Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Multivariate Failure Threshold with Quantifiable Operation Risks in Machine Prognostics

Prognostics and Health Management (PHM) is attracting the attention from both academia and industry due to its great potential to enhance the resilience and responsiveness of the equipment to the potential operation risks. In literature, many methodologies are proposed to predict the Remaining Useful Life (RUL) of the equipment. However, there are two major challenges that limit the practicality of these methodologies. 1) How to generate a quantifiable Health Indicator (HI) to represent the operation risks? 2) How to define a reasonable failure threshold to predict RUL? To answer these two questions, this paper proposes a novel methodology for failure threshold determination with quantifiable operation risk in machine prognostics. In the proposed methodology, Fisher distance and Mann-Kendall (MK) test are firstly used to extract useful sensors based on which HI is estimated by applying Principle Component Analysis (PCA). Then, Rao-Blackwellized Particle Filter (RBPF) is employed to obtain the HI prediction and the uncertainties. Afterwards, a Bivariate-Weibull-distribution-based risk quantification model is designed to quantify the cumulative risk over time and over the increase of HI. The failure threshold, which is the ending point of the RUL, varies over different users and applications depending on the level of risk they want to tolerate. The validation of the methodology is based on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The results validate the effectiveness of the proposed risk quantification method and its potential application on machine prognostics.
Mitochondrial DNA haplogroups and trajectories of cardiometabolic risk factors during childhood and adolescence: A prospective cohort study

Background Mitochondria are organelles responsible for converting glucose into energy. Mitochondrial DNA is exclusively maternally inherited by offspring. The role of mitochondrial DNA haplogroups in the aetiology of cardiometabolic disease risk is not well understood. Methods We examined the sex-specific association between European mitochondrial DNA haplogroups and trajectories of nine cardiometabolic risk factors from birth to 18 years in a prospective English birth cohort. Mitochondrial haplogroups were analysed according to common European haplogroups; H,U,J,T,K,V,W,I and X. Nine cardiometabolic risk factors measured over varying times from birth/mid-childhood to age 18 years included body mass index (BMI), fat mass and lean mass, systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate, high-density lipoprotein cholesterol (HDL-c), non HDL-c and triglycerides. Fractional polynomial and linear spline multilevel models stratified by sex explored the sex-specific association between haplogroups and risk factor trajectories. Results Among 6,360-7,954 participants with 22,864-79,178 repeated measures per outcome, we found no strong evidence that haplogroups U,T,J,K and W were associated with trajectories of cardiometabolic risk factors across childhood and adolescence compared to haplogroup H. In females, haplogroup V was associated with 4.0% (95% CI: 1.4, 6.7) lower BMI at age 7 years and 9.3% (95% CI: 1.9, 16.7) lower fat mass at age 9, though differences did not persist at age 18. Haplogroup X was associated with 1.3kg (95% CI: 0.5, 2.2) lower lean mass and 16.4% (95% CI: 3.5, 29.3) lower fat mass at age 9; associations with lower lean mass persisted at 18 years whereas associations with fat mass did not. In males, haplogroup I was associated with 2.4% (95% CI: 0.2, 4.6) higher BMI at age 7; this difference widened to 5.1% (95% CI: 0.9,9.3) at 18 years. Conclusion Our study demonstrated some evidence of sex-specific associations between mitochondrial DNA haplogroups V, I and X and trajectories of adiposity during childhood and adolescence.
WebRR: self-replay enhanced robust record/replay for web application testing

Record-and-replay tools are important for quality assurance of Web applications by capturing user case scenarios and executing them automatically when needed. However, the tests generated by existing techniques are brittle, and often lead to test breakages as the dynamic behavior and frequent updates of modern Web applications. In this paper, we propose WebRR, a self-replay enhanced robust record-and-replay technique for Web applications testing. The novelty of WebRR is that, it introduces a new self-replay mechanism in the recording phase, which checks the captured event from the record module online, and generates multiple locators (including DOM locators, visual locator and proximity locators) automatically, to improve the robustness of generated test cases. During the replay, it combines multiple locators and new local workflow repair technique to repair test breakages, and can improve the resilience of generated tests to frequent updates of the applications. We applied our approach to 3 enterprise Web applications, which are deployed in a large power grid company of China. The experimental results show that WebRR is effective, and substantially improve the robustness of end-to-end web tests that are generated using record-and-replay technique.
A first look at good first issues on GitHub

