Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of an Immune-Related LncRNA Prognostic Signature for Glioma

Introduction Glioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management. Materials and methods Single-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature. Results A total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p < 0.05) in ssGSEA except neutrophils (p = 0.43). Conclusion The study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.
Effect of 2-Weeks Ischemic Preconditioning on Exercise Performance: A Pilot Study

An acute bout of ischemic preconditioning (IPC) has been reported to increase exercise performance. Nevertheless, the ineffectiveness of acute IPC on exercise performance has also been reported. Similarly, the effect of a shot-term intervention of IPC on exercise performance remains controversial in previous studies. In this study, we examined the effects of short-term IPC intervention on whole and local exercise performances and its-related parameters. Ten healthy young males undertook a 2-weeks IPC intervention (6 days/weeks). The IPC applied to both legs with three episodes of a 5-min ischemia and 5-min reperfusion cycle. Whole-body exercise performance was assessed by peak O2 consumption (VO2: VO2 peak) during a ramp-incremental cycling test. Local exercise performance was assessed by time to task failure during a knee extensor sustained endurance test. A repeated moderate-intensity cycling test was performed to evaluate dynamics of pulmonary VO2 and muscle deoxygenation. The knee extensor maximal voluntary contraction and quadriceps femoris cross-sectional area measurements were performed to explore the potentiality for strength gain and muscle hypertrophy. The whole-body exercise performance (i.e., VO2 peak) did not change before and after the intervention (P = 0.147, Power = 0.09, Effect size = 0.21, 95% confidence interval: −0.67, 1.09). Moreover, the local exercise performance (i.e., time to task failure) did not change before and after the intervention (P = 0.923, Power = 0.05, Effect size = 0.02, 95% confidence interval: −0.86, 0.89). Furthermore, no such changes were observed for all parameters measured using a repeated moderate-intensity cycling test and knee extensor strength and quadriceps femoris size measurements. These findings suggest that a 2-weeks IPC intervention cannot increase whole-body and local exercise performances, corresponding with ineffectiveness on its-related parameters in healthy young adults. However, the statistical analyses of changes in the measured parameters in this study showed insufficient statistical power and sensitivity, due to the small sample size. Additionally, this study did not include control group(s) with placebo and/or nocebo. Therefore, further studies with a larger sample size and control group are required to clarify the present findings.
Interparental Conflict and Korean Children’s Inhibitory Control: Testing Emotional Insecurity as a Mediator

This study examined the association between interparental conflict and inhibitory control of Korean children, and it investigated whether this relationship is mediated by the single latent variable of emotional insecurity or by three dimensions of emotional insecurity (i.e., involvement, emotional reactivity, and avoidance). A total of 166 mother–father dyads with Korean children aged 3–5 years participated in a short-term longitudinal survey at two-time points. Both parents completed measures of interparental conflict and emotional insecurity, and, 6 months later, they completed a measure of the inhibitory control of children. The structural equation modeling results suggested that interparental conflict was related to poor inhibitory control in Korean children. Emotional insecurity did not mediate the association between interparental conflict and inhibitory control. Of the three emotional insecurity dimensions, the involvement partially mediated the relationship. Interestingly, although interparental conflict predicted poor inhibitory control, the involvement of children in the conflict, which was associated with a greater interparental conflict, predicted a subsequent increase in inhibitory control 6 months later. These findings were interpreted in terms of sensory processing patterns, the reformulation of emotional security theory, and the influence of Confucianism on Korean culture.
Global Data Gaps in Our Knowledge of the Terrestrial Cryosphere

The IPCC Special Report on Oceans and Cryosphere in a Changing Climate identified major gaps in our knowledge of snow and glacier ice in the terrestrial cryosphere. These gaps are limiting our ability to predict the future of the energy and water balance of the Earth's surface, which in turn affect regional climate, biodiversity and biomass, the freezing and thawing of permafrost, the seasonal supply of water for one sixth of the global population, the rate of global sea level rise and the risk of riverine and coastal flooding. Snow and ice are highly susceptible to climate change but although their spatial extents are routinely monitored, the fundamental property of their water content is remarkably poorly observed. Specifically, there is a profound lack of basic but problematic observations of the amount of water supplied by snowfall and of the volume of water stored in glaciers. As a result, the climatological precipitation of the mountain cryosphere is, for example, biassed low by 50–100%, and biases in the volume of glacier ice are unknown but are likely to be large. More and better basic observations of snow and ice water content are urgently needed to constrain climate models of the cryosphere, and this requires a transformation in the capabilities of snow-monitoring and glacier-surveying instruments. I describe new solutions to this long-standing problem that if deployed widely could achieve this transformation.
Gene Therapy for Neurodegenerative Disease: Clinical Potential and Directions

