Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of the p16 and p53 Tumor Suppressor Proteins and Viral HPV16 E6 and E7 Oncoproteins in the Assessment of Survival in Patients with Head and Neck Cancers Associated with Human Papillomavirus Infections

Simple Summary The role of human papillomavirus (HPV) in the survival of patients with head and neck squamous cell carcinoma (HNSCC) is an important topic. The recognition of additional markers could play a significant role in survival prognosis. Our study aimed to assess the roles of different molecular and immunohistochemical factors in the survival of patients with HNSCC. We analyzed 106 HNSCC samples and confirmed the roles of HPV DNA and p16, p53, and HPV16 E6 and E7 proteins in different subgroups of HNSCC. In addition to p16, the immunohistochemical overexpression of HPV16 E6 protein should be used for patient survival prognosis. Abstract The role of HPV in the survival prognosis of patients with head and neck squamous cell carcinoma, especially patients with laryngeal squamous cell carcinoma (LSCC) and hypopharyngeal squamous cell carcinoma (HPSCC), is still somewhat ambiguous. The present study aimed to explore the significance of tumor suppressor proteins and HPV16 E6 and E7 oncoproteins in the assessment of survival in patients with oropharyngeal squamous cell carcinoma (OPSCC), LSCC, and HPSCC associated with high-risk (HR-) and low-risk (LR-) HPV infections. By utilizing molecular and immunohistochemical investigations of HNSCC samples and patient data, univariate and multivariate survival analyses were conducted. The presence of HPV DNA (LR- and HR-HPV) was associated with a better 5-year OS and DSS for OPSCC and LSCC. The IHC overexpression of HPV16 E6 protein and p16 protein was associated with better survival in the univariate (for OPSCC) and multivariate (OPSCC and HPSCC) survival analyses. The overexpression of p53 was associated with better survival in OPSCC. HPV infection plays a significant role in the tumorigenesis of HNSCC, and the immunohistochemical assessment of HPV16 E6 protein expression should be interpreted as a useful prognostic marker for OPSCC and HPSCC.
Optimal Filtering of AC Output Anemometers

Abstract The output of pulsed and AC output anemometers suffer from discretization noise when such anemometers are sampled at fast rates (>1 Hz). This paper describes the construction of an optimal filter designed to reduce this noise. By comparing the filtered output from an AC output cup anemometer with a nearby cup anemometer whose output is free from discretization noise, it is shown that the filter significantly reduces the noise. Wind speed time series obtained from the two anemometers are quite similar. Next, deconvolution is applied to the filtered time series to account for the anemometer response. Spectra from the deconvolved time series and a time series measured by a nearby sonic anemometer are compared, and for high-speed flows the spectra from the two instruments match quite well. The time series are also very similar; however, the cup anemometer generally cannot respond to the quick bursts of speed seen by the sonic anemometer. The filtering and deconvolution methods presented here are most.
Machine Learning Generation of Dynamic Protein Conformational Ensembles

Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.
Gauge‐invariant classical Hamiltonian formulation of the electrodynamics of nonrelativistic particles

The Hamiltonian formalism for nonrelativistic charged particles in an electromagnetic field is shown to be manifestly gauge invariant. The Hamiltonian for the subsystem consisting of the interacting particles is shown to be form invariant under gauge transformation, but numerically dependent on the choice of gauge. The energy, which is numerically the same in all gauges, is obtained by subtracting the scalar potential of the time‐dependent radiation field from the Hamiltonian. The time rate of change of the energy of the particles is equal to the Joule power, which is the power lost by the field, so that energy of the entire isolated system consisting of particles and radiation field is conserved. In the quasistatic approximation, the energy of the longitudinal electric field gives the instantaneous Coulomb interaction energy, which can be associated with the particles.
Representative Systems and Decision Support for Multicriteria Optimization Problems

