Month: November 2024

California epitomizes the agriculture-climate challenge, as well as its opportunities. As the United States’ largest agricultural producing state agriculture also accounted for approximately 8% of California’s greenhouse gas emissions statewide for the period 2000–2013. At the same time, California is at the forefront of innovative approaches to CSA . Given the state’s Mediterranean climate, part of an integrated CSA strategy will likely include perennial crops, such as winegrapes, that have a high market value and store C long term in woody biomass. Economically, wine production and retail represents an important contribution to California’s economy, generating $61.5 billion in annual economic impact. In terms of land use, 230,000 ha in California are managed for wine production, with 4.2 million tons of wine grapes harvested annually with an approximate $3.2 billion farm gate value. This high level of production has come with some environmental costs, however, with degradation of native habitats, impacts to wildlife, and over abstraction of water resources. Although many economic and environmental impacts of wine production systems are actively being quantified, and while there is increasing scientific interest in the carbon footprint of vineyard management activities, efforts to quantify C capture and storage in annual and perennial biomass remain less well-examined. Studies from Mediterranean climates have focused mostly on C cycle processes in annual agroecosystems or natural systems. Related studies have investigated sources of GHGs, grow bag gardening on-site energy balance, water use and potential impacts of climate change on productivity and the distribution of grape production. The perennial nature and extent of vineyard agroecosystems have brought increasing interest from growers and the public sector to reduce the GHG footprint associated with wine production.

The ongoing development of carbon accounting protocols within the international wine industry reflects the increased attention that industry and consumers are putting on GHG emissions and offsets. In principle, an easy-to-use, wine industry specific, GHG protocol would measure the carbon footprints of winery and vineyard operations of all sizes. However, such footprint assessment protocols remain poorly parameterized, especially those requiring time-consuming empirical methods. Data collected from the field, such as vine biomass, cover crop biomass, and soil carbon storage capacity are difficult to obtain and remain sparse, and thus limit the further development of carbon accounting in the wine sector. Simple yet accurate methods are needed to allow vineyard managers to measure C stocks in situ and thereby better parameterize carbon accounting protocols. Not only would removing this data bottleneck encourage broader participation in such activities, it would also provide a reliable means to reward climate smart agriculture.Building on research that has used empirical data to compare soil and abovground C stocks in vineyards and adjacent oak woodlands in California, this study sought to estimate the C composition of a vine, including the relative contributions of its component parts . By identifying the allometric relationships among trunk diameter, plant height, and other vine dimensions, growers could utilize a reliable mechanism for translating vine architecture and biomass into C estimates. In both natural and agricultural ecosystems, several studies have been performed using allometric equations in order to estimate above ground biomass to assess potential for C sequestration. For example, functional relationships between the ground-measured Lorey’s height and above ground biomass were derived from allometric equations in forests throughout the tropics.

Similarly, functional relationships have been found in tropical agriculture for above ground, below ground, and field margin biomass and C. In the vineyard setting, however, horticultural intervention and annual pruning constrain the size and shape of vines making existing allometric relationships less meaningful, though it is likely that simple physical measurements could readily estimate above ground biomass. To date, most studies on C sequestration in vineyards have been focused on soil C as sinks and some attempts to quantify biomass C stocks have been carried out in both agricultural and natural systems. In vineyards, studies in California in the late 1990s have reported net primary productivity or total biomass values between 550 g C m−2 and 1100 g C m−2. In terms of spatial distribution, some data of standing biomass collected by Kroodsma et al. from companies that remove trees and vines in California yielded values of 1.0–1.3 Mg C ha−1 year−1 woody C for nuts and stone fruit species, and 0.2–0.4 Mg C ha−1 year−1 for vineyards. It has been reported that mature California orchard crops allocate, on average, one third of their NPP to the harvested portion and mature vines 35–50% of the current year’s production to grape clusters. Pruning weight has also been quantified by two direct measurements which estimated 2.5 Mg of pruned biomass per ha for both almonds and vineyards. The incorporation of trees or shrubs in agroforestry systems can increase the amount of carbon sequestered compared to a monoculture field of crop plants or pasture. Additional forest planting would be needed to offset current net annual loss of above ground C, representing an opportunity for viticulture to incorporate the surrounding woodlands into the system. A study assessing C storage in California vineyards found that on average, surrounding forested wild lands had 12 times more above ground woody C than vineyards and even the largest vines had only about one-fourth of the woody biomass per ha of the adjacent wooded wild lands .

The objectives of this study were to: measure standing vine biomass and calculate C stocks in Cabernet Sauvignon vines by field sampling the major biomass fractions ; calculate C fractions in berry clusters to assess C mass that could be returned to the vineyard from the winery in the form of rachis and pomace; determine proportion of perennially sequestered and annually produced C stocks using easy to measure physical vine properties ; and develop allometric relationships to provide growers and land managers with a method to rapidly assess vineyard C stocks. Lastly, we validate block level estimates of C with volumetric measurements of vine biomass generated during vineyard removal.The study site is located in southern Sacramento County, California, USA , and the vineyard is part of a property annexed into a seasonal floodplain restoration program, which has since removed the levee preventing seasonal flooding. The ensuing vineyard removal allowed destructive sampling for biomass measurements and subsequent C quantification. The vineyard is considered part of the Cosumnes River appellation within the Lodi American Viticultural Area, a region characterized by its Mediterranean climate— cool wet winters and warm dry summers—and by nearby Sacramento-San Joaquin Delta breezes that moderate peak summer temperatures compared to areas north and south of this location. The study site is characterized by a mean summer maximum air temperature of 32 °C, has an annual average precipitation of 90 mm, typically all received as rain from November to April. During summer time, plastic grow bag the daily high air temperatures average 24 °C, and daily lows average 10 °C. Winter temperatures range from an average low 5 °C to average high 15 °C. Total heating degree days for the site are approximately 3420 and the frost-free season is approximately 360 days annually. Similar to other vineyards in the Lodi region, the site is situated on an extensive alluvial terrace landform formed by Sierra Nevada out wash with a San Joaquin Series soil . This soil-landform relationship is extensive, covering approximately 160,000 ha across the eastern Central Valley and it is used extensively for winegrape production. The dominant soil texture is clay loam with some sandy clay loam sectors; mean soil C content, based on three characteristic grab samples processed by the UC Davis Analytical Lab, in the upper 8 cm was 1.35% and in the lower 8–15 cm was 1.1% . The vineyard plot consisted of 7.5 ha of Cabernet Sauvignon vines, planted in 1996 at a density of 1631 plants ha−1 with flood irrigation during spring and summer seasons. The vines were trained using a quadrilateral trellis system with two parallel cordons and a modified Double Geneva Curtain structure attached to T-posts . Atypically, these vines were not grafted to rootstock, which is used often in the region to modify vigor or limit disease .In Sept.–Oct. of 2011, above ground biomass was measured from 72 vines. The vineyard was divided equally in twelve randomly assigned blocks, and six individual vines from each block were processed into major biomass categories of leaf, fruit, cane and trunk plus cordon . Grape berry clusters were collected in buckets, with fruit separated and weighed fresh in the field. Leaves and canes were collected separately in burlap sacks, and the trunks and cordons were tagged. Biomass was transported off site to partially air dry on wire racks and then fully dried in large ventilated ovens. Plant tissues were dried at 60 °C for 48 h and then ground to pass through a 250 μm mesh sieve using a Thomas Wiley® Mini-Mill . Total C in plant tissues was analyzed using a PDZ Europa ANCA-GSL elemental analyzer at the UC Davis Stable Isotope Facility. For cluster and berry C estimations, grape clusters were randomly selected from all repetitions. Berries were removed from cluster rachis. While the berries were frozen, the seeds and skins were separated from the fruit flesh or “pulp”, and combined with the juice . The rachis, skins and seeds were dried in oven and weighed. The pulp was separated from the juice + pulp with vacuum filtration using a pre-weighed Q2 filter paper . The filter paper with pulp was oven dried and weighed to get insoluble solid fraction . The largest portion of grape juice soluble solids are sugars. Sugars were measured at 25% using a Refractometer PAL-1 .

The C content of sugar was calculated at 42% using the formula of sucrose. Below ground biomass was measured by pneumatically excavating the root system with compressed air applied at 0.7 Mpa for three of the 12 sampling blocks, exposing two vines each in 8 m3 pits. The soil was prewetted prior to excavation to facilitate removal and minimize root damage. A root restricting duripan, common in this soil, provided an effective rooting depth of about 40 cm at this site with only 5–10 fine and small roots able to penetrate below this depth in each plot. Roots were washed, cut into smaller segments and separated into four size classes , oven-dried at 60 °C for 48 h and weighed. Larger roots were left in the oven for 4 days. Stumps were considered part of the root system for this analysis.In vineyard ecosystems, annual C is represented by fruit, leaves and canes, and is either removed from the system and/or incorporated into the soil C pools, which was not considered further. Structures whose tissues remain in the plant were considered perennial C. Cordon and trunk diameters were measured using a digital caliper at four locations per piece and averaged, and lengths were measured with a calibrated tape. Sixty vines were used for the analysis; twelve vines were omitted due to missing values in one or more vine fractions. All statistical estimates were conducted in R.An earth moving machine was used to uproot vines and gather them together to form mounds. Twenty-six mounds consisting of trunks plus cordons and canes were measured across this vineyard block . The mounds represented comparable spatial footprints within the vineyard area . Mound C stocks were estimated using their biomass contribution areas, physical size, density and either a semi-ovoid or hemispherical model.The present study provides results for an assessment of vineyard biomass that is comparable with data from previous studies, as well as estimates of below ground biomass that are more precise than previous reports. While most studies on C sequestration in vineyards have focused on soil C, some have quantified above ground biomass and C stocks. For example, a study of grapevines in California found net primary productivity values between 5.5 and 11 Mg C ha−1 —figures that are comparable to our mean estimate of 12.4 Mg C ha−1 . For pruned biomass, our estimate of 1.1 Mg C ha−1 were comparable to two assessments that estimated 2.5 Mg of pruned biomass ha−1 for both almonds and vineyards. Researchers reported that mature orchard crops in California allocated, on average, one third of their NPP to harvestable biomass, and mature vines allocated 35–50% of that year’s production to grape clusters. Our estimate of 50% of annual biomass C allocated to harvested clusters represent the fraction of the structures grown during the season .

ABA concentrations may be higher in the BOD berry skins based upon the higher transcript abundance of important ABA signaling and biosynthesis genes encoding ABF2, SnRK2 kinases and NCED6. We hypothesize that this would be seed derived ABA since water deficits were not apparent in BOD with the recent rainfall and high humidity. In contrast, NCED3 and NCED5 had higher transcript abundance in RNO berry skins, which might occur as the result of the very low humidity and large vapor pressure deficit . The lower expression of NCED6 in RNO berry skins may indicate that the seeds in the berry were more immature than the BOD berries. The higher expression of other seed development and dormancy genes in the berry skins support the argument that BOD berries matured at a lower sugar level than the RNO berries. The ABA concentrations in the berry skins are a function of biosynthesis, catabolism, conjugation and transport. ABA in seeds increase as the seed matures and some of this ABA may be transported to the skin. In fact, a number of ABCG40 genes, which encode ABA transporters, had higher transcript abundance in BOD berry skins than that in RNO . Part of the ABA in skins may be transported from the seed and part of it might be derived from biosynthesis in the skins. NCED6 transcript abundance in the skins was higher in BOD berries. Perhaps the transcript abundance of NCED6 in the skin is regulated by the same signals as the embryo and reflects an increase in seed maturity. AtNCED6 transcript abundance is not responsive to water deficit in Arabidopsis, square black flower bucket wholesale but AtNCED3 and AtNCED5 are. This is consistent with the higher NCED3, NCED5 and BAM1 transcript abundance in RNO berries . Thus, there are complex responses of ABA metabolism and signaling.

