Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Reliability and Quality of Videos on Medial Epicondylitis Shared on YouTube

Introduction Video-sharing websites, especially YouTube search engine, have become popular sources for obtaining health information over the years. The reliability and quality of contents on YouTube are unpredictable and questionable. The purpose of this study was to evaluate the reliability and quality of videos on medial epicondylitis shared on the YouTube platform. Methods A YouTube search with the keywords “medial epicondylitis” and “golfer’s elbow” was conducted. After typing each keyword, the first 50 videos that appeared in the first three pages were evaluated. The titles and duration of the videos, the uploading sources, the time passed since the upload, number of total views, likes, dislikes and comments were recorded. All videos were analyzed and scored using the DISCERN scoring system, modified DISCERN (mod-DISCERN) scale, and the Global Quality Score (GQS). Results Eighty-eight videos met the inclusion criteria and were included in the study. The mean DISCERN score, mod-DISCERN score and GQS were 40.68±10.5, 1.81±0.76, and 2.72±0.9, respectively. Most of the videos had poor and very poor quality content according to the DISCERN instrument, the mod-DISCERN scale and GQS tool. The main upload sources were doctors (36.4%), physiotherapists (25%), patients (2.2%), and other (36.4%). The videos uploaded by doctors had higher quality scores than other uploading sources. Of the 88 videos, 10 were about diagnosis alone, 39 were on treatment alone, and 39 contained information about both of diagnosis and treatment. The mean DISCERN score, mod-DISCERN score and GQS of the videos on “both” were significantly higher than those “diagnosis alone” and “treatment alone” groups. Conclusion Nearly 80% of YouTube videos on medial epicondylitis according to the DISCERN and GQS tool, and also 97.7% of videos according to the mod-DISCERN scale had medium or poor quality. YouTube videos on medial epicondylitis could not be considered as accurate and reliable sources. Because the videos uploaded by doctors had higher quality scores, the physicians should prepare and upload more reliable and quality contents with detailed information on YouTube.
Linear signatures in nonlinear gyrokinetics: interpreting turbulence with pseudospectra

A notable feature of plasma turbulence is its propensity to retain features of the underlying linear eigenmodes in a strongly turbulent state—a property that can be exploited to predict various aspects of the turbulence using only linear information. In this context, this work examines gradient-driven gyrokinetic plasma turbulence through three lenses—linear eigenvalue spectra, pseudospectra, and singular value decomposition (SVD). We study a reduced gyrokinetic model whose linear eigenvalue spectra include ion temperature gradient driven modes, stable drift waves, and kinetic modes representing Landau damping. The goal is to characterize in which ways, if any, these familiar ingredients are manifest in the nonlinear turbulent state. This pursuit is aided by the use of pseudospectra, which provide a more nuanced view of the linear operator by characterizing its response to perturbations. We introduce a new technique whereby the nonlinearly evolved phase space structures extracted with SVD are linked to the linear operator using concepts motivated by pseudospectra. Using this technique, we identify nonlinear structures that have connections to not only the most unstable eigenmode but also subdominant modes that are nonlinearly excited. The general picture that emerges is a system in which signatures of the linear physics persist in the turbulence, albeit in ways that cannot be fully explained by the linear eigenvalue approach; a non-modal treatment is necessary to understand key features of the turbulence.
Sex and Age Differences in Association between Physical Activity and Metabolic Syndrome: Results from NHANES 2003–2006

Objective: To examine whether and how sex and age modify the association between accelerometer-based physical activity (PA) and metabolic syndrome (MetS) among American (US) adults. Method: Adults aged ≥20 years old who participated in the mobile center examination during 2003–2006 in the National Health and Nutrition Examination Survey were included for analysis. The total minutes per day of moderate-to-vigorous PA (MVPA) was estimated using ActiGraph. Multivariable logistic regression was used to estimate the odds ratio (OR) of having MetS at an increasing MVPA time. The modification effects of gender and age on the association between MetS and MVPA time were examined by testing for two-way and three-way interaction terms of MVPA time, sex, and age in the model after adjusting for relevant covariates. Results: The prevalence of MetS generally decreased with the MVPA time and was lower in females than in males, although the sex difference varied across age groups. After adjusting for demographic and lifestyle covariates, there was a significant sex difference in how an increased MVPA time lowered the odds of MetS. This interactive effect also varied with age. MVPA benefitted young and middle-age populations up until about 65 years old for both sexes, and the protective effect weakened with age. Although the effect of MVPA was stronger for males than females at young ages, the rate at which it attenuated was quicker in males. The OR of MetS between males and females per unit change of MVPA time was 0.73 (95% CI: [0.57, 0.93]) at age = 25 years, compared to OR = 1.00 (95% CI: [0.88, 1.16]) at age = 60 years. Before the age of 50, the gender difference in the protective effect on MetS was larger at low MVPA levels and became smaller at higher MVPA levels. The male advantage was quite stable with an increasing MVPA time for ages 50–60, and no longer significant at older ages. Conclusions: Young and middle-age populations benefitted from MVPA, lowering the risk of MetS for both sexes. A longer MVPA time was associated with a greater decrease in the risk of MetS in young men than in women, but the sex difference reduced with age and was no longer apparent in older populations.
Identification of human phosphoglycerate mutase 1 (PGAM1) inhibitors using hybrid virtual screening approaches

