Miracle-dz/UniFAR-FTUs huggingface dataset:
Paeonol Ameliorates Chronic Itch and Spinal Astrocytic Activation via CXCR3 in an Experimental Dry Skin Model in Mice

Paeonol is a bioactive phenol presents mainly in Paeonia suffruticosa Andr. (Paeoniaceae), Paeonia lactiflora Pall. , and Dioscorea japonica Thunb. (Dioscoreaceae), harboring various pharmacological activities including anti-inflammatory, antioxidant, immune regulatory activity and reverse chemoresistance. Recent reports revealed paeonol exhibited good effects on chronic dermatitis, such as atopic dermatitis (AD) and psoriasis. However, whether paeonol is effective for dry skin disease and its mechanism of action still remain unclear. In this study, we analysed the effects of paeonol on a mouse model of dry skin treated with acetone-ether-water (AEW), which showed impressive activities in reducing scratching behavior and skin inflammation. To elucidate the underlying molecular targets for the anti-pruritic ability of paeonol, we screened the expression of possible chemokine pathways in the spinal cord. The expression of CXCR3 was significantly alleviated by paeonol, which increased greatly in the spinal neurons of AEW mice. In addition, treatment of paeonol significantly inhibited AEW-induced expression of astrocyte activity-dependent genes including Tlr4, Lcn2 and Hspb1 et al. The inhibitory effects of paeonol on scratching behavior and astrocytic activation in the spinal cord induced by AEW were abolished when CXCR3 was antagonized or genetically ablated. Taken together, our results indicated that paeonol can ameliorate AEW-induced inflammatory response and itching behavior, and reduce the expression of spinal astrocyte activity-dependent genes induced by AEW, which are driven by CXCR3.
Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma

Background: The alterations in metabolic profile of tumors have been identified as one of the prognostic hallmarks of cancers, including osteosarcoma. These alterations are majorly controlled by groups of metabolically active genes. However, the regulation of metabolic gene signatures in tumor microenvironment of osteosarcoma has not been well explained. Objectives: Thus, we investigated the sets of previously published metabolic genes in osteosarcoma patients and normal samples. Methods: We applied computational techniques to identify metabolic genes involved in the immune function of tumor microenvironment (TME) and survival and prognosis of the osteosarcoma patients. Potential candidate gene PAICS (phosphoribosyl aminoimidazole carboxylase, phosphoribosyl aminoimidazole succino carboxamide synthetase) was chosen for further studies in osteosarcoma cell lines for its role in cell proliferation, migration and apoptosis. Results: Our analyses identified a list of metabolic genes differentially expressed in osteosarcoma tissues. Next, we scrutinized the list of genes correlated with survival and immune cells, followed by clustering osteosarcoma patients into three categories: C1, C2, and C3. These analyses led us to choose PAICS as potential candidate gene as its expression showed association with poor survival and negative correlation with the immune cells. Furthermore, we established that loss of PAICS induced apoptosis and inhibited proliferation, migration, and wound healing in HOS and MG-63 cell lines. Finally, the results were supported by constructing and validating a prediction model for prognosis of the osteosarcoma patients. Conclusion: Here, we conclude that metabolic genes specifically PAICS play an integral role in the immune cell infiltration in osteosarcoma TME, as well as cancer development and metastasis.
Multi-Source and Multi-Representation Adaptation for Cross-Domain Electroencephalography Emotion Recognition

Due to the non-invasiveness and high precision of electroencephalography (EEG), the combination of EEG and artificial intelligence (AI) is often used for emotion recognition. However, the internal differences in EEG data have become an obstacle to classification accuracy. To solve this problem, considering labeled data from similar nature but different domains, domain adaptation usually provides an attractive option. Most of the existing researches aggregate the EEG data from different subjects and sessions as a source domain, which ignores the assumption that the source has a certain marginal distribution. Moreover, existing methods often only align the representation distributions extracted from a single structure, and may only contain partial information. Therefore, we propose the multi-source and multi-representation adaptation (MSMRA) for cross-domain EEG emotion recognition, which divides the EEG data from different subjects and sessions into multiple domains and aligns the distribution of multiple representations extracted from a hybrid structure. Two datasets, i.e., SEED and SEED IV, are used to validate the proposed method in cross-session and cross-subject transfer scenarios, experimental results demonstrate the superior performance of our model to state-of-the-art models in most settings.
Virtual Reality Reduces Pediatric Anxiety During Food Allergy Clinical Trials: A Pilot Randomized, Pragmatic Study

