nils-herrmann/clean_scirepeval huggingface dataset:
The Seroprevalence of Hepatitis C Antibodies in Immigrants and Refugees from Intermediate and High Endemic Countries: A Systematic Review and Meta-Analysis

Hepatitis C virus (HCV) infection is a significant global health issue that leads to 350,000 preventable deaths annually due to associated cirrhosis and hepatocellular carcinoma (HCC). Immigrants and refugees (migrants) originating from intermediate/high HCV endemic countries are likely at increased risk for HCV infection due to HCV exposure in their countries of origin. The aim of this study was to estimate the HCV seroprevalence of the migrant population living in low HCV prevalence countries.



Four electronic databases were searched from database inception until June 17, 2014 for studies reporting the prevalence of HCV antibodies among migrants. Seroprevalence estimates were pooled with a random-effect model and were stratified by age group, region of origin and migration status and a meta-regression was modeled to explore heterogeneity.



Data from 50 studies representing 38,635 migrants from all world regions were included. The overall anti-HCV prevalence (representing previous and current infections) was 1.9% (95% CI, 1.4-2.7%, I2 96.1). Older age and region of origin, particularly Sub-Saharan Africa, Asia, and Eastern Europe were the strongest predictors of HCV seroprevalence. The estimated HCV seroprevalence of migrants from these regions was >2% and is higher than that reported for most host populations.



Adult migrants originating from Asia, Sub-Saharan Africa and Eastern Europe are at increased risk for HCV and may benefit from targeted HCV screening.
Evaluation and Measurement of Software Process Improvement—A Systematic Literature Review

Software Process Improvement (SPI) is a systematic approach to increase the efficiency and effectiveness of a software development organization and to enhance software products. This paper aims to identify and characterize evaluation strategies and measurements used to assess the impact of different SPI initiatives. The systematic literature review includes 148 papers published between 1991 and 2008. The selected papers were classified according to SPI initiative, applied evaluation strategies, and measurement perspectives. Potential confounding factors interfering with the evaluation of the improvement effort were assessed. Seven distinct evaluation strategies were identified, wherein the most common one, “Pre-Post Comparison,” was applied in 49 percent of the inspected papers. Quality was the most measured attribute (62 percent), followed by Cost (41 percent), and Schedule (18 percent). Looking at measurement perspectives, “Project” represents the majority with 66 percent. The evaluation validity of SPI initiatives is challenged by the scarce consideration of potential confounding factors, particularly given that “Pre-Post Comparison” was identified as the most common evaluation strategy, and the inaccurate descriptions of the evaluation context. Measurements to assess the short and mid-term impact of SPI initiatives prevail, whereas long-term measurements in terms of customer satisfaction and return on investment tend to be less used.
Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths

Determine effects of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Determine if limited enforcement in a medical tutoring system inhibits students from learning the optimal and most efficient solution path. Describe the type of deviations from the optimal solution path that occur during tutoring, and how these deviations change over time. Determine if the size of the problem-space (domain scope), has an effect on learning gains when using a tutor with limited enforcement.



Analyzed data mined from 44 pathology residents using SlideTutor-a Medical Intelligent Tutoring System in Dermatopathology that teaches histopathologic diagnosis and reporting skills based on commonly used diagnostic algorithms. Two subdomains were included in the study representing sub-algorithms of different sizes and complexities. Effects of the tutoring system on student errors, goal states and solution paths were determined.



Students gradually increase the frequency of steps that match the tutoring system's expectation of expert performance. Frequency of errors gradually declines in all categories of error significance. Student performance frequently differs from the tutor-defined optimal path. However, as students continue to be tutored, they approach the optimal solution path. Performance in both subdomains was similar for both errors and goal differences. However, the rate at which students progress toward the optimal solution path differs between the two domains. Tutoring in superficial perivascular dermatitis, the larger and more complex domain was associated with a slower rate of approximation towards the optimal solution path.



Students benefit from a limited-enforcement tutoring system that leverages diagnostic algorithms but does not prevent alternative strategies. Even with limited enforcement, students converge toward the optimal solution path.
An advanced Autonomous Underwater Vehicle design and control strategy

In this paper, an advanced design of an Autonomous Underwater Vehicle (AUV) is presented. The design is driven only by four water pumps. The different power combinations of the four motors provides the force and moment for propulsion and maneuvering. No control surfaces are needed in this design, which make the manufacturing cost of such a vehicle minimal and more reliable. Based on the propulsion method of the vehicle, a nonlinear AUV dynamic model is studied. This nonlinear model is linearized at the operation point. A control strategy of the AUV is proposed including attitude control and auto-pilot design. Simulation results for the attitude control loop are presented to validate this approach.
Global epidemiology of hepatitis C virus infection.

