Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Magnoliae Flos on Atopic Dermatitis-Like Inflammation Evaluated via Extracellular Signal-regulated Kinase or Signal Transducers and Activators of Transcription 1/3 Signalling Pathways

Atopic dermatitis is a chronic inflammatory skin disease. Skin is the largest organ and plays a pivotal role in protecting the body. Not only does the skin act as a physical barrier against the external environment, but it also has its own immune system. Atopic dermatitis is caused by prolonged excessive inflammatory responses that worsen under imbalanced cutaneous immune system skin conditions. Although the prevalence and burden of atopic dermatitis is increasing, the standard therapeutic agents remain unclear due to the complicated pathophysiology of the condition. The objective of this study is to examine the use of Magnoliae flos, the dried flower bud of Magnolia biondii or related plants. The effects and underlying mechanism of action of aqueous extract of the buds of Magnoliae flos (MF) were evaluated. Immortalized human keratinocytes (HaCaT) stimulated with tumour necrosis factor-α and interferon-γ mixture and NC/Nga mice stimulated with 2,4-dinitrochlorobenzene were used as atopic dermatitis models, in vitro and in vivo, respectively. The effects of MF were determined by measuring the suppression of pro-inflammatory signalling pathways, such as extracellular signal-regulated kinase or signal transducers and activators of transcription 1/3 and restoring skin barrier molecules. In conclusion, MF is a potential therapeutic alternative for the treatment of atopic dermatitis through repressing inflammatory pathways. SIGNIFICANCE The chronic inflammatory skin disease atopic dermatitis reduces patients’ quality of life due to severe effects of itch and frequent relapse. There is a need for therapeutic agents that will relieve the symptoms of atopic dermatitis in the long-term without side-effects. Magnoliae flos has been used for a long time to treat inflammatory diseases with safety and low toxicity for; therefore this study examined its potential in treating atopic dermatitis. MF showed repressive effects on cutaneous inflammatory responses and recovery on skin barrier molecules which were done regulating through ERK or STAT1/3 signalling pathways.
ASSESSMENT OF RICE AND MAIZE BASED CROPPING SYSTEMS FOR RURAL LIVELIHOOD IMPROVEMENT IN NEPAL

This paper explores the assessment of rice and maize base cropping system in agro-ecological regions for sustainable rural livelihood development in Nepal. Analysis of 1994-2007 data showed rice-dominated cropping systems in the Plain region, as opposed to maize-dominated cropping systems in the Mountain and the Hill regions. The production increase was achieved mainly through increases in area. The growth in the yield of crops was very minimal in all three regions. During the last fifteen years, the cropping pattern changed slightly from Maize-Wheat to Maize-Paddy in the Mountain region, but no significant change was observed in the other two regions. The current rate of fertilizer application is lower than the recommended rate. Improved access to and availability of agricultural inputs is key to improve the production and yield of major food crops to achieve sustainable rural livelihood in the country.
NAT10 Is Involved in Cardiac Remodeling Through ac4C-Mediated Transcriptomic Regulation

