Collective Action in Plant Breeding

Olson (1965) formulated a "Logic of Collective Action". We investigate whether a logic of collective action in plant breeding - and research and development generally - can be constructed. Using a case study on the Latin American Fund for Irrigated Rice (FLAR) as well as other real-world institutions of collective action in R&D, we construct an expanded logic of collective action, which revolves around two core features: Impure public goods and the tragedy of the anticommons. Provisions of FLAR and other institutions are related to game theory and contract theory, and theoretical, methodological and policy implications are outlined.Institutional and Behavioral Economics,

South-North trade, intellectual property jurisdictions, and freedom to operate in agricultural research on staple crops:

A biotechnology revolution is proceeding in tandem with international proliferation of intellectual property regimes and rights. Does the intellectual property impede agricultural research conducted in, or of consequence for, developing countries? This question has important spatial dimensions that link the location of production, the pattern of international trade, and the jurisdiction of intellectual property. Our main conclusion is that the current concerns about the freedom to operate in agricultural research oriented towards food crops for the developing world are exaggerated. Rights to intellectual property are confined to the jurisdictions where they are granted, and, presently, many of the intellectual property (IP) rights for biotechnologies potentially useful to developing-country agricultural producers are valid only in developed countries. IP problems might arise in technologies destined for crops grown in developing countries unencumbered by IP restrictions, if those crops are subsequently exported to countries in which IP is likely to prevail. Thus freedom to trade is also part of the IP story. However, using international production and trade data in the 15 crops critical to food security throughout the developing world, we show that exports from developing to developed countries are generally dwarfed by production and consumption in the developing world, the value of these exports is concentrated in a few crops and a few exporting countries, and the bulk of these exports go to Western Europe. Thus for now, most LDC researchers can focus primarily on domestic IPR in determining their freedom to operate with respect to food staples.Intellectual property., Biotechnology., Agricultural research., Trade regulation.,

Benefits and challenges of Bbg data in healthcare. An overview of the European initiatives

Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being. The present work reports an overview of best practice initiatives in Europe related to Big Data analytics in public health and oncology sectors, aimed to generate new knowledge, improve clinical care and streamline public health surveillance

Hepatic gene expression variations in response to high-fat diet-induced impaired glucose tolerance using RNAseq analysis in collaborative cross mouse population

Hepatic gene expression is known to differ between healthy and type 2 diabetes conditions. Identifying these variations will provide better knowledge to the development of gene-targeted therapies. The aim of this study is to assess diet-induced hepatic gene expression of susceptible versus resistant CC lines to T2D development. Next-generation RNA-sequencing was performed for 84 livers of diabetic and non-diabetic mice of 41 different CC lines (both sexes) following 12 weeks on high-fat diet (42% fat). Data analysis revealed significant variations of hepatic gene expression in diabetic versus non-diabetic mice with significant sex effect, where 601 genes were differentially expressed (DE) in overall population (males and females), 718 genes in female mice, and 599 genes in male mice. Top prioritized DE candidate genes were Lepr, Ins2, Mb, Ckm, Mrap2, and Ckmt2 for the overall population; for females-only group were Hdc, Serpina12, Socs1, Socs2, and Mb, while for males-only group were Serpine1, Mb, Ren1, Slc4a1, and Atp2a1. Data analysis for sex differences revealed 193 DE genes in health (Top: Lepr, Cav1, Socs2, Abcg2, and Col5a3), and 389 genes DE between diabetic females versus males (Top: Lepr, Clps, Ins2, Cav1, and Mrap2). Furthermore, integrating gene expression results with previously published QTL, we identified significant variants mapped at chromosomes at positions 36-49 Mb, 62-71 Mb, and 79-99 Mb, on chromosomes 9, 11, and 12, respectively. Our findings emphasize the complexity of T2D development and that significantly controlled by host complex genetic factors. As well, we demonstrate the significant sex differences between males and females during health and increasing to extent levels during disease/diabetes. Altogether, opening the venue for further studies targets the discovery of effective sex-specific and personalized preventions and therapies

Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks

Retinopathy of Prematurity (ROP) is an ocular disease observed in premature babies, considered one of the largest preventable causes of childhood blindness. Problematically, the visual indicators of ROP are not well understood and neonatal fundus images are usually of poor quality and resolution. We investigate two ways to aid clinicians in ROP detection using convolutional neural networks (CNN): (1) We fine-tune a pretrained GoogLeNet as a ROP detector and with small modifications also return an approximate Bayesian posterior over disease presence. To the best of our knowledge, this is the first completely automated ROP detection system. (2) To further aid grading, we train a second CNN to return novel feature map visualizations of pathologies, learned directly from the data. These feature maps highlight discriminative information, which we believe may be used by clinicians with our classifier to aid in screening

A predictive score for retinopathy of prematurity in very low birth weight preterm infants

