Miracle-dz/UniFAR-FTUs huggingface dataset:
Autophagy and SARS-CoV-2-Old Players in New Games

At present it is well-defined that autophagy is a fundamental process essential for cell life but its pro-viral and anti-viral role has been stated out with the COVID pandemic. However, viruses in turn have evolved diverse adaptive strategies to cope with autophagy driven host defense, either by blocking or hijacking the autophagy machinery for their own benefit. The mechanisms underlying autophagy modulation are presented in the current review which summarizes the accumulated knowledge on the crosstalk between autophagy and viral infections, with a particular emphasizes on SARS-CoV-2. The different types of autophagy related to infections and their molecular mechanisms are focused in the context of inflammation. In particular, SARS-CoV-2 entry, replication and disease pathogenesis are discussed. Models to study autophagy and to formulate novel treatment approaches and pharmacological modulation to fight COVID-19 are debated. The SARS-CoV-2—autophagy interplay is presented, revealing the complex dynamics and the molecular machinery of autophagy. The new molecular targets and strategies to treat COVID-19 effectively are envisaged. In conclusion, our finding underline the importance of development new treatment strategies and pharmacological modulation of autophagy to fight COVID-19.
Gait Recovery in Spinal Cord Injury: A Systematic Review with Metanalysis Involving New Rehabilitative Technologies

Gait recovery is a fundamental goal in patients with spinal cord injury to attain greater autonomy and quality of life. Robotics is becoming a valid tool in improving motor, balance, and gait function in this patient population. Moreover, other innovative approaches are leading to promising results. The aim of this study was to investigate new rehabilitative methods for gait recovery in people who have suffered spinal cord injuries. A systematic review of the last 10 years of the literature was performed in three databases (PubMed, PEDro, andCochrane). We followed this PICO of the review: P: adults with non-progressive spinal cord injury; I: new rehabilitative methods; C: new methods vs. conventional methods; and O: improvement of gait parameters. When feasible, a comparison through ES forest plots was performed. A total of 18 RCTs of the 599 results obtained were included. The studies investigated robotic rehabilitation (n = 10), intermittent hypoxia (N = 3) and external stimulation (N = 5). Six studies of the first group (robotic rehabilitation) were compared using a forest plot for 10MWT, LEMS, WISCI-II, and SCIM-3. The other clinical trials were analyzed through a narrative review of the results. We found weak evidence for the claim that robotic devices lead to better outcomes in gait independence compared to conventional rehabilitation methods. External stimulation and intermittent hypoxia seem to improve gait parameters associated with other rehabilitation methods. Research investigating the role of innovative technologies in improving gait and balance is needed since walking ability is a fundamental issue in patients with SCI.
Pinned Photodiode Imaging Pixel With Floating Gate Readout and Dual Gain

We present an imaging pixel featuring dual conversion gain in a single exposure based on the pinned photodiode (PPD). The signal charge is first converted to voltage nondestructively using a floating gate, and a second conversion is done at a p-n junction-based sense node (SN). Higher signal dynamic range (DR) is achieved due to the sensing of the same charge with two different conversion gains. The results from a prototype 10-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-pitch pixel manufactured in a 180-nm CMOS image sensor process demonstrate a gain ratio of 3, DR of 90 dB, 3.6 e− rms readout noise, and negligible image lag.
The Split Bregman Method for L1 Projection Problems

In order to solve general L1 projection problems,this paper proposes Alternating Split Bregman L1 projection algorithm.The convergence of the iteration scheme is analyzed and proved.The algorithm can solve a very broad class of L1-projection problems.Total variation projection as an example,using the proposed algorithm,we can get faster convergence rate and better result of image restoration.Numerical results show that our algorithm is better than state of the art TV projection method(Dual projection algorithm) to solve denoising,deconvolution and inpainting problems.
Using habitat suitability models for multiscale forensic geolocation analysis

