LLukas22/scidocs huggingface dataset:
Task selection in spatial crowdsourcing from worker's perspective

With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, we study a version of the spatial crowdsourcing problem in which the workers autonomously select their tasks, called the worker selected tasks (WST) mode. Towards this end, given a worker, and a set of tasks each of which is associated with a location and an expiration time, we aim to find a schedule for the worker that maximizes the number of performed tasks. We first prove that this problem is NP-hard. Subsequently, for small number of tasks, we propose two exact algorithms based on dynamic programming and branch-and-bound strategies. Since the exact algorithms cannot scale for large number of tasks and/or limited amount of resources on mobile platforms, we propose different approximation algorithms. Finally, to strike a compromise between efficiency and accuracy, we present a progressive algorithms. We conducted a thorough experimental evaluation with both real-world and synthetic data on desktop and mobile platforms to compare the performance and accuracy of our proposed approaches. A preliminary version of this work [13] appeared in ACM SIGSPATIAL GIS 2013. Dingxiong Deng dingxiod@usc.edu Cyrus Shahabi shahabi@usc.edu Ugur Demiryurek demiryur@usc.edu Linhong Zhu linhong@isi.edu 1 Computer Science Department, University of Southern California, Los Angeles, CA 90089-0781, USA 2 Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina Del Rey, CA 90292, USA
Trends in seroprevalence of herpes simplex virus type 2 among non-Hispanic blacks and non-Hispanic whites aged 14 to 49 years--United States, 1988 to 2010.

OBJECTIVES
Genital herpes simplex virus type 2 (HSV-2) is one of the most prevalent sexually transmitted infections in the United States. We sought to assess differences in HSV-2 seroprevalence among non-Hispanic blacks and non-Hispanic whites and describe trends over time from 1988 to 2010.


METHODS
Data from National Health and Nutrition Examination Surveys (NHANES) were used to determine national HSV-2 seroprevalence estimates from National Health and Nutrition Examination Surveys 1988 to 1994, 1999 to 2002, 2003 to 2006, and 2007 to 2010. Persons aged 14 to 49 years were included in the analyses. Race/Ethnicity was defined by self-report as non-Hispanic white or non-Hispanic black. Purified glycoprotein specific for HSV-2 was used to detect type-specific antibodies using an immunodot assay. The same assay was used in all surveys. History of diagnosed genital herpes was self-reported.


RESULTS
Overall, HSV-2 seroprevalence decreased in the United States between 1988 to 1994 and 2007 to 2010, from 21.2% to 15.5%. Among non-Hispanic white females, HSV-2 seroprevalence decreased from 19.5% (1988-1994) to 15.3% (2007-2010; P < 0.001); HSV-2 seroprevalence remained stable among non-Hispanic black females, 52.5% (1988-1994) to 49.9% (2007-2010; P = 0.1). The female black/white prevalence ratio was 2.7 (95% confidence interval [CI], 2.4-3.0) in 1988 to 1994 increasing to 3.3 (95% CI, 2.9-3.7) in 2007 to 2010 (P = 0.01). Among males, the black/white prevalence ratio was 2.4 (95% CI, 1.9-2.9) in 1988 to 1994 increasing to 4.4 (95% CI, 3.3-5.8) in 2007 to 2010 (P = 0.001). The overall percentage of HSV-2-seropositive survey participants who reported never being told by a doctor or health care professional that they had genital herpes did not change significantly between 1988 to 1994 and 2007 to 2010 and remained high (90.7% and 87.4%, respectively).


CONCLUSIONS
Although HSV-2 seroprevalence decreased overall, the decrease was most marked among non-Hispanic whites, and racial disparities significantly increased over time. These persistent disparities demonstrate the need for innovative prevention strategies among this at-risk population.
The Yale cTAKES extensions for document classification: architecture and application

BACKGROUND
Open-source clinical natural-language-processing (NLP) systems have lowered the barrier to the development of effective clinical document classification systems. Clinical natural-language-processing systems annotate the syntax and semantics of clinical text; however, feature extraction and representation for document classification pose technical challenges.