It would appear that there may be two different ABA pathways affecting ABA concentrations and signaling: one involved with embryo development and one involved with the water status in the skins. Auxin is also involved with ABA signaling during the late stages of embryo development in the seeds. Auxin signaling responses are complex. ABF5 is an auxin receptor that degrades Aux/IAA proteins, which are repressors of ARF transcriptional activity. Thus, a rise in auxin concentration releases Aux/IAA repression of ARF transcription factors, activating auxin signaling. In the berry skins, there was a diversity of transcriptional responses of Aux/IAA and ARF genes in the two locations, some with increased transcript abundance and others with decreased transcript abundance. As with ABA signaling, there may be multiple auxin signaling pathways operating simultaneously. One pathway appears to involve seed dormancy. ARF2 had a higher transcript abundance in BOD berries. ARF2 promotes dormancy through the ABA signaling pathway. This is consistent with the hypothesis that BOD berries reach maturity at a lower sugar level than RNO berries.Grapevines have very dynamic gene expression responses to pathogens. The top 150 DEGs for BOD berries were highly enriched with biotic stress genes. The BOD vineyard site had a higher rainfall and higher relative humidity than RNO and these conditions are likely to be more suitable for fungi to grow. We detected a much higher transcript abundance of powdery mildew-responsive genes in BOD berries and this may be connected to a higher transcript abundance of ethylene and phenylpropanoid genes as part of a defense response. The transcript abundance profiles of some of these genes are remarkably similar. Increased ethylene signaling in grapevines has been associated with powdery mildew infection and phenylpropanoid metabolism and appears to provide plant protection against the fungus.

Genes involved with phenylpropanoid metabolism, especially PAL and STS genes, appear to be quite sensitive to multiple stresses in the environment. In Arabidopsis there are four PAL genes. These PAL genes appear to be involved with flavonoid biosynthesis and pathogen resistance in Arabidopsis. Ten different PAL1 and two PAL2 orthologs had higher transcript abundance in BOD berry skins; many STS genes also had a higher transcript abundance in BOD berry skins . Stilbenes are phytoalexins and provide pathogen resistance in grapes and STS genes are strongly induced by pathogens. Thus, the higher transcript abundance of powdery mildew genes may be associated with the higher transcript abundance of genes in the ethylene and phenylpropanoid pathways.The transcript abundance of a number of iron homeostasis genes were significantly different in the two locations and there was a difference in soil available iron concentrations in the two locations. However, iron uptake and transport in plants is complicated depending on multiple factors, such as pH, soil redox state, organic matter composition, solubility in the phloem, etc. Thus, it is impossible to predict iron concentrations in the berry without direct measurements. The roles of these genes in iron homeostasis and plant physiological functions are diverse. Iron supply can affect anthocyanin concentrations and the transcript abundance of genes in the phenylpropanoid pathway in Cabernet Sauvignon berry skins. One of the DEGs, SIA1, is located in the chloroplast in Arabidopsis and appears to function in plastoglobule formation and iron homeostasis signaling in concert with ATH13. Another DEG, YSL3, is involved in iron transport. It acts in the SA signaling pathway and appears to be involved in defense responses to pathogens.

It also functions in iron transport into seeds. FER1 is one of a family of ferritins found in Arabidopsis. VIT1 and NRAMP3 are vacuolar iron transporters and are also involved in iron storage in seeds. Other DEGs are also responsive to iron supply. IREG3 appears to be involved in iron transport in plastids; its transcript abundance increases with increasing iron concentrations. ABCI8 is an iron-stimulated ATPase located in the chloroplast that functions in iron homeostasis. It is unclear what specific roles these iron homeostasis genes are playing in grape berry skins, but they appear to be involved in iron storage in seeds and protection against oxidative stress responses. One possible explanation for the transcript abundance profiles in the BOD and RNO berry skins is that ferritins are known to bind iron and are thought to reduce the free iron concentrations in the chloroplast, thus, reducing ROS production that is caused by the Fenton reaction. As chloroplasts senesce during berry ripening, iron concentrations mayrise as a result of the catabolism of iron-containing proteins in the thylakoid membranes; thus, berry skins may need higher concentrations of ferritins to keep free iron concentrations low. This might explain the increase in ferritin transcript abundance with increasing sugar levels. Most soils contain 2 to 5% iron including available and unavailable iron; soils with 15 and 25 μg g− 1 of available iron are considered moderate for grapevines, but soils with higher concentrations are not considered toxic. Therefore, for both soils in this study, iron concentrations can be considered to be very high but not toxic. The higher available iron concentrations in the BOD vineyard may be associated with the wetter conditions and the lower soil pH.Other researchers using Omics approaches have identified environmental factors that influence grape berry transcript abundance and metabolites. One study investigated the differences in transcript abundance in berries of Corvina in 11 different vineyards within the same region over 3 years. They determined that approximately 18% of the berry transcript abundance was affected by the environment. Climate had an overwhelming effect but viticultural practices were also significant. Phenylpropanoid metabolism was very sensitive to the environment and PAL transcript abundance was associated with STS transcript abundance. In another study of a white grape cultivar, Garganega, berries were analyzed by transcriptomic and metabolomic approaches. Berries were selected from vineyards at different altitudes and soil types. Again, plastic square flower bucket phenylpropanoid metabolism was strongly influenced by the environment. Carotenoid and terpenoid metabolism were influenced as well. Two studies investigated the grape berry transcriptomes during the ripening phase in two different regions of China, a dry region in Western China and a wet region in Eastern China. These two locations mirror some of the differences in our conditions in our study, namely moisture, light and elevation, although the dry China western region has higher night temperatures and more rainfall than the very dry RNO location. In the Cabernet Sauvignon study, they compared the berry transcriptomes from the two regions at three different stages: pea size, veraison and maturity. The TSS at maturity was slightly below 20°Brix. Similar to our study, the response to stimulus, phenylpropanoid and diterpenoid metabolism GO categories were highly enriched in mature berries between the two locations.

Like in our study, the authors associated the transcript abundance of these proteins to the dry and wet locations, respectively. In the second study comparing these two regions in China, the effects of the environment on the metabolome and transcriptome of Muscat Blanc à Petits Grains berries were investigated over two seasons; specifically, terpenoid metabolism was targeted. Like in our study, the transcripts in terpenoid were in higher abundance in the wetter location. The transcript abundances were correlated with terpenoid concentrations and a coexpression network was constructed. A specific set of candidate regulatory genes were identified including some terpene synthases , glycosyl transferases and 1-hydroxy-2-methyl-2-butenyl 4-diphosphate reductase . We examined the transcript abundance of some of these candidate genes in our own data but did not find significant differences between our two locations. The contrasting results between our study and Wen et al. could be for a variety of reasons such as different cultivar responses, berry versus skin samples, or different environmental conditions that affect terpenoid production. Terpenoid metabolism is influenced by the microclimate and is involved in plant defense responses to pathogens and insects. Light exposure to Sauvignon Blanc grapes was manipulated by removing adjacent leaves without any detectable differences in berry temperatures. Increased light exposure increased specific carotenoid and terpene concentrations in the berry. The responses of carotenoid and terpenoid production to temperature are less clear. Some effect of temperature was associated with carotenoid and terpenoid production, but to a lesser extent than light. Higher concentrations of rotundone, a sesquiterpene, have been associated with cooler temperatures. Water deficit can also alter carotenoid and terpenoid metabolism in grapes. Terpenes can act as signals for insect attacks and attract insect predators. Thus, terpenoid metabolism is highly sensitive to the environment and influenced by many factors. In contrast to these studies, excess light and heat can affect transcript abundance and damage berry quality. In addition to a higher rate of malate catabolism, anthocyanin concentrations and some of the transcript abundances associated with them are decreased as well.BOD berries reached maturity at a lower °Brix level than RNO berries; the cause is likely to be the warmer days and cooler nights in RNO. Higher day temperature may increase photosynthesis and sugar transport and coolernight temperatures may reduce fruit respiration. °Brix or TSS approximates the % sugar in a berry and is a reliable marker of berry maturity in any given location; however, TSS is an unreliable marker of berry maturity when comparing grapes from very different climates. The differences in TSS between BOD and RNO are consistent with other studies on the temperature effects on berry development. Indirect studies have associated gradual warming over the last century to accelerated phenology and increased sugar concentrations in the grape berries. Increasing temperature can accelerate metabolism, including sugar biosynthesis and transport, but the increase in metabolism is not uniform. For example, the increase in anthocyanin concentration during the ripening phase is not affected as much as the increase in sugar concentration. These responses vary with the cultivar, complicating this kind of analysis even further. Direct studies of temperature effects on Cabernet Sauvignon berry composition also are consistent with our data. In one study, the composition of Cabernet Sauvignon berries was altered substantially for vines grown in phytotrons at 20 or 30 °C temperatures. Cooler temperatures promoted anthocyanin development and malate concentrations and higher temperatures promoted TSS and proline concentrations. In a second study, vines were grown at 20 or 30 °C day temperatures with night temperatures 5 °C cooler than the day. In this study, higher temperatures increased berry volume and veraison started earlier by about 3 to 4 weeks. The authors concluded that warmer temperatures hastened berry development. In a third study, Cabernet Sauvignon berry composition was affected in a similar manner by soil temperatures that differed by 13 °C .

We will next perform long distance thermal navigation, at a height of 150 µm above the surface. Retract 150 µm using axis 3 of the coarse positioners. I’d recommend doing this in one or two big steps, because the coarse positioner can slide in response to small excursions. Verify that you can still see the thermal signal on the SQUID. It is Ok if it’s faint or close to the noise floor; it will increase in size, and you know which directions to start travelling. If the resistive encoders are working , then use them to move in 100 um steps, checking the SQUID signal in between movements. There is no need to ground the SQUID in between coarse positioner steps, there will be crosstalk but this is not hazardous for the nanoSQUID. If the resistive encoders are not working, click the Step+ button repeatedly until the SQUID signal increases to a maximum. This might take a few minutes or so of clicking. You can work on a software solution instead if you like , but remember that there is always a simple, safe solution available! Once the signal is at a maximum, take another scan to verify that you’re centered above the device. You should see a local maximum in the temperature in the middle of your scan region. Ground the SQUID. Ramp the current through the device down to zero. Zero and ground any gates you have applied voltages to. Ground the sample. Make sure the SQUID is grounded to the breakout box by a BNC . Hook up the second little red turbo pump to the sample chamber through a plastic clamp and o-ring, and turn it on. Slowly, over 10-20 minutes, flower harvest buckets open the valve to the sample chamber and pump it out. Make sure the sand buckets for vibration isolation are set up and the bellows aren’t touching the ground. If there are vibration issues you can often feel them on the bellows and on the table with your hand.

Repeat the setup for approaching to contact, and approach to contact. Definitely watch the first few rounds of this approach! You can even watch the whole thing- it’ll take 30-45 mintues, but if you’ve messed something up then the approach will destroy both the SQUID and the device, because you’ve carefully aligned the SQUID with the device! Once you’ve reached the surface, you will set up the SQUID circuit. Attach the preamplifier to one of the SMA connectors at the top of the insert. Attach its output to the input of the feedback box. This output goes through the ground breaker that is clamped to the table in Andrea’s lab; all of these analog electronic circuits are susceptible to noise and ringing, so I’m sure there will be different idiosyncracies in other laboratories with other electromagnetic environments. Attach the output of the feedback box to the BNC labelled FEEDBACK . This is the BNC that should get a resistor in series if you wanted to increase the transfer function. We generally use resistors between 1 kΩ and 10 kΩ for this. To start with, just using nothing is fine . Plug the preamp and feedback box into fresh batteries . Turn the preamp on. Turn the feedback OFF. Hook up the SQUID bias wires to SQUID A and SQUID B. You can tell which they are because of the chunky low pass filters on the end, but of course they are also labelled. Make sure both sides of the SQUID are grounded while hooking it up- there is a BNC T there for a grounding cap for this purpose. Hook up Output 2 of the Zurich to signal input on the feedback box. Apply 1 V to signal input. There’s a good chance you just used this same output and cable to apply avoltage to the device, so be careful not to skip this step and apply this voltage to the device itself!