The pathogenesis of neurodegenerative diseases (NDDs) is complex and diverse. Over the decades, our understanding of NDD has been limited to pathological features. However, recent advances in gene sequencing have facilitated elucidation of NDD at a deeper level. Gene editing techniques have uncovered new genetic links to phenotypes, promoted the development of novel treatment strategies and equipped researchers with further means to construct effective cell and animal models. The current review describes the history of evolution of gene editing tools, with the aim of improving overall understanding of this technology, and focuses on the four most common NDD disorders to demonstrate the potential future applications and research directions of gene editing.
Absolute Cardiovascular Disease Risk Is Associated With the Incidence of Non-amnestic Cognitive Impairment in Japanese Older Adults

Background The estimated absolute cardiovascular disease (CVD) risk level is known to be a useful surrogate marker for future cognitive impairment; however, evidence regarding its predictive validity in terms of cognitive subtypes is limited. We aimed to examine subtype-dependent differences in the associations between absolute CVD risk and the incidence of cognitive impairment in a community-dwelling older Japanese cohort. Methods and Results This study comprised 1,641 cognitively intact older Japanese participants without CVDs at baseline. We estimated absolute CVD risk using WHO region-specific risk estimation charts and included age, sex, diabetes mellitus, smoking, systolic blood pressure, and total cholesterol at baseline, and the CVD risk level was stratified into the three following risk categories: low (<10%), moderate (10 to <20%), and high (≥20%). Objective cognitive screening was performed using a multicomponent neurocognitive test at baseline and follow-up, and the incidence of cognitive impairment over 48 ± 2 months was determined. The incidence of cognitive impairment in low-, moderate-, and high-CVD risk participants was 1.2, 3.0, and 5.4%, respectively, for amnestic subtypes and 5.8, 10.1, and 14.0%, respectively, for non-amnestic subtypes. After adjusting for potential confounding factors, the absolute CVD risk level was significantly associated with non-amnestic impairment but not with amnestic impairment. Conclusions The absolute CVD risk estimated using region-specific risk estimation charts in old age is useful to predict incidence of cognitive impairment. Strategies to screen populations at risk of cognitive impairment and to prevent progression to dementia should be cognitive subtype-specific.
Head Injury Exposure in Veterans Presenting to Memory Disorders Clinic: An Observational Study of Clinical Characteristics and Relationship of Event-Related Potentials and Imaging Markers

Objective: Traumatic brain injury (TBI) and repetitive head impacts (RHI) related to blasts or contact sports are commonly reported among military service members. However, the clinical implications of remote TBI and RHI in veterans remains a challenge when evaluating older veterans at risk of neurodegenerative conditions including Alzheimer's disease (AD) and Chronic Traumatic Encephalopathy (CTE). This study aimed to test the hypothesis that veterans in a memory disorders clinic with remote head injury would be more likely to have neurodegenerative clinical diagnoses, increased rates of amyloid PET positivity, higher prevalence of cavum septum pellucidi/vergae, and alterations in event-related potential (ERP) middle latency auditory evoked potentials (MLAEPs) and long latency ERP responses compared to those without head injuries. Methods: Older veterans aged 50–100 were recruited from a memory disorders clinic at VA Boston Healthcare system with a history of head injury (n = 72) and without head injury history (n = 52). Patients were classified as reporting prior head injury including TBI and/or RHI exposure based on self-report and chart review. Participants underwent MRI to determine presence/absence of cavum and an ERP auditory oddball protocol. Results: The head injury group was equally likely to have a positive amyloid PET compared to the non-head injury group. Additionally, the head injury group were less likely to have a diagnosis of a neurodegenerative condition than those without head injury. P200 target amplitude and MLAEP amplitudes for standard and target tones were decreased in the head injury group compared to the non-head injury group while P3b amplitude did not differ. Conclusions: Veterans with reported remote head injury evaluated in a memory disorders clinic were not more likely to have a neurodegenerative diagnosis or imaging markers of neurodegeneration than those without head injury. Decreased P200 target and MLAEP target and standard tone amplitudes in the head injury group may be relevant as potential diagnostic markers of remote head injury.
Soft Devices for High-Resolution Neuro-Stimulation: The Interplay Between Low-Rigidity and Resolution

