Miracle-dz/UniFAR-FTUs huggingface dataset:
Ophthalmic Manifestations of the Monkeypox Virus: A Systematic Review and Meta-Analysis

Background: The accurate estimation of the prevalence of mpox-induced ophthalmic lesions will enable health departments to allocate resources more effectively during the ongoing mpox pandemic. The aim of this meta-analysis was to estimate the global prevalence of ophthalmic manifestations in mpox patients. Methods: A systematic search was carried out in seven databases—Pub Med, Scopus, Web of Science, EMBASE, ProQuest, EBSCOHost, and Cochrane—for studies published on or before 12 December 2022. The pooled prevalence of ophthalmic manifestations was estimated by the random effects model. Risk of bias assessment of the studies and sub-group analysis to explain heterogeneity were undertaken. Results: Overall, 12 studies were included, with 3239 confirmed mpox cases, among which 755 patients reported ophthalmic manifestations. The pooled prevalence of ophthalmic manifestations was 9% (95% confidence interval (CI), 3–24). Studies from Europe reported a very low prevalence of ocular manifestations of 0.98% (95% CI 0.14–2.31), compared to studies from Africa with a substantially higher prevalence of 27.22% (95% CI 13.69–43.26). Conclusions: A wide variation in the prevalence of ocular manifestations among mpox patients was observed globally. Healthcare workers involved in mpox-endemic African countries should be aware of ocular manifestations for early detection and management.
Asymptotic solutions of differential rotation driven by convection in rapidly rotating fluid spheres with the non-slip boundary condition

A strong Coriolis force in a rapidly rotating planet and star not only enforces two-dimensionality of fluid motion driven by thermal instabilities but also generates strong differential rotation even in the vicinity of the onset of instabilities. We derive an asymptotic solution describing convection-driven differential rotation in rotating, self-gravitating Boussinesq fluid spheres with the no-slip boundary condition, taking into account full spherical curvature and being valid for asymptotically small Ekman numbers. For the purpose of validating the asymptotic solution, the corresponding numerical analysis valid for large or small Ekman numbers is also carried out, showing a satisfactory agreement between the asymptotic and numerical solutions.
NURBS Parameterization for Medical Surface Reconstruction

InVesalius is an open-source software for reconstruction of computed tomography and magnetic resonance images, which allows the user to make analysis and segmentation of virtual anatomical models. Physical models can be printed with the aid of rapid prototyping, giving the medical community a reliable instrument to help planning surgeries. To offer the user more control over the model, this work describes a methodology and tool developed for NURBS parameterization that provides mechanisms for adjusting the shape or even selecting a particular region of interest of the surface. Furthermore, the tool gives the option to export the final results of the process to a STEP file, which allows further edition in any well-known CAD software.
The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.
Fingerprint Networked Reinforcement Learning via Multiagent Modeling for Improving Decision Making in an Urban Food–Energy–Water Nexus

Food–energy–water (FEW) nexus analyses are critical to sustainable development. Nexus analyses form a unique multiagent decision-making arena that requires using a system engineering approach to simultaneously improve food and water security as well as energy efficiency. To tackle the complexity in decision making within a FEW nexus with respect to dynamic behaviors and interactive logics, we model the FEW nexus as a multiagent system (MAS) under a mixed competitive and cooperative environment from the perspective of a Markov game. Then, we propose a fingerprint networked reinforcement learning (FNRL) framework for the collective learning of a MAS by following the logic flows of human decision making. FNRL can alleviate the problems caused by stationary issues in a MAS environment by integrating a long short-term-memory-driven neural network model into the context of multiagent reinforcement learning (RL) to extract fingerprint information via historical data. Numerical simulations for an urban FEW nexus analysis in Florida (USA) demonstrate that applying the FNRL framework can drive agents in the MAS toward achieving optimality via RL in a dynamic environment.
Multiple Comparisons for Repeated Measures Means.

The paper shows how multiple comparison procedures for repeated measures means employing a pooled estimate of error variance must conform to the sphericity assumptions of the design in order to provide a valid test. Since it is highly unlikely that behavioral science data will satisfy this condition the paper presents a test statistic that, depending upon the design, will provide either an exact or robust test and is generalizable to designs containing any number of repeated factors. Finally, various critical values are enumerated to limit the joint level of significance at α.
A GAS-PHASE FORMATION ROUTE TO INTERSTELLAR TRANS-METHYL FORMATE