You should see the SQUID array transfer function on the oscilloscope . Turn the rheostat/potentiometer on the preamp until this pattern has maximum amplitude. Turn the Offset rheostat/potentiometer on the feedback box until this passes through zero . There is a more sophisticated procedure for minimizing noise in the SQUID array; this is covered in great detail by documents Martin Huber has provided to the lab. But if you are a beginner this simple procedure will work fine. Flip the On switch on the feedback box, and watch the interference pattern vanish, replaced by a line near V = 0. Turn off the AC voltage going to signal input. You are now ready to characterize the SQUID, although you’ll need to unground it. That includes removing the BNC grounding caps from the T’s downstream of the SQUID bias filters and also flipping the BNC switch on the top of the rack. Click ‘preliminary sweep’ on the nSOT characterizer window. Sweep from 0 to 0.1. If you see a linear slope, a ton of stuff is working! The SQUID bias circuit, the SQUID array, the feedback electronics, all the cryogenics- that’s a really good sign. If you see no signal, don’t panic. Once again, there’s a lot of stuff involved in this circuit and a ton of mistakes you can make. Go back through the list and check everything, then check to make sure the SQUID bias isn’t grounded somewhere. Increase the sweep range until you see a critical current or you get above 3.3 V, which is where the feedback box will fail. If you don’t see a critical current, you have a SHOVET but not a SQUID. If you see a critical current, close the window, switch to the nSOT characterizer, and characterize the SQUID. At this point, you are at the surface and over the device with a working SQUID, and you can begin your imaging campaign, so what comes next is up to you!As wireless technology matured, Wireless Sensor Networks began to emerge as an advantageous alternative to their wired counterparts due in part to easy deployment and scalability. The 802.15.4 IEEE communication standard was developed for use specifically with low-rate wireless personal area networks with a focus on wireless sensor networks. In the early 2000s, the ZigBee alliance worked to construct the ZigBee protocols, communication protocols functioning on the 802.15.4 MAC and Physical layers. The main advantage of the ZigBee protocols over its competitor Bluetooth was ZigBees’ highly efficient sleep mode; ZigBee devices use a basic master-slave configuration suited for low frequency data transmission star topologies, and can wake from sleep and transmit a packet in around 15 miliseconds. As a result, ZigBee devices can last for long periods on a single power supply. In recent years, Digi incorporated the 802.15.4 standard and ZigBee protocols into a proprietary RF module known as the Xbee. Xbee devices have modular firmware capable of constructing various network topologies and have been utilized as end devices in wireless sensor network and monitoring applications. However, Xbee does not contain large processors for signal processing or local data analysis at the End Device. The limited processing capabilities of an Xbee device can be addressed with the implementation of additional hardware for processing support. Current WSN designs utilize an Arduino, a low-cost, round flower buckets reliable microcontroller capable of functioningas a building block for data acquisition or control systems, to augment a sensor nodes processing capabilities. In addition to the Arduino and Xbee, prototype WSN routinely incorporate a Raspberry Pi, a small inexpensive linux computer. The Raspberry Pi usually serves as a hardware platform for the ZigBee network Coordinator, and is used to direct network communication and control in wireless systems. Additionally, the Raspberry Pi can be used to handle WSN data storage by functioning as a database server . Raspberry Pi, Arduino, and Xbee based WSN posit two main questions. First, since ZigBee protocols were developed specifically for facilitating long node lifetimes, how does introducing additional processing hardware in the form of an Arduino impact overall node lifetime? And second, if one reason for the advance of WSN is its scalability, how do developers address the relatively limited storage capabilities of the IoT devices and their potential inability to successfully scale with increasing WSN traffic?Both sensors require signal processing to convert their data into human readable format. The Arduino uses the One Wire and Dallas Temperature libraries to read temperature values from the DS18B20 sensor, and the softSerial and TinyGPS libraries to parse GPS data from the PMB-648 GPS module. The Arduino runs a single loop that manages reading temperature and GPS sensor data, and communicating data via Xbee to the ZigBee network Coordinator. Both the Xbee and Arduino have sleep functions that minimize power consumption by periodically stopping unnecessary internal processes when those processes are notneeded.

The sleep functions were implemented inside the Arduino main code loop to halt superfluous processes while the node was neither gathering nor transmitting data. In oder to address the impact of the Arduino on End Device lifetimes, End Device average power consumption was compared for a range of transmission frequencies to generate a graphical relationship between transmission frequency and power consumption of an Arduino-Xbee End Device. The ZigBee Coordinator node consists of a Raspberry Pi series 2 B running OS Raspbian Jessie and a single Xbee Series 2 loaded with Coordinator firmware via XCTU. The Xbee Coordinator transmission and reception lines are input to the Raspberry Pi via its GPIO pins as a serial communication device. Raspberry Pi uses the Python serial and Xbee libraries to parse incoming API statements from End Devices. In order to address the limited local storage on the Raspberry Pi ZigBee Coordinator, the device is transformed into an SQLite cloud database client. The Raspberry Pi uses the Python requests library to transmit data packets as URL parameters to a cloud server. The cloud database server handles all WSN data storage, alleviating the responsibility from the Raspberry Pi. Web Application Development uses Heroku as a Platform as a Service . Heroku runs a Linux Operating System, a Puma Web Server, and an SQLite database as a framework for development. The Web Application is in charge of managing wireless sensor network data storage in the SQLite database and rendering a useful human readable User Interface for data presentation with a browser request. The Web Application uses Rails Model, View, and Controller architecture to pass incoming URL parameters to the the SQLite database via Object Relational Mapping . User Interface uses the Gmaps4Rails, a Ruby gem to superimpose Sensor and Coordinator GPS data as markers on an interactive map using the google maps API. The markers display relevant sensor data when clicked by the user, such as MAC address for sensor and Coordinator node, and temperature in degrees celsius for sensor node. A full list of the latest received data for each unique Sensor node is displayed in table format underneath the map for easy viewing.Additionally, the cloud database server is designed to be a shared database for multiple wireless sensor networks. A collaborative wireless sensor network cloud database may be useful in monitoring large scale geographically separate areas of interest such as a nationwide average temperature census or large scale environmental monitoring. Examples are given showing the cloud server functioning as a shared database.ZigBee is a global open standard for communication using the 802.15.4 protocol. Maintained by the ZigBee Alliance, transceivers communicate over ISM signal bands with intended ranges of 10-100m.ZigBee device firmware can have one of three functions which combine to form various network topologies. The three types are: ZigBee Coordinator , ZigBee Router , and ZigBee End Device . ZigBee Coordinators act as the central controller and parent node to both end devices and routers. They are in charge of network management functions such as storing security keys and network ids as well as handling network traffic. They are the most resource heavy nodes in terms of processing and local memory, and must be active for a network to exist. ZigBee Routers are capable of performing application layer tasks as well as acting as fully functional sensor nodes. Routers may function as network repeaters, extending network size by relaying information from end devices or other routers out of range of the Coordinator node. Routers are not necessary for a ZigBee network to exist, but are useful in forming sophisticated network structures or when network contains a large number of nodes.

We have already mentioned the most important consequence of a finite net Chern number: the presence of chiral edge states in the gap of a magnetic insulator. We have not yet discussed the consequences of this state of affairs, and we will do so next. The quantum states available in the bulk of trivial materials, i.e. Bloch states, are delocalized over the entire crystal, and as a result, when Bloch states are present at the Fermi level, electronic transport between any two points in the crystal can occur through the rapid local occupation and depletion of these quantum states. The edge states that appear in Chern magnets support a lower-dimensional analog of this property: they are delocalized quantum states restricted to the edge of a two dimensional crystal, and as a result they support electronic transport along the edge of the crystal through the rapid local occupation and depletion of these semi-localized quantum states. They do not support electronic transport through the bulk, and edge states that are not simply connected cannot transmit electrons through the bulk region separating them. As mentioned, the Chern number is a signed integer, and we have not yet discussed the physical meaning of the sign of the Chern number. The edge states in Chern magnets are chiral, meaning that electrons populating a particular edge state can only propagate in one direction around the edge of a two-dimensional crystal. The sign of the Chern number determines the direction or chirality with which propagation of the electronic wave function around the crystal occurs. Electronic bands with opposite Chern numbers produce edge states with opposite chiralities. So in summary, a two dimensional crystal that is a Chern magnet supports electronic transport through chiral edge states that live on its boundaries.

These systems remain bulk insulators, black plastic plant pots bulk and edge states separated by the bulk cannot exchange electrons with each other. The sign of the Chern number is determined by the spin state that is occupied, and thus the chirality of the available edge state is hysteretically switchable, just like the magnetization of the two dimensional magnet.It is important to remember that these quantum states are just as real as Bloch states, and apart from the short list of differences discussed above, they can be analyzed and understood using many of the same tools. For example, in a metallic system, the Fermi level can be raised by exposing a crystal to a large population of free electrons and using an electrostatic gate to draw electrons into the crystal. These Bloch states have a fixed set of allowed momenta associated with their energies, and experiments that probe the momenta of electrons in a crystal will subsequently detect the presence of electrons in newly populated momentum eigenstates. Similarly, attaching a Chern magnet to a reservoir of electrons and using an electrostatic gate to draw electrons into the magnet will populate additional chiral edge states. Properties that depend on the number of electrons occupying these special quantum states will change accordingly. In all of these systems, conductivity strongly depends on the number of quantum states available at the Fermi level. For metallic systems, the number of Bloch states available at any particular energy depends on details of the band structure. The total conductance between any two points within the crystal depends on the relative positions of the two points and the geometry of the crystal.

Thus conductivity is an intrinsic property of a metal, but conductance is an extrinsic property of a metal, and both are challenging to compute precisely from first principles.At finite temperature, electrons occupying Bloch states in metals can dissipate energy by scattering off of phonons, other electrons, or defects into different nearby Bloch states. This is possible because at every position in real space and momentum space there is a near-continuum of available quantum states available for an electron to scatter into with arbitrarily similar momentum and en-ergy. This is not the case for electrons in chiral edge states of Chern magnets, which do not have available quantum states in the bulk. As a result, electrons that enter chiral edge state wave functions do not dissipate energy. There is a dissipative cost for getting electrons into these wave functions this was discussed in the previous paragraph- but this energetic cost is independent of all details of the shape and environment of the chiral edge state, even at finite temperature. This is why the Hall resistance Rxy in a Chern magnet is so precisely quantized; it must take on a value of C 1 e h 2 , and processes that would modify the resistance in other materials are strictly forbidden in Chern magnets. All bands have finite degeneracy- that is, they can only accommodate a certain number of electrons per unit area or volume of crystal. If electrons are forced into a crystal after a particular band is full, they will end up in a different band, generally the band that is next lowest in energy. This degeneracy depends only on the properties of the crystal. Chern bands have electronic degeneracies that change in response to an applied magnetic field; that is to say, when Chern magnets are exposed to an external magnetic field, their electronic bands will change to accommodate more electrons.Simple theoretical models that produce quantized anomalous Hall effects have been known for decades.

The challenge, then, lay in realizing real materials with all of the ingredients necessary to produce a Chern magnet. These are, in short: high Berry curvature, a two-dimensional or nearly two-dimensional crystal, and an interaction-driven gap coupled to magnetic order. It turns out that a variety of material systems with high Berry curvature are known in three dimensions; three dimensional topological insulators satisfy the first criterion, and are relatively straightforward to produce and deposit in thin film form using molecular beam epitaxy, satisfying the second. These systems do not, however, have magnetic order. Researchers attempted to induce magnetic order in these materials with the addition of magnetic dopants. It was hoped that by peppering the lattice with ions with large magnetic moments and strong exchange interactions that magnetic order could be induced in the band structure of the material, as illustrated in Fig. 3.11. This approach ultimately succeeded in producing the first material ever shown to support a quantized anomalous Hall effect. An image of a film of this material and associated electronic transport data are shown in Fig. 3.12.We have already discussed the notion of the Curie temperature and its origin. To reiterate, the Curie temperature is a temperature set by the lowest energy scale at which excitations that change the magnetic order can appear. It is worth emphasizing one point in particular: the set of excitations that change the magnetic order includes but is not limited to all those that promote an electron from the valence band to the conduction band, i.e. the excitations that support charge transport through the bulk of a magnetic insulator. For this reason, the energy scale of the Curie temperature is generally expected to be lower than the energy scale of thermal activation of electrons into the bulk conduction band of the magnetic insulator.There are many a priori reasons to suspect that magnetically doped topological insulators might have strong charge disorder. The strongest is the presence of the magnetic dopants- dopants always generate significant charge disorder; in a sense they are by definition a source of disorder. Because their distribution throughout the host crystal is not ordered, procona system dopants can reduce the effective band gap through the mechanism illustrated in Fig. 3.14. It turns out this concern about magnetically doped topological insulators has been born our in practice; the systems have been improved since their original discovery, but in all known samples the Curie temperatures dramatically exceed the charge gaps . This puts these systems deep in the kBTC > EGap limit. The resolution to this issue has always been clear, if not exactly easy. If a crystal could be realized that had bands with both finite Chern numbers and magnetic interactions strong enough to produce a magnetic insulator, then we could expect such a system to be a clean Chern magnet . Such a system would likely support a QAH effect at much higher temperature then the status quo, since it would not be limited by charge disorder.Other researchers predicted that breaking inversion symmetry in graphene would open a gap nearcharge neutrality with strong Berry curvature at the band edges. The graphene heterostructures we make in this field are almost always encapsulated in the two dimensional crystal hBN, which has a lattice constant quite close to that of graphene. The presence of this two dimensional crystal technically always does break inversion symmetry for graphene crystals, but this effect is averaged out over many graphene unit cells whenever the lattices of hBN and graphene are not aligned with each other.

