Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of DNA Type on the Physicochemical and Biological Properties of Polyplexes Based on Star Polymers Bearing Different Amino Functionalities

The interactions of two star polymers based on poly (2-(dimethylamino)ethyl methacrylate) with different types of nucleic acids are investigated. The star polymers differ only in their functionality to bear protonable amino or permanently charged quaternary ammonium groups, while DNAs of different molar masses, lengths and topologies are used. The main physicochemical parameters of the resulting polyplexes are determined. The influence of the polymer’ functionality and length and topology of the DNA on the structure and properties of the polyelectrolyte complexes is established. The quaternized polymer is characterized by a high binding affinity to DNA and formed strongly positively charged, compact and tight polyplexes. The parent, non-quaternized polymer exhibits an enhanced buffering capacity and weakened polymer/DNA interactions, particularly upon the addition of NaCl, resulting in the formation of less compact and tight polyplexes. The cytotoxic evaluation of the systems indicates that they are sparing with respect to the cell lines studied including osteosarcoma, osteoblast and human adipose-derived mesenchymal stem cells and exhibit good biocompatibility. Transfection experiments reveal that the non-quaternized polymer is effective at transferring DNA into cells, which is attributed to its high buffering capacity, facilitating the endo-lysosomal escape of the polyplex, the loose structure of the latter one and weakened polymer/DNA interactions, benefitting the DNA release.
Effects of weight divisions in time-motion of female high-level Brazilian Jiu-jitsu combat behaviors

Coaches and psychologists can use time-motion analysis to elaborate specific interventions for female BJJ athletes, increasing specific training context and reducing unnecessary psychological and physical demands and injuries. Therefore, the present study aimed to analyze high-level BJJ female athletes in the 2020 Pan-American Games by comparing the weight categories on the time-motion analysis. The time-motion analysis (i.e., approach, gripping, attack, defensive actions, transition, mounting, guard, side control, and submissions) of 422 high-level female BJJ combats was divided and compared by weight category as follows: Rooster (n = 8), Light Feather (n = 18), Feather (n = 122), Light (n = 84), Middle (n = 74), Medium Heavy (n = 44), Heavy (n = 36), Super Heavy (n = 36), using p ≤ 0.05. The main results indicated that the Super heavyweight category [3.1 (5.8;119.9) s] had a shorter gripping time than other weight categories, p ≤ 0.05. In contrast, roosters [7.2 (3.5;64.6) s] had longer gripping, transition [14.0 (4.8;29.6) s], and attack time [76.2 (27.7, 93.2)] than the light feather, middlers, and heavier weight categories, p ≤ 0.05. These findings should be considered for the psychological interventions and training prescription.
Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis—Challenges and Opportunities

Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.
Saturation and Color Constancy

Color constancy is the phenomenon in which the colors of surfaces remain approximately constant despite considerable variations in the physical signals reaching the eyes from those surfaces. The analysis of color constancy is commonly restricted to changes in the intensity and chromaticity of the illuminant. We have been exploring aspects of color constancy which occur in the absence of shifts in the mean intensity or chromaticity of the stimulus, but depend instead on changes in the variance of the tristimulus values of the set of visible surfaces. (In natural settings, such effects may be due either to desaturation by haze, or to changes in the bandwidth of the illuminant.) The effects we consider are inconsistent with most previously published theories of color constancy.
Convergence of dynamic iteration methods for initial value problems

In VLSI-simulation there has recently been interest in an iterative technique called the waveform relaxation method. In this paper we set up a theoretical framework to analyze the convergence of such methods. We restrict the discussion to linear systems and do not consider the effects of time discretization, but assume that the initial value problems are solved exactly.
Long period oscillations of microwave emission of solar active regions: observations with NoRH and SSRT

Abstract In this work we present the first results of study and comparison of the parameters of quasi-periodic long-term oscillations of microwave emission of large (>0.7 arcmin) sunspots as a result of simultaneous observations with two radioheliographs – NoRH (17 GHz) and Siberian Solar Radio Telescope (SSRT) (5.7 GHz) with 1 minute cadence. Radioheliographs have been working with quite large time overlap (about 5 hours) and have the high spatial resolution: 10 arcsec (NoRH) and 20 arcsec (SSRT). We have found that quasi-periodic long-term oscillations are surely observed at both frequencies with the periods in the range of 20–150 min. We detected common periods for common time of observations with two radioheliographs and interpret this as the consequence of the vertical-radial quasi-periodic displacements of sunspot as a whole structure.
Vision-Based Robotic Object Grasping—A Deep Reinforcement Learning Approach

