nils-herrmann/clean_scirepeval huggingface dataset:
GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality
Venue: Statistical Genomics
Year: 2016

The programs GMAP and GSNAP, for aligning RNA Seq and DNA Seq datasets to genomes, have evolved along with advances in biological methodology to handle longer reads, larger volumes of data, and new types of biological assays. The genomic representation has been improved to include linear genomes that can compare sequences using single instruction multiple data (SIMD) instructions, compressed genomic hash tables with fast access using SIMD instructions, handling of large genomes with more than four billion bp, and enhanced suffix arrays (ESAs) with novel data structures for fast access. Improvements to the algorithms have included a greedy match and extend algorithm using suffix arrays, segment chaining using genomic hash tables, diagonalization using segmental hash tables, and nucleotide level dynamic programming procedures that use SIMD instructions and eliminate the need for F loop calculations. Enhancements to the functionality of the programs include standardization of indel positions, handling of ambiguous splicing, clipping and merging of overlapping paired end reads, and alignments to circular chromosomes and alternate scaffolds. The programs have been adapted for use in pipelines by integrating their usage into R/Bioconductor packages such as gmapR and HTSeqGenie, and these pipelines have facilitated the discovery of numerous biological phenomena.
Various Coupling Agents in the Phosphoramidite Method for Oligonucleotide Synthesis
Year: 2018

This review selects some representative coupling agents used for internucleotide bond formation reactions in the phosphoramidite method, which is now the most widely employed method for the chemical synthesis of oligodeoxyribonucleotides and oligoribonucleotides, and it describes their utility, efficiency, and drawbacks. Moreover, the mechanism of the coupling of the nucleoside phosphoramidite and nucleoside promoted by the coupling agent is discussed in some cases. The selected coupling agents are 1H tetrazole, 5 ethylthio 1H tetrazole (ETT) 5 benzylthio 1H tetrazole (BTT) 5 [3,5 bis(trifluoromethyl)phenyl] 1H tetrazole (Activator 42) 4,5 dicyanoimidazole (DCI) certain carboxylic acids, and various acid/azole complexes such as benzimidazolium triflate (BIT) and saccharin 1 methylimidazole (SMI)
Preparation of Roxburgh Rose Extracts and Its Application in Flue cured Tobacco
Year: 2014

[Objective]To study the effects of various factors on extracting of roxburgh rose with ethanol solution. [Method]Extracting time, ratio of material to liquid and extracting times were selected for single factor test and orthogonal test. Result]The results showed that,the influence of the three factors on the amount of roxburgh rose extract was extracting times ratio of material to liquid extracting time. The optimized extracting conditions were obtained and verified. The roxburgh rose extract was analyzed by GC MS method,109 components were indentified. The roxburgh rose extract was also added into cigarettes for sensory evaluation. [Conclusion]It was found that the roxburgh rose extract obviously increased the sensory quality on the amount of aroma,coziness and after taste.
Frontal fibrosing alopecia: a multicenter review of 355 patients.
Venue: Journal of the American Academy of Dermatology
Year: 2014

To our knowledge, there are no large multicenter studies concerning frontal fibrosing alopecia (FFA) that could give clues about its pathogenesis and best treatment.  We sought to describe the epidemiology, comorbidities, clinical presentation, diagnostic findings, and therapeutic choices in a large series of patients with FFA.  This retrospective multicenter study included patients given the diagnosis of FFA. Clinical severity was classified based on the recession of the frontotemporal hairline.  In all, 355 patients (343 women [49 premenopausal] and 12 men) with a mean age of 61 years (range 23 86) were included. Early menopause was detected in 49 patients (14% whereas 46 (13% had undergone hysterectomy. Severe FFA was observed in 131 patients (37% Independent factors associated with severe FFA after multivariate analysis were: eyelash loss, facial papules, and body hair involvement. Eyebrow loss as the initial clinical presentation was associated with mild forms. Antiandrogens such as finasteride and dutasteride were used in 111 patients (31% with improvement in 52 (47% and stabilization in 59 (53% LIMITATIONS The retrospective design is a limitation.  Eyelash loss, facial papules, and body hair involvement were associated with severe FFA. Antiandrogens were the most useful treatment.
Selective Synthesis in Microdroplets of 2 Phenyl 2,3 dihydrophthalazine 1,4 dione from Phenyl Hydrazine with Phthalic Anhydride or Phthalic Acid.
Venue: Chemistry
Year: 2019

