Electrochemical Investigation of Exchange Current Density of Uranium and Rare-earths Couples (M3+/M0) in LiCl-KCl Eutectic Electrolyte

The objective of this work is to use electrochemical techniques to quantify the electrode reaction rate of some rare-earth elements and uranium in a LiCl-KCl eutectic electrolyte at 500oC. The exchange current densities of the oxidation-reduction couples of M3+/M0 (La3+/La0, Ce3+/Ce0, Pr3+/Pr0, Nd3+/Nd0,Gd3+/Gd0, Y3+/Y0, U3+/U0) on a tungsten electrode were measured by applying a linear polarization resistance technique. A region of linear dependence of potential on applied current could be found to describe the reaction rate of oxidation-reduction system. From these measurements, the estimated exchange current density was 0.38 mA/cm2 for uranium, and was within the range of 0.27 to 0.38mA/cm2 for rare-earth elements.open0

Polynomial-based Self-Attention for Table Representation learning

Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning. Various representation learning methods for tabular data have been proposed, ranging from encoder-decoder structures to Transformers. Among these, Transformer-based methods have achieved state-of-the-art performance not only in tabular data but also in various other fields, including computer vision and natural language processing. However, recent studies have revealed that self-attention, a key component of Transformers, can lead to an oversmoothing issue. We show that Transformers for tabular data also face this problem, and to address the problem, we propose a novel matrix polynomial-based self-attention layer as a substitute for the original self-attention layer, which enhances model scalability. In our experiments with three representative table learning models equipped with our proposed layer, we illustrate that the layer effectively mitigates the oversmoothing problem and enhances the representation performance of the existing methods, outperforming the state-of-the-art table representation methods

The role of cGAMP via the STING pathway in modulating germinal center responses and CD4 T cell differentiation

Germinal center (GC) responses are essential for establishing protective, long-lasting immunity through the differentiation of GC B cells (BGC) and plasma cells (BPC), along with the generation of antigen-specific antibodies. Among the various pathways influencing immune responses, the STING (Stimulator of Interferon Genes) pathway has emerged as significant, especially in innate immunity, and extends its influence to adaptive responses. In this study, we examined how the STING ligand cGAMP can modulate these key elements of the adaptive immune response, particularly in enhancing GC reactions and the differentiation of BGC, BPC, and follicular helper T cells (TFH). Employing in vivo models, we evaluated various antigens and the administration of cGAMP in Alum adjuvant, investigating the differentiation of BGC, BPC, and TFH cells, along with the production of antigen-specific antibodies. cGAMP enhances the differentiation of BGC and BPC, leading to increased antigen-specific antibody production. This effect is shown to be type I Interferon-dependent, with a substantial reduction in BPC frequency upon interferon (IFN)-β blockade. Additionally, cGAMP’s influence on TFH differentiation varies over time, which may be critical for refining vaccine strategies. The findings elucidate a complex, antigen-specific influence of cGAMP on T and B cell responses, providing insights that could optimize vaccine efficacy

Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations

Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. In recent years, Linear-based LTSF models showed better performance, pointing out the problem of Transformer-based approaches causing temporal information loss. However, Linear-based approach has also limitations that the model is too simple to comprehensively exploit the characteristics of the dataset. To solve these limitations, we propose LTSF-DNODE, which applies a model based on linear ordinary differential equations (ODEs) and a time series decomposition method according to data statistical characteristics. We show that LTSF-DNODE outperforms the baselines on various real-world datasets. In addition, for each dataset, we explore the impacts of regularization in the neural ordinary differential equation (NODE) framework.Comment: Accepted at IEEE BigData 202

Development and Testing of Thrombolytics in Stroke

Despite recent advances in recanalization therapy, mechanical thrombectomy will never be a treatment for every ischemic stroke because access to mechanical thrombectomy is still limited in many countries. Moreover, many ischemic strokes are caused by occlusion of cerebral arteries that cannot be reached by intra-arterial catheters. Reperfusion using thrombolytic agents will therefore remain an important therapy for hyperacute ischemic stroke. However, thrombolytic drugs have shown limited efficacy and notable hemorrhagic complication rates, leaving room for improvement. A comprehensive understanding of basic and clinical research pipelines as well as the current status of thrombolytic therapy will help facilitate the development of new thrombolytics. Compared with alteplase, an ideal thrombolytic agent is expected to provide faster reperfusion in more patients; prevent re-occlusions; have higher fibrin specificity for selective activation of clot-bound plasminogen to decrease bleeding complications; be retained in the blood for a longer time to minimize dosage and allow administration as a single bolus; be more resistant to inhibitors; and be less antigenic for repetitive usage. Here, we review the currently available thrombolytics, strategies for the development of new clot-dissolving substances, and the assessment of thrombolytic efficacies in vitro and in vivo

Altered resting-state connectivity in subjects at ultra-high risk for psychosis: an fMRI study

<p>Abstract</p> <p>Background</p> <p>Individuals at ultra-high risk (UHR) for psychosis have self-disturbances and deficits in social cognition and functioning. Midline default network areas, including the medial prefrontal cortex and posterior cingulate cortex, are implicated in self-referential and social cognitive tasks. Thus, the neural substrates within the default mode network (DMN) have the potential to mediate self-referential and social cognitive information processing in UHR subjects.</p> <p>Methods</p> <p>This study utilized functional magnetic resonance imaging (fMRI) to investigate resting-state DMN and task-related network (TRN) functional connectivity in 19 UHR subjects and 20 matched healthy controls. The bilateral posterior cingulate cortex was selected as a seed region, and the intrinsic organization for all subjects was reconstructed on the basis of fMRI time series correlation.</p> <p>Results</p> <p>Default mode areas included the posterior/anterior cingulate cortices, the medial prefrontal cortex, the lateral parietal cortex, and the inferior temporal region. Task-related network areas included the dorsolateral prefrontal cortex, supplementary motor area, the inferior parietal lobule, and middle temporal cortex. Compared to healthy controls, UHR subjects exhibit hyperconnectivity within the default network regions and reduced anti-correlations (or negative correlations nearer to zero) between the posterior cingulate cortex and task-related areas.</p> <p>Conclusions</p> <p>These findings suggest that abnormal resting-state network activity may be related with the clinical features of UHR subjects. Neurodevelopmental and anatomical alterations of cortical midline structure might underlie altered intrinsic networks in UHR subjects.</p

Acute Myocardial Infarction due to Polyarteritis Nodosa in a Young Female Patient

Coronary artery aneurysms are uncommon, are usually associated with atherosclerosis, and rarely involve all three major coronary arteries. The present report describes a rare case of a young female patient presenting with acute myocardial infarction (AMI). Coronary angiography revealed multiple severe aneurysmal and stenotic changes. Based on clinical feature and angiographic findings, it was strongly suspected that the patient had polyarteritis nodosa (PAN) complicated by AMI. The patient was treated with standard cardiac medications and immunosuppressive agents and has remained stable without further complications during a follow-up period of 6 months