Miracle-dz/UniFAR-FTUs huggingface dataset:
Soluble RAGE and skeletal muscle tissue RAGE expression profiles in lean and obese young adults across differential aerobic exercise intensities

Nearly 40% of Americans have obesity and are at increased risk for developing type 2 diabetes. Skeletal muscle is responsible for >80% of insulin-stimulated glucose uptake which is attenuated by the inflammatory milieu of obesity and augmented by exercise. The Receptor for Advanced Glycation Endproducts (RAGE) is an inflammatory receptor directly linking metabolic dysfunction with inflammation. Circulating soluble isoforms of RAGE (sRAGE) formed either by proteolytic cleavage (cRAGE) or alternative splicing (esRAGE) act as decoys for RAGE ligands thereby counteracting RAGE-mediated inflammation. PURPOSE We aimed to determine if RAGE expression or alternative splicing of RAGE is altered by obesity in muscle, and whether acute aerobic exercise (AE) modifies RAGE and sRAGE. METHODS Young (20-34y) participants without (n=17; BMI: 22.6±2.6 kg/m2) and with obesity (n=7; BMI: 32.8±2.9 kg/m2) performed acute aerobic exercise (AE) at 40, 65 or 80% of VO2Max (mL/kg/min) on separate visits. Blood was taken before and 30 minutes after each exercise bout. Muscle biopsy samples were taken before, 30 minutes, and 3 hours after the 85% VO2Max exercise bout. RESULTS Individuals with obesity had higher total RAGE and esRAGE mRNA and RAGE protein (p<0.0001). In addition, RAGE and esRAGE transcripts correlated to transcripts of the NF-κB subunit P65 (p<0.05). There was no effect of exercise on total RAGE or esRAGE transcripts, or RAGE protein (p>0.05) and AE tended to decrease circulating sRAGE. CONCLUSIONS RAGE expression is exacerbated in skeletal muscle with obesity, which may contribute to the development of skeletal muscle metabolic dysfunction such as insulin resistance.
Expression of calcitonin gene-related peptide in atopic dermatitis and correlation with distress.

BACKGROUND Atopic dermatitis (AD) is a chronic, inflammatory, often severely itching skin disorder. It may worsen due to stress, depression, or anxiety. Calcitonin gene-related peptide (CGRP) may be involved in inflammation signaling. CGRP has also been suggested in relation to stress, depression, and anxiety. This study aimed to investigate the expression of CGRP in the skin of patients with AD. METHODS Twenty-seven adult patients with AD, characterized with clinical and psychodemographic parameters, were investigated regarding CGRP expression in skin biopsies, using an immunohistochemical technique. RESULTS The total number of CGRP-positive nerve-like fibers was found to be higher in lesional skin than in non-lesional skin. Moreover, more inflammatory cells of dendritic shape intruded into the epidermis in lesional skin compared to non-lesional skin. Keratinocytes showing expression of CGRP were also found in lesional skin. Interestingly, the number of CGRP-positive nerve-like fibers in lesional skin correlated with depressive and anxiety scores. Correlation with depressive score was also found for round CGRP-positive inflammatory cells in the epidermis. CONCLUSIONS CGRP may have a role in both the inflammatory process and distress, in AD.
Optimal selection of learning data for highly accurate QSAR prediction of chemical biodegradability: a machine learning-based approach

ABSTRACT Prior to the manufacture of new chemicals, regulations mandate a thorough review of the chemicals under risk management. This review involves evaluating their effects on the environment and human health. To assess these effects, a review report that conforms to the OECD Test Guidelines must be submitted to the regulatory body. One of the essential components of the report is an assessment of the biodegradability of chemicals in the environment. In addition to conventional methods, quantitative structure-activity relationship (QSAR) models have been developed to predict the properties of chemicals based on their structural features. Although a greater number of chemicals in the learning set may enhance the prediction accuracy, it may also lead to a decrease in accuracy due to the mixing of different structural features and properties of the chemicals. To improve the prediction performance, it is recommended to use only the appropriate data for biodegradability prediction as a training set. In this study, we propose a novel approach for the optimal selection of training set that enables a highly accurate prediction of the biodegradability of chemicals by QSAR. Our findings indicate that the proposed method effectively reduces the root mean squared error and improves the prediction accuracy.
Upper Bounds and Design Guidelines for Realizing Uncorrelated Ports on Multimode Antennas Based on Symmetry Analysis of Characteristic Modes

