Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffusion-weighted MRI of the spinal cord in cervical spondylotic myelopathy after instrumented fusion

Introduction This study investigated tissue diffusion properties within the spinal cord of individuals treated for cervical spondylotic myelopathy (CSM) using post-decompression stabilization hardware. While previous research has indicated the potential of diffusion-weighted MRI (DW-MRI) markers of CSM, the metallic implants often used to stabilize the decompressed spine hamper conventional DW-MRI. Methods Utilizing recent developments in DW-MRI metal-artifact suppression technologies, imaging data was acquired from 38 CSM study participants who had undergone instrumented fusion, as well as asymptomatic (non-instrumented) control participants. Apparent diffusion coefficients were determined in axial slice sections and split into four categories: a) instrumented levels, b) non-instrumented CSM levels, c) adjacent-segment (to instrumentation) CSM levels, and d) non-instrumented control levels. Multi-linear regression models accounting for age, sex, and body mass index were used to investigate ADC measures within each category. Furthermore, the cord diffusivity within CSM subjects was correlated with symptom scores and the duration since fusion procedures. Results ADC measures of the spinal cord in CSM subjects were globally reduced relative to control subjects (p = 0.005). In addition, instrumented levels within the CSM subjects showed reduced diffusivity relative to controls (p = 0.003), while ADC within non-instrumented CSM levels did not statistically deviate from control levels (p = 0.107). Discussion Multi-spectral DW-MRI technology can be effectively employed to evaluate cord diffusivity near fusion hardware in subjects who have undergone surgery for CSM. Leveraging this advanced technology, this study had identified significant reductions in cord diffusivity, relative to control subjects, in CSM patients treated with conventional metallic fusion instrumentation.
Deciphering the single-cell transcriptome network in keloids with intra-lesional injection of triamcinolone acetonide combined with 5-fluorouracil

Objectives Keloid is a highly aggressive fibrotic disease resulting from excessive extracellular matrix deposition after dermal injury. Intra-lesional injection of triamcinolone acetonide (TAC) in combination with 5-fluorouracil (5-FU) is a commonly used pharmacological regimen and long-term repeated injections can achieve sustained inhibition of keloid proliferation. However, the molecular mechanisms underlying the inhibitory effect on keloids remain insufficiently investigated. Methods and materials This study performed single-cell RNA sequencing analysis of keloids treated with TAC+5-FU injections, keloids, and skins to explore patterns of gene expression regulation and cellular reprogramming. Results The results revealed that TAC+5-FU interrupted the differentiation trajectory of fibroblasts toward pro-fibrotic subtypes and induced keloid atrophy possibly by inhibiting the FGF signaling pathway in intercellular communication. It also stimulated partial fibroblasts to develop the potential for self-replication and multidirectional differentiation, which may be a possible cellular source of keloid recurrence. T cell dynamics demonstrated elevated expression of secretory globulin family members, which may be possible immunotherapeutic targets. Schwann cell populations achieved functional changes by increasing the proportion of apoptotic or senescence-associated cell populations and reducing cell clusters that promote epidermal development and fibroblast proliferation. Conclusions Our findings elucidated the molecular and cellular reprogramming of keloids by intra-lesional injection of TAC+5-FU, which will provide new insights to understand the mechanism of action and therapeutic targets.
Establishment of a triplex TaqMan quantitative real-time PCR assay for simultaneous detection of Cymbidium mosaic virus, Odontoglossum ringspot virus and Cymbidium ringspot virus

Orchids are significant ornamental plants whose viral infection results in substantial economic damage. Cymbidium mosaic virus (CymMV), Odontoglossum ringspot virus (ORSV), and Cymbidium ringspot virus (CymRSV) represent three important and prevalent orchid viruses. The detection system proposed in this study uses a triplex TaqMan quantitative real-time PCR assay to identify CymMV, ORSV, and CymRSV in a simultaneous manner. We designed specific primers and probes for CymMV, ORSV, and CymRSV, with amplified sequences of 156 bp, 148 bp, and 145 bp, respectively. The minimum detection limit of the triplex qRT-PCR assay for CymMV and CymRSV was 1 copy/assay, and the minimum detection limit was 10 copies/assay for ORSV. The minimum stable detection limits for CymMV, ORSV, and CymRSV were 10, 102, and 102 copies/assay, respectively. Therefore, this system exhibited higher sensitivity (approximately 10 to 104-fold) than RT–PCR. The intra-and interassay CVs of Cq values are less than 0.55 and 0.95%, respectively, indicating that the triplex assay is highly reliable and accurate. In addition, 66 samples from five different orchid genera were analyzed using the established assay and gene chip. The detection results demonstrated that the triplex probe qRT–PCR demonstrated higher sensitivity than the gene chip, indicating that the triplex real-time PCR assay could be used for the detection of field samples. Our findings suggest that the triplex real-time RT–PCR detection system represents a rapid, simple, and accurate tool for detecting CymMV, ORSV, and CymRSV on orchids.
Monitoring and mapping vegetation greenery in residential communities using street view images and a Normalized Vegetation Greenery Index: a case study in Beijing, China

