Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved specificity of glutamate decarboxylase 65 autoantibody measurement using luciferase-based immunoprecipitation system (LIPS) assays

Autoantibodies to glutamate decarboxylase (GADA) are widely used in the prediction and classification of type 1 diabetes. GADA radiobinding assays (RBAs) using N-terminally truncated antigens offer improved specificity but radioisotopes limit the high-throughput potential for population screening. Luciferase-based Immunoprecipitation System (LIPS) assays are sensitive and specific alternatives to RBAs with the potential to improve risk stratification. The performance of assays using the Luciferase (Nluc-) conjugated GAD65 constructs, Nluc-GAD65(96-585) and full length Nluc-GAD65(1-585) were evaluated in 434 well-characterised sera from recent-onset type 1 diabetes patients and first-degree relatives. Non-radioactive, high-throughput LIPS assays are quicker and require less serum than RBAs. Of 171 relatives previously tested single autoantibody positive for autoantibodies to full-length GAD65 by RBA but had not progressed to diabetes, fewer retested positive by LIPS using either truncated (n=72) or full-length (n=111) antigen. The Nluc-GAD65(96-585) truncation demonstrated the highest specificity in LIPS assays overall but in contrast to RBA, N-terminus truncations did not result in a significant increase in disease-specificity compared with the full-length antigen. This suggests that binding of non-specific antibodies is affected by the conformational changes resulting from addition of the Nluc antigen. Nluc-GAD65(96-585) LIPS assays offer low blood volume, high specificity GADA tests for screening and diagnostics.
Enhancement of proton acceleration by a right-handed circularly polarized laser interaction with a cone target exposed to a longitudinal magnetic field

Our previous research [J. X. Gong et al. Phys. Plasmas 24, 033103 (2017)] shows that in the presence of an external longitudinal magnetic field, there is no cut-off density when a right-handed (RH-) circularly polarized (CP) laser propagates in the plasmas. In this work, the proton acceleration driven by an RH-CP laser interaction with a pre-magnetized cone target filled with a pre-formed plasma is investigated under the mechanism of target normal sheath acceleration. The strength of the external magnetic field considered in this paper is comparable to that of the incident laser. The two-dimensional particle-in-cell simulation results show that with an external longitudinal magnetic field, both the energy and yield of protons accelerated by the sheath electric field at the rear of the target are remarkably increased because of the higher coupling efficiency from RH-CP laser energy to electrons and the more efficient electron acceleration. Electrons can be converged remarkably by the external magnetic fiel.
Machine learning algorithms in spatiotemporal gait analysis can identify patients with Parkinsons disease.

Changes to spatiotemporal gait metrics in gait-altering conditions are characteristic of the pathology. This data can be interpreted by machine learning (ML) models which have recently emerged as an adjunct to clinical medicine. However, the literature is undecided regarding its utility in diagnosing pathological gait and is heterogeneous in its approach to applying ML techniques. This study aims to address these gaps in knowledge. This was a prospective observational study involving 32 patients with Parkinsons disease and 88 normative subjects. Spatiotemporal gait metrics were gathered from all subjects using the MetaMotionC inertial measurement unit and data obtained were used to train and evaluate the performance of 10 machine learning models. Principal component analysis and Genetic Algorithm were amongst the feature selection techniques used. Classification models included Logistic Regression, Support Vector Machine, Naive - Bayes, Random Forest, and Artificial Neural Networks. ML algorithms can accurately distinguish pathological gait in Parkinsons disease from that of normative controls. Two models which used the Random Forest classifier with Principal Component analysis and Genetic Algorithm feature selection techniques separately, were 100% accurate in its predictions and had an [[EQUATION]] score of 1. A third model using principal component analysis and Artificial neural networks was equally as successful (100% accuracy, [[EQUATION]]). We conclude that ML algorithms can accurately distinguish pathological gait from normative controls in Parkinsons Disease. Random Forest classifiers, with Genetic Algorithm feature selection are the preferred ML techniques for this purpose as they produce the highest performing model.
Robust time delay estimation based direction finding in lp-space

