Miracle-dz/UniFAR-FTUs huggingface dataset:
Insights gained from sequencing Australian non-invasive and invasive Streptococcus pyogenes isolates

Epidemiological data have indicated that invasive infections caused by the Gram-positive cocci Streptococcus pyogenes (group A streptococcus, GAS) have increased in many Australian states over the past two decades. In July 2022, invasive GAS (iGAS) infections became nationally notifiable in Australia via public-health agencies. Surveillance for S. pyogenes infections has been sporadic within the state of New South Wales (NSW). This has led to a lack of genetic data on GAS strains in circulation, particularly for non-invasive infections, which are the leading cause of GAS’s burden on the Australian healthcare system. To address this gap, we used whole-genome sequencing to analyse the genomes of 318 S. pyogenes isolates collected within two geographical regions of NSW. Invasive isolates were collected in 2007–2017, whilst non-invasive isolates were collected in 2019–2021. We found that at least 66 different emm-types were associated with clinical disease within NSW. There was no evidence of any Australian-specific clones in circulation. The M1UK variant of the emm1 global pandemic clone (M1global) has been detected in our isolates from 2013 onwards. We detected antimicrobial-resistance genes (mainly tetM, ermA or ermB genes) in less than 10 % of our 318 isolates, which were more commonly associated with non-invasive infections. Superantigen virulence gene carriage was reasonably proportionate between non-invasive and invasive infection isolates. Our study adds rich data on the genetic makeup of historical S. pyogenes infections within Australia. Ongoing surveillance of invasive and non-invasive GAS infections within NSW by whole-genome sequencing is warranted to inform on outbreaks, antimicrobial resistance and vaccine coverage.
Fibroblast growth factor 21 predicts arteriovenous fistula functional patency loss and mortality in patients undergoing maintenance hemodialysis

Abstract Background Arteriovenous fistula (AVF) dysfunction is a common complication in patients undergoing maintenance hemodialysis (MHD). Elevated serum levels of fibroblast growth factor 21 (FGF21) are associated with atherosclerosis and cardiovascular mortality. However, its association with vascular access outcomes remains elusive. The present study evaluated the relationship of serum FGF21 levels with AVF dysfunction and all-cause mortality in patients undergoing MHD. Methods We included patients undergoing MHD using AVF from January 2018 to December 2019. FGF21 concentration was detected using enzyme-linked immunosorbent assay. Patients were followed up to record two clinical outcomes, AVF functional patency loss and all-cause mortality. The follow-up period ended on April 30, 2022. Results Among 147 patients, the mean age was 58.49 ± 14.41 years, and the median serum level of FGF21 was 150.15 (70.57–318.01) pg/mL. During the median follow-up period of 40.83 months, the serum level of FGF21 was an independent risk factor for AVF functional patency loss (per 1 pg/mL increase, HR 1.002 [95% CI: 1.001–1.003, p = 0.003]). Patients with higher serum levels of FGF21 were more likely to suffer from all-cause mortality (per 1 pg/mL increase, HR 1.002 [95% CI: 1.000–1.003, p = 0.014]). The optimal cutoffs for FGF21 to predict AVF functional patency loss and all-cause mortality in patients undergoing MHD were 149.98 pg/mL and 146.43 pg/mL, with AUCs of 0.701 (95% CI: 0.606–0.796, p < 0.001) and 0.677 (95% CI: 0.595–0.752, p = 0.002), respectively. Conclusions Serum FGF21 levels were an independent risk factor and predictor for AVF functional patency loss and all-cause mortality in patients undergoing MHD.
The association between climatic factors and childhood illnesses presented to hospital emergency among young children

Abstract There has been an increasing concern about the effect of climatic change on human health in recent years. It has been suggested that young children are particularly at risk due to the reduced regulating mechanism against extreme climatic changes. However, few studies on the associations between climatic factors and childhood illness, specifically among young children, have been found in the literature. This epidemiological study utilized data collected on all young patients aged less than 6 years who presented to an emergency department for a period of two years. Information on climate and outdoor air quality variables was obtained from the Bureau of Meteorology. Data were analysed with various ARIMA time series models for each common childhood illness. Results suggested that maximum daily temperature was a significant risk factor for fever (t = 5.29, p < 0.001) with a regression coefficient of 0.37 (SE = 0.07) and gastroenteritis (t = 2.69, p = 0.007) with a regression coefficient of 0.10 (SE = 0.04). The UV index was also found to be significantly but negatively related to gastroenteritis (t = −2.37, p = 0.018). However, none of the climatic variables were associated with respiratory problems after adjusting for other air quality variables and infectious disease.
Importance of the gut microbiota in mice with a ‘humanized’ bile acid pool

