Miracle-dz/UniFAR-FTUs huggingface dataset:
Basal lamina: A novel pH regulator at the neuromuscular junction

Proton concentration can change within the cleft during synaptic activity due to vesicular release and Ca2+ extrusion from cellular compartments. These changes within the synaptic cleft can impact neural activity by proton-dependent modulation of ion channel function. The pH transient differs in magnitude and direction between synapses, requiring different synapse types to be measured to generate a complete understanding of this mechanism and its impacts on physiology. With a focus on the mouse neuromuscular junction (NMJ), the recently published “Postsynaptic Calcium Extrusion at the Mouse Neuromuscular Junction Alkalinizes the Synaptic Cleft” measured synaptic cleft pH at a cholinergic synapse and found a biphasic pH transient. The study demonstrated that the changes in proton concentration found were due to postsynaptic signaling when measuring pH at the muscle membrane, despite the expectation of a presynaptic contribution. This result suggests a diffusional barrier within the NMJ isolates pH transients to presynaptic versus postsynaptic compartments. Generating a Donnan equilibrium that impacts protons, evidence suggests the basal lamina may be a key regulator of pH at the NMJ. Exploring synaptic pH, proton regulating factors, and downstream pH transient effects at presynaptic versus postsynaptic membranes may lead to new insight for a variety of diseases.
The Double-Edged Influence of Self-Expansion in the Metaverse: A Two-Wave Panel Assessment of Identity Perception, Self-Esteem, and Life Satisfaction

This study researches the impact of self-expansion experiences in the Metaverse on users' identity perception, self-esteem, and life satisfaction. To do so, the researchers conducted a two-wave panel study with a 3-month interval (N = 486) in VRChat, one of the most popular social virtual reality (VR) platforms. As predicted, the increase in self-expansion experience in VR environments positively predicted users' self-esteem and life satisfaction. However, when self-expansion led to a loss of coherency in the self-concept by causing identity disjunction or self-discrepancy, it damaged self-esteem and life satisfaction, respectively. The current findings exhibit that experimenting with and enlarging identity through immersive experiences in the Metaverse could benefit the individual, but only when it does not cause a disconnection between virtual and offline identities. This article discusses the potential opportunities and risks in the Metaverse, emphasizing the importance of advancing our understanding of the self-expansion experience in immersive media.
M-FANet: Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

Motor imagery (MI) decoding methods are pivotal in advancing rehabilitation and motor control research. Effective extraction of spectral-spatial-temporal features is crucial for MI decoding from limited and low signal-to-noise ratio electroencephalogram (EEG) signal samples based on brain-computer interface (BCI). In this paper, we propose a lightweight Multi-Feature Attention Neural Network (M-FANet) for feature extraction and selection of multi-feature data. M-FANet employs several unique attention modules to eliminate redundant information in the frequency domain, enhance local spatial feature extraction and calibrate feature maps. We introduce a training method called Regularized Dropout (R-Drop) to address training-inference inconsistency caused by dropout and improve the model’s generalization capability. We conduct extensive experiments on the BCI Competition IV 2a (BCIC-IV-2a) dataset and the 2019 World robot conference contest-BCI Robot Contest MI (WBCIC-MI) dataset. M-FANet achieves superior performance compared to state-of-the-art MI decoding methods, with 79.28% 4-class classification accuracy (kappa: 0.7259) on the BCIC-IV-2a dataset and 77.86% 3-class classification accuracy (kappa: 0.6650) on the WBCIC-MI dataset. The application of multi-feature attention modules and R-Drop in our lightweight model significantly enhances its performance, validated through comprehensive ablation experiments and visualizations.
Acute effects of commercial energy drink consumption on exercise performance and cardiovascular safety: a randomized, double-blind, placebo-controlled, crossover trial