In this thesis, we investigate several upcoming issues occurring in the context of conceiving and building a decision support system. We elaborate new algorithms for computing representative systems with special quality guarantees, provide concepts for supporting the decision makers after a representative system was computed, and consider a methodology of combining two optimization problems. We review the original Box-Algorithm for two objectives by Hamacher et al. (2007) and discuss several extensions regarding coverage, uniformity, the enumeration of the whole nondominated set, and necessary modifications if the underlying scalarization problem cannot be solved to optimality. In a next step, the original Box-Algorithm is extended to the case of three objective functions to compute a representative system with desired coverage error. Besides the investigation of several theoretical properties, we prove the correctness of the algorithm, derive a bound on the number of iterations needed by the algorithm to meet the desired coverage error, and propose some ideas for possible extensions. Furthermore, we investigate the problem of selecting a subset with desired cardinality from the computed representative system, the Hypervolume Subset Selection Problem (HSSP). We provide two new formulations for the bicriteria HSSP, a linear programming formulation and a \(k\)-link shortest path formulation. For the latter formulation, we propose an algorithm for which we obtain the currently best known complexity bound for solving the bicriteria HSSP. For the tricriteria HSSP, we propose an integer programming formulation with a corresponding branch-and-bound scheme. Moreover, we address the issue of how to present the whole set of computed representative points to the decision makers. Based on common illustration methods, we elaborate an algorithm guiding the decision makers in choosing their preferred solution. Finally, we step back and look from a meta-level on the issue of how to combine two given optimization problems and how the resulting combinations can be related to each other. We come up with several different combined formulations and give some ideas for the practical approach.
Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks

The communications between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference in the region through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep Q-network reinforcement learning (RL) with low-dimensional fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. In addition, we extend the amount of transmitted data in our work, while adding scenarios where spectrum resources are relatively scarce, i.e. the number of V2V links is significantly larger than the amount of spectrum, to compensate for some of the shortcomings in existing literature studies. We demonstrate that the proposed D3QN-LS algorithm leads to a further improvement in the total capacity of V2I links and the success rate of periodic secure message transmission in V2V links.
Modeling Cues May Reduce Sway Following Sit-To-Stand Transfer for People with Parkinson’s Disease

Cues are commonly used to overcome the effects of motor symptoms associated with Parkinson’s disease. Little is known about the impact of cues on postural sway during transfers. The objective of this study was to identify if three different types of explicit cues provided during transfers of people with Parkinson’s disease results in postural sway more similar to healthy controls. This crossover study had 13 subjects in both the Parkinson’s and healthy control groups. All subjects completed three trials of uncued sit to stand transfers. The Parkinson’s group additionally completed three trials of sit to stand transfers in three conditions: external attentional focus of reaching to targets, external attentional focus of concurrent modeling, and explicit cue for internal attentional focus. Body worn sensors collected sway data, which was compared between groups with Mann Whitney U tests and between conditions with Friedman’s Tests. Sway normalized with modeling but was unchanged in the other conditions. Losses of balance presented with reaching towards targets and cueing for an internal attentional focus. Modeling during sit to stand of people with Parkinson’s disease may safely reduce sway more than other common cues.
Topological transport properties of highly oriented Bi2Te3 thin film deposited by sputtering

Topological insulators (TIs) are the promising materials for next-generation technology due to their exotic features such as spin momentum locking, conducting surface states, etc. However, the high-quality growth of TIs by sputtering technique, which is one of the foremost industrial requirements, is extremely challenging. Also, the demonstration of simple investigation protocols to characterize topological properties of TIs using electron-transport methods is highly desirable. Here, we report the quantitative investigation of non-trivial parameters employing magnetotransport measurements on a prototypical highly textured Bi2Te3 TI thin film prepared by sputtering. Through the systematic analyses of the temperature and magnetic field dependent resistivity, all topological parameters associated with TIs, such as coherency factor α , Berry phase ( ΦB ), mass term (m), the dephasing parameter (p), slope of temperature dependent conductivity correction ( κ ) and the surface state penetration depth (λ) are estimated by using the modified ‘Hikami–Larkin–Nagaoka’, ‘Lu–Shen’ and ‘Altshuler–Aronov’ models. The obtained values of topological parameters are well comparable to those reported on molecular beam epitaxy grown TIs. The epitaxial growth of Bi2Te3 film using sputtering, and investigation of the non-trivial topological states from its electron-transport behavior are important for their fundamental understanding and technological applications.
Kernel Function Optimization in Kernel Principle Component Analysis and Its Application to Feature Extraction of Gear Faults