This paper focuses on developing a robotic object grasping approach that possesses the ability of self-learning, is suitable for small-volume large variety production, and has a high success rate in object grasping/pick-and-place tasks. The proposed approach consists of a computer vision-based object detection algorithm and a deep reinforcement learning algorithm with self-learning capability. In particular, the You Only Look Once (YOLO) algorithm is employed to detect and classify all objects of interest within the field of view of a camera. Based on the detection/localization and classification results provided by YOLO, the Soft Actor-Critic deep reinforcement learning algorithm is employed to provide a desired grasp pose for the robot manipulator (i.e., learning agent) to perform object grasping. In order to speed up the training process and reduce the cost of training data collection, this paper employs the Sim-to-Real technique so as to reduce the likelihood of damaging the robot manipulator due to improper actions during the training process. The V-REP platform is used to construct a simulation environment for training the deep reinforcement learning neural network. Several experiments have been conducted and experimental results indicate that the 6-DOF industrial manipulator successfully performs object grasping with the proposed approach, even for the case of previously unseen objects.
NMR-Based Metabolite Profiling and the Application of STOCSY toward the Quality and Authentication Assessment of European EVOOs

Extra virgin olive oil (EVOO) possesses a high-value rank in the food industry, thus making it a common target for adulteration. Hence, several methods have been essentially made available over the years. However, the issue of authentication remains unresolved with national and food safety organizations globally struggling to regulate and control its market. Over the course of this study, the aim was to determine the origin of EVOOs suggesting a high-throughput, state-of-the-art method that could be easily adopted. A rapid, NMR-based untargeted metabolite profiling method was applied and complemented by multivariate analysis (MVA) and statistical total correlation spectroscopy (STOCSY). STOCSY is a valuable statistical tool contributing to the biomarker identification process and was employed for the first time in EVOO analysis. Market samples from three Mediterranean countries of Spain, Italy, and Greece, blended samples from these countries, as well as monocultivar samples from Greece were analyzed. The NMR spectra were collected, with the help of chemometrics acting as “fingerprints” leading to the discovery of certain chemical classes and single biomarkers that were related to the classification of the samples into groups based on their origin.
An Interpretation of Partial Least Squares

Abstract Univariate partial least squares (PLS) is a method of modeling relationships between a Y variable and other explanatory variables. It may be used with any number of explanatory variables, even far more than the number of observations. A simple interpretation is given that shows the method to be a straightforward and reasonable way of forming prediction equations. Its relationship to multivariate PLS, in which there are two or more Y variables, is examined, and an example is given in which it is compared by simulation with other methods of forming prediction equations. With univariate PLS, linear combinations of the explanatory variables are formed sequentially and related to Y by ordinary least squares regression. It is shown that these linear combinations, here called components, may be viewed as weighted averages of predictors, where each predictor holds the residual information in an explanatory variable that is not contained in earlier components, and the quantity to be predicted is the vecto.
Semidefinite Optimization Applications

Owing to its versatile model power and wide applicability, semidefinite programming (SDP) has received a lot of attention of late in many communities. In this article, we will survey some recent applications of SDP, with a particular emphasis on semidefinite relaxations of hard optimization problems. We will also provide pointers to the literature, for further reading. Keywords: semidefinite programming; semidefinite relaxation; quadratic optimization; polynomial optimization; chance-constrained optimization; approximation algorithms
Uniqueness Criteria for the Adjoint Equation in State-Constrained Elliptic Optimal Control

The article considers linear elliptic equations with regular Borel measures as inhomogeneity. Such equations frequently appear in state-constrained optimal control problems. By a counter example of Serrin [18], it is known that, in the presence of non-smooth data, a standard weak formulation does not ensure uniqueness for such equations. Therefore several notions of solution have been developed that guarantee uniqueness. In this note, we compare different definitions of solutions, namely the ones of Stampacchia [19] and Boccardo-Galouët [4] and the two notions of solutions of [2, 7], and show that they are equivalent. As side results, we reformulate the solution in the sense of [19], and prove the existence of solutions in the sense of [2, 4, 7] in case of mixed boundary conditions.
Exciton lifetime, Auger recombination, and exciton transport by calibrated differential absorption spectroscopy in Cu 2 O

Differential absorption spectroscopy shows a characteristic dependence on the density of Is excitons in Cu 2 O at low temperature. The relation between absorption change and Is exciton density is established and calibratedusing one- and two-photon excitation. The calibrated density evaluation is applied to exciton transport measurements in a quasi-one-dimensional sample geometry. A numerical simulation of the transport yields extremely long exciton lifetimes of up to 3 ms. The Auger recombination of excitons is almost negligible, contrary to previous estimates. A deviation of the transport characteristics from the diffusive regime was not observed.
Investigation of potassium niobate as an ultrasonic transducer material

