Miracle-dz/UniFAR-FTUs huggingface dataset:
Controversial Outcomes in Neck Rehabilitation between Surgically and Conservatively Treated Patients—Results of an Observational Study

The present study aimed to compare changes during inpatient rehabilitation between conservatively and surgically treated patients. A total of n = 162 patients with cervical spine complaints were included in the study (n = 107 conservatively treated, n = 55 after surgery). Patients completed disease-specific (NDI) and generic (NPRS, EQ-5D-5L, HAQ) patient reported outcome measures (PROMs) before and after rehabilitation. In addition, the range of motion (ROM) in the transversal plane of the cervical spine was measured. Changes and correlations between PROMs and ROM values during rehabilitation were assessed. The influence of moderating factors on NDI outcomes was examined. Significant improvements with large effect sizes were found in PROMs and ROM (all p < 0.001). The conservatively treated patients showed significantly greater NDI improvements than operated patients (p = 0.050), but a greater proportion of poor performance in ROM (p = 0.035). Baseline NDI (β = 0.66), HAQ (β = 0.14), and ROM scores (β = −0.17) explained 63.7% of the variance in NDI after rehabilitation. Both patient groups showed different outcomes. The findings of this study indicate that the unique needs of patients may require different therapeutic interventions and highlight the importance of using multidimensional outcome measures when implementing a multimodal rehabilitation approach.
A Study on Smooth Polynomial Functions for SSVM

Deriving smooth approach to the plus function is the most important portion of the Smooth Support Vector Machine (SSVM), and its performance, efficiency, and correctness are dependent on the error of approach. Piecewise polynomial functions are used to approach the plus function in our research; in this paper, we describe an instance at first, and we describe how an optimization model was built to optimize smooth piecewise polynomial functions. Solving the optimization problem for more approachable functions is the primary focus of this paper. Theoretical proof describes the advantages of solutions that the optimization model will achieve.
PD-L1, CD4+, and CD8+ Tumor-Infiltrating Lymphocytes (TILs) Expression Profiles in Melanoma Tumor Microenvironment Cells

(1) Background: Because melanoma is an aggressive tumor with an unfavorable prognosis, we aimed to characterize the PD-L1 expression in melanomas in association with T cell infiltrates because PD-1/PD-L1 blockade represents the target in treating melanoma strategy. (2) Methods: The immunohistochemical manual quantitative methods of PD-L1, CD4, and CD8 TILs were performed in melanoma tumor microenvironment cells. (3) Results: Most of the PD-L1 positive, expressing tumors, have a moderate score of CD4+ TILs and CD8+TILs (5−50% of tumor area) in tumoral melanoma environment cells. The PD-L1 expression in TILs was correlated with different degrees of lymphocytic infiltration described by the Clark system (X2 = 8.383, p = 0.020). PD-L1 expression was observed often in melanoma cases, with more than 2−4 mm of Breslow tumor thickness being the associated parameters (X2 = 9.933, p = 0.014). (4) Conclusions: PD-L1 expression represents a predictive biomarker with very good accuracy for discriminating the presence or absence of malign tumoral melanoma cells. PD-L1 expression was an independent predictor of good prognosis in patients with melanomas.
Polymer mixing in shear-driven turbulence

We investigate numerically the influence of polymer mixing on shear-driven turbulence. Of particular interest is the suppression of mixing that accompanies drag reduction with dilute polymer solutions. The simulations use the finite extensible nonlinear elastic model with the Peterlin closure (FENE-P) to describe the polymer stresses in the momentum equation, with polymer concentration allowed to vary in space and time. A thin slab of concentrated polymer was placed in an initially Newtonian homogeneous turbulent shear flow on a plane perpendicular to the mean velocity gradient, and allowed to mix in the gradient direction while actively altering the turbulence. The initially higher concentration of polymer near the centreplane suppressed production of turbulent kinetic energy and Reynolds stress in that region, while turbulence outside the polymer-rich region remained shear-dominated Newtonian turbulence. The rate of mixing in the shear direction was severely damped by the action of the polymer compared to a passive scalar in the corresponding Newtonian turbulent shear flow. This, in part, was a result of the same damping of vertical velocity fluctuations by the polymer that leads to the suppression of momentum flux. However, the cross-correlation between the polymer concentration and vertical velocity fluctuations was also suppressed, indicating that the explanation for the reduction in polymer mixing involves both the suppression of vertical velocity fluctuations and an alteration of turbulence structure by the polymer–turbulence interactions.
Deep Reinforcement Learning for Adaptive Parameter Control in Differential Evolution for Multi-Objective Optimization