The field of neurostimulation has evolved over the last few decades from a crude, low-resolution approach to a highly sophisticated methodology entailing the use of state-of-the-art technologies. Neurostimulation has been tested for a growing number of neurological applications, demonstrating great promise and attracting growing attention in both academia and industry. Despite tremendous progress, long-term stability of the implants, their large dimensions, their rigidity and the methods of their introduction and anchoring to sensitive neural tissue remain challenging. The purpose of this review is to provide a concise introduction to the field of high-resolution neurostimulation from a technological perspective and to focus on opportunities stemming from developments in materials sciences and engineering to reduce device rigidity while optimizing electrode small dimensions. We discuss how these factors may contribute to smaller, lighter, softer and higher electrode density devices.
Genetic Analysis of Tryptophan Metabolism Genes in Sporadic Amyotrophic Lateral Sclerosis

The essential amino acid tryptophan (TRP) is the initiating metabolite of the kynurenine pathway (KP), which can be upregulated by inflammatory conditions in cells. Neuroinflammation-triggered activation of the KP and excessive production of the KP metabolite quinolinic acid are common features of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). In addition to its role in the KP, genes involved in TRP metabolism, including its incorporation into proteins, and synthesis of the neurotransmitter serotonin, have also been genetically and functionally linked to these diseases. ALS is a late onset neurodegenerative disease that is classified as familial or sporadic, depending on the presence or absence of a family history of the disease. Heritability estimates support a genetic basis for all ALS, including the sporadic form of the disease. However, the genetic basis of sporadic ALS (SALS) is complex, with the presence of multiple gene variants acting to increase disease susceptibility and is further complicated by interaction with potential environmental factors. We aimed to determine the genetic contribution of 18 genes involved in TRP metabolism, including protein synthesis, serotonin synthesis and the KP, by interrogating whole-genome sequencing data from 614 Australian sporadic ALS cases. Five genes in the KP (AFMID, CCBL1, GOT2, KYNU, HAAO) were found to have either novel protein-altering variants, and/or a burden of rare protein-altering variants in SALS cases compared to controls. Four genes involved in TRP metabolism for protein synthesis (WARS) and serotonin synthesis (TPH1, TPH2, MAOA) were also found to carry novel variants and/or gene burden. These variants may represent ALS risk factors that act to alter the KP and lead to neuroinflammation. These findings provide further evidence for the role of TRP metabolism, the KP and neuroinflammation in ALS disease pathobiology.
Sound Quality Characteristics of Importance for Preschool Children’s Perception and Wellbeing After an Acoustic Intervention

In Sweden preschool-age children spend most of their waking hours at preschool. At this age children undergo substantial physical and mental development and their hearing sensations may not be comparable to those of an adult. The preschool sound environment is loud and highly intermittent, and the acoustic may not be supportive for young children’s hearing, and wellbeing. This article describes an acoustic intervention among seven preschools, and comparisons with three reference preschools. The intervention included changing floor mats to plastic mats designed to reduce impact sounds, adding damping cushions under chairs, change of ceiling absorbers and, in some rooms, addition of wall absorbers. The effect of the intervention was studied using a previously developed interview protocol, “Inventory of Noise and Children’s Health,” in combination with sound level and room acoustic measurements. A total of 61 children aged 4–6 years were interviewed before the intervention, and 56 after. A reduction of the sound levels in a range of LAeq 1.2–3.8 dB for meal/craft rooms and play rooms were found for the intervention preschools using stationary noise level meters, while this was not found for the reference preschools. The reverberation time (T20) decreased slightly after the interventions. The average room frequency response for the two room types tended to be more flat after the interventions. Further investigations are needed to see its importance for the perceived acoustic quality. The results linking children’s perception and response to the measured reduction in sound levels, confirmed an association between reduced sound levels after the acoustic intervention and a 30% reduction in stomach ache, as well as in children’s perceptions of scraping, screeching sounds. Children’s perceptions of these sounds were further associated with important oral communication outcomes. Children’s bodily sensations of sounds were also associated with psychoacoustic symptoms and wellbeing. Despite the seemingly modest reduction in sound level, the acoustic intervention was indeed perceived and reported on by the children. Future studies should pay more attention to how a supportive preschool acoustic environment should be optimised and acoustically described to take preschool-age hearing and perception into account.
AUCseg: An Automatically Unsupervised Clustering Toolbox for 3D-Segmentation of High-Grade Gliomas in Multi-Parametric MR Images