Hepatitis C virus (HCV) is a major cause of liver disease worldwide and a potential cause of substantial morbidity and mortality in the future. The complexity and uncertainty related to the geographic distribution of HCV infection and chronic hepatitis C, determination of its associated risk factors, and evaluation of cofactors that accelerate its progression, underscore the difficulties in global prevention and control of HCV. Because there is no vaccine and no post-exposure prophylaxis for HCV, the focus of primary prevention efforts should be safer blood supply in the developing world, safe injection practices in health care and other settings, and decreasing the number of people who initiate injection drug use.
Team THOR's Entry in the DARPA Robotics Challenge Trials 2013

Seung-Joon Yi GRASP Lab, University of Pennsylvania, Philadelphia, Pennsylvania 19104  yiseung@seas.upenn.edu Stephen G. McGill GRASP Lab, University of Pennsylvania, Philadelphia, Pennsylvania 19104  smcgill3@seas.upenn.edu Larry Vadakedathu GRASP Lab, University of Pennsylvania, Philadelphia, Pennsylvania 19104  vlarry@seas.upenn.edu Qin He GRASP Lab, University of Pennsylvania, Philadelphia, Pennsylvania 19104  Inyong Ha Robotis, Seoul, Korea e-mail: dudung@robotis.com Jeakweon Han Robotis, Seoul, Korea e-mail: jkhan@robotis.com Hyunjong Song Robotis, Seoul, Korea e-mail: hjsong@robotis.com Michael Rouleau RoMeLa, Virginia Tech, Blacksburg, Virginia 24061  mrouleau@vt.edu Byoung-Tak Zhang BI Lab, Seoul National University, Seoul, Korea e-mail: btzhang@bi.snu.ac.kr Dennis Hong RoMeLa, University of California, Los Angeles, Los Angeles, California 90095 e-mail: dennishong@ucla.edu Mark Yim GRASP Lab, University of Pennsylvania, Philadelphia, Pennsylvania 19104  yim@seas.upenn.edu Daniel D. Lee GRASP Lab, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks

Deep neural networks have shown promising results for various clinical prediction tasks such as diagnosis, mortality prediction, predicting duration of stay in hospital, etc. However, training deep networks – such as those based on Recurrent Neural Networks (RNNs) – requires large labeled data, high computational resources, and significant hyperparameter tuning effort. In this work, we investigate as to what extent can transfer learning address these issues when using deep RNNs to model multivariate clinical time series. We consider transferring the knowledge captured in an RNN trained on several source tasks simultaneously using a large labeled dataset to build the model for a target task with limited labeled data. An RNN pre-trained on several tasks provides generic features, which are then used to build simpler linear models for new target tasks without training task-specific RNNs. For evaluation, we train a deep RNN to identify several patient phenotypes on time series from MIMIC-III database, and then use the features extracted using that RNN to build classifiers for identifying previously unseen phenotypes, and also for a seemingly unrelated task of in-hospital mortality. We demonstrate that (i) models trained on features extracted using pre-trained RNN outperform or, in the worst case, perform as well as task-specific RNNs; (ii) the models using features from pre-trained models are more robust to the size of labeled data than task-specific RNNs; and (iii) features extracted using pre-trained RNN are generic enough and perform better than typical statistical hand-crafted features.
Human falling detection algorithm using back propagation neural network

A fall monitor system is necessary to reduce the rate of fall fatalities in elderly people. As an accelerometer has been smaller and inexpensive, it has been becoming widely used in motion detection fields. This paper proposes the falling detection algorithm based on back propagation neural network to detect the fall of elderly people. In the experiment, a tri-axial accelerometer was attached to waists of five healthy and young people. In order to evaluate the performance of the fall detection, five young people were asked to simulate four daily-life activities and four falls; walking, jumping, flopping on bed, rising from bed, front fall, back fall, left fall and right fall. The experimental results show that the proposed algorithm can potentially distinguish the falling activities from the other daily-life activities.
Enzyme immunoassay (EIA)/enzyme-linked immunosorbent assay (ELISA).

