Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Unit Test Amplification For DevOps

Over the last decade, strong unit testing has become an essential component of any serious software project, whether in industry or academia. The agile development movement has contributed to this cultural change with the global dissemination of test-driven development techniques. More recently, the DevOps movement has further strengthened the testing practice with an emphasis on continuous and automated testing. However, testing is tedious and costly for industry: it is hard to evaluate return on investment. Thus, developers under pressure, by lack of discipline or time might skip writing the tests. To overcome this problem, research investigates the automation of creating strong tests. The dream was that a command-line would give you a complete test suite, that verifies the whole program. Even if automatically generated test suites achieve high coverage, there are still obstacles on the adoption of such techniques by the industry. This can be explained by the difficulties to understand, integrate and maintain generated test suite. Also, most of the tools rely on weak or partial oracles, \eg absence of run-time errors, which limits their ability to find bugs. In this thesis, I aim at addressing the lack of a tool that assists developers in regression testing. To do so, I use test suite amplification. In this thesis, I define test amplification and review research works that are using test amplification. Test amplification consists of exploiting the knowledge of test methods, in which developers embed input data and expected properties, in order to enhance these manually written tests with respect to an engineering goal. In the state of the art, I reveal main challenges of test amplification and the main lacks. I propose a new approach based on both test inputs transformation and assertions generation to amplify the test suite. This algorithm is implemented in a tool called DSpot. In this thesis, I evaluate DSpot on open-source projects from GitHub. First, I improve the \ms of test suites and propose these improvements to developers through pull-requests. This evaluation shows that developers value the output of \dspot and thus accepted to integrate amplified test methods into their test suite. This proves that DSpot can improve the quality of real projects' test suites. Second, I use DSpot to detect the behavioral difference between two versions of the same program particularly to detect the behavioral change introduced by a commit. This shows that \dspot can be used in the continuous integration to achieve two crucial tasks: 1) generate amplified test methods that specify a behavioral change; 2) generate amplified test methods to improve the ability to detect potential regressions. In this thesis, I also expose three transversal contributions, related to the correctness of program. First, I study the programs' correctness under runtime perturbations. Second, I study the presence of pseudo-tested methods that are methods revealing weaknesses of the tests. Third, I study overfitting patches and test generation for automatic repair.
Erectile dysfunction with elevated serum testosterone

Symptoms of testosterone deficiency with elevated testosterone levels are rare and could occur with elevated sex hormone binding globulin (SHBG) levels. Here, we report a 59 year old patient presenting with erectile dysfunction (ED) with diminished libido for 4 months. He developed these symptoms 6 months after starting anti tuberculosis treatment (AAT). He had elevated total testosterone concentration along with raised SHBG. However, his free testosterone was normal. Repeated total testosterone level 2 months after completion of ATT was normal indicating normalization of SHBG levels.
pH sensitive thread-based wound dressing with integrated drug delivery and wireless bluetooth interface

Wound treatment is a significant field in healthcare, but one with huge potential and need for advancement. Infection monitoring, in its current state, is a largely primitive affair, relying on visual and olfactory inspection to detect bacteria. As a result, early detection is impossible, and doctors and patients are forced to remove dressings to investigate the wound in a laborious, painful, and unsanitary process. When an infection is detected, the treatment is typically systemic administration of antibiotics. Systemic administration reduces the concentration of antibiotics that can be brought to bear on the infection because it interacts with the entire body and is dissipated by the time it reaches the wound and increases the risk of side effects or antibiotic resistance. Within this thesis, a smart, threadbased wound dressing is presented that addresses these issues by providing a pH-based early detection system accompanied by a topical, on-demand drug delivery system. The device has been tested in vitro and in vivo, on bacterial culture and on an animal model, and demonstrated effectiveness at detecting and eliminating bacteria, and at promoting wound healing. This smart wound dressing has the potential to improve treatment and outcomes for a wide variety of injuries, varying from burns to chronic wounds.
PERTUMBUHAN MIKROALGA Nannochloropsis sp., Tetraselmis sp. DAN Dunaliella sp. PADA MEDIA AIR LIMPASAN BUDIDAYA UDANG VANAME (Litopenaeus vannamei)

