Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the Identification of the Co-evolution of Production and Test Code

Many software processes advocate that the test code should co-evolve with the production code. Prior work usually studies such co-evolution based on production-test co-evolution samples mined from software repositories. A production-test co-evolution sample refers to a pair of a test code change and a production code change where the test code change triggers or is triggered by the production code change. The quality of the mined samples is critical to the reliability of research conclusions. Existing studies mined production-test co-evolution samples based on the following assumption: if a test class and its associated production class change together in one commit, or a test class changes immediately after the changes of the associated production class within a short time interval, this change pair should be a production-test co-evolution sample. However, the validity of this assumption has never been investigated. To fill this gap, we present an empirical study, investigating the reasons for test code updates occurring after the associated production code changes, and revealing the pervasive existence of noise in the production-test co-evolution samples identified based on the aforementioned assumption by existing works. We define a taxonomy of such noise, including six categories (i.e., adaptive maintenance, perfective maintenance, corrective maintenance, indirectly related production code update, indirectly related test code update, and other reasons). Guided by the empirical findings, we propose CHOSEN (an identifiCation metHod Of production-teSt co-EvolutioN) based on a two-stage strategy. CHOSEN takes a test code change and its associated production code change as input, aiming to determine whether the production-test change pair is a production-test co-evolution sample. Such identified samples are the basis of or are useful for various downstream tasks. We conduct a series of experiments to evaluate our method. Results show that (1) CHOSEN achieves an AUC of 0.931 and an F1-score of 0.928, significantly outperforming existing identification methods, and (2) CHOSEN can help researchers and practitioners draw more accurate conclusions on studies related to the co-evolution of production and test code. For the task of Just-In-Time (JIT) obsolete test code detection, which can help detect whether a piece of test code should be updated when developers modify the production code, the test set constructed by CHOSEN can help measure the detection method’s performance more accurately, only leading to 0.76% of average error compared with ground truth. In addition, the dataset constructed by CHOSEN can be used to train a better obsolete test code detection model, of which the average improvements on accuracy, precision, recall, and F1-score are 12.00%, 17.35%, 8.75%, and 13.50% respectively.
What Constitutes the Deployment and Run-time Configuration System? An Empirical Study on OpenStack Projects

Modern software systems are designed to be deployed in different configured environments (e.g., permissions, virtual resources, network connections), and adapted at run-time to different situations (e.g., memory limits, enabling/disabling features, database credentials). Such a configuration during the deployment and run-time of a software system is implemented via a set of configuration files, which together constitute what we refer to as a “configuration system”. Recent research efforts investigated the evolution and maintenance of configuration files. However, they merely focused on a limited part of the configuration system (e.g., specific infrastructure configuration files or Dockerfiles), and their results do not generalize to the whole configuration system. To cope with such a limitation, we aim by this paper to better capture and understand what files constitute a configuration system. To do so, we leverage an open Card Sort technique to qualitatively study 1,756 configuration files from OpenStack, a large and widely studied open-source software ecosystem. Our investigation reveals the existence of nine types of configuration files, which cover the creation of the infrastructure on top of which OpenStack will be deployed, along with other types of configuration files used to customize OpenStack after its deployment. These configuration files are interconnected while being used at different deployment stages. For instance, we observe specific configuration files used during the deployment stage to create other configuration files that are used in the run-time stage. We also observe that identifying and classifying these types of files is not straightforward, as five out of the nine types can be written in similar programming languages (e.g., python and bash) as regular source code files. We also found that the same file extensions (e.g., Yaml) can be used for different configuration types, making it difficult to identify and classify configuration files. Thus, we first leverage a machine learning model to identify configuration from non-configuration files, which achieved a median AUC of 0.91, a median Brier score of 0.12, a median precision of 0.86, and a median recall of 0.83. Thereafter, we leverage a multi-class classification model to classify configuration files based on the nine configuration types. Our multi-class classification model achieved a median weighted AUC of 0.92, a median Brier score of 0.04, a median weighted precision of 0.84, and a median weighted recall of 0.82. Our analysis also shows that with only 100 labeled configuration and non-configuration files, our model reached a median AUC higher than 0.69. Furthermore, our configuration model requires a minimum of 100 configuration files to reach a median weighted AUC higher than 0.75.
Description of Paramylonchulus iranicus sp. n. (Nematoda: Mononchida) from Iran

