Miracle-dz/UniFAR-FTUs huggingface dataset:
The SNARE Protein CfVam7 Is Required for Growth, Endoplasmic Reticulum Stress Response, and Pathogenicity of Colletotrichum fructicola

The tea-oil tree Camellia oleifera is native to China and is cultivated in many parts of southern China. This plant has been grown for over 2,000 years, mainly for its high-quality cooking oil. Anthracnose is the main disease of tea-oil tree and results in a huge loss annually. Colletotrichum fructicola is a major pathogen causing anthracnose on tea-oil tree. In a previous study, we characterized that the bZIP transcription factor CfHac1 controlled the development and pathogenicity of C. fructicola. Here, we identified and characterized the function of CfVAM7 gene, which was significantly downregulated at the transcriptional level in the ΔCfhac1 strain under dithiothreitol stress. Targeted gene deletion revealed that CfVam7 is important in growth, pathogenicity, and responses to endoplasmic reticulum-related stresses. Further analysis revealed that CfVam7 is required for appressorium formation and homotypic vacuole fusion, which are important for fungal pathogen invasion. Cytological examinations revealed that CfVam7 is localized to vacuole membranes in the hyphal stage. The Phox homology (PX) and SNARE domains of CfVam7 were indispensable for normal cellular localization and biological function. Taken together, our results suggested that CfVam7-mediated vacuole membrane fusion promotes growth, stress response, and pathogenicity of C. fructicola.
Apolipoprotein E Genetic Variation and Its Association With Cognitive Function in Rural-Dwelling Older South Africans

Apolipoprotein E (APOE) 𝜀4 allele carrier status is well known for its association with an increased likelihood of developing Alzheimer’s disease, but its independent role in cognitive function is unclear. APOE genetic variation is understudied in African populations; hence, this cross-sectional study in a rural South African community examined allele and genotype frequencies, and their associations with cognitive function. Cognitive function was assessed using two different screening methods to produce a total cognition score and four domain-specific cognition scores for verbal episodic memory, executive function, language, and visuospatial ability. Cognitive phenotype and APOE genotype data were used to determine whether APOE variation was significantly associated with cognitive function in this population. Observed allele frequencies for 1776 participants from the HAALSI study [age 40–80years (mean=56.19); 58.2% female] were 58.1% (𝜀3), 25.4% (𝜀4) and 16.5% (𝜀2). Allele distributions were similar to the African super population, but different from all non-African super populations from the 1,000 Genomes Project. The 𝜀3 homozygous genotype was most common (34.9%) and used as the base genotype for comparison in regression models. Four models were tested for each of the five cognitive phenotypes to explore association of APOE variation with cognitive function. In the first model assessing association with all genotypes for all individuals, marginally significant associations were observed for 𝜀2 homozygotes where executive function scored higher by ~0.5 standard deviations (p=0.037, SE=0.23), and for 𝜀3/𝜀4 heterozygotes where visuospatial ability scores were lower (p=0.046, SE=0.14). These did not survive correction for multiple testing. Regional African population differences were observed at the APOE locus. Marginally, significant associations between APOE genotype, and executive function and visuospatial ability indicate the need for larger studies to better examine these associations in African populations. Furthermore, longitudinal data could shed light on APOE genetic association with rate of change, or decline, in cognitive function.
Epidemiology of Corneal Neovascularization and Its Impact on Visual Acuity and Sensitivity: A 14-Year Retrospective Study

Purpose: To quantify the severity and location of corneal neovascularization (cNV) and its impact on the visual acuity and corneal sensitivity in a cohort of the patients referred to a specialist cornea clinic and also to describe the etiology of cNV in the cohort. Methods: We retrospectively evaluated the charts of 13,493 subjects referred to the San Raffaele Cornea Unit between January 2004 and December 2018 to search for cNV diagnosis. The corneal neovascularization severity was measured in the quadrants (range: 1–4) and location was defined as superficial, deep, or both. Best spectacle corrected visual acuity (BSCVA) was measured in logMar. We used the multiple regression analysis to identify the independent predictors of logMAR, after adjusting for age, gender, keratoconus, herpes keratitis, penetrating keratoplasty, trauma, and cataract surgery. Results: Corneal neovascularization was diagnosed in 10.4% of the patients analyzed. The most prevalent etiology of cNV in our population was non-infectious corneal dystrophies/degenerations followed by herpes simplex virus infection. cNV affected OD, OS, or both eyes in 35.6, 40.2, and 24.2 of cases, respectively. Mean BSCVA (SD) was 0.59 (0.76), 0.74 (0.94), and 1.24 (1.08) in cNV one, two, and three or four of the quadrant groups. Superficial, deep, or mixed cNV occurred in 1,029, 348, and 205 eyes. Severe cNV (three or four of the quadrants) was a significant predictor of low visual acuity (p < 0.001) and reduced corneal sensitivity (p < 0.05). cNV location and its severity were associated (p < 0.05). In addition, corneal anesthesia was associated with lower BSCVA (p < 0.001). Conclusion: Severe and deep cNV are associated with the reduced visual acuity and corneal sensitivity. Our data strongly support the relevance of appropriate follow-up as cNV is a major risk factor for graft rejection.
Novel in vitro Experimental Approaches to Study Myelination and Remyelination in the Central Nervous System

