Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenotypic detection and genotyping of Clostridium perfringens associated with enterotoxemia in sheep in the Qassim Region of Saudi Arabia

Background and Aim: Enterotoxemia caused by Clostridium perfringens toxinotypes is an often fatal disease of sheep of all ages, with a substantial economic loss to the sheep industry. This study was conducted to isolate C. perfringens from suspected cases of enterotoxemia in sheep in the central part of the Qassim Region, Saudi Arabia, and to determine the prevalent toxinotype by detecting alpha (cpA), beta (cpB), and epsilon (etX) toxin genes, which might help control this disease locally. Materials and Methods: A total of 93 rectal swabs and intestinal content samples were collected from diseased and animals suspected of having died of enterotoxemia in early 2020. Samples were subjected to bacteriological examination, biochemical analysis of isolates by VITEK 2, and molecular toxinotyping of isolates by LightCycler® real-time polymerase chain reaction (RT-PCR). Results: Our results revealed that only 14 isolates were confirmed by VITEK 2 as being C. perfringens, with excellent identification (probability of 95% and 97%). According to the toxinotyping of isolates by RT-PCR, all 14 isolates possessed both the cpA and etX toxin genes, while the cpB toxin gene was not detected in any of the isolates. Conclusion: Our findings demonstrated that C. perfringens type D was the only toxinotype found in the central part of the Qassim Region in 2020; moreover, according to the culture method, only 15% (14/93) of the suspected cases of enterotoxemia were confirmed to be caused by C. perfringens infection, which highlighted the importance of clinical and laboratory differential diagnosis of enterotoxemia in sheep.
Experiences of Girls Dropping Out of Secondary School due to Unplanned Pregnancies in Southern Tanzania

The study assessed the experiences of girls who dropped out of secondary schools due to pregnancy to inform efforts to addres challenges associated with school age pregnancies. A cross-sectional survey using structured questions involved 165 secondary schoolgirls who had dropped out of school due to pregnancy in Rungwe district, Mbeya Region Tanzania. Descriptive statistics were used to summarise the data. Results shows that by the time girls got pregnant about 67.3% had more than three sexual partners. The main motive for engaging in their first sexual experience was the need for money (51.6%). Awareness of contraceptive methods and sexually transmitted diseases was fairly low. Only 14.5% of the girls knew abstinence as best method of preventing pregnancy. Almost half (46.7%) were chased away from home after being known were pregnant and 49.1% of their male partners denied the responsibility. Only 40% started antenatal clinic during their first trimester as recommended by the World Health Organisation. The majority (69.1%) experienced food problems during pregnancy and the situation worsened after delivery. Girls who get pregnant while school in Tanzania experience severe challenges. However, the solution to their problems lies within the capacity of the parents, the community and the
Toward Generate-and-Test Algorithms for Continual Feature Discovery

The backpropagation algorithm is a fundamental algorithm for training modern artificial neural networks (ANNs). However, it is known the backpropagation algorithm performs poorly on changing problems. We demonstrate the backpropagation algorithm can perform poorly on a clear, generic, changing task. The task is online meaning the agent learns from one sample at a time from a stream of samples. The task is nonstationary since the sample distribution regularly changes. We call it the generic continual feature discovery task (GCFD), as it is sufficiently difficult that the backpropagation algorithm must regularly discover new features to perform well. We propose an explanation for the poor performance of the backpropagation algorithm on the GCFD task. The backpropagation algorithm consists of two phases: initializing an ANN with small random weights, and using stochastic gradient descent to update the weights with data. It is known that the initialization step is crucial to the fast discovery of useful features with the backpropagation algorithm, and a typical initialization step sets the weights to small, random numbers. We corroborate that the small, random weight initialization step leads to conditions that speed up the discovery of useful features with the backpropagation algorithm. Then, we show that these conditions are not maintained during the GCFD task. Without the maintenance of these conditions, there is little reason to expect the backpropagation algorithm to quickly discover useful features for new sample distributions. We demonstrate that the backpropagation algorithm’s performance on the ii GCFD task can be significantly improved with generate-and-test algorithms. The generate-and-test algorithms replace the least useful features of the ANN with features that have small, random weights. By regularly introducing features with small random weights, we restore conditions the backpropagation algorithm can use to quickly discover useful features for new data distributions.
DataExposer: Exposing Disconnect between Data and Systems