The design of multiport antennas for multiple-input multiple-output (MIMO) applications utilizing characteristic modes is investigated. For good MIMO performance, uncorrelated antenna ports are generally required. In order to analyze the port correlation, symmetry analysis based on group theory and matrix representations is applied to the theory of characteristic modes. The characteristic surface current densities act as basis functions of the irreducible representations of the symmetry group of the antenna. Current densities belonging to different representations or belonging to different rows of the same representation are orthogonal to each other and can, thus, be excited separately. Therefore, an upper bound for the number of uncorrelated antenna ports can be derived for a given antenna structure based on the symmetry analysis of the characteristic modes. Furthermore, design guidelines on which antenna geometry to choose in order to realize a given number of uncorrelated antenna ports and on how to implement these ports can be deduced. The concept is illustrated by means of examples.
Chronic endometritis diagnosed using a cut-off of ≥5 CD138 plasma cells significantly affects the reproductive outcomes of frozen embryo transfer: a case-control study

Objective: To investigate the clinical significance of a diagnosis of chronic endometritis (CE) made using a diagnostic cut-off of ≥1 or ≥5 CD138 plasma cells per high power field (HPF) in asymptomatic patients undergoing in vitro fertilization (IVF) with frozen embryo transfer (FET). Material and Methods: In this retrospective case-control study, 1,865 patients underwent freeze-all-IVF treatment between January and December 2019, with 419 undergoing endometrial biopsies at oocyte retrieval. Of the 419 biopsy-patients, 301 have since undergone first FET. The processed endometrial biopsies of the 301 patients underwent immunohistochemical (IHC) examination with anti-CD138 to count CD138+ plasma cells per HPF. CE diagnosis was defined as 0 CD138 plasma cells (control-group), ≥1 CD138 plasma cells (CEcontrol-group) or ≥5 CD138 plasma cells (CEdisease-group) per HPF. Results: Twenty-six (8.6%) patients were retrospectively diagnosed having ≥1 CD138 plasma cells, and five patients (1.7%) having ≥5 CD138 plasma cells (CEdisease-group) per HPF. The live birth and pregnancy loss rates of the three groups were 52.7% and 27.9%, 53.8% and 26.3% and 20.0% and 66.7%, respectively. The antral follicle count (AFC) of the three groups were 15.0 (9.0-22.0), 10.5 (7.75-15.25), and 6.0 (5.0-14.0), respectively. Conclusion: Asymptomatic patients diagnosed with CE with ≥5 CD138 plasma cells per HPF, had the lowest live birth and highest pregnancy loss rates, with these patients also having significantly reduced AFC.
Call for standardization in assessment and reporting of muscle and adipose change using computed tomography analysis in oncology: A scoping review

