Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of pathogenicity islands in uropathogenic Escherichia coli isolated from patients with urinary catheters.

INTRODUCTION Uropathogenic Escherichia coli (UPEC), an important causative agent of urinary tract infections (UTIs), carries virulence factors which are clustered on pathogenicity islands (PAIs). The goal of this study was to characterize the PAIs among the UPEC isolated from patients with urinary catheters. METHODOLOGY A descriptive cross-sectional study was designed and from December 2014 to April 2015, 78 non-duplicate E. coli were collected from hospitalized patients with UTIs, including patients with and without indwelling urinary catheters. Two multiplex polymerase chain reaction (PCR) assays were performed to evaluate the presence of the eight most studied PAIs (I 536, II 536, III 536, IV536, I CFT073, II CFT073, PAI IJ96, and PAI IIJ96). RESULTS Of 78 patients with a UTI 31 (39.7%) used indwelling catheters. Of these 31 patients, 27 (87.1%) carried PAIs markers, including 25 (80.6%) PAI IV536, 12 (38.7%) PAI ICFT073, 6 (19.4%) PAI IICFT073, 6 (19.4%) PAI III536 and 3 (9.7%) PAI II536. PAI I536, PAI IJ96, and PAI IIJ96 were not detected in the UPEC strains. CONCLUSIONS The findings of this study revealed that the frequency of PAI markers in UPEC isolates from patients with indwelling urinary catheters was high. The rate of multiple PAIs carriage was notable among those patients, suggesting that UPEC strains that colonize the indwelling urinary catheters have the potential to cause complicated urinary infections. PAI ICFT073, which was found in association with pyelonephritis, prostatitis, and sepsis, could be considered as a target for medical interventions.
Development of a smoking cessation smartphone application for pregnant smokers focusing on the role of identity

This thesis reports two strands of work leading to the creation of a smartphone application harnessing identity change to promote smoking cessation during pregnancy. The first strand studied the role of smoker identity in smoking cessation. Using data from a national prospective study of smokers in England, Study 1 found that most young women do not have a positive smoker identity but that those who do are less likely to try to quit smoking, independent of other smoking-related attitudes. Study 2 used prospective national survey data and showed that adopting a non-smoker identity after a quit attempt predicts medium-term abstinence. Study 3 was a meta-ethnography of smoker identity in young adults and found that multiple smoker identities evolve depending on the context and that these do appear to play a role in cessation. The second strand related to the development of the application itself. Using interviews with pregnant smokers, Study 4 found that, from their perspective, stopping smoking would require strong enough motivation, improved ability and a supportive social and physical environment. Study 5 involved focus groups of health professionals working with pregnant smokers and solicited recommendations regarding the design and delivery of digital cessation aids in pregnancy. Study 6 used data from a pilot randomised controlled trial of the smoking cessation website, MumsQuit, to assess associations between exposure to particular components and four-week continuous abstinence. Six behaviour change techniques were associated with abstinence: promoting problem solving, providing feedback on behaviour, promoting self-monitoring of outcomes of the behaviour, providing feedback on outcomes of the behaviour, providing social reward, and promoting self-reward. The thesis concludes with a description of the development of a smartphone application, SmokeFree Baby, based on preceding work.
Predictive Score of Maternal and Foetal Prognosis during Labour in Primiparous Woman in a Congolese Hospital

Background: In Africa, primiparity is associated with high maternal and neonatal morbidity and mortality during pregnancy and childbirth. This study aims to establish a clinical score to identify the primiparous parturients at risk of pejorative evolution. Methodology: This was a case-control study nested in ROC analysis, aiming diagnosis. During the period from April 1 st , 2012 to June 30 th 2013, the 1230 primiparous who gave birth at the Oïcha General Reference Hospital in the Democratic Republic of Congo were selected for this study. The primiparous were divided into an evaluation group (n = 686) and a validation group (n = 544). The evaluation group was subjected to a multi-variate analysis with conditional logistic regression to search for risk factors for the pejorative evolution of labour, which allowed the construction of the "Juakali score" for alerting about a bad prognosis of labour. The ROC analysis had revealed the threshold value and the area below the ROC curve, the discriminatory power. Results: The overall prevalence of pejorative evolution was 14.8%. Risk factors: age = 26 years, Height <150cm, Weight <50Kg, BMI (Body Mass Index) ≥ 30Kg / m 2 , cervical dilatation rate <1cm / h at the latent phase. The score varied from 0 to 9 with a threshold value of 6, from which the positive predictive value was 75.3% in the assessment group (AUC = 0.924) and 63% (AUC = 0.922) for the validation. Conclusion: The Juakali Score is a reliable tool for predicting the pejorative evolution of labour, which can be used to adjust driving behaviour early in labour monitoring.
Effects of a parallel electromagnetic field in the three-flavor Nambu–Jona-Lasinio model