Phlebotomy procedures required in food allergy (FA) diagnosis and clinical trials often induce fear and anxiety for pediatric patients. The primary aim of this study was to determine whether virtual reality (VR) applications were effective in reducing anxiety for pediatric FA patients undergoing phlebotomy during FA clinical trials. Secondary aims assessed fear, pain, procedural compliance, and adverse events. Participants undergoing phlebotomy were enrolled and randomized to a VR group or standard of care (SOC) group for this prospective pilot randomized, pragmatic study. Participants in the VR group played interactive applications on a customized Samsung Gear VR headset and those in the SOC group received the standard of care. Participants' anxiety, fear, and pain were assessed with the Children's Anxiety Meter, Children's Fear Scale, and FACES pain scale pre, during, and post phlebotomy procedure. Compliance was assessed using the modified Induction Compliance Checklist during the procedure and compared between two groups. Forty-nine participants were randomized to VR (n = 26) and SOC (n = 23) groups. Although both the VR and SOC groups experienced a decrease in anxiety and fear from pre- to post-procedure, those in the VR group experienced less anxiety and fear during the procedure than SOC participants. Similarly, both groups experienced an increase in pain from pre- to post-procedure; however, the VR group reported less pain during the procedure than SOC. Fewer symptoms of procedural non-compliance were reported in the VR group. Interactive VR applications may be an effective tool for reducing fear, anxiety, and pain during phlebotomy for FA clinical trials.
The Antibacterial Activity of Erythrocytes From Goose (Anser domesticus) Can Be Associated With Phagocytosis and Respiratory Burst Generation

In the lumen of blood vessels, there are large numbers of erythrocytes, which are approximately 95% of the total blood cells. Although the function of erythrocytes is to transport oxygen in the organism, recent studies have shown that mammalian and teleost erythrocytes are involved in the immune response against bacterial infections. However, the immune mechanisms used by avian erythrocytes are not yet clear. Here, we demonstrated that erythrocytes from goose have the ability to phagocytose as well as conduct antimicrobial activity. Firstly, we revealed the phagocytosis or adhesion activity of goose erythrocytes for latex beads 0.1-1.0 μm in diameter by fluorescence microscopy, and scanning and transmission electron microscopy. The low cytometry results also proved that goose erythrocytes had a wide range of phagocytic or adhesion activity for different bacteria. Followed, the low cytometry analysis data further explored that the goose erythrocytes contain the ability to produce reactive oxygen species (ROS) and inducible nitric oxide synthase (iNOS) in response to bacterial stimulation, and also up-regulated the expression of NOX family includes NOX1 and NOX5. Finally, we also found that goose erythrocytes showed a powerful antibacterial activity against all the three bacteria, meanwhile the stimulation of three kinds of bacteria up-regulated the expression of inflammatory factors, and increased the production of antioxidant enzymes to protect the cells from oxidative damage. Herein, our results demonstrate that goose Erythrocytes possess a certain phagocytic capacity and antioxidant system, and that the antimicrobial activity of erythrocytes can occurred through the production of unique respiratory burst against foreign pathogenic bacteria, which provides new clues to the interaction between bacteria and avian erythrocytes.
RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors. Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups. Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression. Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.
Screen Time, Age and Sunshine Duration Rather Than Outdoor Activity Time Are Related to Nutritional Vitamin D Status in Children With ASD

Objective: This study aimed to investigate the possible association among vitamin D, screen time and other factors that might affect the concentration of vitamin D in children with autism spectrum disorder (ASD). Methods: In total, 306 children with ASD were recruited, and data, including their age, sex, height, weight, screen time, time of outdoor activity, ASD symptoms [including Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS) and Autism Diagnostic Observation Schedule–Second Edition (ADOS-2)] and vitamin D concentrations, were collected. A multiple linear regression model was used to analyze the factors related to the vitamin D concentration. Results: A multiple linear regression analysis showed that screen time (β = −0.122, P = 0.032), age (β = −0.233, P < 0.001), and blood collection month (reflecting sunshine duration) (β = 0.177, P = 0.004) were statistically significant. The vitamin D concentration in the children with ASD was negatively correlated with screen time and age and positively correlated with sunshine duration. Conclusion: The vitamin D levels in children with ASD are related to electronic screen time, age and sunshine duration. Since age and season are uncontrollable, identifying the length of screen time in children with ASD could provide a basis for the clinical management of their vitamin D nutritional status.
Size Related Differences in Organic and Mineral Components of Molluscan Shell

