Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Transposon Mutagenesis Screens Identify Group A Streptococcus Genes Affecting Susceptibility to β-Lactam Antibiotics

Beta-lactam antibiotics are the primary drugs prescribed to treat infections caused by group A streptococcus (GAS), an important human pathogen. However, the molecular mechanisms of GAS interactions with beta-lactam antibiotics are not fully understood. ABSTRACT Group A streptococcus (GAS) is a Gram-positive human bacterial pathogen responsible for more than 700 million infections annually worldwide. Beta-lactam antibiotics are the primary agents used to treat GAS infections. Naturally occurring GAS clinical isolates with decreased susceptibility to beta-lactam antibiotics attributed to mutations in PBP2X have recently been documented. This prompted us to perform a genome-wide screen to identify GAS genes that alter beta-lactam susceptibility in vitro. Using saturated transposon mutagenesis, we screened for GAS gene mutations conferring altered in vitro susceptibility to penicillin G and/or ceftriaxone, two beta-lactam antibiotics commonly used to treat GAS infections. In the aggregate, we found that inactivating mutations in 150 GAS genes are associated with altered susceptibility to penicillin G and/or ceftriaxone. Many of the genes identified were previously not known to alter beta-lactam susceptibility or affect cell wall biosynthesis. Using isogenic mutant strains, we confirmed that inactivation of clpX (Clp protease ATP-binding subunit) or cppA (CppA proteinase) resulted in decreased in vitro susceptibility to penicillin G and ceftriaxone. Deletion of murA1 (UDP-N-acetylglucosamine 1-carboxyvinyltransferase) conferred increased susceptibility to ceftriaxone. Our results provide new information about the GAS genes affecting susceptibility to beta-lactam antibiotics. IMPORTANCE Beta-lactam antibiotics are the primary drugs prescribed to treat infections caused by group A streptococcus (GAS), an important human pathogen. However, the molecular mechanisms of GAS interactions with beta-lactam antibiotics are not fully understood. In this study, we performed a genome-wide mutagenesis screen to identify GAS mutations conferring altered susceptibility to beta-lactam antibiotics. In the aggregate, we discovered that mutations in 150 GAS genes were associated with altered beta-lactam susceptibility. Many identified genes were previously not known to alter beta-lactam susceptibility or affect cell wall biosynthesis. Our results provide new information about the molecular mechanisms of GAS interaction with beta-lactam antibiotics.
Light propagation in inhomogeneous and anisotropic cosmologies

The standard model of cosmology is based on the hypothesis that the Universe is spatially homogeneous and isotropic. When interpreting most observations, this cosmological principle is applied stricto sensu ; the main goal of the present thesis was to evaluate how reliable this assumption is, especially when small scales are at stake. After having reviewed the laws of geometric optics in curved spacetime, and the standard interpretation of cosmological observables, the dissertation reports a comprehensive analysis of light propagation in Swiss-cheese models, designed to capture the clumpy character of the Universe. The resulting impact on the interpretation of the Hubble diagram is quantified, and shown to be relatively small, thanks to the cosmological constant. When applied to current supernova data, the associated corrections tend to improve the agreement between the cosmological parameters inferred from the Hubble diagram and from the cosmic microwave background. This is a hint that the effect of small-scale structures on light propagation may become non-negligible in the era of precision cosmology. This motivated the development of a new theoretical framework, based on stochastic processes, which aims at describing small-scale lensing with a better accuracy. Regarding the isotropy side of the cosmological principle, this dissertation addresses, on the one hand, the potential effect of a large-scale anisotropy on light propagation, by solving the equations of geometric optics in the Bianchi I spacetime. On the other hand, possible sources of such an anisotropy, namely scalar-vector models for inflation or dark energy, are analysed.
Fast spontaneous emission and high Förster resonance energy transfer rate in hybrid organic/inorganic plasmonic nanostructures

