Miracle-dz/UniFAR-FTUs huggingface dataset:
Envisioning social drones in education

Education is one of the major application fields in social Human-Robot Interaction. Several forms of social robots have been explored to engage and assist students in the classroom environment, from full-bodied humanoid robots to tabletop robot companions, but flying robots have been left unexplored in this context. In this paper, we present seven online remote workshops conducted with 20 participants to investigate the application area of Education in the Human-Drone Interaction domain; particularly focusing on what roles a social drone could fulfill in a classroom, how it would interact with students, teachers and its environment, what it could look like, and what would specifically differ from other types of social robots used in education. In the workshops we used online collaboration tools, supported by a sketch artist, to help envision a social drone in a classroom. The results revealed several design implications for the roles and capabilities of a social drone, in addition to promising research directions for the development and design in the novel area of drones in education.
Antifungal action and induction of resistance by Bacillus sp. strain YYC 155 against Colletotrichum fructicola for control of anthracnose disease in Camellia oleifera

Anthracnose disease caused by Colletotrichum fructicola is a serious disease that can afflict Camellia oleifera. Biological control is a rapidly growing approach for the management of plant diseases. In this study, we investigated the bio-control efficiency and the defense responses of an endophytic Bacillus tequilensis strain YYC 155, which was isolated from the root nodules of the Crotalaria pallida against anthracnose disease, caused by C. fructicola in C. oleifera. B. tequilensis YYC 155 exhibited significant inhibitory activity against anthracnose disease, caused by C. fructicola in C. oleifera. YYC 155 can secrete extracellular hydrolases, such as chitinase and β-1, 3-glucanase, which produce lipopeptides that are antimicrobial and forms strong biofilms. In addition, in treatment with YYC 155, the cell membranes of C. fructicola were injured and the leakage of cell contents from the mycelia of the pathogen was increased. Spraying 1 × 107 cells mL–1 bacterial suspension of YYC 155 on C. oleifera leaves enhanced the activity of key enzymes in C. oleifera associated with the phenylpropanoid pathway and increased the content of phenolic compounds and flavonoids. Results of our study indicate that B. tequilensis YYC 155 may potentially represent an effective biocontrol agent against anthracnose disease in C. oleifera.
Effects of Artemisia argyi Powder on Egg Quality, Antioxidant Capacity, and Intestinal Development of Roman Laying Hens

This study was conducted to evaluate the effect of dietary supplementation with Artemisia argyi (A. argyi) on egg quality, serum biochemical, antioxidant capacity, and intestinal development in Roman laying hens. A total of 432 (34-week-old) Roman hens were randomly divided into control group and three experimental groups. The control group was fed a basal diet, and the experimental group was fed a basal diet with 1%, 2%, and 3% A. argyi powder, respectively. The results showed that dietary supplementation of 2% A. argyi to the diet increased egg weight and egg white weight, and the daturic acid (C17:0), stearic acid (C18:0), eicosadienoic acid (C20:2), docosahexaenoic acid (C22:6n-3), α-linolenic acid (C18:3n-3), linoleic acid (C18:2n-6c), and polyunsaturated fatty acid (PUFA) in egg yolk. Meanwhile, the addition of 1∼3% A. argyi decreased serum urea. Moreover, dietary supplementation of 1% A. argyi promoted the antioxidative capacity of the hens by increasing hepatic T-SOD and CAT activities, as well as GSH-Px content. However, the addition of 3% A. argyi to the diet significantly increased the content of malondialdehyde in serum and liver and destroyed the intestinal morphology by increasing duodenal crypt depth. In conclusion, the addition level of A. argyi promoting egg quality and antioxidant capacity was at 2% and 1%, respectively.
Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Background Continuously growing of HIV incidence among men who have sex with men (MSM), as well as the low rate of HIV testing of MSM in China, demonstrates a need for innovative strategies to improve the implementation of HIV prevention. The use of machine learning algorithms is an increasing tendency in disease diagnosis prediction. We aimed to develop and validate machine learning models in predicting HIV infection among MSM that can identify individuals at increased risk of HIV acquisition for transmission-reduction interventions. Methods We extracted data from MSM sentinel surveillance in Zhejiang province from 2018 to 2020. Univariate logistic regression was used to select significant variables in 2018–2019 data (P < 0.05). After data processing and feature selection, we divided the model development data into two groups by stratified random sampling: training data (70%) and testing data (30%). The Synthetic Minority Oversampling Technique (SMOTE) was applied to solve the problem of unbalanced data. The evaluation metrics of model performance were comprised of accuracy, precision, recall, F-measure, and the area under the receiver operating characteristic curve (AUC). Then, we explored three commonly-used machine learning algorithms to compare with logistic regression (LR), including decision tree (DT), support vector machines (SVM), and random forest (RF). Finally, the four models were validated prospectively with 2020 data from Zhejiang province. Results A total of 6,346 MSM were included in model development data, 372 of whom were diagnosed with HIV. In feature selection, 12 variables were selected as model predicting indicators. Compared with LR, the algorithms of DT, SVM, and RF improved the classification prediction performance in SMOTE-processed data, with the AUC of 0.778, 0.856, 0.887, and 0.942, respectively. RF was the best-performing algorithm (accuracy = 0.871, precision = 0.960, recall = 0.775, F-measure = 0.858, and AUC = 0.942). And the RF model still performed well on prospective validation (AUC = 0.846). Conclusion Machine learning models are substantially better than conventional LR model and RF should be considered in prediction tools of HIV infection in Chinese MSM. Further studies are needed to optimize and promote these algorithms and evaluate their impact on HIV prevention of MSM.
Emotional exhaustion and unhealthy eating among COVID-19 front-line healthcare workers during recuperation: A cross-sectional study