Investigators are increasingly measuring skeletal muscle (SM) and adipose tissue (AT) change during cancer treatment to understand impact on patient outcomes. Recent meta‐analyses have reported high heterogeneity in this literature, representing uncertainty in the resulting estimates. Using the setting of palliative‐intent chemotherapy as an exemplar, we aimed to systematically summarize the sources of variability among studies evaluating SM and AT change during cancer treatment and propose standards for future studies to enable reliable meta‐analysis. Studies that measured computed tomography‐defined SM and/or AT change in adult patients during palliative‐intent chemotherapy for solid tumours were included, with no date or geographical limiters. Of 2496 publications screened by abstract/title, 83 were reviewed in full text and 38 included for extraction, representing 34 unique cohorts across 8 tumour sites. The timing of baseline measurement was frequently defined as prior to treatment, while endpoint timing ranged from 6 weeks after treatment start to time of progression. Fewer than 50% specified the actual time interval between measurements. Measurement error was infrequently discussed (8/34). A single metric (cm2/m2, cm2 or %) was used to describe SM change in 18/34 cohorts, while multiple metrics were presented for 10/34 and no descriptive metrics for 6/34. AT change metrics and sex‐specific reporting were available for 10/34 cohorts. Associations between SM loss and overall survival were evaluated in 24 publications, with classification of SM loss ranging from any loss to >14% loss over variable time intervals. Age and sex were the most common covariates, with disease response in 50% of models. Despite a wealth of data and effort, heterogeneity in study design, reporting and statistical analysis hinders evidence synthesis regarding the severity and outcomes of SM and AT change during cancer treatment. Proposed standards for study design include selection of homogenous cohorts, clear definition of baseline/endpoint timing and attention to measurement error. Standard reporting should include baseline SM and AT by sex, actual scan interval, SM and AT change using multiple metrics and visualization of the range of change observed. Reporting by sex would advance understanding of sexual dimorphism in SM and AT change. Evaluating the impact of tissue change on outcomes requires adjustment for relevant covariates and concurrent disease response. Adoption of these standards by researchers and publishers would alter the current paradigm to enable meta‐analysis of future studies and move the field towards meaningful application of SM and AT change to clinical care.
Impact of Normal and Overweight Pregnancy in GLUT4 and Glucose-Dependent Vascular Contractility

Introduction: Obesity during pregnancy can contribute to hypertensive complications through changes in glucose utilization. We investigated the impact of vascular glucose uptake, GLUT4 density, and endothelium on agonist-induced vasoconstriction in the aortas of overweight pregnant rats. Methods: Isolated aortic rings with or without endothelium from pregnant or nonpregnant rats fed a standard (SD) or hypercaloric diet (HD) were contracted with phenylephrine or serotonin (10−9 to 10−4M) using standard (11 mm) or without (0 mm) glucose Krebs solution. GLUT4 density in the aortas was measured using the en face method. Results: Aortas from overweight pregnant animals (PHD) showed increased Phe-induced vasoconstriction (p < 0.05 vs. pregnant standard diet [PSD]), which was endothelium-independent. The contraction decreased significantly in the absence of glucose. In contrast, vessels from pregnant SD rats maintained their contraction in glucose-free Krebs solution. 5-HT increases PHD aortic contraction only in the absence of glucose. The fetal aortas from PHD mothers showed blunted vasoconstriction. Overweight significantly reduced GLUT4 expression in maternal and fetal aortas (p < 0.05 vs. PSD). Conclusions: Aortic contractility is independent of glucose uptake during healthy pregnancy. In contrast, overweight pregnancy increases contractility. This increase depends directly on smooth muscle glucose uptake and inversely on GLUT-4 density. The increased contraction observed in the vasculature of overweight mothers was inverted in the fetal aortas.
Mechanism investigation of Duhuo Jisheng pill against rheumatoid arthritis based on a strategy for the integration of network pharmacology, molecular docking and in vivo experimental verification

Abstract Context Duhuo Jisheng pill (DHJS) is a classic traditional Chinese medicine (TCM) formula for rheumatoid arthritis (RA). The effective components and therapeutic mechanisms of DHJS for treating RA are still unclear. Objective To explore the potential mechanism of DHJS against RA by means of network pharmacology and experimental verification. Materials and methods A network pharmacology and molecular docking analysis based on phytochemistry was used to elucidate the mechanism of DHJS against RA. The targets of DHJS anti-RA active ingredient were obtained by searching TCMSP, ETCM and TCMSID. The RA model induced by collagen was established in Wistar rats. The rats in the DHJS group were administered doses of 0.5, 1.0 and 2.0 g/kg for a period of 10 d. The expression of targets was measured with Western blot. Results Network pharmacology analysis showed that the anti-RA effect of DHJS was mediated by targets involved in immunity, inflammation and apoptosis, as well as PI3K-Akt and NF-κB signalling pathways. Of 2.0 g/kg DHJS significantly alleviated the ankle inflammation (IL-6: 62.73 ± 8.39 pg/mL, IL-1β: 50.49 ± 11.47 pg/mL, TNF-α: 16.88 ± 3.05 pg/mL, IL-17A: 12.55 ± 1.87 pg/mL, IL-10: 16.24 ± 3.00 pg/mL), comparing with the model group (IL-6: 92.02 ± 13.25 pg/mL, IL-1β: 71.85 ± 4.12 pg/mL, TNF-α: 25.64 ± 3.69 pg/mL, IL-17A: 22.14 ± 4.56 pg/mL, IL-10: 9.51 ± 3.03 pg/mL) (p < 0.05). Moreover, the protein expression of p-PI3K, p-AKT and p-p65 significantly decreased after DHJS administration. Conclusions DHJS could alleviate the collagen-induced arthritis (CIA) by the PI3K/AKT/NF-κB signalling pathway.
This is How You Lose the Transient Execution War