We report an experimental study of the plasmon-assisted spontaneous emission and the F\"orster resonance energy transfer between organic molecules and semiconductor colloidal quantum dots. The localized plasmonic field in the nanogap between a gold nano-popcorn's tips and a 5-nm separated gold film supports high photonic density of states and provides pathways for the light-matter interaction mechanisms. We demonstrate that, besides the total spontaneous emission rate enhancement factor up to 66 for quantum dots and molecules, the F\"orster resonance energy transfer efficiency and rate constant are simultaneously modified. While the energy transfer efficiency is reduced from 84 to 35 per cent due to the non-radiative quenching effect and fast donor spontaneous emission rate, the energy transfer rate constant is significantly increased from 4 to 20 ns-1. Our results have quantitatively elucidated decay mechanisms that are important toward understanding and controlling of the light-matter interaction at the nanoscale.
Grasping follows Weber's law: How to use response variability as a proxy for JND

Weber's law is a fundamental psychophysical principle. It states that the just noticeable difference (JND) between stimuli increases with stimulus magnitude; consequently, larger stimuli should be estimated with larger variability. However, visually guided grasping seems to violate this expectation: When repeatedly grasping large objects, the variability is similar to that when grasping small objects. Based on this result, it was often concluded that grasping violated Weber's law. This astonishing finding generated a flurry of research, with contradictory results and potentially far-reaching implications for theorizing about the functional architecture of the brain. We show that previous studies ignored nonlinearities in the scaling of the grasping response. These nonlinearities result from, for example, the finger span being limited such that the opening of the fingers reaches a ceiling for large objects. We describe how to mathematically take these nonlinearities into account and apply this approach to our own data, as well as to the data of three influential studies on this topic. In all four datasets, we found that—when appropriately estimated—JNDs increase with object size, as expected by Weber's law. We conclude that grasping obeys Weber's law, as do essentially all sensory dimensions.
A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters

We demonstrate the ability of convolutional neural networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters with remarkably low bias and scatter. We present two models, CNN1D and CNN2D, which leverage this deep learning tool to infer cluster masses from distributions of member galaxy dynamics. Our first model, CNN1D, infers cluster mass directly from the distribution of member galaxy line-of-sight velocities. Our second model, CNN2D, extends the input space of CNN1D to learn on the joint distribution of galaxy line-of-sight velocities and projected radial distances. We train each model as a regression over cluster mass using a labeled catalog of realistic mock cluster observations generated from the MultiDark simulation and UniverseMachine catalog. We then evaluate the performance of each model on an independent set of mock observations selected from the same simulated catalog. The CNN models produce cluster mass predictions with lognormal residuals of scatter as low as 0.132 dex, greater than a factor of 2 improvement over the classical M–σ power-law estimator. Furthermore, the CNN model reduces prediction scatter relative to similar machine-learning approaches by up to 17% while executing in drastically shorter training and evaluation times (by a factor of 30) and producing considerably more robust mass predictions (improving prediction stability under variations in galaxy sampling rate by 30%).
Learning Spatiotemporal Features of Ride-sourcing Services with Fusion Convolutional Network

To collectively forecast the demand for ride-sourcing services in all regions of a city, the deep learning approaches have been applied with commendable results. However, the local statistical differences throughout the geographical layout of the city make the spatial stationarity assumption of the convolution invalid, which limits the performance of CNNs on the demand forecasting task. In this paper, we propose a novel deep learning framework called LC-ST-FCN (locally connected spatiotemporal fully-convolutional neural network) to address the unique challenges of the region-level demand forecasting problem within one end-to-end architecture (E2E). We first employ the 3D convolutional layers to fuse the spatial and temporal information existed in the input and then feed the spatiotemporal features extracted by the 3D convolutional layers to the subsequent 2D convolutional layers. Afterward, the prediction value of each region is obtained by the locally connected convolutional layers which relax the parameter sharing scheme. We evaluate the proposed model on a real dataset from a ride-sourcing service platform (DiDiChuxing) and observe significant improvements compared with a bunch of baseline models. Besides, we also illustrate the effectiveness of our proposed model by visualizing how different types of convolutional layers transform their input and capture useful features. The visualization results show that fully convolutional architecture enables the model to better localize the related regions. And the locally connected layers play an important role in dealing with the local statistical differences and activating useful regions.
Risk factors for initial appointment non-attendance at Improving Access to Psychological Therapy (IAPT) services: A retrospective analysis