PGAM1 plays a critical role in cancer cell metabolism through glycolysis and different biosynthesis pathways to promote cancer. It is generally known as a crucial target for treating pancreatic ductal adenocarcinoma, the deadliest known malignancy worldwide. In recent years different studies have been reported that strived to find inhibitory agents to target PGAM1, however, no validated inhibitor has been reported so far, and only a small number of different inhibitors have been reported with limited potency at the molecular level. Our in silico studies aimed to identify potential new PGAM1 inhibitors that could bind at the allosteric sites. At first, shape and feature-based models were generated and optimized by performing receiver operating characteristic (ROC) based enrichment studies. The best query model was then employed for performing shape, color, and electrostatics complementarity-based virtual screening of the ChemDiv database. The top two hundred and thirteen hits with greater than 1.2 TanimotoCombo score were selected and then subjected to structure-based molecular docking studies. The hits yielded better docking scores than reported compounds, were selected for subsequent structural similarity-based clustering analysis to select the best hits from each cluster. Molecular dynamics simulations and binding free energy calculations were performed to validate their plausible binding modes and their binding affinities with the PGAM1 enzyme. The results showed that these compounds were binding in the reported allosteric site of the enzyme and can serve as a good starting point to design better active selective scaffolds against PGAM1enzyme.
Grouping corrections for circular goodness-of-fit tests

Simple corrections are outlined for the application to grouped seasonal data of Watson's and Maag's tests. These tests are circular or seasonal analogues of one-sample and k-(≥2) sample tests, of Cramer-von Mises type, and have several natural advantages over their competitors. The provision of simple grouping corrections may enhance the use of these tests, which otherwise have very good features. The corresponding grouping corrections are also presented for conventional, i.e. non-circular, Cramer-von Mises types of test
Application of Orthogonal Block Variables and Canonical Correlation Analysis in Modeling Pharmacological Activity of Alkaloids from Plant Medicines

A new kind of orthogonal block variables, derived from subspace projection and canonical correlation analysis, is applied to model pharmaological activity of alkaloids from plant drugs. The al- kaloids are grouped into three cases by intravenous, intraperitoneal, and subcutaneous injections. Four block variables (family of vari- ables) investigated in this work are valence molecular connectiv- ity index, alpha kappa index, E-State index and element counts of molecules, respectively. The regression model embracing only few new orthogonal block variables against pharmaological activity shows significant improvement than those, say multiple linear regression (MLR) simply using original variables, principal component regres- sion (PCR) and the ones selecting only one or two of the original family variables, both in fitting and prediction ability of the corre- lation model. The reason for this might be that the new orthogonal block variables in fact include almost all of the information of the original variables but without collinearity between them.
Recognition of spectrally shaped speech in speech-modulated noise: Effects of age, spectral shape, speech level, and vocoding

This study examined the recognition of spectrally shaped syllables and sentences in speech-modulated noise by younger and older adults. The effect of spectral shaping and speech level on temporal amplitude modulation cues was explored through speech vocoding. Subclinical differences in hearing thresholds in older adults were controlled using threshold matching noise. Older, compared to younger, adults had poorer recognition but similar improvements as the bandwidth of the shaping function increased. Spectral shaping may enhance the sensation level of glimpsed speech, which improves speech recognition in noise, even with mild elevations in hearing thresholds.
Robust multi-institution low-dose CT imaging with semi-supervised federated learning network

Deep learning technology has been utilized in computed tomography, but, it needs centralized dataset to train the neural networks. To solve it, federated learning has been proposed, which collaborates the data from different local medical institutions with privacy-preserving decentralized strategy. However, lots of unpaired data is not included in the local models training and directly aggregating the parameters would degrade the performance of the updated global model. In order to deal with the issues, we present a semi-supervised and semi-centralized federated learning method to promote the performance of the learned global model. Specifically, each local model is trained with an unsupervised strategy locally at a fixed round. After that, the parameters of the local models are shared to aggregate on the server to update the global model. Then, the global model is further trained with a standard dataset, which contains well paired training samples to stabilize and standardize the global model. Finally, the global model is distributed to local models for the next training step. For shorten, we call the presented federated learning method as “3SC-FL”. Experiments demonstrate the presented 3SC-FL outperforms the compared methods, qualitatively and quantitatively.
Improved adaptive tessellation rendering algorithm