Penelitian ini bertujuan mengetahui pertumbuhan Nannochloropsis sp. , Tetraselmis sp. dan Dunaliella sp. yang dikultur pada air limpasan budidaya udang vaname (Litopenaeus vannamei). Rancangan penelitian yang digunakan adalah Rancangan Acak Lengkap (RAL) dengan tiga perlakuan dan tiga ulangan, yaitu perlakuan A (100% air limpasan budidaya udang vaname sebagai media kulturNannochloropsis sp.), B (100% air limpasan budidaya udang vaname sebagai media kultur Tetraselmis sp.)dan C (100% air limpasan budidaya udang vaname sebagai media kultur Dunaliella sp.). Parameter yang diamati meliputi kepadatan populasi Nannochloropsis sp. , Tetraselmis sp. dan Dunaliella sp. , nitrat, ortofosfat, pH, suhu, intensitas cahayadan salinitas. Data parameter kepadatan puncak populasiNannochloropsis sp. , Tetraselmis sp. dan Dunaliella sp.diuji menggunakan uji (ANOVA) dengan tingkat kepercayaan 95%data menunjukkan berpengaruh signifikan setelah itu dilanjutkan uji Duncan dengan tingkat kepercayaan 95% data menunjukkan perlakuan A berbeda nyata terhadap perlakuan C. Hasil penelitian menunjukkan bahwa air limpasan budidaya udang vaname dapat dimanfaatkan sebagai media kultur mikroalga Nannochloropsis sp. , Tetraselmis sp. dan Dunaliella sp. dan mikroalga Nannochloropsis sp. merupakan mikroalga yang menghasilkan kepadatan tertinggi sebesar 34,5 x 104 ind/mL. Kata Kunci:Nannochloropsis sp. , Tetraselmis sp. , Dunaliella sp. , air limpasan, nitrat, fosfat This research was aimed to find out growth of Nannochloropsis sp. , Tetraselmis sp. And Dunaliella sp. that cultivated on vaname (Litopenaeus vannamei) cultivation run off water. Experimental design was used Completely Randomized Design (CRD) with three treatments and three replications, there were A (100% vaname cultivation run off water as Nannochloropsis sp. culture media), B (100% vaname cultivation run off water as Tetraselmis sp. culture media), and C (100% vaname cultivation run off water as Dunaliella sp. culture media). The observed parameters were density population of Nannochloropsis sp. , Tetraselmis sp. and Dunaliella sp. , nitrat, ortofosfat, pH, temperature, light intensity, and salinity. The parameters data of density population Nannochloropsis sp. , Tetraselmis sp. and Dunaliella sp.was tested by ANOVA with 95% level of trust showed significantly affected, then continued Duncan’s test with 95% level of trust showed A treatment significantly different for C treatment. The results showed that vaname cultivation run off water could be used as a microalgae culture medium of Nannochloropsis sp. , Tetraselmis sp. and Dunaliella sp. and microalgae Nannochloropsis sp. is a microalgae that produces the highest density of 34,5 x 104 ind/mL. Key words: Nannochloropsis sp. , Tetraselmis sp. , Dunaliella sp. ,run off water, nitrat, fosfat
Chapter-10 Prostate Cancer

Prostate Cancer is an increasingly common diagnosis in Western societies and in those emulating Western lifestyles and diets. In the year 2001 there were estimated to be 198,100 new cases and almost 31,900 deaths attributable to this condition in the United States (1). Approximately one in seven American men will be diagnosed with prostate cancer during their lifetime, making it the most common solid tissue cancer in the United States.
Characterization of Urban New Particle Formation in Amman—Jordan