Objective Thousands of healthcare workers on the frontlines who have been battling the COVID-19 pandemic could face emotional and mental health risks even after their critical pandemic work. This study examined the impact of affective rumination on emotional exhaustion and the spillover effect of affective rumination on unhealthy food consumption among healthcare workers during recuperation. Methods A total of 418 frontline healthcare workers from 10 Chinese medical institutions were recruited through random cluster sampling. A linear mixed model in SPSS25.0 was performed for hierarchical regression to analyze the effect of affective rumination on unhealthy food consumption via emotional exhaustion. A conditional process analysis was employed to investigate the moderating role of family support in the mediating effect of emotional exhaustion. Results Front-line healthcare workers scored at a medium level on an emotional exhaustion scale (2.45 ± 0.88). Affective rumination mediated by emotional exhaustion had a significant positive predictive effect on unhealthy food consumption. The indirect effect accounted for ~43.9% of the total effect. Family support amplified the effect of emotional exhaustion on unhealthy food consumption (β = 0.092, p < 0.05). Conclusion Affective rumination could be a cause of emotional exhaustion and unhealthy food consumption. First-line healthcare workers could be screened for possible emotional exhaustion through the evaluation of affective rumination in order to provide them with targeted interventions. Family support did not prove to be beneficial in all cases as it enhanced the positive effect of emotional exhaustion on unhealthy eating in the current study. Therefore, family support should be carefully integrated in future interventions. Graphical Abstract In this study, we examined the impact of affective rumination on emotional exhaustion and the spillover effect of affective rumination on unhealthy food consumption among healthcare workers during recuperation. Emotional exhaustion was an important mediator of the relationship between affective rumination and unhealthy food consumption. Interestingly, family support did not prove to be beneficial in all cases, as it enhanced the positive effect of emotional exhaustion on unhealthy eating in the current study.
Diagnostic yield of TOF-MRA for detecting incidental vascular lesions in patients with cognitive impairment: An observational cohort study

Objectives The role of three-dimensional (3D) TOF-MRA in patients with cognitive impairment is not well established. We evaluated the diagnostic yield of 3D TOF-MRA for detecting incidental extra- or intracranial artery stenosis and intracranial aneurysm in this patient group. Methods This retrospective study included patients with cognitive impairment undergoing our brain MRI protocol from January 2013 to February 2020. The diagnostic yield of TOF-MRA for detecting incidental vascular lesions was calculated. Patients with positive TOF-MRA results were reviewed to find whether additional treatment was performed. Logistic regression analysis was conducted to identify the clinical risk factors for positive TOF-MRA findings. Results In total, 1,753 patients (mean age, 70.2 ± 10.6 years; 1,044 women) were included; 199 intracranial aneurysms were detected among 162 patients (9.2%, 162/1,753). A 3D TOF-MRA revealed significant artery stenoses (>50% stenosis) in 162 patients (9.2%, 162/1,753). The overall diagnostic yield of TOF-MRA was 16.8% (294/1,753). Among them, 92 patients (31.3%, 92/294) underwent either medical therapy, endovascular intervention, or surgery. In total, eighty-one patients with stenosis were prescribed with either antiplatelet medications or lipid-lowering agent. In total, fifteen patients (aneurysm: 11 patients, stenosis: 4 patients) were further treated with endovascular intervention or surgery. Thus, the “number needed to scan” was 19 for identifying one patient requiring treatment. Multivariate logistic regression analysis showed that being female (odds ratio [OR] 2.05) and old age (OR 1.04) were the independent risk factors for intracranial aneurysm; being male (OR 1.52), old age (OR 1.06), hypertension (OR 1.78), and ischemic heart disease history (OR 2.65) were the independent risk factors for significant artery stenosis. Conclusions Our study demonstrated the potential benefit of 3D TOF-MRA, given that it showed high diagnostic yield for detecting vascular lesions in patients with cognitive impairment and the considerable number of these lesions required further treatment. A 3D TOF-MRA may be included in the routine MR protocol for the work-up of this patient population, especially in older patients and patients with vascular risk factors.
Impaired sharp-wave ripple coordination between the medial entorhinal cortex and hippocampal CA1 of knock-in model of Alzheimer’s disease