BACKGROUND: Heart failure, characterized by cardiac remodeling, is associated with abnormal epigenetic processes and aberrant gene expression. Here, we aimed to elucidate the effects and mechanisms of NAT10 (N-acetyltransferase 10)-mediated N4-acetylcytidine (ac4C) acetylation during cardiac remodeling. METHODS: NAT10 and ac4C expression were detected in both human and mouse subjects with cardiac remodeling through multiple assays. Subsequently, acetylated RNA immunoprecipitation and sequencing, thiol-linked alkylation for the metabolic sequencing of RNA (SLAM-seq), and ribosome sequencing (Ribo-seq) were employed to elucidate the role of ac4C-modified posttranscriptional regulation in cardiac remodeling. Additionally, functional experiments involving the overexpression or knockdown of NAT10 were conducted in mice models challenged with Ang II (angiotensin II) and transverse aortic constriction. RESULTS: NAT10 expression and RNA ac4C levels were increased in in vitro and in vivo cardiac remodeling models, as well as in patients with cardiac hypertrophy. Silencing and inhibiting NAT10 attenuated Ang II-induced cardiomyocyte hypertrophy and cardiofibroblast activation. Next-generation sequencing revealed ac4C changes in both mice and humans with cardiac hypertrophy were associated with changes in global mRNA abundance, stability, and translation efficiency. Mechanistically, NAT10 could enhance the stability and translation efficiency of CD47 and ROCK2 transcripts by upregulating their mRNA ac4C modification, thereby resulting in an increase in their protein expression during cardiac remodeling. Furthermore, the administration of Remodelin, a NAT10 inhibitor, has been shown to prevent cardiac functional impairments in mice subjected to transverse aortic constriction by suppressing cardiac fibrosis, hypertrophy, and inflammatory responses, while also regulating the expression levels of CD47 and ROCK2 (Rho associated coiled-coil containing protein kinase 2). CONCLUSIONS: Therefore, our data suggest that modulating epitranscriptomic processes, such as ac4C acetylation through NAT10, may be a promising therapeutic target against cardiac remodeling.
Prediction of molecular phenotypes for novel SCN1A variants from a Turkish genetic epilepsy syndromes cohort and report of two new patients with recessive Dravet syndrome

Abstract Dravet syndrome and genetic epilepsy with febrile seizures plus (GEFS+) are both epilepsy syndromes that can be attributed to deleterious mutations occurring in SCN1A, the gene encoding the pore‐forming α‐subunit of the NaV1.1 voltage‐gated sodium channel predominantly expressed in the central nervous system. In this research endeavor, our goal is to expand our prior cohort of Turkish patients affected by SCN1A‐positive genetic epilepsy disorders. This will be accomplished by incorporating two recently discovered and infrequent index cases who possess a novel biallelic (homozygous) SCN1A missense variant, namely E158G, associated with Dravet syndrome. Furthermore, our intention is to use computational techniques to predict the molecular phenotypes of each distinct SCN1A variant that has been detected to date within our center. The correlation between genotype and phenotype in Dravet syndrome/GEFS+ is intricate and necessitates meticulous clinical investigation as well as advanced scientific exploration. Broadened mechanistic and structural insights into NaV1.1 dysfunction offer significant promise in facilitating the development of targeted and effective therapies, which will ultimately enhance clinical outcomes in the treatment of epilepsy.
ASSESSMENT OF BANK EROSION PROBABILITY: A STUDY ON KUNUR RIVER, EASTERN INDIA

High stream bank erosion and failure rates on streams bank may be attributed to land use change and degradation of riparian environment. Excessive rate of river bank erosions and failures result in loss of land, increased stream sediment loads and increased stream instability. For assessing potential risk of river bank erosion, Bank Erosion Hazard Index (BEHI) has applied. Out of 12 selected sites six sites (Dhobani, Malandighi, Ausgram etc.) are highly hazardous and erosion prone. It has found that the probability of erosion increases downward. The main causes that are responsible for such condition in the lower catchment area are loose bank material, less woody vegetation cover, high volume of water discharge. From source to mouth it is also found that the rate of erosion is more on right bank of the river. Such unequal rate of erosion between left and right banks is indicating that the right side migration tendency of channel and erosional vulnerability of the right bank.
Intermediate-depth earthquake generation and shear zone formation caused by grain size reduction and shear heating

The underlying physics of intermediate-depth earthquakes have been an enigmatic topic; several studies support either thermal runaway or dehydration reactions as viable mechanisms for their generation. Here we present fully coupled thermomechanical models that investigate the impact of grain size evolution and energy feedbacks on shear zone and pseudotachylite formation. Our results indicate that grain size reduction weakens the rock prior to thermal runaway and significantly decreases the critical stress needed for thermal runaway, making it more likely to result in intermediate-depth earthquakes at shallower depths. Furthermore, grain size is reduced in and around the shear zone, which agrees with field and laboratory observations where pseudotachylites are embedded in a simultaneously formed mylonite matrix. The decrease in critical stress to initialize localization has important implications for large-scale geodynamics, as this mechanism might induce lithosphere-scale shear zones and subduction initiation. We suggest that the combination of grain size reduction and shear heating explains both the occurrence of intermediate-depth earthquakes and the formation of large-scale shear zones.
DWORF Extends Life Span in a PLN-R14del Cardiomyopathy Mouse Model by Reducing Abnormal Sarcoplasmic Reticulum Clusters

