Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-alcoholic fatty liver and fibrosis is associated with cardiovascular structure and function in young adults

Background: Non-alcoholic fatty liver disease shares many risk factors with other metabolic disorders. We sought to establish whether non-alcoholic fatty liver disease may be associated with cardiovascular health independently of other known risk factors. Methods: In this prospective, population-based cohort of young adults, controlled attenuation parameter-defined liver steatosis, transient elastography-defined liver fibrosis, echocardiography, carotid ultrasonography, and pulse wave analysis were assessed at age 24 years. We examined associations between liver and cardiovascular measures, with and without accounting for demographics, body mass index, alcohol, smoking, blood pressure, lipidemia, glycemia, and inflammation. Results: We included 2047 participants (mean age 24.4 y; 36.2% female): 212 (10.4%) had steatosis, whereas 38 (1.9%) had fibrosis. Steatosis was associated with cardiovascular measures after adjusting for demographics, but with more comprehensive adjustment, steatosis only remained associated with stroke index [β (95% CI) of −1.85 (−3.29, −0.41) mL/m2] and heart rate [2.17 (0.58, 3.75) beats/min]. Fibrosis was associated with several measures of cardiovascular structure and function after full adjustment for risk factors, including left ventricular mass index [2.46 (0.56, 4.37) g/m2.7], E/A ratio [0.32 (0.13, 0.50)], tricuspid annular plane systolic excursion [0.14 (0.01, 0.26) cm], carotid intima-media thickness [0.024 (0.008, 0.040) mm], pulse wave velocity [0.40 (0.06, 0.75) m/s], cardiac index [−0.23 (−0.41, −0.06) L/min⋅m2], and heart rate [−7.23 (−10.16, −4.29) beats/min]. Conclusions: Steatosis was not associated with measures of cardiovascular structure and function nor with subclinical atherosclerosis after adjusting for known cardiovascular risk factors. Fibrosis, however, was associated with several cardiovascular measures, including indicators of subclinical atherosclerosis, even after full adjustment. Further follow-up will help determine whether cardiovascular health worsens later with steatosis alone.
Analgesia after cesarean section – what is new?

Purpose of review Cesarean section is the most frequent surgical intervention, and pain following cesarean delivery unfortunately remains a common issue. The purpose of this article is to highlight the most effective and efficient options for postcesarean analgesia and to summarize current guidelines. Recent findings The most effective form of postoperative analgesia is through neuraxial morphine. With adequate dosing, clinically relevant respiratory depression is extremely rare. It is important to identify women with increased risk of respiratory depression, as they might require more intensive postoperative monitoring. If neuraxial morphine cannot be used, abdominal wall block or surgical wound infiltration are very valuable alternatives. A multimodal regimen with intraoperative intravenous dexamethasone, fixed doses of paracetamol/acetaminophen, and nonsteroidal anti-inflammatory drugs reduce postcesarean opioid use. As the use of postoperative lumbar epidural analgesia impairs mobilization, double epidural catheters with lower thoracic epidural analgesia are a possible alternative. Summary Adequate analgesia following cesarean delivery is still underused. Simple measures, such as multimodal analgesia regimens should be standardized according to institutional circumstances and defined as part of a treatment plan. Neuraxial morphine should be used whenever possible. If it cannot be used, abdominal wall blocks or surgical wound infiltration are good alternatives.
Replicate contact zones suggest a limited role of plumage in reproductive isolation among subspecies of the variable seedeater (Sporophila corvina)

After establishing secondary contact, recently diverged populations may remain reproductively isolated or may hybridize to a varying extent depending on factors such as hybrid fitness and the strength of assortative mating. Here, we used genomic and phenotypic data from three independent contact zones between subspecies of the variable seedeater (Sporophila corvina) to examine how coloration and genetic divergence shape patterns of hybridization. We found that differences in plumage coloration are probably maintained by divergent selection across contact zones; however, the degree of plumage differentiation does not match overall patterns of hybridization. Across two parallel contact zones between populations with divergent phenotypes (entirely black vs. pied plumage), populations hybridized extensively across one contact zone but not the other, suggesting that plumage divergence is not sufficient to maintain reproductive isolation. Where subspecies hybridized, hybrid zones were wide and formed by later‐generation hybrids, suggesting frequent reproduction and high survivorship for hybrid individuals. Moreover, contemporary gene flow has played an important role in shaping patterns of genetic structure between populations. Replicated contact zones between hybridizing taxa offer a unique opportunity to explore how different factors interact to shape patterns of hybridization. Overall, our results demonstrate that divergence in plumage coloration is important in reducing gene flow but insufficient in maintaining reproductive isolation in this clade, and that other factors such as divergence in song and time since secondary contact may also play an important role in driving patterns of reduced hybridization and gene flow.
Bayesian Identification of Multivariate Autoregressive Processes