As data is a central component of many modern systems, the cause of a system malfunction may reside in the data, and, specifically, particular properties of the data. For example, a health-monitoring system that is designed under the assumption that weight is reported in imperial units (lbs) will malfunction when encountering weight reported in metric units (kilograms). Similar to software debugging, which aims to find bugs in the mechanism (source code or runtime conditions), our goal is to debug the data to identify potential sources of disconnect between the assumptions about the data and the systems that operate on that data. Specifically, we seek which properties of the data cause a data-driven system to malfunction. We propose DataExposer, a framework to identify data properties, called profiles, that are the root causes of performance degradation or failure of a system that operates on the data. Such identification is necessary to repair the system and resolve the disconnect between data and system. Our technique is based on causal reasoning through interventions: when a system malfunctions for a dataset, DataExposer alters the data profiles and observes changes in the system's behavior due to the alteration. Unlike statistical observational analysis that reports mere correlations, DataExposer reports causally verified root causes, in terms of data profiles, of the system malfunction. We empirically evaluate DataExposer on three real-world and several synthetic data-driven systems that fail on datasets due to a diverse set of reasons. In all cases, DataExposer identifies the root causes precisely while requiring orders of magnitude fewer interventions than prior techniques.
Oviposition Behavior of Culex quinquefasciatus and Anopheles coluzzii Females According to the Ovitrap Color and Presence of Fertilizer in Breeding Sites

Malaria and lymphatic filariasis are still important public health problems in Cameroon and in most part of sub-Saharan Africa. However there is still not enough informations on the bionomic and oviposition behaviour of their respectives vectors. The present study assesses the influence of color and organics pollutants presence on Culex and Anopheles coluzzii females oviposition behaviour. Laboratory experiments using Culex quinquefasciatus and Anopheles coluzzii laboratory colonies were conducted. Gravid females were offered cups of different colors (red, green, yellow, black, purple and white) with water for oviposition. Experiments were also conducted with cups containing different concentration of NPK fertilizers (1%, 0.75%, 0.5% and 0%). The influence of NPK fertilizer on the larval development time was also assessed. Cx quinquefasciatus and Anopheles coluzzii gravids females were found to be attracted by dark colors over 50% of eggs were laid in cups of black, red or green colors. The experiment was conducted with the mother and the F1 and F2 generations and similar oviposition preference were registered. When mosquitoes were offered cups containing different concentration of NPK for oviposition it appeared that females of the two species prefer laying their eggs in low concentration of NPK. Rearing of larvae in water containing NPK fertilizers was found to accelerate the larval development time of Culex quinquefasciaus but not of Anopheles coluzzii. The study indicated that several factors could be influencing vector bionomic and need further understanding to improve the fight against vector populations.
Analysis Development of Accounting Information System Implementation using Technology Acceptance Model (TAM)

-Current technological developments have penetrated the field of information, especially in the field of accounting information in organizations and companies, because of technological developments, many companies are switching to using computer-based information technology. This study aims to analyze the influence of infrastructure, human resources, costs, usefulness perceptions, and perceived ease of implementation of computerized accounting information systems. The population in this study were employees of the Padang Primary Tax Service Office. Based on the purposive sampling method, this study uses a sample of 47 respondents who work as employees of the accounting and finance department. For the dependent variable (y) of this study is the implementation of computerized accounting information systems. While for the independent variables are infrastructure (x1), human resources (x2), costs (x3), perceived usefulness (x4)
Work beyond the age of 50. What role for mental versus physical health?