Vegetation greenery is essential for the sensory and psychological wellbeing of residents in residential communities. To enhance the quality of regulations and policies to improve people’s living environments, it is crucial to effectively identify and monitor vegetation greenery from the perspective of the residents using effective images and methods. In this study, Baidu street view (BSV) images and a Normalized Vegetation Greenery Index (NVGI) based method were examined to distinguish vegetation greenery in residential communities of Beijing, China. The magnitude of the vegetation was quantified and graded, and spatial analysis techniques were employed to investigate the spatial characteristics of vegetation greenery. The results demonstrated that (1) the identified vegetation greenery using the proposed NVGI-based method was closely correlated with those of the reference classification (r = 0.993, p = 0.000), surpassing the comparison results from the SVM method, a conventional remote sensing classification means; (2) the vegetation greenery was distributed unevenly in residential communities and can be categorized into four grades, 63.79% of the sampling sites were found with relatively low (Grade II) and moderate (Grade III) vegetation greenery distribution, most of the districts in the study area contained zero-value green view index sites; and (3) there was significant spatial heterogeneity observed in the study area, with low-value clustering (cold spots) predominantly located in the central region and high-value clustering (hot spots) primarily concentrated in the peripheral zone. The findings of this study can be applied in other cities and countries that have street view images available to investigate greenery patterns within residential areas, which can help improve the planning and managing efforts in urban communities.
Profile and potential role of novel metabolite biomarkers, especially indoleacrylic acid, in pathogenesis of neuromyelitis optica spectrum disorders

Background: Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune central nervous system (CNS) inflammatory and demyelinating disorder that can lead to serious disability and mortality. Humoral fluid biomarkers with specific, convenient, and efficient profiles that could characterize and monitor disease activity or severity are very useful. We aimed to develop a sensitive and high-throughput liquid chromatography–tandem mass spectrometry (LC-MS)/MS-based analytical method for novel biomarkers finding in NMOSD patients and verified its function tentatively. Methods: Serum samples were collected from 47 NMOSD patients, 18 patients with other neurological disorders (ONDs), and 35 healthy controls (HC). Cerebrospinal fluid (CSF) samples were collected from 18 NMOSD and 17 OND patients. Three aromatic amino acids (phenylalanine, tyrosine, and tryptophan) and nine important metabolites that included phenylacetylglutamine (PAGln), indoleacrylic acid (IA), 3-indole acetic acid (IAA), 5-hydroxyindoleacetic acid (HIAA), hippuric acid (HA), I-3-carboxylic acid (I-3-CA), kynurenine (KYN), kynurenic acid (KYNA), and quinine (QUIN) were analyzed by using the liquid chromatography–tandem mass spectrometry (LC-MS/MS)-based method. The profile of IA was further analyzed, and its function was verified in an astrocyte injury model stimulated by NMO-IgG, which represents important events in NMOSD pathogenesis. Results: In the serum, tyrosine and some of the tryptophan metabolites IA and I-3-CA decreased, and HIAA increased significantly in NMOSD patients. The CSF levels of phenylalanine and tyrosine showed a significant increase exactly during the relapse stage, and IA in the CSF was also increased markedly during the relapse and remission phases. All conversion ratios had similar profiles with their level fluctuations. In addition, the serum IA levels negatively correlated with glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) levels in the serum of NMOSD patients were measured by using ultra-sensitive single-molecule arrays (Simoa). IA showed an anti-inflammatory effect in an in vitro astrocyte injury model. Conclusion: Our data suggest that essential aromatic amino acid tryptophan metabolites IA in the serum or CSF may serve as a novel promising biomarker to monitor and predict the activity and severity of NMOSD disease. Supplying or enhancing IA function can promote anti-inflammatory responses and may have therapeutic benefits.
Editorial: The mechanism of immune cells in the development of inflammatory bowel disease (IBD) and colitis-associated colorectal cancer (CAC)

COPYRIGHT © 2023 Russo, Yin, Sheng, Mao and Amedei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Editorial PUBLISHED 19 May 2023 DOI 10.3389/fimmu.2023. 1218958
Emotional sounds in space: asymmetrical representation within early-stage auditory areas