Identification is one of the most important stages of a time series analysis. This paper develops a direct Bayesian technique to identify the order of multivariate autoregressive processes. By employing the conditional likelihood function and a matrix normal-Wishart prior density, or Jeffrey' vague prior, the proposed identification technique is based on deriving the exact posterior probability mass function of the model order in a convenient form. Then one may easily evaluate the posterior probabilities of the model order and choose the order that maximizes the posterior mass function to be the suitable order of the time series data being analyzed. Assuming the bivariate autoregressive processes, a numerical study, with different prior mass functions, is carried out to assess the efficiency of the proposed technique. The analysis of the numerical results supports the adequacy of the proposed technique in identifying the orders of multivariate autoregressive processes.
Assessing the risk of surgery in patients with cirrhosis

Patients with cirrhosis have an increased perioperative risk relative to patients without cirrhosis. This is related to numerous cirrhosis-specific factors, including severity of liver disease, impaired synthetic function, sarcopenia and malnutrition, and portal hypertension, among others. Nonhepatic comorbidities and surgery-related factors further modify the surgical risk, adding to the complexity of the preoperative assessment. In this review, we discuss the pathophysiological contributors to surgical risk in cirrhosis, key elements of the preoperative risk assessment, and application of risk prediction tools including the Child-Turcotte-Pugh score, Model for End-Stage Liver Disease-Sodium, Mayo Risk Score, and the VOCAL-Penn Score. We also detail the limitations of current approaches to risk assessment and highlight areas for future research.
Cumulative conformance count chart with variable sampling intervals and control limits

The cumulative conformance count (CCC) chart has been used for monitoring processes with very low fraction of nonconforming items. Typically, the items produced from the process were examined using 100% inspection for generating the CCC chart. However, this would be costly when taking the inspection cost and time into consideration and thus limit its application. Instead of inspecting the items one by one, this study takes sample from them, and regards the time between two successive samples as the sampling interval. In order to increase the sensitivity of the CCC chart to process change, the sampling interval and control limits are allowed to vary in this study. The average time to signal process change of the modified CCC chart (called the variable sampling interval and control limit (VSI/VCL) CCC chart) is derived by the Markov chain approach and taken as the performance measure to evaluate its statistical efficiency. With some minor changes, this chart can be reduced to the VSI CCC chart, the VCL CCC chart, and the standard CCC chart. In addition, comparisons among them are made and discussed. Copyright © 2010 John Wiley & Sons, Ltd.
Can SMEs benefit equally from supportive policies in China?

This paper applies quantitative and narrative approaches to fiscal and financial policies of Chinese small and medium enterprises (SMEs) in order to study the support effect of macro policies on micro enterprises. As the first researchers to focus on the firm heterogeneity of SMEs’ policy effects, we show that the “flood irrigation” supportive policies for SMEs have not achieved the expected “help the weaker” effect. Non-state-owned SMEs and small(micro) enterprises have a low sense of policy gain, which is contrary to some “positive” research conclusions from China. The mechanism study found that “ownership” and “scale” discrimination suffered by non-state-owned and small(micro) enterprises in the financing process are key. We suggest the supportive policies for SMEs should shift from “flood” to “precise drip” irrigation. The policy benefits of non-state-owned, small and micro enterprises need to be emphasized. More targeted policies need to be studied and provided. Our findings shed new light on the formulation of supportive policies for SMEs.
Magnetoelectric emission in rare-earth doped ferroelectric crystals La 2 Ti 2 O 7 :R 3+ ( R=Er , Eu, and Nd)

The optical magnetoelectric (OME) effect, i.e., the change of optical response on the reversal of light propagation vector $(\mathbit{k})$, has been investigated for the emission of rare earth ${R}^{3+}$ ion ($R=\text{Nd}$, Eu, Er) doped in a ferroelectric ${\text{La}}_{2}{\text{Ti}}_{2}{\text{O}}_{7}$ single crystal under magnetic field $(\mathbit{H})$. The symmetry condition for the appearance of the OME effect for $\mathbit{H}\ensuremath{\perp}\mathbit{k}$ was confirmed by varying the relative angle between the electric polarization and magnetic field. Another tensor component of the second-order magnetolectric tensor ${\ensuremath{\beta}}_{ijk}$ for $\mathbit{H}\ensuremath{\parallel}\mathbit{k}$, i.e., the magnetochiral effect, is allowed in the Faraday configuration but found to be small compared with the OME effect in the Voigt configuration. The importance of the spin-orbit coupling, the magnetic dipole transition, and the noncentrosymmetric crystal structure is discussed as the origin of the OME effect on the basis of the observed signal magnitude depending on the species of the rare-earth ion and its optical transition moment.
Enzyme function prediction using contrastive learning

Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied proteins or those with previously uncharacterized functions or multiple activities. We present a machine learning algorithm named CLEAN (contrastive learning–enabled enzyme annotation) to assign EC numbers to enzymes with better accuracy, reliability, and sensitivity compared with the state-of-the-art tool BLASTp. The contrastive learning framework empowers CLEAN to confidently (i) annotate understudied enzymes, (ii) correct mislabeled enzymes, and (iii) identify promiscuous enzymes with two or more EC numbers—functions that we demonstrate by systematic in silico and in vitro experiments. We anticipate that this tool will be widely used for predicting the functions of uncharacterized enzymes, thereby advancing many fields, such as genomics, synthetic biology, and biocatalysis. Description A crystal ball for enzyme activities With rapidly growing genomic and metagenomic databases, we have vastly more sequence data than functional data for enzymes. Accurate functional annotation from sparse experimental evidence is therefore crucial for analysis and applications when working from sequence data. Hoping to circumvent the limitations of current approaches, Yu et al. developed a machine learning model based on contrastive learning that performs particularly well at discerning enzyme function. In addition to comparing the performance of the method with existing tools, the authors experimentally validated predicted functions of 36 enzymes that form carbon–halogen bonds. They found excellent prediction accuracy and the ability to distinguish between similar activities. —MAF A contrastive learning algorithm enables accurate enzyme function annotation.
Effect of cues from Amblyseius largoensis (Muma) on the oviposition behavior of Raoiella indica Hirst

Abstract The ability of an organism to defend itself and protect its offspring is closely related to the evolutionary success of the organism. In this study, we examined the responses of red palm mite, Raoiella indica Hirst, to predation risk using cues from the predator Amblyseius largoensis (Muma). Females of A. largoensis were used for the production of cues for 4 h in two types of bioassays, a choice and no-choice tests. The oviposition rate of R. indica was evaluated until to 96 h. In addition, a walking behavior test was conducted (distance/time walked and resting time). For both tests, a control treatment without cues was done. In the no-choice bioassay, the oviposition rate of R. indica decreased by 55% when the cues were present. In the choice bioassay, the oviposition rate was similar to the control treatment, however most of the eggs were deposited in the site without cues. The walking behavior of R. indica was not affected by the presence of predator cues in no-choice bioassay. In choice bioassay, R. indica stayed longer in sites without cues. A. largoensis cues seem to be strong signs of predation risk affecting R. indica oviposition when there is no-choice, even after 96 h. R. indica shows that it is able to identify the cues of A. largoensis. We clearly evidenced the anti-predation behavior of R. indica, showing the trade-off between predation risk and reproduction on A. largoensis cues.
A Switch to Reduce SPH Viscosity

Smoothed particle hydrodynamics is a Lagrangian particle method for fluid dynamics which simulates shocks by using an artificial viscosity. Unlike Eulerian methods it is not convenient to reduce the effects of viscosity by means of switches based on spatial gradients. In this paper we introduce the idea of time-varying coefficients which fits more naturally with a particle formulation. Each particle has a viscosity parameter which evolves according to a simple source and decay equation. The source causes the parameter to grow when the particle enters a shock and the decay term causes it to decay to a small value beyond the shock. Tests on one-dimensional shocks and a two-dimensional shock? bubble interaction confirm that the method gives good results.
Measuring a Binary Response's Range of Influence in Logistic Regression

This article proposes a conceptually simple class of diagnostics to determine the potential of a single binary response to affect the outcome of a logistic regression. We simply rerun the regression once for each observation after changing the response and calculate how much the statistics of interest have changed. We show how statistics of this type may be helpful in checking the robustness of a logistic regression and study their relationship to earlier proposed diagnostics.
ON EMERGENCE TIMES OF COMPETITION PROCESSES

Competition processes are natural extensions of birth-death processes in higher dimensions. At any time, for any type, a birth, a death or a mutation to another type can occur. This work is concerned with the time $S$ at which a particular type emerges in the population through spontaneous birth or mutation. We give some bounds for the tail of the distribution of $S$. Then using properties of quasi-stationary distributions, we give conditions for the existence of an initial law under which $S$ is exponentially distributed. Finally, we provide some examples of applications to the emergence time of pathogen mutants overcoming host resistance in a host-pathogen system.
Channel correlation of transmit diversity FSO systems with partially coherent optical beams

Spatial diversity, including transmit diversity, has been proven to be a promising technique to mitigate turbulence-induced signal fading. However, due to the limitation of size in compact FSO (free-space optical) terminals, the transmit antennas cannot always be separated far enough from each other, resulting in increased channel correlation and deteriorated diversity performance. In this paper, we focus on the channel correlation statistics of transmit diversity FSO systems with two transmit beams. Specifically, we consider partially coherent optical beams, which reduce scintillation over fully coherent optical beams. We perform theoretical analysis and obtain some interesting numerical results, such as the degree of channel correlation which changed mainly with source correlation radius from 0.001 to 0.1 m. These results can be helpful for designing transmit diversity FSO systems with partially coherent beams.
Examining Early Childhood Dimensions of Emotional Awareness Using a Novel Coloring Task