A new class of vulnerabilities related to speculative and out-of-order execution, fault-injection, and microarchitectural side channels rose to attention in 2018. The techniques behind the transient execution vulnerabilities were not new, but the combined application of the techniques was more sophisticated, and the security impact more severe, than previously considered possible. Numerous mitigations have been proposed and implemented for variants of the transient execution vulnerabilities. While Meltdown-type exception-based transient execution vulnerabilities have proven to be tractable, Spectre-type vulnerabilities and other speculation-based transient execution vulnerabilities have been far more resistant to countermeasures. A few proposed mitigations have been widely adopted by hardware vendors and software developers, but combining those commonly deployed mitigations does not produce an effective and comprehensive solution, it only protects against a small subset of the variants. Over the years, newly proposed mitigations have been trending towards more effective and comprehensive approaches with better performance, and yet, older mitigations remain the most popular despite limited security benefits and prohibitive performance penalties. If we continue this way, we can look forward to many generations of hardware debilitated by performance penalties from increasing layers of mitigations as new variants are discovered, and yet still vulnerable to both known and future variants.
Hyperpolarisation of nuclear spins: polarisation blockade

Efficient hyperpolarisation of nuclear spins via optically active defect centers, such as the nitrogen vacancy (NV) center in diamond, has great potential for enhancing NMR based quantum information processing and nanoscale magnetic resonance imaging. Recently, pulse-based protocols have been shown to efficiently transfer optically induced polarisation of the electron defect spin to surrounding nuclear spins -- at particular resonant pulse intervals. In this work, we investigate the performance of these protocols, both analytically and experimentally, with the electronic spin of a single NV defect. We find that whenever polarisation resonances of nuclear spins are near-degenerate with a `blocking' spin, which is single spin with stronger off-diagonal coupling to the electronic central spin, they are displaced out of the central resonant region -- without, in general, significant weakening of the resonance. We analyse the underlying physical mechanism and obtain a closed form expression for the displacement. We propose that spin blocking represents a common but overlooked effect in hyperpolarisation of nuclear spins and suggest solutions for improved protocol performance in the presence of (naturally occurring) blocking nuclear spins.
A host galaxy study of southern narrow-line Seyfert 1 galaxies

We studied seven nearby narrow-line Seyfert 1 (NLS1) galaxies in $J$- and $Ks$-bands with redshifts varying from 0.019 to 0.092. This is the first multi-source study targeting the hosts of southern NLS1 galaxies. Our data was obtained with the FourStar instrument of the 6.5~m Magellan Baade telescope at the Las Campanas Observatory (Chile). The aim of our study is to determine the host galaxy morphologies of these sources by using GALFIT. We were able to model six out of the seven sources reliably. Our conclusion is that all of the reliably modelled sources are disk-like galaxies, either spirals or lenticulars. None of these sources present elliptical morphology. Our findings are in agreement with the hypothesis that disk-like galaxies are the main host of jetted NLS1 galaxies. Taking advantage of observations in two bands, we also produced a $J - Ks$ colour map of each source. Five of the six colour maps show significant dust extinction near the core of the galaxy -- a feature often seen in gamma-ray-detected jetted NLS1 galaxies, and interpreted to be a consequence of a past minor merger.
Study of Poincare map and limit cycles for non-smooth Welander's system

In this work, our primary goal is to study the Poincare map and the existence of limit cycles for Welander's model that describes ocean convection. Welander developed two versions of his model, one with a smooth transition between convective states, and one with an abrupt non-smooth change. Our focus in this paper is to study the non-smooth model. Approaching through the Poincare Map, we demonstrate analytically the bifurcation of a stable limit cycle surrounding an escaping segment.
Uncovering Drift in Textual Data: An Unsupervised Method for Detecting and Mitigating Drift in Machine Learning Models