Myelin is the lipidic insulating structure enwrapping axons and allowing fast saltatory nerve conduction. In the central nervous system, myelin sheath is the result of the complex packaging of multilamellar extensions of oligodendrocyte (OL) membranes. Before reaching myelinating capabilities, OLs undergo a very precise program of differentiation and maturation that starts from OL precursor cells (OPCs). In the last 20 years, the biology of OPCs and their behavior under pathological conditions have been studied through several experimental models. When co-cultured with neurons, OPCs undergo terminal maturation and produce myelin tracts around axons, allowing to investigate myelination in response to exogenous stimuli in a very simple in vitro system. On the other hand, in vivo models more closely reproducing some of the features of human pathophysiology enabled to assess the consequences of demyelination and the molecular mechanisms of remyelination, and they are often used to validate the effect of pharmacological agents. However, they are very complex, and not suitable for large scale drug discovery screening. Recent advances in cell reprogramming, biophysics and bioengineering have allowed impressive improvements in the methodological approaches to study brain physiology and myelination. Rat and mouse OPCs can be replaced by human OPCs obtained by induced pluripotent stem cells (iPSCs) derived from healthy or diseased individuals, thus offering unprecedented possibilities for personalized disease modeling and treatment. OPCs and neural cells can be also artificially assembled, using 3D-printed culture chambers and biomaterial scaffolds, which allow modeling cell-to-cell interactions in a highly controlled manner. Interestingly, scaffold stiffness can be adopted to reproduce the mechanosensory properties assumed by tissues in physiological or pathological conditions. Moreover, the recent development of iPSC-derived 3D brain cultures, called organoids, has made it possible to study key aspects of embryonic brain development, such as neuronal differentiation, maturation and network formation in temporal dynamics that are inaccessible to traditional in vitro cultures. Despite the huge potential of organoids, their application to myelination studies is still in its infancy. In this review, we shall summarize the novel most relevant experimental approaches and their implications for the identification of remyelinating agents for human diseases such as multiple sclerosis.
A Systematic Review and Meta-Analysis of Moderate-to-Vigorous Physical Activity Levels in Children and Adolescents With and Without ASD in Inclusive Schools

Background: The health benefits of physical activity (PA) participation are well-documented. Little was known about the PA levels of students with autism spectrum disorder (ASD) and their typically developing (TD) peers in inclusive schools. This study aimed to synthesize available studies examining PA levels of children and adolescents with and without ASD and its associated factors that affected their PA participation during inclusive schools applying the social–relational model of disability (SRMD). Methods: Eight databases were searched including CINAHL Complete, SPORTDiscus with Full Text, PubMed, Embase, Web of Science, Eric, APA PsycINFO, and Scopus from inception through May 2021 to identify related studies. Two researchers independently screened studies, assessed methodological quality, and summarized relevant data. The McMaster Critical Reviewer Form for quantitative studies was used to evaluate the methodological quality of the included articles. Results: A total of seven articles were included in this systematic review. Overall, meta-analysis results indicated that children and adolescents with ASD had a moderately decreased PA levels compared with their TD peers [SMD = −0.585, 95% CI (−0.774, −0.425), p < 0.01]. Individual-, social-, and environmental-level factors that influence PA levels in children and adolescents with ASD were identified from the perspective of SRMD. Conclusion: This review indicates that children and adolescents with ASD have lower PA levels than their TD peers in inclusive schools and multilevel factors affect their PA.
A Critical Review on the Design, Manufacturing and Assessment of the Bone Scaffold for Large Bone Defects