Pollen is one of the most durable environmental materials that law enforcement agencies recover as trace evidence from people and objects. Although links between objects and geographic locations are essential during legal investigations, the approach of using pollen and other microbial fingerprints to build these links in an analytical framework is still underutilized. This study uses bees as objects that are mobile and collects environmental traces as a test case to determine the efficacy of predictive geolocation efforts with recovered pollen and species distribution models at both subcontinental and global scales. Results demonstrate promising performance in both the predictive capability of species distribution models and identification of possible location history of bees at both study extents. When coupling pollen with other categories of evidentiary items, this geographic attribution framework can aid law enforcement personnel in refining investigation priorities and optimizing search strategies.
DOCTEUR DE L'UNIVERSIT E DE VERSAILLES-SAINT-QUENTIN

Computing the global infimum f? of a multivariate polynomial subject to some constraints is a central question since it appears in many areas of engineering science. For some particular applications, it is of first importance to obtain reliable results. A lot of techniques has emerged to deal with constraints defined by polynomial inequalities. In this thesis, we focus on the optimization problem of a n-variate polynomial subject to constraints defined by n-variate polynomial equations. Our goal is to obtain reliable and efficient tools, algorithms and implementations to solve polynomial optimization problems. To do that, our strategy is to reduce the optimization problem subject to constraints defining algebraic sets of arbitrary dimension to an equivalent optimization problem, subject to constraints defining algebraic sets whose dimension is well-controlled. The algebraic variety defined by these new constraints is the union of the critical locus of the objective polynomial and an algebraic set of dimension at most 1. This is done by means of geometric objects defined as critical loci of linear projections. Since the dimension is well-controlled, the existence of certificates for lower bounds on f? can be proved on this new variety. This is done by means of sums of squares and it does not require that f? is reached. Likewise, we use the properties of our geometric objects to design an exact algorithm computing f? If it exists, a minimizer is also returned. If there are s constraints and if all the polynomials have degree at most D, its complexity is essentially cubic in (sD) and linear in the evaluation complexity of the input. Its implementation, available as a Maple library, reflects the theoretical complexity. It solves problems unreachable by previous exact algorithms. te l-0 09 22 80 5, v er si on 1 30 D ec 2 01 3
Surgical Treatment in Patients with Toxic Phosphorus Osteonecrosis of Facial Skull Middle Zone

During the last few years, in the territory of the Russian Federation, the number of cases of toxic phosphoric osteonecrosis of the jaws has increased against the background of taking drugs of “artisanal” production (pervitin, desomorphin). The aim of our study was to increase the effectiveness of surgical treatment of patients with a diagnosis of toxic phosphorus necrosis of the maxilla. We performed a comprehensive treatment of patients with a history of drug addiction and the above diagnosis. Surgical intervention in the volume of complete resection of pathologically altered tissues and reconstructive techniques using local tissues and a replaced flap made it possible to achieve good aesthetic and functional results in the early and late postoperative period. Thus, our proposed method of surgical treatment can be used in similar clinical situations.
Statistical Theories of Mental Test Scores.

This is a reprint of the orginal book released in 1968. Our primary goal in this book is to sharpen the skill, sophistication, and in- tuition of the reader in the interpretation of mental test data, and in the construction and use of mental tests both as instruments of psychological theory and as tools in the practical problems of selection, evaluation, and guidance. We seek to do this by exposing the reader to some psychologically meaningful statistical theories of mental test scores. Although this book is organized in terms of test-score theories and models, the practical applications and limitations of each model studied receive substantial emphasis, and these discussions are presented in as nontechnical a manner as we have found possible. Since this book catalogues a host of test theory models and formulas, it may serve as a reference handbook. Also, for a limited group of specialists, this book aims to provide a more rigorous foundation for further theoretical research than has heretofore been available.One aim of this book is to present statements of the assumptions, together with derivations of the implications, of a selected group of statistical models that the authors believe to be useful as guides in the practices of test construction and utilization. With few exceptions we have given a complete proof for each major result presented in the book. In many cases these proofs are simpler, more complete, and more illuminating than those originally offered. When we have omitted proofs or parts of proofs, we have generally provided a reference containing the omitted argument. We have left some proofs as exercises for the reader, but only when the general method of proof has already been demonstrated. At times we have proved only special cases of more generally stated theorems, when the general proof affords no additional insight into the problem and yet is substantially more complex mathematically.
Intraoperative OCT for Lamellar Corneal Surgery: A User Guide