Evolutionary algorithms (EA) are efficient population-based stochastic algorithms for solving optimization problems. The performance of EAs largely depends on the configuration of values of parameters that control their search. Previous works studied how to configure EAs, though, there is a lack of a general approach to effectively tune EAs. To fill this gap, this paper presents a consistent, automated approach for tuning and controlling parameterized search of an EA. For this, we propose a deep reinforcement learning (DRL) based approach called ‘DRL-APC-DE’ for online controlling search parameter values for a multi-objective Differential Evolution algorithm. The proposed method is trained and evaluated on widely adopted multi-objective test problems. The experimental results show that the proposed approach performs competitively to a non-adaptive Differential Evolution algorithm, tuned by grid search on the same range of possible parameter values. Subsequently, the trained algorithms have been applied to unseen multi-objective problems for the adaptive control of parameters. Results show the successful ability of DRL-APC-DE to control parameters for solving these problems, which has the potential to significantly reduce the dependency on parameter tuning for the successful application of EAs.
Polarization from scattering in blobs

We describe a Monte Carlo method for calculating polarization due to scattering in various geometries. We apply the method in this paper to the case of scattering in a spherical cloud illuminated by parallel rays. The polarization is largest in small optical depth blobs, where single scattering dominates. The polarization decreases as optical depth increases until a limit is reached where scattering occurs on the surface, and increasing optical depth has little effect on the results. For a given optical depth, decreasing the albedo leads to an increase of the degree of polarization of the scattered light because single scattering increases relative to multiple scattering. As the optical depth of the blob increases, the scattered flux becomes increasingly backward throwing. In the high albedo blobs, the maximum polarization becomes skewed towards the illuminated hemisphere at high optical depths. We discuss the applications to polarization in supergiants and R CrB stars, and scattering in a clumpy interstellar medium.
Postharvest Application of Potato Starch Edible Coatings with Sodium Benzoate to Reduce Sour Rot and Preserve Mandarin Fruit Quality

Starch is a biodegradable polymeric carbohydrate that can easily form films and coatings and can readily be obtained from some food industry by-products and wastes, which may contribute to the circular bioeconomy. In this work, we studied the potential of two edible coating emulsions based on pregelatinized potato starch (PPS) and glyceryl monostearate (GMS) alone (F6 and F10) or formulated with the food additive sodium benzoate (SB, 2%) (F6/SB and F10/SB) to control sour rot, an important citrus postharvest disease caused by the fungus Geotrichum citri-aurantii, and maintain postharvest quality of cold-stored ‘Orri’ mandarins. The PPS-GMS coating application was compared to dipping in water (uncoated controls) and dipping in a 2% SB (w/v) aqueous solution. The results showed that the coating F10/SB was the most promising treatment to control sour rot on mandarins, with reductions in disease incidence with respect to the uncoated control samples of 94, 69, and 55% after 2, 4, and 6 weeks of storage at 5 °C, respectively. Coatings formulated without SB were ineffective. Regarding fruit quality, the coating F10 was the most effective to reduce weight loss, maintain firmness, and provide gloss on mandarins stored at 5 °C for up to 6 weeks followed by a shelf-life period of 1 week at 20 °C. The addition of SB to the PPS-GMS coatings adversely affected these coating properties, but the coating F10/SB still reduced weight loss compared to uncoated controls without negatively affecting the fruit physicochemical (juice titratable acidity, soluble solids content, and volatiles content) and sensory quality (overall flavor, off-flavors, external aspect). Overall, the coating F10/SB showed the greatest potential for commercial use as an efficient and environmentally friendly alternative to conventional fungicides and waxes for sour rot control and quality preservation of cold-stored mandarins.
Transparent Models for Stock Market Price Forecasting

