nils-herrmann/clean_scirepeval huggingface dataset:
IL 18 immunoadjuvanted xenogeneic canine MMP 7 DNA vaccine overcomes immune tolerance and supresses the growth of murine mammary tumor.
Venue: International immunopharmacology
Year: 2020

The development of the tumorigenesis and angiogenesis through proteolytic cleavage of extracellular matrix protein and basement membranes is promoted by Matrix metelloproteinases 7 (MMP 7) Consequently, MMP 7 is presumed as potential target for mammary cancer immunotherapy. However, MMP 7 is an endogenous tumor associated antigen (TAA) therefore, immunization is challenging. In current study, a potent anti tumor immune response has been elicited through recombinant bivalent plasmid pVIVO2.IL18.cMMP7 which subside the highly metastatic 4 T1 cell line induced mammary tumors and efficiently negate the existing challenge of using MMP 7 as immunotherapeutic target. Balb/c mice were immunized with canine MMP 7 (cMMP 7) using interleukine 18 (IL 18) as an immunoadjuvant, to explore the potential of the combination regarding elicitation of a potent anti tumor immune response. Mice vaccinated with pVIVO2.IL18.cMMP7 DNA plasmid reduced the tumor growth significantly along with augmentation of the immune response to fight against tumor antigen as depicted by substantial enrichment of CD4+ and CD8+ population in splenocytes, infiltration of immune system cells in tumor tissue and enhanced survival time of mice. Further, splenocyte supernatant examination of the cytokines revealed that Th1 cytokines (IFN g and IL 2) were remarkably up regulated demonstrating the stimulation of cell mediated immune response. Thus the current observations vividly portray that administration of xenogeneic MMP 7 DNA vaccine bypasses the tolerance barrier.
A generalized finite element method for the strongly damped wave equation with rapidly varying data
Venue: ESAIM: Mathematical Modelling and Numerical Analysis
Year: 2021

We propose a generalized finite element method for the strongly damped wave equation with highly varying coefficients. The proposed method is based on the localized orthogonal decomposition introduced in Malqvist and Peterseim 2014) 2583 2603] and is designed to handle independent variations in both the damping and the wave propagation speed respectively. The method does so by automatically correcting for the damping in the transient phase and for the propagation speed in the steady state phase. Convergence of optimal order is proven in L2(H1) norm, independent of the derivatives of the coefficients. We present numerical examples that confirm the theoretical findings.
TreeBUGS: An R package for hierarchical multinomial processing tree modeling
Venue: Behavior research methods
Year: 2018

Multinomial processing tree (MPT) models are a class of measurement models that account for categorical data by assuming a finite number of underlying cognitive processes. Traditionally, data are aggregated across participants and analyzed under the assumption of independently and identically distributed observations. Hierarchical Bayesian extensions of MPT models explicitly account for participant heterogeneity by assuming that the individual parameters follow a continuous hierarchical distribution. We provide an accessible introduction to hierarchical MPT modeling and present the user friendly and comprehensive R package TreeBUGS, which implements the two most important hierarchical MPT approaches for participant heterogeneity the beta MPT approach (Smith Batchelder,  2010) and the latent trait MPT approach (Klauer, Psychometrika 75:70 98, 2010) TreeBUGS reads standard MPT model files and obtains Markov chain Monte Carlo samples that approximate the posterior distribution. The functionality and output are tailored to the specific needs of MPT modelers and provide tests for the homogeneity of items and participants, individual and group parameter estimates, fit statistics, and within and between subjects comparisons, as well as goodness of fit and summary plots. We also propose and implement novel statistical extensions to include continuous and discrete predictors (as either fixed or random effects) in the latent trait MPT model.
Subgroup analysis of nelipepimut S plus GM CSF combined with trastuzumab versus trastuzumab alone to prevent recurrences in patients with high risk, HER2 low expressing breast cancer.
Venue: Clinical immunology
Year: 2021

HER2 targeted therapy has not benefited patients with low levels of HER2 expression; however, combination therapy may be effective. Primary analysis of a phase IIb trial investigating the HER2 derived vaccine nelipepimut S (NPS) did not benefit the intention to treat population, but subset analysis showed a benefit in triple negative breast cancer (TNBC) patients. The subset analysis of this multicenter, randomized, single blind, phase IIb trial identified significant improvement in 36 month disease free survival (DFS) between NPS (n 55) and placebo (n 44) in TNBC (HR 0.25, p 0.01) and those who express HLA A24 (HR 0.41, p 0.05) The TNBC cohort demonstrated improved 36 month DFS in those with HER2 1+ expression (HR 0.17, p 0.01) HLA A24 positivity (HR 0.08, p 0.01) or in those who received neoadjuvant chemotherapy (HR 0.21, p 0.01) NPS vaccination with trastuzumab was associated with improved 36 month DFS among patients with TNBC. The observed benefit to this high risk subgroup warrants confirmation in a phase III trial.
DNA sequence polymorphism analysis using DnaSP.
Venue: Methods in molecular biology
Year: 2009