Intraoperative OCT is an innovative and promising technology which allows anterior and posterior segment ocular surgeons to obtain a near-histologic cross-sectional and tomographic image of the tissues. Intraoperative OCT has several applications in ocular surgery which are particularly interesting in the context of corneal transplantation. Indeed, iOCT images provide a direct and meticulous visualization of the anatomy, which could guide surgical decisions. In particular, during both big-bubble and manual DALK, the visualization of the relationship between the corneal layers and instruments allows the surgeon to obtain a more desirable depth of the trephination, thus achieving more type 1 bubbles, better regularity of the plane, and a reduced risk of DM perforation. During EK procedures, iOCT supplies information about proper descemetorhexis, graft orientation, and interface quality in order to optimize the postoperative adhesion and reduce the need for re-bubbling. Finally, mushroom PK, a challenging technique for many surgeons, can be aided through the use of iOCT since it guides the correct apposition of the lamellae and their centration. The technology of iOCT is still evolving: a larger field of view could allow for the visualization of all surgical fields, and automated tracking and iOCT autofocusing guarantee the continued centration of the image.
Research Progress on Porous Carbon-Based Non-Precious Metal Electrocatalysts

The development of efficient, stable, and economic electrocatalysts are key to the large-scale application of electrochemical energy conversion. Porous carbon-based non-precious metal electrocatalysts are considered to be the most promising materials to replace Pt-based catalysts, which are limited in large-scale applications due to high costs. Because of its high specific surface area and easily regulated structure, a porous carbon matrix is conducive to the dispersion of active sites and mass transfer, showing great potential in electrocatalysis. This review will focus on porous carbon-based non-precious metal electrocatalysts and summarize their new progress, focusing on the synthesis and design of porous carbon matrix, metal-free carbon-based catalysts, non-previous metal monatomic carbon-based catalyst, and non-precious metal nanoparticle carbon-based catalysts. In addition, current challenges and future trends will be discussed for better development of porous carbon-based non-precious metal electrocatalysts.
Diagnosis of Nonlinear Oscillatory Behavior of a Fluttering Plate with a Periodicity Ratio Approach

Abstract This research focuses on diagnosing the nonlinear dynamic responses of a fluttering plate excited by high-velocity air flow. An approach based on the Periodicity Ratio method is developed such that the characteristics of a nonlinear system subjected to nonperiodic excitations can be diagnosed. The governing equations of the nonlinear dynamic responses of a fluttering plate are derived and solved numerically. Eight modes of motion are used for obtaining accurate results. With the implementation of the present approach, a regular-irregular region diagram is generated for quantitatively analysing and directly visualizing the behavior of the fluttering plate with varying system parameters. Bifurcation of the plate’s motion is analyzed with utilization of the regular-irregular region diagrams. The routes from regular motion to irregular motion are investigated and illustrated. Comparison is made with the diagnosing results available in the literature. The present approach demonstrates the efficiency and accuracy in the characteristic diagnosis for the fluttering plate.
Distribution and Morphology of α-Al, Si and Fe-Rich Phases in Al–Si–Fe Alloys under an Electromagnetic Field