This brief note addresses the historical background of the invention of the enzyme immunoassay (EIA) and enzyme-linked immunosorbent assay (ELISA). These assays were developed independently and simultaneously by the research group of Peter Perlmann and Eva Engvall at Stockholm University in Sweden and by the research group of Anton Schuurs and Bauke van Weemen in The Netherlands. Today, fully automated instruments in medical laboratories around the world use the immunoassay principle with an enzyme as the reporter label for routine measurements of innumerable analytes in patient samples. The impact of EIA/ELISA is reflected in the overwhelmingly large number of times it has appeared as a keyword in the literature since the 1970s. Clinicians and their patients, medical laboratories, in vitro diagnostics manufacturers, and worldwide healthcare systems owe much to these four inventors.
A Transition-Based Directed Acyclic Graph Parser for UCCA

We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.
LD-SDS: Towards an Expressive Spoken Dialogue System based on Linked-Data

In this work we discuss the related challenges and describe an approach towards the fusion of state-of-the-art technologies from the Spoken Dialogue Systems (SDS) and the Semantic Web and Information Retrieval domains. We envision a dialogue system named LD-SDS that will support advanced, expressive, and engaging user requests, over multiple, complex, rich, and open-domain data sources that will leverage the wealth of the available Linked Data. Specifically, we focus on: a) improving the identification, disambiguation and linking of entities occurring in data sources and user input; b) offering advanced query services for exploiting the semantics of the data, with reasoning and exploratory capabilities; and c) expanding the typical information seeking dialogue model (slot filling) to better reflect real-world conversational search scenarios.
Engineered Endolysin-Based “Artilysins” To Combat Multidrug-Resistant Gram-Negative Pathogens

The global threat to public health posed by emerging multidrug-resistant bacteria in the past few years necessitates the development of novel approaches to combat bacterial infections. Endolysins encoded by bacterial viruses (or phages) represent one promising avenue of investigation. These enzyme-based antibacterials efficiently kill Gram-positive bacteria upon contact by specific cell wall hydrolysis. However, a major hurdle in their exploitation as antibacterials against Gram-negative pathogens is the impermeable lipopolysaccharide layer surrounding their cell wall. Therefore, we developed and optimized an approach to engineer these enzymes as outer membrane-penetrating endolysins (Artilysins), rendering them highly bactericidal against Gram-negative pathogens, including Pseudomonas aeruginosa and Acinetobacter baumannii. Artilysins combining a polycationic nonapeptide and a modular endolysin are able to kill these (multidrug-resistant) strains in vitro with a 4 to 5 log reduction within 30 min. We show that the activity of Artilysins can be further enhanced by the presence of a linker of increasing length between the peptide and endolysin or by a combination of both polycationic and hydrophobic/amphipathic peptides. Time-lapse microscopy confirmed the mode of action of polycationic Artilysins, showing that they pass the outer membrane to degrade the peptidoglycan with subsequent cell lysis. Artilysins are effective in vitro (human keratinocytes) and in vivo (Caenorhabditis elegans).  Bacterial resistance to most commonly used antibiotics is a major challenge of the 21st century. Infections that cannot be treated by first-line antibiotics lead to increasing morbidity and mortality, while millions of dollars are spent each year by health care systems in trying to control antibiotic-resistant bacteria and to prevent cross-transmission of resistance. Endolysins--enzymes derived from bacterial viruses--represent a completely novel, promising class of antibacterials based on cell wall hydrolysis. Specifically, they are active against Gram-positive species, which lack a protective outer membrane and which have a low probability of resistance development. We modified endolysins by protein engineering to create Artilysins that are able to pass the outer membrane and become active against Pseudomonas aeruginosa and Acinetobacter baumannii, two of the most hazardous drug-resistant Gram-negative pathogens.
Remote real-time monitoring system for oil and gas well based on wireless sensor networks

In this paper a monitoring system for production well based on WSN is designed, where the sensors can be used as the downhole permanent sensor to measure temperature and pressure analog signals. The analog signals are modulated digital signals by data acquisition system. The digital signals are transmitted to database server of monitoring center. Meanwhile the data can be browsed on internet or by mobile telephone, and the consumer receive alarm message when the data are overflow. The system offered manager and technician credible gist to make decision timely.
From Superpixel to Human Shape Modelling for Carried Object Detection