The abundance of methyl formate in the interstellar medium has previously been underpredicted by chemical models. Additionally, grain surface chemistry cannot account for the relative abundance of the cis- and trans-conformers of methyl formate, and the trans-conformer is not even formed at detectable abundance on these surfaces. This highlights the importance of studying formation pathways to methyl formate in the gas phase. The rate constant and branching fractions are reported for the gas-phase reaction between protonated methanol and formic acid to form protonated trans-methyl formate and water as well as adduct ion: Rate constants were experimentally determined using a flowing afterglow-selected ion flow tube apparatus at 300 K and a pressure of 530 mTorr helium. The results indicate a moderate overall rate constant of (3.19 ± 0.39) × 10−10 cm3 s−1 (± 1σ) and an average branching fraction of 0.05 ± 0.04 for protonated trans-methyl formate and 0.95 ± 0.04 for the adduct ion. These experimental results are reinforced by ab initio calculations at the MP2(full)/aug-cc-pVTZ level of theory to examine the reaction coordinate and complement previous density functional theory calculations. This study underscores the need for continued observational studies of trans-methyl formate and for the exploration of other gas-phase formation routes to complex organic molecules.
On the Multiplication over Finite Fields of Characteristic Three in Hermite Polynomial Representation

Finite fields are used in cryptographic applications, coding theory and digital signal processing. Many cryptosystems such as RSA, AES, elliptic curve cryptography and also recently pairing based cryptography uses the basic arithmetic operations in finite fields. The progress in the pairing-based cryptography increase the importance of arithmetic of finite fields in characteristic three. In this paper, we deal mainly with the arithmetic in certain extension fields of $\mathbb{F}_{3}$. We represent these finite fields by using Hermite polynomials and we explain how to obtain an arithmetic design for finite field multiplication and modular reduction in $\mathbb{F}_{3^{n}}$. We give the multiplication of two elements in $\mathbb{F}_{3^{n}}$ with matrix-vector multiplication design where the matrix is block Toeplitz matrix.
The impact of career expectation on employment anxiety of art students in higher vocational colleges during the COVID-19: A chain mediating role of social support and psychological capital

Objective In the process of college students’ employment, psychological capital, and social support play a vital role. Methods This study examined the relationship between career expectation and employment anxiety of Chinese vocational art college students (N = 634). Participants completed the Career Expectation Scale (CES), Employment Anxiety Scale (EAS), Psychological Capital Scale (PCS), and Social Support Scale (SSS). Results (1) Vocational art students’ career expectation has a positive predictive effect on employment anxiety, social support, and psychological capital; Social support and psychological capital have negative predictive effects on employment anxiety. (2) Social support and psychological capital play a significant chain intermediary role between career expectation and employment anxiety, and there is a masking effect. Conclusion These results are of guiding significance to the improvement of the employment quality of art students in higher vocational colleges and the employment consulting work in colleges.
On Trajectory Tracking Model Predictive Control of an Unmanned Quadrotor Helicopter Subject to Aerodynamic Disturbances

In this article a model predictive control (MPC) strategy for the trajectory tracking of an unmanned quadrotor is presented. The quadrotor's dynamics are modeled using a hybrid systems approach and, specifically, a set of piecewise affine (PWA) systems around different operating points of the translational and rotational motions. The proposed control scheme is dual and consists of an integral MPC for the translational motions, followed by an MPC scheme for the tracking of the quadrotor's attitude motions. By the utilization of PWA representations, the controller is computed for a larger part of the quadrotor's flight envelope, which provides more control authority for aggressive maneuvering. The proposed dual control scheme is able to calculate optimal control actions with robustness against atmospheric disturbances (e.g. wind gusts) and with respect to the physical constraints of the quadrotor (e.g. maximum lifting forces or fixed thrust limitations in order to extend flight endurance). Extended simulation studies indicate the efficiency of the MPC scheme, both in trajectory tracking and aerodynamic disturbance attenuation.
Slope stability analysis for undrained loading conditions

The current state of art for limit equilibrium analysis of slope stability problems lacks a satisfactory procedure for stability evaluation under general, rapid (undrained) loading conditions. Some procedures are available for the analysis of rapid drawdown, but these suffer from several shortcomings and, furthermore, are not applicable to other types of rapid loading. An approach is presented which overcomes these limitations. The approach integrates four components-establishment of soil behaviour on the basis of laboratory testing, estimation of steady-state conditions in the slope using a boundary value analysis, estimation of distribution of undrained strength in the slope using undrained stress paths, and identification of the critical slip surface followed by calculation of its factor of safety. The approach is illustrated through its application to the stability analysis of an earth dam under rapid drawdown and earthquake conditions.
Correlation Analysis between Dietary Intake of Tyrosols and Their Food Sources and Urinary Excretion of Tyrosol and Hydroxytyrosol in a European Population