BACKGROUND: The p.Arg14del variant of the PLN (phospholamban) gene causes cardiomyopathy, leading to severe heart failure. Calcium handling defects and perinuclear PLN aggregation have both been suggested as pathological drivers of this disease. Dwarf open reading frame (DWORF) has been shown to counteract PLN regulatory calcium handling function in the sarco/endoplasmic reticulum (S/ER). Here, we investigated the potential disease-modulating action of DWORF in this cardiomyopathy and its effects on calcium handling and PLN aggregation. METHODS: We studied a PLN-R14del mouse model, which develops cardiomyopathy with similar characteristics as human patients, and explored whether cardiac DWORF overexpression could delay cardiac deterioration. To this end, R14Δ/Δ (homozygous PLN-R14del) mice carrying the DWORF transgene (R14Δ/ΔDWORFTg [R14Δ/Δ mice carrying the DWORF transgene]) were used. RESULTS: DWORF expression was suppressed in hearts of R14Δ/Δ mice with severe heart failure. Restoration of DWORF expression in R14Δ/Δ mice delayed cardiac fibrosis and heart failure and increased life span >2-fold (from 8 to 18 weeks). DWORF accelerated sarcoplasmic reticulum calcium reuptake and relaxation in isolated cardiomyocytes with wild-type PLN, but in R14Δ/Δ cardiomyocytes, sarcoplasmic reticulum calcium reuptake and relaxation were already enhanced, and no differences were detected between R14Δ/Δ and R14Δ/ΔDWORFTg. Rather, DWORF overexpression delayed the appearance and formation of large pathogenic perinuclear PLN clusters. Careful examination revealed colocalization of sarcoplasmic reticulum markers with these PLN clusters in both R14Δ/Δ mice and human p.Arg14del PLN heart tissue, and hence these previously termed aggregates are comprised of abnormal organized S/ER. This abnormal S/ER organization in PLN-R14del cardiomyopathy contributes to cardiomyocyte cell loss and replacement fibrosis, consequently resulting in cardiac dysfunction. CONCLUSIONS: Disorganized S/ER is a major characteristic of PLN-R14del cardiomyopathy in humans and mice and results in cardiomyocyte death. DWORF overexpression delayed PLN-R14del cardiomyopathy progression and extended life span in R14Δ/Δ mice, by reducing abnormal S/ER clusters.
ncRNADrug: a database for validated and predicted ncRNAs associated with drug resistance and targeted by drugs

Abstract Drug resistance is a major barrier in cancer treatment and anticancer drug development. Growing evidence indicates that non-coding RNAs (ncRNAs), especially microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), play pivotal roles in cancer progression, therapy, and drug resistance. Furthermore, ncRNAs have been proven to be promising novel therapeutic targets for cancer treatment. Reversing dysregulated ncRNAs by drugs holds significant potential as an effective therapeutic strategy for overcoming drug resistance. Therefore, we developed ncRNADrug, an integrated and comprehensive resource that records manually curated and computationally predicted ncRNAs associated with drug resistance, ncRNAs targeted by drugs, as well as potential drug combinations for the treatment of resistant cancer. Currently, ncRNADrug collects 29 551 experimentally validated entries involving 9195 ncRNAs (2248 miRNAs, 4145 lncRNAs and 2802 circRNAs) associated with the drug resistance of 266 drugs, and 32 969 entries involving 10 480 ncRNAs (4338 miRNAs, 6087 lncRNAs and 55 circRNAs) targeted by 965 drugs. In addition, ncRNADrug also contains associations between ncRNAs and drugs predicted from ncRNA expression profiles by differential expression analysis. Altogether, ncRNADrug surpasses the existing related databases in both data volume and functionality. It will be a useful resource for drug development and cancer treatment. ncRNADrug is available at http://www.jianglab. cn/ncRNADrug.
A Large Deviations Perspective on Policy Gradient Algorithms