Abstract During a survey of soil nematodes in Iran, a population of a species belonging to the order Mononchida was recovered. The new species, Paramylonchulus iranicus sp. n. is characterized by body length (1292–1535 μm in females and 1476–1670 μm in males), c (20.2–29.0 in females and 19.9–27.4 in males), buccal cavity length (23.0–26.0 μm), post vulval uterine sac length (135–162 μm), spicule length (46.0–50.0 μm), gubernaculum length (8.0–11.0 μm), and tail length (49.0–70.0 μm in females and 55.0–73.0 μm in males). Canonical discriminant analysis clearly separated P. iranicus sp. n. from the closely related species Paramylonchulus based on the important morphometric characters of females and males. A molecular study of the 18S rDNA region of P. iranicus sp. n. places this population in a well-supported clade with other species of the genus.
Image-based pupil plane characterization via a space-domain basis

Abstract. Aberration characterization plays a critical role in the development of any optical system. State-of-the-art lithography systems have the tightest aberration tolerances. We present an approach to image-based pupil plane amplitude and phase characterization using models built with a space-domain basis, in which aberration effects are separable. A polynomial model is constructed between the projections of the image intensity for chosen binary mask targets onto this basis and pupil amplitude or phase variation. This method separates model building and pupil characterization into two distinct steps, thus enabling rapid pupil characterization following data collection. The basis is related to both the transmission cross-coefficient function and the principal components of the image intensity. The pupil plane variation of a zone-plate lens from the Semiconductor High-NA Actinic Reticle Review Project (SHARP) at Lawrence Berkeley National Laboratory is examined using this method. Results are compared to pupil plane characterization using a previously proposed methodology where inverse solutions are obtained through an iterative process involving least-squares regression.
Critical Issues of Human Resource Management in Ageing Care Centres: Case of Malaysia

The entire world is progressing towards an ageing population, as, until 2030, 1.4 billion population will be elderly, which leads to 2.1 billion by 2050. The changing lifestyle and the declining fertility rate are main reasons for drastic makeover of economic environment and business issues. This study aims to examine the critical issues of Human Resource Management (HRM) in elderly care sector. The success of a business is associated with employed staff and ‘human resource’ in elderly care centres entails knowledge, skills, passion, loyalty, and personal traits. A qualitative research strategy is adopted, and semi-structured interviews were conducted with 10 HR executives of elderly care centres throughout Malaysia. Saldana’s structured inductive data analysis method for analysis. The result revealed critical issues of human resource management such as lack of professional and competent workers, absence of flexible and adaptive potential of employees. A recommendation plan is proposed regarding planning, recruitment, selection, training and professional development and supervision, and support of staff. Thus, it is imperative to achieve these objectives of human resource management in elderly care centres to provide graceful ageing to an older adult. The findings of the study may use as a guide for HR executives in elderly care centres.
Mediating Effect of Employee Performance on the Relationship Between Emotional Labour and Intent to Leave Among Nurses

In this study, it was aimed to examine the effect of nurses’ emotional labor levels on their intention to leave and to determine whether employee performance plays a mediating role in this relationship. The study is of descriptive cross-sectional type, which was conducted through a questionnaire on nurses. The surface acting has positive effect on the intent to leave among nurses, whereas deep acting has negative effect; employee performance has partial mediating role on the relationship between the surface acting and intent to leave, and the relationship between deep acting and intent to leave. The study is considered to provide significant tips in the identification of nurses with higher risk to leave the work, and in deciding managerial interventions that would be realised against such circumstances. It is suggested that it may be beneficial for nurse managers to organise training in their units, to evaluate the performance of nurses at regular intervals and thus to determine the reason for the increase and decrease, to plan activities that will reduce emotional burden.
Psychological abuse, internalising, and externalising behaviours of students: A latent profile analysis

This study aimed to explore the types of psychological abuse and their relationship with internalising and externalising behaviours among Chinese junior middle school students (n = 619; male = 49.1%, female = 50.9%; mean age = 13.5 years, SD = 1.152 years). Latent profile analysis revealed four psychological abuse profiles: low, medium-low, medium-high, and high. Those students with high psychological abuse profiles also reported high internalising and externalising behaviours compared to those with lower psychological abuse profiles. Multinomial logistic regression analysis indicated that gender and school grade differences had a significant impact on psychological abuse profiles. This finding suggested that boys were more likely to have a high psychological abuse profile than girls, and students in Grade 8 were more likely to have a high psychological abuse profile than students in Grade 9. Our findings underscore the importance of designing and implementing psychological abuse treatment interventions by profiles of the children for higher efficacy in reducing the risk of internalising and externalising behaviours.
Human neuronal excitation/inhibition balance explains and predicts neurostimulation induced learning benefits

