Miracle-dz/UniFAR-FTUs huggingface dataset:
Whole Exome Sequencing Confirms Molecular Diagnostics of Three Pakhtun Families With Autosomal Recessive Epidermolysis Bullosa

Epidermolysis bullosa (EB) is a genetic skin disorder that shows heterogeneous clinical fragility. The patients develop skin blisters congenitally or in the early years of life at the dermo-epithelial junctions, including erosions, hyperkeratosis over the palms and soles. The other associated features are hypotrichosis on the scalp, absent or dystrophic nails, and dental anomalies. Molecular diagnosis through whole-exome sequencing (WES) has become one of the successful tool in clinical setups. In this study, three Pakhtun families from the Khyber Pakhtunkhwa province of Pakistan were ascertained. WES analysis of a proband in each family revealed two novel variants (COL17A1: NM_000494.4: c.4041T>G: p.Y1347* and PLEC: NM_201380.3: c.1283_1285delGCT: p.L426del) and one previously known COL17A1: NM_000494. 4:c.3067C>T: p.Q1023*) variant in homozygous forms. Sanger sequencing of the identified variants confirmed that the heterozygous genotypes of the obligate carriers. The identified variants have not only increased the mutation spectrum of the COL17A1 and PLEC but also confirms their vital role in the morphogenesis of skin and its associated appendages. WES can be used as a first-line diagnostic tool in genetic testing and counselling families from Khyber Pakhtunkhwa, Pakistan.
Impact of the Internal Carotid Artery Morphology on in silico Stent-Retriever Thrombectomy Outcome

The aim of this work is to propose a methodology for identifying relationships between morphological features of the cerebral vasculature and the outcome of in silico simulations of thrombectomy, the mechanical treatment for acute ischemic stroke. Fourteen patient-specific cerebral vasculature segmentations were collected and used for geometric characterization of the intracranial arteries mostly affected by large vessel occlusions, i.e., internal carotid artery (ICA), middle cerebral artery (MCA) and anterior cerebral artery (ACA). First, a set of global parameters was created, including the geometrical information commonly provided in the clinical context, namely the total length, the average diameter and the tortuosity (length over head-tail distance) of the intracranial ICA. Then, a more exhaustive geometrical analysis was performed to collect a set of local parameters. A total of 27 parameters was measured from each patient-specific vascular configuration. Fourteen virtual thrombectomy simulations were performed with a blood clot with the same length and composition placed in the middle of the MCA. The model of TREVO ProVue stent-retriever was used for all the simulations. Results from simulations produced five unsuccessful outcomes, i.e., the clot was not removed from the vessels. The geometric parameters of the successful and unsuccessful simulations were compared to find relations between the vascular geometry and the outcome. None of the global parameters alone or combined proved able to discriminate between positive and negative outcome, while a combination of local parameters allowed to correctly identify the successful from the unsuccessful simulations. Although these results are limited by the number of patients considered, this study indicates a promising methodology to relate patient-specific geometry to virtual thrombectomy outcome, which might eventually guide decision making in the treatment of acute ischemic stroke.
Synaptotagmin 1 oligomerization via the juxtamembrane linker regulates spontaneous and evoked neurotransmitter release

Synaptotagmin-1 (syt1) is a Ca2+ sensor that regulates synaptic vesicle exocytosis. Cell-based experiments suggest that syt1 functions as a multimer, however biochemical and electron microscopy studies have yielded contradictory findings regarding putative self-association. Here, we performed dynamic light scattering on syt1 in solution, followed by electron microscopy, and used atomic force microscopy to study syt1 self-association on supported lipid bilayers under aqueous conditions. Ring-like multimers were clearly observed. Multimerization was enhanced by Ca2+ and required anionic phospholipids. Large ring-like structures (∼180 nm) were reduced to smaller rings (∼30 nm) upon neutralization of a cluster of juxtamembrane lysine residues; further substitution of residues in the second C2-domain completely abolished self-association. When expressed in neurons, syt1 mutants with graded reductions in self-association activity exhibited concomitant reductions in: a) clamping spontaneous release, and b) triggering and synchronizing evoked release. Thus, the juxtamembrane linker of syt1 plays a crucial role in exocytosis by mediating multimerization.
Deep learning identifies and quantifies recombination hotspot determinants

Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known to be related to the hotspots, their contributions to the recombination hotspots have not been quantified, and other determinants are yet to be elucidated. Here, we develop a computational method, RHSNet, based on deep learning and signal processing, to identify and quantify the hotspot determinants in a purely data-driven manner, utilizing datasets from various studies, populations, sexes, and species. In addition to being able to identify hotspot regions and the well-known determinants accurately, RHSNet is sensitive to the difference between different PRDM9 alleles and different sexes, and can generalize to PRDM9-lacking species. The cross-sex, cross-population, and cross-species studies suggest that the proposed method has the potential to identify and quantify the evolutionary determinant motifs. Teaser RHSNet can accurately identify and quantify recombination hotspot determinants across different studies, sexes, populations, and species.
Across-population genomic prediction in grapevine opens up promising prospects for breeding

Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.
Mediating effect of destination image on the relationship between risk perception of smog and revisit intention: a case of Chengdu

ABSTRACT Major urban centers are suffering from air pollution in China recently, the relation between tourists’ risk perception of smog and behavior intention remains unclear. This study develops and empirically validates an integrated model to examine the relationship between risk perception of smog, destination image and revisit intention. Results show that, destination image played a key mediating role between the three dimensions of risk perception of smog, namely, perceived health, traffic and tourist experience risks, and revisit intention. Findings highlight the necessity to compound destination image in modeling risk perception and provide implications for promoting smog polluted cities as tourist destinations.
A Cost Function for the Uncertainty of Matching Point Distribution on Image Registration

Computing the homography matrix using the known matching points is a key step in computer vision for image registration. In practice, the number, accuracy, and distribution of the known matching points can affect the uncertainty of the homography matrix. This study mainly focuses on the effect of matching point distribution on image registration. First, horizontal dilution of precision (HDOP) is derived to measure the influence of the distribution of known points on fixed point position accuracy on the image. The quantization function, which is the average of the center points’ HDOP* of the overlapping region, is then constructed to measure the uncertainty of matching distribution. Finally, the experiments in the field of image registration are performed to verify the proposed function. We test the consistency of the relationship between the proposed function and the average of symmetric transfer errors. Consequently, the proposed function is appropriate for measuring the uncertainty of matching point distribution on image registration.
Urban Hotspot Area Detection Using Nearest-Neighborhood-Related Quality Clustering on Taxi Trajectory Data

Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It is of great significance to mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics and the operational status of urban traffic. The existing clustering methods mainly concentrate on the number of objects contained in an area within a specified size, neglecting the impact of the local density and the tightness between objects. Hence, a novel algorithm is proposed for detecting urban hotspots from taxi trajectory data based on nearest neighborhood-related quality clustering techniques. The proposed spatial clustering algorithm not only considers the maximum clustering in a limited range but also considers the relationship between each cluster center and its nearest neighborhood, effectively addressing the clustering issue of unevenly distributed datasets. As a result, the proposed algorithm obtains high-quality clustering results. The visual representation and simulated experimental results on a real-life cab trajectory dataset show that the proposed algorithm is suitable for inferring urban hotspot areas, and that it obtains better accuracy than traditional density-based methods.
Estimating the Transmissibility of Mumps: A Modelling Study in Wuhan City, China

Despite the adoption of a national immunization program in China, the incidence of mumps remains high. This study aimed to describe the epidemiological characteristics, including the time, region, occupation, and age, of mumps in Wuhan from 2005 to 2018 and to evaluate its transmissibility. In this study, the susceptible–exposed–infectious–asymptomatic–recovered (SEIAR) model fitted the actual incidence data of mumps. The effective reproduction number (Rt) was used to evaluate and compare the transmission capacity in different areas. From 2005 to 2018, there were 36,415 cases. The incidence of mumps was highest among people aged 5–10 years (460.02 per 100,000). The SEIAR model fitted the reported mumps data well (P < 0.01). The median transmissibility (Rt) was 1.04 (range = 0–2.50). There were two peak spreads every year (from March to May and from October to December). The Rt peak always appeared in the first 2 months of the peak incidence rate. The peak time of the epidemic spread of mumps was 1–2 months earlier than the peak incidence rate. The prevention and control measures of vaccination for children aged 5–10 years should be taken before the peak transmission capacity each year, 2 months before the peak of the outbreak, to reduce the spread of mumps.
Phytoplankton and Bacterial Responses to Monsoon-Driven Water Masses Mixing in the Kuroshio Off the East Coast of Taiwan

