Identification of Class I HLA T Cell Control Epitopes for West Nile Virus

The recent West Nile virus (WNV) outbreak in the United States underscores the importance of understanding human immune responses to this pathogen. Via the presentation of viral peptide ligands at the cell surface, class I HLA mediate the T cell recognition and killing of WNV infected cells. At this time, there are two key unknowns in regards to understanding protective T cell immunity: 1) the number of viral ligands presented by the HLA of infected cells, and 2) the distribution of T cell responses to these available HLA/viral complexes. Here, comparative mass spectroscopy was applied to determine the number of WNV peptides presented by the HLA-A*11:01 of infected cells after which T cell responses to these HLA/WNV complexes were assessed. Six viral peptides derived from capsid, NS3, NS4b, and NS5 were presented. When T cells from infected individuals were tested for reactivity to these six viral ligands, polyfunctional T cells were focused on the GTL9 WNV capsid peptide, ligands from NS3, NS4b, and NS5 were less immunogenic, and two ligands were largely inert, demonstrating that class I HLA reduce the WNV polyprotein to a handful of immune targets and that polyfunctional T cells recognize infections by zeroing in on particular HLA/WNV epitopes. Such dominant HLA/peptide epitopes are poised to drive the development of WNV vaccines that elicit protective T cells as well as providing key antigens for immunoassays that establish correlates of viral immunity. © 2013 Kaabinejadian et al

Widespread forest vertebrate extinctions induced by a mega hydroelectric dam in lowland Amazonia

Mega hydropower projects in tropical forests pose a major emergent threat to terrestrial and freshwater biodiversity worldwide. Despite the unprecedented number of existing, underconstruction and planned hydroelectric dams in lowland tropical forests, long-term effects on biodiversity have yet to be evaluated. We examine how medium and large-bodied assemblages of terrestrial and arboreal vertebrates (including 35 mammal, bird and tortoise species) responded to the drastic 26-year post-isolation history of archipelagic alteration in landscape structure and habitat quality in a major hydroelectric reservoir of Central Amazonia. The Balbina Hydroelectric Dam inundated 3,129 km2 of primary forests, simultaneously isolating 3,546 land-bridge islands. We conducted intensive biodiversity surveys at 37 of those islands and three adjacent continuous forests using a combination of four survey techniques, and detected strong forest habitat area effects in explaining patterns of vertebrate extinction. Beyond clear area effects, edge-mediated surface fire disturbance was the most important additional driver of species loss, particularly in islands smaller than 10 ha. Based on species-area models, we predict that only 0.7% of all islands now harbor a species-rich vertebrate assemblage consisting of ≥80% of all species. We highlight the colossal erosion in vertebrate diversity driven by a man-made dam and show that the biodiversity impacts of mega dams in lowland tropical forest regions have been severely overlooked. The geopolitical strategy to deploy many more large hydropower infrastructure projects in regions like lowland Amazonia should be urgently reassessed, and we strongly advise that long-term biodiversity impacts should be explicitly included in pre-approval environmental impact assessments

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Applying Benford’s law to detect accounting data manipulation in the banking industry

We utilise Benford’s Law to test if balance sheet and income statement data broadly used to assess bank soundness were manipulated prior to and also during the global financial crisis. We find that all banks resort to loan loss provisions to manipulate earnings and income upwards. Distressed institutions that have stronger incentives to conceal their financial difficulties resort additionally to manipulating loan loss allowances and non-performing loans downwards. Moreover, manipulation is magnified during the crisis and expands to encompass regulatory capital

Heterogeneous consumption in OLG model with horizontal innovations

The paper develops a general equilibrium endogenous growth model involving heterogeneous consumption by an age-structured population with uncertain but limited life span and balanced life-time budget without bequests. The heterogeneity is introduced via weights which the individuals attribute in their utility function to consumption of different goods depending on the vintage of the good. The goods are produced by monopolistically competitive firms and the variety of available goods/technologies is determined endogenously through R&D investments. A competitive bank sector provides financial resources for investments, secured by agents’ savings and future firms profits. The general equilibrium is characterized by a system of functional equations and is analytically or numerically determined for several particular weight functions. It is shown that the investments by agents alone may be insufficient to sustain growth, while additional investments provided by the bank sector may lead to growth. The resulting imbalance between agents’ assets and the total value of firms can grow unboundedly in the case of homogeneous consumption. The results exhibit the qualitative difference between the dynamics of the model with heterogeneous versus homogeneous consumption. In particular heterogeneous con- sumption (when old goods are discounted) reduces the additional investments by the financial sector so that the values of firms become balanced by the assets of agents in the long run.info:eu-repo/semantics/publishedVersio

Left atrial volume predicts adverse cardiac and cerebrovascular events in patients with hypertrophic cardiomyopathy

