Miracle-dz/UniFAR-FTUs huggingface dataset:
Serum pepsinogen assay is not recommended for the diagnosis of esophageal squamous cell carcinoma: a systematic review and meta-analysis

Background: Serum pepsinogen I (PGI) concentration and PGI/PGII ratio (PGR) are often used as serological markers for gastric fundus atrophy (AGA) and gastric carcinoma. However, their diagnostic value in esophageal carcinoma (EC) is inaccurate. Methods: This study evaluated the diagnostic value of PGI and PGR in EC by searching the PubMed, Web of Science, Embase, Cochrane Library and Cochrane Central Register of Controlled Trials databases for literature on the diagnosis of EC with PGI and PGR from January 1, 2000 to October 2, 2018. The included literature were systematically evaluated using QUSDAS-2 software. Meta-analysis was conducted using STATA 15.0 software. The summary receiver operating characteristic curve (SROC) accuracy was plotted, the area under the curve was calculated. Results: A total of 84 papers were selected, and after screening, nine papers on esophageal squamous cell carcinoma (ESCC) were finally included. Results showed low an ESCC-specific diagnostic sensitivity (0.27), high specificity (0.85), and 0.63 AUC of SROC when PGI≤70 ng/mL. When PGR≤3, the ESCC-specific diagnostic sensitivity was low (0.29), the specificity was high (0.83), and the AUC of SROC was 0.63. Conclusion: According to the current research results, PGI≤70 ng/mL or PGR≤3 diagnostic ESCC sensitivity is low, and specificity is high. These findings indicate that neither PGI≤70 ng/mL nor PGR≤3 can be used as an ESCC-screening index.
Prevalence of HIV, Hepatitis B and C Virus Co-infections among Iranian High-Risk Groups: A Systematic Review and Meta-Analysis

Co-infection with hepatitis B and C among HIV infected patients are prevalent among high-risk populations. This meta-analysis aimed to estimate the prevalence of HIV, HCV and HBV co-infections among high-risk populations in Iran. We systematically searched the national and international electronic databases until 2016. The primary outcome was the prevalence of HIV, HBV, HCV and HIV co-infections in different high-risk populations in Iran. All English and Persian studies conducted on Iranian high-risk groups were included in the study. The review was reported based on PRISMA guidelines and data were analysed at 95% confidence level using random effect models. Overall, 916 relevant papers were recognised and 14 articles were included in the meta-analysis. The pooled estimates of HBV/HCV, HCV/HIV, HBV/HIV and HBV/HCV/HIV were 1.3% (95%CI: 0.5–2.1), 16.3% (95%CI: 1.1–31.6), 0.5% (95%CI: 0–1.4) and 0.5% (95%CI: 0.2–0.8), respectively. Based on subgroup analysis, there was a higher proportion of all co-infections from the years 2010–2016 as compared to that of the years 2003–2009. Our results highlighted that HCV/HIV co-infection in Iranian high-risk groups including injection drug users (IDUs) and prisoners is common. In addition, the increasing trend of coinfections should be considered alarming for policymakers.
The safety and efficacy of amrubicin in the treatment of previously untreated extensive-disease small-cell lung cancer: a meta-analysis

Background: Extensive-disease small-cell lung cancer (ED-SCLC) has been known to be rapid progression and relapse, despite highly sensitive to chemotherapy. Amrubicin (AMR), a third-generation synthetic anthracycline, was accepted as a feasible alternative compared with the standard first-line chemotherapy for previously untreated ED-SCLC. While, the efficacies of these amrubicin-based regimens are unsatisfactory. Aim: Our meta-analysis was performed to assess the efficacy and toxicity of first-line therapy comparing AMR and chemotherapy in patients with ED-SCLC. Methods: Electronic databases were searched for eligible trials updated on November 2018. Randomized-controlled trials assessing the efficacy and safety of AMR in ED-SCLC were included, of which the interested results were objective response rate (ORR), progression-free survival (PFS), overall survival (OS), and adverse events (AEs). Results: A total of 6 randomized controlled trials were included in this analysis. There are no significant differences in OS (OR=1.03, 95% CI=0.66–1.60, P=0.91), PFS (OR=1.2, 95% CI=10.77–1.88, P=0.41) or ORR (OR=1.31, 95% CI=0.90–1.92, P=0.16) with AMR (OR=0.90, 95% CI=0.76–1.05, P=0.17). The most common treatment-related AEs in the AMR group are leukopenia (OR=3.13, 95% CI=1.22–7.99, P=0.02) and neutropenia (OR=3.25, 95% CI=1.38–7.65, P=0.007). Fatigue, anemia, nausea, vomiting, diarrhea the difference between the two groups had no statistical significance. Conclusion: The results of our analysis indicated that AMR therapy demonstrated non-inferiority to the standard first-line chemotherapy for previously untreated ED-SCLC. Whether it can be accepted as an alternative regimen to the standard first-line chemotherapy is still warranted.
TideHunter: efficient and sensitive tandem repeat detection from noisy long-reads using seed-and-chain

