Miracle-dz/UniFAR-FTUs huggingface dataset:
Nodal and Hedgehog synergize in gill slit formation during development of the cephalochordate Branchiostoma floridae

ABSTRACT The larval pharynx of the cephalochordate Branchiostoma (amphioxus) is asymmetrical. The mouth is on the left, and endostyle and gill slits are on the right. At the neurula, Nodal and Hedgehog (Hh) expression becomes restricted to the left. To dissect their respective roles in gill slit formation, we inhibited each pathway separately for 20 min at intervals during the neurula stage, before gill slits penetrate, and monitored the effects on morphology and expression of pharyngeal markers. The results pinpoint the short interval spanning the gastrula/neurula transition as the critical period for specification and positioning of future gill slits. Thus, reduced Nodal signaling shifts the gill slits ventrally, skews the pharyngeal domains of Hh, Pax1/9, Pax2/5/8, Six1/2 and IrxC towards the left, and reduces Hh and Tbx1/10 expression in endoderm and mesoderm, respectively. Nodal auto-regulates. Decreased Hh signaling does not affect gill slit positions or Hh or Nodal expression, but it does reduce the domain of Gli, the Hh target, in the pharyngeal endoderm. Thus, during the neurula stage, Nodal and Hh cooperate in gill slit development – Hh mediates gill slit formation and Nodal establishes their left-right position. Summary: In cephalochordates (amphioxus), the late gastrula/early neurula stage is critical for the specification of future gill slits; Nodal signaling establishes their left-right position and Hh, downstream of Nodal, regulates Gli to mediate gill slit penetration.
Prevalence and distribution of cervical human papillomavirus genotypes in women with cytological results from Sichuan province, China

The epidemiologic characteristics of human papillomavirus (HPV) genotypes vary by age, ethnicity, and geographic location, and the available data on HPV epidemiological characteristics with cytology results in Sichuan province are limited. Our research was conducted from June 2016 to July 2017. A total of 10 953 women getting HPV testing were enrolled. Liquid‐based cytological and histological results were collected. The overall HPV infection rate was 24.1% in Sichuan province. The prevalence of high‐risk HPV (hrHPV) was 19.9%. For hrHPV genotypes, HPV52 (15.5%) was the most prevalent genotype, followed by HPV16 (13.8%), HPV58 (13.3%), HPV51 (8.6%), HPV39 (8.1%), and HPV68 (7.8%). Among all HPV‐positive women with a cytology or histology result, HPV16‐positive women have the highest cervical intraepithelial neoplasia 1 (CIN1)+ prevalence (11.1%), followed by HPV18 and HPV33; HPV16‐positive women also have the highest CIN2+ prevalence (9.3%), followed by HPV58 and HPV18. To date, this is the largest study done in the Sichuan province for HPV prevalence and subtype distribution with normal and abnormal cytological results. The age‐specific prevalence in patients at gynecology clinics and other clinics is different. Besides, patients at the same age also have a different hrHPV prevalence and lrHPV prevalence. Our result revealed that in every 10 HPV16‐positive women, there is approximately one women with CIN2, CIN3, or cervical cancer. A higher oncogenic potential of HPV58 than that of HPV52 was observed.
A local uPAR-plasmin-TGFβ1 positive feedback loop in a qualitative computational model of angiogenic sprouting explains the in vitro effect of fibrinogen variants