We characterized new particle formation (NPF) events in the urban background of Amman during August 2016–July 2017. The monthly mean of submicron particle number concentration was 1.2 × 104–3.7 × 104 cm−3 (exhibited seasonal, weekly, and diurnal variation). Nucleation mode (10–15 nm) concentration was 0.7 × 103–1.1 × 103 cm−3 during daytime with a sharp peak (1.1 × 103–1.8 × 103 cm−3) around noon. We identified 110 NPF events (≈34% of all days) of which 55 showed a decreasing mode diameter after growth. The NPF event occurrence was higher in summer than in winter, and events were accompanied with air mass back trajectories crossing over the Eastern Mediterranean. The mean nucleation rate (J10) was 1.9 ± 1.1 cm−3 s−1 (monthly mean 1.6–2.7 cm−3 s−1) and the mean growth rate was 6.8 ± 3.1 nm/h (4.1–8.8 nm/h). The formation rate did not have a seasonal pattern, but the growth rate had a seasonal variation (maximum around August and minimum in winter). The mean condensable vapor source rate was 4.1 ± 2.2 × 105 molecules/cm3 s (2.6–6.9 × 105 molecules/cm3 s) with a seasonal pattern (maximum around August). The mean condensation sink was 8.9 ± 3.3 × 10−3 s−1 (6.4–14.8 × 10−3 s−1) with a seasonal pattern (minimum around June and maximum in winter).
A male infertility mutation reverts NANOS1 activity from anti-apoptotic to pro-apoptotic by disrupting repression of GADD45A, GADD45B, GADD45G and RHOB genes

Background While Nanos-mediated maintenance of germ cells in Drosophila and mice has been related to regulation of apoptosis, the relevance of these findings to human physiology is uncertain. Previously we have described the p.[(Pro34Thr);(Ser83del)] double NANOS1 mutation as associated with an absence of germ cells in the testes of infertile patients. The aim of this study was to identify the mechanism underlying infertility phenotype of patients bearing the NANOS1 mutation. Methods Constructs encoding a wild-type or mutated NANOS1 protein were used for transfection of TCam-2 cell line, representing male germ cells in culture. Influence of this mutation on cell proliferation was performed using MTS assay while apoptosis and cell cycle were measured by flow cytometry. RNA-Seq analysis including quantitative RT-PCR was conducted for selecting pro-apoptotic genes, repressed by the wild-type NANOS1. Influence of the p.[(Pro34Thr);(Ser83del)] NANOS1 mutation on that repression was investigated by quantitative RT-PCR. Results We show here that the p.[(Pro34Thr);(Ser83del)] double NANOS1 mutation causes NANOS1 to functionally switch from being anti-apoptotic to pro-apoptotic in the human male germ cell line TCam-2. This mutation disrupts repression of mRNAs encoding pro-apoptotic GADG45A, GADD45B, GADD45G and RHOB factors, which could contribute to an increase in apoptosis. Conclusions This report underscores the conservation of Nanos from flies to humans as a repressor of pro-apoptotic mRNAs in germ cells, and provides a basis for understanding NANOS1 functions in human reproductive health.
Distribution Level Building Load Prediction Using Deep Learning

Load prediction in distribution grids is an important means to improve energy supply scheduling, reduce the production cost, and support emission reduction. Determining accurate load predictions has become more crucial than ever as electrical load patterns are becoming increasingly complicated due to the versatility of the load profiles, the heterogeneity of individual load consumptions, and the variability of consumer-owned energy resources. However, despite the increase of smart grids technologies and energy conservation research, many challenges remain for accurate load prediction using existing methods. This dissertation investigates how to improve the accuracy of load predictions at the distribution level using artificial intelligence (AI), and in particular deep learning (DL), which have already shown significant progress in various other disciplines. Existing research that applies the DL for load predictions has shown improved performance compared to traditional models. The current research using conventional DL tends to be modeled based on the developer’s knowledge. However, there is little evidence that researchers have yet addressed the issue of optimizing the DL parameters using evolutionary computations to find more accurate predictions. Additionally, there are still questions about hybridizing different DL methods, conducting parallel computation techniques, and investigating them on complex smart buildings. In addition, there are still questions about disaggregating the net metered load data into load and behind-the-meter generation associated with solar and electric vehicles (EV). The focus of this dissertation is to improve the distribution level load predictions using DL. Five approaches are investigated in this dissertation to find more accu-
Dual-band asymmetric planar dipole antenna backed by conducting reflector for WLAN applications

