Miracle-dz/UniFAR-FTUs huggingface dataset:
Epigenetic profile of the immune system associated with symptom severity and treatment response in schizophrenia

Background: Environmental modification of genetic information (epigenetics) is often invoked to explain interindividual differences in the phenotype of schizophrenia. In clinical practice, such variability is most prominent in the symptom profile and the treatment response. Epigenetic regulation of immune function is of particular interest, given the therapeutic relevance of this mechanism in schizophrenia. Methods: We analyzed the DNA methylation data of immune-relevant genes in patients with schizophrenia whose disease duration was less than 3 years, with previous lifetime antipsychotic treatment of no more than 2 weeks total. Results: A total of 441 patients met the inclusion criteria. Core symptoms were consistently associated with 206 methylation positions, many of which had previously been implicated in inflammatory responses. Of these, 24 methylation positions were located either in regulatory regions or near the CpG islands of 20 genes, including the SRC gene, which is a key player in glutamatergic signalling. These symptom-associated immune genes were enriched in neuronal development functions, such as neuronal migration and glutamatergic synapse. Compared with using only clinical information (including scores on the Positive and Negative Syndrome Scale), integrating methylation data into the model significantly improved the predictive ability (as indicated by area under the curve) for response to 8 weeks of antipsychotic treatment. Limitations: We focused on a small number of methylation probes (immune-centred search) and lacked nutritional data and direct brain-based measures. Conclusion: Epigenetic modifications of the immune system are associated with symptom severity at onset and subsequent treatment response in schizophrenia.
Identification of hippocampal area CA2 in hamster and vole brain

Prairie voles (Microtus ochrogaster) and Syrian, or golden, hamsters (Mesocricetus auratus) are closely related to mice (Mus musculus) and rats (Rattus norvegicus, for example) and are commonly used in studies of social behavior including social interaction, social memory, and aggression. The CA2 region of the hippocampus is known to play a key role in social memory and aggression in mice and responds to social stimuli in rats, likely owing to its high expression of oxytocin and vasopressin 1b receptors. However, CA2 has yet to be identified and characterized in hamsters or voles. In this study, we sought to determine whether CA2 could be identified molecularly in vole and hamster. To do this, we used immunofluorescence with primary antibodies raised against known molecular markers of CA2 in mice and rats to stain hippocampal sections from voles and hamsters in parallel with those from mice. Here, we report that, like in mouse and rat, staining for many CA2 proteins in vole and hamster hippocampus reveals a population of neurons that express regulator of G protein signaling 14 (RGS14), Purkinje cell protein 4 (PCP4) and striatal-enriched protein tyrosine phosphatase (STEP), which together delineate the borders with CA3 and CA1. These cells were located at the distal end of the mossy fiber projections, marked by the presence of Zinc Transporter 3 (ZnT-3) and calbindin in all three species. In addition to staining the mossy fibers, calbindin also labeled a layer of CA1 pyramidal cells in mouse and hamster but not in vole. However, Wolframin ER transmembrane glycoprotein (WFS1) immunofluorescence, which marks all CA1 neurons, was present in all three species and abutted the distal end of CA2, marked by RGS14 immunofluorescence. Staining for two stress hormone receptors—the glucocorticoid (GR) and mineralocorticoid (MR) receptors—was also similar in all three species, with GR staining found primarily in CA1 and MR staining enriched in CA2. Interestingly, although perineuronal nets (PNNs) are known to surround CA2 cells in mouse and rat, we found that staining for PNNs differed across species in that both CA2 and CA3 showed staining in voles and primarily CA3 in hamsters with only some neurons in proximal CA2 showing staining. These results demonstrate that, like in mouse, CA2 in voles and hamsters can be molecularly distinguished from neighboring CA1 and CA3 areas, but PNN staining is less useful for identifying CA2 in the latter two species. These findings reveal commonalities across species in molecular profile of CA2, which will facilitate future studies of CA2 in these species. Yet to be determined is how differences in PNNs might relate to differences in social behavior across species.
TSC22D, WNK and NRBP gene families exhibit functional buffering and evolved with Metazoa for macromolecular crowd sensing