In experimental assays of angiogenesis in three-dimensional fibrin matrices, a temporary scaffold formed during wound healing, the type and composition of fibrin impacts the level of sprouting. More sprouts form on high molecular weight (HMW) than on low molecular weight (LMW) fibrin. It is unclear what mechanisms regulate the number and the positions of the vascular-like structures in cell cultures. To address this question, we propose a mechanistic simulation model of endothelial cell migration and fibrin proteolysis by the plasmin system. The model is a hybrid, cell-based and continuum, computational model based on the cellular Potts model and sets of partial-differential equations. Based on the model results, we propose that a positive feedback mechanism between uPAR, plasmin and transforming growth factor β1 (TGFβ1) selects cells in the monolayer for matrix invasion. Invading cells releases TGFβ1 from the extracellular matrix through plasmin-mediated fibrin degradation. The activated TGFβ1 further stimulates fibrin degradation and keeps proteolysis active as the sprout invades the fibrin matrix. The binding capacity for TGFβ1 of LMW is reduced relative to that of HMW. This leads to reduced activation of proteolysis and, consequently, reduced cell ingrowth in LMW fibrin compared to HMW fibrin. Thus our model predicts that endothelial cells in LMW fibrin matrices compared to HMW matrices show reduced sprouting due to a lower bio-availability of TGFβ1.
Exploring drivers of litter decomposition in a greening Arctic: results from a transplant experiment across a treeline

Abstract Decomposition of plant litter is a key control over carbon (C) storage in the soil. The biochemistry of the litter being produced, the environment in which the decomposition is taking place, and the community composition and metabolism of the decomposer organisms exert a combined influence over decomposition rates. As deciduous shrubs and trees are expanding into tundra ecosystems as a result of regional climate warming, this change in vegetation represents a change in litter input to tundra soils and a change in the environment in which litter decomposes. To test the importance of litter biochemistry and environment in determining litter mass loss, we reciprocally transplanted litter between heath (Empetrum nigrum), shrub (Betula nana), and forest (Betula pubescens) at a sub‐Arctic treeline in Sweden. As expansion of shrubs and trees promotes deeper snow, we also used a snow fence experiment in a tundra heath environment to understand the importance of snow depth, relative to other factors, in the decomposition of litter. Our results show that B. pubescens and B. nana leaf litter decomposed at faster rates than E. nigrum litter across all environments, while all litter species decomposed at faster rates in the forest and shrub environments than in the tundra heath. The effect of increased snow on decomposition was minimal, leading us to conclude that microbial activity over summer in the productive forest and shrub vegetation is driving increased mass loss compared to the heath. Using B. pubescens and E. nigrum litter, we demonstrate that degradation of carbohydrate‐C is a significant driver of mass loss in the forest. This pathway was less prominent in the heath, which is consistent with observations that tundra soils typically have high concentrations of “labile” C. This experiment suggests that further expansion of shrubs and trees may stimulate the loss of undecomposed carbohydrate C in the tundra.
Genetic counseling students’ experiences with mental illness during training: An exploratory study

Abstract Mental illness is a substantive issue for graduate students. We investigated experiences of mental illness during training among genetic counseling students, a subgroup of graduate students for which little data exists on this topic. Genetic counseling students and recent graduates (n = 227) completed an online survey, from who 11 were selected to participate in semi-structured telephone interviews. Thematic analysis and member checking were employed to interpret the interviews. An overarching theme of importance to participants’ mental health during genetic counseling training was safety, with subthemes of: trust/confidentiality, stigma and fear of labeling, developing a unique professional identity, and ability to engage in self care strategies. Our data could help genetic counseling training programs develop strategies to support students’ mental health.
Predictors of Aggressive Behavior While under the Influence of Illicit Drugs among Young Adult Methamphetamine Users

