Miracle-dz/UniFAR-FTUs huggingface dataset:
A robust benchmark for evaluating and improving mosaic variant calling strategies

The rapid advances in sequencing and analysis technologies have enabled the accurate detection of diverse forms of genomic variants, including germline, somatic, and mosaic mutations. However, unlike for the former two mutations, the best practices for mosaic variant calling still remain chaotic due to the technical and conceptual difficulties faced in evaluation. Here, we present our benchmark of nine feasible strategies for mosaic variant detection based on a systematically designed reference standard that mimics mosaic samples, with 390,153 control positive and 35,208,888 negative single-nucleotide variants and insertion–deletion mutations. We identified the condition-dependent strengths and weaknesses of the current strategies, instead of a single winner, regarding variant allele frequencies, variant sharing, and the usage of control samples. Moreover, feature-level investigation directs the way for immediate to prolonged improvements in mosaic variant calling. Our results will guide researchers in selecting suitable calling algorithms and suggest future strategies for developers.
APPLICATION OF EMERGING LPWAN TECHNOLOGIES IN CONDITION MONITORING

Condition monitoring of electrical equipment is considered a key activity for the efficient asset management and development of distribution infrastructure. LPWAN (Low-power Wide Area Network) is an emerging and fast-growing M2M (machine-to-machine) communication technology which can be implemented as a low-end complement to other communication technologies to extend condition monitoring in distribution grids. In this article, the technical characteristics of LPWAN technologies are identified and summarized. A pilot project to test the application of LoRaWAN technology for condition monitoring in substations is presented. The results demonstrate the outstanding building penetration capabilities as well as the limited effort to deploy a complete solution from sensors to end users.
The Effects of Dietary Nitrate Supplementation on Explosive Exercise Performance: A Systematic Review

Dietary nitrate supplementation is evidenced to induce physiological effects on skeletal muscle function in fast-twitch muscle fibers and may enhance high-intensity exercise performance. An important component of sport-specific skills is the ability to perform explosive movements; however, it is unclear if nitrate supplementation can impact explosive efforts. We examined the existing evidence to determine whether nitrate supplementation improves explosive efforts lasting ≤ 6 s. PubMed, Scopus and Directory of Open Access Journals (DOAJ) were searched for articles using the following search strategy: (nitrate OR nitrite OR beetroot) AND (supplement OR supplementation) AND (explosive OR power OR high intensity OR high-intensity OR sprint* OR “athletic performance”). Out of 810 studies, 18 were eligible according to inclusion criteria. Results showed that 4 of the 10 sprint-type studies observed improved sprint time, power output, and total work in cycling or running, whereas 4 of the 10 resistance-based exercise studies observed improvements to power and velocity of free-weight bench press as well as isokinetic knee extension and flexion at certain angular velocities. These results suggest that nitrate potentially improves explosive exercise performance, but further work is required to clarify the factors influencing the efficacy of nitrate in different exercise modalities.
Recent Trends in the Management of Mango By-products

ABSTRACT Mango is one of the major cultivated tropical fruits in the world. A huge quantity of mango by-products is generated after processing which gives rise to the severe problem of disposal. This could be a menace to the environment and also bring economic loss to fruit processing industries. However, mango by-products are rich in numerous value-added compounds such as dietary fiber, pectin, polyphenols, carotenoids, etc. which can be recovered using effective extraction methods. The implementation of green techniques for the extraction of bioactive compounds from the by-products can be a sustainable valorization strategy that is economically and environmentally feasible. Proper management of mango processing waste using the concept of biorefinery could help mitigate the environmental issues of waste disposal and help gain economic benefits. The development of value-added products along with bio-energy generation from mango waste biomass using different conversion technologies can be a novel approach in the global market. The present review highlights the valuable components and the recent trends in the management of mango by-products.
HLocalExp-CM: confidence map by hierarchical local expansion moves for accurate stereo matching