Pyridazine derivatives are privileged structures because of their potential biological and optical properties. Traditional synthetic methods usually require acid or base as a catalyst under reflux conditions with reaction times ranging from hours to a few days or require microwave assistance to induce the reaction. Herein, this work presents the accelerated synthesis of a pyridazine derivative, 2 phenyl 2,3 dihydrophthalazine 1,4 dione (PDHP) in electrosprayed microdroplets containing an equimolar mixture of phenyl hydrazine and phthalic anhydride or phthalic acid. This reaction occurred on the submillisecond timescale with good yield (over 90 with the choice of solvent) without using an external catalyst at room temperature. In sharp contrast to the bulk reaction of obtaining a mixture of two products, the reaction in confined microdroplets yields only the important six membered heterocyclic product PDHP. Results indicated that surface reactions in microdroplets with low pH values cause selectivity, acceleration, and high yields.
An LR framework incorporating sensitivity analysis to model multiple direct and secondary transfer events on skin surface.
Venue: Forensic science international. Genetics
Year: 2021

Bayesian logistic regression is used to model the probability of DNA recovery following direct and secondary transfer and persistence over a 24 h period between deposition and sample collection. Sub source level likelihood ratios provided the raw data for activity level analysis. Probabilities of secondary transfer are typically low, and there are challenges with small data sets with low numbers of positive observations. However, the persistence of DNA over time can be modelled by a single logistic regression for both direct and secondary transfer, except that the time since deposition must be compensated by an offset value for the latter. This simplifies the analysis. Probabilities are used to inform an activity level Bayesian Network that takes account of alternative propositions e.g. time of assault and time of social activities. The model is extended in order to take account of multiple contacts between person of interest and 'victim' Variables taken into account include probabilities of direct and secondary transfer, along with background DNA from unknown individuals. The logistic regression analysis is Bayesian for each analysis, 4000 separate simulations were carried out. Quantile assignments enable calculation of a plausible range of probabilities and sensitivity analysis is used to describe the corresponding variation of LRs that occur when modelled by the Bayesian network. It is noted that there is need for consistent experimental design, and analysis, to facilitate inter laboratory comparisons. Appropriate recommendations are made. The open source program written in R code ALTRaP (Activity Level, Transfer, Recovery and Persistence) enables analysis of complex multiple transfer propositions that are commonplace in cases work e.g. between those who cohabit. A number of case examples are provided. ALTRaP can be used to replicate the results and can easily be modified to incorporate different sets of data and variables.
Relationships between generalized rough sets based on covering and reflexive neighborhood system
Venue: Inf. Sci.
Year: 2015

Rough set theory is an important tool to deal with uncertainty, granularity and incompleteness of knowledge. The covering based rough sets is one of the most important extensions of the classical Pawlak rough sets. In covering based rough set theory, many types of rough sets were already defined in the literature. To find out the relationships among different types of covering based rough sets and give an axiomatic definition is a very important issue. The generalized rough sets in neighborhood system is another important extension of the classical Pawlak rough sets. In this paper, we investigate relationships between covering based rough sets and generalized rough sets in neighborhood system. Then, twenty three types of the element based, the granule based and the subsystem based definitions of covering approximation operators are unified under the framework of generalized approximation operators in neighborhood systems. Moreover, properties of the generalized approximation operators in neighborhood systems are presented.
The Adhesion of Lactobacillus salivarius REN to a Human Intestinal Epithelial Cell Line Requires S layer Proteins
Venue: Scientific reports
Year: 2017

Lactobacillus salivarius REN, a novel probiotic isolated from Chinese centenarians, can adhere to intestinal epithelial cells and subsequently colonize the host. We show here that the surface layer protein choline binding protein A (CbpA) of L. salivarius REN was involved in adherence to the human colorectal adenocarcinoma cell line HT 29. Adhesion of a cbpA deletion mutant was significantly reduced compared with that of wild type, suggesting that CbpA acts as an adhesin that mediates the interaction between the bacterium and its host. To identify the molecular mechanism of adhesion, we determined the crystal structure of a truncated form of CbpA that is likely involved in binding to its cell surface receptor. The crystal structure identified CbpA as a peptidase of the M23 family whose members harbor a zinc dependent catalytic site. Therefore, we propose that CbpA acts as a multifunctional surface protein that cleaves the host extracellular matrix and participates in adherence. Moreover, we identified enolase as the CbpA receptor on the surface of HT 29 cells. The present study reveals a new class of surface layer proteins as well as the molecular mechanism that may contribute to the ability of L. salivarius REN to colonize the human gut.
Taxonomy of bio inspired algorithms
Year: 2020