Previous research has highlighted the role of the excitation/inhibition ratio (E/I) for typical and atypical development, mental health, cognition, and learning. Other research has highlighted the benefits of high-frequency random noise stimulation (tRNS)—an excitatory form of neurostimulation—on learning. We examined the E/I as a potential mechanism and studied whether tRNS effect on learning depends on E/I as measured by the aperiodic exponent as its putative marker. In addition to manipulating E/I using tRNS, we also manipulated the level of learning (learning/overlearning) that has been shown to influence E/I. Participants (n=102) received either sham stimulation or 20 min tRNS over the dorsolateral prefrontal cortex (DLPFC) during a mathematical learning task. We showed that tRNS increased E/I, as reflected by the aperiodic exponent, and that lower E/I predicted greater benefit from tRNS specifically for the learning task. In contrast to previous Magnetic Resonance Spectroscopy-based E/I studies, we found no effect of the level of learning on E/I. A further analysis using a different data set suggest that both measures of E/I (EEG vs MRS) may reflect, at least partly, different biological mechanisms. Our results highlight the role of E/I as a marker for neurostimulation efficacy and learning. This mechanistic understanding provides better opportunities for augmented learning and personalized interventions.
Unravelling Biosynthesis and Biodegradation Potentials of Microbial Dark Matters in Hypersaline Lakes

Biosynthesis and biodegradation of microorganisms critically underpin the development of biotechnology, new drugs and therapies, and environmental remediation. However, the vast majority of uncultured microbial species along with their metabolic capacities in extreme environments remain obscured. To unravel the metabolic potentials of these microbial dark matters (MDMs), we investigated four deep-inland hypersaline lakes with largely diversified environmental parameters in Xinjiang Uygur Zizhiqu, China. Metagenomic binning obtained 3,030 metagenome-assembled genomes (MAGs) spanning 82 phyla, of which 2,363 MAGs could not be assigned to a known genus. These unknown MAGs were abundantly observed with distinct taxa among lakes, possibly linked to the diversification of physiochemical conditions. Analysis of biosynthetic potentials identified 9,635 biosynthesis gene clusters (BGCs), of which 9,403 BGCs were considered novel. We found that some MAGs from putatively novel phyla consistently comprised enriched BGCs, which may have substantial potentials in biotechnological applications. In addition, biodegradation potentials such as dehalogenation, anaerobic ammonium oxidation (Anammox), polycyclic aromatic hydrocarbon (PAH), and plastic degradation were found in new microbial clades from hypersaline lakes. These findings substantially expanded the genetic repository of biosynthesis and biodegradation potentials, which can further assist the development of new and innovative applications in biotechnology.
Numerical Modelling of Sooting Laminar Diffusion Flames at Elevated Pressures and Microgravity

Numerical Modelling of Sooting Laminar Diffusion Flames at Elevated Pressures and Microgravity Marc Robert Joseph Charest Doctor of Philosophy Graduate Department of Aerospace Science and Engineering University of Toronto 2011 Fully understanding soot formation in flames is critical to the development of practical combustion devices, which typically operate at high pressures, and fire suppression systems in space. Flames display significant changes under microgravity and high-pressure conditions as compared to normal-gravity flames at atmospheric pressure, but the exact causes of these changes are not well-characterized. As such, the effects of gravity and pressure on the stability characteristics and sooting behavior of laminar coflow diffusion flames were investigated. To study these effects, a new highly-scalable combustion modelling tool was developed specifically for use on large multi-processor computer architectures. The tool is capable of capturing complex processes such as detailed chemistry, molecular transport, radiation, and soot formation/destruction in laminar diffusion flames. The proposed algorithm represents the current state of the art in combustion modelling, making use of a second-order accurate finite-volume scheme and a parallel adaptive mesh refinement algorithm on body-fitted, multi-block meshes. An acetylene-based, semi-empirical model was used to predict the nucleation, growth, and oxidation of soot particles. Reasonable agreement with experimental measurements for different fuels and pressures was obtained for predictions of flame height, temperature and soot volume fraction. Overall, the algorithm displayed excellent strong scaling performance by achieving a parallel efficiency of 70% on 384 processors. The effects of pressure and gravity were studied for flames of two different fuels: ethylene-air flames between pressures of 0.5–5 atm and methane-air flames between 1–60 atm. Based on the numerical predictions, zero-gravity flames had lower temperatures, broader soot-containing zones, and higher soot concentrations than normal-gravity flames at the same pressure. Buoyant forces caused the normal-gravity flames to narrow with increasing pressure while the increased soot concentrations and radiation at high pressures lengthened the zero-gravity flames. Low-pressure flames at both gravity levels exhibited a similar powerlaw dependence of the maximum carbon conversion on pressure which weakened as pressure was increased. This dependence decayed at a faster rate in zero gravity when pressure was increased beyond 1–10 atm.
Endogenous tagging using split mNeonGreen in human iPSCs for live imaging studies