Keeping a good influx of newcomers is critical for open source software projects' survival, while newcomers face many barriers to contributing to a project for the first time. To support newcomers onboarding, GitHub encourages projects to apply labels such as good first issue (GFI) to tag issues suitable for newcomers. However, many newcomers still fail to contribute even after many attempts, which not only reduces the enthusiasm of newcomers to contribute but makes the efforts of project members in vain. To better support the onboarding of newcomers, this paper reports a preliminary study on this mechanism from its application status, effect, problems, and best practices. By analyzing 9,368 GFIs from 816 popular GitHub projects and conducting email surveys with newcomers and project members, we obtain the following results. We find that more and more projects are applying this mechanism in the past decade, especially the popular projects. Compared to common issues, GFIs usually need more days to be solved. While some newcomers really join the projects through GFIs, almost half of GFIs are not solved by newcomers. We also discover a series of problems covering mechanism (e.g., inappropriate GFIs), project (e.g., insufficient GFIs) and newcomer (e.g., uneven skills) that makes this mechanism ineffective. We discover the practices that may address the problems, including identifying GFIs that have informative description and available support, and require limited scope and skill, etc. Newcomer onboarding is an important but challenging question in open source projects and our work enables a better understanding of GFI mechanism and its problems, as well as highlights ways in improving them.
A first look at the integration of machine learning models in complex autonomous driving systems: a case study on Apollo

Autonomous Driving System (ADS) is one of the most promising and valuable large-scale machine learning (ML) powered systems. Hence, ADS has attracted much attention from academia and practitioners in recent years. Despite extensive study on ML models, it still lacks a comprehensive empirical study towards understanding the ML model roles, peculiar architecture, and complexity of ADS (i.e., various ML models and their relationship with non-trivial code logic). In this paper, we conduct an in-depth case study on Apollo, which is one of the state-of-the-art ADS, widely adopted by major automakers worldwide. We took the first step to reveal the integration of the underlying ML models and code logic in Apollo. In particular, we study the Apollo source code and present the underlying ML model system architecture. We present our findings on how the ML models interact with each other, and how the ML models are integrated with code logic to form a complex system. Finally, we inspect Apollo in a dynamic view and notice the heavy use of model-relevant components and the lack of adequate tests in general. Our study reveals potential maintenance challenges of complex ML-powered systems and identifies future directions to improve the quality assurance of ADS and general ML systems.
Deep learning library testing via effective model generation

Deep learning (DL) techniques are rapidly developed and have been widely adopted in practice. However, similar to traditional software systems, DL systems also contain bugs, which could cause serious impacts especially in safety-critical domains. Recently, many research approaches have focused on testing DL models, while little attention has been paid for testing DL libraries, which is the basis of building DL models and directly affects the behavior of DL systems. In this work, we propose a novel approach, LEMON, to testing DL libraries. In particular, we (1) design a series of mutation rules for DL models, with the purpose of exploring different invoking sequences of library code and hard-to-trigger behaviors; and (2) propose a heuristic strategy to guide the model generation process towards the direction of amplifying the inconsistent degrees of the inconsistencies between different DL libraries caused by bugs, so as to mitigate the impact of potential noise introduced by uncertain factors in DL libraries. We conducted an empirical study to evaluate the effectiveness of LEMON with 20 release versions of 4 widely-used DL libraries, i.e., TensorFlow, Theano, CNTK, MXNet. The results demonstrate that LEMON detected 24 new bugs in the latest release versions of these libraries, where 7 bugs have been confirmed and one bug has been fixed by developers. Besides, the results confirm that the heuristic strategy for model generation indeed effectively guides LEMON in amplifying the inconsistent degrees for bugs.
Cost measures matter for mutation testing study validity