The segmentation of high-grade gliomas (HGG) using magnetic resonance imaging (MRI) data is clinically meaningful in neurosurgical practice, but a challenging task. Currently, most segmentation methods are supervised learning with labeled training sets. Although these methods work well in most cases, they typically require time-consuming manual labeling and pre-trained models. In this work, we propose an automatically unsupervised segmentation toolbox based on the clustering algorithm and morphological processing, named AUCseg. With our toolbox, the whole tumor was first extracted by clustering on T2-FLAIR images. Then, based on the mask acquired with whole tumor segmentation, the enhancing tumor was segmented on the post-contrast T1-weighted images (T1-CE) using clustering methods. Finally, the necrotic regions were segmented by morphological processing or clustering on T2-weighted images. Compared with K-means, Mini-batch K-means, and Fuzzy C Means (FCM), the Gaussian Mixture Model (GMM) clustering performs the best in our toolbox. We did a multi-sided evaluation of our toolbox in the BraTS2018 dataset and demonstrated that the whole tumor, tumor core, and enhancing tumor can be automatically segmented using default hyper-parameters with Dice score 0.8209, 0.7087, and 0.7254, respectively. The computing time of our toolbox for each case is around 22 seconds, which is at least 3 times faster than other state-of-the-art unsupervised methods. In addition, our toolbox has an option to perform semi-automatic segmentation via manually setup hyper-parameters, which could improve the segmentation performance. Our toolbox, AUCseg, is publicly available on Github. (https://github.com/Haifengtao/AUCseg).
Exploring the Key Genes and Identification of Potential Diagnosis Biomarkers in Alzheimer’s Disease Using Bioinformatics Analysis

Background Alzheimer’s disease (AD) is one of the major threats of the twenty-first century and lacks available therapy. Identification of novel molecular markers for diagnosis and treatment of AD is urgently demanded, and genetic biomarkers show potential prospects. Method We identify and intersected differentially expressed genes (DEGs) from five microarray datasets to detect consensus DEGs. Based on these DEGs, we conducted Gene Ontology (GO), performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, constructed a protein—protein interaction (PPI) network, and utilized Cytoscape to identify hub genes. The least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify potential diagnostic biomarkers. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the key genes. Result We identified 608 consensus DEGs, several dysregulated pathways, and 18 hub genes. Sixteen hub genes dysregulated as AD progressed. The diagnostic model of 35 genes was constructed, which has a high area under the curve (AUC) value in both the validation dataset and combined dataset (AUC = 0.992 and AUC = 0.985, respectively). The model can also differentiate mild cognitive impairment and AD patients from controls in two blood datasets. Brain-derived neurotrophic factor (BDNF) and WW domain-containing transcription regulator protein 1 (WWTR1), which are associated with the Braak stage, Aβ 42 levels, and β-secretase activity, were identified as critical genes of AD. Conclusion Our study identified 16 hub genes correlated to the neuropathological stage and 35 potential biomarkers for the diagnosis of AD. WWTR1 were identified as candidate genes for future studies. This study deepens our understanding of the transcriptomic and functional features and provides new potential diagnostic biomarkers and therapeutic targets for AD.
Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning

Electromyography (EMG) pattern recognition is one of the widely used methods to control the rehabilitation robots and prostheses. However, the changes in the distribution of EMG data due to electrodes shifting results in classification decline, which hinders its clinical application in repeated uses. Adaptive learning can solve this problem but takes additional time. To address this, an efficient scheme is developed by comparing the performance of 12 combinations of three feature selection methods [no feature selection (NFS), sequential forward search (SFS), and particle swarm optimization (PSO)] and four classification methods [non-adaptive support vector machine (N-SVM), incremental SVM (I-SVM), SVM based on TrAdaBoost (T-SVM), and I-SVM based on TrAdaBoost (TI-SVM)] in the classification of EMG data of 12 subjects for 5 consecutive days. Our results showed that TI-SVM achieved the highest classification accuracy among the classification methods (p < 0.05). The SFS method achieved the same classification accuracy as that of the scheme trained with the feature vectors selected by the NFS method (p = 0.999) while achieving a lower training time than that of TI-SVM combined with the NFS method (p = 0.043). Although the PSO method outperformed the NFS and SFS methods by achieving reduced training and response times (p < 0.05), the PSO method achieved a considerably lower classification accuracy than that of the scheme trained with the feature vectors selected by the NFS (p = 0.001) or SFS (p = 0.001) method. Furthermore, TI-SVM combined with the SFS method outperformed the CNN method with fine-tuning in classification accuracy on a small data set (p = 0.001). The results indicate that TI-SVM combined with the SFS method is suitable for improving the performance of EMG pattern recognition in repeated uses.