In recent years, a great variety of nature and bio inspired algorithms have been published in the literature. These algorithms simulate some natural or biological processes such as natural evolution, to optimize complex problems. Last years, the accumulation of this type of algorithms has achieved a number difficult to manage, so it is needed a good taxonomy that allows researchers to organise existing and future meta heuristics in well defined categories. In this work, we have reviewed more than two hundred nature inspired and bio inspired algorithms, and proposed two taxonomies that group them in categories ans subcategories, considering two different criteria. The first category consider the source of inspiration, while the second one consider only the behavior of each algorithm. It is shown the proposals in each one of the categories, and the summarise of each category. Later, results of these two taxonomies are compared, and obtained the most similar classic algorithm for each reviewed papers. We have obtained that there is a poor relationship between the natural inspiration of an algorithm and its behavior, and that, their similarities in behavior are greater than expected. Even more, more than half of reviewed proposals are versions of classic algorithms. Although there are many proposals based in different concepts of reality, the majority of them are actually more similar than expected, so authors should focus more on the behaviour. Also, we give several suggestions to continue improving this growing research field.
Adversarial Training Improved Multi Path Multi Scale Relation Detector for Knowledge Base Question Answering
Venue: IEEE Access
Year: 2020

Knowledge Base Question Answering (KBQA) is a promising approach for users to access substantial knowledge and has become a research focus in recent years. Our paper focuses on relation detection, a subtask of KBQA and proposes an adversarial training improved multi path multi scale relation detector (AdvT MMRD) to improve the performance of a common KBQA system. To solve the problem of matching the casual form of a question with the logical form of a predicate, we use question pattern relation matching, in which an attention based bidirectional recurrent neural network with gated recurrent units (Bi GRUs) is used to match semantic similarity and a convolutional neural network (CNN) is used to learn literal similarity between question and relation. We also explore two ways to measure the relevance of entity type relation pairs through several level representations. Additionally, an adversarial training strategy is conducted to enhance our model. The experimental results demonstrate that our approach not only achieves a state of the art accuracy of 93.8% on relation detection task, but contributes our KBQA system to reaching an outstanding accuracy of 79.0% on the SimpleQuestions benchmark.
Effect of Age on Heart Rate Variability Analysis in Breast Cancer Patients
Year: 2020

Breast Cancer (BC) is the leading cause of death in women, worldwide. The Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) of BC can be studied using HRV measures. The main purpose of this chapter is to give an insight to clinicians via HRV measures with respect to age to make them understand the PS of patients. Data from 114 BC patients was segregated into two age groups, G1 (20 to 40 years) and G2 (41 to 75 years) The 5 minute electrocardiogram of the subjects was taken and HRV measures were extracted. One way ANOVA with Posthoc Tukeys' HSD test was done. Triangular Index, Ratio of standard deviation of poincare plot perpendicular to the line of identity to the standard deviation along line of identity, Detrended Fluctuation Analysis descriptors, Approximate Entropy, Effect of Age on Heart Rate Variability Analysis in Breast Cancer Patients: Heart Rate Variability in Breast Cancer
Components and characteristics of the dopamine reward utility signal
Venue: The Journal of comparative neurology
Year: 2016

Rewards are defined by their behavioral functions in learning (positive reinforcement) approach behavior, economic choices, and emotions. Dopamine neurons respond to rewards with two components, similar to higher order sensory and cognitive neurons. The initial, rapid, unselective dopamine detection component reports all salient environmental events irrespective of their reward association. It is highly sensitive to factors related to reward and thus detects a maximal number of potential rewards. It also senses aversive stimuli but reports their physical impact rather than their aversiveness. The second response component processes reward value accurately and starts early enough to prevent confusion with unrewarded stimuli and objects. It codes reward value as a numeric, quantitative utility prediction error, consistent with formal concepts of economic decision theory. Thus, the dopamine reward signal is fast, highly sensitive and appropriate for driving and updating economic decisions.  2016.
Multi view (Joint) probability linear discrimination analysis for J vector based text dependent speaker verification
Venue: 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Year: 2017

J vector has been proved to be very effective in text dependent speaker verification with short duration speech. However, the current back end classifiers cannot make full use of such deep features. In this paper, we propose a method to model the multi faceted information in the j vector explicitly and jointly. Examples of the multi faceted information include speaker identity and text content. In our approach, the j vector was modeled as a result derived by a generative multi view (joint1) Probability Linear Discriminant Analysis (PLDA) model, which contains multiple kinds of latent variables. The usual PLDA model only considers one single label. However, in practical use, when using multi task learned network as feature extractor, the extracted feature are always associated with several labels. This type of feature is called multi view deep feature (e.g. j vector) With multi view (joint) PLDA, we are able to explicitly build a model that can combine multiple heterogeneous information from the j vectors. In verification step, we calculated the likelihood to describe whether the two j vectors having consistent labels or not. This likelihood is used in the following decision making. Experiments have been conducted on large scale data corpus of different languages. On the public RSR2015 data corpus, the results showed that our approach can achieve 0.02% EER and 0.09% EER for impostor wrong and impostor correct cases respectively.
A Motivational Model of Video Game Engagement
Year: 2010