This paper contributes to the literature on old employment barriers by exploring empirically the relative importance of mental v.s. physical health in determining work. It combines regression and variance decomposition analyses to quantify the respective role of mental v.s. physical health. The data used are from SHARE and inform in great detail on the health but also work status (i.e. employment and hours) of individuals aged 50+, interviewed between 2004 and 2017 in 21 European countries. The main result of the paper is that of the rather limited role of mental health — in comparison to physical health — in accounting for older individuals’ work. The paper also shows that health (physical or mental) is much better at predicting old people’s propensity to be in employment than the number of hours they work. Finally, the paper reveals that, in comparison to women, men’s work is more driven by their health status.
A linear logic approach to arithmetic

Peano Arithmetic is based on classical logic. In this paper, we present a version of arithmetic based instead on linear logic. This version of arithmetic is called µ MALL, and we use it to characterize several topics within computational logic. In particular, we show that µ MALL directly encodes uniﬁcation problems, Horn-clause logic programming, and various model checking problems. We also show how the proof search interpretation of µ MALL can be used to compute general recursive functions. Finally, we identify several situations where Peano Arithmetic is conservative over µ MALL.
Compiler Toolchains for Deep Learning Workloads on Embedded Platforms

As the usage of deep learning becomes increasingly popular in mobile and embedded solutions, it is necessary to convert the framework-specific network representations into executable code for these embedded platforms. This paper consists of two parts: The first section is made up of a survey and benchmark of the available open source deep learning compiler toolchains, which focus on the capabilities and performance of the individual solutions in regard to targeting embedded devices and microcontrollers that are combined with a dedicated accelerator in a heterogeneous fashion. The second part explores the implementation and evaluation of a compilation flow for such a heterogeneous device and reuses one of the existing toolchains to demonstrate the necessary steps for hardware developers that plan to build a software flow for their own hardware.
Does Mutation Testing Improve Testing Practices?

Various proxy metrics for test quality have been defined in order to guide developers when writing tests. Code coverage is particularly well established in practice, even though the question of how coverage relates to test quality is a matter of ongoing debate. Mutation testing offers a promising alternative: Artificial defects can identify holes in a test suite, and thus provide concrete suggestions for additional tests. Despite the obvious advantages of mutation testing, it is not yet well established in practice. Until recently, mutation testing tools and techniques simply did not scale to complex systems. Although they now do scale, a remaining obstacle is lack of evidence that writing tests for mutants actually improves test quality. In this paper we aim to fill this gap: By analyzing a large dataset of almost 15 million mutants, we investigate how these mutants influenced developers over time, and how these mutants relate to real faults. Our analyses suggest that developers using mutation testing write more tests, and actively improve their test suites with high quality tests such that fewer mutants remain. By analyzing a dataset of past fixes of real high-priority faults, our analyses further provide evidence that mutants are indeed coupled with real faults. In other words, had mutation testing been used for the changes introducing the faults, it would have reported a live mutant that could have prevented the bug.
Manufacture and Characterization of Melamine/Urea/Formaldehyde Based Microcapsules for Self-healing Applications

-------------------------------------------------------------------------국문요약 자가손상복구 기법에서 마이크로캡슐은 손상복구 효율을 좌우하는 중요한 역할을 한다. 본 연구에 서는 다양한 종류의 손상복구액을 함유한 마이크로캡슐을 Melamine/Urea/Formaldehyde를 외벽물질 로 하여 in-situ 중합법에 의해서 oil-in-water emulsion 방법으로 제조하였다. 제조된 마이크로캡슐 을 광학현미경, 주사전자현미경으로 캡슐 모폴로지, 외부 및 내부표면, 외벽 두께 등을 조사하였다. 그 리고 입도분석기를 이용하여 캡슐의 크기를 측정하였으며 그 결과 캡슐의 직경은 평균 약 120 마이크 론 정도 였다. --------------------------------------------------------------------------
In-Depth Analysis of the Thylakoid Membrane Proteome of Arabidopsis thalianaChloroplasts:NewProteins,NewFunctions, and a Plastid Proteome Database

