Miracle-dz/UniFAR-FTUs huggingface dataset:
Conduction Velocity of Spinal Reflex in Patients with Acute Lateral Ankle Sprain

Recent literature has highlighted altered spinal-reflex excitability following acute lateral ankle sprain (ALAS), yet there is little information on the conduction velocity of spinal reflex pathways (CV-SRP) in these patients. Therefore, we aimed to investigate the effects of ALAS on the CV-SRP. We employed a cross-sectional study with two groups: ALAS (n = 30) and healthy controls (n = 30). The CV-SRP of the soleus, fibularis longus, and tibialis anterior was assessed using the H-index method. As secondary outcomes, H-reflex and M-wave latencies were assessed as well as acute symptoms including ankle swelling, pain, and self-reported ankle function. Separate group-by-limb ANOVA with repeated measures revealed a significant interaction for soleus CV-SRP (p < 0.001) and H-reflex latency (p < 0.001), showing significant slower CV-SRP and longer H-reflex latency in the involved limb of the ALAS group compared with both limbs in the control group. However, there was no significant interaction or main effect in any other ankle muscles (p > 0.05). A further correlation analysis showed a significant relationship between CV-SRP and acute symptoms, including ankle swelling (r = −0.37, p = 0.048) and self-reported ankle function (r = 0.44, p = 0.017) in ALAS patients. These results suggest a disrupted functionality of the afferent pathway and/or synaptic transmission following ALAS. Level of Evidence: 4.
On the moduli spaces of surfaces of general type

It is nowadays well known that there exists a coarse moduli space Wl for complete smooth curves of genus g, and that Wlg is a quasiprojective normal irreducible variety of dimension 3g — 3. Recently D. Gieseker [11] proved the existence of a quasiprojective coarse moduli space Wl for surfaces of general type modulo birational equivalence. Whereas, through recent work of J. Harris & D. Mumford [12], much is known about the geometry of Wlg, very little is known about Wl, except for particular classes of surfaces. By the results of E. Bombieri [2] (cf. also [3]), the surfaces of general type with given numerical invariants χ (= χ((Ps)) and K 2 (= self intersection of a canonical divisor of the minimal model S) belong to a finite number of families. Therefore, fixing K and χ, which are invariants (under orientation preserving homeomorphisms) of the oriented topological 4-manifold underlying the minimal model 5, isomorphism classes of surfaces S with those invariants are parametrized by a quasiprojective variety Wlκi χ with a finite number of irreducible components Wlλ, -,Wlk and such that, if you take two points belonging to the same connected component of Wlκi χ, they represent isomorphism classes [S]9 [S'] of two minimal models S and S' diffeomorphic to each other. In analogy with the case of curves, we shall say that Wl is a moduli space for a surface So of general type with given K , χ if Wl is the union of all components of Wlκiχ whose points correspond to all the S's which are (orientedly) homeomorphic to So.
A Comprehensive Review on Collagen Type I Development of Biomaterials for Tissue Engineering: From Biosynthesis to Bioscaffold

Collagen is the most abundant structural protein found in humans and mammals, particularly in the extracellular matrix (ECM). Its primary function is to hold the body together. The collagen superfamily of proteins includes over 20 types that have been identified. Yet, collagen type I is the major component in many tissues and can be extracted as a natural biomaterial for various medical and biological purposes. Collagen has multiple advantageous characteristics, including varied sources, biocompatibility, sustainability, low immunogenicity, porosity, and biodegradability. As such, collagen-type-I-based bioscaffolds have been widely used in tissue engineering. Biomaterials based on collagen type I can also be modified to improve their functions, such as by crosslinking to strengthen the mechanical property or adding biochemical factors to enhance their biological activity. This review discusses the complexities of collagen type I structure, biosynthesis, sources for collagen derivatives, methods of isolation and purification, physicochemical characteristics, and the current development of collagen-type-I-based scaffolds in tissue engineering applications. The advancement of additional novel tissue engineered bioproducts with refined techniques and continuous biomaterial augmentation is facilitated by understanding the conventional design and application of biomaterials based on collagen type I.
Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data

