THE EMERGENCY FOOD ASSISTANCE SYSTEM - FINDINGS FROM THE PROVIDER SURVEY, VOLUME II: FINAL REPORT

Findings of the first comprehensive government study of the Emergency Food Assistance System (EFAS) suggest that public and private food assistance may work in tandem to provide more comprehensive food assistance than either could provide by itself. Five major types of organizations (emergency kitchens, food pantries, food banks, food rescue organizations, and emergency food organizations) operate in the EFAS. About 5,300 emergency kitchens provide more than 173 million meals a year, and 32,700 food pantries distribute about 2.9 billion pounds of food a year, which translates into roughly 2,200 million meals. Despite substantial amounts of food distributed by the system, the EFAS remains much smaller in scale than the Federal programs. This study, which was sponsored by USDAs Economic Research Service, provides detailed information about the systems operations and about each of the five types of organizations. This report presents the study results in detail. For a summary of the results, see The Emergency Food Assistance SystemFindings from the Provider Survey, Volume I: Executive Summary at http://www.ers.usda.gov/publications/fanrr16-1. For more information on the survey methodology, see The Emergency Food Assistance SystemFindings from the Provider Survey, Volume III: Survey Methodology at http://www.ers.usda.gov/publications/efan01008.Food pantry, emergency kitchen, food bank, food rescue organization, emergency food organization, TEFAP, Food Consumption/Nutrition/Food Safety, Food Security and Poverty,

Interaction of light with a single atom in the strong focusing regime

We consider the near-resonant interaction between a single atom and a focused light mode, where a single atom localized at the focus of a lens can scatter a significant fraction of light. Complementary to previous experiments on extinction and phase shift effects of a single atom, we report here on the measurement of coherently backscattered light. The strength of the observed effect suggests combining strong focusing with the well-established methods of cavity QED. We consider theoretically a nearly concentric cavity, which should allow for a strongly focused optical mode. Simple estimates show that in a such case one can expect a significant single photon Rabi frequency. This opens new perspectives and a possibility to scale up the system consisting of many atom+cavity nodes for quantum networking due to a significant technical simplification of the atom--light interfaces.Comment: 7 pages, 6 figures, followup of workshop "Single photon technologies" in Boulder, CO, 200

Most nuclear systemic autoantigens are extremely disordered proteins: implications for the etiology of systemic autoimmunity

Patients with systemic autoimmune diseases usually produce high levels of antibodies to self-antigens (autoantigens). The repertoire of common autoantigens is remarkably limited, yet no readily understandable shared thread links these apparently diverse proteins. Using computer prediction algorithms, we have found that most nuclear systemic autoantigens are predicted to contain long regions of extreme structural disorder. Such disordered regions would generally make poor B cell epitopes and are predicted to be under-represented as potential T cell epitopes. Consideration of the potential role of protein disorder may give novel insights into the possible role of molecular mimicry in the pathogenesis of autoimmunity. The recognition of extreme autoantigen protein disorder has led us to an explicit model of epitope spreading that explains many of the paradoxical aspects of autoimmunity – in particular, the difficulty in identifying autoantigen-specific helper T cells that might collaborate with the B cells activated in systemic autoimmunity. The model also explains the experimentally observed breakdown of major histocompatibility complex (MHC) class specificity in peptides associated with the MHC II proteins of activated autoimmune B cells, and sheds light on the selection of particular T cell epitopes in autoimmunity. Finally, the model helps to rationalize the relative rarity of clinically significant autoimmunity despite the prevalence of low specificity/low avidity autoantibodies in normal individuals

A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling

Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks and assignments. Python, one of the most popular programming languages, is open source, free to use, and has plenty of learning resources. The workshop is designed for students without previous experience in programming, and it aims for a deeper understanding of the complexity of concepts in programming and machine learning. The examples used correspond to real data from physicochemical characterizations of wine, a content that is of interest for students. The contents of the workshop are introduction to Python, basic statistics, data visualization, and dimension reduction, classification, and regression.Fil: Lafuente, Deborah. Universidad de Buenos Aires; ArgentinaFil: Cohen, Brenda. Universidad de Buenos Aires; ArgentinaFil: Fiorini, Guillermo. Universidad de Buenos Aires; ArgentinaFil: Garcia, Agustin Alejo. Universidad de Buenos Aires; ArgentinaFil: Bringas, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Morzan, Ezequiel Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Onna, Diego Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentin

A Two-Biomarker Model Predicts Mortality in the Critically Ill with Sepsis.