BACKGROUND: The human body model in the virtual surgery system is generally nested by multiple complex models and each model has quite complex tangent and curvature change. In actual rendering, if all details of the human body model are rendered with high performance, it may cause the stutter due to insufficient hardware performance. If the human body model is roughly rendered, the details of the model cannot be well represented. OBJECTIVE: In order to realize the real-time rendering of complex models in virtual surgical systems, this paper proposes an improved adaptive tessellation rendering algorithm, which includes offline and online parts. METHODS: The offline part mainly completes data reading and data structure constructing. The online part performs the surface subdivision operation in-real time for each frame, which includes the subdivision operation of the control points and surface evaluation. The offline part simplifies the subdivision step by recording the surface subdivision hierarchy using a quadtree and using control templates to record control point information. RESULTS: The online part reduces computation time by using a matrix to record topological relationships between vertices and vertex weights. The online part can compress the time complexity of traversing the quadtree of different subdivision levels to O⁢(n⁢log⁡n) by establishing an association with the quadtree of each subdivision level and using the greedy algorithm to complete the traversal of the quadtree. Finally, the adaptive tessellation rendering algorithm proposed in this paper is compared with other commonly used tessellation algorithms. CONCLUSION: The algorithm has advantages in computational efficiency and graphical display.
Cardiac phase estimation using deep learning analysis of pulsed-mode projections: towards autonomous cardiac CT imaging

Cardiac CT plays an important role in diagnosing heart diseases but is conventionally limited by its complex workflow that requires dedicated phase and bolus tracking [e.g., electrocardiogram (ECG) gating]. This work reports initial progress towards robust and autonomous cardiac CT exams through deep learning (DL) analysis of pulsed-mode projections (PMPs). To this end, cardiac phase and its uncertainty were simultaneously estimated using a novel projection domain cardiac phase estimation network (PhaseNet), which utilizes a sliding-window multi-channel feature extraction approach and a long short-term memory (LSTM) block to extract temporal correlation between time-distributed PMPs. Monte-Carlo dropout layers were utilized to predict the uncertainty of deep learning-based cardiac phase prediction. The performance of the proposed phase estimation pipeline was evaluated using accurate physics-based emulated data. PhaseNet demonstrated improved phase estimation accuracy compared to more standard methods in terms of RMSE (~43% improvement vs. a standard CNN-LSTM; ~17% improvement vs. a multi-channel residual network [ResNet]), achieving accurate phase estimation with <8% RMSE in cardiac phase (phase ranges from 0-100%). These findings suggest that the cardiac phase can be accurately estimated with the proposed projection domain approach. Combined with our previous work on PMP-based bolus curve estimation, the proposed method could potentially be used to achieve autonomous cardiac CT scanning without ECG device or expert-in-the-loop bolus timing.
Dirichlet Process and Stick-Breaking

As k → ∞, we shall have G = ∑∞ c=1 πcδφc , where all the φc are i.i. d. samples from G0, while the random sequence {πc} ∞ c=1 sum up to one, shall be constructed by the “Stick Breaking” process Sethuraman (1994). Suppose there is a stick with length 1. Let βc ∼ Beta (1, α) for c = 1, 2, 3, . , and regard them as fractions for how much we take away from the remainder of the stick every time. Then πc can be calculated by the length we take away each time.
Adopsi E-commerce pada UMKM selama Masa Krisis: dengan Peran Mediator Kapabilitas Dinamis

This study aims to investigate the effect of e-commerce adoption on cultural SMEs’ performance during the pandemic. While the e-commerce market is now highly developed in Indonesia, most global companies are focusing on bigger markets like Indonesia so every business in Indonesia is adapting their own business model to survive. Considering that this pandemic does not look at layers of society, the demand for agility to adapt does not only 'apply' to large-scale businesses but all sectors, including the SMEs. As the pandemic hits all types of organizations, this study also believes that SMEs’ dynamic capabilities would moderate the effect of e-commerce adoption on cultural SMEs’ performance. Furthermore, this study aims to examine the moderating effect of dynamic capabilities among cultural SMEs as an important dimension of business ability to deal with rapid environmental changes in the midst of a crisis. Quantitative data analysis was used to investigate research data. Questionnaires were distributed to 120 SMEs owners in the areas of Temanggung, Magelang, Yogyakarta and Solo in the cultural field. SPSS was used to analyze the relationship between variables. Result shows that the e-commerce adoption effect on business operations proved to affect cultural SMEs’ financial performance significantly. But, the influence of e-commerce adoption on marketing is not proven to significantly affect the financial performance of cultural SMEs. Meanwhile, dynamic capabilities construct was also proved to strengthen the effect of e-commerce adoption on business operations on cultural SMEs’ financial performance.
Mapping Averaged Pairwise Information (MAPI): a new exploratory tool to uncover spatial structure