This paper investigates the use of single crystal potassium niobate (KNbO/sub 3/) as an ultrasonic transducer material. Crystallographic engineering allows the samples to be optimized for high coupling coefficients for the vibration modes of interest. The piezoelectric properties of KNbO/sub 3/ in both thickness and length extensional mode have been measured for two different crystallographic orientations. The measured high coupling coefficients (k/sub t/ =68% and k/sub 33/=75%) mid-range Q/sub m/(mechanical) of 40 and high longitudinal velocities of 8000 m/sec enable the use of KNbO/sub 3/ for high frequency transducers. A potential drawback is high measured values of electrical loss. In order to investigate losses at the frequencies of interest a curve-fitting program was used. The results show low losses above 1 MHz. A 10 Mhz transducer was fabricated using two optimally designed matching layers to match the high acoustic impedance with tissue. Modeling was performed both with the measured losses at 1 kHz and the low loss values obtained through curve fitting. The experimental pulse echo response indicates that the effect of losses on the transducer performance is minimal, indicating that losses are not a major factor. A broad bandwidth of 64% and insertion loss of 17 dB was obtained.
Coastal upwelling on a sloping bottom: the formation of plumes, jets and pinched-off cyclones

An experimental, two-layer, stratified model of coastal upwelling was produced at the outer edge of a cylindrical tank having a conical bottom. As in Narimousa & Maxworthy (1985), we find that most of the characteristics of the flow field can be described by the single non-dimensional parameter θ* = g′h0/μ*fλs (where g′ = gδP is the reduced gravity, h0 is the initial depth of the top layer, μ* is the friction velocity applied to the top surface of the fluid, f is the Coriolis parameter and λs is the distance of the stationary position of the upwelling front at the surface from the wall of the tank). For example, we find that the migration rate of the front at the surface, μf is given by μf/μ* ∼ θ−½*; the size λW of the frontal, circular waves is λW/λs ∼ θ*; that these waves first appear at the front when it has moved a distance λi < λs given by λi/λs ∼ θ−½*; and have a final drift velocity μw ≈ 0.5 U in the direction of the applied stress (where U is the mean velocity of the top layer). At low values of θ* complex interactions between the growing waves created intense meandering jets which transported upwelled fluid far from the mean position of the front.
Reactive Oxygen Species and NRF2 Signaling, Friends or Foes in Cancer?

The imbalance between reactive oxygen species (ROS) production and clearance causes oxidative stress and ROS, which play a central role in regulating cell and tissue physiology and pathology. Contingent upon concentration, ROS influence cancer development in contradictory ways, either stimulating cancer survival and growth or causing cell death. Cells developed evolutionarily conserved programs to sense and adapt redox the fluctuations to regulate ROS as either signaling molecules or toxic insults. The transcription factor nuclear factor erythroid 2-related factor 2 (NRF2)-KEAP1 system is the master regulator of cellular redox and metabolic homeostasis. NRF2 has Janus-like roles in carcinogenesis and cancer development. Short-term NRF2 activation suppresses tissue injury, inflammation, and cancer initiation. However, cancer cells often exhibit constitutive NRF2 activation due to genetic mutations or oncogenic signaling, conferring advantages for cancer cells’ survival and growth. Emerging evidence suggests that NRF2 hyperactivation, as an adaptive cancer phenotype under stressful tumor environments, regulates all hallmarks of cancer. In this review, we summarized the source of ROS, regulation of ROS signaling, and cellular sensors for ROS and oxygen (O2), we reviewed recent progress on the regulation of ROS generation and NRF2 signaling with a focus on the new functions of NRF2 in cancer development that reach beyond what we originally envisioned, including regulation of cancer metabolism, autophagy, macropinocytosis, unfolded protein response, proteostasis, and circadian rhythm, which, together with anti-oxidant and drug detoxification enzymes, contributes to cancer development, metastasis, and anticancer therapy resistance.
Analysis of the Skyscraper Wind around High-Rise Buildings in Coastal Region, South Korea, during Typhoon ‘Hinnamnor’

High-rise buildings in cities adversely affect wind regimes by changing the air currents in their surrounding areas. In particular, extreme climate phenomena caused by climate change are stronger and more frequent, causing damage in cities. To better understand skyscraper wind behaviors around high-rise buildings, actual measurements are necessary to determine the environmental assessment of the wind effect. In this study, field measurements were performed with five anemometers at five points in the vicinity of a skyscraper called the LCT residential complex (411.6 m tall) surrounded by high-rise buildings in the coastal city of Busan, South Korea during Typhoon Hinnamnor. The gust was 3.7 times stronger, while the maximum 1-min mean wind speed was 3.1 times stronger than those measured at a nearby reference weather station operated by the Korean Meteorological Administration. The characteristics of downward and canyon winds were shown to depend on the spatiotemporal characteristics of the five points. The turbulence intensity declined as the wind speed increased and converged to a certain value. The gust factor also dropped as the wind speed increased and converged to 2.0, and was considered to be the parameter that best represents the intensity of instantaneous gust caused by the skyscraper wind effect. These results suggest that high-rise buildings should be designed with the consideration of gusts twice as strong as the average wind speed. In addition, field measurements should be accompanied in order to respond to the skyscraper wind effect.
Causal Model Building in the Context of Cardiac Rehabilitation: A Systematic Review