Biogeochemical responses to mixing were examined in two cruise surveys along a transect across the Kuroshio Current (KC) in May and July 2020. Two stations located at the South China Sea (SCS)–KC mixing and the KC waters were chosen for the diel study. In the euphotic zone (~100 m depth), the average values of nitrate (0.97–1.62 μM), chlorophyll-a (Chl-a, 0.36–0.40 mg/m3), and primary production (PP; 3.46 ± 1.37 mgC/m3/day) of the mixing water station (MWS) of the two cruises were several folds higher than those of the KC station (KCS; nitrate, 0.03–0.10 μM; Chl-a, 0.14–0.24 mg/m3; and PP, 0.91 ± 0.47 mgC/m3/day). In the July cruise, the maximal bacterial production (BP) at the MWS (3.31 mgC/m3/day) was 82% higher in comparison with that of the KCS (1.82 mgC/m3/day); and the readings of Chl-a showed no trend with BP in the oligotrophic KCS, but a positive relationship was found among these measurements at the mesotrophic MWS. This implies that the trophic status of the system might affect phytoplankton–bacteria interactions. The backward-trajectory analyses conducted by an observation-validated three-dimensional model identified that the prevailing southwest monsoon drove a northeastward “intrusion” of the SCS waters in July 2020, resulted in mixing between SCS and Kuroshio (KC) waters off the east coast of southern Taiwan. For the first time, this study demonstrates that the high biological biomass and activities that occur in the KC are induced by the northward intrusion of the SCS waters.
Accelerating Inference of Convolutional Neural Networks Using In-memory Computing

In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One prominent application of IMC is that of performing matrix-vector multiplication in O(1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of an IMC core. However, because of the significantly different pattern of execution compared to previous computational paradigms, IMC requires a rethinking of the architectural design choices made when designing deep-learning hardware. In this work, we focus on application-specific, IMC hardware for inference of Convolution Neural Networks (CNNs), and provide methodologies for implementing the various architectural components of the IMC core. Specifically, we present methods for mapping synaptic weights and activations on the memory structures and give evidence of the various trade-offs therein, such as the one between on-chip memory requirements and execution latency. Lastly, we show how to employ these methods to implement a pipelined dataflow that offers throughput and latency beyond state-of-the-art for image classification tasks.
Current Trends and Research Topics Regarding Intestinal Organoids: An Overview Based on Bibliometrics

Currently, research on intestinal diseases is mainly based on animal models and cell lines in monolayers. However, these models have drawbacks that limit scientific advances in this field. Three-dimensional (3D) culture systems named organoids are emerging as a reliable research tool for recapitulating the human intestinal epithelium and represent a unique platform for patient-specific drug testing. Intestinal organoids (IOs) are crypt–villus structures that can be derived from adult intestinal stem cells (ISCs), embryonic stem cells (ESCs), or induced pluripotent stem cells (iPSCs) and have the potential to serve as a platform for individualized medicine and research. However, this emerging field has not been bibliometric summarized to date. Here, we performed a bibliometric analysis of the Web of Science Core Collection (WoSCC) database to evaluate 5,379 publications concerning the use of organoids; the studies were divided into four clusters associated with the current situation and future directions for the application of IOs. Based on the results of our bibliometric analysis of IO applications, we systematically summarized the latest advances and analyzed the limitations and prospects.
Exploration of the Quorum-Quenching Mechanism in Pseudomonas nitroreducens W-7 and Its Potential to Attenuate the Virulence of Dickeya zeae EC1