Therefore the simplest way to break inversion symmetry in graphene systems is to align the graphene lattice with the lattice of one of its encapsulating hBN crystals. Experiments on such a device indeed realized a large valley hall effect, an analogue for the valley degree of freedom of the spin Hall effect discussed in the previous chapter, a tantalizing clue that the researchers had indeed produced high Berry curvature bands in graphene. Twisted bilayer graphene aligned to hBN thus has all of the ingredients necessary for realizing an intrinsic Chern magnet: it has flat bands for realizing a magnetic insulator, it has strong Berry curvature, and it is highly gate tunable so that we can easily reach the Fermi level at which an interaction-driven gap is realized. Magnetism with a strong anomalous Hall effect was first realized in hBN-aligned twisted bilayer graphene in 2019. Some basic properties of this phase are illustrated in Fig. 4.3. This system was clearly a magnet with strong Berry curvature; it was not gapped and thus did not realize a quantized anomalous Hall effect, but it was unknown whether this was because of disorder or because the system did not have strong enough interactions or small enough bandwidth to realize a gap. The stage was set for the discovery of a quantized anomalous Hall effect in an intrinsic Chern magnet in hBN-aligned twisted bilayer graphene.We return now to our discussion of twisted bilayer graphene; we will be discussing domain dynamics. To investigate the domain dynamics directly, we compare magnetic structure across different states stabilized in the midst of magnetic field driven reversal. Figure 5.13A shows a schematic depictionof our transport measurement, and Fig. 5.13B shows the resulting Rxy data for both a major hysteresis loop spanning the two fully polarized states at Rxy = ±h/e2 and a minor loop that terminates in a mixed polarization state at Rxy ≈ 0 . All three states represented by these hysteresis loops can be stabilized at B = 22 mT for T = 2.1 K, where our nanoSQUID has excellent sensitivity, allowing a direct comparison of their respective magnetic structures . Figures 5.13, F and G, show images obtained by subtracting one of the images at full positive or negative polarization from the mixed state, as indicated in the lower left corners of the panels. Applying the same magnetic inversion algorithm used in Fig. 5.1 produces maps of m corresponding to these differences , allowing us to visualize the domain structure generating the intermediate plateau Rxy ≈ 0 seen in the major hysteresis loop. The domains presented in Figs. 5.13, H and I, are difference images; the domain structures actually realized in experiment are illustrated schematically in Fig. 5.13, J-L. Evidently, the anomalous Hall resistance of the device in this state is dominated by the interplay of two large magnetic domains, each comprising about half of the active area. Armed with knowledge of the domain structure, it is straightforward to understand the behavior of the measured transport in the mixed state imaged in Fig. 5.13D. In particular, the state corresponds to the presence of a single domain wall that crosses the device, separating both the current and the Hall voltage contacts . In the limit in which the chiral edge states at the boundaries of each magnetic domain are in equilibrium, there will be no drop in chemical potential across the domain wall, leading to Rxy = 0. This is very close to the observed value of Rxy = 1.0 kΩ = 0.039 h/e 2 .

A semiclassical model- in which electrons within the system redistribute themselves in the out-of-plane direction to screen this electric field- does not apply; instead, the wave functions hosted by the two dimensional crystal are themselves deformed in response to the applied electric field . This changes the electronic band structure of the crystal directly, without affecting the electron density. So to summarize, when a two dimensional crystal is encapsulated with gates to produce a three-layer capacitor, researchers can tune both the electron density and the band structure of the crystal at their pleasure. In the first case, this represents a degree of control that would require the creation of many separate samples to replicate in a three dimensional system. There is a temptation to focus on the exotic phenomena that these techniques for manipulating the electronic structure of two dimensional crystals have allowed us to discover, and there will be plenty of time for that. I’d first like to take a moment to impress upon the reader the remarkable degree of control and extent of theoretical understanding these technologies have allowed us to achieve over those condensed matter systems that are known not to host any new physics. I’ve included several figures from a publication produced by Andrea’s lab with which I was completely uninvolved. It contains precise calculations of the compressibility of a particular allotrope of trilayer graphene as a function of electron density and out-of-plane electric field based on the band structure of the system .

It also contains a measurement of compressibility as a function of electron density and out-of-plane electric field, plastic growers pots performed using the techniques discussed above . The details of the physics discussed in that publication aren’t important for my point here; the observation I’d like to focus on is the fact that, for this particular condensed matter system, quantitatively accurate agreement between the predictions of our models and the real behavior of the system has been achieved. I remember sitting in a group meeting early in my time working with Andrea’s lab, long before I understood much about Chern magnets or any of the other ideas that would come to define my graduate research work, and marvelling at that fact. Experimental condensed matter physics necessarily involves the study of systems with an enormous number of degrees of freedom and countless opportunities for disorder and complexity to contaminate results. Too often work in this field feels uncomfortably close to gluing wires to rocks and then arguing about how to interpret the results, with no real hope of achieving full understanding, or closure, or even agreement about the conclusions we can extract from our experiments. Within the field of exfoliated heterostructures, it is now clear that we really can hope to pursue true quantitative accuracy in calculations of the properties of condensed matter systems. Rich datasets like these, with a variety of impactful independent variables, produce extremely strong limits on theories. They allow us to be precise in our comparisons of theory to experiment, and as a result they have allowed us to bring models based on band structure theory to new heights of predictive power. But most importantly, under these conditions we can easily identify deviations from our expectations with interesting new phenomena- in particular, situations in which electronic interactions produce even subtle deviations from the predictions of single particle band.

This is more or less how I would explain the explosion of interest in the physics of two dimensional crystalline systems within experimental condensed matter physics over the past decade. If you ask a theorist if two dimensional physical systems have any special properties, they will tell you that they do. They might say that the magnetic phase transitions in a Heisenberg model on a two dimensional lattice differ dramatically from those on a three dimensional one. They might say that high Tc superconductivity is apparently a two dimensional phenomenon. They might note that two dimensional electronic systems can support quantum Hall effects and even be Chern magnets , while three dimensional systems cannot. But it is easy to miss the forest for the trees here, and I would argue that interest in these particular physical phenomena is not behind the recent explosion in the popularity of the study of exfoliated two dimensional crystals in condensed matter physics. Instead, much more basic technical considerations are largely responsible- it is simply much easier for us to use charge density and band structure as independent variables in two dimensional crystals than in three dimensional crystals, and that capability has facilitated rapid progress in our understanding of these systems. The techniques described above still have some limitations, and chief among them is a limited range of electronic densities that they can reach. Of course, the gold standard of electron density modulation is the ability to completely fill or deplete an electronic band, which requires about one electron per unit cell in the lattice. Chemical doping can achieve enormous offsets in charge density, sometimes as high as one electron per unit cell.

Electrostatic gating of graphene can produce crystals with an extra electron per hundred unit cells at most. This limitation isn’t fundamental and there are some ideas in the community for ways to improve it, but for now it remains true that electrostatic gates can modify electron densities only slightly relative to the total electron densities of real two dimensional crystals. As it stands, electrostatic gating can only substantially modify the properties of a crystal if the crystal happens to have large variations in the number and nature of available quantum states near charge neutrality. For many crystals this is not the case; thankfully it is for graphene, and for a wide variety of synthetic crystals we will discuss shortly. Electrostatic gating of two dimensional crystals was rapidly becoming a mature technology by the time I started my PhD. So where does nanoSQUID magnetometry fit into all of this? A variety of other techniques exist for microscopic imaging of magnetic fields; the most capable of these other technologies recently developed the sensitivity and spatial resolution necessary to image stray magnetic fields from a fully polarized two dimensional magnet, with a magnetization of about one electron spin per crystalline unit cell, and this was widely viewed within the community as a remarkable achievement. We will shortly be discussing several ferromagnets composed entirely of electrons we have added to a two dimensional crystal using electrostatic gates. Because of the afore-mentioned limitations of electrostatic gating as a technology, this necessarily means that these will be extremely low density magnets with vanishingly small magnetizations, at least 100 times smaller than those produced by a fully polarized two dimensional magnet like the one in the reference above. It is difficult to summarize performance metrics for magnetometers, especially those used for microscopy. Many magnetometers are sensitive to magnetic flux, not field, so very high magnetic field sensitivities are achievable by simply sampling a large region, but of course that is not a useful option when imaging microscopic magnetic systems. Suffice to say that nanoSQUID sensors, blueberry in pot which had been invented in 2010 and integrated into a scanning probe microscope by their inventors by 2012, combine high spatial resolution with very high magnetic field sensitivity. This combination of performance metrics was and remains unique in its ability to probe the minute magnetic fields associated with gate-tunable electronic phenomena at the length scales demanded by the size of the devices. Gate-tunable phenomena in exfoliated heterostructures and nanoSQUID microscopy were uniquely well-matched to each other, and although at the time I started my graduate research only a small handful of gate-tunable magnetic phenomena had so far been discovered in exfoliated two dimensional crystals, nanoSQUID microscopy seemed like the perfect tool for investigating them.So what exactly is nanoSQUID microscopy? We can start by discussing Superconducting Quantum Interference Devices, or SQUIDs. In summary, SQUIDs are electronic devices with properties that strongly depend on the magnetic field to which they are exposed, which makes them useful as magnetometers. I won’t delve into the details of how and why SQUIDs work here, but I will explain briefly how SQUIDs are made, since that will be necessary for understanding how nanoSQUID imaging differs from other SQUID-based imaging technologies. A SQUID is a pair of superconducting wires in parallel, each with a thin barrier in series . The electronic transport properties of this device depend strongly on the magnetic flux through the region between the wires, i.e. inside the hole in the center of the device in Fig. 1.3.

To be a little bit more precise, superconductors transport current without dissipation, so long as the current density stays below a sharp threshold. When this threshold is exceeded, the superconductor revertsto dissipative transport, like a normal metal. Above this critical current, in the so-called ‘voltage state,’ electronic transport is dissipative and highly sensitive to B. Any non-superconductor can function as a barrier, including insulators, metals, and superconducting regions thinner than the coherence length.This is sufficient for many applications, but it presents some issues for producing sensors for scanning probe microscopy. Scanning probe microscopy is a technique through which any sensor can be used to generate images; we simply move the sensor to every point in a grid, perform a measurement, and use those measurements to populate the pixels of a two dimensional array . This can of course be done with a SQUID, and many researchers have used SQUIDs fabricated this way to great effect. But the spatial resolution of a scanning SQUID magnetometry microscope is set by the size of the SQUID, and there are limits to how small SQUIDs can be fabricated using photo lithography. It is also challenging to fashion these SQUIDs into probes that can be safely brought close to a surface for scanning; photo lithography produces SQUIDs on large, flat silicon substrates, and these must subsequently be cut out and ground down into a sharp cantilever with the SQUID on the apex in order to get the SQUID close enough to a surface for microscopy. In summary, the ideal SQUID sensor for microscopy would be one that was smaller than could be achieved using traditional photo lithography and located precisely on the apex of a sharp needle to facilitate scanning. As is so often the case when developing new technologies, we have to make the best of the tools other clever people have already developed. In the case of nanoSQUID microscopy, the inventors of the technique took advantage of a lot of legwork done by biologists. Long ago, glass blowers found that hollow glass tubes could be heated close to their melting point and drawn out into long cones without crushing their hollow interiors. Chemists used this fact to make pipettes for manipulating small volumes of liquid, and biologists later used the techniques they developed to fashion microscopic hypodermic needles that could be used to inject chemicals into and monitor the chemical environment inside individual cells in a process called patch-clamping. A rich array of tools exist for producing these structures, called micropipettes, for chemists and biologists. Eli Zeldov noticed that these structures already had the perfect geometry to serve as substrates for tiny SQUIDs. By depositing superconducting materials onto these substrates from a few different directions, one can produce superconducting contacts and a tiny torus of superconductor on the apex of the micropipette. The same group of researchers successfully integrated these sensors into a scanning probe microscope at cryogenic temperatures. The sizes of these SQUIDs are limited only by how small a micropipette can be made, and since the invention of the technique SQUIDs as small as 30 nm have been realized. We call these sensors nanoSQUIDs, or nanoSQUID-on-tip sensors. A few representative examples of nanoSQUID sensors are shown in Fig. 1.4. A characterization of the electronic transport properties of such a sensor, and in particular the sensor’s response to an applied magnetic field, is shown in Fig. 1.5. NanoSQUID microscopes share many of the core competencies of more traditional, planar scan-ning SQUID microscopes. They dissipate little power, and the measurements they generate are quantitative and can be easily calibrated by measuring the period of the SQUID’s electronic response to an applied magnetic field.