A kernel function parameter optimization method based on the matrix similarity is proposed.The method first gives a fine matrix of kernel function out,then searchs an approximate kernel function parameter value in a finite range based on the similarity between the given matrix and the kernel function matrix.The method is applied to the feature extraction of the helieoptor gearbox fault.The result shows KPCA using optimization kernel parameter value has a better fault classification performance.
Providing a new method for link prediction in social networks based on the meta-heuristic algorithm

Social network analysis is one of the most important research fields in data mining today. The purpose of the analysis of these networks is to extract the embedded knowledge in the data set and to learn the behavior of users in the social networking environment. One of the most attractive and central applications of social network analysis is link prediction. The purpose of link prediction in social networks is to identify missing and unknown information from users or to predict the future link between two users. In recent years, various artificial intelligence algorithms have been introduced as one of the most important tools for resolving link prediction and big data. In this research, a strategy based on a meta-heuristic is used to improve link prediction in social networks. The proposed method is based on the characteristics of the signed social networks provided and turns the link prediction problem into a two-class classification problem. Then uses the capability of the Particle Swarm Optimization (PSO) and the topological properties of the social network graph to create a database with two classes, the first class pointing to the existence of a connection between the users and the second class pointing to the absence of this relationship. Creates a database using the support vector machine model for categorization work and uses the classic Katz similarity criterion for end-user suggestions. Twitter social network information has been used to compare and evaluate the proposed method. The results of the experiments show the superiority of the proposed method with 0.23, 0.99, and 6.32, respectively, compared to Meta-Path, Katz and CN algorithms in F-measure criterion.
An Optimal Nonlinear Model Predictive Control- Based Motion Cueing Algorithm Using Cascade Optimization and Human Interaction

Nonlinear model predictive control has been used in motion cueing algorithms recently to consider the nonlinear dynamics model of the system. The entire motion cueing algorithm indexes, including the physical and dynamical constraints of the actuators and physical constraints of passive joints, can be controlled with precision using nonlinear model predictive control. However, several weighting parameters in the nonlinear model predictive control-based motion cueing algorithm (including driving sensation, motion description of the actuators, and passive joints) require proper and laborious tuning to attain an optimal design structure. In this work, the optimal weighting parameters of a nonlinear predictive control-based motion cueing algorithm model are calculated using cascade optimisation and human interaction. A cascade optimisation method consisting of a particle swarm optimisation and genetic algorithm is designed to identify the best weighting parameters compared to those from one optimiser. In addition, the human decision-making units are added to the two-level cascade optimiser to determine the best solution from a Pareto front. The proposed cascade optimiser decreases the run-time with better extraction of the optimal weighting parameters to increase the motion fidelity compared to a single optimiser. It should be noted that the proposed methodology is applied along longitudinal channel. While the same methodology can be applied along lateral, heave and yaw channels for further evaluation of the proposed method. The proposed model is simulated utilising the MATLAB software and the results prove the efficiency of the newly proposed model compared to those from the previous single optimiser in reproducing more accurate motion signals with better usage of the driving motion platform workspace.
Smart angular displacement sensor for agricultural field robot manipulators

This study presents the electromagnetic angular displacement sensor and its technical characteristics. The existing electromagnetic angular displacement sensors have been thoroughly analyzed and compared with other types of sensors. The reason for the low sensitivity of the electromagnetic angular displacement sensor has been investigated and some technical modification to the existing sensor has been made. The magnetic circuits of the electromagnetic sensors have been analyzed. A method is proposed for expanding the range of angular measurements up to 180 degrees and increasing the sensitivity of the electromagnetic sensor without compromising the measurement accuracy. This, in turn, allows high-precision control and measurement of rotating mechanisms of all types of mechatronic systems and agricultural robots.
On challenges in the uncertainty evaluation for time-dependent measurements

The measurement of quantities with time-dependent values is a common task in many areas of metrology. Although well established techniques are available for the analysis of such measurements, serious scientific challenges remain to be solved to enable their routine use in metrology. In this paper we focus on the challenge of estimating a time-dependent measurand when the relationship between the value of the measurand and the indication is modeled by a convolution. Mathematically, deconvolution is an ill-posed inverse problem, requiring regularization to stabilize the inversion in the presence of noise. We present and discuss deconvolution in three practical applications: thrust-balance, ultra-fast sampling oscilloscopes and hydrophones. Each case study takes a different approach to modeling the convolution process and regularizing its inversion. Critically, all three examples lack the assignment of an uncertainty to the influence of the regularization on the estimation accuracy. This is a grand challenge for dynamic metrology, for which to date no generic solution exists. The case studies presented here cover a wide range of time scales and prior knowledge about the measurand, and they can thus serve as starting points for future developments in metrology. The aim of this work is to present the case studies and demonstrate the challenges they pose for metrology.
Cosmic acceleration with bulk viscosity in an anisotropic f(R, L m ) background

