Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive multi-omics analysis reveals the core role of glycerophospholipid metabolism in rheumatoid arthritis development

Objectives Rheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis and eventually lead to joint deformity. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis. Methods A total of 113 RA patients and 75 controls were included in our study. Plasma and stool samples were obtained for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing and metabolomics analysis. And PBMCs were obtained for RNA sequencing. We used three models, logistic regression, least absolute shrinkage and selection operator (LASSO), and random forest, respectively, to distinguish NORA from CRA, and finally we validated model performance using an external cohort of 26 subjects. Results Our results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 29 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. Additionally, we applied three approaches to develop disease classifier models that were based on plasma metabolites and gut microbiota, which effectively distinguished between new-onset and chronic RA patients in both discovery cohort and external validation cohort. Conclusions These findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.
mmVAE: multimorbidity clustering using Relaxed Bernoulli β-Variational Autoencoders

The prevalence of chronic disease multi-morbidity is a significant and increasing challenge for health systems. In many cases, the occurrence of one chronic disease leads to the development of one or more other chronic conditions. This exerts a significant challenge in improving patient outcomes and is a growing challenge globally as average population age increases. Using electronic health record information to identify patterns of co-occurring conditions is seen as an unbiased means of understanding multimorbidity but most studies have adopted off-the-shelf algorithmic techniques that are not tai-lored for the application. We present a novel bespoke approach for multimorbidity clustering based on a highly cus-tomised version of a β -variational autoencoder. We incorporate the use of minimum entropy clustering to identify sparse, low-dimensional factored representations that link at a feature-level to the observed patient-level multimorbidity profiles. We demonstrate how the approach can be used to explore complex structure in a population-scale health data sets by examining data from a UK population of nearly 300,000 women in pregnancy suffering from multimorbidity.
A systematic benchmark of machine learning methods for protein–RNA interaction prediction

RNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation. Experiments to profile binding sites of RBPs in vivo are limited to transcripts expressed in the experimental cell type, creating the need for computational methods to infer missing binding information. While numerous machine-learning based methods have been developed for this task, their use of heterogeneous training and evaluation datasets across different sets of RBPs and CLIP-seq protocols makes a direct comparison of their performance difficult. Here, we compile a set of 37 machine learning (primarily deep learning) methods for in vivo RBP-RNA interaction prediction and systematically benchmark a subset of 11 representative methods across hundreds of CLIP-seq datasets and RBPs. Using homogenized sample pre-processing and two negative-class sample generation strategies, we evaluate methods in terms of predictive performance and assess the impact of neural network architectures and input modalities on model performance. We believe that this study will not only enable researchers to choose the optimal prediction method for their tasks at hand, but also aid method developers in developing novel, high-performing methods by introducing a standardized framework for their evaluation.
Accelerating Lloyd’s Algorithm for k-Means Clustering

The k-means clustering algorithm, a staple of data mining and unsupervised learning, is popular because it is simple to implement, fast, easily parallelized, and offers intuitive results. Lloyd’s algorithm is the standard batch, hill-climbing approach for minimizing the k-means optimization criterion. It spends a vast majority of its time computing distances between each of the k cluster centers and the n data points. It turns out that much of this work is unnecessary, because points usually stay in the same clusters after the first few iterations. In the last decade researchers have developed a number of optimizations to speed up Lloyd’s algorithm for both lowand high-dimensional data. In this chapter we survey some of these optimizations and present new ones. In particular we focus on those which avoid distance calculations by the triangle inequality. By caching known distances and updating them efficiently with the triangle inequality, these algorithms can provably avoid many unnecessary distance calculations. All the optimizations examined produce the same results as Lloyd’s algorithm given the same input and initialization, so are suitable as drop-in replacements. These new algorithms can run many times faster and compute far fewer distances than the standard unoptimized implementation. In our experiments, it is common to see speedups of over 30–50x compared to Lloyd’s algorithm. We examine the trade-offs for using these methods with respect to the number of examples n, dimensions d , clusters k, and structure of the data.
KCRL: A Prior Knowledge Based Causal Discovery Framework with Reinforcement Learning