Endogenous tags have become invaluable tools to visualize and study native proteins in live cells. However, generating human cell lines carrying endogenous tags is difficult due to the low efficiency of homology-directed repair. Recently, an engineered split mNeonGreen protein was used to generate a large-scale endogenous tag library in HEK293 cells. Using split mNeonGreen for large-scale endogenous tagging in human iPSCs would open the door to studying protein function in healthy cells and across differentiated cell types. We engineered an iPS cell line to express the large fragment of the split mNeonGreen protein (mNG21-10) and showed that it enables fast and efficient endogenous tagging of proteins with the short fragment (mNG211). We also demonstrate that neural network-based image restoration enables live imaging studies of highly dynamic cellular processes such as cytokinesis in iPSCs. This work represents the first step towards a genome-wide endogenous tag library in human stem cells.
Highly divergent and diverse viral community infecting sylvatic mosquitoes from Northeast Brazil

Mosquitoes can transmit several pathogenic viruses to humans, but their natural viral community is also composed of a myriad of other viruses such as insect-specific viruses (ISV) and those that infect symbiotic microorganisms. Besides a growing number of studies investigating the mosquito virome the majority are focused on few urban species and relatively little is known about the virome of sylvatic mosquitoes particularly in high biodiverse brazilian biomes. Here, we characterized the RNA virome of 10 sylvatic mosquitos species from Atlantic forest remains at a sylvatic-urban interface in Northeast Brazil employing a metatranscriptomic approach. A total of 16 viral families were detected. The phylogenetic reconstructions of 14 viral families revealed that the majority of the sequences are putative ISVs. The phylogenetic positioning and, in most cases, the association with a high RdRp amino acid divergence from other known viruses suggests that the viruses characterized here represent at least 34 new viral species. Therefore, the sylvatic mosquitoes viral community is majorly composed of highly divergent viruses highlighting the limited knowledge we still have about the natural virome of mosquitoes in general. Moreover, we found that none of the viruses recovered were shared between the species investigated. These findings will help to understand the interactions and coevolution between mosquitoes and viruses in nature. Importance Mosquitoes are medically important insects as they transmit pathogenic viruses to humans and animals during blood feeding. However, their natural microbiota is also composed of a diverse set of viruses that cause no harm to the insect and other hosts, such as ISVs. In this study, we characterized the RNA virome of sylvatic mosquitoes from Northeast Brazil using unbiased metatranscriptomic sequencing and in depth bioinformatics approaches. Our analysis revealed that these mosquitoes species harbor a diverse set of highly divergent viruses and the majority comprises new viral species. Our findings revealed many new virus lineages characterized for the first time broadening our understanding about the natural interaction between mosquitoes and viruses. Lastly, it also provided several complete genomes that warrants further assessment for mosquito and vertebrate host pathogenicity and their potential interference with pathogenic arboviruses.
Quantitative Spatial Analysis of Neuroligin-3 mRNA Expression in the Enteric Nervous System Reveals a Potential Role in Neuronal–Glial Synapses and Reduced Expression in Nlgn3R451C Mice

Mutations in the Neuroligin-3 (Nlgn3) gene are implicated in autism spectrum disorder (ASD) and gastrointestinal (GI) dysfunction but its cellular expression in the GI tract remains to be characterised. Localisation of NLGN3 protein is challenging in intestinal tissue due to the lack of target-specific antibodies. Here, we combined RNAScope in situ hybridization for Nlgn3 mRNA and immunofluorescence for markers of all enteric neurons, cholinergic submucosal neurons, non-cholinergic submucosal neurons, nitregic and calretinin-containing myenteric neurons as well as glial cells. We also developed a quantitative 3-dimensional image analysis method to measure Nlgn3 mRNA cellular expression levels in enteric neurons and glia. We show that Nlgn3 mRNA is expressed in most submucosal and myenteric neurons as well as in enteric glia. The R451C mutation reduces Nlgn3 mRNA expression levels in cholinergic, nitrergic and calretinin enteric neuronal subpopulations but does not affect Nlgn3 mRNA expression in VIPergic submucosal neurons. In summary, we show that the autism-associated R451C mutation in Nlgn3 reduces Nlgn3 mRNA expression in the mouse ENS. These findings could shed light on the pathophysiology of GI dysfunction in ASD.