Abstract Motivation Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) sequencing technologies can produce long-reads up to tens of kilobases, but with high error rates. In order to reduce sequencing error, Rolling Circle Amplification (RCA) has been used to improve library preparation by amplifying circularized template molecules. Linear products of the RCA contain multiple tandem copies of the template molecule. By integrating additional in silico processing steps, these tandem sequences can be collapsed into a consensus sequence with a higher accuracy than the original raw reads. Existing pipelines using alignment-based methods to discover the tandem repeat patterns from the long-reads are either inefficient or lack sensitivity. Results We present a novel tandem repeat detection and consensus calling tool, TideHunter, to efficiently discover tandem repeat patterns and generate high-quality consensus sequences from amplified tandemly repeated long-read sequencing data. TideHunter works with noisy long-reads (PacBio and ONT) at error rates of up to 20% and does not have any limitation of the maximal repeat pattern size. We benchmarked TideHunter using simulated and real datasets with varying error rates and repeat pattern sizes. TideHunter is tens of times faster than state-of-the-art methods and has a higher sensitivity and accuracy. Availability and implementation TideHunter is written in C, it is open source and is available at https://github.com/yangao07/TideHunter
Antiviral and immunomodulatory effects of polyphenols on macrophages infected with dengue virus serotypes 2 and 3 enhanced or not with antibodies

Background: There is a lack of specific antiviral therapy against dengue virus (DENV) in current use. Therefore, a great proportion of dengue cases progress to severe clinical forms due to a complex interplay between virus and host immune response. It has been hypothesized that heterotypic non-neutralizing antibodies enhance DENV infection in phagocytic cells, and this induces an inflammatory response that is involved in the pathogenesis of severe dengue. Purpose: To identify the antiviral and immunomodulatory effects of polyphenols on dengue virus infection. Methods: Human U937-DC-SIGN macrophages were infected with DENV serotypes 2 or 3 in the presence or not of enhancing antibody 4G2. Viral titers and the secretion of tumor necrosis factor-alpha, IL-6, IL-10 and interferon-alpha were analyzed timely. Results: DENV infection alone induced high production of IL-6 and TNF-α, but in the presence of 4G2 antibody, viral titers and TNF-α secretion were potentiated. Based on anti-inflammatory antecedents, the polyphenols curcumin, fisetin, resveratrol, apigenin, quercetin and rutin were tested for antiviral and immunomodulatory properties. Only quercetin and fisetin inhibited DENV-2 and DENV-3 infection in the absence or presence of enhancing antibody (>90%, p<0.001); they also inhibited TNF-α and IL-6 secretion (p<0.001). Conclusion: Quercetin and fisetin down-regulate the production of proinflammatory cytokines induced by DENV infection enhanced by antibodies a mechanism involved in severe dengue.
Enhancing the drug discovery process: Bayesian inference for the analysis and comparison of dose–response experiments

Abstract Motivation The efficacy of a chemical compound is often tested through dose–response experiments from which efficacy metrics, such as the IC50, can be derived. The Marquardt–Levenberg algorithm (non-linear regression) is commonly used to compute estimations for these metrics. The analysis are however limited and can lead to biased conclusions. The approach does not evaluate the certainty (or uncertainty) of the estimates nor does it allow for the statistical comparison of two datasets. To compensate for these shortcomings, intuition plays an important role in the interpretation of results and the formulations of conclusions. We here propose a Bayesian inference methodology for the analysis and comparison of dose–response experiments. Results Our results well demonstrate the informativeness gain of our Bayesian approach in comparison to the commonly used Marquardt–Levenberg algorithm. It is capable to characterize the noise of dataset while inferring probable values distributions for the efficacy metrics. It can also evaluate the difference between the metrics of two datasets and compute the probability that one value is greater than the other. The conclusions that can be drawn from such analyzes are more precise. Availability and implementation We implemented a simple web interface that allows the users to analyze a single dose–response dataset, as well as to statistically compare the metrics of two datasets.
Affect detection in home-based educational software for young children

Research on automated aﬀect detection in educational software using play log data has shown promising results. Yet most studies use classroom-based software designed for adolescents or adults. In this paper, we aim to detect aﬀection in an online educational platform primarily aimed at home use by young children. This presents two challenges: we have to rely on a self-report instrument of aﬀect that users can utilize at home, and we have to make sure that this instrument is properly understood by children. To this end, we developed and validated an emoticon-based self-report instrument to derive ground-truth labels of four emotions: Joy, frustration, confusion, and boredom. Training a number of diﬀerent classiﬁers for automated aﬀect detection yields promising re-sults, in particular for detecting joy and frustration.
Mining Android crash fixes in the absence of issue- and change-tracking systems