Abstract Bile acids are signaling mediators, enabling intricate communication between tissues and the gut microbiota, and are involved in the pathophysiology of several immune and metabolic disorders. In this commentary, we discuss the importance of the gut microbiota in the Cyp2c70 knock-out mice, which are considered as a promising ‘humanized’ experimental resource for studying bile acids and their role in pathological conditions. We also discuss how Cyp2c70-deficient mice contribute to enhancing the translatability of preclinical studies in murine models to humans.
Effectiveness of radiology modalities in diagnosing and characterizing brain disorders

Objectives: To observe the accuracy of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans in evaluating neurological disorders. Methods: This retrospective research used CT or MRI to diagnose and characterize brain disorders. Patients’ records suffering from neurological disorders were considered eligible for inclusion, regardless of the time of appearance of symptoms, the severity of their symptoms, or their final clinical diagnosis. The exclusion criteria for this study involved patients who did not undergo either a CT or MRI scan. A chi-square test was performed to observe the association between the study variables. A total of 3155 cases were analyzed. Results: The most prevalent comorbid was dyslipidemia 670 (21.6%) followed by hypertension 548 (17.6%). Overall brain disorders were confirmed in 2426 (77%) patients. It was observed that half of the patients 1543 (48.9%) were diagnosed with stroke. It was found that the accuracy of CT and MRI was 78% and 74% respectively. The association of modalities, patient type, and gender with the confirmation of diseases was not found significant (p=>0.05). Conclusion: Our study revealed that CT and MRI were accurate by more than 75% and no difference was between both techniques to detect neurological disorders.
KINEMATIC ANALYSIS OF THE SNATCH TECHNIQUE OF COLOMBIAN 62 KG DIVISION WEIGHTLIFTERS PARTICIPATING IN THE 52nd NATIONAL CHAMPIONSHIP, "CARTAGENA 2014"

The purpose of this study was to identify the behaviour of some kinematic lineal variables during the execution of the snatch of the Colombian 62 kg division weightlifters participating in the 52nd National Championship in Cartagena in 2014, to recognize the movement patterns in this population, to identify the principal values of velocity peaks, the percentage of the height reached by the bar at its maximal point in relation to the weightlifter’s height, the relationship between the time of execution and the maximal peak of 3D velocity. All of the lifts, 100%, were analyzed of which 56% (n32) were valid.
Beekeeping in Jalisco, México

The purpose of this study was to analyze the socioeconomic factors that influence the beekeeping process and describe the current situation in beekeeping technology development in the south and southeast regions of Jalisco. The study was conducted by reviewing secondary sources of documentary information and the primary information was obtained by means of a survey, analyzing demographic, social, technological, and economic variables. From January to April 2011, a stratified sampling was conducted of six strata of beekeepers, with a final sampling of 183 beekeepers. We applied a frequency analysis, ANOVA (Waller-Duncan), and contingency tables (χ2). The average age observed for the beekeepers was 47 years, with fewer women participating in the activity, and an above national average level of education. The majority keep their apiaries in rented premises, a high percentage outside the municipality where they live. The honey obtained is multiflora and the main harvest is in the autumn, with a honey yield per hive below the national average. A number of problems affect the production sector including environmental factors, production costs, and varroa. We observed little diversification; in addition to honey only beeswax is recovered, and only a minority keep a record of production costs. There is wide participation in beekeeping associations and in training provided by different public and private bodies. There is a willingness to adopt new technologies and equipment for honey production with good practice standards.
Posttraumatic stress and depression symptom classes in parents of trauma-exposed children: a transdiagnostic perspective using pooled individual participant data

