Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cyclooxygenase 2 Inhibitor Etoricoxib as Adjunctive Therapy in Tuberculosis Impairs Macrophage Control of Mycobacterial Growth

Abstract Background Current tuberculosis treatment regimens could be improved by adjunct host-directed therapies (HDT) targeting host responses. We investigated the antimycobacterial capacity of macrophages from patients with tuberculosis in a phase 1/2 randomized clinical trial (TBCOX2) of the cyclooxygenase-2 inhibitor etoricoxib. Methods Peripheral blood mononuclear cells from 15 patients with tuberculosis treated with adjunctive COX-2i and 18 controls (standard therapy) were collected on day 56 after treatment initiation. The ex vivo capacity of macrophages to control mycobacterial infection was assessed by challenge with Mycobacterium avium, using an in vitro culture model. Macrophage inflammatory responses were analyzed by gene expression signatures, and concentrations of cytokines were analyzed in supernatants by multiplex. Results Macrophages from patients receiving adjunctive COX-2i treatment had higher M. avium loads than controls after 6 days, suggesting an impaired capacity to control mycobacterial infection compared to macrophages from the control group. Macrophages from the COX-2i group had lower gene expression of TNF, IL-1B, CCL4, CXCL9, and CXCL10 and lowered production of cytokines IFN-β and S100A8/A9 than controls. Conclusions Our data suggest potential unfavorable effects with impaired macrophage capacity to control mycobacterial growth in patients with tuberculosis receiving COX-2i treatment. Larger clinical trials are required to analyze the safety of COX-2i as HDT in patients with tuberculosis. Clinical Trials Registration NCT02503839.
Subphotospheric Emission from Short Gamma-Ray Bursts: Protons Mold the Multimessenger Signals

The origin of the observed Band-like photon spectrum in short gamma-ray bursts (sGRBs) is a long-standing mystery. We carry out the first general relativistic magnetohydrodynamic simulation of an sGRB jet with initial magnetization σ 0 = 150 in dynamical ejecta from a binary merger. From this simulation, we identify regions along the jet of efficient energy dissipation due to magnetic reconnection and collisionless subshocks. Taking into account electron and proton acceleration processes, we solve for the first time the coupled transport equations for photons, electrons, protons, neutrinos, and intermediate particle species up to close to the photosphere (i.e., up to 1 × 1012 cm), accounting for all relevant radiative and cooling processes. We find that the subphotospheric multimessenger signals carry strong signatures of the hadronic interactions and their resulting particle cascades. Importantly, the spectral energy distribution of photons is significantly distorted with respect to the Wien one, commonly assumed below the photosphere. Our findings suggest that the bulk of the nonthermal photon spectrum observed in sGRBs can stem from hadronic processes occurring below the photosphere and previously neglected, with an accompanying energy flux of neutrinos peaking in the GeV energy range.
SN 2022crv: IIb, Or Not IIb: That is the Question

We present optical and near-infrared observations of SN~2022crv, a stripped envelope supernova in NGC~3054, discovered within 12 hrs of explosion by the Distance Less Than 40 Mpc Survey. We suggest SN~2022crv is a transitional object on the continuum between SNe Ib and SNe IIb. A high-velocity hydrogen feature ($\sim$$-$20,000 -- $-$16,000 $\rm km\,s^{-1}$) was conspicuous in SN~2022crv at early phases, and then quickly disappeared around maximum light. By comparing with hydrodynamic modeling, we find that a hydrogen envelope of $\sim 10^{-3}$ \msun{} can reproduce the behaviour of the hydrogen feature observed in SN~2022crv. The early light curve of SN~2022crv did not show envelope cooling emission, implying that SN~2022crv had a compact progenitor with extremely low amount of hydrogen. The analysis of the nebular spectra shows that SN~2022crv is consistent with the explosion of a He star with a final mass of $\sim$4.5 -- 5.6 \msun{} that has evolved from a $\sim$16 -- 22 \msun{} zero-age main sequence star in a binary system with about 1.0 -- 1.7 \msun{} of oxygen finally synthesized in the core. The high metallicity at the supernova site indicates that the progenitor experienced a strong stellar wind mass loss. In order to retain a small amount of residual hydrogen at such a high metallicity, the initial orbital separation of the binary system is likely larger than $\sim$1000~$\rm R_{\odot}$. The near-infrared spectra of SN~2022crv show a unique absorption feature on the blue side of He I line at $\sim$1.005~$\mu$m. This is the first time that such a feature has been observed in a Type Ib/IIb, and could be due to \ion{Sr}{2}. Further detailed modelling on SN~2022crv can shed light on the progenitor and the origin of the mysterious absorption feature in the near infrared.
Observational constraints on the Emergent Universe with interacting non-linear fluids and its stability analysis