In this work, we explore the ground state of QCD system and the relevant collective modes in a parallel electromagnetic (EM) field within the effective three-flavor Nambu--Jona-Lasinio model. Competition between QCD and QED anomalies is found from the features of neutral chiral condensates: the negativeness of $\pi_{\rm d}^*$ and $\pi_{\rm s}^*$ is a signal of QCD anomaly dominance for all the flavors, and the positiveness of $\pi_{\rm s}^*$ beyond $e\bar{I}_2^{c_1}$ indicates QED anomaly dominance for strange quark. For the lowest-lying neutral collective modes, the masses of $\Pi$ and $H$ modes reduce to zero around the critical EM fields and the scalar-pseudoscalar components exchange between $\Sigma$ and $H$ modes show itself through the crossing structure.
Stellar populations across galaxy bars in the MUSE TIMER project

Stellar populations in barred galaxies save an imprint of the influence of the bar on the host galaxy’s evolution. We present a detailed analysis of star formation histories (SFHs) and chemical enrichment of stellar populations in nine nearby barred galaxies from the TIMER project. We used integral field observations with the MUSE instrument to derive unprecedented spatially resolved maps of stellar ages, metallicities, [Mg/Fe] abundances, and SFHs, as well as Hαas a tracer of ongoing star formation. We find a characteristic V-shaped signature in the SFH that is perpendicular to the bar major axis, which supports the scenario where intermediate-age stars (∼2 − 6 Gyr) are trapped on more elongated orbits shaping a thinner part of the bar, while older stars (> 8 Gyr) are trapped on less elongated orbits shaping a rounder and thicker part of the bar. We compare our data to state-of-the-art cosmological magneto-hydrodynamical simulations of barred galaxies and show that such V-shaped SFHs arise naturally due to the dynamical influence of the bar on stellar populations with different ages and kinematic properties. Additionally, we find an excess of very young stars (< 2 Gyr) on the edges of the bars, predominantly on the leading side, thus confirming typical star formation patterns in bars. Furthermore, mass-weighted age and metallicity gradients are slightly shallower along the bar than in the disc, which is likely due to orbital mixing in the bar. Finally, we find that bars are mostly more metal-rich and less [Mg/Fe]-enhanced than the surrounding discs. We interpret this as a signature that the bar quenches star formation in the inner region of discs, usually referred to as star formation deserts. We discuss these results and their implications on two different scenarios of bar formation and evolution.
Effect of cytosol viscosity on the flow behavior of red blood cell suspensions in microvessels

The flow behavior of blood is strongly affected by red blood cell (RBC) properties, such as the viscosity ratio C between cytosol and suspending medium, which can significantly be altered in several pathologies (e.g. sickle‐cell disease, malaria). The main objective of this study is to understand the effect of C on macroscopic blood flow properties such as flow resistance in microvessels, and to link it to the deformation and dynamics of single RBCs.
Masked Face Recognition Dataset and Application

In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. Most current advanced face recognition approaches are designed based on deep learning, which depend on a large number of face samples. However, at present, there are no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD). Among them, to the best of our knowledge, RMFRD is currently theworld's largest real-world masked face dataset. These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed. The multi-granularity masked face recognition model we developed achieves 95% accuracy, exceeding the results reported by the industry. Our datasets are available at: this https URL.
Comparative study between cryotherapy and salicylic acid in the treatment of plantar warts in Erbil - Iraq

Background and objective: Plantar warts are hard, grainy growths that usually appear on the heels or balls of feet. Plantar warts are caused by the human papillomavirus, usually self-limiting, but treatment is generally recommended to lessen symptoms, decrease duration, and reduce transmission. The study aimed to evaluate the clinical effectiveness of topical 40% salicylic acid in comparison to cryotherapy in the treatment of plantar wart. Methods: This study was conducted from March 2015 to January 2016 on 70 patients with plantar wart who attended the consultation clinic of dermatology department in Rizgary teaching hospital. Patients were randomly allocated to two groups: cryotherapy treatment with liquid nitrogen every two or three weeks and self-application of salicylic acid daily. Results: Sixty patients aged 12 to 60 years were included in the analysis at 13 weeks. Cure rates were 66.7% in the cryotherapy group and 80% in the salicylic acid group. A significant association was found between the duration of wart before treatment and cure rates (P <0.001). Conclusion: No difference in effectiveness between cryotherapy and topical salicylic acid 40% in the treatment of plantar warts was found.
Optimization of Bathymetry for Long Waves with Small Amplitude

This paper deals with bathymetry-oriented optimization in the case of long waves with small amplitude. Under these two assumptions, the free-surface incompressible Navier-Stokes system can be written as a wave equation where the bathymetry appears as a parameter in the spatial operator. Looking then for time-harmonic fields and writing the bottom topography as a perturbation of a flat bottom, we end up with a heterogeneous Helmholtz equation with impedance boundary condition. In this way, we study some PDE-constrained optimization problem for a Helmholtz equation in heterogeneous media whose coefficients are only bounded with bounded variation. We provide necessary condition for a general cost function to have at least one optimal solution. We also prove the convergence of a finite element approximation of the solution to the considered Helmholtz equation as well as the convergence of discrete optimum toward the continuous ones. We end this paper with some numerical experiments to illustrate the theoretical results and show that some of their assumptions could actually be removed.
From identification of random contributions to determination of the optimum forecast of a soccer match.

The forecasting of sports events is of broad interest from the applied but also from the theoretical perspective. In this work the question is addressed for the example of the German soccer Bundesliga how a theoretically optimum forecast of the goal difference of a match can be characterized. This involves a careful analysis of the random contributions in a match and its disentanglement from the informative contributions, resulting from the individual team strengths. An important aspect is the consideration of the time dependence of the team strength which turns out to mainly fluctuate around a team-specific value during the course of a season. Two types of time-dependent properties have to be distinguished, one being uncorrelated between different match-days, the other being correlated and thus accessible by an appropriate correlation analysis. For some performance indicators, which may be used to estimate the team strength, the quality of the respective forecast is compared to the theoretical optimum. Knowledge of the informative contribution allows one to conclude that the offensive team strength is more important than the defensive team strength for the final success.
Holonomy and (stated) skein algebras in combinatorial quantization

The algebra $\mathcal{L}_{g,n}(H)$ was introduced by Alekseev-Grosse-Schomerus and Buffenoir-Roche and quantizes the character variety of the Riemann surface $\Sigma_{g,n}\! \setminus\! D$ ($D$ is an open disk). In this article we define a holonomy map in that quantized setting, which associates a tensor with components in $\mathcal{L}_{g,n}(H)$ to tangles in $(\Sigma_{g,n}\! \setminus\! D) \times [0,1]$, generalizing previous works of Buffenoir-Roche and Bullock-Frohman-Kania-Bartoszynska. We show that holonomy behaves well for the stack product and the action of the mapping class group; then we specialize this notion to links in order to define a generalized Wilson loop map. Thanks to the holonomy map, we give a geometric interpretation of the vacuum representation of $\mathcal{L}_{g,0}(H)$ on $\mathcal{L}_{0,g}(H)$. Finally, the general results are applied to the case $H=U_{q^2}(\mathfrak{sl}_2)$ in relation to skein theory and the most important consequence is that the stated skein algebra of a compact oriented surface with just one boundary edge is isomorphic to $\mathcal{L}_{g,n}\big( U_{q^2}(\mathfrak{sl}_2) \big)$. Throughout the paper we use a graphical calculus for tensors with coefficients in $\mathcal{L}_{g,n}(H)$ which makes the computations and definitions very intuitive.
Human Activity Recognition from Wearable Sensor Data Using Self-Attention

Human Activity Recognition from body-worn sensor data poses an inherent challenge in capturing spatial and temporal dependencies of time-series signals. In this regard, the existing recurrent or convolutional or their hybrid models for activity recognition struggle to capture spatio-temporal context from the feature space of sensor reading sequence. To address this complex problem, we propose a self-attention based neural network model that foregoes recurrent architectures and utilizes different types of attention mechanisms to generate higher dimensional feature representation used for classification. We performed extensive experiments on four popular publicly available HAR datasets: PAMAP2, Opportunity, Skoda and USC-HAD. Our model achieve significant performance improvement over recent state-of-the-art models in both benchmark test subjects and Leave-one-subject-out evaluation. We also observe that the sensor attention maps produced by our model is able capture the importance of the modality and placement of the sensors in predicting the different activity classes.
Gaussian Curvature Filter on 3D Mesh

Minimizing Gaussian curvature of meshes is fundamentally important for obtaining smooth and developable surfaces. However, there is a lack of computationally efficient and robust Gaussian curvature optimization method. In this paper, we present a simple yet effective method that can efficiently reduce Gaussian curvature for 3D meshes. We first present the mathematical foundation of our method, which states that for any point on a developable surface there must be another point that lives on its tangent plane. Then, based on this theoretical insight, we introduce a simple and robust implicit Gaussian curvature optimization method named Gaussian Curvature Filter (GCF). GCF implicitly minimizes Gaussian curvature without the need to explicitly calculate the Gaussian curvature itself. GCF is highly efficient and is 20 times faster than the classical standard Gaussian curvature flow method. We present extensive experiments to demonstrate that GCF significantly outperforms state-of-the-art methods in minimizing Gaussian curvature, geometric feature preserving smoothing and mesh noise removal. This method can be used in a large range of applications that involve Gaussian curvature.
The mass discrepancy in intermediate- and high-mass eclipsing binaries: The need for higher convective core masses

Context. Eclipsing, spectroscopic double-lined binary star systems are excellent laboratories for calibrating theories of stellar interior structure and evolution. Their precise and accurate masses and radii measured from binary dynamics offer model-independent constraints and challenge current theories of stellar evolution. Aims. We aim to investigate the mass discrepancy in binary stars. This is the significant difference between stellar components’ masses measured from binary dynamics and those inferred from models of stellar evolution via positions of the components in the Teff − log g Kiel diagram. We study the effect of near-core mixing on the mass of the convective core of the stars and interpret the results in the context of the mass discrepancy. Methods. We fitted stellar isochrones computed from a grid of MESA stellar evolution models to a homogeneous sample of eleven high-mass binary systems. Two scenarios are considered where individual stellar components of a binary system are treated independent of each other and where they are forced to have the same age and initial chemical composition. We also study the effect of the microturbulent velocity and turbulent pressure on the atmosphere model structure and stellar spectral lines, and its link with the mass discrepancy. Results. We find that the mass discrepancy is present in our sample and that it is anti-correlated with the surface gravity of the star. No correlations are found with other fundamental and atmospheric parameters, including the stellar mass. The mass discrepancy can be partially accounted for by increasing the amount of near-core mixing in stellar evolution models. We also find that ignoring the microturbulent velocity and turbulent pressure in stellar atmosphere models of hot evolved stars results in the overestimation of their effective temperature by up to 8%. Together with enhanced near-core mixing, this can almost entirely account for the ∼30% mass discrepancy found for the evolved primary component of V380 Cyg. Conclusions. We find a strong link between the mass discrepancy and the convective core mass. The mass discrepancy can be solved by considering the combined effect of extra near-core boundary mixing and the consistent treatment in the spectrum analysis of hot evolved stars. Our binary modelling results in convective core masses between 17 and 35% of the stellar mass, which is in excellent agreement with the results from gravity-mode asteroseismology of single stars. This implies larger helium core masses near the end of the main sequence than have been anticipated so far.
STUDY OF GLYCATED HEMOGLOBIN LEVELS IN POLYCYSTIC OVARY SYNDROME

Objective: Polycystic ovarian syndrome (PCOS) is often accompanied by insulin resistance, obesity, and cardiometabolic risk factors. Androgen excess-PCOS recommends oral glucose tolerance test or glycated hemoglobin (HbA1C) to evaluate dysglycemia in PCOS subjects. We undertook this study to evaluate the prevalence of elevated HbA1C levels in PCOS women. Methods: The study was carried out among 100 PCOS patients from SRM Hospital, 100 healthy individuals were included as controls. Fasting glucose, HbA1C, Insulin and Homeostasis Model Assessment-Insulin Resistance Index were estimated. Results: Patients with polycystic ovary syndrome showed a significant increase in HbA1C levels (5.799±1.022; 4.96±0.625, p=0.001) when compared to the control group. Conclusion: We found elevated HbA1C levels in PCOS women categorizing 26% as prediabetes and 28% as having type 2 diabetes mellitus.
Exploring Categorical Regularization for Domain Adaptive Object Detection

In this paper, we tackle the domain adaptive object detection problem, where the main challenge lies in significant domain gaps between source and target domains. Previous work seeks to plainly align image-level and instance-level shifts to eventually minimize the domain discrepancy. However, they still overlook to match crucial image regions and important instances across domains, which will strongly affect domain shift mitigation. In this work, we propose a simple but effective categorical regularization framework for alleviating this issue. It can be applied as a plug-and-play component on a series of Domain Adaptive Faster R-CNN methods which are prominent for dealing with domain adaptive detection. Specifically, by integrating an image-level multi-label classifier upon the detection backbone, we can obtain the sparse but crucial image regions corresponding to categorical information, thanks to the weakly localization ability of the classification manner. Meanwhile, at the instance level, we leverage the categorical consistency between image-level predictions (by the classifier) and instance-level predictions (by the detection head) as a regularization factor to automatically hunt for the hard aligned instances of target domains. Extensive experiments of various domain shift scenarios show that our method obtains a significant performance gain over original Domain Adaptive Faster R-CNN detectors. Furthermore, qualitative visualization and analyses can demonstrate the ability of our method for attending on the key regions/instances targeting on domain adaptation. Our code is open-source and available at https://github.com/Megvii-Nanjing/CR-DA-DET.