Causal discovery is an important problem in many sciences that enables us to estimate causal relationships from observational data. Particularly, in the healthcare domain, it can guide practitioners in making informed clinical decisions. Several causal discovery approaches have been developed over the last few decades. The success of these approaches mostly rely on a large number of data samples. In practice, however, an infinite amount of data is never available. Fortunately, often we have some prior knowledge available from the problem domain. Particularly, in healthcare settings, we often have some prior knowledge such as expert opinions, prior RCTs, literature evidence, and systematic reviews about the clinical problem. This prior information can be utilized in a systematic way to address the data scarcity problem. However, most of the existing causal discovery approaches lack a systematic way to incorporate prior knowledge during the search process. Recent advances in reinforcement learning techniques can be explored to use prior knowledge as constraints by penalizing the agent for their violations. Therefore, in this work, we propose a framework KCRL 1 that utilizes the existing knowledge as a constraint to penalize the search process during causal discovery. This utilization of existing information during causal discovery reduces the graph search space and enables a faster convergence to the optimal causal mechanism. We evaluated our framework on benchmark synthetic and real datasets as well as on a real-life healthcare application. We also compared its performance with several baseline causal discovery methods. The experimental findings show that penalizing the search process for constraint violation yields better performance compared to existing approaches that do not utilize prior knowledge.
Adjustment uncertainty in effect estimation

Often there is substantial uncertainty in the selection of confounders when estimating the association between an exposure and health. We define this type of uncertainty as `adjustment uncertainty'. We propose a general statistical framework for handling adjustment uncertainty in exposure effect estimation for a large number of confounders, we describe a specific implementation, and we develop associated visualization tools. Theoretical results and simulation studies show that the proposed method provides consistent estimators of the exposure effect and its variance. We also show that, when the goal is to estimate an exposure effect accounting for adjustment uncertainty, Bayesian model averaging with posterior model probabilities approximated using information criteria can fail to estimate the exposure effect and can over- or underestimate its variance. We compare our approach to Bayesian model averaging using time series data on levels of fine particulate matter and mortality. Copyright 2008, Oxford University Press.
Explicit algebraic Reynolds stress and non-linear eddy-viscosity models

This article reviews explicit algebraic Reynolds stress models and other non-linear eddy-viscosity turbulence models utilised in the context of Reynolds-Averaged Navier-Stokes simulations of turbulent flows. Since the 1990s, these modelling classes have become important intermediate classes between the linear eddy viscosity models and full Reynolds stress transport models. The derivation of explicit algebraic Reynolds stress models from the Reynolds stress transport models, and the required simplifications are discussed. The most important simplification is the weak-equilibrium assumption. Properties of the basic weak-equilibrium assumption and its extended form are discussed. Differences between explicit solutions based on complete tensor representation and those based on reduced representation as well as other non-linear eddy-viscosity models are pointed out. The scale-determining models are also discussed briefly.
LOCAL H i EMISSIVITY MEASURED WITH FERMI-LAT AND IMPLICATIONS FOR COSMIC-RAY SPECTRA

Cosmic-ray (CR) electrons and nuclei interact with the Galactic interstellar gas and produce high-energy γ-rays. The γ-ray emission rate per hydrogen atom, called emissivity, provides a unique indirect probe of the CR flux. We present the measurement and the interpretation of the emissivity in the solar neighborhood for γ-ray energy from 50 MeV to 50 GeV. We analyzed a subset of 4 yr of observations from the Large Area Telescope (LAT) aboard the Fermi Gamma-ray Space Telescope (Fermi) restricted to absolute latitudes 10 ° < ∣ b ∣ < 70 ° ? > . From a fit to the LAT data including atomic, molecular, and ionized hydrogen column density templates, as well as a dust optical depth map, we derived the emissivities, the molecular hydrogen–to–CO conversion factor X CO = ( 0.902 ± 0.007 ) × 10 20 ? > cm−2 (K km s−1)−1, and the dust-to-gas ratio X DUST = ( 41.4 ± 0.3 ) × 10 20 ? > cm−2 mag−1. Moreover, we detected for the first time γ-ray emission from ionized hydrogen. We compared the extracted emissivities to those calculated from γ-ray production cross sections and to CR spectra measured in the heliosphere. We observed that the experimental emissivities are reproduced only if the solar modulation is accounted for. This provides a direct detection of solar modulation observed previously through the anticorrelation between CR fluxes and solar activity. Finally, we fitted a parameterized spectral form to the heliospheric CR observations and to the Fermi-LAT emissivity and obtained compatible local interstellar spectra for proton and helium kinetic energy per nucleon between between 1 and 100 GeV and for electron–positrons between 0.1 and 100 GeV.
Precise Detection of Car License Plates by Locating Main Characters