Motivated by policy gradient methods in the context of reinforcement learning, we derive the first large deviation rate function for the iterates generated by stochastic gradient descent for possibly non-convex objectives satisfying a Polyak-Lojasiewicz condition. Leveraging the contraction principle from large deviations theory, we illustrate the potential of this result by showing how convergence properties of policy gradient with a softmax parametrization and an entropy regularized objective can be naturally extended to a wide spectrum of other policy parametrizations.
THOS: A Benchmark Dataset for Targeted Hate and Offensive Speech

Detecting harmful content on social media, such as Twitter, is made difficult by the fact that the seemingly simple yes/no classification conceals a significant amount of complexity. Unfortunately, while several datasets have been collected for training classifiers in hate and offensive speech, there is a scarcity of datasets labeled with a finer granularity of target classes and specific targets. In this paper, we introduce THOS, a dataset of 8.3k tweets manually labeled with fine-grained annotations about the target of the message. We demonstrate that this dataset makes it feasible to train classifiers, based on Large Language Models, to perform classification at this level of granularity.
Sum-Rate Optimization for RIS-Aided Multiuser Communications with Movable Antenna

Reconfigurable intelligent surface (RIS) is known as a promising technology to improve the performance of wireless communication networks, which has been extensively studied. Movable antenna (MA) is a novel technology that fully exploits the antenna position for enhancing the channel capacity. In this paper, we propose a new RIS-aided multiuser communication system with MAs. The sum-rate is maximized by jointly optimizing the beamforming, the reflection coefficient (RC) values of RIS and the positions of MAs. A fractional programming-based iterative algorithm is proposed to solve the formulated non-convex problem, considering three assumptions for the RIS. Numerical results are presented to verify the effectiveness of the proposed algorithm and the superiority of the proposed MA-based system in terms of sum-rate.
The Last Decade in Review: Tracing the Evolution of Safety Assurance Cases through a Comprehensive Bibliometric Analysis

Safety assurance is of paramount importance across various domains, including automotive, aerospace, and nuclear energy, where the reliability and acceptability of mission-critical systems are imperative. This assurance is effectively realized through the utilization of Safety Assurance Cases. The use of safety assurance cases allows for verifying the correctness of the created systems capabilities, preventing system failure. The latter may result in loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. Still, the emergence of complex technologies such as cyber-physical systems (CPSs), characterized by their heterogeneity, autonomy, machine learning capabilities, and the uncertainty of their operational environments poses significant challenges for safety assurance activities. Several papers have tried to propose solutions to tackle these challenges, but to the best of our knowledge, no secondary study investigates the trends, patterns, and relationships characterizing the safety case scientific literature. This makes it difficult to have a holistic view of the safety case landscape and to identify the most promising future research directions. In this paper, we, therefore, rely on state-of-the-art bibliometric tools(e.g., VosViewer) to conduct a bibliometric analysis that allows us to generate valuable insights, identify key authors and venues, and gain a birds eye view of the current state of research in the safety assurance area. By revealing knowledge gaps and highlighting potential avenues for future research, our analysis provides an essential foundation for researchers, corporate safety analysts, and regulators seeking to embrace or enhance safety practices that align with their specific needs and objectives.
A 3D Conditional Diffusion Model for Image Quality Transfer - An Application to Low-Field MRI

Low-field (LF) MRI scanners (<1T) are still prevalent in settings with limited resources or unreliable power supply. However, they often yield images with lower spatial resolution and contrast than high-field (HF) scanners. This quality disparity can result in inaccurate clinician interpretations. Image Quality Transfer (IQT) has been developed to enhance the quality of images by learning a mapping function between low and high-quality images. Existing IQT models often fail to restore high-frequency features, leading to blurry output. In this paper, we propose a 3D conditional diffusion model to improve 3D volumetric data, specifically LF MR images. Additionally, we incorporate a cross-batch mechanism into the self-attention and padding of our network, ensuring broader contextual awareness even under small 3D patches. Experiments on the publicly available Human Connectome Project (HCP) dataset for IQT and brain parcellation demonstrate that our model outperforms existing methods both quantitatively and qualitatively. The code is publicly available at \url{https://github.com/edshkim98/DiffusionIQT}.
Adaptive Language-based Mental Health Assessment with Item-Response Theory