<p>Abstract</p> <p>Aims</p> <p>To prospectively evaluate the relationship between left atrial volume (LAV) and the risk of clinical events in patients with hypertrophic cardiomyopathy (HCM).</p> <p>Methods</p> <p>We enrolled a total of 141 HCM patients with sinus rhythm and normal pump function, and 102 patients (73 men; mean age, 61 ± 13 years) who met inclusion criteria were followed for 30.8 ± 10.0 months. The patients were divided into two groups with or without major adverse cardiac and cerebrovascular events (MACCE), a composite of stroke, sudden death, and congestive heart failure. Detailed clinical and echocardiographic data were obtained.</p> <p>Results</p> <p>MACCE occurred in 24 patients (18 strokes, 4 congestive heart failure and 2 sudden deaths). Maximum LAV, minimum LAV, and LAV index (LAVI) corrected for body surface area (BSA) were significantly greater in patients with MACCE than those without MACCE (maximum LAV: 64.3 ± 25.0 vs. 51.9 ± 16.0 ml, p = 0.005; minimum LAV: 33.9 ± 15.1 vs. 26.2 ± 10.9 ml, p = 0.008; LAVI: 40.1 ± 15.4 vs. 31.5 ± 8.7 ml/mm², p = 0.0009), while there were no differences in the other echocardiographic parameters.</p> <p>LAV/BSA of ≥ 40.4 ml/m²to identify patients with cardiovascular complications with a sensitivity of 73% and a specificity of 88%.</p> <p>Conclusion</p> <p>LAVI may be an effective marker for detecting the risk of MACCE in patients with HCM and normal pump function.</p

Determining the neurotransmitter concentration profile at active synapses

Establishing the temporal and concentration profiles of neurotransmitters during synaptic release is an essential step towards understanding the basic properties of inter-neuronal communication in the central nervous system. A variety of ingenious attempts has been made to gain insights into this process, but the general inaccessibility of central synapses, intrinsic limitations of the techniques used, and natural variety of different synaptic environments have hindered a comprehensive description of this fundamental phenomenon. Here, we describe a number of experimental and theoretical findings that has been instrumental for advancing our knowledge of various features of neurotransmitter release, as well as newly developed tools that could overcome some limits of traditional pharmacological approaches and bring new impetus to the description of the complex mechanisms of synaptic transmission

Psychometric Curve and Behavioral Strategies for Whisker-Based Texture Discrimination in Rats

The rodent whisker system is a major model for understanding neural mechanisms for tactile sensation of surface texture (roughness). Rats discriminate surface texture using the whiskers, and several theories exist for how texture information is physically sensed by the long, moveable macrovibrissae and encoded in spiking of neurons in somatosensory cortex. However, evaluating these theories requires a psychometric curve for texture discrimination, which is lacking. Here we trained rats to discriminate rough vs. fine sandpapers and grooved vs. smooth surfaces. Rats intermixed trials at macrovibrissa contact distance (nose >2 mm from surface) with trials at shorter distance (nose <2 mm from surface). Macrovibrissae were required for distant contact trials, while microvibrissae and non-whisker tactile cues were used for short distance trials. A psychometric curve was measured for macrovibrissa-based sandpaper texture discrimination. Rats discriminated rough P150 from smoother P180, P280, and P400 sandpaper (100, 82, 52, and 35 µm mean grit size, respectively). Use of olfactory, visual, and auditory cues was ruled out. This is the highest reported resolution for rodent texture discrimination, and constrains models of neural coding of texture information

History of adversity, health and psychopathology among prisoners: comparison between men and women

Adversity in childhood, risk behaviors and psychopathology are highly prevalent phenomena in inmate populations and have a strong impact on health. Knowing the differences in these variables between the sexes is most important in order to develop appropriate intervention strategies in a prison context. By administering the Socio-demographic and Life History Questionnaire and the Brief Symptoms Inventory, we sought to characterize adverse childhood experiences and relate them to risk behaviors and to psychopathological symptoms, and study the differences between the 65 male and 42 female detainees in Portuguese prison establishments. Men and women report a complex web of adversity in childhood. In a range of ten possible categories, a medium value of 5.05 (DP = 2.63) in total adversity for women and 2.63 (DP = 2.18) for men was encountered, with the prevalence being significantly higher within the female population (Z = -4.33; p = .000). A high prevalence of risk behaviors and psychopathological symptoms was found in both groups, the latter being higher among females. We concluded that the differences between men and women calls for in depth studies in order to provide guidelines for intervention projects in specific populations.Adversidade na infância, comportamentos de risco e psicopatologia são fenómenos muito prevalentes na população reclusa e com forte impacto na saúde. Conhecer as diferenças entre sexos, no que diz respeito a tais variáveis, é de elevada importância no sentido de adequar estraté- gias de intervenção em contexto prisional. Utilizando o Questionário Sociodemográfico e Histó- ria de Vida, o Questionário de Adversidade na Infância e o Brief Symptons Inventory, procuramos caracterizar a adversidade na infância, os comportamentos de risco e as dimensões psicopatológicas, e averiguar as diferenças entre 65 homens e 42 mulheres reclusos em estabelecimentos prisionais Portugueses. Homens e mulheres relatam um quadro complexo de adversidade na infância. Num total possível de dez categorias, verificamos uma média de adversidade total de 5.05 (DP = 2.63) para as mulheres e de 2.63 (DP = 2.18) para os homens, sendo a prevalência significativamente mais elevada junto da população feminina (Z = -4.33; p = .000). Foi ainda encontrada uma elevada prevalência de comportamentos de risco e de sintomatologia psicopatológica em ambos os grupos, sendo esta última superior nas mulheres. Concluímos que as diferenças entre sexos devem ser estudadas para guiarem a adequação dos projetos

An OLG model of growth with longevity : when grandparents take care of grandchildren

By assuming that grandparents take care of grandchildren, this paper aims at studying the effects of longevity on long-term economic growth in a model with overlapping generations and endogenous fertility. We show that an increase in longevity may: (i) reduce the long-term economic growth; (ii) increase the supply of labour, and (iii) cause fertility either to increase of decrease depending on the size of time spent by grandparents to rise grandchildren. These findings also hold in an endogenous growth setting a` la Romer (J Polit Econ 94:1002–1037, 1986).info:eu-repo/semantics/publishedVersio