METHODS
The authors developed extensions to the clinical Text Analysis and Knowledge Extraction System (cTAKES) that simplify feature extraction, experimentation with various feature representations, and the development of both rule and machine-learning based document classifiers. The authors describe and evaluate their system, the Yale cTAKES Extensions (YTEX), on the classification of radiology reports that contain findings suggestive of hepatic decompensation.


RESULTS AND DISCUSSION
The F(1)-Score of the system for the retrieval of abdominal radiology reports was 96%, and was 79%, 91%, and 95% for the presence of liver masses, ascites, and varices, respectively. The authors released YTEX as open source, available at http://code.google.com/p/ytex.
HTSeq—a Python framework to work with high-throughput sequencing data

MOTIVATION
A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed.


RESULTS
We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.


AVAILABILITY AND IMPLEMENTATION
HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq.
Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study

BACKGROUND
Long-term exposure to particulate matter air pollution has been associated with increased cardiopulmonary mortality in the USA. We aimed to assess the relation between traffic-related air pollution and mortality in participants of the Netherlands Cohort study on Diet and Cancer (NLCS), an ongoing study.


METHODS
We investigated a random sample of 5000 people from the full cohort of the NLCS study (age 55-69 years) from 1986 to 1994. Long-term exposure to traffic-related air pollutants (black smoke and nitrogen dioxide) was estimated for the 1986 home address. Exposure was characterised with the measured regional and urban background concentration and an indicator variable for living near major roads. The association between exposure to air pollution and (cause specific) mortality was assessed with Cox's proportional hazards models, with adjustment for potential confounders.


FINDINGS
489 (11%) of 4492 people with data died during the follow-up period. Cardiopulmonary mortality was associated with living near a major road (relative risk 1.95, 95% CI 1.09-3.52) and, less consistently, with the estimated ambient background concentration (1.34, 0.68-2.64). The relative risk for living near a major road was 1.41 (0.94-2.12) for total deaths. Non-cardiopulmonary, non-lung cancer deaths were unrelated to air pollution (1.03, 0.54-1.96 for living near a major road).


INTERPRETATION
Long-term exposure to traffic-related air pollution may shorten life expectancy.
A Mapping of Drug Space from the Viewpoint of Small Molecule
                    Metabolism

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the "effect space" comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.
Better multiclass classification via a margin-optimized single binary problem

We develop a new multiclass classification method that reduces the multiclass problem to a single binary classifier (SBC). Our method constructs the binary problem by embedding smaller binary problems into a single space. A good embedding will allow for large margin classification. We show that the construction of such an embedding can be reduced to the task of learning linear combinations of kernels. We provide a bound on the generalization error of the multiclass classifier obtained with our construction and outline the conditions for its consistency. Our empirical examination of the new method indicates that it outperforms one-vs-all, all-pairs and the error-correcting output coding scheme at least when the number of classes is small.
Breast Cancer Classification in Histopathological Images using Convolutional Neural Network

Computer based analysis is one of the suggested means that can assist oncologists in the detection and diagnosis of breast cancer. On the other hand, deep learning has been promoted as one of the hottest research directions very recently in the general imaging literature, thanks to its high capability in detection and recognition tasks. Yet, it has not been adequately suited to the problem of breast cancer so far. In this context, I propose in this paper an approach for breast cancer detection and classification in histopathological images. This approach relies on a deep convolutional neural networks (CNN), which is pretrained on an auxiliary domain with very large labelled images, and coupled with an additional network composed of fully connected layers. The network is trained separately with respect to various image magnifications (40x, 100x, 200x and 400x). The results presented in the patient level achieved promising scores compared to the state of the art methods. Keywords—Convolutional neural network (CNN); histopathological images; imagenet; classification
Associations between physical fitness and HbA1c in type 2 diabetes mellitus