More Americans now play video games than go to the movies (NPD Group, 2009) The meteoric rise in popularity of video games highlights the need for research approaches that can deepen our scientific understanding of video game engagement. This article advances a theory based motivational model for examining and evaluating the ways by which video game engagement shapes psychological processes and influences well being. Rooted in self determination theory (Deci Ryan, 2000; Ryan Deci, 2000a) our approach suggests that both the appeal and well being effects of video games are based in their potential to satisfy basic psychological needs for competence, autonomy, and relatedness. We review recent empirical evidence applying this perspective to a number of topics including need satisfaction in games and short term well being, the motivational appeal of violent game content, motivational sources of postplay aggression, the antecedents and consequences of disordered patterns of game engagement, and the determinants and effects of immersion. Implications of this model for the future study of game motivation and the use of video games in interventions are discussed.
Plasma chemistry simulation of surface microdischarge in humid air for wound healing
Year: 2011

Submitted for the GEC11 Meeting of The American Physical Society Plasma chemistry simulation of surface microdischarge in humid air for wound healing DAVID GRAVES, YUKINORI SAKIYAMA, MARAT ORAZOV, University of California at Berkeley, GREGOR MORFILL, Max Planck Institute for Extraterrestrial Physics Controlling fluxes of reactive oxygen species and reactive nitrogen species (RONS) created in humid air plasma is important for biomedical applications of surface micro discharges (SMD) We report results from a well mixed, dual zone model that couples a plasma region with a reactiontransport region, and incorporates about 50 charged and neutral species coupled through about 500 elementary reactions. Dominant species in dry air are O3, NO2, and N2O5, while HNO3 and H2O2 approach similar densities in humid air. Finally, we report initial results from coupling the plasma chemistry model to a transient, one dimensional wound healing model. Wound healing models, based on a reactiondiffusion description of cell dynamics, describe the spatial and temporal variations of chemo attractants, blood vessels, fibroblasts and the extracellular matrix. Yukinori Sakiyama University of California at Berkeley Date submitted: 16 Jul 2011  1.4
Robust Single object Visual Tracking Framework via Fully Convolutional Siamese Network with Correlation Filter
Venue: 2020 13th International Symposium on Computational Intelligence and Design (ISCID)
Year: 2020

Recently, there are two kinds of methods in the research field of visual tracking, one is correlation filtering, the other is siamese network. In order to fully utilize the advantages of these two framework, this paper proposed a robust single object visual tracking framework based on fully convolutional siamese network and correlation filter. Two branches are constructed in this network, namely training template branch and searching template branch, which are share the convolutional layer parameters. Each branch is divided into two sub branches. In the sub branches of the training template, two correlation filtering layers are embedded to replace the traditional convolutional layer. Finally, the location of object to be tracked is calculated by constructing response graph. Extensive experiments on challenging benchmark show that the tracking framework in this paper can further improve the accuracy.
How Has COVID 19 Affected the Costs of the Surgical Fellowship Interview Process?
Venue: Journal of Surgical Education
Year: 2020

To evaluate the effect of COVID 19 on the costs of the surgical fellowship interview process.  A literature review of the historical costs of surgical fellowship interviews and a summary of how the shift to virtual interviews has unintended positive and negative effects on costs for applicants and training programs.  Transitioning fellowship interviews to virtual platforms affects expenditures of finances and time. Each fellowship candidate saves close to $6,000 in interview travel expenses. Applicants require less time off from their residency programs during this critical time of need for frontline healthcare workers. However, applicants miss some of the live aspects of interviewing, and training programs invest more effort upfront altering their interviews to virtual formats.  The COVID 19 public health crisis has had a significant impact on surgical education, including how selection is conducted. Virtual recruitment has the potential for cost savings but should continue to be refined. This is an opportune time to innovate and rethink how to recruit prospective surgical residency and fellowship candidates during the current and forthcoming interview seasons.
A Comprehensive Analysis of Classification Methods for Big Data Stream
Year: 2020

Traditional tools for mining the big data have become insufficient due to ever growing data in the world. For handling big data, real time and distributed processing is adopted. From so many mining tools options, it can be difficult for a researcher to opt an efficient tool. This paper is intended to aid the researcher who understands WEKA but is inexperienced with big data. The preliminary stage of data mining is classification, which categorizes the data into predefined groups. In this paper, WEKA with MOA package is used to classify big data stream with four different classifiers. Performance of these classifiers is analyzed on the basis of accuracy, i.e. correctly and incorrectly classified instances, time taken to test the model, and time taken to build the model. For this particular scenario, obtained results prove that naive Bayes is the most accurate classifier and decision stump is least effective classifier for big data.
Strategies for Comparative Research in Political Science
Year: 2013

Preface 1. The Importance of Comparison 2. The Logic of Comparison 3. The Number of Cases and Which Ones? 4. Measurement and Bias 5. The Role of Theory in Comparative Politics 6. The Case Study 7. Building on Case Analysis 8. Events History and Change Over Time 9. Statistical Analysis 10. The Future of Comparative Politics