Abstract. We present a stereo matching approach referred to as HLocalExp-CM by exploiting the hierarchical local contextual information and a confidence map based on a new grid structure. The proposed approach preserves fine depth edges and extracts accurate disparities in weak texture, textureless, and repeated texture regions. The proposed approach adopts a two-stage optimization strategy. In the framework of first stage, a multiresolution cost aggregation is minimized to reduce the search space of the disparity plane of each pixel. The second stage iteratively optimizes the confidence map and a global energy function to progressively improve the disparity accuracy for each pixel. The confidence map is estimated through classifying the pixels into distinctive and ambiguous ones by computing the decreasing rate of the multiresolution cost aggregation and then performs a spatial propagation and plane refinement for the update of the disparity of each pixel, thereby successfully eliminating the ambiguity of nondistinctive pixels. The global energy function based on a pairwise Markov random field uses cross-scale cost aggregation for taking advantage of context information of objects in different scenarios on local grid regions, which is different from the deep learning technique uses convolution layers extracting the context information. The proposed approach is evaluated on Middlebury benchmark V3, and is ranked first based on “bad 2.0 all metric,” a widely used criterion for the evaluation of stereo images, while the eighth place on “bad 2.0 nonocc metric” (recorded on July 24, 2021).
Functional dissection of neural connectivity in generalized anxiety disorder

Differences in the correlated activity of networked brain regions have been reported in individuals with generalized anxiety disorder (GAD) but an overreliance on the null-hypothesis significance testing (NHST) framework limits the identification and characterization of disorder-relevant relationships. In this preregistered study, we applied a Bayesian statistical framework as well as NHST to the analysis of resting-state fMRI scans from females with GAD and demographically matched healthy comparison females. Eleven a-priori hypotheses about functional correlativity (FC) were evaluated using Bayesian (multilevel model) and frequentist (t-test) inference. Reduced FC between the ventromedial prefrontal cortex (vmPFC) and the posterior-mid insula (PMI) was confirmed by both statistical approaches. FC between the vmPFC-anterior insula, the amygdala-PMI, and the amygdala-dorsolateral prefrontal cortex (dlPFC) region pairs did not survive multiple comparison correction using the frequentist approach. However, the Bayesian model provided evidence for these region pairs having decreased FC in the GAD group. Leveraging Bayesian modeling, we demonstrate decreased FC of the vmPFC, insula, amygdala, and dlPFC in females with GAD. Exploiting the Bayesian framework revealed FC abnormalities between region pairs excluded by the frequentist analysis, as well as other previously undescribed regions, demonstrating the benefits of applying this statistical approach to resting state FC data.
Learning about the meanings of ambiguous words: evidence from a word-meaning priming paradigm with short narratives

Fluent language comprehension requires people to rapidly activate and integrate context-appropriate word meanings. This process is challenging for meanings of ambiguous words that are comparatively lower in frequency (e.g., the “bird” meaning of “crane”). Priming experiments have shown that recent experience makes such subordinate (less frequent) word meanings more readily available at the next encounter. These experiments used lists of unconnected sentences in which each ambiguity was disambiguated locally by neighbouring words. In natural language, however, disambiguation may occur via more distant contextual cues, embedded in longer, connected communicative contexts. In the present experiment, participants (N = 51) listened to 3-sentence narratives that ended in an ambiguous prime. Cues to disambiguation were relatively distant from the prime; the first sentence of each narrative established a situational context congruent with the subordinate meaning of the prime, but the remainder of the narrative did not provide disambiguating information. Following a short delay, primed subordinate meanings were more readily available (compared with an unprimed control), as assessed by responses in a word association task related to the primed meaning. This work confirms that listeners reliably disambiguate spoken ambiguous words on the basis of cues from wider narrative contexts, and that they retain information about the outcome of these disambiguation processes to inform subsequent encounters of the same word form.
Physical Layer Node Authentication in Underwater Acoustic Sensor Networks Using Time-Reversal