Quorum quenching (QQ) is a novel, promising strategy that opens up a new perspective for controlling quorum-sensing (QS)-mediated bacterial pathogens. QQ is performed by interfering with population-sensing systems, such as by the inhibition of signal synthesis, catalysis of degrading enzymes, and modification of signals. In many Gram-negative pathogenic bacteria, a class of chemically conserved signaling molecules named N-acyl homoserine lactones (AHLs) have been widely studied. AHLs are involved in the modulation of virulence factors in various bacterial pathogens including Dickeya zeae. Dickeya zeae is the causal agent of plant-rot disease of bananas, rice, maize, potatoes, etc., causing enormous economic losses of crops. In this study, a highly efficient AHL-degrading bacterial strain W-7 was isolated from activated-sludge samples and identified as Pseudomonas nitroreducens. Strain W-7 revealed a superior ability to degrade N-(3-oxododecanoyl)-l-homoserine lactone (OdDHL) and completely degraded 0.2 mmol/L of OdDHL within 48 h. Gas chromatography-mass spectrometry (GC-MS) identified N-cyclohexyl-propanamide as the main intermediate metabolite during AHL biodegradation. A metabolic pathway for AHL in strain W-7 was proposed based on the chemical structure of AHL and intermediate products. In addition to the degradation of OdDHL, this strain was also found to be capable of degrading a wide range of AHLs including N-(3-oxohexanoyl)-l-homoserine lactone (OHHL), N-(3-oxooctanoyl)-l-homoserine lactone (OOHL), and N-hexanoyl-l-homoserine lactone (HHL). Moreover, the application of strain W-7 as a biocontrol agent could substantially attenuate the soft rot caused by D. zeae EC1 to suppress tissue maceration in various host plants. Similarly, the application of crude enzymes of strain W-7 significantly reduced the disease incidence and severity in host plants. These original findings unveil the biochemical aspects of a highly efficient AHL-degrading bacterial isolate and provide useful agents that exhibit great potential for the control of infectious diseases caused by AHL-dependent bacterial pathogens.
Age-Related Variations in Regional White Matter Volumetry and Microstructure During the Post-adolescence Period: A Cross-Sectional Study of a Cohort of 1,713 University Students

Human brain white matter undergoes a protracted maturation that continues well into adulthood. Recent advances in diffusion-weighted imaging (DWI) methods allow detailed characterizations of the microstructural architecture of white matter, and they are increasingly utilized to study white matter changes during development and aging. However, relatively little is known about the late maturational changes in the microstructural architecture of white matter during post-adolescence. Here we report on regional changes in white matter volume and microstructure in young adults undergoing university-level education. As part of the MRi-Share multi-modal brain MRI database, multi-shell, high angular resolution DWI data were acquired in a unique sample of 1,713 university students aged 18–26. We assessed the age and sex dependence of diffusion metrics derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) in the white matter regions as defined in the John Hopkins University (JHU) white matter labels atlas. We demonstrate that while regional white matter volume is relatively stable over the age range of our sample, the white matter microstructural properties show clear age-related variations. Globally, it is characterized by a robust increase in neurite density index (NDI), and to a lesser extent, orientation dispersion index (ODI). These changes are accompanied by a decrease in diffusivity. In contrast, there is minimal age-related variation in fractional anisotropy. There are regional variations in these microstructural changes: some tracts, most notably cingulum bundles, show a strong age-related increase in NDI coupled with decreases in radial and mean diffusivity, while others, mainly cortico-spinal projection tracts, primarily show an ODI increase and axial diffusivity decrease. These age-related variations are not different between males and females, but males show higher NDI and ODI and lower diffusivity than females across many tracts. These findings emphasize the complexity of changes in white matter structure occurring in this critical period of late maturation in early adulthood.
Sleep-Wake Behavior in Elite Athletes: A Mixed-Method Approach

Purpose Using a mixed-method approach, this investigation aimed to examine athletes’ sleeping patterns along with the socio-physiological acute and chronic stressors affecting their sleep. Methods Fifteen elite athletes (Mage = 21.7 years; SD = 2.2) were monitored daily during a typical in-season training period (20 ± 1 days) and completed individual semi-structured interviews. Their sleep was analyzed using actigraphy and sleep diaries. A content analysis method was used to assess qualitative interviews. Results Three factors influencing sleep emerged from the qualitative interview analyses, i.e., sleep environment, training and study requirements. Half the athletes (8/15) complained about their sleeping environment with noise and/or bedroom temperature and/or mattress quality and/or light exposure presented as an issue. “Complainers” notably exhibited impaired (p < 0.05) sleep efficiency and wake after sleep onset compared with “non-complainers. ” Daily training load showed correlations (p < 0.05) with wake after sleep onset and sleep onset latency. “Student athletes” exhibited later bedtime and earlier wake-up time compared with “non-student athletes,” leading to a reduced total sleep time (6:50 ± 1:13 vs. 7:06 ± 0:53; p < 0.05; d = 0.20). Conclusion An individualized assessment of sleep using actigraphy and interviews, with consideration to various socio-physiological factors, is recommended. Individualized sleep interventions with consideration to physiological (e.g., training load), behavioral (e.g., screen use) and environmental factors (e.g., room temperature, noise, mattress), can then be provided to each athlete.