ABSTRACT Background: Prior research indicates that patterns of combined alcohol and methamphetamine use may be associated with experiencing subjective feelings of aggression or hostility during methamphetamine use episodes. Objectives: This study examines whether subjective effects of methamphetamine use (i.e., aggression or hostility and paranoia) are associated with aggressive behavior while under the influence of any illicit drugs, controlling for combined alcohol and methamphetamine use and a number of other potential predictors. Methods: Data from a population-based sample of Australian young adult methamphetamine users (n = 101) collected in 2010 was analyzed. A prediction model of aggressive behavior under the influence of illicit drugs was developed using penalized maximum likelihood logistic regression. Results: Over one-third (34.7%) of methamphetamine users had engaged in verbal and/or physical aggression under the influence of illicit drugs in the last 12 months. In the prediction model, recurrent feelings of aggression or hostility attributed to methamphetamine use (≥3 times in the last 12 months) were associated with aggressive behavior (adjusted odds ratio 4.95, 95% confidence interval 1.67, 14.69). This association was independent of methamphetamine-attributed paranoia, combined alcohol and methamphetamine use, methamphetamine, ecstasy, cocaine, and cannabis use patterns, heavy episodic drinking, gender, and age. No association was found for combined alcohol and methamphetamine use. Conclusions: These findings indicate a link between methamphetamine-related subjective feelings of aggression or hostility and self-reported aggressive behavior while under the influence of illicit drugs. This suggests that subjective feelings of aggression or hostility may distinguish those who are involved in aggression from other methamphetamine users.
‘Brussels will land on its feet like a cat’: motivations for memefying #Brusselslockdown

ABSTRACT This article explores user motivations for sharing and creating internet memes in a crisis situation. For this purpose, we investigate the kitten memes in #Brusselslockdown on Twitter, following the Brussels security lockdown in November 2015 that resulted from information about potential terrorist attacks. We use a social network analysis to identify three user groups: content producers, content sharers, and conversationalists. On the basis of interviews with users from these three groups, we argue that the motivations for sharing and creating memes range from personal involvement in the crisis situation to acts of resistance to creative self-realization. We conclude by arguing that a combination of motifs and activities employ the visuality of internet memes to express popular internet culture, resistance, and solidarity as well as a humorous means of coping with the crisis situation. As a form of solidarity beyond the political, internet memes create tension between a crisis event, the mundane, terror, resistance, and humor.
Biologic refractory disease in rheumatoid arthritis: results from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis

Objectives Biologic disease-modifying antirheumatic drugs (bDMARDs) have revolutionised treatment and outcomes for rheumatoid arthritis (RA). The expanding repertoire allows the option of switching bDMARD if current treatment is not effective. For some patients, even after switching, disease control remains elusive. This analysis aims to quantify the frequency of, and identify factors associated with, bDMARD refractory disease. Methods Patients with RA starting first-line tumour necrosis factor inhibitor in the British Society for Rheumatology Biologics Register for RA from 2001 to 2014 were included. We defined patients as bDMARD refractory on the date they started their third class of bDMARD. Follow-up was censored at last follow-up date, 30 November 2016, or death, whichever came first. Switching patterns and stop reasons of bDMARDs were investigated. Cox regression identified baseline clinical factors associated with refractory disease. Multiple imputation of missing baseline data was used. Results 867 of 13 502 (6%) patients were bDMARD refractory; median time to third bDMARD class of 8 years. In the multivariable analysis, baseline factors associated with bDMARD refractory disease included patients registered more recently, women, younger age, shorter disease duration, higher patient global assessment, higher Health Assessment Questionnaire score, current smokers, obesity and greater social deprivation. Conclusions This first national study has identified the frequency of bDMARD refractory disease to be at least 6% of patients who have ever received bDMARDs. As the choice of bDMARDs increases, patients are cycling through bDMARDs quicker. The aetiopathogenesis of bDMARD refractory disease requires further investigation. Focusing resources, such as nursing support, on these patients may help them achieve more stable, controlled disease.
[Journal First] Are Fix-Inducing Changes a Moving Target?: A Longitudinal Case Study of Just-in-Time Defect Prediction