Mutation testing research has often used the number of mutants as a surrogate measure for the true execution cost of generating and executing mutants. This poses a potential threat to the validity of the scientific findings reported in the literature. Out of 75 works surveyed in this paper, we found that 54 (72%) are vulnerable to this threat. To investigate the magnitude of the threat, we conducted an empirical evaluation using 10 real-world programs. The results reveal that: i) percentages of randomly sampled mutants differ from the true execution time, on average, by 44%, varying in difference from 19% to 91%; ii) errors arising from using the surrogate correlate with program size (ρ = 0.74) and number of mutants (ρ = 0.76), making the problem more pernicious for more realistic programs; iii) scientific findings concerning sampling strategies would have approximately 37% rank disagreement, indicating potentially dramatic impact on experiment validity. To investigate whether this threat matters in practice, we reproduced a seminal study on Selective Mutation (widely relied upon for more than two decades). The impact is stark: an inconclusive scientific finding using the surrogate is transformed to an unequivocal finding when using the true execution cost.
Guillain-Barré syndrome and COVID-19: an observational multicentre study from two Italian hotspot regions

Objective Single cases and small series of Guillain-Barré syndrome (GBS) have been reported during the SARS-CoV-2 outbreak worldwide. We evaluated incidence and clinical features of GBS in a cohort of patients from two regions of northern Italy with the highest number of patients with COVID-19. Methods GBS cases diagnosed in 12 referral hospitals from Lombardy and Veneto in March and April 2020 were retrospectively collected. As a control population, GBS diagnosed in March and April 2019 in the same hospitals were considered. Results Incidence of GBS in March and April 2020 was 0.202/100 000/month (estimated rate 2.43/100 000/year) vs 0.077/100 000/month (estimated rate 0.93/100 000/year) in the same months of 2019 with a 2.6-fold increase. Estimated incidence of GBS in COVID-19-positive patients was 47.9/100 000 and in the COVID-19-positive hospitalised patients was 236/100 000. COVID-19-positive patients with GBS, when compared with COVID-19-negative subjects, showed lower MRC sum score (26.3±18.3 vs 41.4±14.8, p=0.006), higher frequency of demyelinating subtype (76.6% vs 35.3%, p=0.011), more frequent low blood pressure (50% vs 11.8%, p=0.017) and higher rate of admission to intensive care unit (66.6% vs 17.6%, p=0.002). Conclusions This study shows an increased incidence of GBS during the COVID-19 outbreak in northern Italy, supporting a pathogenic link. COVID-19-associated GBS is predominantly demyelinating and seems to be more severe than non-COVID-19 GBS, although it is likely that in some patients the systemic impairment due to COVID-19 might have contributed to the severity of the whole clinical picture.
Genomic analysis of circular RNAs in heart

Background Heart failure is a leading cause of human morbidity and mortality. Circular RNAs (circRNAs) are a newly discovered class of RNA that have been found to have important physiological and pathological roles. In the current study, we de novo analyzed existing whole transcriptome data from 5 normal and 5 dilated cardiomyopathy (DCM) human heart samples and compared the results with circRNAs that have been previously reported in human, mouse and rat hearts. Results Our analysis identifies a list of cardiac circRNAs that are reliably detected in multiple studies. We have also defined the top 30 most abundant circRNAs in healthy human hearts which include some with previously unrecognized cardiac roles such as circHIPK3_11 and circTULP4_1. We further found that many circRNAs are dysregulated in DCM, particularly transcripts originating from DCM-related gene loci, such as TTN and RYR2 . In addition, we predict the potential of cardiac circRNAs to sponge miRNAs that have reported roles in heart disease. We found that circALMS1_6 has the highest potential to bind miR-133, a microRNA that can regulate cardiac remodeling. Interestingly, we detected a novel class of circRNAs, referred to as read-though (rt)-circRNAs which are produced from exons of two different neighboring genes. Specifically, rt-circRNAs from SCAF8 and TIAM2 were observed to be dysregulated in DCM and these rt-circRNAs have the potential to sponge multiple heart disease-related miRNAs. Conclusions In summary, this study provides a valuable resource for exploring the function of circRNAs in human heart disease and establishes a functional paradigm for identifying novel circRNAs in other tissues.
Association of Maternal Folate and Vitamin B12 in Early Pregnancy With Gestational Diabetes Mellitus: A Prospective Cohort Study