Android apps are prone to crash. This often arises from the misuse of Android framework APIs, making it harder to debug since official Android documentation does not discuss thoroughly potential exceptions.Recently, the program repair community has also started to investigate the possibility to fix crashes automatically. Current results, however, apply to limited example cases. In both scenarios of repair, the main issue is the need for more example data to drive the fix processes due to the high cost in time and effort needed to collect and identify fix examples. We propose in this work a scalable approach, CraftDroid, to mine crash fixes by leveraging a set of 28 thousand carefully reconstructed app lineages from app markets, without the need for the app source code or issue reports. We developed a replicative testing approach that locates fixes among app versions which output different runtime logs with the exact same test inputs. Overall, we have mined 104 relevant crash fixes, further abstracted 17 fine-grained fix templates that are demonstrated to be effective for patching crashed apks. Finally, we release ReCBench, a benchmark consisting of 200 crashed apks and the crash replication scripts, which the community can explore for evaluating generated crash-inducing bug patches.
Face Recognition via Ensemble SIFT Matching of Uncorrelated Hyperspectral Bands and Spectral PCTs

Face recognition through hyperspectral images is a concept which is still in its infancy. Although the conventional method of face recognition using Red-Green-Blue (RGB) or grayscale images has been advanced over the last twenty years, these methods are still shown to have weak performance whenever there are variations or changes in lighting, pose, or temporal aspect of the subjects. A hyperspectral representation of an image captures more information that is available within a scene than a RGB image therefore it is beneficial to study the performance of face recognition using a hyperspectral representation of the subjects’ faces. The results from a variety of methods of performing face recognition using the Scale Invariant Transformation Feature (SIFT) algorithm as a matching function on uncorrelated spectral bands, principal component representation of the spectral bands, and the ensemble decision of the two were studied. We conclude that there is no dominating method in the scope of our research; however, we do obtain three algorithms that outperform the results obtained from a previous study which only considered a SIFT application on a single hyperspectral band, and our algorithm that utilizes fusion performs very well under temporal variation as supported by the findings in our results. The novelty of this research is the application of SIFT on hyperspectral face images as a matching function in performing face recognition which has never been performed prior to this study.
Interactive metamorphic testing of debuggers

When improving their code, developers often turn to interactive debuggers. The correctness of these tools is crucial, because bugs in the debugger itself may mislead a developer, e.g., to believe that executed code is never reached or that a variable has another value than in the actual execution. Yet, debuggers are difficult to test because their input consists of both source code and a sequence of debugging actions, such as setting breakpoints or stepping through code. This paper presents the first metamorphic testing approach for debuggers. The key idea is to transform both the debugged code and the debugging actions in such a way that the behavior of the original and the transformed inputs should differ only in specific ways. For example, adding a breakpoint should not change the control flow of the debugged program. To support the interactive nature of debuggers, we introduce interactive metamorphic testing. It differs from traditional metamorphic testing by determining the input transformation and the expected behavioral change it causes while the program under test is running. Our evaluation applies the approach to the widely used debugger in the Chromium browser, where it finds eight previously unknown bugs with a true positive rate of 51%. All bugs have been confirmed by the developers, and one bug has even been marked as release-blocking.
Modelling End-of-Session Actions in Educational Systems

In this paper we consider the problem of modelling when students end their session in an online mathematics educational system. Being able to model this accurately will help us optimize the way content is presented and consumed. This is done by modelling the probability of an action being the last in a session, which we denote as the End-of-Session probability. We use log data from a system where students can learn mathematics through various kinds of learning materials, as well as multiple types of exercises, such that a student session can consist of many different activities. We model the End-of-Session probability by a deep recurrent neural network in order to utilize the long term temporal aspect, which we experimentally show is central for this task. Using a large scale dataset of more than 70 million student actions, we obtain an AUC of 0.81 on an unseen collection of students. Through a detailed error analysis, we observe that our model is robust across different session structures and across varying session lengths.
A new dimension of test quality: assessing and generating higher quality unit test cases

Unit tests form the first defensive line against the introduction of bugs in software systems. Therefore, their quality is of a paramount importance to produce robust and reliable software. To assess test quality, many organizations relies on metrics like code and mutation coverage. However, they are not always optimal to fulfill such a purpose. In my research, I want to make mutation testing scalable by devising a lightweight approach to estimate test effectiveness. Moreover, I plan to introduce a new metric measuring test focus—as a proxy for the effort needed by developers to understand and maintain a test— that both complements code coverage to assess test quality and can be used to drive automated test case generation of higher quality tests.
Some challenges for software testing research (invited talk paper)