In recent years, bone tissue engineering has emerged as a promising solution for large bone defects. Additionally, the emergence and development of the smart metamaterial, the advanced optimization algorithm, the advanced manufacturing technique, etc. have largely changed the way how the bone scaffold is designed, manufactured and assessed. Therefore, the aim of the present study was to give an up-to-date review on the design, manufacturing and assessment of the bone scaffold for large bone defects. The following parts are thoroughly reviewed: 1) the design of the microstructure of the bone scaffold, 2) the application of the metamaterial in the design of bone scaffold, 3) the optimization of the microstructure of the bone scaffold, 4) the advanced manufacturing of the bone scaffold, 5) the techniques for assessing the performance of bone scaffolds.
A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom’s Taxonomy of Cognition Objectives Perspective

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.
Epithelial-Macrophage Crosstalk Initiates Sterile Inflammation in Embryonic Skin

Macrophages are highly responsive to the environmental cues and are the primary responders to tissue stress and damage. While much is known about the role of macrophages during inflammatory disease progression; the initial series of events that set up the inflammation remains less understood. In this study, we use next generation sequencing (NGS) of embryonic skin macrophages and the niche cells - skin epithelia and stroma in the epidermis specific knockout of integrin beta 1 (Itgβ1) model to uncover specific roles of each cell type and identify how these cell types communicate to initiate the sterile inflammatory response. We demonstrate that while the embryonic skin fibroblasts in the Itgβ1 knockout skin are relatively inactive, the keratinocytes and macrophages are the critical responders to the sterile inflammatory cues. The epidermis expresses damage associated molecular patterns (DAMPs), stress response genes, pro-inflammatory cytokines, and chemokines that aid in eliciting the inflammatory response. The macrophages, in-turn, respond by acquiring enhanced M2-like characteristics expressing ECM remodeling and matrisome signatures that exacerbate the basement membrane disruption. Depletion of macrophages by blocking the CSF1 receptor (CSF1R) results in improved basement membrane integrity and reduced ECM remodeling activity in the KO skin. Further, blocking the skin inflammation with celecoxib reveals that the acquired fate of macrophages in the KO skin is dependent on its interaction with the epidermal compartment through COX2 dependent cytokine production. Taken together, our study highlights a critical crosstalk between the epithelia and the dermal macrophages that shapes macrophage fate and initiates sterile inflammation in the skin. The insights gained from our study can be extrapolated to other inflammatory disorders to understand the early events that set up the disease.
Joint Image Registration and Blur Kernel Learning for Pansharpening

Image registration and the estimation of spatial and spectral blur kernels are essential steps before fusing panchromatic image (PAN) and multispectral image (MSI). Usually, these basic steps are performed separately, which will lead to error accumulation and ultimately affect the fusion performance. In this paper, we propose a novel deep learning (DL) based framework which can jointly register images and learn the blur kernels of original PAN and MSI. Specifically, we first construct a convolutional neural network (CNN) to learn the offsets between PAN and MSI, and the offsets are utilized to align the two images. Then, we analyze the relationship between the registered PAN and MSI, and design a tiny network for blur kernel learning. After solving the gradient derivation problems, we can combine the two networks and train them end-to-end. Experimental results on GF-2 satellite images demonstrate that the proposed method can significantly improve the fusion performance of some popular pansharpening methods.
Using The GAF Transform and MODIS Time-Series to Perform Landcover Classification and Change Detection

The Gramien Angular Field (GMF) transform encodes time-series into images so that image based classification approaches can be exploited to perform time-series classification. Settlement expansion is one of the most pervasive forms of landcover change in sub-saharan Africa. In this paper, we show that the GAF transform outperforms the conventional techniques we compared against when it is employed in conjunction with Moderate Resolution Imaging Spectroradiometer (MODIS) time-series to detect settlement expansion in the Gauteng province of South Africa.
Inter-Orbit Change Detection for High-Resolution SAR Imagery Using Conditional Siamese Network

This paper proposes a method of inter-orbit change detection for high-resolution synthetic aperture radar (SAR) imagery using a conditional Siamese network. The proposed method introduces a sub-network with a condition, which is satellite orbit information, into the multitask Siamese network (MS-net). To introduce the conditions effectively, the weights sharing in one fully-connected layer after connecting the subnetwork is canceled. These tricks enable the proposed network to learn the absorption of layover effects depending on the orbit, which improves change detection performance. Experiments were conducted for detecting car changes in a parking lot by using Asnaro-2 images captured from five different orbits. Compared with the conventional MS-net, the proposed model improves AUC-ROC by 0.015 on average and is more robust to input orbit combinations.
Bayesian Channel Estimation in Multi-User Massive MIMO With Extremely Large Antenna Array