The imaging unit consists of 24 independent objectives attached to a vertical sliding stage using 4 maker beam vertical columns and 2 Nema-11 stepper motors , an example of a row can be seen in Fig. 4a. The fine threads are necessary for focusing on specific biological features and collecting z-stack imaging . With this fixed lens system, the system has a field of view of approximately 5 mm. The Picroscope is able to resolve Group 7, Element 1 targets , corresponding to a resolution of 7 μm . If higher resolution is needed the lens can be swapped out for more magnification . The lens currently on the system was chosen due to our interest in imaging whole organisms. The objectives are distributed on 4-rows and 6-columns to match a standard 24-well culture plate. Each objective consists of a 3D printed camera body that hosts a 5 MegaPixel camera and an off-the-shelf Arducam 1/2” M12 Mount 16 mm Focal Length. Each objective is controlled by a single-board computer , which is connected to an individual slot on one of the three custom-made power distribution boards . All 24 single-board computers computers communicate to a hub board computer that manages the images and autonomously uploads them to a remote server. The hub single-board computer has the MIPI CSI-2 camera port and is connected to an Arduino Uno, which has a motor shield attachment, to control the motors and lift the elevator piece . As a safety feature, the system also includes a custom-made Relay Board that is attached to the Arduino and motor driver stack. The relay board provides control of the LED boards and in the event of an overheat allows us to shut down the system, protecting the system and the biological sample. After each set of pictures, the imaging unit returns to the lowest position, plastic planters bulk which is determined by a limiting switch attached to the elevator unit.

The entire system sits on a 3D printed base, that includes a fan for heat dissipation. Supplementary Fig. 3 shows thermal images of the Picroscope to demonstrate that heat from the system does not impact the experiment. A guide on how to assemble the Picroscope and components needed can be found in Table 2 and Supplementary Note 1. During the course of an experiment, the pictures are autonomously uploaded on a remote computer/server using the ethernet connection of the hub computer board, where they can be viewed or processed in near real time .As proof of principle of the longitudinal live imaging capabilities of the Picroscope, we imaged the development of Xenopus tropicalis embryos from the onset of gastrulation through organogenesis . The fertilization and development of Xenopus occur entirely externally, which allows scientists to easily observe and manipulate the process. For decades, Xenopus have been heavily used in biology studies to model a variety of developmental processes and early onset of diseases, particularly those of the nervous system. While several species of Xenopus are used in different laboratories around the world, Xenopus tropicalis is one of the preferred species due to its diploid genomic composition and fast sexual maturation. Normal development and optimal husbandry of Xenopus tropicalis occur at 25∘ –27 ∘ C, closely approximating standard room temperature, which eliminates the need of special environmental control for most experiments. Given these convenient experimental advantages and their large size, Xenopus embryos have been used extensively to understand the development of the vertebrate body plan, with particular success in elaborating the complex cellular rearrangements that occur during gastrulation and neural tube closure.

These experiments rely on longitudinal imaging of developing embryos, often at single-embryoscale with dyes, fluorescent molecules, and computational tracking of single cells. These studies have elucidated key cellular mechanical properties and interactions critical to vertebrate development, often replayed and co-opted during tumorigenesis. There exists an opportunity to scale these experiments to be more high-throughput with the Picroscope, as one could image hundreds of developing embryos simultaneously, rather than having to move the objective from embryo to embryo during development, or repeating the experiment many times. We imaged Xenopus tropicalis embryos over a 28 h time period. Four embryos were placed in each of the 23 wells used in a 24-well plate, and we used an extra well as calibration . The embryos were grown in simple saline solution and the experiment took place at room temperature. Imaging was performed hourly starting at gastrulation . Then, we visually inspected each image and mapped the embryos to the standard stages of frog development, categorizing their development in gastrulation, neurulation, and organogenesis . Finally, we took a subset of 27 embryos and measured the diameter of the blastopore as the embryos underwent gastrulation . Only 27 embryos were used because those were the only embryos with their blastopores clearly visible throughout the image set. We observed a progressive reduction of blastopore diameter over a 6 h time period, consistent with progression through gastrulation and the start of neurulation. This simple experiment demonstrated that the Picroscope can be used for longitudinal sequential imaging and tracking of biological systems.

While many biological systems including zebra- fish, planaria, and frogs develop at room temperature and atmospheric gas concentrations, mammalian models require special conditions requiring an incubator enclosure. Mammalian models include 2D monolayer cell cultures, as well as 3D organoid models of development and organogenesis. They have been used to assess molecular features and effects of drugs for a variety of phenotypes including cell proliferation, morphology, and activity, among others. Deploying electronics and 3D printed materials inside tissue culture incubators presents some unique challenges. The temperature and humidity conditions can cause electronics to fail and cause certain plastics to off gas toxins. Plastics can also be prone to deformation in these conditions. A common solution for protecting electronics and preventing off gassing is to use inert protective coatings e.g., Parylene C. This requires expensive clean room equipment. Instead, we print all of the components with PLA, a nontoxic and biodegradable material, to prevent deformation we print using 100% infill and reinforce vulnerable elements with aluminum MakerBeam profiles. We coat all electronic components with Corona Super Dope Coating to protect the electronics from the conditions of an incubator. We tested the functionality of the Picroscope inside a standard tissue culture incubator by imaging 2D-monolayers of human embryonic stem cells . To demonstrate the capacity of our system to perform longitudinal imaging across the z-axis, we imaged human cortical organoids embedded in Matrigel . Using this system, we could monitor and measure the growth of the organoids over 86 h . Tracking of individual cells within organoid outgrowths allowed us observe their migration patterns and behavior . Altogether, we show the feasibility of using our system for longitudinal imaging of mammalian cell and organoid models.The combination of 3D printed technology and open-source software has significantly increased the accessibility of academic and teaching laboratories to biomedical equipment. Thermocyclers, for example, were once an expensive commodity unattainable for many laboratories around the world. Now, lowcost thermocyclers have been shown to perform as well as high end commercially available equipment. Inexpensive thermocyclers can be used in a variety of previously unimaginable contexts, including conservation studies in the Amazon, collection pot diagnostics of Ebola, Zika and SARS-CoV-2, teaching high-school students in the developing world and epigenetic studies onboard the International Space Station. Simultaneous imaging of biological systems is crucial for drug discovery, genetic screening, and high-throughput phenotyping of biological processes and disease. This technique typically requires expensive multi-camera and robotic equipment, making it inaccessible to most. While the need for a low-cost solution has long been appreciated, few solutions have been proposed. Currently, the low-cost solutions can be grouped in two categories: those that use gantry systems that move an individual camera through multiple wells, performing “semi-simultaneous” imaging or those that use acquisition of large fields of view encompassing multiple wells , where they can be viewed and/or processed , with minimal intervention. Commercial electronic systems for simultaneous imaging of biological samples are typically designed to image cells plated in monolayers. Yet, significant attention has been given to longitudinal imaging-based screens using whole organisms. These have included zebrafish, worms, and plants. Many times, the results of the screens are based on single-plane images or in maximal projections obtained from external microscopes.

This is accomplished with fine adjustment by two stepper motors that lift the elevator unit that holds all 24 camera objectives .To date, few 3D printed microscopes are designed to function inside incubators. We have run the Picroscope in the incubator for three weeks. This makes the Picroscope compatible with screens in 3D mammalian models including organoids. We have shown a proof of principle of this function by performing longitudinal imaging of human cortical organoids and analyzing the behavior and movement of individual cells . We anticipate many useful applications of the Picroscope and derivatives of it. Here, we demonstrated the versatility of the Picroscope across animal and cell models in different environmental conditions. The modular nature of the system, allows for new features to be easily built and added. For example, defined spectrum LED light sources and filters for fluorescent imaging would enable longitudinal studies of the appearance and fate of defined sub populations of cells in a complex culture by taking advantage of genetically encoded fluorescent reporter proteins. Similarly, the use of fluorescent reporters or dyes that respond to dynamic cell states such as calcium sensors allow long-term imaging of cell activity.Most studies that monitor plants and their environment, whether it be in the field or in the laboratory, require sensors that convert physical or chemical energy into an electrical signal. Some examples of sensors commonly used in plant research are thermocouples, which convert temperature gradients into an electrical potential; photodiodes, which convert light into an electrical current; and strain gauges, which have an electrical resistance that changes when deformed. Many existing methods, such as sap flow measurement , measuring chloroplast movement , and lysimeters , utilize these types of sensors, and nearly all methods that use sensors require a data acquisition system to record measurements. Such systems usually have two basic components: an analog‐ to‐digital converter that converts the electrical signal from the sensor into digital information and a microcontroller or computer that records and processes the digital information from the ADC . There are many commercially available DAQ systems, but these products are often expensive and lack flexibility; a project may need a custom DAQ system to overcome these limitations. One ideal choice for a custom system is a Raspberry Pi computer paired with a high‐resolution ADC. The low cost, flexibility, and high resolution of such a system is ideal for improving existing plant research methods or for developing new ones. The Raspberry Pi is an inexpensive, single‐board computer that has many easily accessible and configurable input/output interfaces, including multiple serial peripheral interfaces and general‐purpose input/output pins , which allow it to be used with a wide variety of ADCs and other peripheral devices. It can run many different operating systems, but the most common is the Linux‐based Raspberry Pi OS, which supports most programming languages. The Raspberry Pi and other similar single‐board computers have many possible applications in life science research. Its small size and low cost make it suitable for data logging in a variety of environments. The easily accessed I/O interfaces can be connected to many different types of sensors for data acquisition, including cameras for high‐throughput plant imaging , microphones for bioacoustic data collection , or gas sensors for air quality monitoring . These same interfaces can also be used to control external components such as mechanical actuators, lighting, or temperature control. To use sensors for data logging with a Raspberry Pi, an ADC is needed to convert the analog output of a sensor into digital information that the computer can use. Many different ADCs are available for this purpose, and it is important to choose one that is appropriate for the application. A few important specifications to consider when choosing an ADC are bit resolution, sampling rate, and number of channels. There is a necessary tradeoff between an ADC’s sampling rate and effective resolution, in that ADCs with very high resolutions are limited to sampling rates in the kilohertz range or less and that as the sampling rate of a given ADC is increased the effective resolution declines . For applications where ultra‐high‐resolution is not critical, there are many ADCs on the market that have readily available open‐ source software libraries and schematics for interfacing with a Raspberry Pi.

Hyperspectral imaging was deployed to identify contaminated mangos. The algorithm’s overall error proportion of high infested samples ranges between 2% and 6%, whereas the algorithm’s overall error rate for low infested samples is 12.3 percent. To detect contaminated cherries, Xing et al. used reflectance and transmittance spectra. According to the extent of damage, the cherries were separated into two categories: “acceptable” and “nonacceptable.” On transmittance spectra, Canonical Discriminant Analysis achieved 85 percent classification accuracy. Potamitis et al. used optoacoustic spectrum analysis to construct an olive fruit fly detection system. The optoacoustic spectrum analysis detects the species of insects based on wing beat analysis. The authors examined the recorded signal’s temporal and frequency domains. The random forest classifier is fed the retrieved features from the time and frequency domains. The random forest classifier had a precision of 0.93, a recall of 0.93, and an F1-Score of 0.93. The optoacoustic approach, on the other hand, cannot distinguish between different types of fruit flies, including peaches and figs. Furthermore, solar radiation affects sensor readings, and the trap is susceptible to sudden strikes or shocks that cause false alarms on windy days. Böckmann et al. utilizes Bag of Visual Words to encode clusters of key points extracted by scale-invariant feature transform into some meaningful local features in a so-called visual codebook. This kind of dictionary is then used to incorporate how frequent each feature appears in each patch of newly extracted key points as the input to train an SVM classifier for different classes of flies as well as one background class for a patch of nothing of interest.