An extensive analysis of the Arabidopsis thaliana peripheral and integral thylakoid membrane proteome was performed by sequential extractions with salt, detergent, and organic solvents, followed by multidimensional protein separation steps (reverse-phase HPLC and oneand two-dimensional electrophoresis gels), different enzymatic and nonenzymatic protein cleavage techniques, mass spectrometry, and bioinformatics. Altogether, 154 proteins were identified, of which 76 (49%) were a-helical integral membrane proteins. Twenty-seven new proteins without known function but with predicted chloroplast transit peptides were identified, of which 17 (63%) are integral membrane proteins. These new proteins, likely important in thylakoid biogenesis, include two rubredoxins, a potential metallochaperone, and a new DnaJ-like protein. The data were integrated with our analysis of the lumenal-enriched proteome. We identified 83 out of 100 known proteins of the thylakoid localized photosynthetic apparatus, including several new paralogues and some 20 proteins involved in protein insertion, assembly, folding, or proteolysis. An additional 16 proteins are involved in translation, demonstrating that the thylakoid membrane surface is an important site for protein synthesis. The high coverage of the photosynthetic apparatus and the identification of known hydrophobic proteins with low expression levels, such as cpSecE, Ohp1, and Ohp2, indicate an excellent dynamic resolution of the analysis. The sequential extraction process proved very helpful to validate transmembrane prediction. Our data also were cross-correlated to chloroplast subproteome analyses by other laboratories. All data are deposited in a new curated plastid proteome database (PPDB) with multiple search functions (http:// cbsusrv01.tc.cornell.edu/users/ppdb/). This PPDB will serve as an expandable resource for the plant community.
IMGDroid: Detecting Image Loading Defects in Android Applications

Images are essential for many Android applications or apps. Although images play a critical role in app functionalities and user experience, inefficient or improper image loading and displaying operations may severely impact the app performance and quality. Additionally, since these image loading defects may not be manifested by immediate failures, e.g., app crashes, existing GUI testing approaches cannot detect them effectively. In this paper, we identify five anti-patterns of such image loading defects, including image passing by intent, image decoding without resizing, local image loading without permission, repeated decoding without caching, and image decoding in UI thread. Based on these anti-patterns, we propose a static analysis technique, IMGDroid, to automatically and effectively detect such defects. We have applied IMGDroid to a benchmark of 21 open-source Android apps, and found that it not only successfully detects the 45 previously-known image loading defects but also finds 15 new such defects. Our empirical study on 1,000 commercial Android apps demonstrates that the image loading defects are prevalent.
Real-Time Dynamic Fur and Hair Simulation using Verlet Integration

- Throughout the history of game development, the physics behind the real-time hair simulation has continued to pose a challenge due to lack of availability of computational resources required by the system. Unlike rendering an animation, where the requirement of real-time simulation is absent, game hair physics needs more efficiency when it comes to utilization of computational resources. Generally, for making a hair strand mesh, a cylinder or a capsule mesh is an obvious choice despite its requirement of a higher number of draw calls or resources. This paper proposes the use of an innovative and highly efficient use of quad polygons, whose normals face the render in conjunction with the use of Verlet integration, which delivers optimal results by keeping the frames per second (FPS) stable. Additionally, the proposed physics also allows for physical forces, such as gravity and wind, to affect hair movement as well as simulate a natural curl in the hair strand.
An in silico Approach for Epitope Based Vaccine Design Against Ebola virus

Ebola viral disease remains a great threat to the society due to the limited availability of licensed drugs or vaccines. As the pathogen belongs to Biosafety Level 4, it is extremely dangerous to design a vaccine using conventional techniques. Therefore, reverse vaccinology approach is used to select the suitable epitopes from the genomic sequence of the virus. Using several algorithms, specific epitopes were chosen and 3D structures were obtained. The HLA alleles vary in different population and hence those alleles that commonly occur in African and world population were selected. Homology models were built for those HLA alleles whose 3D structures were unavailable. Then selected 20 epitopes were docked with selected 8 HLAs and further, selected epitope NQDGLICGL was validated by molecular dynamics study.
Effectiveness of personal tailored risk information and taster sessions to increase the uptake of smoking cessation services