We propose a novel method to precisely detect car license plates by locating main characters, which are printed with large font size. The regions of the main characters are directly detected without detecting the plate region boundaries, so that license regions can be detected more precisely than by other existing methods. To generate a binary image, multiple thresholds are applied, and segmented regions are selected from multiple binarized images by a criterion of size and compactness. We do not employ any character matching methods, so that many candidates for main character groups are detected; thus, we use a neural network to reject non-main character groups from the candidates. The relation of the character regions and the intensity statistics are used as the input to the neural network for classification. The detection performance has been investigated on real images captured under various illumination conditions for 1000 vehicles. 980 plates were correctly detected, and almost all non-detected plates were so stained that their characters could not be isolated for character recognition. In addition, the processing time is fast enough for a commercial automatic license plate recognition system. Therefore, the proposed method can be used for recognition systems with high performance and fast processing.
Development of a total reflection double-slit for evaluation of spatial coherence in hard X-ray region

A new total reflection double-slit for hard X-ray region was developed. It consists of Au narrow stripes deposited on a SiO2 substrate and can function equivalently as a conventional Young's interferometer by operating with a small grazing incident angle. The Young's interferometer using the total reflection double-slit was constructed at Hyogo-ID BL of SPring-8, and the spatial coherence at 10 keV X-rays was evaluated by analyzing the interference fringes.
Validation and Intercomparison of Satellite Rainfall Estimates over Colombia

Abstract Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between the two extremes. The evaluated satellite rainfall products are the Tropical Rainfall Measuring Mission 3B42 and 3B42RT products, the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), the Naval Research Laboratory’s blended product (NRLB), and two versions of the Global Satellite Mapping of Precipitation m.
Synthesis of contact‐aided compliant mechanisms for non‐smooth path generation

Topology optimization is used in this paper for the systematic synthesis of contact‐aided compliant mechanisms that trace prescribed, non‐smooth paths in response to a single, monotonically increasing input force. Intermittent contact interactions that enable these mechanisms to exhibit non‐smooth responses also lead to algorithmic difficulties when the techniques from the synthesis of ordinary compliant mechanisms are used to design contact‐aided compliant mechanisms. A sequential optimization approach based on a regularized normal contact model for large displacements is used in this work to circumvent these difficulties and to enable the use of computationally efficient, gradient‐based optimization methods. We use an objective function based on Fourier shape descriptors, which allows the designer to emphasize different aspects of the design intent (such as the shape, the size and the orientation of the output path) separately. A variable‐stiffness input spring is used to allow the synthesis procedure to choose the appropriate magnitude of the input force. An arc‐length finite element solver and heuristic measures that guard against local and global instabilities add to the robustness of the synthesis procedure as demonstrated by the two design examples presented in this paper. Copyright © 2006 John Wiley & Sons, Ltd.
Large-scale systems with symmetrically interconnected subsystems: analysis and synthesis of decentralized controllers

A number of large-scale interconnected systems often encountered in practice are composed of subsystems with similar dynamics interconnected in a symmetrical fashion, and the synthesis of controllers for such systems must exploit the special structural properties in order to avoid overly conservative designs and to take advantage of the possible beneficial effects of the interconnections. An analysis of some important qualitative properties of such symmetrically interconnected systems focusing on the spectrum characterization, controllability, and observability and the solutions of the algebraic Riccati equation and the matrix Lyapunov equation are presented. Procedures for constructing the solutions to the analysis problems at the overall system level from the computationally simple subsystem level solutions are developed. A decentralized controller design procedure is presented as an illustration of the utilization of the available structural information in addressing synthesis problems. Numerical examples are included to demonstrate the superiority of the designs over existing approach which do not take full advantage of the structural knowledge of these large-scale systems. <<ETX>>
Fourier reconstruction in optoacoustic imaging using truncated regularized inverse k-space interpolation