The analysis of DNA sequence polymorphisms and SNPs (single nucleotide polymorphisms) can provide insights into the evolutionary forces acting on populations and species. Available population genetic methods, and particularly those based on the coalescent theory, have become the primary framework to analyze such DNA polymorphism data. Here, I explain some essential analytical methods for interpreting DNA polymorphism data and also describe the basic functionalities of the DnaSP software. DnaSP is a multi propose program that allows conducting exhaustive DNA polymorphism analysis using a graphical user friendly interface.
IoT Based Smart Health Monitoring System for Diabetes Patients Using Neural Network
Year: 2020

In improvement of the quality of health care services, Internet of Things (IoT) has evolved rapidly for monitoring patient from distance. However, notifying health status based on continuous change of health condition for immediate healing to patient, existing systems has some limitations. In this paper, we demonstrate a smart health monitoring technology for diabetic patients which follows up their health condition depending on sugar level, heart pulse, food intake, sleep time and exercise. To illustrate, this technology takes the variables (data) as input through sensors continuously and process with neural network to evaluate the data, resulting four modes of health risk status: low, medium, high and extreme. The range of the risk status can differ based on patient's type and previous histories of their health. In addition, an automatic phone call and/or SMS notification is being sent to patient's relative along with patient's location if his/her health condition is at high or extreme risk. Besides, it also calls patients nearest hospital in case of extreme risk. However, the system provides allied instruction as voice command to patient's mobile in both cases. This technology has been experimented on 25 diabetic patients successfully and achieved 84.29% accuracy to identify the proper risk level, which is a highly acceptable level of identifying health risk status.
CHARACTERIZATION OF RABIES EXPOSURE IN COLOMBIA, 2007 2011
Year: 2018

The objective of this study using secondary data from the national health surveillance system was to describe the characteristics of the cases exposed to rabies in the Colombian population during 2007 2011. The cases exposed to rabies were categorized into no risk exposure, lowrisk exposure and high risk exposure. An increase in cases exposed to rabies was observed during 2007 (8.7% and 2011 (31.5% In Casanare, 31.4% of the cases exposed to rabies were classified as no risk exposure, whereas in Arauca 86.7% of the cases corresponded to low risk exposure cases. Vaupes reported 88.5% high risk exposure cases. Rabies exposure was most prevalent in men (56.3 and the population belonging to the subsidized health insurance scheme (42.2% The most common way of transmission was animal bites (88% The observed increase in the number of cases exposed to rabies indicates a need to develop interventions targeting people in high risk exposure regions.
Coarse to Fine Volumetric Prediction for Single Image 3D Human Pose
Venue: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2017

This paper addresses the challenge of 3D human pose estimation from a single color image. Despite the general success of the end to end learning paradigm, top performing approaches employ a two step solution consisting of a Convolutional Network (ConvNet) for 2D joint localization and a subsequent optimization step to recover 3D pose. In this paper, we identify the representation of 3D pose as a critical issue with current ConvNet approaches and make two important contributions towards validating the value of end to end learning for this task. First, we propose a fine discretization of the 3D space around the subject and train a ConvNet to predict per voxel likelihoods for each joint. This creates a natural representation for 3D pose and greatly improves performance over the direct regression of joint coordinates. Second, to further improve upon initial estimates, we employ a coarse to fine prediction scheme. This step addresses the large dimensionality increase and enables iterative refinement and repeated processing of the image features. The proposed approach outperforms all state of the art methods on standard benchmarks achieving a relative error reduction greater than 30% on average. Additionally, we investigate using our volumetric representation in a related architecture which is suboptimal compared to our end to end approach, but is of practical interest, since it enables training when no image with corresponding 3D groundtruth is available, and allows us to present compelling results for in the wild images.
Loss of Function Mutation in Toll Like Receptor 4 Prevents Diet Induced Obesity and Insulin Resistance
Venue: Diabetes
Year: 2007