Clinical evidence suggests that the entorhinal cortex is a primary brain area triggering memory impairments in Alzheimer’s disease (AD), but the underlying brain circuit mechanisms remain largely unclear. In healthy brains, sharp-wave ripples (SWRs) in the hippocampus and entorhinal cortex play a critical role in memory consolidation. We tested SWRs in the MEC layers 2/3 of awake amyloid precursor protein knock-in (APP-KI) mice, recorded simultaneously with SWRs in the hippocampal CA1. We found that MEC→CA1 coordination of SWRs, found previously in healthy brains, was disrupted in APP-KI mice even at a young age before the emergence of spatial memory impairments. Intriguingly, long-duration SWRs critical for memory consolidation were mildly diminished in CA1, although SWR density and amplitude remained intact. Our results point to SWR incoordination in the entorhinal-hippocampal circuit as an early network symptom that precedes memory impairment in AD.
Novel machine learning models to predict pneumonia events in supratentorial intracerebral hemorrhage populations: An analysis of the Risa-MIS-ICH study

Background Stroke-associated pneumonia (SAP) contributes to high mortality rates in spontaneous intracerebral hemorrhage (sICH) populations. Accurate prediction and early intervention of SAP are associated with prognosis. None of the previously developed predictive scoring systems are widely accepted. We aimed to derive and validate novel supervised machine learning (ML) models to predict SAP events in supratentorial sICH populations. Methods The data of eligible supratentorial sICH individuals were extracted from the Risa-MIS-ICH database and split into training, internal validation, and external validation datasets. The primary outcome was SAP during hospitalization. Univariate and multivariate analyses were used for variable filtering, and logistic regression (LR), Gaussian naïve Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and ensemble soft voting model (ESVM) were adopted for ML model derivations. The accuracy, sensitivity, specificity, and area under the curve (AUC) were adopted to evaluate the predictive value of each model with internal/cross-/external validations. Results A total of 468 individuals with sICH were included in this work. Six independent variables [nasogastric feeding, airway support, unconscious onset, surgery for external ventricular drainage (EVD), larger sICH volume, and intensive care unit (ICU) stay] for SAP were identified and selected for ML prediction model derivations and validations. The internal and cross-validations revealed the superior and robust performance of the GNB model with the highest AUC value (0.861, 95% CI: 0.793–0.930), while the LR model had the highest AUC value (0.867, 95% CI: 0.812–0.923) in external validation. The ESVM method combining the other six methods had moderate but robust abilities in both cross-validation and external validation and achieved an AUC of 0.843 (95% CI: 0.784–0.902) in external validation. Conclusion The ML models could effectively predict SAP in sICH populations, and our novel ensemble model demonstrated reliable robust performance outcomes despite the populational and algorithmic differences. This attempt indicated that ML application may benefit in the early identification of SAP.
Quantitative myelin-related maps from R1 and T2* ratio images using a single ME-MP2RAGE sequence in 7T MRI