In contrast, blueberry containers the precision values decreased after 7 days of the insects remaining on the Yellow Sticky Paper by approximately 20% compared to the test results of the initialization measurement on day 0. Regarding class mean accuracy, the dictionary size had no obvious influence but on the recall in individual categories. Within the individual categories, the recall of the background class was the highest, as expected. A maximum value of 99.13% was achieved without differences in color space conversion or dictionary size. The best classification results were achieved with greyscale images and dictionary sizes of 200 and 500 words. Regarding deep learning techniques, Zhong et al. created a deep-learning-based multi-class classifier that can classify and count six different types of flying insects. The You Only Look Once algorithm is used for detection and coarse counting. To increase the number of training images required by the YOLO deep learning model, the scientists considered the six species of flying insects as a single class. The authors augment the images with translation, rotation, flipping, scaling, noise addition, and contrast adjustment to extend the data set size. They also employed a pre-trained YOLO to fine-tune its parameters on an insect dataset. Support Vector Machine is used for classification and fine counting, with global features. The technique was run on Raspberry PI, with detection and counting performed locally in each trap. The system attained a 92.5 percent average counting accuracy and a 90.18 percent average categorization accuracy. The Dacus Image Recognition Toolkit was created by Kalamatianos et al.. The toolkit includes MATLAB code samples for fast experimentation, as well as a collection of annotated olive fruit fly photos acquired by McPhail traps.

On the DIRT dataset, the authors tested various forms of the pre-trained Faster Region Convolutional Neural Networks deep learning detection technique. Prior to classification, RCNNs are convolutional neural networks containing region proposals that suggest the regions of objects. Faster-RCNN had a mAP of 91.52 percent, where mAP is the average maximum precision for various recall levels. The authors demonstrated that image size has a substantial impact on the detection, but RGB and grayscale images have almost the same detection accuracy. Because Faster RCNN is computationally costly, each e-trap regularly uploads its collected image to a server for processing. Ding et al. created a technique for detecting moth flies. Translation, rotation, and flipping are used to enhance the visuals. To balance the average intensities of the red, green, and blue channels, the photos are pre-processed with a color-correcting algorithm. The moths in the photos are then detected using a sliding window Convolutional Neural Network . CNNs are supervised learning algorithms that use learned weights to apply filters on picture pixels. Back propagation is used to learn the weights. Finally, Non-Max Suppression is used to remove the overlapping bounding boxes . Using an end-to-end deep learning neural network, Xia et al. detect 24 kinds of insects in agriculture fields. A pre-trained VGG-19 network is utilized to retrieve the features. The insect’s position is then determined through the Region Proposal Network . The proposed model had a mAP of 89.22 percent. Recently, YOLO is proving its notable performance in the work in pest detection. Especially, the reported results of YOLO v5 by the authors illustrate the mAP of 94.7 percent, where it has the highest recall score of 0.92 among all the other state-of-the-art methods, such as Fast RCNN, Faster RCNN and RetinaNet.

The models have been pretrained on COCO dataset and later fine-tuned on a training dataset of 4480 sub-images made from 280 images of yellow sticky pheromone traps. However, YOLO v5 is considered slower than YOLOv4. For the AI implementation on edge devices, works in demonstrate the AI applications on edge devices pest monitoring as well. In [30], Lynfield-inspired trap was used with naled-and fipronilintoxicated methyl eugenol in replacement of the yellow sticky paper trap combined with object detection system to detect only targeted oriental yellow flies. Unlike the yellow sticky paper, the substance is proved to only attract harmful fruit flies and the detection problem is thus reduced to one class detection for detecting the existence of the fruit flies and verifying whether the detection is correct. The work showed primary work and provided foundation to further develop real-time system for yellow fly detection in on-field scenario. Compared to [27], the application Single Shot Multibox Detector with variant backbones and YOLOv4- tiny show significant speed performance to YOLO v5, while taking the raw images as input instead of segmented sub-images. Nevertheless, the work also showed limitation of applying detection models on edge device due to the slow processing speed, which will be further addressed in this article.Most of the time, insects are not stationary, so it is difficult to get a clear image of flying insects. In studies [32 – 35], the authors chose insect specimens that were well-preserved in an ideal laboratory environment to capture images of the insects at high resolution. However, since fewer environmental factors are considered in this method, it is limited in specific applications. In this study, we designed a unique automatic autonomous environment data reading and pest identification system to try to eliminate the above problems. Being largely motivated by preventing the oriental fruit flies from destroying citrus fruits such as oranges and grapefruits, we come up with a trap which targets only that one type of the species, best indoor plant pots which is specifically named B. Dorsalis. This can be achieved by replacing the yellow sticky paper with the naled-and fipronil-intoxicated methyl eugenol attractant to assure only B. Dorsalis flies are lured into the trap. It eases the classification and counting process as no other insects will get attracted by the methyl eugenol attractant . The system involves a two-fold setting: a) an electronic system reads environment data with a sticky trap installed and a digital camera is set up to collect images of the flies, b) the object detection software to recognize fruit flies on the image before sending all information via email or SMS to alert farmers independently. The whole system is autonomous and powered by a solar system. This system is implemented on an Arduino Uno and Raspberry Pi system. The results provide precise prevention and treatment methods based on the combination of pest information and other environmental information. Based on this edge computing design, the computation pressure on the server is alleviated and the network burden is largely reduced.

The edge-computing traps are designed to work separately and individually re-port the count of fruit flies to the farmers. They are spread, based on the effectiveness of the attractant, such that each 2-3 devices can cover an area of 1000 square meters.Overall, the hardware part of the system consists of five interconnected subsystems with distinctive functions and behaviors, which are described in Figure 1, namely the solar panel system, the control system, the sensor system, the trap, and the object detection and communication system. The power system of the trap contains a solar panel, a battery, and a solar charge controller . The solar panel converts the solar energy to DC current with 830 mA to power the trap system. The converted energy is stored in an electro-chemical energy storage with a capacity of 5 Ah and a voltage of 12 V. The Arduino in the operating system will check voltage of the battery with a voltage sensor to make sure the battery voltage is above a certain level required for the system’s operation. If the condition is not met, the object detection module will not be operated. The Pulse Width Modulation solar charge controller is used to control the device voltage, open the circuit, and halt the charging process if the battery voltage is above a certain level. The operation system is controlled by an Arduino micro-controller board. As aforementioned, the Arduino module reads the battery voltage with a voltage sensor from the sensor system to decide whether to turn on or off the object detection system, which is controlled by the Raspberry module. The SSR10D is used to control activate and deactivate the object detection system. The SSR10D is a solid-state relay and uses lower power electrical signal to generate an optical semiconductor signal as an activate signal for the opto-transistor to allow high voltage going into and powering the device’s output device, which is the Raspberry device in this case. In addition, the lower electrical signal is the output from the 2N2222 bipolar junction transistor receiving control signal from the Arduino module. Hence, the Arduino can stop the Raspberry Pi 3b+ computer drawing current from the solar system after it is shut down. The sensor system takes responsibility for measuring the three important factors, temperature, humidity, and light. Also, it records the current created by the solar system and the voltage battery. The humidity and temperature, which also affect the living environment of the yellow flies, are measured with the AM2315 I2C sensor. RGB and clear light is measured with the TCS34725 light sensor with IR filter and white LED. In addition to sensor system, INA219 is used to read the solar current and battery voltage information. Moreover, a DS1307, which is a battery-backed real time clock , is used to help the microcontroller keep track of time. The information from the sensors along with their corresponding time are stored in an SD card attached on the device. These two factors, the operation system and sensor system, help the microcontroller decide whether to turn on the object detection or not. The object detection system, shown in Figure 1e, is operated by the Raspberry Pi 3b+ and collect images for its fruit fly detection algorithm with a Waveshare Pi camera with 5 MP. The camera is placed at the top of a double-size Lynfield shape trap with several holes at the bottom, shown in Figure 1d. To attract and capture only the yellow flies, methyl eugenol is used as the attractant to the insects, which later helps to simplify the detection and classification problem. The Raspberry Pi module will receive data from the sensor system and send all data to the notification system to notify or alert farmers about the environmental data and the number of detected fruit flies through email or SMS. The behavior of the whole system is described in the flow chart shown in Figure 2.The architectures used to train the yellow fly detection models are SSD with MobilenetV1 and MobilenetV2 backbones, and YOLOv4-tiny. The selected models are all single-stage detection models since, compared to their counterpart, the two-stage detection models, the single-stage detection models have been shown to have a faster processing speed with a competitive performance. Moreover, the three models were selected because of their comparable parameter size and their feasibility for real-time implementation on edge devices.

We show that six of eight ant species limit CBB colonization of berries and that the effect of ants is independent of ant activity on branches. This study is the first field experiment to provide evidence that a diverse group of ant species limits the CBB from colonizing coffee berries.To test the effects of each ant on CBB colonization of berries, we performed an ant exclusion experiment. We surveyed bushes occupied by one of the eight target ant species. We excluded coffee bushes with few branches to control for the size of the foraging area of each ant species. On each bush, we searched for two branches of equal age and position and roughly the same number of coffee berries . On each branch, we removed all berries that had CBB entrance holes. We then removed all ants from one branch and applied tangle foot to the base of the branch near the coffee trunk. On the second branch, we left ants to forage freely . To estimate ant activity, we counted the total number of ants foraging on the stem, leaves, and berries of each branch for 1-min including those that travelled onto the branch during the 1-min survey. We also counted ants on exclusion branches after the experiment and if a branch had more than one ant individual present, we excluded the bush from analysis . To release CBB onto control and treatment branches, we created a leaf platform to aid their chances of encountering berries. The leaf was wedged between the branch stem and a cluster of berries to create a platform surrounding the cluster . A coffee leaf was used as a platform because artificial structures attract attention from many ant species. After waiting several minutes to ensure normal ant activity, blueberries in pots we released 20 CBBs on the leaf platforms of the control and exclusion branches.

After 24 h, we counted the number of berries per branch that had CBBs inside entrance holes. We did not count partially bored holes in berries, nor CBBs that had bored into twigs and leaves. Multiple bored entrance holes per berry were only counted as one bored berry. We modified the experiment slightly for P. simplex and P. ejectus because of the difficulty in locating these species within a bush using visual cues . For these two species, we used the living branch to which the nest was attached to as the control branch . This was done because we wanted to make sure that ants were actively foraging on control branches after the disturbance of removing nests. To statistically analyze experimental data, we opted to use linear mixed models instead of paired t tests because mixed models allow inclusions of experimental non-independencies through the incorporation of covariates. We included bush as a random effect in the model to pair control and exclusion branches within each bush. Ant species and treatment and the species 9 treatment interaction were included as fixed effects in the model. To control for differences between each branch and bush, we included the number of berries per branch, the number of berries in contact with the leaf platform, and the number of worker ants per branch as covariates in the model. We performed type III F tests of significance for main effects with maximum likelihood to estimate the fixed effect parameters and variance of random effects . We removed non-significant factors from models and compared nested and null models with likelihood ratio tests to determine the best-fit model. We also compared ant activity across different species to determine if this factor might correlate with berries bored and vary across ant species.

To determine if ant activity correlated with the number of coffee berries bored, we limited the dataset to only control branches and used a generalized linear model with a Poisson log-link function because data did not meet the assumptions of normality. To determine if ant activity varied by species, we again limited the dataset to only control branches and used ANOVA with Tukey’s HSD analysis. We tested the normality of the data with qq-plots and Kolmogorov–Smirnov tests of model residuals. We conducted all statistical analyses with SPSS .Our study represents one of the first field experiments showing that a broad survey of ants reduce colonization of coffee berries by the CBB. This is in contrast to previous studies that suggest ants may not have any effects on CBB, especially in field experiments . Our results are in accordance with other observational studies that show that specific ant species may limit CBB in coffee plantations, yet these studies have either focused on the most dominant or abundant species observed or investigated the broad community-wide impacts of ants on the CBB . Our experimental approach is limited to our understanding of how ants control CBB colonization of berries and not other life stages of the CBB. Our study suggests that ant occupation of coffee bushes is very important during a seasonal period when new coffee berries develop and the CBB begins to disperse from old infested berries to developing un-infested berries . It is surprising that Crematogaster spp. and S. picea did not limit the colonization of berries, considering that other studies have shown species within these two genera have important effects on herbivores .