Obesity is associated with insulin resistance and a state of abnormal inflammatory response. The Toll like receptor (TLR)4 has an important role in inflammation and immunity, and its expression has been reported in most tissues of the body, including the insulin sensitive ones. Because it is activated by lipopolysaccharide and saturated fatty acids, which are inducers of insulin resistance, TLR4 may be a candidate for participation in the cross talk between inflammatory and metabolic signals. Here, we show that C3H/HeJ mice, which have a loss of function mutation in TLR4, are protected against the development of diet induced obesity. In addition, these mice demonstrate decreased adiposity, increased oxygen consumption, a decreased respiratory exchange ratio, improved insulin sensitivity, and enhanced insulin signaling capacity in adipose tissue, muscle, and liver compared with control mice during high fat feeding. Moreover, in these tissues, control mice fed a high fat diet show an increase in IkB kinase complex and c Jun NH2 terminal kinase activity, which is prevented in C3H/HeJ mice. In isolated muscles from C3H/HeJ mice, protection from saturated fatty acid induced insulin resistance is observed. Thus, TLR4 appears to be an important mediator of obesity and insulin resistance and a potential target for the therapy of these highly prevalent medical conditions.
Bucking the Trend in Wolf Dog Hybridization: First Evidence from Europe of Hybridization between Female Dogs and Male Wolves
Venue: PloS one
Year: 2012

Studies on hybridization have proved critical for understanding key evolutionary processes such as speciation and adaptation. However, from the perspective of conservation, hybridization poses a concern, as it can threaten the integrity and fitness of many wild species, including canids. As a result of habitat fragmentation and extensive hunting pressure, gray wolf (Canis lupus) populations have declined dramatically in Europe and elsewhere during recent centuries. Small and fragmented populations have persisted, but often only in the presence of large numbers of dogs, which increase the potential for hybridization and introgression to deleteriously affect wolf populations. Here, we demonstrate hybridization between wolf and dog populations in Estonia and Latvia, and the role of both genders in the hybridization process, using combined analysis of maternal, paternal and biparental genetic markers. Eight animals exhibiting unusual external characteristics for wolves six from Estonia and two from Latvia proved to be wolf dog hybrids. However, one of the hybridization events was extraordinary. Previous field observations and genetic studies have indicated that mating between wolves and dogs is sexually asymmetrical, occurring predominantly between female wolves and male dogs. While this was also the case among the Estonian hybrids, our data revealed the existence of dog mitochondrial genomes in the Latvian hybrids and, together with Y chromosome and autosomal microsatellite data, thus provided the first evidence from Europe of mating between male wolves and female dogs. We discuss patterns of sexual asymmetry in wolf dog hybridization.
Impact of Problematic Smartphone Use and Instagram Use Intensity on Self Esteem with University Students from Physical Education
Venue: International journal of environmental research and public health
Year: 2020

Mobile devices are a revolutionary element that offer many possibilities, although they can also cause problems for users. This is the case with the development of addictive behaviors that can affect personal well being. The purpose of this paper has been to analyze the influence of smartphone addiction and Instagram use intensity on the self esteem of Physical Education students. A cross sectional research design was adopted by applying an online survey to a sample of undergraduate students (n 385) The results showed that gender and age were factors that influenced the problematic use of the smartphone. In turn, there was a significant positive correlation between smartphone addiction and Instagram use intensity. The influence of smartphone addiction on students' self esteem was also highlighted. In contrast, Instagram use intensity did not affect self esteem. Finally, the findings are discussed, and the main implications of the study are established, where physical education students take on a special role in order to avoid the improper use of smartphones and Instagram through sport.
The impact of Positive Reinforcement on Employees' Performance in Organizations
Year: 2014

Each employee's performance is important in an organization. A way to motivate it is through the application of reinforcement theory which is developed by B. F. Skinner. One of the most commonly used methods is positive reinforcement in which one's behavior is strengthened or increased based on consequences. This paper aims to review the impact of positive reinforcement on the performances of employees in organizations. It can be applied by utilizing extrinsic reward or intrinsic reward. Extrinsic rewards include salary, bonus and fringe benefit while intrinsic rewards are praise, encouragement and empowerment. By applying positive reinforcement in these factors, desired positive behaviors are encouraged and negative behaviors are eliminated. Financial and non financial incentives have a positive relationship with the efficiency and effectiveness of staffs.
How Do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking
Venue: EMNLP
Year: 2020

Attribution methods assess the contribution of inputs (e.g. words) to the model prediction. One way to do so is erasure: a subset of inputs is considered irrelevant if it can be removed without affecting the model prediction. Despite its conceptual simplicity, erasure is not commonly used in practice. First, the objective is generally intractable, and approximate search or leave one out estimates are typically used instead; both approximations may be inaccurate and remain very expensive with modern deep (e.g. BERT based) NLP models. Second, the method is susceptible to the hindsight bias: the fact that a token can be dropped does not mean that the model `knows' it can be dropped. The resulting pruning is over aggressive and does not reflect how the model arrives at the prediction. To deal with these two challenges, we introduce Differentiable Masking. DiffMask relies on learning sparse stochastic gates (i.e. masks) to completely mask out subsets of the input while maintaining end to end differentiability. The decision to include or disregard an input token is made with a simple linear model based on intermediate hidden layers of the analyzed model. First, this makes the approach efficient at test time because we predict rather than search. Second, as with probing classifiers, this reveals what the network `knows' at the corresponding layers. This lets us not only plot attribution heatmaps but also analyze how decisions are formed across network layers. We use DiffMask to study BERT models on sentiment classification and question answering.
Statistical Machine Translation Philipp Koehn (University of Edinburgh) Cambridge University Press, 2010, xii+433 pp; ISBN 978 0 521 87415 1, $60.00
Venue: Computational Linguistics
Year: 2010

