Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:Predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of followup data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.</p

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

An event study on the effect of captive formation on the stock returns of parent companies in the Asia-Pacific.

This report analyzes the effect of captive formation on the stock returns of parent companies incorporated in the Asia-Pacific. The study applies specifically to 2 countries: Japan and Australia. The cumulative abnormal return (CAR) model is used in the analysis

Pt Nanoparticles Densely Coated on SnO₂‑Covered Multiwalled Carbon Nanotubes with Excellent Electrocatalytic Activity and Stability for Methanol Oxidation

A new electrocatalyst exhibiting enhanced activity and stability is designed from SnO₂-covered multiwalled carbon nanotubes coated with 85 wt % ratio Pt nanoparticles (NPs). This catalyst showed a mass activity 6.2 times as active as that of the commercial Pt/C for methanol oxidation, owing to the unique one-dimensional structure. Moreover, the durability and antipoisoning ability were also improved greatly. The enhanced intrinsic performance was ascribed to the densely connected networks of Pt NPs on the SnO₂ NPs

Hydrogen Peroxide Activated by Biochar-Supported Sulfidated Nano Zerovalent Iron for Removal of Sulfamethazine: Response Surface Method Approach

Biochar (BC)-supported sulfide-modified nanoscale zerovalent iron (S-nZVI/BC) was prepared using the liquid-phase reduction method for the application of the removal of sulfamethazine (SMZ) from water. The reaction conditions were optimized by the Box&ndash;Behnken response surface method (RSM). A model was constructed based on the influence factors of the removal rate, i.e., the carbon-to-iron ratio (C/Fe), iron-sulfur ratio (Fe/S), pH, and hydrogen peroxide (H2O2) concentration, and the influence of each factor on the removal efficiency was investigated. The optimal removal process parameters were determined based on theoretical and experimental results. The results showed that the removal efficiency was significantly affected by the C/Fe ratio and pH (p &lt; 0.0001) but relatively weakly affected by the Fe/S ratio (p = 0.0973) and H2O2 concentration (p = 0.022). The optimal removal process parameters were as follows: 0.1 mol/L H2O2, a pH of 3.18, a C/Fe ratio of 0.411, and a Fe/S ratio of 59.75. The removal rate of SMZ by S-nZVI/BC was 100% under these conditions. Therefore, it is feasible to use the Box&ndash;Behnken RSM to optimize the removal of emerging pollutants in water bodies by S-nZVI/BC

An Effective Integrated Design for Enhanced Cathodes of Ni Foam-Supported Pt/Carbon Nanotubes for Li‑O₂ Batteries

Designing an effective microstructural cathode combined with a highly efficient catalyst is essential for improving the electrochemical performance of Li-O₂ batteries (LOBs), especially for long-term cycling. We present a nickel foam-supported composite of Pt nanoparticles (NPs) coated on self-standing carbon nanotubes (CNTs) as a binder-free cathode for LOBs. The assembled LOBs can afford excellent electrochemical performance with a reversible capacity of 4050 mAh/g tested at 20 mA/g and superior cyclability for 80 cycles with a limited capacity of 1500 mAh/g achieved at a high current density of 400 mA/g. The capacity corresponds to a high energy density of ∼3000 Wh/kg. The improved performance should be attributed to the excellent catalytic activity of highly dispersed Pt NPs, facile electron transport via loose CNTs connected to the nickel foam current collector, and fast O₂ diffusion through the porous Pt/CNTs networks. In addition, some new insights from impedance analysis have been proposed to explain the enhanced mechanism of LOBs

Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)

Abstract Background Cell-free DNA (cfDNA) contains a large amount of molecular information that can be used for multi-cancer early detection (MCED), including changes in epigenetic status of cfDNA, such as cfDNA fragmentation profile. The fragmentation of cfDNA is non-random and may be related to cfDNA methylation. This study provides clinical evidence for the feasibility of inferring cfDNA methylation levels based on cfDNA fragmentation patterns. We performed whole-genome bisulfite sequencing and whole-genome sequencing (WGS) on both healthy individuals and cancer patients. Using the information of whole-genome methylation levels, we investigated cytosine–phosphate–guanine (CpG) cleavage profile and validated the method of predicting the methylation level of individual CpG sites using WGS data. Results We conducted CpG cleavage profile biomarker analysis on data from both healthy individuals and cancer patients. We obtained unique or shared potential biomarkers for each group and built models accordingly. The modeling results proved the feasibility to predict the methylation status of single CpG sites in cfDNA using cleavage profile model from WGS data. Conclusion By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites. Therefore, methylation profile, a widely used epigenetic biomarker, can be obtained from a single WGS assay for MCED