We investigate wideband uplink channel estimation for a multi-user (MU) multiple-input single-output (MISO) OFDM system, in which the base station (BS) is equipped with an extremely large antenna array (ELAA). The existing compressive sensing massive multiple-input multiple-output (MIMO) channel estimation approach with a traditional sparsity promoting prior model becomes invalid in the ELAA scenario due to the spatial non-stationary effects caused by the spherical wavefront and visibility region (VR) issue. We therefore propose a new structured prior with the Hidden Markov Model (HMM) to promote the structured sparsity of the spatial non-stationary ELAA channel. Based on this, a Bayesian inference problem on the posterior of the ELAA channel coefficients is formulated. In addition, we propose the turbo orthogonal approximate message passing (Turbo-OAMP) algorithm to achieve a low-complexity channel estimation. Comprehensive simulations verify that the proposed algorithm has supreme performance under spatial non-stationary ELAA channels compared to various state-of-the-art baselines.
Adaptive Visual Information Gathering for Autonomous Exploration of Underwater Environments

This work presents the development and field testing of a novel adaptive visual information gathering (AVIG) framework for autonomous exploration of benthic environments using AUVs. The objective is to adapt dynamically the robot exploration using the visual information gathered online. This framework is based on a novel decision-time adaptive replanning (DAR) behavior that works together with a sparse Gaussian process (SGP) for environmental modeling and a Convolutional Neural Network (CNN) for semantic image segmentation. The framework is executed in mission time. The SGP uses semantic data obtained from stereo images to probabilistically model the spatial distribution of certain species of seagrass that colonize the sea bottom forming widespread meadows. The uncertainty of the probabilistic model provides a measure of sampling informativeness to the DAR behavior. The DAR behavior has been designed to execute successive informative paths, without stopping, considering the newest information obtained from the SGP. We solve the information path planning (IPP) problem by means of a novel depth-first (DF) version of the Monte Carlo tree search (MCTS). The DF-MCTS method has been designed to explore the state-space in a depth-first fashion, provide solution paths of a given length in an anytime manner, and reward smooth paths for field realization with non-holonomic robots. The complete framework has been integrated in a ROS environment as a high level layer of the AUV software architecture. A set of simulations and field testing show the effectiveness of the framework to gather data in P. oceanica environments.
Sub-Pixel Width Road Network Extraction Using Sentinel-2 Imagery

Nowadays, road maps play a key role in our society. Therefore, keeping those maps up-to-date is highly important. The extraction of road networks from satellite imagery is a complex problem, not only because of occlusions, shadows produced by non-road objects, but also due to the limited spatial resolution of the imagery used. The feasibility to detect a road depends on its width, which can reach sub-pixel size in some satellite products. In the last decade, many attempts have been carried out to automatize this labour. However, the vast majority of methods rely on aerial imagery, whose costs are not yet affordable for maintaining up-to-date maps. This work demonstrates that it is also possible to accurately detect roads using freely available Sentinel-2 imagery, regardless of their width. For that purpose, a new deep learning architecture which combines semantic segmentation and super-resolution techniques is proposed. As a result, fine-grained road network maps at 2.5 m are generated from 10 m imagery taken as input. To evaluate this proposal a data-set composed of 20 cities spread across the Spanish territory is used.
The Covid-19 Detection with Contactless Method based on Deep Learning

The spread of corona virus diseases among medical workers is a big problem in this pandemic corona virus. The are many medical workers suffer and some of them died. The infected medical workers of corona virus are caused through directed or closed contacts between infected patients and medical workers. The closed and directed contact in medical services take place especially in diagnostic process. In order to handle this problem, the proposed method prevents the closed or directed contacts of medical workers to the suspected patients. The method uses RMI images or chest X-ray images data to predict the infected suspects. The proposed method is detecting the infected lung X-Ray image through Deep Learning model. It uses pre-trained model of GoogleNet, with modification. The Confusion Matrix and ROC curve are used to measure accuracy the predicted. They show that proposed method has high accuracy. Finally, the proposed method is able to replace closed or directed contact diagnostic done by medical workers to Covid-19 suspects. It has ability to handle the spread of Covid-19 among medical workers.