In this work we study features of inertia-gravity wave turbulence in the rotating shallow water equations. On examining the dynamics of waves with varying rotation rates, we find that the turbulent cascade of waves is strongest at low rotation rates, forming a $k^{-2}$ energy spectrum, and a rich distribution of shocks in physical space. At high rotation rates, the forward cascade of waves weakens along with a steeper energy spectra and vanishing of shocks in physical space. The wave cascade is seen to be scale-local, resulting in a noticeable time interval for energy to get transferred from domain scale to dissipative scale. Furthermore, we find that the vortical flow has a non-negligible effect on the wave cascade, especially at high rotation rates. The vortical flow assists in the forward cascade of waves and shock formation at high rotation rates, while the waves by themselves in the absence of the vortical flow lack a forward cascade and shock formation at such high rotation rates. On investigating the physical space structures in the vortical flow and their connections to the wave cascade, we find that strain-dominant regions, that are located around the boundaries of coherent vortices, are the physical space regions that contribute majorly to the forward cascade of waves. Our results in general highlight intriguing features of dispersive inertia-gravity wave turbulence that are qualitatively similar to those seen in three-dimensional homogeneous isotropic turbulence and are beyond the predictions of asymptotic resonant wave interaction theory.

New advancements in radio data post-processing are underway within the Square Kilometre Array (SKA) precursor community, aiming to facilitate the extraction of scientific results from survey images through a semi-automated approach. Several of these developments leverage deep learning methodologies for diverse tasks, including source detection, object or morphology classification, and anomaly detection. Despite substantial progress, the full potential of these methods often remains untapped due to challenges associated with training large supervised models, particularly in the presence of small and class-unbalanced labelled datasets.

Self-supervised learning has recently established itself as a powerful methodology to deal with some of the aforementioned challenges, by directly learning a lower-dimensional representation from large samples of unlabelled data. The resulting model and data representation can then be used for data inspection and various downstream tasks if a small subset of labelled data is available.

In this work, we explored contrastive learning methods to learn suitable radio data representations by training the SimCLR model on large collections of unlabelled radio images taken from the ASKAP EMU and SARAO MeerKAT GPS surveys. The resulting models were fine-tuned over smaller labelled datasets, including annotated images from various radio surveys, and evaluated on radio source detection and classification tasks. Additionally, we employed the trained self-supervised models to extract features from radio images, which were used in an unsupervised search for objects with peculiar morphology in the ASKAP EMU pilot survey data. For all considered downstream tasks, we reported the model performance metrics and discussed the benefits brought by self-supervised pre-training, paving the way for building radio foundational models in the SKA era.

Background: Chordomas are rare, malignant bone tumors that present significant challenges in management and treatment due to their complex anatomical locations and propensity for recurrence. Advancements in artificial intelligence (AI) and machine learning (ML) show promise in improving chordoma management. Methods: A comprehensive literature search was conducted following PRISMA guidelines across multiple databases, including MEDLINE, Cochrane, Embase, Scopus, and Web of Science. The search targeted articles related to AI and ML applications in clinical tasks associated with chordoma management. The selection process involved systematic screening, data extraction, and assessment of inter-rater variability. Results: The search yielded 1,006 records, with 18 included for analysis. Convolutional neural networks (CNNs) excelled in tumor volume estimation, with the state-of-the-art model achieving a Dice similarity score of 74.2%, sensitivity of 79.4%, and positive predictive value of 74.3%. Clustering algorithms were effective in prognostic evaluations. Bayesian models and logistic regression demonstrated robustness in diagnostics. Support vector machines (SVMs) were noted for their diagnostic precision. Conclusions: AI and ML algorithms, particularly CNNs, clustering algorithms, Bayesian models, logistic regression, and SVMs, show promise in improving chordoma management through enhanced imaging, diagnostics, and prognostics. Future research should focus on larger, externally validated datasets and explore underutilized techniques like multi-modal data integration.

