The exposure variable as well as covariates were all measured using self-reported survey data and subject to recall bias, which has been well described for exposure and disease studies. We limited recall bias in the survey by anchoring the past in memorable events such as recent rainy and dry seasons as well as holidays. While survey respondents sometimes found it difficult to precisely quantify household land area, the evidence for recall bias in agricultural surveys in sub-Saharan Africa is limited. Additionally, infection outcomes are limited by the sensitivity and specificity of available diagnostic methods: urine filtration for S. haematobium and duplicate Kato-Katz examination of two stool samples for S. mansoni. The detection methods used for S. haematobium are more sensitive compared to those used for S. mansoni , but the low sensitivity of diagnostic techniques used to detect S. mansoni infections—especially low intensity infections—may have contributed to the inconclusive results we observed for this parasite species. Our findings add a new dimension to the notion that the benefits of water resources development for food security are offset by infectious disease. While we cannot speak to the dam’s net impact, we find that schistosomiasis risk may be a result of land use for subsistence livelihoods as well as landscape-level environmental change. Residents of the lower basin of the Senegal River face an unfortunate trade-of where the prevailing economic activity may make them sick.Every bio-process in which cells are the final product or used in the production process requires suitable culture conditions for cell growth and product quality. In the rapidly growing cellular agriculture/cultivated meat industry, where cells are grown for consumption to replace carbon‐intensive and often unethical animal agriculture,plastic plant container cost‐effective media has been identified as the most critical aspect in scale‐up and commercialization .
Optimizing these conditions is difficult due to a large number of media components with nonlinear and interacting effects between cells, medium, matrix material, and reactor environment . Typically, culture media used for processes in cellular agriculture consist of a basal medium of glucose, amino acids, vitamins, and salts supplemented with fetal bovine serum for improved cell survival. FBS is an undefined, animal‐derived serum consisting of proteins, hormones, and other large molecular weight components, and contributes substantially to the cost of media . Even when enriched with additional growth factors or FBS, media is often far from optimal for all cell types and requires adaptation and/or optimization , which is difficult for media mixtures with >30 components, as is common in cell culture. To manage this complexity, design‐of‐experiments methods are often employed in which factors are set to a user‐specified value and outputs are measured . These DOE designs are arranged in such a way that statistically meaningful correlations can be found in fewer experiments than techniques like intuition, “one‐factor‐at‐a‐time” sequences, or random designs. A more advanced form of this is to use sequential, model‐based DOEs such as a radial basis function or Gaussian Process , combined with an optimizer/sampling policy, to automatically select sequences of optimal designs. These approaches are often more efficient than traditional DOE at optimizing systems using fewer experiments and allow for more natural incorporation of process priors , measurement noise , probabilistic output constraints and constraint learning , multi-objective , multi-point , and multi‐information source designs . Even with these methods available, limitations still exist. In previous work, we applied a machine learning approach to optimize complex media design spaces but had limited success due to the difficulty in measuring cell number for multi-passage growth . Therefore, in this study, we utilized a multi‐information source Bayesian model to fuse “cheap” measures of cell biomass with more “expensive” but higher quality measurements to predict long‐term medium performance.
We refer to the simpler and cheap assays as “low‐fidelity” IS, and more complex and expensive assays as “high‐fidelity” IS. While not always predictive of long‐term growth, these lower fidelity assays are at least correlated with cell health and can help in identifying interesting regions of the design space for further study with the high‐fidelity IS. We used this model, with Bayesian optimization tools, to optimize a cell culture medium with 14 components while minimizing the number of experiments, optimally allocating laboratory resources, and building process knowledge to improve our optimization scheme and model. In Section 2 we discuss the computational and experimental components of this BO method. In Section 3 we present the results of the BO method in comparison to a traditional DOE method, followed by Section 4 where we demonstrate the importance of fusing multiple sources of information to obtain relevant process knowledge and/or optimization results.The system under consideration was the proliferation of C2C12 cells. These cells are immortalized muscle cells with similar metabolism and growth characteristics as other adherent cell lines useful in the cellular agriculture industry. Cells were stored in 70% DMEM , 20% FBS , 10% dimethyl sulfoxide freeze medium at −196°C until thawed. Vials were thawed to 25°C and the freezing medium was removed by centrifugation at 1500 g for 5 min. The centrifuged cell pellet was resuspended in 17 ml of DMEM with 10% FBS and placed on 15 cm sterile plastic tissue culture dishes . Cells were incubated in a 37°C and 5% CO2 environment. After 24 h the medium was removed, the culture dish‐washed with Phosphate Buffer Solution , and fresh DMEM with 10% FBS was introduced. After an additional 24 h, cells were harvested using tripLE solution , diluted in PBS, and counted using Countess II with trypan blue exclusion and disposable slides . The process of removing cells from a plate, counting, and re‐plating them with fresh medium is called sub-culturing or passaging.