Natural convection is present in all liquid alloys whereas forced convection may be applied as the method to improve material properties. To understand the effect of forced convection, the solidification in simple cylindrical samples was studied using a rotating magnetic field with a low cooling rate and low temperature gradient. The composition of Al–Si–Fe alloys was chosen to enable independent growth or joint growth of occurring α-Al, β-Al5FeSi, δ-AlFeSi_T4 phases and Si crystals and analysis of structure modifications. Stirring produced rosettes instead of equiaxed dendrites, which altered the secondary dendrite arm spacing and the specific surface of α-Al and also modified β-Al5FeSi. The melt flow caused a modification of iron rich δ-AlFeSi_T4 phases and gathered them inside the sample of the β/Si alloy, where δ together with Si were the first precipitating phases. The separation of δ and β phases and Si crystals was found by their joint growth along the monovariant line. A reduction in the amount of Si crystals and the formation of a thin Si-rich layer outside the sample was observed in the hypereutectic alloy. The separation and reduction in iron-rich phases may play a role in the removal of Fe from Al–Si alloys, and the control of Si may be applied in materials for the solar photovoltaic industry.
ASYMPTOTIC EXPANSIONS OF GENERALIZED QUANTILES AND EXPECTILES FOR EXTREME RISKS

Generalized quantiles of a random variable were defined as the minimizers of a general asymmetric loss function, which include quantiles, expectiles and M-quantiles as their special cases. Expectiles have been suggested as potentially better alternatives to both Value-at-Risk and expected shortfall risk measures. In this paper, we first establish the first-order expansions of generalized quantiles for extreme risks as the confidence level α↑ 1, and then investigate the first-order and/or second-order expansions of expectiles of an extreme risk as α↑ 1 according to the underlying distribution belonging to the max-domain of attraction of the Fréchet, Weibull, and Gumbel distributions, respectively. Examples are also presented to examine whether and how much the first-order expansions have been improved by the second-order expansions.
Quantifying Surface Recombination—Improvements in Determination and Simulation of the Surface Recombination Parameter J0s

The recombination parameter <italic>J</italic><sub>0</sub><italic><sub>s</sub></italic> provides an important metric to characterize surface recombination. For its calculation, numerous methods and models have to be applied. Since the models for the Auger and radiative recombination in crystalline silicon were recently revised, it is important to investigate the influence of these changes on <italic>J</italic><sub>0</sub><italic><sub>s</sub></italic>. The origin and possible ways of obtaining <italic>J</italic><sub>0</sub><italic><sub>s</sub></italic> from effective lifetime measurements as well as simulations are described in detail, including the potential to fit the full lifetime curve and a new approach that is based upon the reparameterization of the excess charge carrier density Δ<italic>n</italic>. Using the effective lifetime measurements, we find that <italic>J</italic><sub>0</sub><italic><sub>s</sub></italic> values determined with the older parameterization by Richter et al. will result in erroneous values up to 5 fA/cm<sup>2</sup>, depending on the chosen conditions. By simulating the recombination parameter <italic>J</italic><sub>0</sub><italic><sub>s</sub></italic> in near surface, highly doped structures, such as emitters, it is shown that these errors can even go up to 50%. If used in a simulation, we highlight the importance of having the parameterizations of surface recombination being determined with the corresponding parameterization of intrinsic recombination. Therefore, an update for the recombination at oxide-passivated and phosphorous doped surfaces is given that can be used with the new intrinsic recombination models. Finally, we give some best-practice examples on how recent improvements in effective lifetime measurements affect <italic>J</italic><sub>0</sub><italic><sub>s</sub></italic> values as well as possible pitfalls.
Fibrates Affect Levels of Phosphorylated p38 in Intestinal Cells in a Differentiation-Dependent Manner

Fibrates are widely used hypolipidaemic agents that act as ligands of the peroxisome proliferator-activated receptor α (PPARα). p38 is a protein kinase that is mainly activated by environmental and genotoxic stress. We investigated the effect of the PPARα activators fenofibrate and WY-14643 and the PPARα inhibitor GW6471 on the levels of activated p38 (p-p38) in the colorectal cancer cell lines HT-29 and Caco2 in relation to their differentiation status. Fibrates increased p-p38 in undifferentiated HT-29 cells, whereas in other cases p-p38 expression was decreased. HT-29 cells showed p-p38 predominantly in the cytoplasm, whereas Caco2 cells showed higher nuclear positivity. The effect of fibrates may depend on the differentiation status of the cell, as differentiated HT-29 and undifferentiated Caco2 cells share similar characteristics in terms of villin, CYP2J2, and soluble epoxide hydrolase (sEH) expression. In human colorectal carcinoma, higher levels of p-p38 were detected in the cytoplasm, whereas in normal colonic surface epithelium, p-p38 showed nuclear positivity. The decrease in p-p38 positivity was associated with a decrease in sEH, consistent with in vitro results. In conclusion, fibrates affect the level of p-p38, but its exact role in the process of carcinogenesis remains unclear and further research is needed in this area.
Performance Analysis of an Improved Variable Step Size LMS Algorithm