Experimental and theoretical studies on millimetre-sized droplets suggest that at low Reynolds number the difference between the drag force on a circulating water droplet and that on a rigid sphere is very small (less than 1 %) (LeClair et al., J. Atmos. Sci., vol. 29, 1972, pp. 728–740). While the drag force on a spherical liquid droplet at high viscosity ratios (of the liquid to the gas), is approximately the same as that on a rigid sphere of the same size, the other quantities of interest (e.g. the temperature) in the case of a rarefied gas flow over a liquid droplet differ from the same quantities in the case of a rarefied gas flow over a rigid sphere. The goal of this article is to study the effects of internal motion within a spherical microdroplet/nanodroplet – such that its diameter is comparable to the mean free path of the surrounding gas – on the drag force and its overall dynamics. To this end, the problem of a slow rarefied gas flowing over an incompressible liquid droplet is investigated analytically by considering the internal motion of the liquid inside the droplet and also by accounting for kinetic effects in the gas. Detailed results for different values of the Knudsen number, the ratio of the thermal conductivities and the ratio of viscosities are presented for the pressure and temperature profiles inside and outside the liquid droplet. The results for the drag force obtained in the present work are in good agreement with the theoretical and experimental results existing in the literature.

The gharial Gavialis gangeticus is a Critically Endangered crocodilian endemic to the Indian subcontinent. Habitat modification by river damming and water extraction has caused a severe decline in its population. The status of the gharial is known within protected areas, but there have been few surveys for this species in unprotected areas. In Katerniaghat Wildlife Sanctuary, a breeding gharial population in Girijapuri Barrage Reservoir has low recruitment, and it has been hypothesized that yearlings disperse downstream into the unprotected Ghaghara River when the barrage gates are opened. We surveyed a 100-km stretch of the Ghaghara River from the Girijapuri Barrage to Chahlari Ghat, observing a total of 84 gharials, including a high proportion of juveniles. A survey in 2021 from Chahlari Ghat to Ayodhya observed 174 gharials, giving a combined total of 258 gharials in a 219-km stretch of the Ghaghara River for the two surveys. Together, these findings confirm the presence of a significant population of gharials in the Ghaghara River. We recommend the adoption of an integrated approach, involving government agencies and local communities along the river, to conserve the protected Girwa–Kaudiyala Rivers and the unprotected Ghaghara River for gharial conservation and recovery. Such a programme will need to tackle the threats facing the gharial and establish baseline data and long-term monitoring protocols for freshwater species conservation in this river system.

Soybean is a major source of vegetable oil and protein worldwide. Globally, India is among the top five producers where soybean is a major oilseed grown under diverse agro-climatic conditions by small and marginal farmers. The present study aims to identify soybean varieties with higher yield levels, resistance to pestdiseases and adaptability to climatic fluctuations. One hundred and twenty-five (125) indigenous and exotic soybean germplasm accessions and five checks were evaluated and characterized for eight agro-morphological traits at five testing locations and also screened for frog-eye leaf spot (FLS) and yellow mosaic virus (YMV) diseases under hot-spot locations during the rainy season. A wide range of variability was observed among accessions for days to 50% flowering (39–59), plant height (41–111 cm), number of nodes/plant (10–30), pod clusters/plant (14–39), number of pods/plant (40–102), days to maturity (96–115), grain yield/plant (4.89–16.54 g) and 100-seed weight (6.02–13.72 g). Among various traits, 100-seed weight (0.45), number of pods/plant (0.60) and number of pod clusters/plant (0.38) were found to be major yield-contributing traits as they exhibited highly significant correlation with grain yield/plant. Principal components PCI and PCII with eigen value >1 accounted for 42.66 and 27.08% of the total variation, respectively. Accessions G24 (EC 393222) from Taiwan and G40 (IMP-1) from the USA belonging to cluster IV were found promising for multiple yield traits and JS 20–38 from cluster III for earliness as per cluster analysis. GGE biplot average environment coordination (AEC) view revealed that the accessions viz., G11 (EC 333872), G2 (EC 251506) and G47 (TNAU-S-55) were the best performing stable genotypes in terms of grain yield/plant across locations. Twelve accessions had a high level of resistance against both FLS and YMV diseases under natural hot-spot conditions which can be utilized as promising donors in the soybean breeding programme.