To aid in the prediction of stock prices, forecasting algorithms can be beneficial. Black box modelling techniques can be, and have been, applied to the prediction of stock prices, but do not offer any clarity on the parameters that have impact on the model predictions. Hence, the focus of this work is to utilise a transparent model to enable the understanding and interpretation of the model output. Stock market data between 2009 and 2019 (a 10-year period) were analysed to determine if a Nonlinear AutoRegressive Moving Average model with eXogenous input (NARMAX) model could accurately predict day ahead stock prices. A NARMAX model was initially developed using a single input variable (Open) and then extended using multiple input variables which included Open, High and Low stock prices. Obtained results revealed that the NARMAX model has strong potential for day ahead price prediction and can be compared against existing techniques for stock market price prediction. Performance evaluation, demonstrated across multiple stock market datasets, demonstrate that NARMAX is efficient in predicting stock market closing price.
Effects of Posttraumatic Stress Disorder and Mental Disorders on the Labor Market Integration of Young Syrian Refugees

Civil war experience in the Syrian home country, insecurity and critical life events during migration, or adverse events in the receiving country might affect refugees’ mental health. This paper addresses the effects of psychological distress and mental disorders on refugees’ labor market integration in Germany between 2016 and 2021. We employ survey data from about 2700 young Syrians, delivering information on individuals’ experience of migration and arrival in Germany in 2016. The survey data were successfully merged with register data, delivering detailed information regarding individuals’ process of labor market integration and employment status from 2016 to 2021. Overall, the labor market integration of young refugees improved remarkably over time. In 2021, about 69% of the study population was integrated in a wider sense, and 30% was employed in fulltime contracts in 2021. However, the results indicate long-lasting effects of PTSD and mental disorders on individuals’ labor market integration, whilst individuals’ characteristics related to migration and arrival lose relevance over time and hardly affect labor market integration around five years after arrival. High PTSD scores in 2016 indicate a significantly reduced full-time employment probability in 2021. Anxiety and depression show significant negative effects on individuals’ labor market integration, but with a less severe impact compared to a PTSD diagnosis.
Effects of the cold tongue in the South China Sea on the monsoon, diurnal cycle and rainfall in the Maritime Continent

We investigate the effects of the cold tongue in the South China Sea (SCS) on the winter monsoon, rainfall and diurnal cycle in the maritime continent using a numerical model verified with satellite rainfall and reanalysis data. Composite analysis of the observation and reanalysis data based on cold tongue index indicates that the penetration of the monsoon to Java Sea is enhanced when the cold tongue is strong. A sensitivity experiment without the cold tongue shows that the winter monsoon is diminished over SCS and around coastal regions because of anomalous low‐level cyclonic circulation associated with enhanced convection over SCS due to the warmer sea surface temperature (SST). The diurnal cycle, in particular the night–morning rainfall, over the sea in coastal regions is modified. The effect on daytime rainfall over the land is relatively weaker. Along the northern coast of Java far from the SCS, the night–morning rainfall is much reduced over the Java Sea when the cold tongue is suppressed because of the weakened land‐breeze front as a result of the weakened northerly monsoon. In contrast, the afternoon–evening rainfall on Java Island is slightly enhanced, showing that the local impacts are not simply the result of large‐scale subsidence from the convective anomaly in the SCS. Along the northwestern coast of Borneo adjacent to the SCS, the weakened winter monsoon tends to reduce the rainfall at the land‐breeze front near the coastline. On the other hand, the warmer SST forces a stronger land breeze and the weakened monsoon encourages further and faster offshore propagation of the land‐breeze front. Consequently, the rainfall peak shifts further offshore in the sensitivity experiment. We conclude that the cold tongue has two effects, the sustenance of a strong monsoon (indirect effect) and the cooling of local SST (direct effect), which have opposite influences on the diurnal cycle in the Maritime Continent.
Challenges and the Way forward in Diagnosis and Treatment of Tuberculosis Infection