ABSTRACT A robust time delay estimation (TDE)-based direction-finding method for multipath signals in the presence of impulsive noise is proposed in this paper. Compared to traditional algorithms, developed for Gaussian distribution, this method performs more robust in the presence of non-Gaussian noise. The robust TDE is first obtained by utilising the matched filtering in -space which is less sensitive to outliers. Then direction-of-arrival (DOA) is estimated by ‘steering vector fitting’ approach based on the delay estimates. An iterative approach is employed to estimate each multipath component separately and improve the estimation accuracy. The method is developed in -space, which minimises the -norm of residual fitting error vector instead of the Frobenius norm. The number of antennas required can be less than that of the sources. Simulation results show that the method outperforms several outliers-resistant algorithms both in cases of impulsive noise and Gaussian distribution noise.
Tracking X-Ray Source Movement in a Retracting Flux Tube

Solar flares produce sources of localized, enhanced X-ray emission, thought to be due to the acceleration of nonthermal electrons and the transport of energy away from the reconnection site. The 2002 November 28 C1.6 limb flare showed clear X-ray source motion in the Reuven Ramaty High Energy Solar Spectroscopic Imager observations at 3–10 keV propagating from the apex of the flaring arcade, down toward the footpoints, and then rising back into the corona. Previous work attempted to model this motion using simulations driven by heating with an electron beam or thermal conduction front, finding reasonable agreement only if there were large initial densities. This work extends the previous model by considering a flux tube that retracts through a current sheet away from a magnetic reconnection site. The retraction model includes drag to slow motion in the current sheet, which allows us to vary the energy released by the retraction. This retraction causes a dense and superhot plug of material to form at the loop apex, naturally causing a thermal X-ray source to form in the corona. We find that the observed X-ray source motion, however, is most likely thermal and a signature of the evaporation fronts after initially filling the flux tube.
Measuring exciton-phonon coupling in semiconductor nanocrystals

At low excitation density, the dynamics of excitons in semiconductor nanocrystals are largely dictated by their interactions with the underlying atomic lattice. This exciton-phonon coupling (EPC) is responsible, for example, for absorption and luminescence linewidths at elevated temperatures, relaxation processes following optical excitation, and even degradation of quantum coherent applications. Characterizing and understanding EPC is therefore central to guiding rational design of colloidal nanocrystal materials and their device applications. In this review, we compare different spectroscopic methods of measuring exciton-phonon interactions and the complementary information that they provide. We emphasize the development of a new technique, termed multidimensional coherent spectroscopy, that circumvents many of the limitations of traditional methods.
Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Ontology and Corpus-based Semantic Representation for Biomedical Concepts Biomedical Concepts

Objective Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that a concept can be referenced in various forms across diﬀerent texts. This paper introduces Multi-Ontology Reﬁned Embeddings (MORE), a novel hybrid framework for incorporating domain knowledge from various ontologies into a distributional semantic model, learned from a corpus of clinical text. This approach generates word embeddings that are more accurate and extensible for computing the semantic similarity of biomedical concepts than previous methods
Journal of Mathematical Psychology

We introduce a new approach to modelling decision confidence, with the aim of enabling computationally cheap predictions while taking into account, and thereby exploiting, trial-by-trial variability in stochastically fluctuating stimuli. Using the framework of the drift diffusion model of decision making, along with time-dependent thresholds and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of ‘‘pipeline’’ evidence that has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli that change over the course of a trial with normally-distributed fluctuations in the evidence they provide. A number of approximations are made to arrive at the final expressions, and we test all approximations via simulation. The derived expressions contain only a small number of standard functions, and require evaluating only once per trial, making trial-by-trial modelling of confidence data in stochastically fluctuating stimuli tasks more feasible. We conclude by using the expressions to gain insight into the confidence of optimal observers, and empirically observed patterns.
Fixed points of dictionary learning algorithms for sparse representations

