Miracle-dz/UniFAR-FTUs huggingface dataset:
Principal Structure Identification: Fast Disentanglement of Multi-source Dataset

Analysis of multi-source data, where data on the same objects are collected from multiple sources, is of rising importance in many fields, e.g., multi-omics biology. Major challenges in multi-source data analysis include heterogeneity among different data sources and the entanglement of their association structure among several groups of variables. Our goal is to disentangle the association structure by identifying shared score subspaces among all or some of data blocks. We propose a sequential algorithm that gathers score subspaces of different data blocks within certain angle threshold and identifies partially-shared score components, using the concept of principal angles between subspaces of different dimensions. Our method shows better performance in identifying the linear association structure than competing methods in this field. In real data analysis, we apply our method to an oncological multi-omics dataset associated with drug responses. The proposed method boasts super-fast computational speed and results in revealing the scores in the estimated shared component showing strong correlations with well-known biological pathways. Supplementary materials for this article are available online.
Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation.
Heavy Vehicles on Minor Highway Bridges: calculation of dynamic impact factors from selected crossing scenarios

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Towards Formal Verification of HotStuff-based Byzantine Fault Tolerant Consensus in Agda: Extended Version

LibraBFT is a Byzantine Fault Tolerant (BFT) consensus protocol based on HotStuff. We present an abstract model of the protocol underlying HotStuff / LibraBFT, and formal, machine-checked proofs of their core correctness (safety) property and an extended condition that enables non-participating parties to verify committed results. (Liveness properties would be proved for specific implementations, not for the abstract model presented in this paper.) A key contribution is precisely defining assumptions about the behavior of honest peers, in an abstract way, independent of any particular implementation. Therefore, our work is an important step towards proving correctness of an entire class of concrete implementations, without repeating the hard work of proving correctness of the underlying protocol. The abstract proofs are for a single configuration (epoch); extending these proofs across configuration changes is future work. Our models and proofs are expressed in Agda, and are available in open source.
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art method for graph-based learning tasks. However, training GCNs at scale is still challenging, hindering both the exploration of more sophisticated GCN architectures and their applications to real-world large graphs. While it might be natural to consider graph partition and distributed training for tackling this challenge, this direction has only been slightly scratched the surface in the previous works due to the limitations of existing designs. In this work, we first analyze why distributed GCN training is ineffective and identify the underlying cause to be the excessive number of boundary nodes of each partitioned subgraph, which easily explodes the memory and communication costs for GCN training. Furthermore, we propose a simple yet effective method dubbed BNS-GCN that adopts random Boundary-Node-Sampling to enable efficient and scalable distributed GCN training. Experiments and ablation studies consistently validate the effectiveness of BNS-GCN, e.g., boosting the throughput by up to 16.2x and reducing the memory usage by up to 58%, while maintaining a full-graph accuracy. Furthermore, both theoretical and empirical analysis show that BNS-GCN enjoys a better convergence than existing sampling-based methods. We believe that our BNS-GCN has opened up a new paradigm for enabling GCN training at scale. The code is available at https://github.com/RICE-EIC/BNS-GCN.
Deprescribing Education vs Usual Care for Patients With Cognitive Impairment and Primary Care Clinicians

Key Points Question Can increasing awareness about deprescribing prior to primary care visits reduce the use of potentially inappropriate long-term medications for individuals with cognitive impairment? Findings In this pragmatic cluster randomized clinical trial of deprescribing education for 3012 older adults with cognitive impairment taking 5 or more medications and their primary care clinicians, patients from intervention clinics and control clinics were taking a similar mean number of medications (6.4 vs 6.5) at 6 months, and similar proportions of patients were taking 1 or more potentially inappropriate medications after 6 months. Meaning Educating patients and clinicians about deprescribing in primary care did not have an effect on the number of long-term medications or percentage of potentially inappropriate medications for older adults taking 5 or more long-term medications; findings suggest such interventions should target older adults taking relatively more medications.
An updated overview of glucagon-like peptide-2 analog trophic therapy for short bowel syndrome in adults

Short bowel syndrome (SBS) is a clinical condition characterized by a failure to achieve optimal intestinal adaptation, which is necessary to maintain oral/enteral autonomy. At present, the treatment options for SBS are primarily intestinal replacement and rehabilitation. Intestinal rehabilitation mainly includes non-transplantation surgery and intestinal rehabilitation measures. In recent years, intestinal rehabilitation in patients with SBS using nutritional intestinal hormones, especially glucagon-like peptide-2 analogs, has made great progress. Many high-quality studies have provided evidence-based medical findings to support the development of clinical guidelines. This article reviews the latest research advancements regarding the use of glucagon-like peptide-2 analogs (teduglutide, glepaglutide, and apraglutide) in the treatment of SBS.
Protein Biomarkers in Monocytes and CD4+ Lymphocytes for Predicting Lithium Treatment Response of Bipolar Disorder: a Feasibility Study with Tyramine-Based Signal-Amplified Flow Cytometry.