Abstract Background Approximately 1.5 million referrals are made to Improving Access to Psychological Therapy (IAPT) services annually. However, treatment is received in less than half of cases due to ineligibility or non-attendance. The aim was to explore risk factors for non-attendance at the initial two IAPT appointments following referral. Methods An exploratory, retrospective analysis of referral and attendance data from five IAPT services in the North of England. Participants were 97,020 referrals received 2010–2014. Main outcome was attendance at the first two offered appointments (assessment and initial treatment). Results Based on data from two services, 66% of referrals resulted in assessment attendance. Across all five services 57% of patients who attended for assessment subsequently attended the first treatment appointment. The odds of attending an assessment appointment were more than 3 times higher for self-referrals than for GP referrals (OR 3.46, 95% CI 3.27–3.66, p < 0.001). Factors important to treatment appointment attendance following assessment were the service, referral source, presenting problem, and anxiety severity. Conclusion Initial appointment non-attendance is a consistent problem for IAPT services. Specific factors that may support IAPT services to improve non-attendance rates are identified. IAPT indicators of success should take account of non-attendance at initial appointments.
Kauffman–Jones polynomial of a curve on a surface

We introduce a Kauffman–Jones type polynomial ℒγ(A) for a curve γ on an oriented surface, whose endpoints are on the boundary of the surface. The polynomial ℒγ(A) is a Laurent polynomial in one variable A and is an invariant of the homotopy class of γ. As an application, we obtain an estimate in terms of the span of ℒγ(A) for the minimum self-intersection number of a curve within its homotopy class. We then give a chord diagrammatic description of ℒγ(A) and show some computational results on the span of ℒγ(A).
THE IMPACT OF COLD GAS ACCRETION ABOVE A MASS FLOOR ON GALAXY SCALING RELATIONS

Using the cosmological baryonic accretion rate and normal star formation (SF) efficiencies, we present a very simple model for star-forming galaxies that accounts for the mass and redshift dependences of the star formation rate (SFR)–mass and Tully–Fisher (TF) relations from z ∼ 2 to the present. The time evolution follows from the fact that each modeled galaxy approaches a steady state where the SFR follows the (net) cold gas accretion rate. The key feature of the model is a halo mass floor Mmin ≃ 1011 M☉ below which accretion is quenched in order to simultaneously account for the observed slopes of the SFR–mass and TF relations. The same successes cannot be achieved via an SF threshold (or delay) nor by varying the SF efficiency or the feedback efficiency. Combined with the mass ceiling for cold accretion due to virial shock heating, the mass floor Mmin explains galaxy “downsizing,” where more massive galaxies formed earlier and over a shorter period of time. It turns out that the model also accounts for the observed galactic baryon and gas fractions as a function of mass and time, and the cosmic SFR density, which are all resulting from the mass floor Mmin. The model helps us to understand that it is the cosmological decline of accretion rate that drives the decrease of cosmic SFR density between z ∼ 2 and z = 0 and the rise of the cosmic SFR density from z ∼ 6 to z ∼ 2 that allows us to put a constraint on our main parameter Mmin ≃ 1011 M☉. Among the physical mechanisms that could be responsible for the mass floor, our view is that photoionization feedback (from first in situ hot stars) lowering the cooling efficiency is likely to play a large role.
Critical Kerr nonlinear optical cavity in the presence of internal loss and driving noise