Summary Background National Health Service Stop Smoking Services (SSSs) oﬀ er help to smokers motivated to quit; however, attendance rates are low and recent ﬁ gures show a downward trend. We aimed to assess the eﬀ ectiveness of a two-component personalised intervention on attendance at SSSs. Methods We did this randomised controlled trial in 18 SSSs in England. Current smokers (aged ≥16 years) were identiﬁ ed from medical records in 99 general practices and invited to participate by their general practitioner. Individuals who gave consent, were motivated to quit, and had not attended the SSS within the past 12 months, were randomly assigned (3:2), via computer-generated randomisation with permuted blocks (block size of ﬁ ve), to receive either an individually tailored risk letter and invitation to attend a no-commitment introductory session run by the local SSS (intervention group) or a standard generic letter advertising the local SSS (control group). Randomisation was stratiﬁ ed by sex. Masking of participants to receipt of a personal letter and invitation to a taster session was not possible. The personal letter was generated by a research assistant, but the remainder of the research team were masked to group allocation. General practitioners, practice staﬀ , and SSS advisers were unaware of their patients’ allocation. The primary outcome was attendance at the ﬁ rst session of an SSS course within 6 months from randomisation. We did analysis by intention to treat. This trial is registered with Current Controlled Trials, number ISRCTN 76561916. Findings Recruitment, collection of baseline data, delivery of the intervention, and follow up of participants took place between Jan 31, 2011, and July 12, 2014. We randomly assigned 4384 smokers to the intervention group (n=2636) or the control group (n=1748); 4383 participants comprised the intention-to-treat population. Attendance at the ﬁ rst session of an SSS course was signiﬁ cantly higher in the intervention group than in the control group (458 [17·4%] vs 158 [9·0%] participants; unadjusted odds ratio 2·12 [95% CI 1·75–2·57]; p<0·0001). Interpretation Delivery of personalised risk information alongside an invitation to an introductory session more than doubled the odds of attending the SSS compared with a standard generic invitation to contact the service. This result suggests that a more proactive approach, combined with an opportunity to experience local services, can reduce patient barriers to receiving treatment and has high potential to increase uptake.
On matchings, T‐joins, and arc routing in road networks

Matchings and T‐joins are fundamental and much‐studied concepts in graph theory and combinatorial optimization. One important application of matchings and T‐joins is in the computation of strong lower bounds for arc routing problems (ARPs). An ARP is a special kind of vehicle routing problem, in which the demands are located along edges or arcs, rather than at nodes. We point out that the literature on applying matchings and T‐joins to ARPs does not fully exploit the structure of real‐life road networks. We propose some ways to exploit this structure. Computational results show significant running time improvements, without deteriorating the quality of the lower bounds.
Sapphire disk dielectric resonator temperature coefficient of frequency dependence on temperature, disk configuration and resonant mode

Results are presented for leucosapphire disk dielectric resonator temperature coefficient of frequency (TCF) dependence on temperature, disk configuration and a resonant mode type in the temperature range 70.350 K. It is found that the most intensively the enumerated factors influence resonant oscillations with HE-type polarization. The revealed TCF(T) difference for different modes permitted to build a novel long term frequency stabilization method. <<ETX>>
Effect of Glycyrrhizic Acid based Inductors to Fungi Pathogen (Fusarium solani L.)

Large-scale planting of agricultural crops and some of their varieties also contributes to the development of fungal diseases.This fungi disease is a significantly reducing the quality and productivity of crops. Therefore, it is economically, medico-toxicologically, ecologically important issue to increase the resistance of plants to fungal diseases.Some fungi species are a parasitics, them’s live cycle depends to other organism livestock (animals and plants). The Fusarium Solani is one of the species in Fusarium L. fungi family that casing a major disease in cereal crops(1). Fusarium toxins are esealy contaminated with cereal grains, resulting a harmfull cereal based production(2).