ABSTRACT The aim of this study was to examine the acute effects of a non-caloric energy drink (C4E) compared to a traditional sugar-containing energy drink (MED) and non-caloric placebo (PLA) on exercise performance and cardiovascular safety. Thirty healthy, physically active males (25 ± 4 y) completed three experimental visits under semi-fasted conditions (5–10 h) and in randomized order, during which they consumed C4E, MED, or PLA matched for volume, appearance, taste, and mouthfeel. One hour after drink consumption, participants completed a maximal, graded exercise test (GXT) with measurement of pulmonary gases, an isometric leg extension fatigue test (ISOFTG), and had their cardiac electrical activity (ECG), leg blood flow (LBF), and blood pressure (BP) measured throughout the visit. Neither MED nor C4E had an ergogenic effect on maximal oxygen consumption, time to exhaustion, or peak power during the GXT (p > 0.05). Compared to PLA, MED reduced fat oxidation (respiratory exchange ratio (RER) +0.030 ± 0.01; p = 0.026) during the GXT and did not influence ISOFTG performance. Compared to PLA, C4E did not alter RER (p = 0.94) and improved impulse during the ISOFTG (+0.658 ± 0.25 V·s; p = 0.032). Relative to MED, C4E did not significantly improve gas exchange threshold (p = 0.05–0.07). Both MED and C4E increased systolic BP at rest (+7.1 ± 1.2 mmHg; p < 0.001 and + 5.7 ± 1.0 mmHg; p < 0.001, respectively), C4E increased SBP post-GXT (+13.3 ± 3.8 mmHg; p < 0.001), and MED increased SBP during recovery (+3.2 ± 1.1 mmHg; p < 0.001). Neither MED nor C4E influenced ECG measures (p ≥ 0.08) or LBF (p = 0.37) compared to PLA. C4E may be more efficacious for improving performance in resistance-type tasks without altering fat oxidation under semi-fasted conditions during fatiguing exercise bouts, but promotes similar changes in BP and HR to MED.
Mid‐ to Late Holocene climate transition and moisture dynamics inferred from South Swedish tree‐ring data

A 1561‐year tree‐ring width (TRW) chronology covering the period 2668–1108 BC (4617–3057 BP) has been developed from 159 moisture‐sensitive peatland pines (Pinus sylvestris L.) sampled at Åbuamossen, southern Sweden. Tree population dynamics and annual growth responses of the trees were shown to reflect and give absolute age to regional hydro‐climatological changes. The main wet‐shifts recorded in the TRW data were precisely dated to 2150–2100, 1550 and 1230–1150 BC (4100–4050, 3500 and 3180–3000 BP) and are likely to be related to the stepwise Mid‐ to Late Holocene climate transition, during which the condition changed from relatively warm and dry towards cold and moist in the northern hemisphere. The tree‐colonization phase was ended by several long‐lasting periods showing depressed annual tree growth and an abrupt dying‐off phase, possibly caused by increasing water level in the peatland as well as the adjacent river Helge Å. This study demonstrates that TRW data obtained from subfossil peatland trees can provide detailed information and exact age control on moisture variability in peatlands associated with regional hydroclimatic changes. Moreover, the combined information from the stratigraphical and the TRW analyses enabled a detailed temporal and spatial site development reconstruction.
On edge melting under the Colorado Plateau margin