Randomization is an effective design option to prevent bias from confounding in the evaluation of the causal effect of interventions on outcomes. However, in some cases, randomization is not possible, making subsequent adjustment for confounders essential to obtain valid results. Several methods exist to adjust for confounding, with multivariable modeling being among the most widely used. The main challenge is to determine which variables should be included in the causal model and to specify appropriate functional relations for continuous variables in the model. While the statistical literature gives a variety of recommendations on how to build multivariable regression models in practice, this guidance is often unknown to applied researchers. We set out to investigate the current practice of explanatory regression modeling to control confounding in the field of cardiac rehabilitation, for which mainly non-randomized observational studies are available. In particular, we conducted a systematic methods review to identify and compare statistical methodology with respect to statistical model building in the context of the existing recent systematic review CROS-II, which evaluated the prognostic effect of cardiac rehabilitation. CROS-II identified 28 observational studies, which were published between 2004 and 2018. Our methods review revealed that 24 (86%) of the included studies used methods to adjust for confounding. Of these, 11 (46%) mentioned how the variables were selected and two studies (8%) considered functional forms for continuous variables. The use of background knowledge for variable selection was barely reported and data-driven variable selection methods were applied frequently. We conclude that in the majority of studies, the methods used to develop models to investigate the effect of cardiac rehabilitation on outcomes do not meet common criteria for appropriate statistical model building and that reporting often lacks precision.
On the analyticity of stationary gravitational fields at spatial infinity

It is proved that all stationary, vacuum solutions of Einstein’s equations which satisfy certain weak differentiability conditions characterizing asymptotic flatness, possess an analytic structure near spatial infinity. This analyticity theorem implies the existence of a multipole expansion whose coefficients can be expressed in terms of the Geroch–Hansen multipole moments defined at the point at infinity on the conformal manifold. This proves a longstanding conjecture that these moments uniquely determine the local structure of a stationary, asymptotically flat, vacuum metric.
Surveillance of ground vehicles for airport security

Future surveillance systems will work in complex and cluttered environments which require systems engineering solutions for such applications such as airport ground surface management. In this paper, we highlight the use of a L1 video tracker for monitoring activities at an airport. We present methods of information fusion, entity detection, and activity analysis using airport videos for runway detection and airport terminal events. For coordinated airport security, automated ground surveillance enhances efficient and safe maneuvers for aircraft, unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs) operating within airport environments.
Forecast of Short-Term Passenger Flow in Multi-Level Rail Transit Network Based on a Multi-Task Learning Model

With the refinement of the urban transportation network, more and more passengers choose the combined mode. To provide better inter-trip services, it is necessary to integrate and forecast the passenger flow of multi-level rail transit network to improve the connectivity of different transport modes. The difficulty of multi-level rail transit passenger flow prediction lies in the complexity of the spatiotemporal characteristics of the data, the different characteristics of passenger flow composition, and the difficulty of research. At present, most of the research focuses on one mode of transportation or the passenger flow within the city, while the comprehensive analysis of passenger flow under various modes of transportation is less. This study takes the key nodes of the multi-level rail transit railway hub as the research object, establishes a multi-task learning model, and forecasts the short-term passenger flow of rail transit by combining the trunk railway, intercity rail transit and subway. Different from the existing research, the model introduces convolution layer and multi-head attention mechanism to improve and optimize the Transformer multi-task learning framework, trains and processes the data of trunk railway, intercity railway, and subway as different tasks, and considers the correlation of passenger flow of trunk railway, intercity railway, and subway in the prediction. At the same time, a new residual network structure is introduced to solve the problems of over-fitting, gradient disappearance, and gradient explosion in the training process. Taking the large comprehensive transportation hub in Guangzhou metropolitan area as an example, the proposed multi-task learning model is evaluated. The improved Transformer has the highest prediction accuracy (Average prediction accuracy of passenger flow of three traffic modes) 88.569%, and others methods HA, FC-LSTM and STGCN are 81.579%, 82.230% and 81.761%, respectively. The results show that the proposed multi-task learning model has better prediction performance than the existing models.