The ability to sense and respond to osmotic fluctuations is critical for the maintenance of cellular integrity. Myriad redundancies have evolved across all facets of osmosensing in metazoans, including among water and ion transporters, regulators of cellular morphology, and macromolecular crowding sensors, hampering efforts to gain a clear understanding of how cells respond to rapid water loss. In this study, we harness the power of gene co-essentiality analysis and genome-scale CRISPR-Cas9 screening to identify an unappreciated relationship between TSC22D2, WNK1 and NRBP1 in regulating cell volume homeostasis. Each of these genes have paralogs and are functionally buffered for macromolecular crowd sensing and cell volume control. Within seconds of hyperosmotic stress, TSC22D, WNK and NRBP family members physically associate into cytoplasmic biocondensates, a process that is dependent on intrinsically disordered regions (IDRs). A close examination of these protein families across metazoans reveals that TSC22D genes evolved alongside a domain in NRBPs that specifically binds to TSC22D proteins, which we have termed NbrT (NRBP binding region with TSC22D), and this co-evolution is concomitant with rapid IDR length expansion in WNK family kinases. Our study identifies functions for unrecognized components of the cell volume sensing machinery and reveals that TSC22D, WNK and NRBP genes evolved as cytoplasmic crowding sensors in metazoans to co-regulate rapid cell volume changes in response to osmolarity.
A new paradigm considering multicellular adhesion, repulsion and attraction represents diverse cellular tile patterns

Cell sorting by differential adhesion is one of the basic mechanisms explaining spatial organization of neurons in early stage brain development of fruit flies. The columnar arrangements of neurons determine the large scale patterns in the fly visual center. Experimental studies indicate that hexagonal configurations regularly appear while tetragonal configurations can be induced in mutants. Mathematical models based on macroscopic approximations of agent based models (ARA models) are shown to produce a similar behavior changing from hexagonal to tetragonal steady configurations when medium range repulsion and longer-range attraction between individuals is incorporated in previous successful models for cell sorting based on adhesion and volume constraints. We analyse the angular configurations of these patterns based on angle summary statistics and compare between experimental data and parameter fitted ARA models showing that intermediate patterns between hexagonal and tetragonal configuration are common in experimental data as well as in our ARA mathematical model. Our studies indicate an overall qualitative agreement of ARA models in tile patterning and pave the way for their quantitative studies. 2010 MSC 92C17, 92C37, 35Q92
Contributions of sub-community based on short and long-range white matter tracts in personalized age-associated neurocompensatory mechanism

Brain function is shaped by the local and global connections between its dynamical units and biological parameters. As we age, the anatomical topology undergoes significant deterioration (e.g., long-range white matter fiber loss) that affects the brain’s overall function. Despite the structural loss, existing studies have pinpointed that normative patterns of functional integrity, defined as the compensatory mechanism of the aging brain, remain intact across the lifespan. However, the crucial components in guiding the adaptive mechanism by which the brain readjusts its biological parameters to maintain optimal compensatory function with age still needs to be uncovered. Here, we provide a parsimonious mechanism, which, together with the data-driven whole-brain generative model, establishes an individualized structure-function link with aging, and uncover sub-communities’ role in driving the neurocompensation process. We use two neuroimaging datasets of healthy human cohorts with large sample sizes to systematically investigate which of the brain sub-graphs (connected via short- or long-range white-matter tracts) drives the compensatory mechanisms and modulates intrinsic global scaling parameters, such as interaction strength and conduction delay, in preserving functional integrity. The functional integrity is evaluated under the hypothesis of preserved metastability, measured from individuals’ fMRI BOLD signals. Our findings uncover that the sub-graph connected via short-range tracts mainly modulates global coupling strength to compensate for structural loss. In contrast, long-range connections contribute to the conduction delay, which may play a complementary role in neurocompensation. For the first time, these findings shed light on the underlying neural mechanisms of age-related compensatory mechanisms and provide a mechanistic explanation for the importance of short-range connections in the face of the loss of long-range connections during aging using BOLD fMRI data. This crucial insight could open avenue to understand the role of subgraphs for targeted interventions to address aging-associated neurodegenerative diseases where long-range connections are significantly deteriorated.
Phosphorylation code of human nucleophosmin includes four cryptic sites for hierarchical binding of 14-3-3 proteins

Nucleophosmin (NPM1) is the 46th most abundant human protein with many functions whose dysregulation leads to various cancers. Pentameric NPM1 resides in the nucleolus but can also shuttle to the cytosol. NPM1 is regulated by multisite phosphorylation, yet molecular consequences of site-specific NPM1 phosphorylation remain elusive. Here we identify four 14-3-3 protein binding sites in NPM1 concealed within its oligomerization and α-helical C-terminal domains that are found phosphorylated in vivo. By combining mutagenesis, in-cell phosphorylation and PermaPhos technology for site-directed incorporation of a non-hydrolyzable phosphoserine mimic, we show how phosphorylation promotes NPM1 monomerization and partial unfolding, to recruit 14-3-3 dimers with low-micromolar affinity. Using fluorescence anisotropy we quantified pairwise interactions of all seven human 14-3-3 isoforms with four recombinant NPM1 phosphopeptides and assessed their druggability by fusicoccin. This revealed a complex hierarchy of 14-3-3 affinities toward the primary (S48, S293) and secondary (S106, S260) sites, differentially modulated by the small molecule. As three of these 14-3-3 binding phospho-sites in NPM1 reside within signal sequences, this work highlights a key mechanism of NPM1 regulation by which NPM1 phosphorylation promotes 14-3-3 binding to control nucleocytoplasmic shuttling. It also provides further evidence that phosphorylation-induced structural rearrangements of globular proteins serve to expose otherwise cryptic 14-3-3-binding sites that are important for cellular function.
BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery

Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and beneﬁting downstream tasks. However, computational challenges arise due to joint inference over combinatorial space of Directed Acyclic Graphs (DAGs) and nonlinear functions. Despite recent progress towards efﬁcient posterior inference over DAGs, existing methods are either limited to variational inference on node permutation matrices for linear causal models, leading to compromised inference accuracy, or continuous relaxation of adjacency matrices constrained by a DAG regularizer, which cannot ensure resulting graphs are DAGs. In this work, we introduce a scalable Bayesian causal discovery framework based on stochastic gradient Markov Chain Monte Carlo (SG-MCMC) that overcomes these limitations. Our approach directly samples DAGs from the posterior without requiring any DAG regularization, simultaneously draws function parameter samples and is applicable to both linear and nonlinear causal models. To enable our approach, we derive a novel equivalence to the permutation-based DAG learning, which opens up possibilities of using any relaxed gradient estimator deﬁned over permutations. To our knowledge, this is the ﬁrst framework applying gradient-based MCMC sampling for causal discovery. Empirical evaluations on synthetic and real-world datasets demonstrate our approach’s effectiveness compared to state-of-the-art baselines.
LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis

—Automated log analysis is crucial in modern software-intensive systems for ensuring reliability and resilience throughout software maintenance and engineering life cycles. Existing methods perform tasks such as log parsing and log anomaly detection by providing a single prediction value without interpretation. However, given the increasing volume of system events, the limited interpretability of analysis results hinders analysts’ trust and their ability to take appropriate actions. Moreover, these methods require substantial in-domain training data, and their performance declines sharply (by up to 62.5%) in online scenarios involving unseen logs from new domains, a common occurrence due to rapid software updates. In this paper, we propose LogPrompt, a novel zero-shot and interpretable log analysis approach. LogPrompt employs large language models (LLMs) to perform zero-shot log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs’ performance by up to 107.5% compared with simple prompts. Experiments on nine publicly available evaluation datasets across two tasks demonstrate that LogPrompt, despite using no training data, outperforms existing approaches trained on thousands of logs by up to around 50%. We also conduct a human evaluation of LogPrompt’s interpretability, with six practitioners possessing over 10 years of experience, who highly rated the generated content in terms of usefulness and read-ability (averagely 4.42/5). LogPrompt also exhibits remarkable compatibility with open-source and smaller-scale LLMs, making it flexible for practical deployment.
Enhanced Locational FDIA Detection in Smart Grids: A Scalable Distributed Framework

Locational detection of the false data injection attack (FDIA) is essential for smart grid cyber-security. However, the FDIA detection techniques often falter in scalability as power network complexity increases. To address the research gap, this paper introduces an innovative distributed framework for locational FDIA detection that optimizes both performance and scalability. The proposed framework initially partitions the power grid using the improved Louvain community detection algorithm. The proposed solution utilizes the Electrical Functional Strength (EFS) matrix and power supply modularity. Subsequently, a dedicated multi-label one-dimensional convolutional neural network model (1D CNN) locational detector is designed for each derived cluster. The proposed methodology is designed to increase detection accuracy and enhance the scalability of the model. This is achieved by reducing training and detection times, as well as lowering memory requirements, compared to traditional centralized approaches. The effectiveness of the proposed framework is validated through simulations on the IEEE 39-bus system. These simulations demonstrate the framework’s capability to enhance detection accuracy by simplifying the locational FDIA detection challenge, achieved through strategic grid partitioning.
Low-Light Image Reconstruction Based On Improved Convolutional Autoencoder

Machine vision target detection can be mainly divided into traditional detection methods represented by Hof circle detection and template matching, and detection methods based on deep learning. Traditional target detection methods need to de-noise the image, binarization and other pre-processing methods to ensure that the contour of the target can be extracted, but in the actual operating environment, the interference generated by factors such as light and the surrounding environment often leads to the contour of the target in the image is difficult to be extracted, reducing the success rate of the traditional methods to extract the circle in the complex environment. And the deep learning based circle detection method can solve this problem. Deep learning methods can obtain more specific features, have a higher degree of recognition of the learned features, and have improved performance over traditional machine learning methods. Therefore, we propose a convolutional autoencoder-based low-light image processing method, which improves the loss function by introducing the luminance module of SSIM in order to realize the adaptive enhancement of the luminance of low-light images.
SAR Image Despeckling Based on Local Noise Variance