Underwater acoustic sensor networks (UASNs) have played a vital role in many security-sensitive applications like offshore oil exploration, tsunami forecast, and tactical surveillance. Due to the complex marine environment and harsh acoustic channel, UASNs face severe security challenges and attacks. Exploiting the multi-path energy from the richly scattering underwater environment, the time-reversal (TR) process can form the resonating strength based on the channel impulse response (CIR). Inspired by the natural link signature resulting from the spatial dependency of acoustic links, an authentication scheme using the maximum TR resonating strength is proposed, with the aim of effectively detecting the spoofing attacks in UASNs. To accommodate the time-varying nature of the underwater acoustic link, a database correlation method is exploited to capture the link CIR pattern over time for each link, which efficiently improves the accuracy of the authentication scheme using the TR process. Hence, the proposed algorithm enables each node to make the authentication decision based on maximum time-reversal resonating strength (MTRRS) locally with little overhead. It was evaluated by the probabilities of authentication, attack detection, and false alarm through extensive simulations. Finally, this MTRRS-based authentication was verified using the sea trial CIR data.
Low Complement C1q/TNF-related Protein-13 Levels are Associated with Childhood Obesity But not Binge Eating Disorder

Objective: C1q/tumor necrosis factor-related proteins (CTRPs) are recently described members of the adipokine family. CTRP-13, a new member of this family, has been shown to increase insulin sensitivity and had an anorexigenic effect on food intake in experimental studies. The aim was to investigate serum CTRP-13 levels in children with obesity, and its relationship with other adipokines, metabolic parameters, or binge eating disorder (BED). Methods: A cross-sectional study was conducted with 105 pubertal children attending a single center. Clinical (metabolic syndrome, BED) and biochemical (glucose, insulin, lipids, leptin, adiponectin, CTRP-13 levels) parameters were assessed. Results: Sixty children with obesity [24 males (40%); median age 14.7 (13.0-16.4) years] and 45 healthy controls [15 males (33.3%); median age 15.2 (14.1-16.5) years] were included. Serum adiponectin and CTRP-13 levels were significantly lower in children with obesity than controls (7.1 vs 20.1 μg/mL, p<0.001; 64.7 vs 103.8 ng/mL, p<0.001, respectively). CTRP-13 levels correlated negatively with body mass index (Spearman rho=-0.230, p=0.018) and positively with high-density lipoprotein-cholesterol levels (Spearman rho=0.218, p=0.026). There was no significant difference in serum CTRP-13 concentrations in terms of the presence of metabolic syndrome or BED. Conclusion: Childhood obesity seems to be causing dysregulation in adipokine production and function, including the down-regulation of CTRP-13. The positive correlation between CTRP-13 and HDL-C levels suggested a possible effect of this adipokine on lipid metabolism. Thus CTRP-13 may be a novel biomarker for dyslipidemia in childhood obesity.
Comparative Microbiomes of the Respiratory Tract and Joints of Feedlot Cattle Mortalities

A comparative study of microbiota of the respiratory tract and joints of bovine respiratory disease (BRD) cattle mortalities was undertaken. Nasopharynx, trachea, lung and joint samples were collected from 32 cattle that died of BRD, “cases”, and 8 that died of other causes, “controls”. Bacterial diversity was lower (p < 0.05) in the nasopharynx, trachea and lungs of cases as compared to controls. In cases, alpha-diversity (p < 0.05) was lower in the lungs and joints than the nasopharynx. Proteobacteria, Tenericutes, Bacteroidetes, Firmicutes and Actinobacteria were the most abundant phyla in all samples. Relative abundances of Mycoplasma spp. in the lung, Pasteurella spp. in the trachea and lung, and Histophilus spp. in the lung, trachea and nasopharynx of cases were higher (p < 0.001) than controls. Mycoplasma spp. comprised 20.5% of bacterial flora in the joint, 36.0% in the lung, 22.4% in the trachea and 8.8% in the nasopharynx. Mannheimia spp. (21.8%) and Histophilus spp. (10.4%) were more abundant in lungs. Cattle that died of BRD possessed less diverse respiratory microbiomes with a higher abundance of respiratory pathogens. Mycoplasma spp. were prominent members of pneumonic lungs and joints displaying septic arthritis.
Decrease in Sleep Duration and Poor Sleep Quality over Time Is Associated with an Increased Risk of Incident Non-Alcoholic Fatty Liver Disease

