Miracle-dz/UniFAR-FTUs huggingface dataset:
EMITTING ELECTRONS AND SOURCE ACTIVITY IN MARKARIAN 501

We study the variation of the broadband spectral energy distribution (SED) of the BL Lac object Mrk 501 as a function of source activity, from quiescent to flaring. Through χ2-minimization we model eight simultaneous SED data sets with a one-zone synchrotron self-Compton (SSC) model, and examine how model parameters vary with source activity. The emerging variability pattern of Mrk 501 is complex, with the Compton component arising from γ–e scatterings that sometimes are (mostly) Thomson and sometimes (mostly) extreme Klein–Nishina. This can be seen from the variation of the Compton to synchrotron peak distance according to source state. The underlying electron spectra are faint/soft in quiescent states and bright/hard in flaring states. A comparison with Mrk 421 suggests that the typical values of the SSC parameters are different in the two sources: however, in both jets the energy density is particle-dominated in all states.
WHAT DETERMINES THE SIZES OF RED EARLY-TYPE GALAXIES?

The sizes of galaxies are known to be closely related with their masses, luminosities, redshifts, and morphologies. However, when we fix these quantities and morphology, we still find large dispersions in the galaxy size distribution. We investigate the origin of these dispersions for red early-type galaxies using two SDSS-based catalogs. We find that the sizes of faint galaxies (log (M dyn/M☉) ≲ 10.3 or 0.1Mr ≳ −19.5, where 0.1Mr is the r-band absolute magnitude, k-corrected to z = 0.1) are affected more significantly by luminosity, while the sizes of bright galaxies (log (M dyn/M☉) ≳ 11.4 or 0.1Mr ≲ −21.4) are by dynamical mass. At fixed mass and luminosity, the sizes of low-mass galaxies (log (M dyn/M☉) ∼ 10.45 and 0.1Mr ∼ −19.8) are relatively less sensitive to their colors, color gradients, and axis ratios. On the other hand, the sizes of intermediate-mass (log (M dyn/M☉) ∼ 10.85 and 0.1Mr ∼ −20.4) and high-mass (log (M dyn/M☉) ∼ 11.25 and 0.1Mr ∼ −21.0) galaxies significantly depend on those parameters, in the sense that larger red early-type galaxies have bluer colors, more negative color gradients (bluer outskirts), and smaller axis ratios. These results indicate that the sizes of intermediate- and high-mass red early-type galaxies are significantly affected by their recent minor mergers or rotations, whereas the sizes of low-mass red early-type galaxies are affected by some other mechanisms. Major dry mergers also seem to have influenced on the size growth of high-mass red early-type galaxies.
Finite-frequency counting statistics of electron transport: Markovian theory

We present a theory of frequency-dependent counting statistics of electron transport through nanostructures within the framework of Markovian quantum master equations. Our method allows the calculation of finite-frequency current cumulants of arbitrary order, as we explicitly show for the second- and third-order cumulants. Our formulae generalize previous zero-frequency expressions in the literature and can be viewed as an extension of MacDonald's formula beyond shot noise. When combined with an appropriate treatment of tunneling using, e.g., the Liouvillian perturbation theory in Laplace space, our method can deal with arbitrary bias voltages and frequencies, as we illustrate with the paradigmatic example of transport through a single resonant level model. We discuss various interesting limits, including the recovery of the fluctuation-dissipation theorem near linear response, as well as some drawbacks inherent to the Markovian description arising from the neglect of quantum fluctuations.
RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors

Cell surface receptors play essential roles in perceiving and processing external and internal signals at the cell surface of plants and animals. The receptor-like protein kinases (RLK) and receptor-like proteins (RLPs), two major classes of proteins with membrane receptor configuration, play a crucial role in plant development and disease defense. Although RLPs and RLKs share a similar single-pass transmembrane configuration, RLPs harbor short divergent C-terminal regions instead of the conserved kinase domain of RLKs. This RLP receptor structural design precludes sequence comparison algorithms from being used for high-throughput predictions of the RLP family in plant genomes, as has been extensively performed for RLK superfamily predictions. Here, we developed the RLPredictiOme, implemented with machine learning models in combination with Bayesian inference, capable of predicting RLP subfamilies in plant genomes. The ML models were simultaneously trained using six types of features, along with three stages to distinguish RLPs from non-RLPs (NRLPs), RLPs from RLKs, and classify new subfamilies of RLPs in plants. The ML models achieved high accuracy, precision, sensitivity, and specificity for predicting RLPs with relatively high probability ranging from 0.79 to 0.99. The prediction of the method was assessed with three datasets, two of which contained leucine-rich repeats (LRR)-RLPs from Arabidopsis and rice, and the last one consisted of the complete set of previously described Arabidopsis RLPs. In these validation tests, more than 90% of known RLPs were correctly predicted via RLPredictiOme. In addition to predicting previously characterized RLPs, RLPredictiOme uncovered new RLP subfamilies in the Arabidopsis genome. These include probable lipid transfer (PLT)-RLP, plastocyanin-like-RLP, ring finger-RLP, glycosyl-hydrolase-RLP, and glycerophosphoryldiester phosphodiesterase (GDPD, GDPDL)-RLP subfamilies, yet to be characterized. Compared to the only Arabidopsis GDPDL-RLK, molecular evolution studies confirmed that the ectodomain of GDPDL-RLPs might have undergone a purifying selection with a predominance of synonymous substitutions. Expression analyses revealed that predicted GDPGL-RLPs display a basal expression level and respond to developmental and biotic signals. The results of these biological assays indicate that these subfamily members have maintained functional domains during evolution and may play relevant roles in development and plant defense. Therefore, RLPredictiOme provides a framework for genome-wide surveys of the RLP superfamily as a foundation to rationalize functional studies of surface receptors and their relationships with different biological processes.
The effects of berberine supplementation on cardiovascular risk factors in adults: A systematic review and dose-response meta-analysis

Cardiovascular disease (CVD) is a major concern today. Herbal medicine is one helping way to control CVD risks. One conclusive of herbal medicine is Berberine (BBR) and converse about it still exists, to clarify this issue, this meta-analysis was performed. PubMed/Medline, Scopus, and Web of Science were searched for RCTs in adults on the effect of BBR supplementation on CVD risk factors up to July 2022. The pooled results showed BBR significantly reduced triglyceride (WMD = −23.70 mg/dl; 95%CI −30.16, −17.25; P < 0.001), total cholesterol (WMD = −20.64 mg/dl; 95%CI −23.65, −17.63; P < 0.001), low-density lipoprotein WMD = −9.63 mg/dl; 95%CI, −13.87, −5.39; P < 0.001), fasting blood glucose (FBG) (WMD = −7.74 mg/dl; 95%CI −10.79, −4.70; P < 0.001), insulin (WMD = −3.27 mg/dl; 95%CI −4.46,−2.07; P < 0.001), HbA1c (WMD = −0.45%; 95%CI −0.68, −0.23; P < 0.001), HOMA-IR (WMD = −1.04; 95%CI −1.55, −0.52; P < 0.001), systolic blood pressure (WMD = −5.46 mmHg; 95%CI −8.17, −2.76; P < 0.001), weight (WMD = −0.84; 95%CI −1.34,−0.34; P < 0.001), body mass index (WMD = −0.25 kg/m2; 95%CI −0.46, −0.04; P = 0.020), while increased high-density lipoprotein (HDL) (WMD = 1.37 mg/dl; 95%CI 0.41,2.23; P = 0.005). The optimal dose of BBR was 1 g/day for TG, TC, and weight, 1.8 g/day for insulin and HOMA-IR, and 5 g/day for HDL. FBG's most efficient time frame was 40 weeks from the beginning of supplementation, whereas DBP and waist circumference was 50 weeks. In conclusion, the lipid profile, FBG balance, obesity parameters, and SBP were improved with BBR supplementation. Systematic review registration CRD42022347004.
A long way to liberalization, or is it? Public perceptions of women empowerment in the Middle East and North Africa