In this article, we investigate the observed cosmic acceleration in the framework of a cosmological f(R, L m ) model dominated by bulk viscous matter in an anisotropic background. We consider the locally rotationally symmetric Bianchi type I metric and derive the Friedmann equations that drive the gravitational interactions in f(R, L m ) gravity. Further, we assume the functional form f(R,Lm)=R2+Lmα , where α is a free model parameter, and then find the exact solutions of field equations corresponding to our viscous matter dominated model. We incorporate the updated H(z) data and the Pantheon data to acquire the best-fit values of parameters of our model by utilizing the χ 2 minimization technique along with the Markov Chain Monte Carlo random sampling method. Further, we present the behavior of physical parameters that describe the Universe’s evolution phase, such as density, effective pressure and EoS parameters, skewness parameter, and the statefinder diagnostic parameters. We find that the energy density indicates expected positive behavior, whereas the negative behavior of bulk viscous pressure contributes to the Universe’s expansion. The effective EoS parameter favors the accelerating phase of the Universe’s expansion. Moreover, the skewness parameter shows the anisotropic nature of spacetime during the entire evolution phase of the Universe. Finally, from the statefinder diagnostic test, we found that our cosmological f(R, L m ) model lies in the quintessence region, and it behaves like a de-Sitter universe in the far future. We analyze different energy conditions in order to test the consistency of the obtained solution. We find that all energy conditions except strong energy condition (SEC) show positive behavior, while the violation of SEC favors the recently observed acceleration with the transition from decelerated to an accelerated epoch of the Universe’s expansion in the recent past.
Measuring Firm Quality Using Machine Learning

__________________________________________________________________________ Firm quality is a foundational construct in the fundamental analysis literature. Asness et al. (2019), a recent representative example of this literature, measures firm quality based on 19 fundamental signals guided by valuation theory (referred to as Asness’ Q score). We examine whether it is possible to leverage the power of machine learning to construct a better measure of firm quality using the same 19 fundamental signals. We show that an advanced machine learning model called XGBoost based on the 19 signals can outperform a linear OLS regression model based on Asness’ Q score (our benchmark) by 27%. However, we fail to find economically significant evidence that adding more raw accounting data items identified by the prior literature or commercial databases to XGBoost can generate a stronger prediction model. We show that our measure of firm quality based on XGBoost and the 19 signals can better explain contemporaneous stock prices than Asness’ Q score. In addition, a value investing trading strategy using our XGBoost model outperforms the same trading strategy based on Asness’ Q score by an economically significant margin. ___________________________________________________________________________
Recent Advances in Tissue-Engineered Cardiac Scaffolds—The Progress and Gap in Mimicking Native Myocardium Mechanical Behaviors

Heart failure is the leading cause of death in the US and worldwide. Despite modern therapy, challenges remain to rescue the damaged organ that contains cells with a very low proliferation rate after birth. Developments in tissue engineering and regeneration offer new tools to investigate the pathology of cardiac diseases and develop therapeutic strategies for heart failure patients. Tissue -engineered cardiac scaffolds should be designed to provide structural, biochemical, mechanical, and/or electrical properties similar to native myocardium tissues. This review primarily focuses on the mechanical behaviors of cardiac scaffolds and their significance in cardiac research. Specifically, we summarize the recent development of synthetic (including hydrogel) scaffolds that have achieved various types of mechanical behavior—nonlinear elasticity, anisotropy, and viscoelasticity—all of which are characteristic of the myocardium and heart valves. For each type of mechanical behavior, we review the current fabrication methods to enable the biomimetic mechanical behavior, the advantages and limitations of the existing scaffolds, and how the mechanical environment affects biological responses and/or treatment outcomes for cardiac diseases. Lastly, we discuss the remaining challenges in this field and suggestions for future directions to improve our understanding of mechanical control over cardiac function and inspire better regenerative therapies for myocardial restoration.