In people with type 2 diabetes, exercise improves glucose control (as reflected in HbA1c) and physical fitness, but it is not clear to what extent these exercise-induced improvements are correlated with one another. We hypothesised that reductions in HbA1c would be related: (1) to increases in aerobic fitness and strength respectively in patients performing aerobic training or resistance training; and (2) to changes in strength and aerobic fitness in patients performing aerobic and resistance training. We randomly allocated 251 type 2 diabetes patients to aerobic, resistance, or aerobic plus resistance training, or to a sedentary control group. Peak oxygen consumption ( $$ \mathop {V}\limits^\cdot {{\text{O}}_{\text{2 peak}}} $$ ), workload, treadmill time and ventilatory threshold measurements from maximal treadmill exercise testing were measured at baseline and 6 months. Muscular strength was measured as the maximum weight that could be lifted eight times on the leg press, bench press and seated row exercises. With aerobic training, significant associations were found between changes in both $$ \mathop {V}\limits^\cdot {{\text{O}}_{\text{2 peak}}} $$ (p = 0.040) and workload (p = 0.022), and changes in HbA1c. With combined training, improvements in $$ \mathop {V}\limits^\cdot {{\text{O}}_{\text{2 peak}}} $$ (p = 0.008), workload (p = 0.034) and ventilatory threshold (p = 0.003) were significantly associated with changes in HbA1c. Increases in strength on the seated row (p = 0.006) and in mid-thigh muscle cross-sectional area (p = 0.030) were significantly associated with changes in HbA1c after resistance exercise, whereas the association between increases in muscle cross-sectional area and HbA1c in participants doing aerobic plus resistance exercise (p = 0.059) was of borderline significance. There appears to be a link between changes in fitness and HbA1c. The improvements in cardiorespiratory fitness with aerobic training may be a better predictor of changes in HbA1c than improvements in strength.
Exploring the risks of phage application in the environment

Interest in using bacteriophages to control the growth and spread of bacterial pathogens is being revived in the wake of widespread antibiotic resistance. However, little is known about the ecological effects that high concentrations of phages in the environment might have on natural microbial communities. We review the current evidence suggesting phage-mediated environmental perturbation, with a focus on agricultural examples, and describe the potential implications for human health and agriculture. Specifically, we examine the known and potential consequences of phage application in certain agricultural practices, discuss the risks of evolved bacterial resistance to phages, and question whether the future of phage therapy will emulate that of antibiotic treatment in terms of widespread resistance. Finally, we propose some basic precautions that could preclude such phenomena and highlight existing methods for tracking bacterial resistance to phage therapeutic agents.
Specific attenuation coefficients of optically active substances and their contribution to the underwater ultraviolet and visible light climate in shallow lakes and ponds

Sunlight penetration through the water column is controlled by the amount and kind of materials dissolved and suspended in the water. Understanding UV penetration in its complexity is essential for the prediction of the impact of UV radiation on aquatic ecosystems. However, only limited data are available on the penetration of UVR into shallow waters rich in inorganic suspended solids and chromophoric dissolved organic matter (CDOM). The same is true for the specific attenuation coefficients of light-absorbing components at the UV waveband. This study analyses the role of CDOM, algal-free suspended solids and algae in the formation of underwater UVR and PAR climate in 30 water bodies from clear gravel pit lakes trough the shallow Lake Balaton to turbid soda pans. Irradiance-depth profiles were obtained at 305, 313, 320 nm (UV-B), 340, 380, 395 nm (UV-A) and 400–700 nm (PAR) with a Biospherical PUV-2500 radiometer. Vertical attenuation coefficients (K d) were calculated. Water samples were taken for the laboratory measurement of the concentration of light-absorbing components: algae as chlorophyll a (CHL), chromophoric dissolved organic matter as colour (CDOM), and algal-free suspended solids (TSS-Alg) parallel with the in situ light measurements. Specific attenuation coefficient values were calculated by multiple regression analysis (n = 140). The obtained specific UV attenuation coefficient values of CHL, CDOM and TSS-Alg made it possible to establish light attenuation at different wavelengths based on the knowledge of the concentration of these light-absorbing components.
Atypical perception in autism: A failure of perceptual specialization?