Synthetic Aperture Radar (SAR) image despeckling is a basic task in SAR image processing. The size of noise variance affects the level of image speckle removal. Excessive variance can lead to the loss of details in the image, while conversely, it can still result in low-frequency noise in the denoised image. In this paper, we start from a sample matrix consisting of similar patches and calculate the local noise variance of each sample matrix. The PCA coefficients are contracted using the local noise variance with LMMSE. For ease of computation, we degenerate the coefficient contraction method to a kernel-paradigm minimization form using the mean of the local noise variance. Finally, the estimation bias induced by linear versus nonlinear signals is balanced by dividing the patches into homogeneous and heterogeneous categories using a region partitioning technique. The despeckling experimental results of simulated and real SAR images show good performance in both quantitative measurement and visual perception.
Fine-Tuning BERT-Based Models for Negative Content Identification on Indonesian Tweets

Social media platforms like Twitter have become substantial sources of user-generated content, enabling people to easily express their emotions and opinions. However, this freedom has increased the spread of harmful content, such as abusive language, sexually explicit content, and hate speech. This poses challenges for content moderation and user safety. In order to guarantee a safer, more receptive, and more pleasurable online environment for users of all ages, it is essential to develop a system capable of recognizing abusive and sexually explicit material on Twitter. Despite the growing importance of content moderation, a research gap exists in Indonesian tweets, with limited comprehensive studies on negative content identification. This research addresses this gap by evaluating the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) models in the Indonesian context, which were primarily developed for English and other languages. This research aims to identify abusive, adult, and neutral content in Indonesian tweets by examining and fine-tuning BERT-based models to maintain a healthy online environment for optimal tweet classification. Based on our experiments, the BERT-based models showed promising results in detecting negative tweets. Among the BERT-based models, IndoBERTweet achieved the best precision, recall, and macro F1 scores with 97.03, 96.88, and 96.94, respectively.
Dynamic Template Learning for Top-Level Computer Management Using Bayesian Optimization

Computing system administration is a difficult undertaking, particularly in complicated and shifting situations. Classical static methods often fall short of efficiently adjusting for shifting circumstances. In this study, we offer Dynamic Template Learning (DTL), a new method for automating top-level administration of computers that makes use of Bayesian optimization. The aim is to employ continuous performance evaluation and user-defined optimizations targets to constantly alter the system's settings and utilization of resources. The DTL architecture includes a feedback process that continually gathers performance information, evaluates the condition of the entire system, and draws lessons from the past in order to make wise choices. To effectively simulate system behavior while investigating the setting space, Bayesian optimizations is used. In order to find the best layouts, DTL uses probability modelling and acquisition procedures to dynamically examine choices. We experimented on a modelled machine with various strain conditions to assess the efficacy of DTL. The findings show that DTL performs better than conventional static techniques in aspects of system sales, usage of resources, and environmental adaption. Finding near-optimal solutions and quickly scanning the set-up field are both largely dependent on the Bayesian optimizations element. Additionally, we illustrate possible uses in real-world computing settings and talk about the advantages and disadvantages of the DTL technique. DTL's flexibility and adaptability makes it appropriate for a variety of infrastructure, including dispersed systems, data centers, and cloud computing systems. Organizations may increase system effectiveness, decrease staff involvement, and improve success overall by automated managerial processes.
An Efficient Quantum Transfer Learning for Cancer Prediction Using Tumour Markers: New Era of Computer in Medical

Ovarian cancer prediction models or algorithms estimate a person's risk of getting the disease based on different variables, such as their medical history, genetics, and biomarkers. Early identification and intervention will enhance patient successive diagnosis outcomes. Tumour markers are chemicals frequently detected in higher concentrations than usual in cancer patient's blood, urine, or tissues. They could be certain chemicals or proteins linked to the presence of tumours or cancer kinds. Tumour markers are employed for diagnosis, prognosis, and treatment response monitoring. Applying information or models from one quantum job to enhance the performance of another requires quantum transfer learning. Transferring knowledge from one domain to another seeks to increase learning effectiveness in novel quantum contexts. The main goal of efficient Quantum Transfer Learning (QTL) is to minimize the resources (computer power, data, or time) necessary to transfer between tasks successfully. In this research work, QTL is used to predict Ovarian Cancer (OC) with the assistance of biomarkers. The Quantum Transfer Learning- Ovarian Cancer (QTL-OC) achieves 93.78% accuracy and outperforms the existing techniques.