A novel Fourier transform based reconstruction algorithm for solving the inverse problem in optoacoustic imaging is presented, which improves reconstruction efficiency and image quality. Fourier algorithms make use of an interpolation law when signal Fourier components are mapped to source Fourier components. To overcome inadequacies affiliated with interpolation methods such as nearest neighbour, linear, cubic or spline interpolation, together with signal data zero padding, we present a regularized interpolation method based on a forward model explicitly formulated for the compactly supported signal data. Simulations performed on a digital tissue phantom reveal the potential of this novel reconstruction method, which results in images of enhanced quality but without the need of using time-consuming zero-padding.
Nonparametric cooling schedules in simulated annealing using the normal score transformation

The author presents the application of the normal score transformation to simulated annealing methods. This application allows Gaussian-based cooling schedules to be used without the risk of violating their underlying assumptions. The normal score transformation is shown to make the simulated annealing algorithm unaffected by the location and scale of the function. The resulting method yields an equivalent search for any monotonic transformation of the objective function, if the same random number sequence is used. The performance of the new method is examined using a standard set of test functions. <<ETX>>
Modelling association football scores

Previous authors have rejected the Poisson model for association football scores in favour of the Negative Binomial. This paper, however, investigates the Poisson model further. Parameters representing the teams' inherent attacking and defensive strengths are incorporated and the most appropriate model is found from a hierarchy of models. Observed and expected frequencies of scores are compared and goodness‐of‐fit tests show that although there are some small systematic differences, an independent Poisson model gives a reasonably accurate description of football scores. Improvements can be achieved by the use of a bivariate Poisson model with a correlation between scores of 0.2.
Dimensionality Reduction in Hyperbolic Data Spaces: Bounding Reconstructed-Information Loss

We have started to see non-Euclidean geometries used in many applications, including visualization, network measurement, and geometric routing. Therefore, we need new mechanisms for managing and exploring non-Euclidean data. In this paper, we specifically address the dimensionality reduction problem for Hyperbolic data that are represented by the Poincare disk model. A desirable property of our solution is its reconstructed boundedness. In other words, we can reconstruct the data from its dimension-reduced version to obtain an approximation whose deviation from the original data is bounded and independent of the boundary measure of the original data space. We also propose a similarity search technique as an application of our reduction approach. We provide both theoretical and simulation analyses to substantiate the findings of our work.
A hierarchical model for the analysis of spatial rainfall extremes

In this article, we propose a spatial model for analyzing extreme rainfall values over the Triveneto region (Italy). We assess the existence of a long-term trend in the extremes. To integrate data coming from the different stations, we propose a hierarchical model. At the first level, for each monitoring station we model data by making use of a generalized extreme value distribution; at the second level, we combine results from the first stage by exploiting recent advances in modeling nonstationary spatial random fields.
DisasChat: An End-to-End Encrypted Off-Grid LoRa-Based Smartphone Communication System for Disaster and Crisis Scenarios

Large-scale disasters like earthquakes, storms, and floods often severely damage the existing infrastructures for telecommunication and make them unavailable. This prevents the otherwise seamless flow of information, toughening the job of rescue and relief. In such a scenario, the need for rapidly deployable network solutions becomes important for restoring connectivity and providing assistance to the people and rescuers to coordinate their relief efforts. In this work, we propose a novel strategy for long-range communication based on the LoRa technology for such post-disaster situations where resources are scarce. A decentralized, low-power, secure, and robust communication system is proposed that can be helpful in challenging scenarios. We implemented and tested our system named Disaschat, which also protects the privacy of its users with end-to-end encryption while enabling long-range communication efficiently. We demonstrated the system’s feasibility by sending text messages from one Smartphone to another using our proposed system and display our findings.
A Cavity Backed Printed Blade Antenna for S-Band 5G Access Points

A co-axial fed Cavity Backed Printed blade antenna for S-Band 5G application is discussed. A blade dipole antenna is designed at operating frequency 3.5 GHz on Roger 5880 20 mil substrate. Antenna is placed on a cavity with λ/4 height and 4mm thickness. Proposed Cavity Backed Printed Blade Antenna is fabricated and tested for its radiation characteristics and return loss. The measurement results are in good agreement with the simulated one. Simulation of a 1X4 Linear array using MathWorks Antenna Toolbox also discussed.