ABSTRACT Maize is an important cereal crop and plays an important role in agriculture. The leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FPAR) are important vegetation canopy structure parameters that are closely related to crop photosynthesis, transpiration and respiration and are of great significance for crop yield estimation. Hyperspectral data can obtain the spectral characteristic parameters of crops, and LiDAR data can obtain the vertical structure index. The former focuses on horizontal characteristic information, and the latter focuses on vertical characteristic information. In this paper, random forest method is used to carry out the inversion modelling of maize LAI and FPAR, and the effects of hyperspectral data resolution, LiDAR data density and scanning angle on the inversion accuracy are tested and analysed. The results show that there is good accuracy and stability of the random forest inversion model (LAI: 0.53 <R2 < 0.78, 0.094 < RMSE < 0.158, 0.009 < MSE < 0.025, 0.074 < MAE < 0.136; FPAR: 0.323 < R2 < 0.594, 0.09 < RMSE < 0.261, 0.008 < MSE < 0.068, 0.068 < MAE < 0.212). When using Hyperspectral data to invert LAI, the accuracy is the highest when the resolution is 1 metre (R2 = 0.742, RMSE = 0.141, MSE = 0.02, MAE = 0.119). When using LiDAR data to invert LAI, the accuracy is the highest when the density is 30% (R2 = 0.78, RMSE = 0.115, MSE = 0.013, MAE = 0.094). When using Hyperspectral data to invert FPAR, the accuracy is the highest when the resolution is 0.128 metre(R2 = 0.594, RMSE = 0.09, MSE = 0.008, MAE = 0.068). When using LiDAR data to invert FPAR, the accuracy is the highest when the density is 20% (R2 = 0.525, RMSE = 0.141, MSE = 0.02, MAE = 0.113).
A Survey on Deep Learning Model for Improved Disease Prediction with Multi Medical Data Sets

Human society has great influence and threat from vibrant diseases as they get affected by different diseases every day. The cardiac disease prediction method predicts the disease based on the data set available and the features, whereas the performance of the method in predicting at higher accuracy is depend on the method and measures used. This article presents a detailed survey on predicting the cardiac disease with the support of deep learning Algorithms using varying data sets. The methods of disease prediction are classified according to the core approach and each class of disease prediction has been categorized based on the approach used. In general, the problem of disease prediction is approached with PCA (Principle Component Analysis), PSO (Particle Swarm Optimization), Genetic Algorithm, Fuzzy logic, neural network, and so on. This article is focusing on analyzing the deep learning method towards the prediction of different disease with various data sets. Further, the performance analysis is conducted and presented in detail. A comparative study has been presented for the better understanding of the methods and how they have been adapted. However, there are a lot of approaches available, the deep learning-based approaches produced higher performance in disease prediction.
Toward Better Risk Stratification for Implantable Cardioverter-Defibrillator Recipients: Implications of Explainable Machine Learning Models

Abstract Background: Current guideline-based implantable cardioverter-defibrillator (ICD) implants fail to meet the demands for precision medicine. Machine learning (ML) designed for survival analysis might facilitate personalized risk stratification. We aimed to develop explainable ML models predicting mortality and the first appropriate shock and compare these to standard Cox proportional hazards (CPH) regression in ICD recipients. Methods and Results: Forty-five routine clinical variables were collected. Four fine-tuned ML approaches (elastic net Cox regression, random survival forests, survival support vector machine, and XGBoost) were applied and compared with the CPH model on the test set using Harrell’s C-index. Of 887 adult patients enrolled, 199 patients died (5.0 per 100 person-years) and 265 first appropriate shocks occurred (12.4 per 100 person-years) during the follow-up. Patients were randomly split into training (75%) and test (25%) sets. Among ML models predicting death, XGBoost achieved the highest accuracy and outperformed the CPH model (C-index: 0.794 vs. 0.760, p < 0.001). For appropriate shock, survival support vector machine showed the highest accuracy, although not statistically different from the CPH model (0.621 vs. 0.611, p = 0.243). The feature contribution of ML models assessed by SHAP values at individual and overall levels was in accordance with established knowledge. Accordingly, a bi-dimensional risk matrix integrating death and shock risk was built. This risk stratification framework further classified patients with different likelihoods of benefiting from ICD implant. Conclusions: Explainable ML models offer a promising tool to identify different risk scenarios in ICD-eligible patients and aid clinical decision making. Further evaluation is needed.
Stability Analysis and Control Synthesis for Dynamical Transportation Networks