Aims This study describes the development of a score based on cumulative risk factors for the prediction of severe retinopathy of prematurity (ROP) comparing the performance of the score against the birth weight (BW) and gestational age (GA) in order to predict the onset of ROP.Methods A prospective cohort of preterm infants with BWp1500 g and/or GAp32 weeks was studied. the score was developed based on BW, GA, proportional weight gain from birth to the 6th week of life, use of oxygen in mechanical ventilation, and need for blood transfusions from birth to the 6th week of life. the score was established after linear regression, considering the impact of each variable on the occurrences of any stage and severe ROP. Receiver operating characteristic (ROC) curves were used to determine the best sensitivity and specificity values for the score. All variables were entered into an Excel spreadsheet (Microsoft) for practical use by ophthalmologists during screening sessions.Results the sample included 474 patients. the area under the ROC curve for the score was 0.77 and 0.88 to predict any stage and severe ROP, respectively. These values were significantly higher for the score than for BW (0.71) and GA (0.69) when measured separately.Conclusions ROPScore is an excellent index of neonatal risk factors for ROP, which is easy to record and more accurate than BW and GA to predict any stage ROP or severe ROP in preterm infants. the scoring system is simple enough to be routinely used by ophthalmologists during screening examination for detection of ROP. Eye (2012) 26, 400-406; doi: 10.1038/eye. 2011.334; published online 23 December 2011Hosp Clin Porto Alegre, Dept Ophthalmol, BR-90035903 Porto Alegre, RS, BrazilUniv Fed Rio Grande do Sul, Dept Ophthalmol, Sch Med, Porto Alegre, RS, BrazilUniversidade Federal de São Paulo, Dept Ophthalmol, Sch Med, São Paulo, BrazilUniv Fed Rio Grande do Sul, Dept Paediat, Newborn Sect, Sch Med, Porto Alegre, RS, BrazilUniversidade Federal de São Paulo, Dept Ophthalmol, Sch Med, São Paulo, BrazilWeb of Scienc

Abrogating GPT2 in triple negative breast cancer inhibits tumor growth and promotes autophagy

Uncontrolled proliferation and altered metabolic reprogramming are hallmarks of cancer. Active glycolysis and glutaminolysis are characteristic features of these hallmarks and required for tumorigenesis. A fine balance between cancer metabolism and autophagy is a prerequisite of homeostasis within cancer cells. Here we show that glutamate pyruvate transaminase 2 (GPT2), which serves as a pivot between glycolysis and glutaminolysis, is highly upregulated in aggressive breast cancers, particularly the triple negative breast cancer (TNBC) subtype. Abrogation of this enzyme results in decreased TCA cycle intermediates, which promotes the rewiring of glucose carbon atoms and alterations in nutrient levels. Concordantly, loss of GPT2 results in an impairment of mechanistic target of rapamycin complex 1 (mTORC1) activity as well as the induction of autophagy. Furthermore, in vivo xenografts studies have shown that autophagy induction correlates with decreased tumor growth and that markers of induced autophagy correlate with low GPT2 levels in patient samples. Taken together, these findings indicate that cancer cells have a close network between metabolic and nutrient sensing pathways necessary to sustain tumorigenesis, and that aminotransferase reactions play an important role in maintaining this balance

Enhancement of COPD biological networks using a web-based collaboration interface

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks

Consensus statement on abusive head trauma in infants and young children

Abusive head trauma (AHT) is the leading cause of fatal head injuries in children younger than 2 years. A multidisciplinary team bases this diagnosis on history, physical examination, imaging and laboratory findings. Because the etiology of the injury is multifactorial (shaking, shaking and impact, impact, etc.) the current best and inclusive term is AHT. There is no controversy concerning the medical validity of the existence of AHT, with multiple components including subdural hematoma, intracranial and spinal changes, complex retinal hemorrhages, and rib and other fractures that are inconsistent with the provided mechanism of trauma. The workup must exclude medical diseases that can mimic AHT. However, the courtroom has become a forum for speculative theories that cannot be reconciled with generally accepted medical literature. There is no reliable medical evidence that the following processes are causative in the constellation of injuries of AHT: cerebral sinovenous thrombosis, hypoxic-ischemic injury, lumbar puncture or dysphagic choking/vomiting. There is no substantiation, at a time remote from birth, that an asymptomatic birth-related subdural hemorrhage can result in rebleeding and sudden collapse. Further, a diagnosis of AHT is a medical conclusion, not a legal determination of the intent of the perpetrator or a diagnosis of murder. We hope that this consensus document reduces confusion by recommending to judges and jurors the tools necessary to distinguish genuine evidence-based opinions of the relevant medical community from legal arguments or etiological speculations that are unwarranted by the clinical findings, medical evidence and evidence-based literature

Collective Action in Plant Breeding

Olson (1965) formulated a "Logic of Collective Action". We investigate whether a logic of collective action in plant breeding - and research and development generally - can be constructed. Using a case study on the Latin American Fund for Irrigated Rice (FLAR) as well as other real-world institutions of collective action in R&D, we construct an expanded logic of collective action, which revolves around two core features: Impure public goods and the tragedy of the anticommons. Provisions of FLAR and other institutions are related to game theory and contract theory, and theoretical, methodological and policy implications are outlined