Asthenosphere beneath the relatively thin lithosphere of the Basin and Range province appears to be juxtaposed in step‐like fashion against the Colorado Plateau's thick lithospheric keel. Primary to near‐primary basalts are found above this edge, in the San Francisco‐Morman Mountain volcanic fields, north central Arizona, western USA. We show that at least two distinct peridotite‐dominated mantle end‐members contributed to the origin of the basalts. One has paired Nd and Hf isotopic characteristics that cluster near the mantle array and trace element patterns as expected for melts generated in the asthenosphere, possibly in the presence of garnet. The second has isotopic compositions displaced above the εHf ‐ εNd mantle array which, together with its particular trace element characteristics, indicate contributions from hydrogenous sediments and/or melt (carbonatite or silicate)‐related metasomatism. Melt equilibration temperatures obtained from Si‐ and Mg‐thermobarometry are mostly 1340–1425°C and account for the effects of water (assumed to be 2 wt.%) and estimated CO2 (variable). Melt equilibration depths cluster at the inferred location of the lithosphere‐asthenosphere boundary at ∼70–75 km beneath the southwestern margin of the Colorado Plateau but scatter to somewhat greater values (∼100 km). Melt generation may have initiated in or below the garnet‐spinel facies transition zone by edge‐driven convection and continued as mantle and/or melts upwelled, assimilating and sometimes equilibrating with shallower contaminated mantle, until melts were finally extracted.
Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish

PURPOSE A critical task in oncology is extracting information related to cancer metastasis from electronic health records. Metastasis-related information is crucial for planning treatment, evaluating patient prognoses, and cancer research. However, the unstructured way in which findings of distant metastasis are often written in radiology reports makes it difficult to extract information automatically. The main aim of this study was to extract distant metastasis findings from free-text imaging and nuclear medicine reports to classify the patient status according to the presence or absence of distant metastasis. MATERIALS AND METHODS We created a distant metastasis annotated corpus using positron emission tomography-computed tomography and computed tomography reports of patients with prostate, colorectal, and breast cancers. Entities were labeled M1 or M0 according to affirmative or negative metastasis descriptions. We used a named entity recognition model on the basis of a bidirectional long short-term memory model and conditional random fields to identify entities. Mentions were subsequently used to classify whole reports into M1 or M0. RESULTS The model detected distant metastasis mentions with a weighted average F1 score performance of 0.84. Whole reports were classified with an F1 score of 0.92 for M0 documents and 0.90 for M1 documents. CONCLUSION These results show the usefulness of the model in detecting distant metastasis findings in three different types of cancer and the consequent classification of reports. The relevance of this study is to generate structured distant metastasis information from free-text imaging reports in Spanish. In addition, the manually annotated corpus, annotation guidelines, and code are freely released to the research community.
Destabilisation of T cell-dependent humoral immunity in sepsis

Abstract Sepsis is a heterogeneous condition defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. For some, sepsis presents as a predominantly suppressive disorder, whilst others experience a pro-inflammatory condition which can culminate in a ‘cytokine storm’. Frequently, patients experience signs of concurrent hyper-inflammation and immunosuppression, underpinning the difficulty in directing effective treatment. Although intensive care unit mortality rates have improved in recent years, one-third of discharged patients die within the following year. Half of post-sepsis deaths are due to exacerbation of pre-existing conditions, whilst half are due to complications arising from a deteriorated immune system. It has been suggested that the intense and dysregulated response to infection may induce irreversible metabolic reprogramming in immune cells. As a critical arm of immune protection in vertebrates, alterations to the adaptive immune system can have devastating repercussions. Indeed, a marked depletion of lymphocytes is observed in sepsis, correlating with increased rates of mortality. Such sepsis-induced lymphopenia has profound consequences on how T cells respond to infection but equally on the humoral immune response that is both elicited by B cells and supported by distinct CD4+ T follicular helper (TFH) cell subsets. The immunosuppressive state is further exacerbated by functional impairments to the remaining lymphocyte population, including the presence of cells expressing dysfunctional or exhausted phenotypes. This review will specifically focus on how sepsis destabilises the adaptive immune system, with a closer examination on how B cells and CD4+ TFH cells are affected by sepsis and the corresponding impact on humoral immunity.
Arcuate Nucleus of the Hypothalamus: Anatomy, Physiology, and Diseases

The hypothalamus is part of the diencephalon and has several nuclei, one of which is the arcuate nucleus. The arcuate nucleus of hypothalamus (ARH) consists of neuroendocrine neurons and centrally-projecting neurons. The ARH is the center where the homeostasis of nutrition/metabolism and reproduction are maintained. As such, dysfunction of the ARH can lead to disorders of nutrition/metabolism and reproduction. Here, we review various types of neurons in the ARH and several genetic disorders caused by mutations in the ARH.
The societal impact of individual placement and support implementation on employment outcomes for young adults receiving temporary health-related welfare benefits: a difference-in-differences study.