This paper explores the question of what explains public opinion of women empowerment in the Middle East and North Africa. Muslim societies have often been accused of conservatism toward empowerment, stripping women of equal access to education and opportunities. However, many predominantly Muslim societies in the MENA region seem to be on the way to implement change to provide women with more rights. Prior research points to exposure to diversity as a contributor to the acceptance of a more egalitarian role of women in society. This article analyzes different mechanisms of the exposure hypothesis and whether they contribute to predicting positive public perceptions of women empowerment in the region. The empirical analyses rely on public opinion data collected by the Arab Barometer in 2018–19. The descriptive findings suggest attitudinal differences across countries, but also significant gender gaps and divergences across core explanatory factors found under the umbrella of the exposure hypothesis, such as diverse urban living, keeping religion a private matter, and connecting with the world via social media. These factors seem important to shift people's minds and to pave women's long way to liberalization.
A laboratory study of the effect of varying beach slopes on bore-driven swash hydrodynamics

Swash zone hydrodynamics has significant coastal geomorphological and engineering implications. However, there is still a research gap in fully understanding the response of bore-driven swash hydrodynamics to varying beach slopes. Therefore, in this study, laboratory experiments were performed in a flume to investigate the hydrodynamics of bore-driven swash flows over impermeable smooth beaches with a mild slope (1:35), a steep slope (1:10), and a composite slope (1:35–1:10), respectively. The designed swash events are produced by a collapse of dam-break-generated bores. Wave gauges, ultrasonic displacement sensors, acoustic Doppler velocimeter, and particle image velocimetry are used simultaneously to capture different phases (bore collapse, uprush, and backwash) of bore evolution in the entire swash zone. The impacts of beach slope on the swash hydrodynamics in view of the shoreline movement, swash depth, and swash velocity are first analyzed. The formation and evolution of the vortex structure on the three beaches are also reported in this study.
Psychopathic tendency in violent offenders is associated with reduced aversive Pavlovian inhibition of behavior and associated striatal BOLD signal

Background Violent offenders with psychopathic tendencies are characterized by instrumental, i.e., planned, callous, and unemotional (aggressive) behavior and have been shown to exhibit abnormal aversive processing. However, the consequences of abnormal aversive processing for instrumental action and associated neural mechanisms are unclear. Materials and methods Here we address this issue by using event-related functional magnetic resonance imaging (fMRI) in 15 violent offenders with high psychopathic tendencies and 18 matched controls during the performance of an aversive Pavlovian-to-instrumental transfer paradigm. This paradigm allowed us to assess the degree to which aversive Pavlovian cues affect instrumental action and associated neural signaling. Results Psychopathic tendency scores were associated with an attenuation of aversive Pavlovian inhibition of instrumental action. Moreover, exploratory analyses revealed an anomalous positive association between aversive inhibition of action and aversive inhibition of BOLD signal in the caudate nucleus of violent offenders with psychopathic tendencies. In addition, psychopathic tendency also correlated positively with amygdala reactivity during aversive versus neutral cues in Pavlovian training. Conclusion These findings strengthen the hypothesis that psychopathic tendencies in violent offenders are related to abnormal impact of aversive processing on instrumental behavior. The neural effects raise the possibility that this reflects deficient transfer of aversive Pavlovian inhibitory biases onto neural systems that implement instrumental action, including the caudate nucleus.
A unified framework for analyzing complex systems: Juxtaposing the (Kernel) PCA method and graph theory