Purpose To determine if enhanced flow cytometry (CellPrint™) can identify intracellular proteins of lithium responsiveness in monocytes and CD4+ lymphocytes from patients with bipolar disorder. Methods Eligible bipolar I or II patients were openly treated with lithium for 16-weeks. Baseline levels of Bcl2, BDNF, calmodulin, Fyn, phospho-Fyn/phospho-Yes, GSK3β, phospho-GSK3αβ, HMGB1, iNOS, IRS2, mTor, NLPR3, PGM1, PKA C-α, PPAR-γ, phospho-RelA, and TPH1 in monocytes and CD4+ lymphocytes of lithium responders and non-responders were measured with CellPrint™. Their utility of discriminating responders from non-responders was explored. Protein-protein network and pathway enrichment analyses were conducted. Results Of the 24 intent-to-treat patients, 12 patients completed the 16-week study. Eleven of 13 responders and 8 of 11 non-responders were available for this analysis. The levels of the majority of analytes in lithium responders were lower than non-responders in both cell types, but only the level of GSK3β in monocytes was significantly different (p = 0.034). The combination of GSK3β and phospho-GSK3αβ levels in monocytes correctly classified 11/11 responders and 5/8 non-responders. Combination of GSK3β, phospho-RelA, TPH1 and PGM1 correctly classified 10/11 responders and 6/7 non-responders, both with a likelihood of ≥ 85%. Prolactin, leptin, BDNF, neurotrophin, and epidermal growth factor/epidermal growth factor receptor signaling pathways are involved in the lithium treatment response. GSK3β and RelA genes are involved in 4 of 5 these pathways. Conclusion CellPrint™ flow cytometry was able to detect differences in multiple proteins in monocytes and CD4+ lymphocytes between lithium responders and non-responders. A large study is warranted to confirm or refute these findings.
A review of procedural and declarative metamemory development across childhood

ABSTRACT Metamemory is a component of metacognition that includes both the knowledge of factors that affect memory (i.e. declarative metamemory) and knowledge and application of factors in one’s own learning and recall performance (i.e. procedural metamemory). The current paper aims to provide a comprehensive review of studies examining metamemory ability development from preschool into adolescence in order to improve the understanding of metamemory, its developmental course, and the available assessment methods. We examined the developmental trajectory of procedural and declarative metamemory abilities for both typically developing children and clinical populations. We found procedural metamemory abilities emerge around 4 to 5 years old, and significantly improve across childhood and into adulthood, although less is known about metamemory development across adolescence in typically developing children. Additionally, metamemory abilities vary significantly based on clinical pathology, although relatively fewer studies have examined these abilities in children with neurodevelopmental disorders or other neurologic conditions, such as acquired brain injury. The methods of metamemory assessment varied significantly across studies as well, indicating a need for a standardized metamemory measure, which would have high utility for clinical care.
Deep-learning based image reconstruction for MRI-guided near-infrared spectral tomography

Non-invasive near-infrared spectral tomography (NIRST) can incorporate the structural information provided by simultaneous magnetic resonance imaging (MRI), and this has significantly improved the images obtained of tissue function. However, the process of MRI guidance in NIRST has been time consuming because of the needs for tissue-type segmentation and forward diffuse modeling of light propagation. To overcome these problems, a reconstruction algorithm for MRI-guided NIRST based on deep learning is proposed and validated by simulation and real patient imaging data for breast cancer characterization. In this approach, diffused optical signals and MRI images were both used as the input to the neural network, and simultaneously recovered the concentrations of oxy-hemoglobin, deoxy-hemoglobin, and water via end-to-end training by using 20,000 sets of computer-generated simulation phantoms. The simulation phantom studies showed that the quality of the reconstructed images was improved, compared to that obtained by other existing reconstruction methods. Reconstructed patient images show that the well-trained neural network with only simulation data sets can be directly used for differentiating malignant from benign breast tumors.
Persistence of an epidemic cluster of Rhodotorula mucilaginosa in multiple geographic regions in China and the emergence of a 5-flucytosine resistant clone