Detecting carried objects is one of the requirements for developing systems to reason about activities involving people and objects. We present an approach to detect carried objects from a single video frame with a novel method that incorporates features from multiple scales. Initially, a foreground mask in a video frame is segmented into multi-scale superpixels. Then the human-like regions in the segmented area are identified by matching a set of extracted features from superpixels against learned features in a codebook. A carried object probability map is generated using the complement of the matching probabilities of superpixels to human-like regions and background information. A group of superpixels with high carried object probability and strong edge support is then merged to obtain the shape of the carried object. We applied our method to two challenging datasets, and results show that our method is competitive with or better than the state-of-the-art.
Exact variable-length anomaly detection algorithm for univariate and multivariate time series

The problem of anomaly detection in time series has received a lot of attention in the past two decades. However, existing techniques cannot locate where the anomalies are within anomalous time series, or they require users to provide the length of potential anomalies. To address these limitations, we propose a self-learning online anomaly detection algorithm that automatically identifies anomalous time series, as well as the exact locations where the anomalies occur in the detected time series. In addition, for multivariate time series, it is difficult to detect anomalies due to the following challenges. First, anomalies may occur in only a subset of dimensions (variables). Second, the locations and lengths of anomalous subsequences may be different in different dimensions. Third, some anomalies may look normal in each individual dimension but different with combinations of dimensions. To mitigate these problems, we introduce a multivariate anomaly detection algorithm which detects anomalies and identifies the dimensions and locations of the anomalous subsequences. We evaluate our approaches on several real-world datasets, including two CPU manufacturing data from Intel. We demonstrate that our approach can successfully detect the correct anomalies without requiring any prior knowledge about the data.
A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices

The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.
Implementation of the Discrete Wavelet Transform Used in the Calibration of the Enzymatic Biosensors

The extensive use of insecticides in modern agriculture has raised serious public concern regarding the environment and food safety, and considerable efforts have been devoted to the development of highly sensitive detection methods (Bachmann et al., 2000; Istamboulie et al., 2007). Biosensors have been described for many years (Andreescu and Marty, 2006a) as good candidates to replace or complement conventional analytical methods, as they can provide real-time qualitative information about the composition of a sample with minimum treatment. In recent years, biosensors have played an important role in the determination of pesticides, because the compounds used in its composition have anticholinergic properties and therefore strongly inhibit cholinesterase enzymes. Evaluation of cholinesterase activity is the crucial factor in the construction of biosensors, however, in the case of multicomponent samples (more common in reality) is absolutely essential include appropriate data processing tools to find relationships between the biosensor responses and the measured data. In most cases, it is necessary a first data pretreatment step in order to explore and validate these obtained information (Ehrentreich, 2002). Many applications related with the use of biosensor responses entail data interpretation problem related to: (1) noisy records due to temperature changes; (2) data acquisition noise present in records, (3) presence of interference signals in the biosensor response mainly contaminated by signals coming for the electrochemical equipment i.e. potentiostats, magnetic stirrings and thermostats (Cai and Harrington, 1998; Zanchettin and Ludermir, 2007): (4) according with the inhibition method the responses can be slow which implies signals with information in low frequencies and a large number of samples per essay (Arduini et al., 2010). In this way, different data processing strategies have been proposed in order to achieve better interpretation models and discard irrelevant content coming from original data. During the last decade, Wavelet Transform (WT) has been widely employed in signal processing analysis were denoising and compression is an important step in the data mining
Engineered synthesis of rosmarinic acid in Escherichia coli resulting production of a new intermediate, caffeoyl-phenyllactate

To achieve high production of rosmarinic acid and derivatives in Escherichia coli which are important phenolic acids found in plants, and display diverse biological activities. The synthesis of rosmarinic acid was achieved by feeding caffeic acid and constructing an artificial pathway for 3,4-dihydroxyphenyllactic acid. Genes encoding the following enzymes: rosmarinic acid synthase from Coleus blumei, 4-coumarate: CoA ligase from Arabidopsis thaliana, 4-hydroxyphenyllactate 3-hydroxylase from E. coli and d-lactate dehydrogenase from Lactobacillus pentosus, were overexpressed in an l-tyrosine over-producing E. coli strain. The yield of rosmarinic acid reached ~130 mg l−1 in the recombinant strain. In addition, a new intermediate, caffeoyl-phenyllactate (~55 mg l−1), was also produced by the engineered E. coli strain. This work not only leads to high yield production of rosmarinic acid and analogues, but also sheds new light on the construction of the pathway of rosmarinic acid in E. coli.