OBJECTIVE To investigate the association of folate and vitamin B12 in early pregnancy with gestational diabetes mellitus (GDM) risk. RESEARCH DESIGN AND METHODS The data of this study were from a subcohort within the Shanghai Preconception Cohort Study. We included pregnancies with red blood cell (RBC) folate and vitamin B12 measurements at recruitment (between 9 and 13 gestational weeks) and those with three samples available for glucose measurements under an oral glucose tolerance test. GDM was diagnosed between 24 and 28 weeks’ gestation. Odds ratio (OR) and 95% CI of having GDM was used to quantify the association. RESULTS A total of 1,058 pregnant women were included, and GDM occurred in 180 (17.01%). RBC folate and vitamin B12 were significantly higher in pregnancies with GDM than those without GDM (P values were 0.045 and 0.002, respectively) and positively correlated with 1-h and 2-h serum glucose. Daily folic acid supplementation in early pregnancy increases the risk of GDM; OR (95% CI) was 1.73 (1.19–2.53) (P = 0.004). Compared with RBC folate <400 ng/mL, pregnancies with RBC folate ≥600 ng/mL were associated with ∼1.60-fold higher odds of GDM; the adjusted OR (95% CI) was 1.58 (1.03–2.41) (P = 0.033). A significant trend of risk effect on GDM risk across categories of RBC folate was observed (Ptrend = 0.021). Vitamin B12 was significantly associated with GDM risk (OR 1.14 per 100 pg/mL; P = 0.002). No significant association of serum folate and percentile ratio of RBC folate/vitamin B12 with GDM was observed. CONCLUSIONS Higher maternal RBC folate and vitamin B12 levels in early pregnancy are significantly associated with GDM risk, while the balance of folate/vitamin B12 is not significantly associated with GDM.
Virulence, Resistance, and Genomic Fingerprint Traits of Vibrio cholerae Isolated from 12 Species of Aquatic Products in Shanghai, China

Vibrio cholerae is a waterborne bacterium and can cause epidemic cholera disease worldwide. Continuous monitoring of V. cholerae contamination in aquatic products is imperative for assuring food safety. In this study, we determined virulence, antimicrobial susceptibility, heavy metal tolerance, and genomic fingerprints of 370 V. cholerae isolates recovered from 12 species of commonly consumed aquatic products collected from July to September of 2018 in Shanghai, China. Among the species, Leiocassis longirostris, Ictalurus punetaus, Ophiocephalus argus Cantor, and Pelteobagrus fulvidraco were for the first time detected for V. cholerae. Toxin genes ctxAB, tcpA, ace, and zot were absent from all the V. cholerae isolates. However, high occurrence of virulence-associated genes was detected, such as hapA (82.7%), hlyA (81.4%), rtxCABD (81.4%, 24.3%, 80.3%, and 80.8%, respectively), and tlh (80.5%). Approximately 62.2% of the 370 V. cholerae isolates exhibited resistance to streptomycin, followed by ampicillin (60.3%), rifampicin (53.8%), trimethoprim (38.4%), and sulfamethoxazole-trimethoprim (37.0%). Moreover, ∼57.6% of the isolates showed multidrug resistant phenotypes with 57 resistance profiles, which was significantly different among the 12 species (multiple antimicrobial resistance index, p < 0.001). Meanwhile, high incidence of tolerance to heavy metals Hg2+ (69.5%), Ni2+ (32.4%), and Cd2+ (30.8%) was observed among the isolates. The enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR)-based fingerprinting profiles classified the 370 V. cholerae isolates into 239 different ERIC-genotypes, which demonstrated diverse genomic variation among the isolates. Overall, the results in this study meet the increasing need of food safety risk assessment of aquatic products.
Influence of Single Nucleotide Polymorphisms of ELOVL on Biomarkers of Metabolic Alterations in the Mexican Population