Visualisation of spatial networks based on pairwise metrics such as (dis)similarity coefficients provides direct information on spatial organisation of biological systems. However, for large networks, graphical representations are often unreadable as nodes (samples), and edges (links between samples) strongly overlap. We present a new method, MAPI, allowing translation from spatial networks to variation surfaces. MAPI relies on (i) a spatial network in which samples are linked by ellipses and (ii) a grid of hexagonal cells encompassing the study area. Pairwise metric values are attributed to ellipses and averaged within the cells they intersect. The resulting surface of variation can be displayed as a colour map in Geographical Information System (GIS), along with other relevant layers, such as land cover. The method also allows the identification of significant discontinuities in grid cell values through a nonparametric randomisation procedure. The interest of MAPI is here demonstrated in the field of spatial and landscape genetics. Using simulated test data sets, as well as observed data from three biological models, we show that MAPI is (i) relatively insensitive to confounding effects resulting from isolation by distance (i.e. over‐structuring), (ii) efficient in detecting barriers when they are not too permeable to gene flow and, (iii) useful to explore relationships between spatial genetic patterns and landscape features. MAPI is freely provided as a PostgreSQL/PostGIS data base extension allowing easy interaction with GIS or the r software and other programming languages. Although developed for spatial and landscape genetics, the method can also be useful to visualise spatial organisation from other kinds of data from which pairwise metrics can be computed.
Holomorphic Mappings of the Hyperbolic Space into the Complex Euclidean Space and the Bloch Theorem

This paper is to study various properties of holomorphic mappings defined on the unit ball B in the complex euclidean space Cn with ranges in the space Cm. Furnishing B with the standard invariant Kähler metric and Cm with the ordinary euclidean metric, we define, for each holomorphic mapping f : B → Cm, a pair of non-negative continuous functions qf and Qf on B ; see § 2 for the definition. Let (Ω), Ω > 0, be the family of holomorphic mappings f : B → Cn such that Qf(z) ≦ Ω for all z ∈ B. (Ω) contains the family (M) of bounded holomorphic mappings as a proper subfamily for a suitable M > 0.
Bivariate Test for Testing the EQUALITY of the Average Areas under Correlated Receiver Operating Characteristic Curves (Test for Comparing of AUC’s of Correlated ROC Curves)

Methodology developed for comparing correlated ROC curves are mainly based on nonparametric methods. These nonparametric methods have several disadvantages. In this paper the authors propose an asymptotic bivariate test for comparing pairs of AUCs for independent data based on the Dorfman and Alf maximum likelihood approach. The properties of the test are examined by using simulation studies. The method is illustrated on an example of angiogram results from Sri Lanka. The test applied to the example found that there was a significant difference in the predictive power of three different cut-offs examined.
A Deep Learning Approach Towards Generating High-Fidelity Diverse Synthetic Battery Datasets

Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the number of vehicles powered by fossil fuels. Lithium-ion based batteries are presently dominating the electric automotive sector. Energy research efforts are also focussed on accurate computation of State-of-Charge of such batteries to provide reliable vehicle range estimates. Although such estimation algorithms provide precise estimates, all such techniques available in literature presume availability of superior quality battery datasets. In reality, gaining access to proprietary battery usage datasets is very tough for battery scientists. Moreover, open access datasets lack the diverse battery charge/discharge patterns needed to build generalized models. Curating battery measurement data is time consuming and needs expensive equipment. To surmount such limited data scenarios, we introduce few Deep Learning-based methods to synthesize high-fidelity battery datasets, these augmented synthetic datasets will help battery researchers build better estimation models in the presence of limited data. We have released the code and dataset used in the present approach to generate synthetic data. The battery data augmentation techniques introduced here will alleviate limited battery dataset challenges.
Pressure Oscillations Numerical Simulation in Solid Rocket Motors

Large solid rocket motors can be a ected by sustained pressure and thrust oscillations during the quasi-steady state. The root cause of this phenomenon is represented by the coupling between the vortices generation, shedding, advection and interaction with the SRM chamber geometry and the acoustic chamber modes excitement, which may act together, in a feedback coupled loop. In the present work, a Q1D model for the simulation of the pressure oscillations in solid rocket motors, named AGAR (Aerodynamically Generated Acoustic Resonance), is described, discussing a new formulation of the Q1D vorticity equation and of the closure terms for modeling the vortex sound generation. The results of the pressure oscillations simulation yielded with the new model formulation are discussed for the P80 solid rocket motor, rst stage of the European launcher VEGA.