Low ant activity on coffee bushes with Crematogaster spp. or S. picea cannot explain these results because thesespecies had greater activity per branch than P. ejectus and P. simplex and equivalent activity to A. instabilis and P. synanthropica, species that did limit CBB damage. One explanation could be that because we grouped five Crematogaster spp. together into a single treatment, effects of individual species may be masked. Solenopsis picea may have an effect on CBB colonization, but only with higher ant activity or when CBB are in closer proximity to nest entrances. This species also has a small body size and moves relatively slowly in comparison to the species that did have an effect, which might have limited it from removing or easily capturing CBBs. Wasmannia auropunctata is of similar size to S. picea and still had strong effects on CBB. However, W. auropunctata had significantly higher ant activity on branches as compared to S. picea. Perhaps the combination of low activity, small body size, and slower movement limited S. picea from affecting the CBB. While we found no effect of S. picea on CBB colonization of berries, it may be that S. picea, and other smaller ants, have important impacts on the CBB at other stages of the CBB life cycle because they can pass into entrance holes of the CBB . Experiments with both P. simplex and P. ejectus employed slightly different methodologies than the other ant species, which may have intensified the effect of these ants. For these two species, hollow twigs that contained ants were attached to a branch with berries and this branch was used as the control branch in the experiment. This likely elevated the number of ants per branch per minute. However, in the lab, P. simplex had similar effects on the CBB . Additionally these two species had the lowest densities on control branches of all other species, averaging 3.6 and 3.7 ants per branch for P. ejectus and P. simplex, respectively. Thus, these species have effects at very low numbers, and the results of this study should only pertain to branches for which the density of these species reaches this mark. Certain aggressive ants that limit CBB colonization of berries might also benefit CBB after colonization. Larger ants cannot enter berries, but if they are aggressive competitors for space, square plant pots they will prevent other ants from occupying the branches they patrol . These ants, likely A. instabilis and P. synanthropica, may provide CBB with enemy free space after the CBBs colonize berries in their territories. In conclusion, we find that six of eight ant species limited CBB colonization of coffee berries suggesting that ants, generally, provide important pest control services within coffee agroecosystems. This is the first field experiment to demonstrate general ant limitation of CBB colonization. This finding is important considering that chemical pesticides are thought to be ineffective at controlling the CBB . Nonetheless, ants do not completely control the CBB, other control agents like birds, parasitoids, and fungal pathogens also aid in the control of the CBB . Further work should look at larger scale impacts of ants on the CBB, such as farm scale impacts. Also, more theoretical work is needed to understand how ants impact the CBB at different stages of its life cycle and to reveal which stage of the life cycle is most important for population regulation. Nonetheless, this study provides strong evidence that ants defend coffee from CBB colonization.

Seminal work by Thouless and coworkers pointed out that band insulators are not identical, but can differ in fundamental respects, that are characterized by a topological property of the bands. The central example discussed was the integer quantum Hall state, whose topological properties are characterized by an integer which is essentially the Hall conductance. Realizing such a state naturally requires breaking of time reversal symmetry, typically by the application of a strong magnetic field on a two-dimensional system. The topological nature of the integer quantum Hall state is also revealed by studying the edge of a two-dimensional sample, where chiral edge states occur at energies within the bulk energy gap. Recently, it has been realized that band insulators with spin orbit interactions can also be characterized by their band topology. In two dimensions, the quantum spin hall phase is closely analogous to the quantum Hall state. However, since it preserves time reversal symmetry, it has a pair of counter-propagating one-dimensional modes at the edge. Such a state can occur with SOIs that preserve spin rotation symmetry about an axis. It was shown in Ref., that even in the absence of such spin rotation invariance, the counter-propagating modes remain protected by time reversal symmetry. The topological property of these insulators are characterized, not by an integer, but by aZ2 number, so that all topologically non-trivial insulators of this kind fall within the same topological class. An experimental realization of this phase has been reported in HgTe heterostructures. Turing to three dimensions, an insulator with nontrivial band topology can be realized just by stacking such two-dimensional QSH states. These are called the weak topological insulators . However, a more surprising possibility, the strong topological insulator , has been predicted theoretically. Once again, the surface physics is exotic, which provides a physical characterization of this phase. STIs have an odd number of Dirac nodes on their surface, which are stable against moderate perturbations that preserve time reversal symmetry. Such a band structure cannot be realized in any two-dimensional system with time reversal invariance. There have been experimental realizations of these predictions in bismuth antimony and in bismuth selenium, which have been verified by angle resolved photoemission spectroscopy. Note, in contrast to the QSH state, in order to realize the STI the SRS must be completely broken. The topological insulator and QSH phases normally exist in systems with strong SOI that explicitly breaks SRS. However, as pointed out in Ref.an extended Hubbard model on a two-dimensional honeycomb lattice can have spontaneous SRS breaking and result in a QSH phase, with the right kind of repulsive interactions. SRS is only preserved about an axis ˆn, which is spontaneously chosen, leading to gapless Goldstone modes. This was termed a topological Mott insulator – the separation of energy scales between the low lying magnetic excitations and the gapped charge excitations being typical of Mott insulators. We will also adopt this nomenclature although it must be noted that local moment physics, often associated with Mott insulators, does not occur here. Subsequently, it was argued in Ref. that skyrmions of ˆn carry charge 2e. Here, we consider the analogous problem of a three-dimensional system without bare spin orbit couplings, and full SRS, being driven into a TI state by strong interactions. The key difference from the two-dimensional case, is that in order to realize the STI, SRS must be completely broken. Hence the order parameter in this case is a rotation matrix ←→R ∈ O, similar to super fluid Helium-3 A and B-phases. Physically, this order parameter describes the orientation of the spin coordinate system, relative to the spatial coordinates. Spatial variations of the order parameter lead to a rich set of topological textures.

The Axion Maestro Edge is designed as an out-of-the-box benchtop electrophysiology system with maximum comfort and usability. Although it has the highest price per channel, it is also an incubator. The Intan RHD USB interface board and head stages require more effort to calibrate, ground, and shield. Unlike Axion, Intan designs and code are open source. Intan bio-amplifier chips have been used in many open source systems, including Intsy, Willow, Open Ephys, and now Piphys. Both Intan and Axion systems provide valuable perspectives for comparison to Piphys. Axion produces the lowest noise baseline but has a different bio-amplifier circuit. Piphys and Intan have the same bio-amplifier chip; therefore Intan is a good reference for ensuring Piphys has the same noise floor and low EMI. Piphys and Intan RHD interface board differ in the way they sample the bio-amplifier. Specifically, Intan has more stable sampling with FPGA, while Piphys samples the chip with a CPU, which has more clock jitter . Overall the neural waveforms recorded on both systems are statistically comparable in shape for neural spikes for the detected neuron. Other comparable platforms in the literature include Intsy, Willow, and Open Ephys. Intsy was designed for measuring gastrointestinal , cardiac , neural , and neuromuscular signals. Willow was designed for high channel count neural probes and resolved the need for many computers by writing data directly to hard drives. Open Ephys is an alternative system to Intan integrating more features into their GUI for closed-loop experiments and plugin-based workflows *. Noise measurements for Piphys, Intan, and Axion were experimentally recorded, blueberry pot while noise measurements for Intsy, Willow, and Open Ephys were cited. Intan claims 2.4 μV RMS as typical in the datasheet for their chips # which was likely inherited into Open Ephys documentation.

Remote longitudinal recording of neural circuits on an accessible platform will open up many exciting avenues for research into the physiology, organization, development, and adaptation of neural tissue. Integration with cloud software will allow in-depth experimentation and automation of analysis. The proof of principle for Piphys has been shown on 2D cultures. As experiments with other devices have shown, it should be applicable to measurements of 3D brain organoids, which are becoming an increasingly popular model for studying human brain tissue developmentand function. One example application of Piphys would be monitoring how genotypes affect neural activity over the course of organoid development. More generally, IoT devices would allow less invasive and less laborious collection of longitudinal datasets of organoid development, to benchmark what wild-type organoid activity looks like throughout the first few months of growth. It would be interesting to compare whether different protocols and cell lines affect organoid activity over the course of development. IoT devices could be distributed and shared to compare whether organoid datasets are replicable and comparable between different labs, using the same low-cost hardware. Many electrode probes have been designed to interface with tissues to provide measurement points for voltage recordings. Future work on Piphys would involve expanding the number of different electrodes types for long-term culture of the biological sample through collaborations with other research groups. Future work on Piphys also includes increasing sampling rate and precision of timing in between samples. Currently, the Raspberry Pi CPU samples the Intan RHD2132 bio-amplifier chip, and the sampling rates are limited by the CPU’s ability to multitask. Future solutions may involve adding another CPU or FPGA to the hardware shield.

The platform will continue to be improved, and its modularity allows adapting hardware and software components as different needs arise. The current proof of concept design is based on a Raspberry Pi chip and uses one 32 channel chip attached to one of the SPI ports. The system can be easily extended to sample 64 channels . The channel number can be doubled if the design would include an FPGA and alternative Intan chips that have 64 channels/chip . However, the true scalability advantage of the proposed system lies in its open source and open hardware architecture. If the number of channels is insufficient, the shield board could be modified to accept multiple Raspberry Pi’s, therefore, increase the number of channels. Piphys is the only electrophysiology device that supports Internet of Things software integration out of the box. The IoT hardware modules and cloud software allow for horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale, and integration with other IoT sensors. Piphys has a low entry cost, and the cost per channel can also be significantly lowered by increasing the number of channels supported per device. This would be accomplished by engineering an inexpensive FPGA into the controller shield to sample multiple bio-amplifier chips and buffer those readings for the Pi. Piphys can have a large cost reduction if extra specialty connectors and adapters are removed and it is fitted with a USB cable which is less expensive. The signal-to-noise ratio could be improved by enabling and tuning on-chip filtering, and improving Faraday cage shielding. In vitro cultures typically fire with amplitudes between 10 – 40 μV . They demand sensitive recording equipment, as an increase of just afew μV in noise for spikes on the lower end of the spectrum can be considered a non-trivial variable. Overall, the open source Piphys design, programmability, and extreme flexibility of the Raspberry Pi significantly lowers the entry barrier of the electrophysiology system, providing an opportunity for broader applications in education and research.Prior to cell culture, the electrode surfaces of 6-well Axion plates were coated with 10 mg/mL poly-D-lysine at room temperature overnight.

The following day, plates were rinsed 4 times with water and dried at room temperature. Primary cells were obtained from human brain tissue at gestational week 21. Briefly, cortical tissue was cut into small pieces, incubated in 0.25% trypsin for 30 minutes, then triturated in the presence of 10 mg/mL DNAse and passed through a 40 μm cell strainer. Cells were spun down and resuspended in BrainPhys supplemented with B27 , N2 , and penicillin-streptomycin , then diluted to a concentration of 8,000,000 cells/mL. Laminin was added to the final aliquot of cells, and a 10 μL drop of cells was carefully pipetted directly onto the dried, PDL-coated electrodes, forming an intact drop. The plate was transferred to a 37 °C, 5% CO2 incubator for 1 hour to allow the cells to settle, then 200 μL of supplemented BrainPhys media was gently added to the drops. The following day, another 800 μL of media was added, and each well was kept at 1 mL media for the duration of the cultures, with half the volume exchanged with fresh media every other day. Activity was first observed at 14 days in culture, and the second recordings were performed on day 42 of culture.The power supplied to the Raspberry Pi is through a mains adapter plugged into the wall outlet. To reduce environmental noise and maximize the signal-to-noise ratio , we use a Faraday cage during recording. The Faraday cage is made of 1 mm thick steel and connected to the wall outlet ground. For noise measurement benchmarks on Piphys, nursery pots an empty Axion plate was filled with the same media used in cell culture and placed in the Faraday cage. The noise baseline of this media-only system was an average of 2.36 ± 0.4 μV RMS for all the channels with software filters. Comparison of the baseline noise we measured for Piphys, Intan, and Axion is in Table 1. During the experiment, the systems were compared by measuring the same neural culture on the same plate within a similar time frame. Recordings occurred within 1 to 3 hours of each other. A 300–6000 Hz 3rd order Butterworth bandpass filter was used to attenuate frequency components outside the neural activity range after the recording. Data was analyzed by a spike sorting algorithm and shown side by side in Figure 6 over an identical time length. Instructions and source files for construction of Piphys hardware and software are available open source on GitHub ††. All files are provided ‘as is’ and endusers are encouraged to freely use and adapt the system for their own application-specific protocols.The printed circuit board was designed in Autodesk Eagle. The board has four layers of copper. The top and bottom layers of the board are GND, while the two layers inside are signal and power. Every signal via has a ground via next to it to sink EMI as signals switch layers. The layout of the circuit board is done in modules. Via stitching was done around the perimeter and throughout the board area to separate modules and fill in areas with no components. The amplifier chip and Raspberry Pi computer are separated by a cable such that noise from the computer would not interfere with the sensitive neural signal recording. During data acquisition, all of the electronics and biology were shielded by a 1 mm thick steel faraday cage.We deployed servers and cloud computing platforms to achieve permanent data storage and messaging between the local device and the dashboard. We used Remote Dictionary Server , Amazon Web Services Internet of Things , and Simple Storage Service .