Abstract Emotional awareness supports emotion regulation. Psychologists have children “color in feelings” to assess emotional awareness, yet whether this relates to emotion regulation is unknown. The present study used a novel coloring task examining behaviors related to coloring in and dictating emotions to assess children’s (N = 95) emotional awareness. Furthermore, it was tested whether performance on this task predicted emotion regulation. Children’s coloring behaviors indicating physiological emotional awareness predicted better emotion regulation. Results may inform the use of emotional awareness tasks in community and clinical settings. Findings also suggest that physiological emotional awareness may be a more salient clinical target in children.
Conceptual Modeling: Topics, Themes, and Technology Trends

Conceptual modeling is an important part of information systems development and use that involves identifying and representing relevant aspects of reality. Although the past decades have experienced continuous digitalization of services and products that impact business and society, conceptual modeling efforts are still required to support new technologies as they emerge. This paper surveys research on conceptual modeling over the past five decades and shows how its topics and trends continue to evolve to accommodate emerging technologies, while remaining grounded in basic constructs. We survey over 5,300 papers that address conceptual modeling topics from the 1970s to the present, which are collected from 35 multidisciplinary journals and conferences, and use them as the basis from which to analyze the progression of conceptual modeling. The important role that conceptual modeling should play in our evolving digital world is discussed, and future research directions proposed.
Agricultural Pest Detection based on Improved Yolov5

Agricultural pest detection is an important research direction in image detection. Due to the high similarity between many pests and the extremely similar color of pests and background, agricultural pest detection has always been a difficulty. In the early work, many methods were made to improve the detection accuracy by fusing gray space, YUV and HSV. In recent years, with the development of neural networks, the Yolo series began to show better performance in agricultural pest detection. In order to further improve the performance of Yolo series, the method of combining EIOU with Yolov5 is applied in this paper, and the accuracy on IP102 is improved.
Self–Adaptive Dynamic Connection Pool Management Method

At present, microservice architecture has become the mainstream architecture of software system implementation. In the system built by microservices, multiple microservices must frequently communicate on the network. Due to the high network communication delay, frequent communication between microservices will reduce the system's quality of service (QoS). The long connection can effectively reduce the delay of network communication. However, the traditional static long connection management mechanism can not effectively meet the requirements of frequently changing microservice communication links. This paper proposes an adaptive dynamic connection pool management method based on prediction to solve this problem. Based on the queuing model, this method can predict the number of communication connections in real time, thus effectively realizing the dynamic management of long connections in the connection pool under frequent link changes. The experimental results show that compared with the widely used working connection pool management method, this method effectively improves the communication performance between services.
An Image Segmentation and Transfer Learning based Prediagnosis of Melanoma beyond pathological

Globally, for every three cancer patients, one is diagnosed as skin cancer. During which, melanoma is regarded as the most lethal one. Accurate and timely diagnosis of melanoma is crucial for effective treatment and life quality improvement of patients. Pathological diagnosis has always been regarded as the gold standard of diagnosis Clinically. However extra risks come with biopsy or operation for Pathological diagnosis should also be concerned. Hence, deep-learning based image classification becomes an alternative and effective way for melanoma diagnosis beyond Pathological. However, due to melanoma images have the characteristics of uneven color, fuzzy boundary, and low contrast. Besides, compared to datasets for other visual classification tasks, the number of data samples in melanoma diagnosis is relatively low. To combat these challenges, in this paper, we propose a two-step melanoma classification framework. Firstly, we propose to abstract the lesion area through image segmentation with XBound-Former. Secondly, a transfer learning-based network architecture is proposed to conduct melanoma classification on the obtained lesion area. We introduce EfficientNet B0 as the backbone network and change its activation function to the Mish activation function for improving accuracy. Finally, through extensive experiments on ISIC2018 and ISIC2020 Dataset, our proposed method achieves a promising result compared with other state-of-art algorithms, which have validated the effectiveness of our proposed method.
Research on Wireless Sensor Network Applied to Smart Grounding Wire of Power Distribution Grid

It is important to improve maintenance efficiency for the power grid equipment based on Internet of things technology. Grounding wire is an important tool to protect the safety for maintenance workers, but in fact power system often has some accidents when the traditional non smart grounding wire is hanged or removed. Smart grounding wire is performed which is configured with various types of sensors. Reliability and low power consumption are the main technical problems because these sensors are powered by batteries. For this reason, this paper proposes a wireless sensor network with high immunity and low power consumption based on the design of data packet splitting mechanism of physical layer, which can support the long life and maintenance free features for the smart grounding wire.