Abstract: The organic component of the molluscan shell allows for orderly biomineralization and ensures structural integrity that is crucial to survival. This organic contribution to the shell typically composes 2-5% of the total adult shell by weight. Because macro- and microstructure of the shell is known to vary with ontogeny and across taxa, we examined if the organic to mineral ratio components in shell also varied with growth across taxa. To assess intraspecific differences in the organic to mineral ratio of the shell during growth, we examined ratios in three marine [Crepidula fornicata (Linnaeus, 1758), Littorina littorea (Linnaeus, 1758), and Littorina saxatilis (Olivi, 1792)] and two freshwater [Corbicula fluminea (Müller, 1774) and Bellamya chinensis (Gray, 1834)] mollusks across size ranges. In the marine gastropods, the average organic component by weight of the small size class was significantly larger than the average organic proportions of the medium and large size classes. The smallest size class of L. saxatilis had an average shell organic proportion of 11.12%, while the smallest size classes of C. fornicata (3.53%) and L. littorea (2.60%) had percentages below 5%. The smallest size class of C. fluminea had a greater average shell organic proportion than the largest size class (6.19% vs 2.68% organics). Adult specimens of B. chinensis had an average shell organic proportion of 3.93%, while in utero shelled juveniles had an average of 10.05%. In both freshwater and marine species, the smallest size class had a greater organic proportion. As the organic matrix is energetically more expensive than the calcified shell portion, we hypothesize that energy expended in these smaller (usually pre-reproductive maturity) stages of growth allows for a more rapid production of shell and that this “expense” is a valuable trade-off for the protection the shell offers young mollusks.
Large-Scale Structure-Based Screening of Potential T Cell Cross-Reactivities Involving Peptide-Targets From BCG Vaccine and SARS-CoV-2

Although not being the first viral pandemic to affect humankind, we are now for the first time faced with a pandemic caused by a coronavirus. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been responsible for the COVID-19 pandemic, which caused more than 4.5 million deaths worldwide. Despite unprecedented efforts, with vaccines being developed in a record time, SARS-CoV-2 continues to spread worldwide with new variants arising in different countries. Such persistent spread is in part enabled by public resistance to vaccination in some countries, and limited access to vaccines in other countries. The limited vaccination coverage, the continued risk for resistant variants, and the existence of natural reservoirs for coronaviruses, highlight the importance of developing additional therapeutic strategies against SARS-CoV-2 and other coronaviruses. At the beginning of the pandemic it was suggested that countries with Bacillus Calmette-Guérin (BCG) vaccination programs could be associated with a reduced number and/or severity of COVID-19 cases. Preliminary studies have provided evidence for this relationship and further investigation is being conducted in ongoing clinical trials. The protection against SARS-CoV-2 induced by BCG vaccination may be mediated by cross-reactive T cell lymphocytes, which recognize peptides displayed by class I Human Leukocyte Antigens (HLA-I) on the surface of infected cells. In order to identify potential targets of T cell cross-reactivity, we implemented an in silico strategy combining sequence-based and structure-based methods to screen over 13,5 million possible cross-reactive peptide pairs from BCG and SARS-CoV-2. Our study produced (i) a list of immunogenic BCG-derived peptides that may prime T cell cross-reactivity against SARS-CoV-2, (ii) a large dataset of modeled peptide-HLA structures for the screened targets, and (iii) new computational methods for structure-based screenings that can be used by others in future studies. Our study expands the list of BCG peptides potentially involved in T cell cross-reactivity with SARS-CoV-2-derived peptides, and identifies multiple high-density “neighborhoods” of cross-reactive peptides which could be driving heterologous immunity induced by BCG vaccination, therefore providing insights for future vaccine development efforts.
An Information Systems Artifact Approach to Evaluate e-Government Services in Rwanda

Technology is universal, but use contexts differ widely. Technology develops rapidly but social contexts are slow to change, hence digitalization efforts must go hand in hand with process innovation. This research aims at designing an improved process for evaluating e-government services in Rwanda, a least developed country pursuing ambitious digitalization. The research draws on the information systems artifact (ISA) model, which considers alignment between technology, information, and social context as the key to successful technology-based innovation, and all three aspects as artifacts, subject to design. We build a prototype technical artifact and suggest an information and a social one. The ambition is making the currently control-based summative evaluation process more cooperative and inclusive of both service providers and users. The new design uses feedback from users to allow service providers and monitoring organizations to conduct also formative evaluations in order to be able to better align service processes with user needs and behavior and thereby make them more attractive and more used. The prototype “Rwanda Online Service Evaluation” (ROSE) was tested through semi-structured interviews with senior managers in six Rwandan organizations, and through an online user survey (n= 60). The findings can guide decision makers and practitioners involved with e-government design and evaluation. As a contribution to research, the research shows the usefulness of the ISA model for communicating the design of technology aiming at comprehensive process change.
MEMS multi-sensor attitude determination system based on the EKF