: This paper proposes a dual-band planar antenna composed of asymmetric dipole elements backed by a conducting re ﬂ ector that is realized by incorporating a LC trap circuit. The proposed antenna achieves the 2.4 GHz and 5 GHz dual-band operations for the purpose of WLAN applications. It is shown that the measured gains were 3.0 dBi at 2.44 GHz, 5.3 dBi at 5.25 GHz, and 5.8 dBi at 5.6 GHz, respectively. It is also con ﬁ rmed that this antenna approximately retains a unidirectional property and a superdirectivity as well.
Insulin pump therapy: a meta-analysis: Response to Weissberg-Benchell et al. Authors' reply

RESEARCH DESIGN AND METHODS — Studies were identified and data regarding study design, year of publication, sample size, patient’s age, diabetes duration, and duration of CSII therapy were collected. Means and SDs for glycohemoglobin, blood glucose, insulin dosages, and body weight for CSII and comparison conditions were subjected to meta-analytic procedures. Data regarding pump complications and psychosocial functioning were reviewed descriptively.
Ligand expression of the EGFR ligands amphiregulin, epiregulin, and amplification of the EGFR gene to predict for treatment efficacy in KRAS wild-type mCRC patients treated with cetuximab plus CAPIRI and CAPOX: Analysis of the randomized AIO CRC-0104 trial.

3519^ Background: We investigated the expression of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) as well as the amplification of the EGFR-gene in tumor specimens of mCRC patients (pts) treated first-line with anti-EGFR targeted cetuximab together with CAPOX or CAPIRI. Expression levels were correlated with overall response rate (ORR), progression free survival (PFS) and overall survival (OS) to determine their relationship with effectiveness in this setting. Methods: A total of 185 mCRC pts were randomized to cetuximab (400mg/m² day 1, followed by 250mg/m² weekly) plus CAPIRI (irinotecan 200mg/m², day 1; capecitabine 800mg/m² twice daily days 1-14, every 3 weeks; 20% dose reduction of both agents for pts older than 65 years) or plus CAPOX (oxaliplatin 130mg/m² day 1; capecitabine 1000mg/m² twice daily days 1-14, every three weeks). The primary study endpoint was ORR. KRAS mutational status did not correlate with treatment outcome. The cut-offs for EGFR-amplification using FISH, AREG and EREG.
Dietary restriction transforms the protein sulfhydrome in a tissue-specific and cystathionine γ-lyase-dependent manner

Hydrogen sulfide (H2S) is a cytoprotective redox-active metabolite that signals through protein sulfhydration (R-SSnH). Despite the known importance of sulfhydration on relatively few identified proteins, tissue-specific sulfhydrome profiles and their associated functions are not well characterized, specifically under conditions known to modulate H2S production. We hypothesized that dietary restriction (DR), which increases lifespan and boosts endogenous H2S production, expands functional tissue-specific sulfhydromes. Here, we found that 50% DR enriched total sulfhydrated proteins in liver, kidney, muscle, and brain but decreased these in heart of adult male mice. DR promoted sulfhydration in numerous metabolic and aging-related pathways. Mice lacking the H2S producing enzyme cystathionine γ-lyase (CGL) had decreased liver and kidney protein sulfhydration and failed to functionally augment their sulfhydrome in response to DR. Overall, we defined tissue- and CGL-dependent sulfhydromes and how diet transforms their makeup, underscoring the breadth for DR and H2S to impact biological processes and organismal health. One Sentence Summary Dietary restriction altered the tissue-specific enrichment of sulfhydrated proteins and their downstream signaling pathways in liver, kidney, skeletal muscle, brain, heart, and plasma that was partly dependent on the hydrogen sulfide producing enzyme cystathionine γ-lyase.
An unlimited scope for novel formulations as orally disintegrating systems: Present and future prospects.