This study analyzed the correlations between the acute and habitual intake of dietary tyrosols, their main food sources, and 24 h urine excretions of tyrosol (Tyr) and hydroxytyrosol (OHTyr) in participants from the European Prospective Investigation into Cancer and Nutrition study (EPIC). Participants (n = 419) were healthy men and women aged from 34 to 73 years from 8 EPIC centers belonging to France, Italy, and Germany. Acute and habitual dietary data were collected using a standardized 24 h dietary recall software and validated country-specific dietary questionnaires, respectively. The intake of 13 dietary tyrosols was estimated using the Phenol-Explorer database. Excretions of Tyr and OHTyr in a single 24 h urine sample were analyzed using tandem mass spectrometry. Urinary excretions of Tyr, OHTyr, and their sum (Tyr + OHTyr) correlated more strongly with their corresponding acute (rhopartial~0.63) rather than habitual intakes (rhopartial~0.47). In addition, individual and combined urinary excretions of Tyr and OHTyr were weakly to moderately correlated with the acute and habitual intake of other individual tyrosol precursors (rhopartial = 0.10–0.44) and especially with major food sources, such as wine (rhopartial = 0.41–0.58), olive oil (rhopartial = 0.25–0.44), and beer (rhopartial = 0.14–0.23). Urinary Tyr + OHTyr excretions were similarly correlated with the acute intake of total tyrosols but differently correlated with food sources among countries. Based on these results, we conclude that 24 h urinary excretions of Tyr + OHTyr could be proposed as biomarkers of total tyrosol intake, preferably for acute intakes.
Learning-Based Path Planning Under Co-Safe Temporal Logic Specifications

This paper presents a path planning algorithm for efficiently generating low-cost trajectories that meet mission requirements specified in Linear Time Logic (LTL), where cost functions are defined throughout the configuration space. The main idea of the paper is to increase efficiency by adding learning-based extensions to sampling-based path planning algorithms. The proposed method includes two layers: a high-level layer that determines how to expand the search tree to satisfy logical specifications and a low-level layer that extends the search tree to search for low-cost trajectories. To efficiently find low-cost trajectories, it is learned how to extend the search tree from the data via deep learning. By leveraging the conditional variational autoencoder, we learn the ideal search tree extension distribution in a given situation, which increases solution search efficiency. Simulations show that the proposed method finds a low-cost trajectory while meeting a given mission specification. Furthermore, it is confirmed that the proposed approach performs better than the existing methods.
Outliers and Residual Distributions in Logistic Regression

Abstract Detection of outliers and other diagnostics based on residuals have gained widespread use in linear regression. Logistic regression has been both blessed and hindered by this development. Certainly logistic regression requires procedures to detect global and local model weaknesses. Thus the wealth of work done in linear regression provides guides and suggestions that may, with care and ingenuity, be applied to logistic regression. Several innovative attempts in this direction have been made by authors such as Tsiatis (1980), Pregibon (1979, 1981), Landwehr, Pregibon, and Shoemaker (1984), and Cook and Weisberg (1982). Unfortunately the similarities that allow such techniques to adapt to logistic regression seem, in addition, to hide many of the differences. Two such differences are discussed here. The first examines the effect of events of small probability on estimation in logistic regression. The simple conclusion is that to estimate the probability of success p in an area in which p is small, .
Siphoning novel sources of seedling salinity tolerance from the diverse chickpea landraces

ABSTRACT Context. Chickpea (Cicer arietinum L.) are highly sensitive to elevated salinity, particularly at initial seedling establishment stage. Seedling screening would be an effective means to identify novel sources of donors for salt tolerance. Aim.This study aimed to identify salt stress tolerant genotypes at seedling stage from 50 chickpea accessions. Methods. The screening of 50 chickpea accessions was done under two salinity conditions including salt stress (8 dS m−1) and control (no salt stress). Accessions were studied for morphological traits, root system architectural analysis, and CSTI (Cumulative salt tolerance index). Further, principal component analysis was conducted to validate these results for more accuracy and reliability. Key results. For morphological traits, a high degree of genetic variation was seen among genotypes, and root traits were found to be the better indicators of salt stress tolerance. CSTI was used to classify the accessions; 22 (44%) were identified as salt sensitive, 21 (42%) were found to be moderately salt tolerant, and 7 (14%) had moderate to high salt tolerance. The most salt tolerant and salt sensitive genotypes were found to be ICCV10 and ILC5595, respectively. Conclusions. Early seedling screening has a great potential to identify genotypes with robust root systems, which can withstand salinity. Implications. We used a novel approach to classify chickpea landraces based on the combination of CSTI and principal component analysis methods. By choosing suitable donors and prospective genotypes at early growth stages, the knowledge gathered from this study may aid scientists and chickpea breeders in developing salt tolerant cultivars.
Interaction between a fluoroquinolone derivative KG022 and RNAs: Effect of base pairs 3′ adjacent to the bulged residues