ABSTRACT Background: In the aftermath of child trauma, post-traumatic stress (PTS) and depression symptoms often co-occur among trauma exposed children and their parents. Studies have used latent class analysis (LCA) to examine PTS and depression symptoms and identify homogeneous subgroups among trauma exposed children. However, little is known about subgroups or classes of PTS and depression reactions of parents of traumatised children. Objectives: (1) Determine PTS and depression symptom classes at 2–9 months post-trauma, and (2) to examine sociodemographic covariates among parents of trauma exposed children. Methods: Using harmonised individual participant data (n = 702) from eight studies (Australia, UK, US) included in the Prospective studies of Acute Child Trauma and Recovery Data Archive (PACT/R), we modelled these phenomena at the symptom level using LCA. Results: Our LCA yielded three solutions: ‘high internalizing symptom’ class (11%); ‘low PTS-high depression’ class (17%); and ‘low internalizing symptom’ class (72%). Parents of children in the ‘low PTS-high depression’ class were more likely to have children of older age and be part of an ethnic minority, compared to the ‘low internalizing symptoms’ class. Mothers were more likely to be in the ‘high internalizing symptom’ class compared to the ‘low internalizing symptoms’ class. Conclusions: These findings reveal a qualitative structure and relationship between depression and PTS symptoms that highlights the importance of assessing and targeting a broad range of internalising symptoms in post-trauma psychological treatment.
Antibacterial activity of silver nanoparticles against Escherichia coli and methicillin-resistant Staphylococcus pseudintermedius is affected by incorporation into carriers for sustained release.

OBJECTIVE To establish the lowest effective dose of commercially available nanoparticulate silver (AgNP) for antibacterial activity against Escherichia coli (E coli) and methicillin-resistant Staphylococcus pseudintermedius (MRSP), in vitro, and to establish the effect of incorporating AgNP into carriers for sustained release on this antibacterial activity. SAMPLES Silver nanoparticle dispersion (0.02 mg/mL) composed of citrate-stabilized, spherical, 10 nm diameter nanoparticles in aqueous buffer. PROCEDURES E coli and MRSP were treated with 0.01 mg/mL AgNP. The highest concentration of bacteria where growth was inhibited by AgNP was selected for treatment with 0.01 mg/mL AgNP incorporated 3 carriers for sustained release: calcium sulfate hemihydrate (CSH) beads, poloxamer 407 gel, and gelatin sponge, respectively. The antibacterial activity of AgNP and AgNP incorporated into carriers for sustained release was compared with a mixed linear effects model. RESULTS AgNP inhibited bacterial growth at a concentration of 101 for MRSP and 103 for E coli. For MRSP, the treatment group was associated with bacterial growth (P < .001) while the concentration of bacteria and time were not (P = .292 and P = .289, respectively). For E coli, the treatment group and concentration of bacteria were associated with bacterial growth (P < .001 and = .029, respectively) while time was not (P = .095). Poloxamer 407 gel exerted standalone antibacterial activity against both species of bacteria; sponge and CSH beads did not. CLINICAL RELEVANCE AgNP has antibacterial activity against E coli and MRSP, which can be reduced when incorporated into carriers for sustained release. Poloxamer 407 gel alone and combined with AgNP exerts antibacterial activity against E coli and MRSP.
Genesis and Multi-Episodic Alteration of Zircon-Bearing Chromitites from the Ayios Stefanos Mine, Othris Massif, Greece: Assessment of an Unconventional Hypothesis on the Origin of Zircon in Ophiolitic Chromitites