BACKGROUND Individual placement and support (IPS) is an evidence-based practice that helps individuals with mental illness gain and retain employment. IPS was implemented for young adults at a municipality level through a cross-sectoral collaboration between specialist mental healthcare, primary mental healthcare, and the government funded employment service (NAV). We investigated whether IPS implementation had a causal effect on employment outcomes for all young adults in receipt of a temporary health-related rehabilitation (work assessment allowance, WAA) welfare benefit, measured at the societal level compared to municipalities that did not implement IPS. METHOD We used a difference in differences design to estimate the effects of IPS implementation on the outcome of workdays per year using longitudinal registry data. We estimate the average effect of being exposed to IPS implementation during four-years of implementation compared to ten control municipalities without IPS for all WAA recipients. RESULTS We found a significant, positive, causal effect on societal level employment outcomes of 5.6 (p = 0.001, 95% CI 2.7-8.4) increased workdays per year per individual, equivalent to 12.7 years of increased work in the municipality where IPS was implemented compared to municipalities without IPS. Three years after initial exposure to IPS implementation individuals worked, on average, 10.5 more days per year equating to 23.8 years of increased work. CONCLUSIONS Implementing IPS as a cross sectoral collaboration at a municipality level has a significant, positive, causal, societal impact on employment outcomes for all young adults in receipt of a temporary health-related rehabilitation welfare benefit.
ReRAM-Based In-Memory Computing for Search Engine and Neural Network Applications

Resource-constrained computing devices such as those used in IoTs require low-power, high performance, and small size to be enabled to operate efficiently. Resistive random access memory (ReRAM) is a promising technology for building novel in-memory computing architectures, due to its ability to perform storage and computation using the same physical element with low energy and high density. ReRAM-based search engines and neural network (NN) accelerators have grown significantly especially for IoT devices. In this paper, we propose a memristor-based voltage-resistance xnor (VR-XNOR) cell. The advantages of this cell are demonstrated through building a reconfigurable content-addressable memory (CAM) architecture that can support binary-CAM (BCAM) and ternary-CAM (TCAM) and enable approximate search operations. Moreover, the memristor-based VR-XNOR cell is utilized for binarized convolutional neural networks (CNN) with focus on the convolution operation. This is achieved by replacing the convolution module with XNOR-based filter banks. Simulations of the proposed architectures for search engine and feature extraction were carried out using VTEAM model in Cadence Virtuoso Analog Design Environment. The proposed filter bank architecture achieves a 1-ns extraction cycle time over ${N}$ filters and produces multiple output feature maps in a single processing cycle. The filter uses two memristor devices to realize each XNOR gate and has shown a significant reduction in the number of multiply-add operations.
Using machine learning algorithms to link volumetric water content to complex dielectric permittivity in a wide (33–2000 MHz) frequency band for hydraulic concretes

This paper focuses on the development and validation of an innovative method for estimating volumetric water content in concrete mixtures. A supervised learning method (support vector machine) has been used to resolve the inverse problem, i.e., generate in-laboratory calibration curves correlating the controlled water content in various concrete mixtures with the frequency-dependent complex dielectric permittivity originating from the coaxial electromagnetic transition line. An extrapolation procedure using a frequency-power-law model has been developed and validated for estimating the complex permittivity over a broad frequency bandwidth. Implementation of this extrapolation method allows considering various physical phenomena (i.e., polarisation versus water content) that typically affect the dielectric behaviour of concrete as a function of frequency. The two-step estimation procedure (involving extrapolation and support vector regression methods) proposed in this paper has been validated on a wide array of moisture-controlled concrete specimens in the laboratory. The procedure helps building calibration curves that rely on both complex effective permittivity and volumetric water content, taking into consideration the frequency dependence.
Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach

Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional principal component analysis to summarize the available longitudinal information, and employ a Cox proportional hazards model for prediction. Additionally, we consider a centred functional principal component analysis procedure in an attempt to remove the natural variation incurred by the difference in age of the considered subjects. We formalize the difference between a ‘relaxed’ landmarking approach where only validation data is landmarked and a ‘strict’ landmarking approach where both the training and validation data are landmarked. We show that a relaxed landmarking approach fails to effectively use the information contained in the longitudinal outcomes, thereby producing substantially worse prediction accuracy than a strict landmarking approach.
Cerebral Cavernous Malformation (CCM)-like Vessel Lesion in the Aged ANKS1A-deficient Brain

In this study, we show that ANKS1A is specifically expressed in the brain endothelial cells of adult mice. ANKS1A deficiency in adult mice does not affect the differentiation, growth, or patterning of the cerebrovascular system; however, its absence significantly impacts the cerebrovascular system of the aged brain. In aged ANKS1A knock-out (KO) brains, vessel lesions exhibiting cerebral cavernous malformations (CCMs) are observed. In addition, CCM-like lesions show localized peripheral blood leakage into the brain. The CCM-like lesions reveal immune cells infiltrating the parenchyma. The CCM-like lesions also contain significantly fewer astrocyte endfeets and tight junctions, indicating that the integrity of the BBB has been partially compromised. CCM-like lesions display increased fibronectin expression in blood vessels, which is also confirmed in cultured endothelial cells deficient for ANKS1A. Therefore, we hypothesize that ANKS1A may play a role in maintaining or stabilizing healthy blood vessels in the brain during aging.
Answer Retrieval in Legal Community Question Answering

The task of answer retrieval in the legal domain aims to help users to seek relevant legal advice from massive amounts of professional responses. Two main challenges hinder applying existing answer retrieval approaches in other domains to the legal domain: (1) a huge knowledge gap between lawyers and non-professionals; and (2) a mix of informal and formal content on legal QA websites. To tackle these challenges, we propose CE_FS, a novel cross-encoder (CE) re-ranker based on the fine-grained structured inputs. CE_FS uses additional structured information in the CQA data to improve the effectiveness of cross-encoder re-rankers. Furthermore, we propose LegalQA: a real-world benchmark dataset for evaluating answer retrieval in the legal domain. Experiments conducted on LegalQA show that our proposed method significantly outperforms strong cross-encoder re-rankers fine-tuned on MS MARCO. Our novel finding is that adding the question tags of each question besides the question description and title into the input of cross-encoder re-rankers structurally boosts the rankers' effectiveness. While we study our proposed method in the legal domain, we believe that our method can be applied in similar applications in other domains.
Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks

The deployment of federated learning (FL) within vertical heterogeneous networks, such as those enabled by high-altitude platform station (HAPS), offers the opportunity to engage a wide array of clients, each endowed with distinct communication and computational capabilities. This diversity not only enhances the training accuracy of FL models but also hastens their convergence. Yet, applying FL in these expansive networks presents notable challenges, particularly the significant non-IIDness in client data distributions. Such data heterogeneity often results in slower convergence rates and reduced effectiveness in model training performance. Our study introduces a client selection strategy tailored to address this issue, leveraging user network traffic behaviour. This strategy involves the prediction and classification of clients based on their network usage patterns while prioritizing user privacy. By strategically selecting clients whose data exhibit similar patterns for participation in FL training, our approach fosters a more uniform and representative data distribution across the network. Our simulations demonstrate that this targeted client selection methodology significantly reduces the training loss of FL models in HAPS networks, thereby effectively tackling a crucial challenge in implementing large-scale FL systems.