This work provides theoretical arguments to compare dictionary learning algorithms for sparse rep- resentations. Three algorithms are considered: Sparsenet, MOD and K-SVD. The main theoretical result is that the fixed points of the Sparsenet and MOD dictionary update stages are the critical points of the residual error energy function (i.e. points with null gradient, not necessarily local minima), whereas the set of K-SVD fixed points is strictly included in the critical point set of the error energy. An example of a point is also provided where Sparsenet and MOD would stop whereas K-SVD can reach a solution with lower residual error. Further experiments show that the result of Sparsenet is a very good starting point for K-SVD. The combination of Sparsenet followed by K-SVD provides a significant improvement in terms of exact recovery rate and approximation quality.
Hypoxic Microenvironment Promotes PTBP1 Lactonization and IGF2BP2 Read Defects Mediate the Development of Preeclampsia

Objective: Pre-eclampsia (PE) remains a major cause of maternal and neonatal morbidity and mortality, with no effective strategy for causal treatment.Methods: This study was performed by downloading the GEO database based on the GSE173193 dataset, including single-cell sequencing data from placental samples of two PE patients and two normal controls. Placental cell subpopulations and their transcriptional heterogeneity were compared between PE and healthy pregnancies, and the mechanisms of PE cell dynamics in the hypoxic microenvironment were confirmed by in vitro experiments.Results: In this study, we constructed a large-scale single-cell transcriptome ecological landscape of 26,416 cells from healthy pregnant and PE patients placenta and further identified a PE-specific CSNK2B-positive subpopulation of EVT cells. Specifically, this study revealed that the EVT subpopulation PTBP1 was inactivated by lactonization in the hypoxic microenvironment, resulting in low expression of the N6-methyladenosine (m6A) reading protein IGF2BP2. Low expression of IGF2BP2 inhibits mitochondrial autophagy, causes the accumulation of damaged mitochondria, exacerbates lactic acid accumulation while inducing EVT apoptosis on the one hand. On the other hand, it inhibits EVT adherent spot signaling, decreases EVT invasive ability, leads to impaired placental spiral vessel recast, and promotes PE disease process. In addition, there are interactions between abnormal metabolic signaling of PE-specific EVT subpopulations and microenvironmental immune cells, which activate metabolic inflammation.Conclusion: The present study not only provides a new cell biological and genetic basis for elucidating the pathogenesis of PE, but also contributes to the design of an allopathic treatment strategy for PE.
Immunogenicity and safety of heterologous Omicron BA.1 and bivalent SARS-CoV-2 recombinant spike protein booster vaccines: a phase 3, randomized, clinical trial

Background. Mutations present in emerging SARS-CoV-2 variants permit evasion of neutralization with prototype vaccines. A novel Omicron BA.1 subvariant-specific vaccine (NVX-CoV2515) was tested alone, or as a bivalent preparation in combination with the prototype vaccine (NVX CoV2373), to assess antibody responses to SARS-CoV-2. Methods. Participants aged 18 to 64 years immunized with 3 doses of prototype mRNA vaccines were randomized 1:1:1 to receive a single dose of NVX-CoV2515, NVX-CoV2373, or bivalent mixture in a phase 3 study investigating heterologous boosting with SARS-CoV-2 recombinant spike protein vaccines. Immunogenicity was measured 14 and 28 days after vaccination for the SARS-CoV-2 Omicron BA.1 sublineage and ancestral strain. Safety profiles of vaccines were assessed. Results. Of participants who received trial vaccine (N=829), those administered NVX-CoV2515 (n=286) demonstrated superior neutralizing antibody response to BA.1 versus NVX-CoV2373 (n=274) at Day 14 (geometric mean titer ratio [95% CI]: 1.6 [1.33, 2.03]). Seroresponse rates [n/N; 95% CI] were 73.4% [91/124; 64.7, 80.9] for NVX-CoV2515 versus 50.9% [59/116; 41.4, 60.3] for NVX-CoV2373. All formulations were similarly well-tolerated. Conclusions. NVX-CoV2515 elicited a superior neutralizing antibody response against the Omicron BA.1 subvariant compared with NVX-CoV2373 when administered as a fourth dose. Safety data were consistent with the established safety profile of NVX-CoV2373.
Thoracic limb activity of Simocephalus vetulus and its descendants is shaped by the combination of delafloxacin and calcium