ABSTRACT Rhodotorula mucilaginosa, an environmental yeast widely used in industry and agriculture, is also an opportunistic pathogen resistant to multi-antifungals. During the national surveillance in China, R. mucilaginosa has been documented from various hospitals and regions. At present, the molecular epidemiology of invasive infections caused by R. mucilaginosa and their resistance profiles to antifungals were unknown. Here we collected 49 strains from four hospitals located in different geographic regions from 2009 to 2019 in China, determined their genotypes using different molecular markers and quantified susceptibilities to various antifungals. Sequencing of ITS and D1/D2 regions in rDNA indicated that 73.5% (36/49) of clinical strains belong to same sequence type (rDNA type 2). Microsatellite (MT) genotyping with 15 (recently developed) tandem repeat loci identified 5 epidemic MT types, which accounted for 44.9% (22/49) of clinical strains, as well as 27 sporadic MT types. Microsatellite data indicated that the presence of an epidemic cluster including 35 strains (71.4%) repeatedly isolated in four hospitals for eight years. Single nucleotide variants (SNVs) from the whole genome sequence data also supported the clustering of these epidemic strains due to low pairwise distance. In addition, phylogenetic analysis of SNVs from these clinical strains, together with environmental and animal strains showed that the closely related epidemic cluster strains may be opportunistic, zoonotic pathogens. Also, molecular data indicated a possible clonal transmission of pan echinocandins-azoles-5-flucytosine resistant R. mucilaginosa strains in hospital H01. Our study demonstrated that R. mucilaginosa is a multi-drug resistant pathogen with the ability to cause nosocomial infection.
Conduct Problems As a Pathway From Childhood Adversity to Community Violence Exposure: The Protective Roles of Caregiver Knowledge and Involvement

Exposure to community violence (ECV) poses a prevalent threat to the health and development of adolescents. Research indicates those who have more Adverse Childhood Experiences (ACEs) are at higher risk for ECV, which further exacerbates risk of negative mental and physical health impacts. Additionally, those with more ACEs are more likely to exhibit conduct problems, which has also been linked to risk for ECV. Despite the prevalence and impact of ECV, there is limited longitudinal research on the risk factors that precede this exposure as well as family-level factors that may prevent it. The current study examined conduct problems as a potential mediator between ACEs and future indirect (i.e. witnessing) ECV in adolescents. Additionally, this study included caregiver factors, such as caregiver knowledge about their adolescent, caregiver involvement, and caregiver-adolescent relationship quality as potential protective moderators. Participants included (N = 1137) caregiver-adolescent dyads identified as at-risk for child maltreatment prior to child’s age four for inclusion in the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN). Conduct problems at age 14 mediated the relationship between ACEs from ages 0-12 and indirect ECV at age 16 (standardized indirect effect = .03, p = .005). Caregiver knowledge moderated the indirect relationship (b = −.40, p = .030), and caregiver involvement moderated the direct relationship between ACEs and indirect ECV (b = −.03, p = .033). Findings expand our knowledge about the longitudinal pathways that increase risk of violence exposure over the course of adolescent development, as well as the protective benefits caregivers can offer to disrupt these pathways and reduce risk of future traumatization. Implications are discussed for interventions that aim to address and prevent trauma and adverse outcomes among youth exposed to child maltreatment, household dysfunction, and community violence.
Interprofessional simulation-based team-training and self-efficacy in emergency medicine situations

ABSTRACT Teamwork quality has been shown to influence patient safety, and simulation-based team-training (SBTT) is an effective means to increase this quality. However, long-term effects are rarely studied. This study aims to investigate the long-term effects of interprofessional SBTT in emergency medicine in terms of global confidence, self-efficacy in interprofessional communication and in emergency medicine situations. Newly graduated doctors, nurses, auxiliary nurses, and medical and nursing students participated. Four emergency medicine scenarios focused on teamwork according to the A-B-C-D-E-strategy. All participants increased their global confidence from 5.3 (CI 4.9–5.8) before to 6.8 (CI 6.4–7.2; p < .0001) after SBTT. Confidence in interprofessional communication increased from 5.3 (CI 4.9–5.8) to 7.0 (CI 6.6–7.4; p < .0001). Students had the greatest gain. The self-efficacy following the A-B-C-D-E strategy increased from 4.9 (CI 4.4–5.3) to 6.6 (CI 6.2–7.0). Again, students had the steepest increase. Newly graduated doctors achieved a superior increase in global confidence as compared to nurses and auxiliary nurses (p < .0001). Their propensity to recommend SBTT to colleagues was 9.9 (CI 9.8–10.0). The positive effects were sustained over a six-month period, indicating that interprofessional SBTT had a positive impact on competence development, and a potential to contribute to increased team quality in emergency medicine care.
Pay attention! The influence of coach-, content-, and player-related factors on focus of attention statements during tennis training

ABSTRACT It is well established that adopting an external focus of attention (EF) enhances motor learning when contrasted to an internal focus (IF) or a neutral condition. Despite consistent evidence for this performance-enhancing effect of an EF, recent studies show that coaches predominantly induce an IF in the communication with athletes. It remains, however, unknown whether and to what degree coach-, content- or player-related factors have an impact on the focus of attention during youth tennis training. Therefore, we recorded all statements from 10 tennis coaches during six training sessions with a total of 87 youth athletes aged 18 years or younger. All statements were categorized according to the focus they induced (IF, EF, neutral, best place to hit the ball, mix, no focus) and the form of communication (instruction, feedback). Of the relevant statements (n = 3049), 45.1% promoted an EF, whereas 33.1% induced an IF. Evaluation of coaches-, player- and content-specific features (education, age of coach/player, training content, skill level) showed that more statements with an IF were given when training beginners. The data also show more statements with an EF for instructions (59.0%) compared to feedback (43.0%). Our results contrast with other sports where instructions with an IF predominate. Thus, instrumented sports with external targets (e.g. tennis) might be better suited for inducing an EF. The data also show that coaches are more likely to use IF when communicating with beginners. Consequently, an EF should not be induced in every possible situation, even if this is often communicated so far. Highlights This study shows for the first time that statements that induce an external focus of attention predominate in tennis. This result contrasts with results from other sports and suggests that racquet sports may be better suited for inducing an external attentional focus. This study shows that coaches adapt their communication to the performance level of their athletes. The use of an external focus of attentions predominates when working with tennis players. However, the likelihood of inducing an EF is even higher when working with skilled tennis players when compared to beginners. The way of communication (feedback vs. instruction) influences how coaches phrase their statements to athletes. The analysis revealed that coaches use more statements with an external focus of attentions for instructions compared to feedback.
Phase II multi-institutional clinical trial result of concurrent cetuximab and nivolumab in recurrent and/or metastatic head and neck squamous cell carcinoma.

PURPOSE A phase II multi-institutional clinical trial was conducted to determine overall survival (OS) in patients with recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) treated with a combination of cetuximab and nivolumab. EXPERIMENTAL DESIGN Patients with R/M HNSCC were treated with cetuximab 500 mg/m2 IV Day (D) -14 as a lead-in followed by cetuximab 500 mg/m2 IV and nivolumab 240 mg IV on D1 and D15 of each 28-D cycle. Expression of p16 and programmed cell death-ligand 1 (PD-L1) in archived tumors were determined. Tumor-tissue-modified human papillomavirus (TTMV) DNA was quantified in plasma. RESULTS Ninety-five patients were enrolled, and 88 patients were evaluable for OS with a median follow-up of 15.9 months. Median OS in the 45 patients who had prior therapy for R/M HNSCC (Cohort A) was 11.4 months, with a 1-year OS 50% (90% CI, 0.43-0.57). Median OS in the 43 patients who had no prior therapy (Cohort B) was 20.2 months, with a 1-year OS 66% (90% CI, 0.59-0.71). In the combined cohorts, the p16-negative immunostaining was associated with higher response rate (RR, p=0.02) but did not impact survival while higher PD-L1 combined positive score was associated with higher RR (p=0.03) and longer OS (log-rank p=0.04). In the p16-positive patients, median (log-rank p=0.05). CONCLUSION The combination of cetuximab and nivolumab is effective in patients with both previously treated and untreated R/M HNSCC and warrants further evaluation.
The role of astrocytes in epileptic disorders

Epilepsy affects about 1% of the population and approximately 30% of epileptic patients are resistant to current antiepileptic drugs. As a hallmark in epileptic tissue, many of the epileptic patients show changes in glia morphology and function. There are characteristic changes in different types of glia in different epilepsy models. Some of these changes such as astrogliosis are enough to provoke epileptic seizures. Astrogliosis is well known in mesial temporal lobe epilepsy (MTLE), the most common form of refractory epilepsy. A better understanding of astrocytes alterations could lead to novel and efficient pharmacological approaches for epilepsy. In this review, we present the alterations of astrocyte morphology and function and present some instances of targeting astrocytes in seizure and epilepsy.