Statistical Machine Translation provides a comprehensive and clear introduction to the most prominent techniques employed in the field of the same name (SMT) This textbook is aimed at students or researchers interested in a thorough entry point to the field, and it does an excellent job of providing basic understanding for each of the many pieces of a statistical translation system. I consider this book to be an essential addition to any advanced undergraduate course or graduate course on SMT. The book is divided into three parts: Foundations, Core Methods, and Advanced Topics. Foundations (75 pages) covers an introduction to translation, working with text, and probability theory. Core Methods (170 pages) covers the main components of a standard phrase based SMT system. Advanced Topics (125 pages) covers discriminative training and linguistics in SMT, including an in depth discussion of syntactic SMT. The text as a whole assumes a certain familiarity with natural language processing; though the Foundations section provides an effort to fill in the gaps, the book's focus is decidedly translation. As such, students unfamiliar with NLP may sometimes need to consult a general NLP text. The book aims to provide a thorough introduction to each component of a statistical translation system, and it definitely succeeds in doing so. Supplementing this core material for each chapter is a highly inclusive Further Reading section. These sections provide brief narratives highlighting many relevant papers and alternative techniques for each topic addressed in the chapter. I suspect many readers will find these literature pointers to be quite valuable, from students wishing to dive deeper, to experienced SMT researchers wishing to get started in a new sub field. Each chapter also closes with a short list of exercises. Many of these are very challenging (accurately indicated by a star rating system) and involve getting your hands dirty with tools downloaded from the Web. The usefulness of these exercises will depend largely on the instructor's tastes; I view them as a bonus rather than a core feature of the book.
Meta Analysis of Action Video Game Impact on Perceptual, Attentional, and Cognitive Skills
Venue: Psychological bulletin
Year: 2018

The ubiquity of video games in today's society has led to significant interest in their impact on the brain and behavior and in the possibility of harnessing games for good. The present meta analyses focus on one specific game genre that has been of particular interest to the scientific community action video games, and cover the period 2000 2015. To assess the long lasting impact of action video game play on various domains of cognition, we first consider cross sectional studies that inform us about the cognitive profile of habitual action video game players, and document a positive average effect of about half a standard deviation (g 0.55) We then turn to long term intervention studies that inform us about the possibility of causally inducing changes in cognition via playing action video games, and show a smaller average effect of a third of a standard deviation (g 0.34) Because only intervention studies using other commercially available video game genres as controls were included, this latter result highlights the fact that not all games equally impact cognition. Moderator analyses indicated that action video game play robustly enhances the domains of top down attention and spatial cognition, with encouraging signs for perception. Publication bias remains, however, a threat with average effects in the published literature estimated to be 30% larger than in the full literature. As a result, we encourage the field to conduct larger cohort studies and more intervention studies, especially those with more than 30 hours of training.
Perceptions of healthcare quality in Ghana: Does health insurance status matter?
Venue: PloS one
Year: 2018

This study's objective is to provide an alternative explanation for the low enrolment in health insurance in Ghana by analysing differences in perceptions between the insured and uninsured of the non technical quality of healthcare. It further explores the association between insurance status and perception of healthcare quality to ascertain whether insurance status matters in the perception of healthcare quality. Data from a survey of 1,903 households living in the catchment area of 64 health centres were used for the analysis. Two sample independent t tests were employed to compare the average perceptions of the insured and uninsured on seven indicators of non technical quality of healthcare. A generalised ordered logit regression, controlling for socio economic characteristics and clustering at the health facility level, tested the association between insurance status and perceived quality of healthcare. The perceptions of the insured were found to be significantly more negative than the uninsured and those of the previously insured were significantly more negative than the never insured. Being insured was associated with a significantly lower perception of healthcare quality. Thus, once people are insured, they tend to perceive the quality of healthcare they receive as poor compared to those without insurance. This study demonstrated that health insurance status matters in the perceptions of healthcare quality. The findings also imply that perceptions of healthcare quality may be shaped by individual experiences at the health facilities, where the insured and uninsured may be treated differently. Health insurance then becomes less attractive due to the poor perception of the healthcare quality provided to individuals with insurance, resulting in low demand for health insurance in Ghana. Policy makers in Ghana should consider redesigning, reorganizing, and reengineering the National Healthcare Insurance Scheme to ensure the provision of better quality healthcare for both the insured and uninsured.