Abstract Due to its robust antibacterial activity in acidic environments and effectiveness against old fluoroquinolone-resistant bacterial strains, the use of delafloxacin (DFX) is projected to increase in the coming years. Here, we investigated the effect on the thoracic limb rate (TLR) of the water flea Simocephalus vetulus under the combined exposure of DFX (0, 100, 200, and 400 µg L−1) and calcium (Ca) (26 and 78 mg L−1) using Bayesian statistics-based linear models. The findings showed unexpected increases and decreases in the TLR of maternal S. vetulus exposed to DFX in the 26 mg Ca L−1. In the 78 mg Ca L−1 condition, we found a clearer decrease in TLR during DFX treatments. In addition, in the 26 mg Ca L−1, elusive changes in the TLR of neonatal S. vetulus whose mothers were exposed to DFX were observed, whereas the reduced TLR of the neonate, whose mother was exposed to 400 µg DFX L−1, was apparent in the higher Ca condition. While the elevated Ca content did not appear to have a clear effect on organisms’ TLR, it acted as a synergist in conjunction with DFX, leading to a reduction in TLR, the underlying mechanism of which remains poorly understood. The results of our study indicate that DFX may pose an ecological risk as a xenobiotic; therefore, taking into account the abundance of Ca in specific aquatic environments, a comprehensive ecological risk assessment should be conducted. Moreover, the inclusion of Bayesian statistics in ecotoxicological studies is highly recommended to improve data analysis and interpretation.
Global dynamics of a stochastic reaction–diffusion predator–prey system with space-time white noise

This paper proposes a stochastic reaction–diffusion predator–prey system with fear under the interference of space-time white noise by using the related theories of stochastic partial differential equations. The motivation of this paper is twofold: (i) mathematically, to try to investigate the effects of environmental noise and diffusion on population dynamics; (ii) biologically, to study how environmental noise affects the permanence of species. First, we analyse the well-posedness of solutions. Then sufficient conditions for extinction and permanence of prey and predator populations are derived. Moreover, we present the existence and uniqueness of stationary distribution of this system. Finally, based on numerical simulations and theoretical analysis, it is revealed that (i) high-intensity white noise can lead to the extinction of predator populations; (ii) low-intensity white noise may prolong the period of periodic solutions. This study provides new theoretical guidance for exploring ecological issues in the face of environmental disturbance and spatial diffusion.
How does environmental relative performance evaluation work? The case of China’s urban air quality ranking

Abstract Adapting to the situation of one principal versus several agents, the Relative Performance Evaluation (RPE) is an effective incentive tool to motivate subordinates. The Chinese central government has adopted RPE to combat intractable air pollution, which is the Urban Air Quality Ranking (UAQR) system. This study uses a series of econometric methods including regression discontinuity design to examine its effectiveness in environmental governance. The results show that (1) since the implementation of UAQR, the overall air quality of cities has improved; (2) air quality of the bottom 3 cities in the ranking will improve in the coming month compared with their counterparts; (3) while top 3 cities in the ranking do not experience such change in air quality. Therefore, this paper argues that the activating based RPE does not stimulate effective competition, whereas competency based RPE does. UAQR with credible incentive commitment can be a useful measure to motivate local authorities to strengthen air pollution reduction. In this study, the results of the empirical analysis enrich the theoretical framework of RPE, and an innovative theoretical framework is adopted to explain the UAQR system. However, as a top-down environmental monitoring system, such ranking institution could mainly eliminate centralized discharged pollutants. A sound judicial system is the fundamental guarantee for sustainable development.
Confined active Brownian particles: theoretical description of propulsion-induced accumulation