Just-In-Time (JIT) models identify fix-inducing code changes. JIT models are trained using techniques that assume that past fix-inducing changes are similar to future ones. However, this assumption may not hold, e.g., as system complexity tends to accrue, expertise may become more important as systems age. In this paper, we study JIT models as systems evolve. Through a longitudinal case study of 37,524 changes from the rapidly evolving Qt and OpenStack systems, we find that fluctuations in the properties of fix-inducing changes can impact the performance and interpretation of JIT models. More specifically: (a) the discriminatory power (AUC) and calibration (Brier) scores of JIT models drop considerably one year after being trained; (b) the role that code change properties (e.g., Size, Experience) play within JIT models fluctuates over time; and (c) those fluctuations yield over- and underestimates of the future impact of code change properties on the likelihood of inducing fixes. To avoid erroneous or misleading predictions, JIT models should be retrained using recently recorded data (within three months). Moreover, quality improvement plans should be informed by JIT models that are trained using six months (or more) of historical data, since they are more resilient to period-specific fluctuations in the importance of code change properties.
Automation of Train Cab Front Cleaning With a Robot Manipulator

In this letter we present a control and trajectory tracking approach for wiping the train cab front panels, using a velocity controlled robotic manipulator and a force/torque sensor attached to its end effector, without using any surface model or vision-based surface detection. The control strategy consists in a simultaneous position and force controller, adapted from the operational space formulation, that aligns the cleaning tool with the surface normal, maintaining a set-point normal force, while simultaneously moving along the surface. The trajectory tracking strategy consists in specifying and tracking a two dimensional path that, when projected onto the train surface, corresponds to the desired pattern of motion. We first validated our approach using the Baxter robot to wipe a highly curved surface with both a spiral and a raster scan motion patterns. Finally, we implemented the same approach in a scaled robot prototype, specifically designed by ourselves to wipe a $1/8$ scaled version of a train cab front, using a raster scan pattern.
The Experience of Developing the UJAmI Smart Lab

In the context of ambient-assisted living and monitoring activities and behaviors, the concept of smart homes/environments has emerged as a research and development field with many examples appearing in a range of different contexts (universities, research centers, hospitals, and residences). Recently, a smart lab called UJAmI based on ambient intelligence was created at the University of Jaén in order to provide an environment where solutions in assistive technologies could be developed. This paper presents the experience of developing this smart lab within its first year. The initial space assigned to the smart lab is described through the creation of the smart lab in five perspectives: the layout of the smart lab, the selection of the devices, the deployment of the middleware, the technical infrastructures, and the furniture. Finally, following our experience of creating the lab we reflect upon our experience and provide a set of guidelines and recommendations. Ongoing projects within this Smart lab are also introduced.
Algorithm of creating of an efficient cooperation between universities, business companies and agriculture enterprises during studying and implementation of information systems

In this work there was described the algorithm of effective cooperation between universities and business companies during investigation of modern information systems which were destined for automation of production processes in agriculture enterprises. Common directions of work for higher education institutions, software developers, and manufacturing companies are based on identifying and combining their multilateral shared interests that are focused on training of qualified professionals, consulting of business companies and users at enterprises during the implementation of information systems.
Blind Spectrum Sensing Using Extreme Eigenvalues for Cognitive Radio Networks

Here, a new spectrum sensing method, called mean-to-square extreme eigenvalue (MSEE), is proposed. Considering a multiple antenna communication system, the proposal is drawn from the arithmetic-to-geometric mean (AGM) algorithm using only the smallest and the largest eigenvalues of the covariance matrix of the received signal. The aim of MSEE is to avoid the heavy computational costs of AGM method. First, based on the random matrix theory, a theoretical development to set the threshold of the proposal is provided. Then, the validity of the expression is verified by simulations. Finally, simulation results show an interesting performance of MSEE compared with several spectrum sensing methods in the literature.
Self-interference cancellation in chipless RFID readers for reading range enhancement

In this work, we studied the effect of self-interference cancellation in single-antenna monostatic chipless RFID readers on its reading range. Two theoretical limits for this range were obtained by taking into account both the signal and noise leaked from the transmitter. Moreover, a software-defined radio-based reader was implemented, whose performance was predicted to be restricted by the leakage signal power rather than the noise power, fact that was verified through measurements of a chipless tag. The experimental results also showed an improvement of more than four-times in the reading range through a 27-dB attenuation of the self-interference signal.
Device-to-Device Communications Underlying UAV-Supported Social Networking

The integration of device-to-device (D2D) communications technology can further boost system performance for unmanned aerial vehicles supported pervasive social networking. In this paper, we maximize the sum social group utility of the social networking by power optimization, whereas the quality-of-service requirements of all D2D users and downlink users must be satisfied. In the proposed formulation, we consider the physical interference and social connections between users in the physical/social domain, respectively. However, the formulated problem is strongly NP-hard and intractable, to solve it we reveal the hidden non-convex components, and approximate the original problem with a sequence of more tractable convex problems. We propose an efficient semi-decentralized algorithm by decomposing the complex problem into several simple subproblems, which is proved to converge to the stationary solutions of the original problem. Simulation results show that our proposed method can achieve comparable performance but much less time complexity than the centralized method and obtain superior performance.
VMD and HMM based rolling bearing fault diagnosis

Aiming at the problem that the vibration signal of rolling bearings has many nonlinear and non-stationary characteristics and leads to the difficulty of signal characteristic fault extraction, a rolling bearing fault diagnosis method based on the variational mode decomposition and the hidden Markov model is proposed in this paper. The variational mode decomposition method is a novel multi-component decomposition method with a solid theoretical foundation. First of all, the vibration signals are decomposed into different modes to obtain several mode functions with band-limited. Then, the singular value decomposition is used to reduce the dimension of the feature matrix which is formed by the number of the mode functions. After that the fault features of original signal are obtained. Finally, the fault features are input to the hidden Markov model for training, and the corresponding models in each state are obtained. The actual acquisition data of the fault simulation test platform of INV1618 transmission system developed by China Orient Institute of Noise and Vibration was used to test and verify the proposed method. The experimental results demonstrate that the rolling bearing can still be effectively diagnosed when only a few samples are trained.
Characterization of the gravity gradiometer based on the Sr optical clock transition

We present a gravity gradiometer with the strontium optical clock transition by integrating a double lattice launch technique to develop two atomic samples vertically and a new method to add an artificial phase shift between two samples. We experimentally characterize the launch performance and the robustness of adding the artificial phase shift. The tunable differential phase shift is particularly important for characterizing the performance of a gravity gradiometer when the separation of the two clouds is limited by the geometrical constraints. Moreover, numerical simulation results indicate that the systematic error of the differential phase measurement can be largely reduced when the ellipse phase approaches ±π/2 rad by simply tuning the relative phase between two RF signals.
GraphDuo: A Dual-Model Graph Processing Framework

Algorithms for large-scale natural graph processing can be categorized into two types based on their value propagation behaviors: the unidirectional value propagation algorithms and the bidirectional value propagation algorithms. The graph computation in different types exhibits different properties on how vertices interact with their neighbors and has different requirements on system design. Current distributed graph processing systems usually try to support both types in one general-purpose computing model, which can result in suboptimial performance, high communication overhead, and high resource consumption. In this paper, we propose GraphDuo, a new Spark-based graph processing framework that provides two efficient computing models for unidirectional and bidirectional value propagation algorithms, respectively. Combined with a degree-based graph partitioning scheme, a locality-aware graph layout, and other optimization techniques, GraphDuo achieves low computation cost, low communication overhead, small memory footprint, and good communication balance for two types of algorithms. According to the experimental results, GraphDuo can outperform GraphX from <inline-formula> <tex-math notation="LaTeX">$1.76\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$6.56\times $ </tex-math></inline-formula> and from <inline-formula> <tex-math notation="LaTeX">$1.11\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$1.26\times $ </tex-math></inline-formula> for two types of algorithms, respectively, with much less memory consumption and communication cost. The source code of GraphDuo is publicly available from <uri>http://prof.ict. ac.cn/GraphDuo</uri>.