We study dynamical transportation networks in a framework that includes extensions of the classical Cell Transmission Model to arbitrary network topologies. The dynamics are modeled as systems of ordinary differential equations describing the traffic flow among a finite number of cells interpreted as links of a directed network. Flows between contiguous cells, in particular at junctions, are determined by merging and splitting rules within constraints imposed by the cells' demand and supply functions as well as by the drivers' turning preferences, while inflows at on-ramps are modeled as exogenous and possibly time-varying. First, we analyze stability properties of dynamical transportation networks. We associate to the dynamics a state-dependent dual graph whose connectivity depends on the signs of the derivatives of the inter-cell flows with respect to the densities. Sufficient conditions for the stability of equilibria and periodic solutions are then provided in terms of the connectivity of such dual graph. Then, we consider synthesis of control policies that use a combination of turning preferences, speed limits, and ramp metering, in order to optimize convex objectives. We first show that, in the general case, the optimal control synthesis problem can be cast as a convex optimization problem, and that the equilibrium of the controlled network is in free-flow. If the control policies are restricted to speed limits and ramp metering, then the resulting synthesis problem is still convex for networks where every node is either a merge or a diverge junction, and where the dynamics is monotone. These results apply both to the optimal selection of equilibria and periodic solutions, as well as to finite-horizon network trajectory optimization. Finally, we illustrate our findings through simulations on a road network inspired by the freeway system in southern Los Angeles.
Accurately Detecting Symmetries of 3D Shapes

Many shapes and geometrical models exhibit symmetries. These symmetries are sometimes explicitly represented in the model. However, symmetry information is often lost in automatic transla- tions between file formats. Using symmetries, one can, for example, manipulate models through coherent re-meshing or intelligent mesh editing programs. We present two algorithms that automatically retrieve symmetries in a 3D model through the use of an intermediate set of functions: The Generalized Even Moments
Quasiparticle description of the hot and dense quark-gluon plasma

The collective modes of QCD at temperatures and densities above its phase-transition are analyzed for isotropic systems as well as for systems having an anisotropy in momentum-space using the HTL approximation. For isotropic systems, results for the thermodynamic pressure and entropy at finite densities based on a phenomenological description of lattice calculations at vanishing densities are derived and compared to those following from different methods. From the resulting equation of state the mass-radius relationship of so-called quark-stars and other applications are investigated. It is shown that systems with anisotropic momentum-space distributions contain unstable modes in addition to the stable quasiparticle modes, which may be of great importance for the dynamical evolution of an incompletely thermalized quark-gluon plasma. Finally, the effects of the anisotropy on the collisional energy loss are studied.
Combinatorial Dynamics and Entropy in Dimension One

Preliminaries: general notation graphs, loops and cycles. Interval maps: the Sharkovskii Theorem maps with the prescribed set of periods forcing relation patterns for interval maps antisymmetry of the forcing relation P-monotone maps and oriented patterns consequences of Theorem 2.6. 13 stability of patterns and periods primary patterns extensions characterization of primary oriented patterns more about primary oriented patterns. Circle maps: liftings and degree of circle maps lifted cycles cycles and lifted cycles periods for maps of degree different from -1, 0 and 1 periods for maps of degree 0 periods for maps of degree -1 rotation numbers and twist lifted cycles estimate of a rotation interval periods for maps of degree 1 maps of degree 1 with the prescribed set of periods other results. Appendix: lifted patterns. Entropy: definitions entropy for interval maps horseshoes entropy of cycles continuity properties of the entropy semiconjugacy to a map of a constant slope entropy for circle maps proof of Theorem 4.7. 3.
Sonographic Diagnosis of Cervical Lymph Node Metastasis in Patients with Thyroid Cancer and Comparison of European and Korean Guidelines for Stratifying the Risk of Malignant Lymph Node

Objective To evaluate the ultrasonography (US) features for diagnosing metastasis in cervical lymph nodes (LNs) in patients with thyroid cancer and compare the US classification of risk of LN metastasis between European and Korean guidelines. Materials and Methods From January 2014 to December 2018, US-guided fine-needle aspiration was performed on 836 LNs from 714 patients for the preoperative nodal staging of thyroid cancer. The US features of LNs were retrospectively reviewed for the following features: size, presence of hilum, margin, orientation, cystic change, punctate echogenic foci (PEF), large echogenic foci, eccentric cortical thickening, abnormal vascularity, and cortical hyperechogenicity. A multiple logistic regression analysis was performed to identify the independent US features for the diagnosis of metastatic LNs. The diagnostic performance of independent US features was subsequently evaluated. LNs were categorized according to the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and European Thyroid Association (ETA) guidelines, and the correlation between the two sets of classifications was assessed. Results Absence of the hilum, presence of cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity were independent US features of metastatic LNs. Cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity showed high specificity (86.8%–99.6%). The absence of the hilum had the highest sensitivity yet low specificity (66.4%). When LNs were classified according to the ETA guidelines and K-TIRADS, they yielded similar categorizations of malignancy risks and were strongly correlated (Spearman coefficient, 0.9766 [95% confidence interval, 0.973–0.979]). According to the ETA guidelines, 9.8% (82/836) of LNs were classified as “not specified. ” Conclusion Absence of hilum, cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity were independent US features suggestive of metastatic LNs in thyroid cancer. Both K-TIRADS and the ETA guidelines provided similar risk stratification for metastatic LNs with a high correlation; however, the ETA guidelines failed to classify 9.8% of LNs into a specific risk stratum. These results may provide a basis for revising LN classification in future guidelines.
SR Splicing Factors Promote Cancer via Multiple Regulatory Mechanisms