We examined whether reduced perceptual specialization underlies atypical perception in autism spectrum disorder (ASD) testing classifications of stimuli that differ either along integral dimensions (prototypical integral dimensions of value and chroma), or along separable dimensions (prototypical separable dimensions of value and size). Current models of the perception of individuals with an ASD would suggest that on these tasks, individuals with ASD would be as, or more, likely to process dimensions as separable, regardless of whether they represented separable or integrated dimensions. In contrast, reduced specialization would propose that individuals with ASD would respond in a more integral manner to stimuli that differ along separable dimensions, and at the same time, respond in a more separable manner to stimuli that differ along integral dimensions. A group of nineteen adults diagnosed with high functioning ASD and seventeen typically developing participants of similar age and IQ, were tested on speeded and restricted classifications tasks. Consistent with the reduced specialization account, results show that individuals with ASD do not always respond more analytically than typically developed (TD) observers: Dimensions identified as integral for TD individuals evoke less integral responding in individuals with ASD, while those identified as separable evoke less analytic responding. These results suggest that perceptual representations are more broadly tuned and more flexibly represented in ASD. Autism Res 2017, 10: 1510-1522. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.
Coronary artery bypass with only in situ bilateral internal thoracic arteries and right gastroepiploic artery.

BACKGROUND
With the rapid advance of catheter intervention, the direction taken by surgeons is not only to make conventional CABG less invasive but also to pursue better long-term results by using more arterial conduits.


METHODS AND RESULTS
Between July 1989 and April 2000, 239 patients (218 men, 21 women) with a mean age of 59.7 (range 39 to 79) years underwent CABG with exclusive use of both internal thoracic arteries (ITAs) and the right gastroepiploic artery (RGEA). ITA grafts were harvested by using the skeletonization technique. Most patients (96%) had either triple-vessel or left main disease. Fifty percent of the patients were diabetic, and 16 were being treated with insulin. The left ventricular ejection fraction was </=40% in 46 patients (19%). Eleven patients (5%) had chronic renal failure and were on hemodialysis. Follow-up was completed in 235 patients (98%). Postoperative follow-up averaged 43 (range 1 to 129) months. Sequential grafting was performed in 64 patients, and the mean number of anastomoses was 3.3. One patient (0.4%) died of mediastinitis on the 53rd postoperative day. Graft patency was confirmed angiographically in 230 patients (96%) 2 to 3 weeks after surgery. The patency rate was 97.1% for the left ITA, 99.6% for the right ITA, and 95.5% for the RGEA. Five-year actuarial survival rate was 92.9%, and the cardiac death-free rate was 97.8%.


CONCLUSIONS
Complete arterial grafting with both ITAs and RGEA was associated with minimal operative risk, a high early graft patency rate, and excellent long-term results.
Some Open Problems in Combinatorial Physics

We point out four problems which have arisen during the recent research in the domain of Combinatorial Physics.
Piecewise Growth Mixture Modeling of Adolescent Alcohol Use Data

Developmental research involves the identification of individual differences in change as well as understanding the process of change itself (Baltes & N esselroade, 1979; Collins & Horn, 1991). The contemporary approach to the analysis of change, as presented in prior chapters, has focused on growth curve modeling that explicitly considers both intraindividual change and interindividual differences in such change, but treats the data as if collected from a single population. This assumption of homogeneity in the growth parameters is often unrealistic. If heterogeneity is ignored, statistical analyses and their effects can be seriously biased. This paper presents a procedure that accounts for sample heterogeneity – finite mixtures – and their application to longitudinal data. The underlying theory of finite mixture modeling assumes that the population of interest is not homogeneous (as measured by response probabilities) but consists of heterogeneous subpopulations with varying parameters. Mixture models are well known in the context of latent class models (e. describes latent class models as comprising a subset of the general class of latent structure models including factor analysis models, covariance structure models, latent profile models, and latent trait models. Others approach the mixture problem from a finite mixture distribution perspective (e. The common theme of mixture modeling is to partition the population into an unknown number of latent classes or subpopulations with class membership determined by specific parameters. The purpose of latent class analysis is, therefore, to 1) estimate the number and size of the latent classes in the mixture, 2) estimate the response probabilities for each indicator given the latent class, 3) assign latent class membership to individuals, and 4) examine the fit of the model to the data using various test statistics.
New identities involving Bernoulli and Euler polynomials

We derive several symmetric identities for Bernoulli and Euler polynomials which imply some known identities. Our proofs depend on the new technique developed in part I and some identities obtained in [European J. Combin. 24(2003), 709--718].