The attitude information is one of the most important parameters in flight control systems.This paper describes an attitude measurement system based on a MEMS multi-sensor.The data from 3-axis MEMS accelerators and 3-axis MEMS gyros is used to determine the pitch angle and the roll angle of the air vehicle using an extended Kalman filter(EKF) equation deduced from the direction cosine matrix.An expert system was designed to estimate the vehicle motion to adjust the measurement noise matrix of the extended Kalman filter equation so that the system can be used in dynamic as well as static environments.The measured airspeed and altitude are also used to revise the pitch angle.The heading is determined from GPS information.Test results compare well with an imported commercial autopilot in a static test with less accurate results in a dynamic test.
DATA DRIVEN ANOMALY CONTROL DETECTION FOR RAILWAY PROPULSION CONTROL SYSTEMS

The popularity of railway transportation has been on the rise over the past decades, as it has been able to provide safe, reliable, and highly available service. The main challenge within this domain is to reduce the costs of preventive maintenance and improve operational eﬃciency. To tackle these challenges, one needs to investigate and provide new approaches to enable quick and timely data collection, transfer, and storage aiming at easier and faster analysis whenever needed. In this thesis, we aim at enabling the monitoring and analysis of collected signal data from a train propulsion system. The main idea is to monitor and analyze collected signal data gathered during the regular operation of the propulsion control unit or data recorded during the regular train tests in the real-time simulator. To do so, we have implemented a solution to enable train signal data collection and its storage into a . txt and . CSV ﬁle to be further analyzed in the edge node and in the future connected to the cloud for further analysis purposes. In our analysis, we focus on identifying signal anomalies and predicting potential failures using MathWorks tools. Two machine learning techniques, unsupervised and supervised learning, are implemented. Additionally, in this thesis, we have investigated ways of how data can be eﬃciently managed. We have also reviewed existing edge computing solutions and anomaly detection approaches using a survey as a suitable method to identify relevant works within the state of the art.
Rigid, Soft, Passive, and Active: a Hybrid Occupational Exoskeleton for bimanual multijoint assistance

Physically demanding work is still common in the western countries, with large proportions of the workforce that are exposed for more than a quarter of their working time to tiring postures or repetitive tasks: the shoulder is one of the main body areas susceptible to work-related MSDs. Recent advancements in assistive technology have provided new instruments to promote safety and reduce workload. Colloquially referred as occupational exoskeletons (OE), these wearable devices are usually spring-loaded, and provide gravity support for overhead tasks. OEs for upper limbs are usually single-joint exoskeletons and assist shoulder flexion/extension; they do not provide support to distal joints such as the elbow. In the present work, starting from a commercially available exoskeleton, we propose an innovative concept of hybrid upper-limb OEs. We combined a spring-loaded shoulder exoskeleton with an active elbow exosuit to extend the capability of the OEs to provide gravitational support to both shoulder and elbow flexion-extension in strenuous manual tasks. The proposed device can reduce up to 32% of the biceps activity during the elbow flexion and up to 31% of the deltoids activity during the shoulder abduction. In-lab experimentation showed the potentials of such a hybrid approach in reducing the strain of the upper-limb muscles.
Exploiting Correlation in Stochastic Computing based Deep Neural Networks

Edge Artificial Intelligence or Edge Intelligence, is beginning to receive a tremendous amount of interest. Unfortunately, the incorporation of AI characteristics to edge computing devices presents the drawbacks of being power and area hungry for typical machine learning techniques such as Convolutional Neural Networks (CNN). In this work, we propose a new power-and-area-efficient architecture for implementing Artificial Neural Networks (ANNs) in hardware, based on the exploitation of correlation phenomenon in Stochastic Computing (SC) systems. The architecture proposed can solve the difficult implementation challenges that SC presents for CNN applications, such as the high resources used in binary-to-stochastic conversion, the inaccuracy produced by an undesired correlation between signals, and the stochastic maximum function implementation. Compared with traditional binary logic implementations, experimental results showed an improvement of 19.6x and 6.3x in terms of speed performance and energy efficiency for the FPGA implementation. For the first time, a fully-parallel CNN as LENET-5 is embedded and tested in a single FPGA, showing the benefits of using stochastic computing for embedded applications, in contrast to traditional binary logic implementations.