Mental health issues widely vary across individuals - the manifestations of signs and symptoms can be fairly heterogeneous. Recently, language-based depression and anxiety assessments have shown promise for capturing this heterogeneous nature by evaluating a patient's own language, but such approaches require a large sample of words per person to be accurate. In this work, we introduce adaptive language-based assessment - the task of iteratively estimating an individual's psychological score based on limited language responses to questions that the model also decides to ask. To this end, we explore two statistical learning-based approaches for measurement/scoring: classical test theory (CTT) and item response theory (IRT). We find that using adaptive testing in general can significantly reduce the number of questions required to achieve high validity (r ~ 0.7) with standardized tests, bringing down from 11 total questions down to 3 for depression and 5 for anxiety. Given the combinatorial nature of the problem, we empirically evaluate multiple strategies for both the ordering and scoring objectives, introducing two new methods: a semi-supervised item response theory based method (ALIRT), and a supervised actor-critic based model. While both of the models achieve significant improvements over random and fixed orderings, we find ALIRT to be a scalable model that achieves the highest accuracy with lower numbers of questions (e.g. achieves Pearson r ~ 0.93 after only 3 questions versus asking all 11 questions). Overall, ALIRT allows prompting a reduced number of questions without compromising accuracy or overhead computational costs.
Singularities of orthogonal and symplectic determinantal varieties

Let either $GL(E)\times SO(F)$ or $GL(E)\times Sp(F)$ act naturally on the space of matrices $E\otimes F$. There are only finitely many orbits, and the orbit closures are orthogonal and symplectic generalizations of determinantal varieties, which can be described similarly using rank conditions. In this paper, we study the singularities of these varieties and describe their defining equations. We prove that in the symplectic case, the orbit closures are normal with good filtrations, and in characteristic $0$ have rational singularities. In the orthogonal case we show that most orbit closures will have the same properties, and determine precisely the exceptions to this.
Accurate Time-segmented Loss Model for SiC MOSFETs in Electro-thermal Multi-Rate Simulation

Compared with silicon (Si) power devices, Silicon carbide (SiC) devices have the advantages of fast switching speed and low on-resistance. However, the effects of non-ideal characteristics of SiC MOSFETs and stray parameters (especially parasitic inductance) on switching losses need to be further evaluated. In this paper, a transient loss model based on SiC MOSFET and SiC Schottky barrier diode (SBD) switching pairs is proposed. The transient process analysis is simplified by time segmentation of the transient process of power switching devices. The electro-thermal simulation calculates the junction temperature and updates the temperature-related parameters with the proposed loss model and the thermal network model. A multi-rate data exchange strategy is proposed to solve the problem of disparity in timescales between circuit simulation and thermal network simulation. The CREE CMF20120D SiC MOSFET device is used for the experimental verification. The experimental results verify the accuracy of the model which provides guidance for the circuit design of SiC MOSFETs. All the parameters of the loss model can be extracted from the datasheet, which is practical in power electronics design.
Geometric and Feedback Linearization on UAV: Review

The pervasive integration of Unmanned Aerial Vehicles (UAVs) across multifarious domains necessitates a nuanced understanding of control methodologies to ensure their optimal functionality. This exhaustive review meticulously examines two pivotal control paradigms in the UAV landscape, Geometric Control and Feedback Linearization. Delving into the intricate theoretical underpinnings, practical applications, strengths, and challenges of these methodologies, the paper endeavors to provide a comprehensive overview. Geometric Control, grounded in the principles of differential geometry, offers an elegant and intuitive approach to trajectory tracking and mission execution. In contrast, Feedback Linearization employs nonlinear control techniques to linearize UAV dynamics, paving the way for enhanced controllability. This review not only dissects the theoretical foundations but also scrutinizes real-world applications, integration challenges, and the ongoing research trajectory of Geometric Control and Feedback Linearization in the realm of UAVs.