Science in the Supply Chain: Collaboration Opportunities for Advancing Sustainable Agriculture in the United States

Consumers and corporations are increasingly interested in understanding the sustainability of agricultural supply chains and reducing the environmental impacts of food, fiber, feed, and fuel production. This emerging need to quantify environmental impacts from agricultural production creates an opportunity for collaboration with the scientific community. Without such collaboration, sustainability efforts risk failure by adopting unrealistic goals or misguided approaches. This commentary explores the role of science in Field to Market, a nonprofit organization developing a sustainability program for US commodity crops, and highlights opportunities to address emerging science challenges. We evaluate changes over the past 35 years in key environmental impacts of crop production used to inform land managers as well as companies that are committed to improvements. Achieving improvements will only be possible if three key gaps are addressed regarding available simulation models and data, scale of implementation and uncertainty, and effectiveness of conservation practices. Filling these gaps presents an opportunity for dialogue between scientists, farmers, and private-sector stakeholders to advance scientific knowledge and promote the common objective of sustainable agriculture

Insights gained from the reverse engineering of gene networks in keloid fibroblasts

<p>Abstract</p> <p>Background</p> <p>Keloids are protrusive claw-like scars that have a propensity to recur even after surgery, and its molecular etiology remains elusive. The goal of reverse engineering is to infer gene networks from observational data, thus providing insight into the inner workings of a cell. However, most attempts at modeling biological networks have been done using simulated data. This study aims to highlight some of the issues involved in working with experimental data, and at the same time gain some insights into the transcriptional regulatory mechanism present in keloid fibroblasts.</p> <p>Methods</p> <p>Microarray data from our previous study was combined with microarray data obtained from the literature as well as new microarray data generated by our group. For the physical approach, we used the fREDUCE algorithm for correlating expression values to binding motifs. For the influence approach, we compared the Bayesian algorithm BANJO with the information theoretic method ARACNE in terms of performance in recovering known influence networks obtained from the KEGG database. In addition, we also compared the performance of different normalization methods as well as different types of gene networks.</p> <p>Results</p> <p>Using the physical approach, we found consensus sequences that were active in the keloid condition, as well as some sequences that were responsive to steroids, a commonly used treatment for keloids. From the influence approach, we found that BANJO was better at recovering the gene networks compared to ARACNE and that transcriptional networks were better suited for network recovery compared to cytokine-receptor interaction networks and intracellular signaling networks. We also found that the NFKB transcriptional network that was inferred from normal fibroblast data was more accurate compared to that inferred from keloid data, suggesting a more robust network in the keloid condition.</p> <p>Conclusions</p> <p>Consensus sequences that were found from this study are possible transcription factor binding sites and could be explored for developing future keloid treatments or for improving the efficacy of current steroid treatments. We also found that the combination of the Bayesian algorithm, RMA normalization and transcriptional networks gave the best reconstruction results and this could serve as a guide for future influence approaches dealing with experimental data.</p

Comparative mitochondrial proteomics: perspective in human diseases

Mitochondria are the most complex and the most important organelles of eukaryotic cells, which are involved in many cellular processes, including energy metabolism, apoptosis, and aging. And mitochondria have been identified as the "hot spot" by researchers for exploring relevant associated dysfunctions in many fields. The emergence of comparative proteomics enables us to have a close look at the mitochondrial proteome in a comprehensive and effective manner under various conditions and cellular circumstances. Two-dimensional electrophoresis combined with mass spectrometry is still the most popular techniques to study comparative mitochondrial proteomics. Furthermore, many new techniques, such as ICAT, MudPIT, and SILAC, equip researchers with more flexibilities inselecting proper methods. This article also reviews the recent development of comparative mitochondrial proteomics on diverse human diseases. And the results of mitochondrial proteomics enhance a better understanding of the pathogenesis associated with mitochondria and provide promising therapeutic targets

Learned Human-in-the-Loop Decision Making

Human beings are decision making engines. From low level automatic activities, such as walking, to high-level reasoning, we readily solve complex perception and decision problems in real time. On the other end of the spectrum, computers and robots have proven particularly useful in dull, dirty, and dangerous domains---inspecting nuclear disaster sites, solving vast optimization problems, and generally accomplishing tasks difficult for humans. Recent advances in robotics and computer science have only served to underscore the difference between humans and robots, namely that robots tend to excel at lower level structured tasks, while humans are unmatched at higher level decision making, particularly when problems involve unstructured data, soft constraints, and ambiguous objectives. This thesis explores decision problems that lie in the gap between current robot capability and human ability. A generic framework for representing large-scale decision problems, referred to as the Generalized Planning Problem (GPP), is defined. A learning-based GPP solution method is presented that captures multiple stochastic problem elements and soft constraints, which arise in many real world problems. An example routing problem is presented to showcase the expressiveness of the GPP framework. To leverage human intuition with such problems, the GPP framework allows for incorporating human input in a structured way as an external reward signal. Adding human feedback into the learning process results in a variably autonomous decision making engine that can vary continuously between fully autonomous, fully manual, and everywhere in between

Science in the Supply Chain: Collaboration Opportunities for Advancing Sustainable Agriculture in the United States

Consumers and corporations are increasingly interested in understanding the sustainability of agricultural supply chains and reducing the environmental impacts of food, fiber, feed, and fuel production. This emerging need to quantify environmental impacts from agricultural production creates an opportunity for collaboration with the scientific community. Without such collaboration, sustainability efforts risk failure by adopting unrealistic goals or misguided approaches. This commentary explores the role of science in Field to Market, a nonprofit organization developing a sustainability program for US commodity crops, and highlights opportunities to address emerging science challenges. We evaluate changes over the past 35 years in key environmental impacts of crop production used to inform land managers as well as companies that are committed to improvements. Achieving improvements will only be possible if three key gaps are addressed regarding available simulation models and data, scale of implementation and uncertainty, and effectiveness of conservation practices. Filling these gaps presents an opportunity for dialogue between scientists, farmers, and private-sector stakeholders to advance scientific knowledge and promote the common objective of sustainable agriculture

Diagnosing myocarditis in endomyocardial biopsies: survey of current practice

Dallas criteria (DC) and European Society of Cardiology criteria (ESCC) have provided valuable frameworks for the histologic diagnosis and classification of myocarditis in endomyocardial biopsy (EMB) specimens. However, the adaptation and the usage of these criteria are variable and depend on local practice settings and regions/countries. Moreover, several ancillary tests that are not included in the current criteria, such as immunohistochemistry (IHC) or viral polymerase chain reaction (PCR), have proven useful for the diagnosis of myocarditis

Science in the Supply Chain: Collaboration Opportunities for Advancing Sustainable Agriculture in the United States

Consumers and corporations are increasingly interested in understanding the sustainability of agricultural supply chains and reducing the environmental impacts of food, fiber, feed, and fuel production. This emerging need to quantify environmental impacts from agricultural production creates an opportunity for collaboration with the scientific community. Without such collaboration, sustainability efforts risk failure by adopting unrealistic goals or misguided approaches. This commentary explores the role of science in Field to Market, a nonprofit organization developing a sustainability program for US commodity crops, and highlights opportunities to address emerging science challenges. We evaluate changes over the past 35 years in key environmental impacts of crop production used to inform land managers as well as companies that are committed to improvements. Achieving improvements will only be possible if three key gaps are addressed regarding available simulation models and data, scale of implementation and uncertainty, and effectiveness of conservation practices. Filling these gaps presents an opportunity for dialogue between scientists, farmers, and private-sector stakeholders to advance scientific knowledge and promote the common objective of sustainable agriculture