All services are platform agnostic and can be hosted anywhere. For our particular experimental setup, Redis and S3 were hosted on the Pacific Research Platform [30]. The Internet of Things service with MQTT messaging and device management was coordinated through Amazon Web Services . The dashboard was hosted on a server at UC Santa Cruz. The thresholding spike detection shown in the dashboard runs inside a Docker container, which reads from the Piphys data Redis stream and writes to another Redis stream shown in the dashboard. In our case the Docker, the Redis service and the dashboard run on the Pacific Research Platform . However, this could be transferred to any cloud storage provider . The IoT architecture of these cloud services is explicitly described in [53].Redis, near real-time data stream—Neuronal action potential recording with a high sample rate and multiple channels requires a high throughput pipeline to make near real-time streaming possible. Remote Dictionary Server is a good choice for the implementation of this objective. It is a high-speed cloud-based data structure store that can be used as a cache, message broker, and database. Based on bench marking results, Redis can handle hundreds of thousands of requests per second. The highest data rate for every push from Piphys system to Redis is 7.68 Mb for each second .Pi data stream to Redis requires the network bandwidth to be at least 7.68 Mbps so that uploading to the Dashboard through Redis can be uninterrupted. Internet of Things communication—The dashboard is programmed to be an IoT device that sends Message Queuing Telemetry Transport messages to control and check the Piphys system. In response, the Piphys subscribes to a particular MQTT topic to wait for instructions. The AWS IoT supports the communication of hundreds of devices, making the Piphys system’s extension to a large scale possible in the future. Simple Storage Service —The Simple Storage Service is the final data storage location. S3 is accessible from anywhere at any time on the internet. It supports both management from a terminal session and integration to a custom web browser application. After each experiment, a new identifier will be updated on the dashboard. When a user asks for a specific experiment result, the dashboard can pull the corresponding data file directly from S3 for visualization.Managing the benefits people receive from nature, or ecosystem services, requires a detailed understanding of ecosystem processes. In particular, biodiversity-driven services, such as pest control on farms, requires knowledge of cropping systems, the habitats in and around croplands, and the interactions among the many organisms that inhabit them. Interactions are complex and often change over space and time ; therefore, a critical first step is identifying the species and populations that provide benefits to society . Identifying service providers, however, may not be straightforward. For example, predation is rarely witnessed directly, making it difficult to identify the predators of crop pests. Pest control is a critical service; in the United States, insect predators save farmers billions of dollars annually in avoided pest damage . Several different techniques have been utilized to identify predator–prey interactions. An indirect approach is using stable isotopes to determine trophic positions .

Univariate analysis of variance was used for all data in determining significant differences. For descriptive analysis data, multivariate analysis of variance was used prior to ANOVA to determine the main treatment effect. ANOVA was used for judge, treatment, and replicate effects along with a pseudo mixed model. Fisher’s least significant difference was used for pairwise comparisons of means. Statistical significance was set at 5% for all tests.Analysis of Brix, pH, and TA of the musts showed minimal differences among treatments for each variety . There were no significant differences for all three parameters of the BA must and only the reject treatment for GN had a significantly higher TA; however, this difference was not large. It is possible that this difference could be the result of the inclusion of underripe berries in the must, which have a higher TA. Raisins were also rejected from the sorter, which are high in sugar and could have compensated for the difference in sugar from the less ripe berries. The CS must exhibited the most differences among treatments, which was unexpected considering this variety had the lowest percentage of rejected fruit . The Brix was significantly higher in the sorted treatment compared to the control and reject treatments. This may indicate that the sorter was effective at removing less ripe berries for CS. The pH also differed significantly among treatments for CS; pH was highest in the reject must at 3.8, followed by sort and control at 3.71 and 3.67 respectively. Although the difference in pH between the sort and control was statistically significant, they are very similar with only a 0.04 pH unit difference. Overall, growing blueberries in pots the differences seen in the must chemistry were minimal and likely made little to no difference in the progression of the wines.

It is possible that the reject must composition was made to be more similar to the control and sort treatments due to the addition of juice that accumulated in the vibrating table trays. If this was not done perhaps there would be more differences in must composition when comparing the reject to the sort and control treatments. Wine chemical compositions are shown in Table 5. All wines progressed consistently through fermentation and fermented dry with less than 1 g/L residual sugar. For the most part, wine chemical compositions are similar among treatments for each variety, especially between the sort and control treatments. However, there are some important exceptions. This mostly corresponds to differences in the starting sugar content, although there is a discrepancy as BA reject wines were not significantly lower in Brix. However, Brix was determined after mixing of the must, and especially if a significant number of raisins were present, soak up in the next 24 h could have resulted in sugar increases. The malolactic fermentations for GN and CS wines progressed to completion; however, the control and sort treatments for BA did not finish and were left with close to 1 g/L malic acid for each of the treatments . This is likely due to the high ethanol content in addition to high TA in the wines which can inhibit malolactic bacteria. This would also explain why the reject treatment for BA progressed further in the malolactic fermentation given that these wines were lower in ethanol content and TA. This difference could have important implications for the sensory analysis of BA wines. The volatile acidity for the CS reject treatment was significantly higher than that for the control and sort treatments . The sensory threshold has been reported to be approximately 0.8 g/L for red table wines, therefore, this discrepancy may not have a large impact on sensory analysis.

It was surprising that GN musts/wines showed few significant differences despite having the largest rate of rejection . It is possible that sorting parameters were too aggressive when processing GN, which may have inadvertently led to the rejection of optimal fruit. As previously mentioned, there was significant variation in color for GN fruit . It may also be possible that observed color difference in GN fruit did not correlate well with sugar content. This would mean that optical sorting based on color for GN fruit from this vineyard is potentially less effective than for the other varieties.Differences among treatments were observed in total phenolics, tannin, and anthocyanin content as measured by the Adams-Harbertson assay . In general, the reject wines were higher than control and sort wines in total phenolics and tannin, and lower in anthocyanin. This may be explained by the inclusion of MOG in the reject fermentations which can lead to greater extraction of phenolics. Lower anthocyanin levels wereobserved in the reject wines for all varieties. This is most likely due to the inclusion of green, underripe berries, which contain less anthocyanin.In general, the results from the RP-HPLC analysis of phenolics agree with the results obtained from the Adams-Harbertson assay . Higher levels of most phenolic compounds were observed in the reject treatments. Concentrations of gallic acid and catechin were higher in the reject treatments for all three varieties and dimer B1 was higher in reject wines for BA and CS. Less ripe berries have been shown to contain more of these compounds, which can explain this trend. Higher levels of identified flavan- 3-ols were also observed in the reject treatments of BA and CS wines, which is also in agreement with results found by Obreque-Slier. An interesting trend was found in relation to the proportions of simple hydroxycinnamic acids and their respective tartaric acid esters. All the reject treatments had very low amounts of caftaric and coutaric acid compared to caffeic and p-coumaric acid. It is possible that hydroxycinnamoyl esterase, the enzyme responsible for hydrolyzing the ester linkage, had a greater activity in the reject wines, possibly due to differences in pH . Another possibility is that there could be higher levels of this enzyme in less-ripe fruit. The reject wines for all three varieties were also significantly lower in anthocyanin, which matches results obtained by the Adam-Harbertson assay.

Although reject wines had higher levels of most phenolic compounds, this did not lead to large differences between sorted and control wines. It is likely that not enough material was removed during processing for there to be a significant effect. This may also explain why there were no significant differences in anthocyanin content between sorted and control wines despite reject wines being significantly lower. Perhaps a greater effect would be observed with more aggressive sorting parameters and/or fruit with more variability. Overall, the levels of most phenolic compounds identified were very similar between the sort and control treatments. It can be concluded that optical sorting had little impact on the composition of phenolic compounds between sorted and control wines in all three varieties tested.For CS wines, 37 volatile compounds were identified, 20 of which differed significantly among treatments ; however, only one compound differed significantly between wines made from sorted and control treatments . A Principle Component Analysis biplot plot of significant compounds is presented in Figure 1. It appears the separation is driven primarily by ethyl esters on the left and higher alcohols on the right. Most ethyl esters have higher concentrations in wines made from control and sort treatments . Esters in wine can be formed by an acid catalyzed esterification reaction between an acid and alcohol.Higher amounts of either acid or alcohol can result in increased formation of esters. Wines made from control and sorted treatments had higher ethanol content than wines made from the reject treatment, which would explain this trend. Another important trend is the association of reject treatment wines with a larger concentration of higher alcohols. The suspended solids concentration was significantly higher in reject treatment musts which may explain the difference in the concentration of higher alcohols among the treatments, as previous research has shown that suspended solids during fermentation can lead to greater production of higher alcohols. PCA loading and score plots of volatile analysis for BA wines are given in Figure 2. Thirty-seven compounds were identified, drainage gutter and nine differed significantly among treatments . Again, separation seems to be driven by the proportionally larger presence of higher alcohols in the reject treatments. Like the CS reject musts, the BA reject musts also had significantly higher levels of soluble solids compared to the sort and control treatments . Although most ethyl esters did not differ significantly among treatments for BA, there was a general trend indicating that ethyl ester content was higher in the control and sort treatments . A PCA biplot using all identified ethyl esters and higher alcohols is provided in Figure S1 and there is a clear trend in the separation of these compounds. This agrees with the previous discussion regarding ethyl ester content in the CS wines. The BA control and sort treatments had significantly higher ethanol content compared to the rejects so it is expected that ethyl ester concentration would be higher as well. One exception to this trend is that ethyl lactate was significantly higher in the reject treatment.

The reject wines completed ML fermentation, but the control and sort wines got stuck with almost 1 g/L malic acid . Therefore, ethyl lactate is significantly higher in reject wines because there was more lactic acid present from the conversion of nearly all the malic acid.For GN wines, 32 compounds were identified, nine of which differed significantly among treatments and four differed significantly between sort and control treatments. The same trend was observed for higher alcohols for GN as for the other varieties driving separation in the PCA plot . The concentrations of cis-3-hexen-1-ol, trans-3- hexen-1-ol, and hexanol were all significantly higher in the reject treatments. Again, this is most likely due to higher suspended solids content in the reject treatment musts . The trend with ethyl esters was not observed for GN wines, most likely because all treatments had similar ethanol content . Overall, the results indicate that optical sorting had a minimal effect on the aroma profile for all three varieties, particularly when comparing sort and control treatments.Given the uniformity of chemical results among biological replications, it is fair to assume that the two replications used for descriptive analysis are representative, and the chemical results can therefore be used to discuss sensory trends. MANOVA was performed and revealed a non-significant treatment effect for all three varieties . From this result, it can be concluded that all three treatments for each variety were similar in sensory properties. Despite this result, ANOVA was carried out on individual attributes and some significant differences were found for each variety . For GN, only one attribute out of twenty differed significantly among treatments. It is possible that sensory analysis was done too soon after the wines were bottled and the levels of molecular sulfur dioxide may have been above sensory threshold of about 2 mg/L. Figure 4 gives a PCA biplot with all attributes from the GN descriptive analysis panel. There are no clear trends from the PCA; therefore, it can be concluded from MANOVA, ANOVA, and PCA results that all treatments lead to wines of similar character for GN wines. When ANOVA was performed on data from the BA descriptive analysis panel, three out of twenty-six attributes were found to be significantly different among treatments. “Alcohol hotness” had a significant judge-by-treatment interaction. Results from the pseudo mixed model indicated the interaction effect was more important than the treatment effect. Thus, “alcohol hotness” will not be included in any further discussion of significant attributes for BA wines. The significant difference in malic acid content in the wines among treatments appears to have had little impact on sensory evaluation given that there was no significant difference in the perception of sourness in the wines. From the PCA generated from BA descriptive analysis results , the control and sort wines appear to be correlated more closely with “alcohol” . Wines made from these treatments were higher in ethanol content, which may explain this trend. However, the small number of significant attributes indicate that BA wines made by different treatments were very similar in sensory properties.For the CS descriptive analysis panel, three out of twenty-two attributes were found to be significantly different when ANOVA was performed. A PCA biplot from the CS panel is provided in Figure 6.