Globally, it is estimated that one-quarter of the world’s population is latently infected with Mycobacterium tuberculosis (Mtb), also known as latent tuberculosis infection (LTBI). Recently, this condition has been referred to as tuberculosis infection (TBI), considering the dynamic spectrum of the infection, as 5–10% of the latently infected population will develop active TB (ATB). The chances of TBI development increase due to close contact with index TB patients. The emergence of multidrug-resistant TB (MDR-TB) and the risk of development of latent MDR-TB has further complicated the situation. Detection of TBI is challenging as the infected individual does not present symptoms. Currently, there is no gold standard for TBI diagnosis, and the only screening tests are tuberculin skin test (TST) and interferon gamma release assays (IGRAs). However, these tests have several limitations, including the inability to differentiate between ATB and TBI, false-positive results in BCG-vaccinated individuals (only for TST), false-negative results in children, elderly, and immunocompromised patients, and the inability to predict the progression to ATB, among others. Thus, new host markers and Mtb-specific antigens are being tested to develop new diagnostic methods. Besides screening, TBI therapy is a key intervention for TB control. However, the long-course treatment and associated side effects result in non-adherence to the treatment. Additionally, the latent MDR strains are not susceptible to the current TBI treatments, which add an additional challenge. This review discusses the current situation of TBI, as well as the challenges and efforts involved in its control.
A New Developmental Approach for Judo Focusing on Health, Physical, Motor, and Educational Attributes

Judo is currently the most practiced combat sport in the world. There is no doubt of its importance for children, adolescents, adults, and seniors. However, due to its inherent complexity, coaches tend to place greater or lesser emphasis only on one of its multiple domains during the judoka formative years. The present study presents a novel developmental approach signaling Judo as an excellent way for physical, motor, educational, and health development across the lifespan. In this study, we consulted 23 Judo coaches, aiming to clarify the relevance and adequacy of the domains (Competitive, Health, Technical-Tactical, and Philosophical-Educational) and phases of the proposed model. Our findings showed that the model contents—physical, motor, educational, and health—are entwined with its four domains, which were considered of utmost importance by experts in the development of judokas. We, therefore, contend that our model is essential to better understand the growth and development of young judokas. Also, it may be a useful tool for Motor Development experts.
Edge cooling experiments and non-local transport phenomena in Tore Supra

Edge cooling experiments have been performed in Tore Supra by impurity injection and oblique pellet injection. Non-local transport (NLT) features have been confirmed in both cases in the ohmic regime. The pellet injection experiments have shown the existence of a threshold on the plasma density ne and current Ip for the observation of this effect: ne/Ip1/2? 1.0? 1016? (m-3? A-1/2). A second threshold on the relative density variation caused by the pellet injection has also been observed: ? ne/ne? 0.5. It has been observed that in the L regime during lower hybrid current drive (LHCD) experiments these limits are extended: [ne/Ip1/2]crit = 1.4? 1016? (m-3? A-1/2) and [? ne/ne]crit? 0.65. Observations in a LHCD scheme with fully non-inductive plasma current seem to indicate that the mechanism governing the NLT is unlikely to be linked to the plasma current profile effects including the spatial redistribution, the magnetic shear, and the low-m MHD modes.
Cardiac Arrhythmias in Post-COVID Syndrome: Prevalence, Pathology, Diagnosis, and Treatment