RATIONALE: Improving the prospective identification of patients with systemic inflammatory response syndrome (SIRS) and sepsis at low risk for organ dysfunction and death is a major clinical challenge. OBJECTIVES: To develop and validate a multibiomarker-based prediction model for 28-day mortality in critically ill patients with SIRS and sepsis. METHODS: A derivation cohort (n = 888) and internal test cohort (n = 278) were taken from a prospective study of critically ill intensive care unit (ICU) patients meeting two of four SIRS criteria at an academic medical center for whom plasma was obtained within 24 hours. The validation cohort (n = 759) was taken from a prospective cohort enrolled at another academic medical center ICU for whom plasma was obtained within 48 hours. We measured concentrations of angiopoietin-1, angiopoietin-2, IL-6, IL-8, soluble tumor necrosis factor receptor-1, soluble vascular cell adhesion molecule-1, granulocyte colony-stimulating factor, and soluble Fas. MEASUREMENTS AND MAIN RESULTS: We identified a two-biomarker model in the derivation cohort that predicted mortality (area under the receiver operator characteristic curve [AUC], 0.79; 95% confidence interval [CI], 0.74-0.83). It performed well in the internal test cohort (AUC, 0.75; 95% CI, 0.65-0.85) and the external validation cohort (AUC, 0.77; 95% CI, 0.72-0.83). We determined a model score threshold demonstrating high negative predictive value (0.95) for death. In addition to a low risk of death, patients below this threshold had shorter ICU length of stay, lower incidence of acute kidney injury, acute respiratory distress syndrome, and need for vasopressors. CONCLUSIONS: We have developed a simple, robust biomarker-based model that identifies patients with SIRS/sepsis at low risk for death and organ dysfunction

Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria

New evidence and consensus has led to further revision of the McDonald Criteria for diagnosis of multiple sclerosis. The use of imaging for demonstration of dissemination of central nervous system lesions in space and time has been simplified, and in some circumstances dissemination in space and time can be established by a single scan. These revisions simplify the Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use. Ann Neurol 201

Structural basis for the sequence-dependent effects of platinum–DNA adducts

The differences in efficacy and molecular mechanisms of platinum based anti-cancer drugs cisplatin (CP) and oxaliplatin (OX) have been hypothesized to be in part due to the differential binding affinity of cellular and damage recognition proteins to CP and OX adducts formed on adjacent guanines in genomic DNA. HMGB1a in particular exhibits higher binding affinity to CP-GG adducts, and the extent of discrimination between CP- and OX-GG adducts is dependent on the bases flanking the adducts. However, the structural basis for this differential binding is not known. Here, we show that the conformational dynamics of CP- and OX-GG adducts are distinct and depend on the sequence context of the adduct. Molecular dynamics simulations of the Pt-GG adducts in the TGGA sequence context revealed that even though the major conformations of CP- and OX-GG adducts were similar, the minor conformations were distinct. Using the pattern of hydrogen bond formation between the Pt–ammines and the adjacent DNA bases, we identified the major and minor conformations sampled by Pt–DNA. We found that the minor conformations sampled exclusively by the CP-GG adduct exhibit structural properties that favor binding by HMGB1a, which may explain its higher binding affinity to CP-GG adducts, while these conformations are not sampled by OX-GG adducts because of the constraints imposed by its cyclohexane ring, which may explain the negligible binding affinity of HMGB1a for OX-GG adducts in the TGGA sequence context. Based on these results, we postulate that the constraints imposed by the cyclohexane ring of OX affect the DNA conformations explored by OX-GG adduct compared to those of CP-GG adduct, which may influence the binding affinities of HMG-domain proteins for Pt-GG adducts, and that these conformations are further influenced by the DNA sequence context of the Pt-GG adduct