By building a nonlinear function relationship between the step size μ(n) and the error signal e(n),this paper proposes an improved variable step size LMS(Least Mean Square) adaptive filtering algorithm.This variable step size algorithm avoids the shortcoming of changing step size of SVSLMS(variable step size LMS based on Sigmoid function). Also in the stage of adaptive steady state it has the virtue of e(n) slightly changing at point close to zero.Meanwhile the algorithm efficiently overcome the discrepancy between the convergence rate and the steady error.This algorithm constitutes a significant improvement in the identificated speed with very small steady error in stationary environment and is of better tracking capability,as compared with the traditional algorithms with step size.
Discounted branching random walks

Let F(·) be a c.d. f. on [0,∞), f(s) = ∑∞ 0 pjsi a p.g. f. with p 0 = 0, < 1 < m = Σj p j < ∞ and 1 < ρ <∞. For the functional equation for a c.d. f. H(·) on [0,∞] we establish that if 1 – F(x) = O(x –θ ) for some θ > α =(log m)/(log p) there exists a unique solution H(·) to (∗) in the class C of c.d. f. ’s satisfying 1 – H(x) = o(x –α ). We give a probabilistic construction of this solution via branching random walks with discounting. We also show non-uniqueness if the condition 1 – H(x) = o(x –α ) is relaxed.
Cascade-based Adversarial Optimization for Influence Prediction

Influence prediction methods based on specific diffusion models are not suitable for information spread in real social networks. In addition, influence prediction methods based on information such as structure graphs and text content are difficult to promote due to limitations in information acquisition. Aiming at the two problems faced in the research of influence prediction, the sequential and non-sequential dependencies in the information diffusion process are captured through the information cascades time-series information, and the Multi-Dependency Diffusion Attention Neural (MDDAN) network is proposed. The sequential dependency and non-sequential dependency of cascades are obtained through recurrent neural networks and attention mechanisms, respectively. At the same time, the model captures the user’s dynamic preferences through the attention mechanism because the information has a time decay characteristic. To reduce the noise interference in the information diffusion, a Cascade-based Adversarial Optimization (CAO) strategy is proposed. To prove that this strategy effectively enhances the generalization ability of cascade-based influence prediction models, we apply it to MDDAN and propose an Adversarial Multi-Dependency Diffusion Attention Neural (AMDDAN) network. Experiments on three real social network datasets show that MDDAN outperforms state-of-the-art cascade prediction models, and the addition of adversarial perturbation to AMDDAN improves the robustness of MDDAN.
Refinement of Sobel Operator-edge Detection

Edge detection is a fundamental and important issue in digital image processing. Sobel operator is one of the methods commonly used in edge detection.This paper starts with analysis and comparison between Sobel operator and several other commonly used edge detection methods,contributing to a more in-depth study on classical Sobel operator.Sobel operator's advantage is its faster detection rate,with unique function to smooth and restrain noise to certain extent.Although the edge effect is great,there are still some problems;some false edges are detected,which makes edges rougher and reduces accuracy of location detection.Researchers proposed some improved methods to solve the problems of Sobel operator,such as: detection template expansion,edge tracking,etc,. Based on the analysis of these methods,the paper presents a new and simple Sobel operator enhancement.This approach realizes edge refinement based on classic Sobel edge detection algorithm.Experimental results show that the method effectively solves the problem of rough edges in Sobel operator,achieves finer and precise positioning.