We investigate a flat Emergent Universe (EU) with a nonlinear equation of state which is equivalent to three different compositions of fluids. In the EU, initially, the evolution of the universe began with no interaction, but as time evolves, an interaction sets in among the three fluids leading to the observed universe. The characteristic of an EU is that it is a singularity-free universe that evolves with all the basic features of the early evolution. A given nonlinear equation of state parameter permits a universe with three different fluids. We get a universe with dark energy, cosmic string, and radiation domination to begin with, which at a later epoch transits into a universe with three different fluids with matter domination, dark matter, and dark energy for a given interaction strength among the cosmic fluids. Later the model parameters are constrained using the observed Hubble data and Type Ia Supernova (SnIa) data from the Pantheon data set. The classical stability analysis of the model is performed using the square speed of sound. It is found that a theoretically stable cosmological model can be obtained in this case, however, the model becomes classically unstable at the present epoch when the observational bounds on the model parameters are taken into account.
Existence and Morse Index of two free boundary embedded geodesics on Riemannian 2-disks with convex boundary

We prove that a free boundary curve shortening flow on closed surfaces with a strictly convex boundary remains noncollapsed for a finite time in the sense of the reflected chord-arc profile introduced by Langford-Zhu. This shows that such flow converges to free boundary embedded geodesic in infinite time, or shrinks to a round half-point on the boundary. As a consequence, we prove the existence of two free boundary embedded geodesics on a Riemannian $2$-disk with a strictly convex boundary. Moreover, we prove that there exists a simple closed geodesic with Morse Index $1$ and $2$. This settles the free boundary analog of Grayson's theorem.
Phytoremediation of Composition-B Derived TNT and RDX in Herbaceous Plant-vegetated and Bare Lysimeters

Abstract : This report describes a study in which phytoremediation of composition-B (comp-B) derived TNT and RDX was quantified in 0.5-m S. nutans (Indian grass)-vegetated organic matter and nutrient-poor soil over a 92-day period. The vegetation was allowed to establish in 0.5-m-high soil cores prior to amendment with ground comp-B mixed with the same soil, and effects and fate of comp-B derived TNT and RDX were followed in plants, soil, and leachate under greenhouse conditions. Remediation in vegetated soils exceeded that in bare soils for TNT up to a comp-B level of 218 mg kg-1, and for RDX up to a comp-B level of 73 mg kg-1. Thus, phytoremediation can be used as an effective remediation technology in a given range of explosives contamination. The greatest annual remediation potential was 58.5 g TNT m-2 and 42.4 g RDX m-2 in vegetated soils, and 54.5 g TNT m-2 and 51.0 g RDX m-2 in unvegetated soils. Remediation was attributed to a large degree to processes other than plant uptake, including bioremediation (plant-assisted or not), complexation with plant material and soil components leading to non-extractability, and photolysis (limited to the upper soil layer). Results of a comparison between 15N-based and chemical-RDX-based mass balances, with 15N derived from uniformly labeled 15N-RDX, indicated greater incorporation of 15N than of RDX in soil and plants of vegetated units than in soil of non-vegetated units.
From nonalcoholic steatohepatitis, metabolic dysfunction-associated fatty liver disease, to steatotic liver disease: Updates of nomenclature and impact on clinical trials

Associated Steatotic Liver Disease; HBV, hepatitis B virus; HCV, hepatitis C virus; SLD, Steatotic Liver Disease; CMRFs, cardiometabolic risk factors; ALD, Alcohol-related liver disease; MASH, metabolic associated steatohepatitis; HDL, high density lipoprotein; SGLT2, glucose-lowering agents, sodium-glucose cotransporter 2; GLP1, glucagon-like peptide 1.
Analysis of modular bioengineered antimicrobial lanthipeptides at nanoliter scale

1. Currently, the deployment of tracking devices is one of the most frequently used approaches to study movement ecology of birds. Recent miniaturization of light-level geolocators enabled studying small bird species whose migratory patterns were widely unknown. However, geolocators may reduce vital rates in tagged birds and may bias obtained movement data. 2. There is a need for a thorough assessment of the potential tag effects on small birds, as previous meta-analyses did not evaluate unpublished data and impact of multiple life-history traits, focused mainly on large species and the number of published studies tagging small birds has increased substantially
Frequency Estimation Using Complex-Valued Shifted Window Transformer