The impact of changes in sleep duration and sleep quality over time on the risk of non-alcoholic fatty liver disease (NAFLD) is not known. We investigated whether changes in sleep duration and in sleep quality between baseline and follow-up are associated with the risk of developing incident NAFLD. The cohort study included 86,530 Korean adults without NAFLD and with a low fibrosis score at baseline. The median follow-up was 3.6 years. Sleep duration and quality were assessed using the Pittsburgh Sleep Quality Index. Hepatic steatosis (HS) and liver fibrosis were assessed using ultrasonography and the fibrosis-4 index (FIB-4). Cox proportional hazard models were used to determine hazard ratios (HRs) and 95% confidence intervals (Cis). A total of 12,127 subjects with incident HS and 559 with incident HS plus intermediate/high FIB-4 was identified. Comparing the decrease in sleep duration of >1 h, with stable sleep duration, the multivariate-adjusted HR (95% CIs) for incident HS was 1.24 (1.15–1.35). The corresponding HRs for incident HS plus intermediate/high FIB-4 was 1.58 (1.10–2.29). Comparing persistently poor sleep quality with persistently good sleep quality, the multivariate-adjusted HR for incident HS was 1.13 (95% CI, 1.05–1.20). A decrease in sleep duration or poor sleep quality over time was associated with an increased risk of incident NAFLD, underscoring an important potential role for good sleep in preventing NAFLD risk.
Monomodular Pseudomonas aeruginosa phage JG004 lysozyme (Pae87) contains a bacterial surface-active antimicrobial peptide-like region and a possible substrate-binding subdomain

Phage lysins are a source of novel antimicrobials to tackle the bacterial antibiotic resistance crisis. The engineering of phage lysins is being explored as a game-changing technological strategy for introducing a more precise approach in the way we apply antimicrobial therapy. Such engineering efforts will benefit from a better understanding of lysin structure and function. In this work, the antimicrobial activity of the endolysin from Pseudomonas aeruginosa phage JG004, termed Pae87, has been characterized. This lysin had been previously identified as an antimicrobial agent candidate, able to interact with the Gram-negative surface and disrupt it. Further evidence is hereby provided on this matter, based on a structural and biochemical study. A high-resolution crystal structure of Pae87 complexed with a peptidoglycan fragment showed a separate substrate-binding region within the catalytic domain, 18 Å away from the catalytic site and located at the opposite side of the lysin molecule. This substrate binding region was conserved among phylogenetically related lysins lacking an additional cell wall binding domain, but not among those containing such a module. Two glutamic acids were identified as relevant for the peptidoglycan degradation activity, although Pae87 antimicrobial activity was seemingly unrelated to it. In contrast, an antimicrobial peptide-like region within Pae87 C-terminus, named P87, was found to be able to actively disturb the outer membrane and have antibacterial activity by itself. Therefore, we propose an antimicrobial mechanism for Pae87 in which the P87 peptide plays the role of binding to the outer membrane and disrupting the cell wall function, either with or without the participation of Pae87 catalytic activity. Synopsis The structure of the monomodular Pseudomonas aeruginosa bacteriophage JG004 lysin Pae87 is presented and investigated in relationship with its function repurposed as an antimicrobial agent. The structure with its peptidoglycan ligand revealed a possible cell wall binding region. A C-terminal antimicrobial peptide-like region is shown to be important for disrupting the bacterial cell wall.
Multipoint Aerodynamic Design of Ultrashort Nacelles for Ultrahigh-Bypass-Ratio Engines