Evidence from behavioral studies suggests that the spatial origin of sounds may influence the perception of emotional valence. Using 7T fMRI we have investigated the impact of the categories of sound (vocalizations; non-vocalizations), emotional valence (positive, neutral, negative) and spatial origin (left, center, right) on the encoding in early-stage auditory areas and in the voice area. The combination of these different characteristics resulted in a total of 18 conditions (2 categories x 3 valences x 3 lateralizations), which were presented in a pseudo-randomized order in blocks of 11 different sounds (of the same condition) in 12 distinct runs of 6 min. In addition, two localizers, i.e., tonotopy mapping; human vocalizations, were used to define regions of interest. A three-way repeated measure ANOVA on the BOLD responses revealed bilateral significant effects and interactions in the primary auditory cortex, the lateral early-stage auditory areas, and the voice area. Positive vocalizations presented on the left side yielded greater activity in the ipsilateral and contralateral primary auditory cortex than did neutral or negative vocalizations or any other stimuli at any of the three positions. Right, but not left area L3 responded more strongly to (i) positive vocalizations presented ipsi- or contralaterally than to neutral or negative vocalizations presented at the same positions; and (ii) to neutral than positive or negative non-vocalizations presented contralaterally. Furthermore, comparison with a previous study indicates that spatial cues may render emotional valence more salient within the early-stage auditory areas.
Net cleaning impacts Atlantic salmon gill health through microbiome dysbiosis

Introduction Net biofouling has a significant impact for the global salmon industry in the seawater grow-out stage in terms of its management. Current mitigation strategies occur primarily through the regular removal of biofouling using in situ cleaning. While in situ net cleaning is effective there is uncertainty as to whether the equipment or dispersed material has an impact upon the fish in the cages. Through direct contact with the environment, the significant surface area of the gill including its microbiome is directly exposed to the acute environmental changes generated by net cleaning. This study aimed to provide a detailed understanding of the impact of in situ net cleaning on Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss) gill health. Methods Three field trials were conducted on commercial fish farms in western Norway. Fouling organisms on net pens and flushed particles during in situ cleaning were identified and screened for major fish pathogens. Hydrographic profile measurements were performed to measure the impact on water quality. Gill samples were examined for histopathological changes, immune gene expression, and the prevalence of major pathogens. 16S rRNA amplicon sequencing was employed to explore the impact of net cleaning on gill microbiome. Results and discussion Data obtained from these trials identified a diversity of fouling species including hydroids, algae, skeleton shrimps, and filter feeders on net pens, a direct impact on measured water quality indicators, a moderate change in gill inflammatory and antigen presentation activity at the level of mRNA, and a large significant change in gill microbiome. Observed changes in gill microbial community involved a decrease in bacterial richness coupled to an increase in identified bacterial genera related to negative health consequences. Parallel analyses for pathogens load in biofouling organisms and flushed particles highlighted the presence of several fish bacteria and parasites. However, minor changes were detected in salmon gill pathogen diversity and loading. Our results suggest that biofouling organisms may act as transient reservoirs for some fish pathogens but not viruses and that gill microbial dysbiosis could be related to the host stress response during and post net cleaning.
Chitosan and chito-oligosaccharide: a versatile biopolymer with endless grafting possibilities for multifarious applications

Chito-oligosaccharides (COS), derived from chitosan (CH), are attracting increasing attention as drug delivery carriers due to their biocompatibility, biodegradability, and mucoadhesive properties. Grafting, the process of chemically modifying CH/COS by adding side chains, has been used to improve their drug delivery performance by enhancing their stability, targeted delivery, and controlled release. In this review, we aim to provide an in-depth study on the recent advances in the grafting of CH/COS for multifarious applications. Moreover, the various strategies and techniques used for grafting, including chemical modification, enzymatic modification, and physical modification, are elaborated. The properties of grafted CH/COS, such as stability, solubility, and biocompatibility, were reported. Additionally, the review detailed the various applications of grafted CH/COS in drug delivery, including the delivery of small drug molecule, proteins, and RNA interference therapeutics. Furthermore, the effectiveness of grafted CH/COS in improving the pharmacokinetics and pharmacodynamics of drugs was included. Finally, the challenges and limitations associated with the use of grafted CH/COS for drug delivery and outline directions for future research are addressed. The insights provided in this review will be valuable for researchers and drug development professionals interested in the application of grafted CH/COS for multifarious applications.
Gliosis, misfolded protein aggregation, and neuronal loss in a guinea pig model of pulmonary tuberculosis