Several small chromium (Cr) ore bodies are hosted within a unit of tectonically thinned dunite in the retired Ayios Stefanos mine of the western Othris ophiolite complex in Greece. Chromium ores consist of tectonically imprinted bodies of semi-massive to massive, podiform and lenticular chromitites composed of chromian spinel [Cr-spinel] with high Cr# [Cr/(Cr + Al) = 0.51–0.66] and Mg# [Mg/(Mg + Fe2+) = 0.58–0.76], low Fe3+# [Fe3+/(Fe3+ + Fe2+) ≤ 0.43] and low TiO2 (≤0.21 wt %) content. This composition is characteristic of Cr-spinels in equilibrium with melts of intermediate affinity between island-arc tholeiites (IATs) and mid-ocean ridge basalts (MORBs). Several Cr-spinel crystals in these ores exhibit imperfect zones made up of spinel hosting oriented lamellae of Mg-silicates (mostly chlorite) locally overgrown by porous domains along grain boundaries and fractures. From the Cr-spinel core to the lamellae-rich rim Cr#, Mg# and Fe3+# generally increase (0.68–0.87, 0.78–0.88 and 0.55–0.80, respectively), whereas from the core or the spinel zones with oriented lamellae to the porous domains Mg# and Fe3+# generally decrease (0.45–0.74 and ≤0.51, correspondingly). The lamellae-rich rims formed at oxidizing conditions, whereas the porous rims resulted from a later reducing event. Several tiny (≤30 μm), subhedral to anhedral and elongated Zr-bearing silicate mineral grains were discovered mainly along open and healed fractures cutting Cr-spinel. Most of the Zr-bearing silicate minerals (30 out of 35 grains) were found in a chromitite boulder vastly intruded by a complex network of gabbroic dykes. The dominant Zr-bearing silicate phase is by far zircon displaying a homogeneous internal texture in cathodoluminescence (CL) images. Raman spectroscopy data indicate that zircons have experienced structural damage due to self-irradiation. Their trace-element contents suggest derivation from a plagioclase-bearing, low-SiO2 intermediate to mafic source. Combined micro-textural and minerochemical data repeat the possibility of zircon derivation from limited volumes of high-T fluids emanating from the gabbroic intrusions. Once zircon is precipitated in cracks, it may be altered to Ca-rich Zr-bearing silicate phases (i.e., armstrongite, calciocatapleiite). Almost all zircons in these samples show evidence of gains in solvent compounds (CaO, Al2O3 and FeO) possibly due to re-equilibration with late deuteric fluids.
Computing the Field of moduli of some non-hyperelliptic pseudo-real curves

The explicit computation of the field of moduli of a closed Riemann surface is, in general, a difficult task. In this paper, for each even integer $k \geq 2$, we consider a suitable $2$-real parameter family of non-hyperelliptic pseudo-real Riemann surfaces of genus $g=1+(2k-3)k^{4}$. For each of them, we compute its field of moduli and also a minimal field of definition.
Accelerated Bregmann divergence optimization with SMART: an information geometry point of view

We investigate the problem of minimizing Kullback-Leibler divergence between a linear model $Ax$ and a positive vector $b$ in different convex domains (positive orthant, $n$-dimensional box, probability simplex). Our focus is on the SMART method that employs efficient multiplicative updates. We explore the exponentiated gradient method, which can be viewed as a Bregman proximal gradient method and as a Riemannian gradient descent on the parameter manifold of a corresponding distribution of the exponential family. This dual interpretation enables us to establish connections and achieve accelerated SMART iterates while smoothly incorporating constraints. The performance of the proposed acceleration schemes is demonstrated by large-scale numerical examples.
OTAS: An Elastic Transformer Serving System via Token Adaptation

Transformer model empowered architectures have become a pillar of cloud services that keeps reshaping our society. However, the dynamic query loads and heterogeneous user requirements severely challenge current transformer serving systems, which rely on pre-training multiple variants of a foundation model, i.e., with different sizes, to accommodate varying service demands. Unfortunately, such a mechanism is unsuitable for large transformer models due to the additional training costs and excessive I/O delay. In this paper, we introduce OTAS, the first elastic serving system specially tailored for transformer models by exploring lightweight token management. We develop a novel idea called token adaptation that adds prompting tokens to improve accuracy and removes redundant tokens to accelerate inference. To cope with fluctuating query loads and diverse user requests, we enhance OTAS with application-aware selective batching and online token adaptation. OTAS first batches incoming queries with similar service-level objectives to improve the ingress throughput. Then, to strike a tradeoff between the overhead of token increment and the potentials for accuracy improvement, OTAS adaptively adjusts the token execution strategy by solving an optimization problem. We implement and evaluate a prototype of OTAS with multiple datasets, which show that OTAS improves the system utility by at least 18.2%.
Rydberg molecules bound by strong light fields

The coupling of an isolated quantum state to a continuum is typically associated with decoherence and decreased lifetime. Here, we demonstrate that Rydberg macrodimers, weakly bound pairs of Rydberg atoms, can overcome this dissipative mechanism and instead form bound states with the continuum of free motional states. This is enabled by the unique combination of extraordinarily slow vibrational motion in the molecular state and the optical coupling to a non-interacting continuum. Under conditions of strong coupling, we observe the emergence of distinct resonances and explain them within a Fano model. For atoms arranged on a lattice, we predict the strong continuum coupling to even stabilize molecules consisting of more than two atoms and find first signatures of these by observing atom loss correlations using a quantum gas microscope. Our results present an intriguing mechanism to control decoherence and bind multiatomic molecules using strong light-matter interactions.
Modality-Aware Representation Learning for Zero-shot Sketch-based Image Retrieval

Zero-shot learning offers an efficient solution for a machine learning model to treat unseen categories, avoiding exhaustive data collection. Zero-shot Sketch-based Image Retrieval (ZS-SBIR) simulates real-world scenarios where it is hard and costly to collect paired sketch-photo samples. We propose a novel framework that indirectly aligns sketches and photos by contrasting them through texts, removing the necessity of access to sketch-photo pairs. With an explicit modality encoding learned from data, our approach disentangles modality-agnostic semantics from modality-specific information, bridging the modality gap and enabling effective cross-modal content retrieval within a joint latent space. From comprehensive experiments, we verify the efficacy of the proposed model on ZS-SBIR, and it can be also applied to generalized and fine-grained settings.
Tournaments, Contestant Heterogeneity and Performance

Tournaments are frequently used incentive mechanisms to enhance performance. In this paper, we use field data and show that skill disparities among contestants asymmetrically affect the performance of contestants. Skill disparities have detrimental effects on the performance of the lower-ability contestant but positive effects on the performance of the higher-ability contestant. We discuss the potential of different behavioral approaches to explain our findings and discuss the implications of our results for the optimal design of contests. Beyond that, our study reveals two important empirical results: (a) affirmative action-type policies may help to mitigate the adverse effects on lower-ability contestants, and (b) the skill level of potential future contestants in subsequent tournament stages can detrimentally influence the performance of higher-ability contestants but does not affect the lower-ability contestant.
DeepSweep: Parallel and Scalable Spectrum Sensing via Convolutional Neural Networks

Spectrum sensing is an essential component of modern wireless networks as it offers a tool to characterize spectrum usage and better utilize it. Deep Learning (DL) has become one of the most used techniques to perform spectrum sensing as they are capable of delivering high accuracy and reliability. However, current techniques suffer from ad-hoc implementations and high complexity, which makes them unsuited for practical deployment on wireless systems where flexibility and fast inference time are necessary to support real-time spectrum sensing. In this paper, we introduce DeepSweep, a novel DL-based transceiver design that allows scalable, accurate, and fast spectrum sensing while maintaining a high level of customizability to adapt its design to a broad range of application scenarios and use cases. DeepSweep is designed to be seamlessly integrated with well-established transceiver designs and leverages shallow convolutional neural network (CNN) to"sweep"the spectrum and process captured IQ samples fast and reliably without interrupting ongoing demodulation and decoding operations. DeepSweep reduces training and inference times by more than 2 times and 10 times respectively, achieves up to 98 percent accuracy in locating spectrum activity, and produces outputs in less than 1 ms, thus showing that DeepSweep can be used for a broad range of spectrum sensing applications and scenarios.
GNNShap: Scalable and Accurate GNN Explanation using Shapley Values

Graph neural networks (GNNs) are popular machine learning models for graphs with many applications across scientific domains. However, GNNs are considered black box models, and it is challenging to understand how the model makes predictions. Game theoric Shapley value approaches are popular explanation methods in other domains but are not well-studied for graphs. Some studies have proposed Shapley value based GNN explanations, yet they have several limitations: they consider limited samples to approximate Shapley values; some mainly focus on small and large coalition sizes, and they are an order of magnitude slower than other explanation methods, making them inapplicable to even moderate-size graphs. In this work, we propose GNNShap, which provides explanations for edges since they provide more natural explanations for graphs and more fine-grained explanations. We overcome the limitations by sampling from all coalition sizes, parallelizing the sampling on GPUs, and speeding up model predictions by batching. GNNShap gives better fidelity scores and faster explanations than baselines on real-world datasets. The code is available at https://github.com/HipGraph/GNNShap.