This paper presents a newly developed methodology for multipoint aerodynamic design of ultra-short nacelles for ultra-high bypass ratio turbofan engines. An integrated aero-dynamic framework, based on parametric geometry generation and ﬂow ﬁeld solution via three-dimensional Reynolds-Averaged Navier-Stokes equations, was built and used for designing several ultra-short nacelle shapes and to evaluate their aerodynamic performance. An approach for estimating the static pressure distribution at the fan face, based on the parallel compressor theory, is presented and validated. A design strategy is introduced and various test cases are evaluated under the following critical operating conditions: mid-cruise, low speed/ high angle of attack, and pure crosswind. The major design parameters are highlighted and their inﬂuence in the ﬂow ﬁeld is discussed in detail for all the chosen ﬂight conditions. Performance was evaluated by assessing inlet ﬂow distortion and by bookkeeping of thrust and drag. The framework has proven to be suitable for designing high performance nacelles capable of operating under critical ﬂight conditions, without ﬂow separation or high levels of distortion. Drooping the inlet by 4 degrees is shown to reduce the drag at cruise by 1.9%, which also has a large beneﬁcial impact on internal lip separation at high incidence conditions. Furthermore, crosswind was identiﬁed as the most severe of the conditions, requiring a drastic reshaping of the nacelle to avoid internal lip separation. Two ﬁnal nacelle designs were compared: the ﬁrst allowing inlet separation under a 90-degree crosswind condition, whilst the second was reshaped to be separation-free under all operating conditions. Reshaping to avoid separation
Quality Optimization of Adaptive Applications via Deep Reinforcement Learning in Energy Harvesting Edge Devices

Applications with adaptability are widely available on the edge devices with energy harvesting capabilities. For their runtime quality optimization, however, current approaches cannot tackle the variations of quality modeling and harvested energy simultaneously. Therefore, in this article, we are the first to propose a deep reinforcement learning (DRL)-based dynamic voltage frequency scaling (DVFS) method that optimizes the application execution quality of energy harvesting edge devices to mitigate the variations. First, we propose a baseline DRL formulation that novelly migrates the objective of quality maximization into a reward function and constructs a DRL quality agent. Second, we devise a long short-term memory (LSTM)-based selector that performs DRL quality agent selection based on the energy harvesting history. Third, we further propose two optimization methods to alleviate the nonnegligible overhead of DRL computations: 1) an improved thinking-while-moving concurrent DRL scheme to compromise the “state drifting” issue during the DRL decision process and 2) a variable interstate duration decision scheme that compromises the DVFS overhead incurred in each action taken. The experiments take an adaptive stereo matching application as a case study. The results show that the proposed DRL-based DVFS method on average achieves 17.9% runtime reduction and 22.05% quality improvement compared to state-of-the-art solutions.
Polygenic Prediction of Type 2 Diabetes in Africa

OBJECTIVE Polygenic prediction of type 2 diabetes (T2D) in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of T2D from Africa and the poor transferability of European-derived polygenic risk scores (PRSs) in diverse ethnicities. We set out to evaluate if African American, European, or multiethnic-derived PRSs would improve polygenic prediction in continental Africans. RESEARCH DESIGN AND METHODS Using the PRSice software, ethnic-specific PRSs were computed with weights from the T2D GWAS multiancestry meta-analysis of 228,499 case and 1,178,783 control subjects. The South African Zulu study (n = 1,602 case and 981 control subjects) was used as the target data set. Validation and assessment of the best predictive PRS association with age at diagnosis were conducted in the Africa America Diabetes Mellitus (AADM) study (n = 2,148 case and 2,161 control subjects). RESULTS The discriminatory ability of the African American and multiethnic PRSs was similar. However, the African American–derived PRS was more transferable in all the countries represented in the AADM cohort and predictive of T2D in the country combined analysis compared with the European and multiethnic-derived scores. Notably, participants in the 10th decile of this PRS had a 3.63-fold greater risk (odds ratio 3.63; 95% CI 2.19–4.03; P = 2.79 × 10−17) per risk allele of developing diabetes and were diagnosed 2.6 years earlier than those in the first decile. CONCLUSIONS African American–derived PRS enhances polygenic prediction of T2D in continental Africans. Improved representation of non-European populations (including Africans) in GWAS promises to provide better tools for precision medicine interventions in T2D.