Drift in machine learning refers to the phenomenon where the statistical properties of data or context, in which the model operates, change over time leading to a decrease in its performance. Therefore, maintaining a constant monitoring process for machine learning model performance is crucial in order to proactively prevent any potential performance regression. However, supervised drift detection methods require human annotation and consequently lead to a longer time to detect and mitigate the drift. In our proposed unsupervised drift detection method, we follow a two step process. Our first step involves encoding a sample of production data as the target distribution, and the model training data as the reference distribution. In the second step, we employ a kernel-based statistical test that utilizes the maximum mean discrepancy (MMD) distance metric to compare the reference and target distributions and estimate any potential drift. Our method also identifies the subset of production data that is the root cause of the drift. The models retrained using these identified high drift samples show improved performance on online customer experience quality metrics.
Trap induced broadening in a potential hydrogen lattice clock

We consider the potential use of optical traps for precision measurements in atomic hydrogen (H). Using an implicit summation method, we calculate the atomic polarisability, the rates of elastic/inelastic scattering and the ionisation rate in the wavelength range 395 to 1000\,nm. We extend previous work to predict three new magic wavelengths for the 1S--2S transition. At the magic wavelengths, the 1S--2S transition is unavoidably and significantly broadened due to trap-induced ionisation associated with the high intensity required to trap the 1S state. However, we also find that this effect is partially mitigated by the low mass of H, which increases the trap frequency, enabling Lamb-Dicke confinement in shallow lattices. We find that a H optical lattice clock, free from the motional systematics which dominate in beam experiments, could operate with an intrinsic linewidth of the order of 1 kHz. Trap-induced losses are shown not to limit measurements of other transitions.
Conversational Swarm Intelligence, a Pilot Study

Conversational Swarm Intelligence (CSI) is a new method for enabling large human groups to hold real-time networked conversations using a technique modeled on the dynamics of biological swarms. Through the novel use of conversational agents powered by Large Language Models (LLMs), the CSI structure simultaneously enables local dialog among small deliberative groups and global propagation of conversational content across a larger population. In this way, CSI combines the benefits of small-group deliberative reasoning and large-scale collective intelligence. In this pilot study, participants deliberating in conversational swarms (via text chat) (a) produced 30% more contributions (p<0.05) than participants deliberating in a standard centralized chat room and (b) demonstrated 7.2% less variance in contribution quantity. These results indicate that users contributed more content and participated more evenly when using the CSI structure.
A boundary-aware point clustering approach in Euclidean and embedding spaces for roof plane segmentation

Roof plane segmentation from airborne LiDAR point clouds is an important technology for 3D building model reconstruction. One of the key issues of plane segmentation is how to design powerful features that can exactly distinguish adjacent planar patches. The quality of point feature directly determines the accuracy of roof plane segmentation. Most of existing approaches use handcrafted features to extract roof planes. However, the abilities of these features are relatively low, especially in boundary area. To solve this problem, we propose a boundary-aware point clustering approach in Euclidean and embedding spaces constructed by a multi-task deep network for roof plane segmentation. We design a three-branch network to predict semantic labels, point offsets and extract deep embedding features. In the first branch, we classify the input data as non-roof, boundary and plane points. In the second branch, we predict point offsets for shifting each point toward its respective instance center. In the third branch, we constrain that points of the same plane instance should have the similar embeddings. We aim to ensure that points of the same plane instance are close as much as possible in both Euclidean and embedding spaces. However, although deep network has strong feature representative ability, it is still hard to accurately distinguish points near plane instance boundary. Therefore, we first group plane points into many clusters in the two spaces, and then we assign the rest boundary points to their closest clusters to generate final complete roof planes. In this way, we can effectively reduce the influence of unreliable boundary points. In addition, we construct a synthetic dataset and a real dataset to train and evaluate our approach. The experiments results show that the proposed approach significantly outperforms the existing state-of-the-art approaches.