The elongation of very long chain fatty acids (ELOVL) is a family of seven enzymes that have specific functions in the synthesis of fatty acids. Some have been shown to be related to insulin secretion (ELOVL2), and in the lipid profile (ELOVL6) and patients with various pathologies. The present work focused on the study of ELOVL polymorphs with clinical markers of non-communicable chronic diseases in the Mexican population. A sample of 1075 participants was obtained, who underwent clinical, biochemical, and nutritional evaluation, and a genetic evaluation of 91 genetic variants of ELOVL was considered (2–7). The results indicate a 33.16% prevalence of obesity by body mass index, 13.84% prevalence of insulin resistance by homeostatic model assessment (HOMA) index, 7.85% prevalence of high cholesterol, and 20.37% prevalence of hypercholesterolemia. The deprived alleles showed that there is no association between them and clinical disease risk markers, and the notable finding of the association studies is that the ELOVL2 variants are exclusive in men and ELVOL7 in women. There is also a strong association of ELOVL6 with various markers. The present study shows, for the first time, the association between the different ELOVLs and clinical markers of chronic non-communicable diseases.
Similar Long‐Term Clinical Outcomes of Deep Brain Stimulation With Different Electrode Targets for Primary Meige Syndrome: One Institution's Experience of 17 Cases

Deep brain stimulation (DBS) is considered an effective and safe treatment for patients with primary Meige syndrome (MS). Both the subthalamic nucleus (STN) and globus pallidus pars internus (Gpi) have been shown to be optional targets for electrode implantation to improve clinical symptoms, but the relationship between clinical outcomes and target is still unclear. The current study aims to compare the clinical outcomes of DBS with different electrode targets for primary MS.
Aging Induces Profound Changes in sncRNA in Rat Sperm and These Changes Are Modified by Perinatal Exposure to Environmental Flame Retardant

Advanced paternal age at fertilization is a risk factor for multiple disorders in offspring and may be linked to age-related epigenetic changes in the father’s sperm. An understanding of aging-related epigenetic changes in sperm and environmental factors that modify such changes is needed. Here, we characterize changes in sperm small non-coding RNA (sncRNA) between young pubertal and mature rats. We also analyze the modification of these changes by exposure to environmental xenobiotic 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47). sncRNA libraries prepared from epididymal spermatozoa were sequenced and analyzed using DESeq 2. The distribution of small RNA fractions changed with age, with fractions mapping to rRNA and lncRNA decreasing and fractions mapping to tRNA and miRNA increasing. In total, 249 miRNA, 908 piRNA and 227 tRNA-derived RNA were differentially expressed (twofold change, false discovery rate (FDR) p ≤ 0.05) between age groups in control animals. Differentially expressed miRNA and piRNA were enriched for protein-coding targets involved in development and metabolism, while piRNA were enriched for long terminal repeat (LTR) targets. BDE-47 accelerated age-dependent changes in sncRNA in younger animals, decelerated these changes in older animals and increased the variance in expression of all sncRNA. Our results indicate that the natural aging process has profound effects on sperm sncRNA profiles and this effect may be modified by environmental exposure.
ITGA6+ Human Testicular Cell Populations Acquire a Mesenchymal Rather than Germ Cell Transcriptional Signature during Long-Term Culture

Autologous spermatogonial stem cell transplantation is an experimental technique aimed at restoring fertility in infertile men. Although effective in animal models, in vitro propagation of human spermatogonia prior to transplantation has proven to be difficult. A major limiting factor is endogenous somatic testicular cell overgrowth during long-term culture. This makes the culture both inefficient and necessitates highly specific cell sorting strategies in order to enrich cultured germ cell fractions prior to transplantation. Here, we employed RNA-Seq to determine cell type composition in sorted integrin alpha-6 (ITGA6+) primary human testicular cells (n = 4 donors) cultured for up to two months, using differential gene expression and cell deconvolution analyses. Our data and analyses reveal that long-term cultured ITGA6+ testicular cells are composed mainly of cells expressing markers of peritubular myoid cells, (progenitor) Leydig cells, fibroblasts and mesenchymal stromal cells and only a limited percentage of spermatogonial cells as compared to their uncultured counterparts. These findings provide valuable insights into the cell type composition of cultured human ITGA6+ testicular cells during in vitro propagation and may serve as a basis for optimizing future cell sorting strategies as well as optimizing the current human testicular cell culture system for clinical use.
Immune checkpoint expression in HNSCC patients before and after definitive chemoradiotherapy

Primary platinum‐based chemoradiotherapy (CRT) remains the treatment of choice for nonresectable squamous cell carcinoma of the head and neck (HNSCC). Immune‐checkpoint modulators are used as palliative therapy and studied in combination with definitive CRT. However, the immunological changes by CRT need yet to be understood.