Age-specific sex-differences in cerebral blood flow velocity in relation to haemoglobin levels

Introduction: Cerebral blood flow (CBF) declines with age and abnormalities in CBF are associated with age-related cerebrovascular disease and neurodegeneration. Women have higher CBF than men, although this sex-difference diminishes to some extent with age in healthy subjects. The physiological drivers of these age/sex differences are uncertain, but might be secondary to age and sex-differences in haemoglobin (Hb) level. Hb levels are inversely correlated with CBF, are lower in women, and decline with age in men, but the interrelations between these factors have not been explored systematically either in healthy subjects or across the full age-range in patients with vascular risk factors. We aimed to determine the age-specific interrelations between sex, Hb, and CBF velocity in a large cohort of patients with cerebrovascular disease. Patients and methods: In patients with a recent transient ischaemic attack or minor stroke (Oxford Vascular Study) and no ipsilateral or contralateral stenosis of the carotid or intracranial arteries, we related peak-systolic velocity (PSV) and other parameters on transcranial Doppler ultrasound (TCD) of the middle cerebral artery to sex, age, Hb and vascular risk factors. Results: Of 958 eligible subjects (mean age/SD = 68.04/14.26, 53.2% male), younger women (age < 55 years) had higher CBF velocities than men (mean sex difference in PSV at age < 55 years = 16.31 cm/s; p < 0.001), but this difference declined with age (interaction p < 0.001), such that it was no longer significant at age 75–84 (∆PSV = 3.26 cm/s; p = 0.12) and was reversed at age ⩾ 85 (∆PSV = −7.42 cm/s; p = 0.05). These changes mirrored trends in levels of Hb, which were higher in men at age < 55 (∆Hb = 1.92 g/dL; p < 0.001), but steadily decreased with age in men but not in women (interaction p < 0.001), with no residual sex-difference at age ⩾ 85 (∆Hb = 0.12 g/dL; p = 0.70). There was an inverse correlation between Hb and PSV in both women and men (both p ⩽ 0.01), and the sex-difference in PSV at age < 55 was substantially diminished after adjustment for Hb (∆PSV = 6.92; p = 0.036; ∆PSV = 5.92, p = 0.13 with further adjustment for end-tidal CO2). In contrast, the sex difference in PSV was unaffected by adjustment for systolic and diastolic blood pressure, heart rate, and vascular risk factors (history of hypertension, diabetes, hyperlipidaemia and smoking). Discussion: CBF velocity is strongly correlated with Hb level at all ages, and sex-differences in CBF velocity appear to be explained in major part by age-related sex-differences in Hb

Better Explain Transformers by Illuminating Important Information

Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings. Prior methods explain Transformers by focusing on the raw gradient and attention as token attribution scores, where non-relevant information is often considered during explanation computation, resulting in confusing results. In this work, we propose highlighting the important information and eliminating irrelevant information by a refined information flow on top of the layer-wise relevance propagation (LRP) method. Specifically, we consider identifying syntactic and positional heads as important attention heads and focus on the relevance obtained from these important heads. Experimental results demonstrate that irrelevant information does distort output attribution scores and then should be masked during explanation computation. Compared to eight baselines on both classification and question-answering datasets, our method consistently outperforms with over 3\% to 33\% improvement on explanation metrics, providing superior explanation performance. Our anonymous code repository is available at: https://github.com/LinxinS97/Mask-LR

NLPBench: Evaluating Large Language Models on Solving NLP Problems

Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a dearth of research dedicated to the NLP problem-solving abilities of LLMs. To fill the gap in this area, we present a unique benchmarking dataset, NLPBench, comprising 378 college-level NLP questions spanning various NLP topics sourced from Yale University's prior final exams. NLPBench includes questions with context, in which multiple sub-questions share the same public information, and diverse question types, including multiple choice, short answer, and math. Our evaluation, centered on LLMs such as GPT-3.5/4, PaLM-2, and LLAMA-2, incorporates advanced prompting strategies like the chain-of-thought (CoT) and tree-of-thought (ToT). Our study reveals that the effectiveness of the advanced prompting strategies can be inconsistent, occasionally damaging LLM performance, especially in smaller models like the LLAMA-2 (13b). Furthermore, our manual assessment illuminated specific shortcomings in LLMs' scientific problem-solving skills, with weaknesses in logical decomposition and reasoning notably affecting results

Efficient Bi-Level Optimization for Recommendation Denoising

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.Comment: 11pages, 5 figures, 6 table

Berberine Improves Insulin Sensitivity by Inhibiting Fat Store and Adjusting Adipokines Profile in Human Preadipocytes and Metabolic Syndrome Patients

Berberine is known to inhibit the differentiation of 3T3-L1 cells in vitro, improve glycemic control, and attenuate dyslipidemia in clinical study. The aim of this study was to investigate the effects of berberine on preadipocytes isolated from human omental fat and in metabolic syndrome patients treated with berberine for 3 months. We have shown that treatment with 10 μM berberine resulted in a major inhibition of human preadipocyte differentiation and leptin and adiponectin secretion accompanied by downregulation of PPARγ2, C/EBPα, adiponectin, and leptin mRNA expression. After 3 months of treatment, metabolic syndrome patients showed decrease in their BMI (31.5 ± 3.6 versus 27.4 ± 2.4 kg/m2) and leptin levels (8.01 versus 5.12 μg/L), as well as leptin/adiponectin ratio and HOMA-IR. These results suggest that berberine improves insulin sensitivity by inhibiting fat store and adjusting adipokine profile in human preadipocytes and metabolic syndrome patients

