A comprehensive two-sample Mendelian randomization analysis of trigeminal neuralgia and modifiable risk factors

ObjectiveTo conduct a comprehensive search and causality study of potential modifiable risk factors for trigeminal neuralgia. To provide new ideas for subsequent treatment and management of patients with trigeminal neuralgia.MethodsData were obtained from large GWAS databases and then analyzed by Mendelian randomization analysis. The causal relationship between 36 potentially modifiable risk factors and trigeminal neuralgia was explored based on the results of the inverse variance weighting method(IVW). p < 0.05 was considered statistically significant.ResultsYears of schooling [OR (95%CI), 0.59(0.42–0.84), p = 0.003] to be a significant protective factor. Anxiety disorders [OR (95%CI), 1.62(1.05–2.48), p = 0.028], Depression [OR (95%CI), 1.53(1.03–2.28), p = 0.035] and Autoimmune [OR (95%CI), 1.16(1.01–1.32), p = 0.033] were significant risk factors. Sleep duration [OR (95%CI), 0.43(0.18–1.01), p = 0.051] was a close protective factor. Body mass index [OR (95%CI), 1.24(0.98–1.57), p = 0.077] was a close risk factor.ConclusionMendelian randomization analysis shows Years of schooling and Sleep duration as protective factors. Anxiety disorders, Depression, Autoimmune, and Body mass index are risk factors. This will help in the research of diagnosis, treatment, and mechanism of trigeminal neuralgia. And reduce the prevalence of trigeminal neuralgia through positive psychological and lifestyle interventions

Trigeminal neuralgia associated with dural arteriovenous fistula: a case report and literature reviews

Trigeminal neuralgia is a paroxysmal, intense electric shock-like, or knife-like, recurrent pain that affects one or more sense areas of the unilateral facial trigeminal nerve. It can be classified into two groups from an etiological standpoint: primary and secondary. The pain episodes brought on by such vascular compression are still categorized as primary trigeminal neuralgia, despite the fact that microvascular compression of the trigeminal nerve root has now been demonstrated to be the primary cause. A rare and complicated condition known as a dural arteriovenous fistula (DAVF) can irritate the Gasserian ganglion or compress the trigeminal nerve’s root entry zone (REZ), leading to secondary trigeminal neuralgia (TN). At present, the treatment of DAVF-induced trigeminal neuralgia is not conclusive. This article reports a case of DAVF-induced trigeminal neuralgia cured by MVD and reviews the relevant literature

Spin-dependent structure functions g^1\hat g_1 and g^2\hat g_2 for inclusive spin-half baryon production in electron-positron annihilation

Two spin-dependent structure functions $\hat g_1$ and $\hat g_2$ for the inclusive spin-half baryon production in electron-positron annihilation are studied in the context of QCD factorization as well as in the naive quark parton model. As a result, it is found that the sum of $\hat g_1$ and $\hat g_2$ is related to $\hat h_1$ and $\hat g_T$, two quark fragmentation functions defined by Jaffe and Ji. In connection with the measurement of quark fragmentation functions, the possible phenomenological consequences are discussed.Comment: RevTex, four Ps figures, to appear in Phys. Rev.

NEOLAF, an LLM-powered neural-symbolic cognitive architecture

This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents. The NEOLAF framework is a superior approach to constructing intelligent agents than both the pure connectionist and pure symbolic approaches due to its explainability, incremental learning, efficiency, collaborative and distributed learning, human-in-the-loop enablement, and self-improvement. The paper further presents a compelling experiment where a NEOLAF agent, built as a problem-solving agent, is fed with complex math problems from the open-source MATH dataset. The results demonstrate NEOLAF's superior learning capability and its potential to revolutionize the field of cognitive architectures and self-improving adaptive instructional systems

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Parallel hierarchical cross entropy optimization for on-chip decap budgeting

Decoupling capacitor (decap) placement has been widely adopted as an effective way to suppress dynamic power supply noise. Traditional decap budgeting algorithms usually explore the sensitivity-based nonlinear optimizations or conjugate gradient methods, which can be prohibitively expensive for large-scale decap budgeting problems. We present a hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. CE is an advanced optimization framework which explores the power of rare-event probability theory and importance sampling. To achieve high efficiency, a sensitivity-guided cross entropy (SCE) algorithm is proposed which integrates CE with a partitioningbased sampling strategy to effectively reduce the dimensionality in solving the large scale decap budgeting problems. Extensive experiments on industrial power grid benchmarks show that the proposed SCE method converges 2X faster than the prior methods and 10X faster than the standard CE method, while gaining up to 25% improvement on power grid supply noise. Importantly, the proposed SCE algorithm is parallel-friendly since the simulation samples of each SCE iteration can be independently obtained in parallel. We obtain up to 1.9X speedup when running the SCE decap budgeting algorithm on a dual-core-dual-GPU system. Copyright 2010 ACM

Power grid analysis with hierarchical support graphs

It is increasingly challenging to analyze present day large-scale power delivery networks (PDNs) due to the drastically growing complexity in power grid design. To achieve greater runtime and memory efficiencies, a variety of preconditioned iterative algorithms has been investigated in the past few decades with promising performance, while incremental power grid analysis also becomes popular to facilitate fast re-simulations of corrected designs. Although existing preconditioned solvers, such as incomplete matrix factor-based preconditioners, usually exhibit high efficiency in memory usage, their convergence behaviors are not always satisfactory. In this work, we present a novel hierarchical support-graph preconditioned iterative algorithm that constructs preconditioners by generating spanning trees in power supply networks for fast power grid analysis. The support-graph preconditioner is efficient for handling complex power grid structures (regular or irregular grids), and can facilitate very fast incremental analysis. Our experimental results on IBM power grid benchmarks show that compared with the best direct or iterative solvers, the proposed support-graph preconditioned iterative solver achieves up to 3.6X speedups for DC analysis, and up to 22X speedups for incremental analysis, while reducing the memory consumption by a factor of four. © 2011 IEEE