Tuberculosis, caused by Mycobacterium tuberculosis infection, is an ongoing epidemic with an estimated ten million active cases of the disease worldwide. Pulmonary tuberculosis is associated with cognitive and memory deficits, and patients with this disease are at an increased risk for Parkinson’s disease and dementia. Although epidemiological data correlates neurological effects with peripheral disease, the pathology in the central nervous system is unknown. In an established guinea pig model of low-dose, aerosolized Mycobacterium tuberculosis infection, we see behavior changes and memory loss in infected animals. We correlate these findings with pathological changes within brain regions related to motor, cognition, and sensation across disease progression. This includes microglial and astrocytic proliferation and reactivity. These cellular changes are followed by the aggregation of neurotoxic amyloid β and phosphorylated tau and, ultimately, neuronal degeneration in the hippocampus. Through these data, we have obtained a greater understanding of the neuropathological effects of a peripheral disease that affects millions of persons worldwide.
Generalization bounds for Kernel Canonical Correlation Analysis

We study the problem of multiview representation learning using kernel canonical correlation analysis (KCCA) and establish non-asymptotic bounds on generalization error for regularized empirical risk minimization. In particular, we give ﬁne-grained high-probability bounds on generalization error ranging from O ( n − 1 / 6 ) to O ( n − 1 / 5 ) depending on underlying distributional properties, where n is the number of data samples. For the special case of ﬁnite-dimensional Hilbert spaces (such as linear CCA), our rates improve, ranging from O ( n − 1 / 2 ) to O ( n − 1 ). Finally, our results generalize to the problem of functional canonical correlation analysis over abstract Hilbert spaces.
Implicit Ensemble Training for Efficient and Robust Multiagent Reinforcement Learning

An important issue in competitive multiagent scenarios is the distribution mismatch between training and testing caused by variations in other agents’ policies. As a result, policies optimized during training are typically sub-optimal (possibly very poor) in testing. Ensemble training is an effective approach for learning robust policies that avoid significant performance degradation when competing against previously unseen opponents. A large ensemble can improve diversity during the training, which leads to more robust learning. However, the computation and memory requirements increase linearly with respect to the ensemble size, which is not scalable as the ensemble size required for learning robust policy can be quite large (Czarnecki et al., 2020). This paper proposes a novel parameterization of a policy ensemble based on a deep latent variable model with a multi-task network architecture, which represents an ensemble of policies implicitly within a single network. Our implicit ensemble training (IET) approach strikes a better trade-off between ensemble diversity and scalability compared to standard ensemble training. We demonstrate in several competitive multiagent scenarios in the board game and robotic domains that our new approach improves robustness against unseen adversarial opponents while achieving higher sample-efficiency and less computation.
Clustering using Approximate Nearest Neighbour Oracles

We study the problem of clustering data points in a streaming setting when one has access to the geometry of the space only via approximate nearest neighbour (ANN) oracles. In this setting, we present algorithms for streaming O (1)-approximate k -median clustering and its (streaming) coreset construction. In certain domains of interest, such as spaces with constant expansion, our algorithms improve upon the best-known runtime of both these problems. Furthermore, our results extend to cost functions satisfying the approximate triangle inequality, which subsumes k -means clustering and M -estimators. Finally, we run experiments on Census1990 dataset wherein the results empirically support our theory.
Two Level Load Balancing Strategy in Cloud

Load balancing is the efficient distribution of incoming client requests in a server pool or virtual machines. For example, it allows high-audience website servers not to be overloaded. One of the challenging scheduling problems in Cloud load balancing is the choice of Virtual Machines (VM) when assigning tasks or migrating VM. This paper proposes a load balancing strategy for distributed cloud data center to be applied in two levels control: Physicals Machines and Clusters. Load balancing is done by two managers; they ensure exchange information and decide afterwards the level concerned with load balancing. Measurement of load is based on load information, including CPU utilization and memory utilization. Virtual Machines are allowed to be migrated between different federations to distribute loads while the communication costs are also incurred. Therefore, the objectives of this strategy are twofold: reducing the load of the overloaded physicals machines and decreasing the communication costs among different federations. The proposed method attempts not only giving a good Cloud balancing but also ensures reducing response time and communication cost and enhancing performance of the whole system.
Classroom Teaching Feedback System Based on Facial Expression Recognition

: During classes, students’ mastery of knowledge points is reflected in their facial expressions. Teachers usually judge their degrees of understanding through the expressions and then adjust the teaching schedule. However, at least 30 students are in a class, and it is impossible for the teachers to take care of each student at all times in a classroom. As a result, teachers can not fully understand each student’s mastery of knowledge points, thus affecting the quality of teaching. To solve this problem, this study introduces a classroom teaching feedback system based on facial expression recognition, which can analyze the facial expression of each student in the classroom and demonstrate their mastery of knowledge points from the expressions. On that basis, it can help teachers understand the real-time learning effect of each student in the classroom, thereby improving teaching quality.