Au@h-Al2O3 Analogic Yolk–Shell Nanocatalyst for Highly Selective Synthesis of Biomass-Derived D-xylonic Acid via Regulation of Structure Effects

Selective oxidation of biomass-based monosaccharides into value-added sugar acids is highly desired, but limited success of producing D-xylonic acid has been achieved. Herein, we report an efficient catalyst system, viz., Au nanoparticles anchored on the inner walls of hollow Al2O3 nanospheres (Au@h- Al2O3), which could catalyze the selective oxidation of D-xylose into D-xylonic acid under base-free conditions. The mesoporous Al2O3 shell as the adsorbent first adsorbed D-xylose. Then, the interface of Au nanoparticles and Al2O3 as active sites spontaneously dissociated O2, and the exposed Au nanoparticle surface as the catalytic site drove the transformation. With this catalyst system, the valuable D-xylonic acid was produced with excellent yields in the aerobic oxidation of D-xylose. Extensive investigation showed that Au@h- Al2O3 is an efficient catalyst with high stability and recyclability

White Matter Imaging Correlates of Early Cognitive Impairment Detected by the Montreal Cognitive Assessment after Transient Ischemic Attack and Minor Stroke

BACKGROUND AND PURPOSE:&nbsp; Among screening tools for cognitive impairment in large cohorts, the Montreal Cognitive assessment (MoCA) appears to be more sensitive to early cognitive impairment than the Mini-Mental State Examination (MMSE), particularly after transient ischemic attack (TIA) or minor stroke. We reasoned that if MoCA-detected early cognitive impairment is pathologically significant, then it should be specifically associated with the presence of white matter hyperintensities (WMH) and reduced fractional anisotropy (FA) on MRI. METHODS:&nbsp;Consecutive eligible patients with TIA or minor stroke (Oxford Vascular Study) underwent MRI and cognitive assessment. We correlated MoCA and MMSE scores with WMH and FA, then specifically studied patients with low MoCA and normal MMSE. RESULTS: Among 400 patients, MoCA and MMSE scores were significantly correlated (all p&lt;0.001) with WMH volumes (rMoCA=-0.336, rMMSE=-0.297) and FA (rMoCA=0.409, rMMSE=0.369), and -on voxel-wise analyses- with WMH in frontal white matter and reduced FA in almost all white matter tracts. However, only the MoCA was independently correlated with WMH volumes (r=-0.183, p&lt;0.001), average FA values (r=0.218, p&lt;0.001), and voxel-wise reduced FA in anterior tracts after controlling for the MMSE. In addition, patients with low MoCA but normal MMSE (N=57) had higher WMH volumes (t=3.1,p=0.002), lower average FA (t=-4.0,p&lt;0.001), and lower voxel-wise FA in almost all white matter tracts than those with normal MoCA and MMSE (N=238). CONCLUSIONS:&nbsp;In patients with TIA or minor stroke, early cognitive impairment detected with the MoCA but not with the MMSE was independently associated with white matter damage on MRI, particularly reduced FA

The relationship between 25-hydroxy vitamin D and serum asprosin in patients with type 2 diabetes in the community

ObjectivesThis study aimed to investigate the link between 25-hydroxy vitamin D and serum asprosin in individuals with type 2 diabetes within the community. The goal was to provide a foundation for clinical interventions.MethodsBetween November 2019 and July 2021, data from 463 patients with type 2 diabetes were consistently gathered at a community health service station in Southeast Shanxi Province. General information and laboratory metrics were compiled, including serum asprosin levels. The participants were categorized based on three serum asprosin quantiles, allowing for a comparison of various factors among the groups. The correlation between serum asprosin levels and other factors was analyzed. Employing a general linear model, the connection between 25-hydroxy vitamin D and serum asprosin levels was studied. Utilizing three quantiles of 25-hydroxy vitamin D, serum asprosin was treated as the dependent variable, while 25-hydroxy vitamin D served as the independent variable for linear regression analysis.ResultsAs serum asprosin increased, there were gradual increments in age, disease duration, SBP, BMI, WC, creatinine, and SUA levels (P&lt;0.05). Conversely, HbA1c, HDL-C, GFR, and 25-hydroxy vitamin D levels exhibited gradual declines (P&lt;0.05). Age, 25-hydroxy vitamin D, SUA, creatinine, and LDL-C emerged as independent influencing factors for serum asprosin. Across the 1st to 3rd 25-hydroxy vitamin D quantiles, elevated 25-hydroxy vitamin D levels correlated with a gradual reduction in mean serum asprosin (P&lt;0.05).ConclusionSerum asprosin levels demonstrate an inverse correlation with 25-hydroxy vitamin D levels in community-dwelling individuals with type 2 diabetes. Serum asprosin levels might independently contribute to 25-hydroxy vitamin D levels