Background: There still are limitations associated with quantifying myelin content using brain magnetic resonance imaging (MRI) despite several studies conducted on this subject. Therefore, this study aimed: (1) to propose a myelin-related mapping technique to obtain the quantitative R1/T2* (q-Ratio) that has the advantage of quick processing and less dependency on imaging parameters, (2) to validate this adapted q-Ratio method by comparing the quantitative myelin-related map with those acquired through an existing mapping method [T1-weighted/T2*-weighted (w-Ratio)], and (3) to determine the q-Ratio myelin-related values in the white and gray matter, and the relationship between the q-Ratio myelin-related value and cerebral volume size in regions of interest (ROIs) in a healthy population. Methods: The multi-echo magnetization-prepared 2 rapid gradient echoes (ME-MP2RAGE) sequence was used in a 7 Tesla (7T) MRI for the acquisition of data regarding myelin content in 10 healthy participants. A correlation analysis was performed between myelin-related values obtained through the q-Ratio and w-Ratio methods. Additionally, myelin distribution was analyzed and compared in the white and gray matter, and the correlation between cerebral volume size and q-Ratio myelin-related value was analyzed in ROIs in the brain. Results: The myelin-related maps acquired through the q-Ratio and w-Ratio methods were significantly correlated (p < 0.001), but the q-Ratio myelin-related map was much clearer. Additionally, the cerebral volume size in the gray matter was 399.40% larger than that in the white matter, but the q-Ratio myelin-related value in the gray matter was 80.83% lower than that of the white matter. Furthermore, volume size was positively correlated with q-Ratio myelin-related values in the white matter (r = 0.509, p = 0.006) but not in the gray matter (r = -0.133, p = 0.402). Conclusions: In this study, we validated using a q-Ratio myelin-related map that was acquired in one imaging sequence at 7T MRI. In addition, we found a significant correlation between ROI volume size and the q-Ratio myelin-related value in the white matter but not in the gray matter. It is expected that this technique could be applied to the study of various neuropsychiatric diseases related to demyelination in the future.
Adaptation to stress in football athletes: The importance of cognitive appraisal

This study analysed the adaptation of football athletes to competitive stressors regarding the upcoming match. For that, the study adopted a cross-sectional methodology using a critical incident approach. The participants were 352 young male football athletes, aged between 15 and 19 years (M = 16.91, SD = 0.99), who were competing in the national football championship. The results indicated that cognitive appraisal partially mediated the relationship between competitive stressors and emotions: athletes who perceived stressors as a challenge, tended to feel more control over the situation and more resourceful (coping perception), leading to a more positive emotional experience, while those perceiving the stressors as a threat were more prone to experience less control and more negative emotions. This mediation model was moderated by athletes’ competitive level (U17 or U19), as the role of challenge perception was more pronounced in the U19 team, while the relationship between threat perception and less control was only observed for the U17 team. In sum, the data reveals the importance of cognitive appraisal in young football athletes’ adaptation to competitive stressors, bolstering the theoretical models in this area and the importance of psychologists to consider these variables during intervention, particularly cognitive appraisal.
A novel cuproptosis-related gene signature for predicting prognosis in cervical cancer

Purpose: Cuproptosis, a form of copper-induced cell death, can be a promising therapeutic target for refractory cancers. Hence, we conducted this research to explore the association between cuproptosis and prognosis in cervical cancer (CC). Methods: For constructing a prognostic signature based on cuproptosis-related genes from TCGA database, the least absolute shrinkage and selection operator Cox regression was utilized. The GSE44001 cohort was utilized for validation. Results: A total of nine cuproptosis-related genes showed distinct expression in CC and normal samples in TCGA-GTEx cohort. Two risk groups were identified based on a seven-gene signature. A significant decrease in overall survival was observed in the high-risk group (p < 0.001). The risk score (HR = 2.77, 95% CI = 1.58–4.86) was an autocephalous predictor with a better predictive ability than the clinical stage. Functional analysis indicated that immune activities were suppressed more in the high-risk group than in the low-risk group. A total of 11 candidate compounds targeting the signature were identified. Conclusion: A total of seven cuproptosis-related gene signatures were constructed to predict prognosis and propose a new therapeutic target for patients with CC.
The chemistry and efficacy benefits of polysaccharides from Atractylodes macrocephala Koidz

Atractylodes macrocephala Koidz (AM), traditional Chinese medicine (TCM) with many medicinal values, has a long usage history in China and other oriental countries. The phytochemical investigation revealed the presence of volatile oils, polysaccharides, lactones, flavonoids, and others. The polysaccharides from AM are important medicinal components, mainly composed of glucose (Glc), galactose (Gal), rhamnose (Rha), arabinose (Ara), mannose (Man), galacturonic acid (GalA) and xylose (Xyl). It also showed valuable bioactivities, such as immunomodulatory, antitumour, gastroprotective and intestinal health-promoting, hepatoprotective, hypoglycaemic as well as other activities. At the same time, based on its special structure and pharmacological activity, it can also be used as immune adjuvant, natural plant supplement and vaccine adjuvant. The aim of this review is to summarize and critically analyze up-to-data on the chemical compositions, biological activities and applications of polysaccharide from AM based on scientific literatures in recent years.
A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study

Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the “happiness emotional brain state” of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4–1 Median = 6.563%; Range = 4.10–27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1–15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.