We theoretically analyze the noise transformation of a high-power continuous-wave light field that is reflected off a critical Kerr nonlinear cavity (KNLC). Our investigations are based on a rigorous treatment in the time domain. Thereby, realistic conditions of a specific experimental environment including optical intracavity loss and strong classical driving noise can be modeled for any KNLC. We show that, even in the presence of optical loss and driving noise, considerable squeezing levels can be achieved. We find that the achievable squeezing levels are not limited by the driving noise but solely by the amount of optical loss. Amplitude-quadrature squeezing of the reflected mean field is obtained if the KNLC's operating point is chosen properly. Consistently, a KNLC can provide a passive purely optical reduction of laser-power noise as experimentally demonstrated in Khalaidovski et al.[Phys. Rev. A 80, 053801 (2009)]. We apply our model to this experiment and find good agreement with measured noise spectra.
Monkeypox outbreak 2022: What we know so far and its potential drug targets and management strategies

Monkeypox is a rare zoonotic disease caused by infection with the monkeypox virus. The disease can result in flu‐like symptoms, fever, and a persistent rash. The disease is currently spreading throughout the world and prevention and treatment efforts are being intensified. Although there is no treatment that has been specifically approved for monkeypox virus infection, infected patients may benefit from using certain antiviral medications that are typically prescribed for the treatment of smallpox. The drugs are tecovirimat, brincidofovir, and cidofovir, all of which are currently in short supply due to the spread of the monkeypox virus. Resistance is also a concern, as widespread replication of the monkeypox virus can lead to mutations that produce monkeypox viruses that are resistant to the currently available treatments. This article discusses monkeypox disease, potential drug targets, and management strategies to overcome monkeypox disease. With the discovery of new drugs, it is hoped that the problem of insufficient drugs will be resolved, and it is not anticipated that drug resistance will become a major issue in the near future.
HAT-P-34b–HAT-P-37b: FOUR TRANSITING PLANETS MORE MASSIVE THAN JUPITER ORBITING MODERATELY BRIGHT STARS

We report the discovery of four transiting extrasolar planets (HAT-P-34b–HAT-P-37b) with masses ranging from 1.05 to 3.33 MJ and periods from 1.33 to 5.45 days. These planets orbit relatively bright F and G dwarf stars (from V = 10.16 to V = 13.2). Of particular interest is HAT-P-34b which is moderately massive (3.33 MJ), has a high eccentricity of e = 0.441 ± 0.032 at a period of P = 5.452654 ± 0.000016 days, and shows hints of an outer component. The other three planets have properties that are typical of hot Jupiters.
Warped branches of flux compactifications

We consider Freund-Rubin-type compactifications which are described by (p+q)-dimensional Einstein gravity with a positive cosmological constant and a q-form flux. Using perturbative expansions of Kinoshita's ansatz for warped dS_pxS^q and AdS_pxS^q spacetimes, we obtain analytical solutions describing the warped branches and their respective phase spaces. These equations are given by inhomogeneous Gegenbauer differential equations which can be solved by the Green's function method. The requirement that the Green's functions are regular provides constraints which determine the structure of the phase space of the warped branches. We apply the perturbation results to calculate the thermodynamic variables for the warped dS_pxS^q branch. In particular, the first law of thermodynamics can be reproduced using this method.
Hjorth Analysis of General Polish Group Actions

Hjorth has introduced a Scott analysis for general Polish group actions, and has asked whether his notion of rank satisfies a boundedness principle similar to the one of Scott rank - namely, the orbit equivalence relation is Borel if and only if Hjorth ranks are bounded. We present the principles of Hjorth analysis and Hjorth rank, and answer Hjorth's question positively. From that we get a positive answer to a conjecture due to Hjorth - for every limit ordinal $\alpha$ , the set of elements whose orbit is of complexity less than $\alpha$ is a Borel set. We then show Nadel's theorem for Hjorth rank - the rank of $x$ is no more than $\omega_{1}^{ck(x)}$.
Identifying supersingular elliptic curves

Given an elliptic curve E over a field of positive characteristic p , we consider how to efficiently determine whether E is ordinary or supersingular. We analyze the complexity of several existing algorithms and then present a new approach that exploits structural differences between ordinary and supersingular isogeny graphs. This yields a simple algorithm that, given E and a suitable non-residue in 𝔽 p 2 , determines the supersingularity of E in O ( n 3 log 2 n ) time and O ( n ) space, where n = O (log p ) . Both these complexity bounds are significant improvements over existing methods, as we demonstrate with some practical computations.
Advances in Extracellular Vesicle Nanotechnology for Precision Theranostics