Estimating closely spaced frequency components of a signal is a fundamental problem in statistical signal processing. In this letter, we introduce 1-D real-valued and complex-valued shifted window (Swin) transformers, referred to as SwinFreq and CVSwinFreq, respectively, for line-spectra frequency estimation on 1-D complex-valued signals. Whereas 2-D Swin transformer-based models have gained traction for optical image super-resolution, we introduce for the first time a complex-valued Swin module designed to leverage the complex-valued nature of signals for a wide array of applications. The proposed approach overcomes the limitations of the classical algorithms such as the periodogram, MUSIC, and OMP in addition to state-of-the-art deep learning approach cResFreq. SwinFreq and CVSwinFreq boast superior performance at low signal-to-noise ratio SNR and improved resolution capability while requiring fewer model parameters than cResFreq, thus deeming it more suitable for edge and mobile applications. We find that the real-valued Swin-Freq outperforms its complex-valued counterpart CVSwinFreq for several tasks while touting a smaller model size. Finally, we apply the proposed techniques for radar range profile super-resolution using real data. The results from both synthetic and real experimentation validate the numerical and empirical superiority of SwinFreq and CVSwinFreq to the state-of-the-art deep learning-based techniques and traditional frequency estimation algorithms. The code and models are publicly available at https://github.com/josiahwsmith10/spectral-super-resolution-swin.
A sex-structured model of Wolbachia invasion to design sex-biased release strategies in Aedes spp mosquitoes populations

Laboratory experiments as well as some ﬁeld essays have revealed that the intracellular bacterium Wolbachia , deliberately introduced in Aedes spp female mosquitoes, drastically reduces their vector competence for dengue virus and other pathogens. However, female mosquitoes infected with Wolbachia still need to ingest human blood while male mosquitoes, either wild or Wolbachia -carrying, do not bite people. Moreover, Wolbachia -carrying females may transmit the virus to people during blood-feeding, even though with a far less probability than the wild ones. Therefore, massive releases of Wolbachia -carrying females may increase both the nuisance and the epidemiological risk among human residents. In this paper, we propose a sex-structured model of Wolbachia invasion that brings forward the possibility of developing male-biased release strategies of Wolbachia -carriers leading to Wolbachia invasion. Thanks to this model, we study the minimal amount of mosquitoes necessary to complete this task, according to the relative sex-ratio of the released mosquitoes and to the release schedule. We also pay attention to the estimate of the time needed to achieve the ultimate population replacement.
A moment-based approach for the analysis of the infinitesimal model in the regime of small variance

We provide an asymptotic analysis of a nonlinear integro-differential equation describing the evolutionary dynamics of a population which reproduces sexually and which is subject to selection and competition. The sexual reproduction is modeled via a nonlinear integral term, known as the ''infinitesimal model''. We consider a regime of small segregational variance, where a parameter in the infinitesimal operator, which measures the deviation between the trait of the offspring and the mean parental trait, is small. We prove that, in this regime, the phenotypic distribution remains close to a Gaussian profile with a fixed small variance and we characterize the dynamics of the mean phenotypic trait via an ordinary differential equation. While similar properties were already proved for a closely related model using a Hopf-Cole transformation and perturbative analysis techniques, we provide an alternative proof which relies on a direct study of the dynamics of the moments of the phenotypic distribution and a contraction property of the Wasserstein distance.
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering

Systematic generalization is a crucial aspect of intelligence, which refers to the ability to generalize to novel tasks by combining known subtasks and concepts. One critical factor that has been shown to influence systematic generalization is the diversity of training data. However, diversity can be defined in various ways, as data have many factors of variation. A more granular understanding of how different aspects of data diversity affect systematic generalization is lacking. We present new evidence in the problem of Visual Question Answering (VQA) that reveals that the diversity of simple tasks (i.e. tasks formed by a few subtasks and concepts) plays a key role in achieving systematic generalization. This implies that it may not be essential to gather a large and varied number of complex tasks, which could be costly to obtain. We demonstrate that this result is independent of the similarity between the training and testing data and applies to well-known families of neural network architectures for VQA (i.e. monolithic architectures and neural module networks). Additionally, we observe that neural module networks leverage all forms of data diversity we evaluated, while monolithic architectures require more extensive amounts of data to do so. These findings provide a first step towards understanding the interactions between data diversity design, neural network architectures, and systematic generalization capabilities.
Regularised Diffusion-Shock Inpainting

We introduce regularised diffusion--shock (RDS) inpainting as a modification of diffusion--shock inpainting from our SSVM 2023 conference paper. RDS inpainting combines two carefully chosen components: homogeneous diffusion and coherence-enhancing shock filtering. It benefits from the complementary synergy of its building blocks: The shock term propagates edge data with perfect sharpness and directional accuracy over large distances due to its high degree of anisotropy. Homogeneous diffusion fills large areas efficiently. The second order equation underlying RDS inpainting inherits a maximum--minimum principle from its components, which is also fulfilled in the discrete case, in contrast to competing anisotropic methods. The regularisation addresses the largest drawback of the original model: It allows a drastic reduction in model parameters without any loss in quality. Furthermore, we extend RDS inpainting to vector-valued data. Our experiments show a performance that is comparable to or better than many inpainting methods based on partial differential equations and related integrodifferential models
Genome‐wide association study of soluble solids content, flesh color, and fruit shape in citron watermelon