Substantial emerging evidence supports that dysregulated RNA metabolism is associated with tumor initiation and development. Serine/Arginine-Rich proteins (SR) are a number of ultraconserved and structurally related proteins that contain a characteristic RS domain rich in arginine and serine residues. SR proteins perform a critical role in spliceosome assembling and conformational transformation, contributing to precise alternative RNA splicing. Moreover, SR proteins have been reported to participate in multiple other RNA-processing-related mechanisms than RNA splicing, such as genome stability, RNA export, and translation. The dysregulation of SR proteins has been reported to contribute to tumorigenesis through multiple mechanisms. Here we reviewed the different biological roles of SR proteins and strategies for functional rectification of SR proteins that may serve as potential therapeutic approaches for cancer.
Mining generalized fuzzy quantitative association rules with fuzzy generalization hierarchies

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining for taking into account the user's interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies for categorical attributes and generalization hierarchies of fuzzy linguistic terms for quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights for attributes.
A representation of orthogonal components in analysis of variance

The history of orthogonal components in the analysis of variance and of the Helmert transformation is allied to more recent mathematical representations of Kronecker products. The latter are well known tools useful in the teaching of factorial designs. Presented here are quick derivations of the usual orthogonal projection matrices associated with independent sums of squares for some familiar balanced designs. Succinct representations used to form orthogonal contrasts clearly illustrate the proofs of independence of sums of squares and give obvious interpretation to degrees of freedom. Resultant central and noncentral chi-squared distributions follow easily. A relationship between recursive residuals and the Helmert transformation is also noted.
AvaliaSmart: Avaliação Automática de Questões Discursivas

Este artigo apresenta brevemente a AvaliaSmart, startup que possui como principal aplicação um modelo para avaliação automática de questões discursivas. Esse tipo de avaliação automática é inédita no Brasil e possui como principal benefício o auxílio a alunos e professores, podendo interessar à diversas instituições ligadas à área da educação que queiram otimizar esse processo internamente. O artigo descreve como a ferramenta passou pelas etapas de identificação do problema, ideia, pesquisa científica até chegar ao mercado como um produto/serviço.
From Riches to Raags: 3-Manifolds, Right-Angled Artin Groups, and Cubical Geometry

This book presents an introduction to the geometric group theory associated with nonpositively curved cube complexes. It advocates the use of cube complexes to understand the fundamental groups of hyperbolic 3-manifolds as well as many other infinite groups studied within geometric group theory. The main goal is to outline the proof that a hyperbolic group $G$ with a quasiconvex hierarchy has a finite index subgroup that embeds in a right-angled Artin group. The supporting ingredients of the proof are sketched: the basics of nonpositively curved cube complexes, wallspaces and dual CAT(0) cube complexes, special cube complexes, the combination theorem for special cube complexes, the combination theorem for cubulated groups, cubical small-cancellation theory, and the malnormal special quotient theorem. Generalizations to relatively hyperbolic groups are discussed. Finally, applications are described towards resolving Baumslag's conjecture on the residual finiteness of one-relator groups with torsion, and to the virtual specialness and virtual fibering of certain hyperbolic 3-manifolds, including those with at least one cusp. The text contains many figures illustrating the ideas.
A super-resolution beamforming method for autonomous underwater vehicle towed nest linear array based on Sparse Bayesian Learning

Compared with stationary sonar, Autonomous Underwater Vehicle (AUV) sonar has the advantages of strong maneuverability and low cost, which is more suitable for ocean intelligent unmanned systems. However, due to the limited size of the AUV platform, the aperture of the sonar hydrophone array is usually limited, and it is difficult to obtain high resolution by conventional beamforming (CBF) methods. In this paper, a super-resolution beamforming method based on Sparse Bayesian Learning (SBL) is introduced for the direction-of-arrival estimation (DOA) estimation of the AUV towed nest linear array. Based on the spatial sparsity of underwater acoustic radiation sources, the amplitudes of sources are approximated by using the hyperparameter variational method, and the super-resolution DOA estimation result is achieved by iterative solution of hyper-parameter and noise estimation. The simulation results show that the proposed method has higher angular resolution and lower sidelobe levels than the CBF when the array structure parameters are determined. In addition, it has a good performance of weak signal detection under strong interference background, which can be effectively applied to low-cost AUV sonar systems.