The stationary-state distribution function of confined active Brownian particles (ABPs) is analyzed by computer simulations and analytical calculations. We consider a radial harmonic as well as an anharmonic confinement potential. In the simulations, the ABP is propelled with a prescribed velocity along a body-fixed direction, which is changing in a diffusive manner. For the analytical approach, the Cartesian components of the propulsion velocity are assumed to change independently; active Ornstein–Uhlenbeck particle (AOUP). This results in very different velocity distribution functions. The analytical solution of the Fokker–Planck equation for an AOUP in a harmonic potential is presented and a conditional distribution function is provided for the radial particle distribution at a given magnitude of the propulsion velocity. This conditional probability distribution facilitates the description of the coupling of the spatial coordinate and propulsion, which yields activity-induced accumulation of particles. For the anharmonic potential, a probability distribution function is derived within the unified colored noise approximation. The comparison of the simulation results with theoretical predictions yields good agreement for large rotational diffusion coefficients, e.g. due to tumbling, even for large propulsion velocities (Péclet numbers). However, we find significant deviations already for moderate Péclet number, when the rotational diffusion coefficient is on the order of the thermal one.
Numerical solution of distributed-order fractional 2D optimal control problems using the Bernstein polynomials

In this work, a new class of two-dimensional optimal control problems is introduced with the help of distributed-order fractional derivative in the Caputo form. The orthonormal Bernstein polynomials are used to make a numerical method to solve these problems. Through this way, some operational matrices are obtained for the classical and fractional derivatives of these polynomials to make effective utilisation of them in constructing the proposed method. The main advantage of the established method is that it turns the solution of the problem under consideration into a system of algebraic equations by approximating the state and control variables by the expressed polynomials and applying the method of Lagrange multipliers. The accuracy and capability of the proposed approach are investigated by solving some numerical examples.
Effects of groove distributions on supersonic turbulent channel flows

This paper investigates the influences of the distribution of the grooves on the wall on the turbulent statistics, transport of turbulent kinetic energy, and flow structures in supersonic turbulent channel flows at the bulk Mach number of 3.0 by performing direct numerical simulations. It is found that the existence of the grooves leads to the enhancement of the turbulent kinetic energy close to the wall and the abatement thereof above the buffer layer. The density and temperature fluctuations are also enhanced, but only within the buffer layer, above which the influences of the grooves can be disregarded. The pressure fluctuations, however, are significantly increased, which is attributed to the radiated acoustic waves from the wall generated by the disturbances on the wall. Such inference is substantiated by the fact that the inclination angles of the phase averaged pressure are related to the Mach number. Nevertheless, the acoustic and dynamic processes seem to be decoupled, leading to insignificant pressure-dilatation terms in the transport of turbulent kinetic energy.
Gabor Filter Using on Graph Regularized Non-negative Matrix Factorization for Face Recognition

Gabor Filter is a famous linear filter which is usually used for edge extraction. In this paper, we introduced Gabor Filter into Graph regularized Non-negative Matrix Factorization for face recognition. Thanks to the Gabor Filter, important information of the original picture can be highlighted so the recognition rate can be improved. We have performed a series of experiments on NMF algorithm with different Gabor Filter to exhibit the improvement on recognition rate. Next, we make some discussion on the accuracy of the neighbor graph construction method under different Gabor Filters. We also focus on the recognition rate of the Gabor Filter based GNMF algorithm with and without knowledge. Experiments demonstrate the effectiveness of our method.