An orally disintegrating tablet or orodispersible tablet (ODT) is a drug dosage form available for a limited amount of over-the-counter (OTC) and prescription medications. ODTs differ from traditional tablets in that they are designed to be dissolved on the tongue rather than swallowed whole. The ODT serves as an alternative dosage form for patients who experience dysphasia (difficulty in swallowing) or for where compliance is a known issue and therefore an easier dosage form to take ensures that medication is taken. Common among all age groups, dysphasia is observed in about 35% of the general population, as well as up to 60% of the elderly institutionalized population and 18-22% of all patients in long-term care facilities. During the last decade, ODTs have become available in a variety of therapeutic markets, both OTC and by prescription. An additional reason to use ODTs is the convenience of a tablet that can be taken without water.
Software Vulnerability Discovery via Learning Multi-Domain Knowledge Bases

Machine learning (ML) has great potential in automated code vulnerability discovery. However, automated discovery application driven by off-the-shelf machine learning tools often performs poorly due to the shortage of high-quality training data. The scarceness of vulnerability data is almost always a problem for any developing software project during its early stages, which is referred to as the cold-start problem. This article proposes a framework that utilizes transferable knowledge from pre-existing data sources. In order to improve the detection performance, multiple vulnerability-relevant data sources were selected to form a broader base for learning transferable knowledge. The selected vulnerability-relevant data sources are cross-domain, including historical vulnerability data from different software projects and data from the Software Assurance Reference Database (SARD) consisting of synthetic vulnerability examples and proof-of-concept test cases. To extract the information applicable in vulnerability detection from the cross-domain data sets, we designed a deep-learning-based framework with Long-short Term Memory (LSTM) cells. Our framework combines the heterogeneous data sources to learn unified representations of the patterns of the vulnerable source codes. Empirical studies showed that the unified representations generated by the proposed deep learning networks are feasible and effective, and are transferable for real-world vulnerability detection. Our experiments demonstrated that by leveraging two heterogeneous data sources, the performance of our vulnerability detection outperformed the static vulnerability discovery tool Flawfinder. The findings of this article may stimulate further research in ML-based vulnerability detection using heterogeneous data sources.
Teaching online is different: critical perspectives fromthe literature

Teaching online is different. In this report we attempt to explain why. This report arises from the #Openteach: Professional Development for Open Online Educators project, which is funded by the National Forum for the Enhancement of Teaching and Learning in Higher Education. In this project we plan to uncover and promote the keys to effective online teaching practice, while recognising that effective teaching is an art, craft and science. We aim to harness this knowledge to support the professional learning of online educators. Ultimately we want to support online students to learn online by helping and inspiring their educators. This report was developed to help lay a foundation for the project through a critical analysis of relevant literature.
Detecting Incivility and Impoliteness in Online Discussions

Impoliteness and incivility in online discussions have recently been discussed as relevant issues in the field of communication science. However, automatically detecting such concepts with computational methods is challenging. In our study, we develop supervised classification models to predict impoliteness and incivility in German user comments. Using a sample of 10,000 hand-coded user comments and a theory-grounded coding scheme, we train and test classifiers based on unigram, bigram, and trigram feature models and on Naive Bayes and Support Vector Machine algorithms. Our classification models, based on word frequency distributions in user comments, predict both impoliteness and incivility with an accuracy of about 80 percent. The models also reveal predictive features that include obviously offensive language, uncivil rhetoric, and topic and context-related words. Our study thereby contributes both to the understanding of impolite and uncivil communication in user comment sections and to the development and applications of text classification using machine learning.
Estimating the Local Effectiveness of Institutions: A Latent-Variable Approach

This paper develops an approach that allows constructing regional proxies of government effectiveness at a highly dis-aggregated level. Our idea builds on the well documented interdependence between institutions and exports, which allows estimating the latent government effectiveness using methods of structural equation modeling. Unobserved institutional quality for each individual region is predicted using the regression outcomes. The impact of this novel identification strategy is tested using various panel applications. Results show that the magnitude of the effect of institutional quality can be biased in estimates that neglect regional differences in the effectiveness of institutions.