In this article, we present a unified framework for the analysis and characterization of a complex system and demonstrate its application in two diverse fields: neuroscience and astrophysics. The framework brings together techniques from graph theory, applied mathematics, and dimensionality reduction through principal component analysis (PCA), separating linear PCA and its extensions. The implementation of the framework maps an abstract multidimensional set of data into reduced representations, which enable the extraction of its most important properties (features) characterizing its complexity. These reduced representations can be sign-posted by known examples to provide meaningful descriptions of the results that can spur explanations of phenomena and support or negate proposed mechanisms in each application. In this work, we focus on the clustering aspects, highlighting relatively fixed stable properties of the system under study. We include examples where clustering leads to semantic maps and representations of dynamic processes within the same display. Although the framework is composed of existing theories and methods, its usefulness is exactly that it brings together seemingly different approaches, into a common framework, revealing their differences/commonalities, advantages/disadvantages, and suitability for a given application. The framework provides a number of different computational paths and techniques to choose from, based on the dimension reduction method to apply, the clustering approaches to be used, as well as the representations (embeddings) of the data in the reduced space. Although here it is applied to just two scientific domains, neuroscience and astrophysics, it can potentially be applied in several other branches of sciences, since it is not based on any specific domain knowledge.
Side-to-side differences in knee laxity and side hop test may predispose an anterior cruciate ligament reinjury in competitive adolescent alpine skiers

An anterior cruciate ligament (ACL) injury is a common, severe injury in alpine skiing, and anterior cruciate ligament reconstruction (ACLR) is frequently performed in competitive alpine skiers younger than 20 years old. To reduce the reinjury rate, both intrinsic and extrinsic risk factors should be examined. The aim of this study was to investigate possible intrinsic risk factors for an ACL reinjury in competitive alpine skiers. A cohort of 384 alpine skiers (191 males/193 females) from the Swedish ski high schools were prospectively followed during their high school years. The students were clinically examined and physically tested prior to each ski season. In addition, the RAND 36-Item health survey 1.0 (SF-36, Copyright 1994 Medical Outcome Trust, distributed by RAND Corporation) and injuries were prospectively registered. Thirty-one of the skiers (five males/26 females) had undergone an ACLR before entering the ski high school. This cohort was analyzed with respect to the occurrence of, and possible risk factors for an ACL reinjury (including ipsilateral and contralateral ACL injuries). Skiers who sustained an ACL reinjury were called the “ACL reinjury group,” and those who did not sustain an ACL reinjury were called the “ACL injury group. ” Notably, 12 of the 31 students (39%), ten female and two male skiers, aged 16.5 (SD 0.5) years, sustained an ACL reinjury during the two first years at the ski high school. In addition, 10 of the 12 ACL reinjuries occurred within 10–23 months from the first injury [m 14.8 (SD4.7)] and two ACL reinjuries occurred at 29 and 47 months, respectively, from the first injury. It is noted that eight of the ACL reinjuries were to the ipsilateral knee and four to the contralateral knee. There were no differences between the groups with respect to muscle flexibility in the lower extremity, Beighton score, and one leg hop for distance or square hop test. Side-to-side differences were found with respect to knee joint laxity, >3 mm, measured with KT-1000 arthrometer (p = 0.02), and the side hop test (p = 0.04). RAND 36-Item health survey did not predict an ACL reinjury. In conclusion, a side-to-side difference in the side hop test and knee joint laxity (KT-1000) may predispose an ACL reinjury in competitive adolescent alpine skiers.
Associations of body image with depressive symptoms and PTG among breast cancer patients: The mediating role of social support

Background Cancer diagnosis and treatment usually trigger positive and negative psychological health outcomes. Social support is a coping resource for psychological health outcomes. However, little research is available on the relationships between social support, body image, and overall psychological health outcomes in breast cancer (BC) patients. This study aimed to estimate the prevalence of depressive symptoms and post-traumatic growth (PTG) and examine the mediating roles of social support between body image and depressive symptoms and PTG among BC patients, respectively. Methods A cross-sectional study was conducted in the Northeast China from December 2015 to August 2017. All the participants were diagnosed with BC and underwent surgery. This study was conducted with 405 BC patients from the First Affiliated Hospital of China Medical University. Participants completed the Center for Epidemiologic Studies Depression scale, Post Traumatic Growth Inventory, Body Image Scale, and Perceived Social Support Scale. The associations of social support, body image with depressive symptoms, and PTG were examined by hierarchical linear regression analysis. Asymptotic and resampling strategies were used to explore the mediating role of social support. Results The prevalence of depressive symptoms was 88.1%, and 67.2% of the patients had moderate-high PTG, 52.84% of the patients had body concerns, and 264 (65.19%) patients had high-level social support. Body image was positively associated with depressive symptoms (β = 0.445, P < 0.001) and social support was negatively associated with depressive symptoms (β = −0.219, P < 0.001). Body image was negatively associated with PTG (β = −0.095, P = 0.023), whereas social support was positively associated with PTG (β = 0.533, P < 0.001). Social support significantly mediated the associations among body image, depressive symptoms (effect size = 0.057), and PTG (effect size = −0.304), respectively. Conclusions Social support played mediating role in the relationships between body image and depressive symptoms and PTG. The interventions based on social support and body image should be included in psychological health prevention.
A nomogram for predicting atrial fibrillation detected after acute ischemic stroke