RNA-targeted small molecules are a promising modality in drug discovery. Recently, we found that a fluoroquinolone derivative, KG022, can bind to RNAs with bulged C or G. To clarify the RNA specificity of KG022, we analyzed the effect of the base pair located at the 3′side of the bulged residue. It was found that KG022 prefers G-C and A-U base pairs at the 3′side. Solution structures of the complexes of KG022 with the four RNA molecules with bulged C or G and G-C or A-U base pairs at the 3′side of the bulged residue were determined to find that the fluoroquinolone moiety is located between two purine bases, and this may be the mechanism of the specificity. This work provides an important example of the specificity of RNA-targeted small molecules.
Fast Angle Estimation Based on MSWF and Polynomial Rooting for Monostatic MIMO Radar

A novel method employing Multi-Stage Wiener Filter (MSWF) and polynomial rooting is proposed for the one-dimensional angle estimation of MIMO radar. In this algorithm, the signal subspace is obtained using MSWF, which avoids the known expected signal, the estimation of the virtual data autocorrelation matrix and eigen-decomposition. Then, a polynomial rooting is adopted to realize angle estimation. The effectiveness of the proposed method is verified by numerical examples.
Numerical Study for the Effects of Mean Wind on the Intensity and Evolution of Dust Devils

Large eddy simulations of dust devil-like vortices under various mean wind conditions were performed with the Weather Research and Forecasting model to investigate the effects of mean wind on the intensity and evolution of dust devils. It is shown that intense vortices are generated for all the mean-wind cases examined. Significant characteristics among the cases with the different mean winds are that vertical vortices are generated most preferably in medium-wind conditions in terms of their number and intensity, and that the lifetime of intense vortices becomes shortened with the increase in mean wind. It is also found that a stability parameter well describes the behavior of vortices in response to mean wind and provides a necessary condition for dust devil occurrence.
Construction and validation of an angiogenesis-related lncRNA prognostic model in lung adenocarcinoma

Background: There is increasing evidence that long non-coding RNAs (lncRNAs) can be used as potential prognostic factors for cancer. This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) using angiogenesis-related long non-coding RNAs (lncRNAs) as potential prognostic factors. Methods: Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify aberrantly expressed angiogenesis-related lncRNAs in LUAD. A prognostic signature was constructed using differential expression analysis, overlap analysis, Pearson correlation analysis, and Cox regression analysis. The model’s validity was assessed using K-M and ROC curves, and independent external validation was performed in the GSE30219 dataset. Prognostic lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) networks were identified. Immune cell infiltration and mutational characteristics were also analyzed. The expression of four human angiogenesis-associated lncRNAs was quantified using quantitative real-time PCR (qRT-PCR) gene arrays. Results: A total of 26 aberrantly expressed angiogenesis-related lncRNAs in LUAD were identified, and a Cox risk model based on LINC00857, RBPMS-AS1, SYNPR-AS1, and LINC00460 was constructed, which may be an independent prognostic predictor for LUAD. The low-risk group had a significant better prognosis and was associated with a higher abundance of resting immune cells and a lower expression of immune checkpoint molecules. Moreover, 105 ceRNA mechanisms were predicted based on the four prognostic lncRNAs. qRT-PCR results showed that LINC00857, SYNPR-AS1, and LINC00460 were significantly highly expressed in tumor tissues, while RBPMS-AS1 was highly expressed in paracancerous tissues. Conclusion: The four angiogenesis-related lncRNAs identified in this study could serve as a promising prognostic biomarker for LUAD patients.
Heart rate and oxygen uptake kinetics obtained from continuous measurements with wearable devices during outdoor walks of patients with COPD

Objective Continuous physiological measurements during a laboratory-based exercise test can provide physiological biomarkers, such as heart rate (HR) and oxygen uptake (V̇O2) kinetics, that carry clinically relevant information. In contrast, it is not clear how continuous data generated by wearable devices during daily-life routines could provide meaningful biomarkers. We aimed to determine whether valid HR and V̇O2 kinetics can be obtained from measurements with wearable devices during outdoor walks in patients with chronic obstructive pulmonary disease (COPD). Methods HR (Polar Belt) and V̇O2(METAMAX3B) were measured during 93 physical activity transitions performed by eight patients with COPD during three different outdoor walks (ntr = 77) and a 6-minute walk test (ntr = 16). HR and V̇O2 kinetics were calculated every time a participant started a walk, finished a walk or walked upstairs. HR and V̇O2 kinetics were considered valid if the response magnitude and model fit were adequate, and model parameters were reliable. Results Continuous measurements with wearable devices provided valid HR kinetics when COPD patients started or finished (range 63%–100%) the different outdoor walks and valid V̇O2 kinetics when they finished (range 63%–100%) an outdoor walk. The amount of valid kinetics and kinetic model performance was comparable between outdoor walks and a laboratory-based exercise test (p > .05). Conclusion We envision that the presented approach could improve telemonitoring applications of patients with COPD by providing regular, unsupervised assessments of HR kinetics during daily-life routines. This could allow to early identify a decline in the patients’ dynamic physiological functioning, physical fitness and/or health status.