How well the C2C12 cells survive and grow after passaging is indicative of their long‐term potential in a large cellular agriculture process. The design space was comprised of the components and minimum/maximum concentrations listed in Table 1. These components were chosen because they are often used to supplement standard DMEM to improve cell growth; this represents a reasonable test case for the industrial application of these multi‐IS BO methods to the cellular agricultural industry. The composition of standard DMEM , is shown in Table 3, and should not be confused with the base DMEM “supplement” , which contains only amino acids, trace metals, salts, and vitamins and none of the other 14 components. pH and osmolarity are not controlled in this study, so act as latent variables.Production scale cellular agricultural processes will require >10 passages of cell growth so optimizing growth based on single‐passage information is not adequate . However, multi-passage growth assays are difficult/ expensive to measure, and even more difficult to optimize when given many components. We managed this complexity by coupling long‐term cell number measurements with simpler but less valuable rapid growth chemical assays in murine C2C12 cultures as a model system for cellular agricultural applications, capturing a more wholistic model of the process. We combined this with an optimization algorithm that efficiently allocates laboratory resources toward solving argmax D x for desirability function D x , a function that incorporates both cell growth and medium cost. This resulted in a 38% reduction in experimental effort, relative to a comparable DOE method, to find a media 227% more proliferative than the DMEM control at nearly the same cost. As the longer‐term passaging study suggests, our Passage 2 objective function and IS were well‐calibrated to mimicking the complex industrial process of growing large batches of cells over many passages,blueberry container with Passage 4 cell numbers well‐predicted by this objective function. The reasons for the success of the BO are myriad. The BO method iteratively refines a single process model to improve certainty in D x‐optimal regions, whereas the DOE relies on a series of BB designs where the older data sets are ignored because they were outside of the optimal factor space. The BO also used a variety of IS, whereas the DOE only used a single low‐fidelity AlamarBlue metric . Looking at Figure 8c, the AlamarBlue and LIVE tended to cluster around the point y = 1, making it difficult to distinguish between high‐quality and low‐quality media. This may be due to the deviation of linearity of the %AB and F530 metric at high biomass. The BO method also refined its multi‐IS model over the entire feasible design space, allowing it to take advantage of optimal combinations and concentrations of all 14 components over the entire domain, whereas the DOE needed to reduce the design and factor spaces to reduce the number of experiments needed, and may have identified the wrong optimal boundary locations resulting in suboptimal experimental designs. The BO method was also able to leverage information about process uncertainty to improve the model is poorly understood regions of the design space, whereas the steepest accent method used by the DOE chased after improved D x with little regard for overall noise or experimental errors.
This was worsened by the sensitivity of the polynomial model to random inter‐batch fluctuations in %AB, which may have driven the DOE to suboptimal media. Note that the success of our BO method should not be taken as generic superiority over all potential instantiations of DOE or commercial media used for C2C12 growth. While the BO method worked well at solving the experimental optimization problem, the multi‐IS GP accuracy was limited to highly sampled regions of the design space, thus limiting the efficacy of sensitivity analysis. This was a conscious decision made to trade off postfacto analysis for sampling media with high desirability D x . Accuracy was also limited by the low amount of data N available relative to the large dimensionality p, which is inherently the case in complex biological experiments where each batch of q experiments takes >1 week to evaluate. Finally, the hyperparameters θ* used in the multi‐IS squared exponential kernel were deliberately regularized with prior distributions to smooth the posterior of the prediction μ x . Regularization may have diminished the quality of the inter‐IS correlations; the model hyperparameters ignored features where IS differed in favor of a simpler correlative structure to explain the data. This is seen in Figure 8b,c, where the kernel evaluations show nearly equal inter‐IS correlative strength for most IS used. This may have “squished”/ignored features that could have provided additional information, but at the cost of sampling the design space too widely, again a deliberate choice of model skepticism towards outliers. Even with these limitations, the BO method clearly performs well on media optimization systems relevant to cellular agriculture, that is, those with multiple and potentially conflicting information sources with varying levels of difficulty in measuring. The media resulting from the BO algorithm supported significantly more C2C12 cell growth with only a small increase in cost. This algorithm performs better than traditional DOE in this case, especially in incorporating critical data from growth after the multiple passages in an affordable manner. With these results, it should be possible to implement this type of experimental optimization algorithm in other systems of importance to cellular agriculture and cell culture production processes with difficult‐to‐measure output spaces, including for optimization of serum‐free media for cell growth and for differentiation.Water management is becoming more challenging by the effects of climate change, population growth, and severe competition for water by the municipal, agricultural, industrial, and energy sectors. Accordingly, integrated water resources management focuses on water demand and supply management to achieve sustainable development. Water is a scarce resource essential for societal survival and functioning. This makes the application of integrated water resources management essential to cope with scarcity and the challenges posed by climate change and increased water demand to by expanding economies. A conceptual framework combining integrated landscape management and institutional design principles perspectives was applied to analyze cooperation initiatives involving water suppliers and agricultural stakeholders from agricultural wastewater. A national drought risk assessment for agricultural lands taking into account the complex interaction between different risk components was presented. The research showed that crop diversification, crop pattern management, and conjunctive water management can be effective in improving agricultural water.