This paper outlines 4 open challenges for Software Testing in general and Search Based Software Testing in particular, arising from our experience with the Sapienz System Deployment at Facebook. The challenges may also apply more generally, thereby representing opportunities for the research community to further benefit from the growing interest in automated test design in industry.
Generic attribute revocation systems for attribute-based encryption in cloud storage

Attribute-based encryption (ABE) has been a preferred encryption technology to solve the problems of data protection and access control, especially when the cloud storage is provided by third-party service providers. ABE can put data access under control at each data item level. However, ABE schemes have practical limitations on dynamic attribute revocation. We propose a generic attribute revocation system for ABE with user privacy protection. The attribute revocation ABE (AR-ABE) system can work with any type of ABE scheme to dynamically revoke any number of attributes.
IoT Smart Home Using Eye Tracking and Voice Interfaces for Elderly and Special Needs People

Eye tracking has become increasingly important in many sectors because of its ability to facilitate day to day activities, especially for users with special needs, where tasks as simple as turning on a light require effort. To tackle similar issues, we propose a model that utilizes Video Oculography approach through Tobii technology with added voice interfaces using Azure cloud to help control home appliances. This model traces the user via reflected infrared light patterns and calculates the gaze position automatically. This method uses no wearable technology through Video Oculography with multiple cameras which deliver several advantages; it provides accurate gaze estimates, portability of the video recording system, and fully remote recordings. Eye tracking facilitates interactions to control home appliances when the user cannot or does not wish to use their hands by means of the IoT and cloud Technologies. Eye tracking has great impact in countless fields such as neurology, cognition, communication and security of different categories. Focus groups and usability tests show that many users were satisfied with the straightforward use of the model, flexibility and reliability in interacting with the system, and accuracy in the movement of the pointer, make this model a reliable solution to simplifying some daily tasks.
Delaunay Triangulation Based Key Distribution for Wireless Sensor Network

The key distribution in a wireless sensor network is a security phase, which involves distribution secret keys among sensor nodes prior to deployment. The distributed keys are used for encryption and decryption process in later stages. There are mainly two types of key distribution strategies namely location independent and location dependent. Location independent key distribution scheme is used when the location of the sensor nodes are not known prior. If the expected locations of sensor nodes are known prior to deployment, the location dependent key distribution scheme is used. Here in this paper, we proposed a location dependent key distribution scheme using Delaunay Triangulation (DTKPS). Delaunay triangulation is a triangulation method which forms a set of triangles for a given set of points such that no point falls inside the Delaunay disk of any triangle. Implementation of the key distribution scheme is done using Matlab software and results were presented. 
Detecting interaction/complexity within crowd movements using braid entropy

The segmentation of moving and non-moving regions in an image within the field of crowd analysis is a crucial process in terms of understanding crowd behavior. In many studies, similar movements were segmented according to the location, adjacency to each other, direction, and average speed. However, these segments may not in turn indicate the same types of behavior in each region. The purpose of this study is to better understand crowd behavior by locally measuring the degree of interaction/complexity within the segment. For this purpose, the flow of motion in the image is primarily represented as a series of trajectories. The image is divided into hexagonal cells and the finite time braid entropy (FTBE) values are calculated according to the different projection angles of each cell. These values depend on the complexity of the spiral structure that the trajectories generated throughout the movement and show the degree of interaction among pedestrians. In this study, behaviors of different complexities determined in segments are pictured as similar movements on the whole. This study has been tested on 49 different video sequences from the UCF and CUHK databases.
A comparison of learning models

Abstract We investigate learning in a probabilistic task, called "medical diagnosis. " On each trial, a subject is presented with a stimulus configuration indicating the value of four medical symptoms. The subject responds by guessing which of two diseases is present and is then given feedback about which disease was actually present. The feedback is determined according to fixed conditional probabilities unknown to the subject. We test a normative Bayesian model as well as simple variants of well-known psychological models including the Fuzzy Logical Model of Perception, an Exemplar model, a two-layer Connectionist model and an ALCOVE model. Both the asymptotic predictions of these models (i.e., predictions regarding behavior after it has stabilized and learning is complete) and predictions of trial-by-trial changes in behavior are tested. The models are tested against existing data from Estes et al. (1989, Journal of Experimental Psychology: Learning, Memory, & Cognition, 15, 556-571) and new data from medical diagnosis tasks that include not only asymmetric but also symmetric base rates. Learning was observed in all cases in that subjects tended to match the objective probabilities of the symptom configurations more closely in later trials. All of the descriptive models give a more accurate account of performance than the normative Bayesian model. Relative to a benchmark measure, however, none of these models does an especially good job of characterizing asymptotic performance or the learning process. We suggest that future experiments should address individual performance, rather than group learning curves.