Extracellular vesicles (EVs) have increasingly been recognized as important cell surrogates influencing many pathophysiological processes, including cellular homeostasis, cancer progression, neurologic disease, and infectious disease. These behaviors enable EVs broad application prospects for clinical application in disease diagnosis and treatment. Many studies suggest that EVs are superior to conventional synthetic carriers in terms of drug delivery and circulating biomarkers for early disease diagnosis, opening up new frontiers for modern theranostics. Despite these clinical potential, EVs containing diverse cellular components, such as nucleic acids, proteins, and metabolites are highly heterogeneous and small size. The limitation of preparatory, engineering and analytical technologies for EVs poses technical barriers to clinical translation. This article aims at present a critical overview of emerging technologies in EVs field for biomedical applications and challenges involved in their clinic translations. The current methods for isolation and identification of EVs are discussed. Additionally, engineering strategies developed to enhance scalable production and improved cargo loading as well as tumor targeting are presented. The superior clinical potential of EVs, particularly in terms of different cell origins and their application in the next generation of diagnostic and treatment platforms, are clarified.
Person‐centred and task‐centred care: Impact on mealtime behaviours in nursing home residents with dementia

Abstract Introduction Mealtime is a critical daily activity to ensure nutrition, hydration, function and socialisation. Interactions between staff and residents during mealtimes are complex and dynamic processes including verbal and/or nonverbal communication that can be positive/neutral or challenging. This study examined characteristics of and relationships between person‐centred and task‐centred care and positive/neutral and challenging mealtime behaviours in persons with dementia. Methods This study was a secondary behavioural analyses of videotaped mealtime observations (n = 110) involving 42 unique staff‐resident dyads (29 staff and 25 residents with dementia) in nine nursing homes. The refined Cue Utilization and Engagement in Dementia mealtime video‐coding scheme was used to code videos during 2019–2020. Dependent variables representing resident mealtime behaviours included positive verbal behaviours, positive/neutral nonverbal behaviours, functional impairments (nonverbal) and resistive behaviours (verbal and nonverbal). Independent variables were staff person‐centred and task‐centred approaches (verbal and nonverbal). Relationships between resident mealtime behaviours and staff approaches were examined using bivariate analysis and logistic regression. Results Staff person‐centred verbal approaches were associated with resident positive verbal behaviours (OR = 1.38, 95% CI = 1.09–1.76), functional impairments (OR = 0.81, 95% CI = 0.66–1.00) and resistive behaviours (OR = 1.65, 95% CI = 1.18–2.31). Staff person‐centred nonverbal approaches were associated with resident functional impairments (OR = 1.33, 95% CI = 1.02–1.74). Staff task‐centred approaches were not associated with resident positive/neutral or challenging mealtime behaviours. Conclusion This study characterised staff approaches and resident behaviours during mealtime care and provided preliminary support on associations between staff person‐centred approaches and resident positive and challenging behaviours. Person‐centred mealtime care practice is recommended by focusing on support of common, challenging behaviours and reinforcement of positive behaviours with adequate consideration of individual needs and preferences.
Linear convergence of the generalized PPA and several splitting methods for the composite inclusion problem

For the inclusion problem involving two maximal monotone operators, under the metric subregularity of the composite operator, we derive the linear convergence of the generalized proximal point algorithm and several splitting algorithms, which include the over-relaxed forward-backward splitting algorithm, the generalized Douglas-Rachford splitting algorithm and Davis' three-operator splitting algorithm. To the best of our knowledge, this linear convergence condition is weaker than the existing ones that almost all require the strong monotonicity of the composite operator. Withal, we give some sufficient conditions to ensure the metric subregularity of the composite operator. At last, the preliminary numerical performances on some toy examples support the theoretical results.