Background Atrial fibrillation detected after stroke (AFDAS) is associated with an increased risk of ischemic stroke (IS) recurrence and death. Early diagnosis can help identify strategies for secondary prevention and improve prognosis. However, there are no validated predictive tools to assess the population at risk for AFDAS. Therefore, this study aimed to develop and validate a predictive model for assessing the incidence of AFDAS after acute ischemic stroke (AIS). Methods This study was a multicenter retrospective study. We collected clinical data from 5332 patients with AIS at two hospitals between 2014.01 and 2021.12 and divided the development and validation of clinical prediction models into a training cohort (n = 3173) and a validation cohort (n = 2159). Characteristic variables were selected from the training cohort using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariable logistic regression analysis. A nomogram model was developed, and its performance was evaluated regarding calibration, discrimination, and clinical utility. Results We found the best subset of risk factors based on clinical characteristics and laboratory variables, including age, congestive heart failure (CHF), previous AIS/transient ischemia attack (TIA), national institutes of health stroke scale (NIHSS) score, C-reactive protein (CRP), and B-type natriuretic peptide (BNP). A predictive model was developed. The model showed good calibration and discrimination, with calibration values of Hosmer-Lemeshow χ2 = 4.813, P = 0.732 and Hosmer-Lemeshow χ2 = 4.248, P = 0.834 in the training and validation cohorts, respectively. The area under the ROC curve (AUC) was 0.815, 95% CI (0.777–0.853) and 0.808, 95% CI (0.770–0.847). The inclusion of neuroimaging variables significantly improved the performance of the integrated model in both the training cohort (AUC. 0.846 (0.811–0.882) vs. 0.815 (0.777–0.853), P = 0.001) and the validation cohort (AUC: 0.841 (0.804–0.877) vs. 0.808 (0.770–0.847), P = 0.001). The decision curves showed that the integrated model added more net benefit in predicting the incidence of AFDAS. Conclusion Predictive models based on clinical characteristics, laboratory variables, and neuroimaging variables showed good calibration and high net clinical benefit, informing clinical decision-making in diagnosing and treating patients with AFDAS.
Morphologic identification of clinically encountered moulds using a residual neural network

The use of morphology to diagnose invasive mould infections in China still faces substantial challenges, which often leads to delayed diagnosis or misdiagnosis. We developed a model called XMVision Fungus AI to identify mould infections by training, testing, and evaluating a ResNet-50 model. Our research achieved the rapid identification of nine common clinical moulds: Aspergillus fumigatus complex, Aspergillus flavus complex, Aspergillus niger complex, Aspergillus terreus complex, Aspergillus nidulans, Aspergillus sydowii/Aspergillus versicolor, Syncephalastrum racemosum, Fusarium spp. , and Penicillium spp. In our study, the adaptive image contrast enhancement enabling XMVision Fungus AI as a promising module by effectively improve the identification performance. The overall identification accuracy of XMVision Fungus AI was up to 93.00% (279/300), which was higher than that of human readers. XMVision Fungus AI shows intrinsic advantages in the identification of clinical moulds and can be applied to improve human identification efficiency through training. Moreover, it has great potential for clinical application because of its convenient operation and lower cost. This system will be suitable for primary hospitals in China and developing countries.