An increase in post-COVID patients with late sequelae of acute COVID-19 infection is emerging as an ongoing challenge for physicians and healthcare professionals. Since the beginning of the pandemic, it has rapidly become evident that the acute infection is not limited to the respiratory tract but that several organs, including the cardiovascular system, can be affected. Moreover, in a significant proportion of patients (ranging from about 10 to up to 50%) with former COVID-19, cardiopulmonary symptoms such as dyspnea, palpitations, restricted physical capacity, and cardiac arrhythmias can persist weeks and months after the acute SARS-CoV-2 infection. The spectrum of COVID-19-associated arrhythmias is rather wide, most likely due to various pathomechanisms. In this article, the prevalence of cardiac arrhythmias and underlying pathologies are reviewed, including direct myocardial injury and abnormal consequences with an impact on cardiac electric instability. The hyperinflammatory reaction of the host immune system is specifically considered. Moreover, several distinct rhythm disorders occurring in post-COVID patients are discussed with regard to their clinical management.
Optimization of Antenna Performance based on VAE-BPNN-PCA

In this paper, a machine learning method combining variational auto-encoder (VAE) and back propagation network (BPNN) is proposed to predict the performance of a double-T monopole antenna, which is the return loss $(S_{11})$ in frequency 1.5-6GHz. Then the trained machine learning model can take the place of the traditional electromagnetic simulation software CST, to predict $S_{11}$ in a few seconds. Genetic algorithm (GA) is used to optimize $S_{11}$ in frequency band 2.4-3.0GHz and 5.15-5.6GHz. This method shows an accurate and efficient prediction performance for that the average prediction errors of training and testing samples for all frequency points are 1.08% and 1.07%, respectively. The optimized antenna performance based on VAE-BPNN-GA is almost in accordance with that acquired by CST, that is, the average prediction error for all frequency points is 1.20%, while occupying less computation time. VAE-BPNN-GA is also compared with PCA-BPNN-GA, and proved to have higher prediction accuracy.
State of the Art in 2022 PET/CT in Breast Cancer: A Review

Molecular imaging with positron emission tomography is a powerful and well-established tool in breast cancer management. In this review, we aim to address the current place of the main PET radiopharmaceuticals in breast cancer care and offer perspectives on potential future radiopharmaceutical and technological advancements. A special focus is given to the following: the role of 18F-fluorodeoxyglucose positron emission tomography in the clinical management of breast cancer patients, especially during staging; detection of recurrence and evaluation of treatment response; the role of 16α-18Ffluoro-17β-oestradiol positron emission tomography in oestrogen receptors positive breast cancer; the promising radiopharmaceuticals, such as 89Zr-trastuzumab and 68Ga- or 18F-labeled fibroblast activation protein inhibitor; and the application of artificial intelligence.
gp130 Activates Mitochondrial Dynamics for Hepatocyte Survival in a Model of Steatohepatitis

Obesity is the main cause of metabolic complications. Fatty liver infiltration is a companion of obesity. NAFLD is associated with impaired energy metabolism with an excess of nutrients. Mitochondrial dynamics are important for the regulation of energy balance, which regulates mitochondrial function, apoptosis, and mitophagy. The aim of this study was to investigate the effect of gp130 on the components of mitochondrial dynamics in a cellular model of steatohepatitis. Addition of IL-6/gp130 contributed to an increase in the percentage of live cells and a decrease in the percentage of dead and apoptotic cells. Addition of IL-6/gp130 increased the expression of NF-kB1 gene and mitochondrial dynamics markers (MFN2 and TFAM) in HepG2 with tBHP/Oleic. Addition of IL-6 or gp130 reduced the expression of cytoprotector genes (HSF1 and HSP70) in HepG2 cell cultures with tBHP/Oleic. Increased mitochondrial dynamics gene activity protected against HepG2 cell death in the steatohepatitis model. Trans-signaling resulted in increased TFAM and MAPLC3B, and decreased DNM1L gene expression in HepG2 with tBHP/Oleic.