Fruit quality traits are crucial determinants of consumers’ willingness to purchase watermelon produce, making them major goals for breeding programs. There is limited information on the genetic underpinnings of fruit quality traits in watermelon. A total of 125 citron watermelon (Citrullus amarus) accessions were genotyped using single nucleotide polymorphisms (SNPs) molecular markers generated via whole‐genome resequencing. A total of 2,126,759 genome‐wide SNP markers were used to uncover marker‐trait associations using single and multi‐locus GWAS models. High broad‐sense heritability for fruit quality traits was detected. Correlation analysis among traits revealed positive relationships, with the exception of fruit diameter and fruit shape index (ratio of fruit length to fruit diameter), which was negative. A total of 37 significant SNP markers associated with soluble solids content, flesh color, fruit length, fruit diameter, and fruit shape index traits were uncovered. These peak SNPs accounted for 2.1%–23.4% of the phenotypic variation explained showing the quantitative inheritance nature of the evaluated traits. Candidate genes relevant to fruit quality traits were uncovered on chromosomes Ca01, Ca03, Ca06, and Ca07. These significant molecular markers and candidate genes will be useful in marker‐assisted breeding of fruit quality traits in watermelon.
Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data

Data augmentation is a common practice to help generalization in the procedure of deep model training. In the context of physiological time series classification, previous research has primarily focused on label-invariant data augmentation methods. However, another class of augmentation techniques (i.e., Mixup) that emerged in the computer vision field has yet to be fully explored in the time series domain. In this study, we systematically review the mix-based augmentations, including mixup, cutmix, and manifold mixup, on six physiological datasets, evaluating their performance across different sensory data and classification tasks. Our results demonstrate that the three mix-based augmentations can consistently improve the performance on the six datasets. More importantly, the improvement does not rely on expert knowledge or extensive parameter tuning. Lastly, we provide an overview of the unique properties of the mix-based augmentation methods and highlight the potential benefits of using the mix-based augmentation in physiological time series data. Our code and results are available at https://github.com/comp-well-org/Mix-Augmentation-for-Physiological-Time-Series-Classification.
Analytical structure of the binary collision integral and the ultrarelativistic limit of transport coefficients of an ideal gas

In this paper we discuss the analytical properties of the binary collision integral for a gas of ultrarelativistic particles interacting via a constant cross-section. Starting from a near-equilibrium expansion over a complete basis of irreducible tensors in momentum space we compute the linearized collision matrices analytically. Using these results we then numerically compute all transport-coefficients of relativistic fluid dynamics with various power-counting schemes that are second-order in Knudsen and/or inverse Reynolds numbers. Furthermore, we also exactly compute the leading-order contribution with respect to the particle mass to the coefficient of bulk viscosity, the relaxation time, and other second-order transport coefficients of the bulk viscous pressure.
Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup

The standard recipe applied in transfer learning is to finetune a pretrained model on the task-specific dataset with different hyperparameter settings and pick the model with the highest accuracy on the validation dataset. Unfortunately, this leads to models which do not perform well under distribution shifts, e.g. when the model is given graphical sketches of the object as input instead of photos. In order to address this, we propose the manifold mixing model soup, an algorithm which mixes together the latent space manifolds of multiple finetuned models in an optimal way in order to generate a fused model. We show that the fused model gives significantly better out-of-distribution performance (+3.5 % compared to best individual model) when finetuning a CLIP model for image classification. In addition, it provides also better accuracy on the original dataset where the finetuning has been done.
Stripe rust of wheat

There have been two introductions of wheat stripe rust into Australia. These introductions may have entered Australia on clothing. The first introduction occurred in Victoria in 1979, and stripe rust rapidly spread across eastern Australia. This original rust mutated, and a number of pathotypes (also known as races or strains) developed enabling the rust to attack more wheat varieties over time. This first introduction, even though wide spread in the east, did not move to Western Australia. The second introduction of stripe rust into Australia occurred in Western Australia in 2002. In 2003, this pathotype was detected in eastern Australia. This second introduction, now known as the "WA" pathotype, quickly became dominant in eastern Australia. Since 2003 the "WA" pathotype has undergone several mutations. There are now many pathotypes of wheat stripe rust that are common in Victoria. The resistance ratings provided in disease guides often represent the worse of the pathotypes.
Efficient Concept Drift Handling for Batch Android Malware Detection Models

The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze sampling methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data and state-of-the-art active learning methods that select those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample selection mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments.