Miracle-dz/UniFAR-FTUs huggingface dataset:
Are School-going Adolescents Mentally Healthy? Case Study from Sabarkantha, Gujarat, India

Background: Mental health issues becoming the global public health challenge, especially among the youth (12–24 years of age), although they are often detected later in life. In India, the adolescent population constitutes a quarter of the country's population and burden of disease varies from 9.5 to 102/1000 population. Most of the mental health disorders remain unidentified due to negligence and ignorance of multiple factors. Keeping this in mind and lack of population-based studies with good quality for guiding the mental health policies, this study aims to document the prevalence of emotional and behavioral difficulties among adolescents in Sabarkantha district of Gujarat, India. Methods: This is a school-based cross-sectional study conducted among 11–19 years of school-going adolescents during August–September 2016. About 477 adolescents who gave consent to participate were selected from 20 randomly primary and secondary schools. A prevalidated questionnaire for sociodemographic information including global validated standard questionnaire for mental health scoring known as Strengths and Difficulties Questionnaire (SDQ) were administered and self-reported responses were documented. Statistical analysis was conducted through SPSS version 20. Results: Mean age of the study population was 14.2 ± 1.4 years. About 14.6% boys and 12.6% of girls had abnormal total SDQ score, while 15.3% boys and 21.9% of girls had borderline SDQ score. Thus, 70.1% of boys compared to 65.6% girls had normal SDQ score. The difference between mean (higher mean score among girls) of total SDQ score of boys and girls was statically significant at the level of P < 0.05. Major risk factors for self-reported mental health issues were illiterate mother, occupation of parents, which make them away from family during daytime, nuclear family, severe addiction to alcohol in the family, financial problem in the family, and adolescent getting daily physical punishment. One-seventh adolescents are vulnerable for mental health problems found in this study. About one-fifth adolescents have internalizing (emotional) and about one-sixth have externalizing (conduct) manifestations. Conclusion: There is an urgent need to address the emotional and conduct manifestation among school-going adolescents. Rashtriya Kishor Swasthya Karyakram framework needs to address these issues on priority.
Preparedness of Young Girls for Prevention of Cervical Cancer and Strategy to introduce the HPV Vaccine

Objective: Cervical cancer incidence and mortality rates remain high in developing country due to low levels of awareness on preventive measures. The main study objective was to sensitize the young girls on the prevention of cervical cancer. Study Design: Quasi-experimental pretest and posttest design was used. Methods: A total of 240 young girls enrolled and 60 were selected randomly for the sensitization program. The preparedness for prevention of cervical cancer was assessed through structured preparedness criteria and perception toward vaccination was ascertained through rating scale. Results: Changes in knowledge level and intention to be vaccinated and for developing positive attitude to be screened in future were assessed using paired t-tests and found to be statistically significant. There is been an improvement in knowledge score noticed from pretest to posttest. The greatest improvements were observed in responses to questions on knowledge on symptoms (60%) and prevention (88%) that is maximum responded vaginal bleeding is the most common symptoms of cervical cancer and avoiding early sexual intercourse is the preventive measures of cancer cervix. Out of the 60 girls, 52 (86%) shown their agreement for human papillomavirus (HPV) vaccination, 35 (58.33%) took the HPV vaccine as a preventive measure. Conclusion: Study concludes that periodical sensitization through educational intervention may act as a cascade for girls and help them to be more aware about the preventive aspects of cervical cancer.
Bone health in elite Norwegian endurance cyclists and runners: a cross-sectional study

Background Athletes who compete in non-weight-bearing activities such as swimming and cycling are at risk of developing low bone mineral density (BMD). Athletes in long-distance running are at risk of low BMD. Objective (1) To evaluate the bone health in Norwegian male and female national elite road cyclists and middle-distance and long-distance runners, and to identify cases of low BMD. (2) To identify possible risk factors associated with low BMD. Methods Twenty-one runners (11 females and 10 males) and 19 road cyclists (7 females and 12 males) were enrolled in this cross-sectional study. Dual-energy X-ray absorptiometry measurement of BMD in total body, femoral neck and lumbar spine was measured. Participants completed a questionnaire regarding training, injuries, calcium intake and health variables. Results The cyclists had lower BMD for all measured sites compared with the runners (p≤0.05). Ten of 19 cyclists were classified as having low BMD according to American College of Sports Medicine criteria (Z-score ≤−1), despite reporting to train heavy resistance training on the lower extremities. Low BMD was site specific having occurred in the lumbar spine and the femoral neck and was not confined to females. Type of sport was the only factor significantly associated with low BMD. Conclusion National elite Norwegian road cyclists had lower BMD compared with runners, and a large proportion was classified as having low BMD, despite having performed heavy resistance training. Interventions to increase BMD in this population should be considered.
Complete Genome Sequence of Bradyrhizobium sp. Strain ORS3257, an Efficient Nitrogen-Fixing Bacterium Isolated from Cowpea in Senegal

Here, we report the complete genome sequence of Bradyrhizobium sp. strain ORS3257, which forms efficient symbioses with cowpea, peanut, or groundnut. ABSTRACT Here, we report the complete genome sequence of Bradyrhizobium sp. strain ORS3257, which forms efficient symbioses with cowpea, peanut, or groundnut. These genomic data will be useful to identify genes associated with symbiotic performance and host compatibility on several legumes, including Aeschynomene species, with which a Nod-independent type III secretion system (T3SS)-dependent symbiosis can be established.
Infrared thermography study as a complementary method of screening and prevention of muscle injuries: pilot study

Objective To evaluate thermography application as a complementary method in preventing muscle injury in professional soccer players. Methods﻿ A longitudinal prospective study with 28 professional soccer players that composed a first division of Brazilian’s soccer team between 2015 and 2016. In both seasons (2015 and 2016), muscle injuries were documented and classified in grade of severity, by ultrasound. During the following season (2016), infrared medical thermography was applied twice a week (48 hours after game) and if a difference of temperature was detected higher than 0.4°C, a prevention protocol was initiated. Muscle injuries in 2016 were documented. Results In 2015, the total number of muscle injuries was 11. In 2016, the total number of muscle injuries was 4 (p=0.04). It represents an incidence/player of 78% in 2015 and 28% in 2016, corresponding to a decrease of 64% in 2016. Seven players played in the first team in both seasons. Among these seven players, muscle injuries were reduced from 8 (in 2015) to 3 (in 2016)—a decrease of 63% in the season we used thermographic monitoring (p=0.06). Conclusion The pilot data provide a promising catalyst for a rigorous RCT that could examine whether thermography can contribute to a muscle injury prevention programme.
A Diagnostic Challenge: Pancreatic Cancer or Autoimmune Pancreatitis?

ABSTRACT: We report a rare case of seronegative autoimmune pancreatitis (AIP) that presented as a pancreatic focal lesion and was considered to be pancreatic cancer based on the clinical presentation and imaging findings. The endoscopic ultrasound-guided biopsies of the pancreatic mass revealed no malignant cells and the pancreatic swelling had become diffuse on repeat imaging. AIP was suspected and a trial of steroids was considered as a diagnostic and therapeutic method. The patient responded dramatically to corticosteroid treatment with resolution of symptoms and normal imagining and laboratory parameters. This case highlights the challenge in the diagnostic approach of a pancreatic mass.
Comparison of Platforms for Recommender Algorithm on Large Datasets

One of the challenges our society faces is the ever increasing amount of data. Among existing platforms that address the system requirements, Hadoop is a framework widely used to store and analyze “big data”. On the human side, one of the aids to finding the things people really want is recommendation systems. This paper evaluates highly scalable parallel algorithms for recommendation systems with application to very large data sets. A particular goal is to evaluate an open source Java message passing library for parallel computing called MPJ Express, which has been integrated with Hadoop. As a demonstration we use MPJ Express to implement collaborative filtering on various data sets using the algorithm ALSWR (Alternating-Least-Squares with Weighted-λ-Regularization). We benchmark the performance and demonstrate parallel speedup on Movielens and Yahoo Music data sets, comparing our results with two other frameworks: Mahout and Spark. Our results indicate that MPJ Express implementation of ALSWR has very competitive performance and scalability in comparison with the two other frameworks. 2012 ACM Subject Classification Information systems→ Database management system engines
Automatic generation of GUI test cases using Ant Colony Optimization and Greedy algorithm

The increasing complexity of new applications means GUIs are also getting more complex, and generating tests cases manually for them becomes harder. Generating automatic, good quality GUI test cases is a growing concern in application testing. Actions performed by the user on the GUI can be regarded as events, which can be performed in sequences, forming a graph of event sequences, and therefore multiple execution paths or routes, known as test cases, are possible. The quality of a set of test cases is measured by the coverage criteria (all actions or events must be performed at least one time in the set), which depend on the length and partial coverage of each execution path. Finding feasible paths and complying with the coverage criteria is a highly combinatorial problem. For such problems, due to high computing power that it would take to ﬁnd an exact solution, it is well justiﬁed to use heuristics and metaheuristics algorithms, allowing us to ﬁnd approximate solutions of good quality. Those methods have been successfully used in chemistry, physics, biology, and recently, in software engineering. In this paper, the use of a metaheuristic known as Ant Colony Optimization Algorithm (ACO) for generating test cases is proposed. The ACO metaheuristic has been adapted in order to ﬁnd individual routes that could lead to a set of test cases of good quality. A individual test, path or route is desirable if it is long (it tests a lot of events or actions) and do not share events (or share few events) with other paths. After a appropiate number of candidate test cases are generated, we express the problem of
Transfer to Rank for Heterogeneous One-Class Collaborative Filtering

Heterogeneous one-class collaborative filtering is an emerging and important problem in recommender systems, where two different types of one-class feedback, i.e., purchases and browses, are available as input data. The associated challenges include ambiguity of browses, scarcity of purchases, and heterogeneity arising from different feedback. In this article, we propose to model purchases and browses from a new perspective, i.e., users’ roles of mixer, browser and purchaser. Specifically, we design a novel transfer learning solution termed role-based transfer to rank (RoToR), which contains two variants, i.e., integrative RoToR and sequential RoToR. In integrative RoToR, we leverage browses into the preference learning task of purchases, in which we take each user as a sophisticated customer (i.e., mixer) that is able to take different types of feedback into consideration. In sequential RoToR, we aim to simplify the integrative one by decomposing it into two dependent phases according to a typical shopping process. Furthermore, we instantiate both variants using different preference learning paradigms such as pointwise preference learning and pairwise preference learning. Finally, we conduct extensive empirical studies with various baseline methods on three large public datasets and find that our RoToR can perform significantly more accurate than the state-of-the-art methods.
Seed-Guided Topic Model for Document Filtering and Classification

One important necessity is to filter out the irrelevant information and organize the relevant information into meaningful categories. However, developing text classifiers often requires a large number of labeled documents as training examples. Manually labeling documents is costly and time-consuming. More importantly, it becomes unrealistic to know all the categories covered by the documents beforehand. Recently, a few methods have been proposed to label documents by using a small set of relevant keywords for each category, known as dataless text classification. In this article, we propose a seed-guided topic model for the dataless text filtering and classification (named DFC). Given a collection of unlabeled documents, and for each specified category a small set of seed words that are relevant to the semantic meaning of the category, DFC filters out the irrelevant documents and classifies the relevant documents into the corresponding categories through topic influence. DFC models two kinds of topics: category-topics and general-topics. Also, there are two kinds of category-topics: relevant-topics and irrelevant-topics. Each relevant-topic is associated with one specific category, representing its semantic meaning. The irrelevant-topics represent the semantics of the unknown categories covered by the document collection. And the general-topics capture the global semantic information. DFC assumes that each document is associated with a single category-topic and a mixture of general-topics. A novelty of the model is that DFC learns the topics by exploiting the explicit word co-occurrence patterns between the seed words and regular words (i.e., non-seed words) in the document collection. A document is then filtered, or classified, based on its posterior category-topic assignment. Experiments on two widely used datasets show that DFC consistently outperforms the state-of-the-art dataless text classifiers for both classification with filtering and classification without filtering. In many tasks, DFC can also achieve comparable or even better classification accuracy than the state-of-the-art supervised learning solutions. Our experimental results further show that DFC is insensitive to the tuning parameters. Moreover, we conduct a thorough study about the impact of seed words for existing dataless text classification techniques. The results reveal that it is not using more seed words but the document coverage of the seed words for the corresponding category that affects the dataless classification performance.
SMAPH

We study the problem of linking the terms of a web-search query to a semantic representation given by the set of entities (a.k.a. concepts) mentioned in it. We introduce SMAPH, a system that performs this task using the information coming from a web search engine, an approach we call “piggybacking. ” We employ search engines to alleviate the noise and irregularities that characterize the language of queries. Snippets returned as search results also provide a context for the query that makes it easier to disambiguate the meaning of the query. From the search results, SMAPH builds a set of candidate entities with high coverage. This set is filtered by linking back the candidate entities to the terms occurring in the input query, ensuring high precision. A greedy disambiguation algorithm performs this filtering; it maximizes the coherence of the solution by iteratively discovering the pertinent entities mentioned in the query. We propose three versions of SMAPH that outperform state-of-the-art solutions on the known benchmarks and on the GERDAQ dataset, a novel dataset that we have built specifically for this problem via crowd-sourcing and that we make publicly available.
Jointly Minimizing the Expected Costs of Review for Responsiveness and Privilege in E-Discovery

Discovery is an important aspect of the civil litigation process in the United States of America, in which all parties to a lawsuit are permitted to request relevant evidence from other parties. With the rapid growth of digital content, the emerging need for “e-discovery” has created a strong demand for techniques that can be used to review massive collections both for “responsiveness” (i.e., relevance) to the request and for “privilege” (i.e., presence of legally protected content that the party performing the review may have a right to withhold). In this process, the party performing the review may incur costs of two types, namely, annotation costs (deriving from the fact that human reviewers need to be paid for their work) and misclassification costs (deriving from the fact that failing to correctly determine the responsiveness or privilege of a document may adversely affect the interests of the parties in various ways). Relying exclusively on automatic classification would minimize annotation costs but could result in substantial misclassification costs, while relying exclusively on manual classification could generate the opposite consequences. This article proposes a risk minimization framework (called MINECORE, for “minimizing the expected costs of review”) that seeks to strike an optimal balance between these two extreme stands. In MINECORE (a) the documents are first automatically classified for both responsiveness and privilege, and then (b) some of the automatically classified documents are annotated by human reviewers for responsiveness (typically by junior reviewers) and/or, in cascade, for privilege (typically by senior reviewers), with the overall goal of minimizing the expected cost (i.e., the risk) of the entire process. Risk minimization is achieved by optimizing, for both responsiveness and privilege, the choice of which documents to manually review. We present a simulation study in which classes from a standard text classification test collection (RCV1-v2) are used as surrogates for responsiveness and privilege. The results indicate that MINECORE can yield substantially lower total cost than any of a set of strong baselines.
Achieving reuse in casual game developers - A Blender Game Engine approach?

The Blender Game Engine (part of an open source 3D-modeling suite) o ↵ ers a graphical logic editor interface, that’s close to a node-based representation of game context behavior. The original design makes basic logic easy to make, but more complex logical solutions become hard to read, modify and reuse. This article describes a proposal for a visual programming node editor for the Blender Game Engine, by providing a Component Based System and building the node editor like a special-purpose, component-oriented system. A small, publicly available, proof of concept is made with basic functionality and tested by two external experts to validate the improvements that this work tries to achieve. With this proposal reusing and sharing generic logic between projects and teams is encouraged and modifying previous work products becomes much easier.
Polymorphism in the Spotlight: How Developers Use it in Practice

Polymorphism—the ability of a client to send a message that is implemented by objects of diﬀerent types without needing to know the exact type of the object that will respond to the message—is a corner-stone of object-oriented programming. It is intended as a way to facilitate the modularity and extensibility of software systems by hiding the variability in behavior behind a uniﬁed interface and allowing the client and provider to be decoupled in their evolution. The downside of polymorphism is that it scatters the implementation of the behavior over multiple classes, which could result in code that is harder to understand. In this research we study how polymorphism is used in one of the oldest and in one of the most widespread object-oriented programming languages: Smalltalk and Java. For this we study a large corpus of several hundreds of systems to answer questions about the prevalence of polymorphism in practice. We also estimate the burden polymorphism imposes on developers maintaining software systems by determining how scattered are the implementations of polymorphic methods in class hierarchies.
Therapeutic Cancer Vaccines—T Cell Responses and Epigenetic Modulation

There is great interest in developing efficient therapeutic cancer vaccines, as this type of therapy allows targeted killing of tumor cells as well as long-lasting immune protection. High levels of tumor-infiltrating CD8+ T cells are associated with better prognosis in many cancers, and it is expected that new generation vaccines will induce effective production of these cells. Epigenetic mechanisms can promote changes in host immune responses, as well as mediate immune evasion by cancer cells. Here, we focus on epigenetic modifications involved in both vaccine-adjuvant-generated T cell immunity and cancer immune escape mechanisms. We propose that vaccine-adjuvant systems may be utilized to induce beneficial epigenetic modifications and discuss how epigenetic interventions could improve vaccine-based therapies. Additionally, we speculate on how, given the unique nature of individual epigenetic landscapes, epigenetic mapping of cancer progression and specific subsequent immune responses, could be harnessed to tailor therapeutic vaccines to each patient.
Marine Alkaloid 2,2-Bis(6-bromo-3-indolyl) Ethylamine and Its Synthetic Derivatives Inhibit Microbial Biofilms Formation and Disaggregate Developed Biofilms

The antimicrobial activity of the marine bisindole alkaloid 2,2-bis(6-bromo-3-indolyl) ethylamine (1) and related synthetic analogues (compounds 2–8) against target microorganisms was investigated by Minimum Inhibitory Concentration (MIC) determination. Compound 1 showed the greatest antimicrobial activity with the lowest MIC (8 mg/L) against Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae, while the derivatives exhibited higher MICs values (from 16 to 128 mg/L). Compounds 1, 3, 4, and 8, the most active ones, were then tested against E. coli, S. aureus, K. pneumoniae, and Candida albicans during biofilms formation as well as on 24 h developed biofilms. The natural alkaloid 1 inhibited the biofilm formation of all the tested microorganisms up to 82.2% and disaggregated biofilms of E. coli, S. aureus, K. pneumoniae, and C. albicans after 30 min of contact, as assessed by viable plate count and crystal violet (CV) staining (optical density at 570 nm). Synthetic derivatives 3, 4, and 8 displayed anti-biofilm activity toward individual bacterial populations. This study highlights the potential of marine bisindole alkaloid 1 as anti-biofilm agent and shows, through a preliminary structure activity relationship (SAR), the importance of halogens and ethylamine side chain for the antimicrobial and antibiofilm activities of this bisindole series.