The prevalence of diet-related obesity is increasing dramatically worldwide, making it important to understand the associated metabolic alterations in the liver. It is well known that obesity is a multifactorial condition that is the result of complex integration between many gene expressions and dietary factors. Obesity alone or in conjunction with other chronic diseases such as diabetes and insulin resistance causes many health problems and is considered a major risk factor for developing non-alcoholic steatohepatitis (NASH) and cirrhosis. In this study, we aimed to understand the molecular mechanisms underlying early hepatic changes in the pathophysiology of high-fat diet (HFD)-induced abdominal obesity in rats. Hepatic protein profiles of normal diet and HFD-induced obesity for 24 weeks were analysed using two-dimensional differential gel electrophoresis (DIGE) and protein identification by MS. Fifty-two proteins were identified by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF), and computer-assisted DIGE image software analysis showed that eighteen major proteins were significantly differentially expressed between comparable groups, with 2·0–4·0-fold change/more (P < 0·01). These proteins are regulated in response to a HFD, and differentially expressed proteins are involved in key metabolic pathways such as lipid metabolism, energy metabolism, detoxification, urea cycle and hepatic Ca homoeostasis. In addition, Western blot and immunohistochemistry of liver-specific arginase-1 (Arg-1) showed significant increased expression in the liver of high-fat-fed rats (P < 0·01). Further, Arg-1 expression was correlated with NASH patients with obesity-related fibrosis (F0–F4). It is concluded that high-fat content may affect changes in liver pathways and may be a therapeutic target for obesity-related liver disease. Arg-1 expressions may be a potential pathological marker for assessing the progression of the disease.

Currently, 7 named Sarcocystis species infect cattle: Sarcocystis hirsuta, S. cruzi, S. hominis, S. bovifelis, S. heydorni, S. bovini and S. rommeli; other, unnamed species also infect cattle. Of these parasites of cattle, a complete life cycle description is known only for S. cruzi, the most pathogenic species in cattle. The life cycle of S. cruzi was completed experimentally in 1982, before related parasite species were structurally characterized, and before the advent of molecular diagnostics; to our knowledge, no archived frozen tissues from the cattle employed in the original descriptions remain for DNA characterization. Here, we isolated DNA from a paraffin-embedded kidney of a calf experimentally infected with S. cruzi in 1980; we then sequenced portions of 18S rRNA, 28S rRNA, COX1 and Acetyl CoA genes and verified that each shares 99–100% similarity to other available isolates attributed to S. cruzi from naturally infected cattle. We also reevaluated histological sections of tissues of calves experimentally infected with S. cruzi in the original description, exploiting improvements in photographic technology to render clearer morphological detail. Finally, we reviewed all available studies of the life cycle of S. cruzi, noting that S. cruzi was transmitted between bison (Bison bison) and cattle (Bos taurus) and that the strain of parasite derived from bison appeared more pathogenic than the cattle strain. Based on these newfound molecular, morphological and physiological data, we thereby redescribed S. cruzi and deposited reference material in the Smithsonian Museum for posterity.

The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $\mu$Jy beam$^{-1}$. We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP$_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM

From the continuation of colonial power structures in global economic development institutions, to immigration policies that favor applicants from white-majority European countries, to the use of counter-terrorism law to target primarily Muslim people, international law and its domestic analogues reflect and further inscribe racial distinctions and hierarchies. Racialization in international law occurs in the more visible areas of public decision making but also in mundane, administrative practices. In this essay, I argue that digital technologies are at the heart of automating processes of racialization in international law. Digital technological instruments effectively divide the global population, decision by decision, in adherence to the logics of racial hierarchy: they distribute social and material rights and privileges through financial, welfare, and immigration decisions while simultaneously deepening and entrenching state surveillance, policing, and violence.

A survey was conducted among Canadian tertiary neonatal intensive care units. Of the 27 sites who responded, 9 did not have any form of antimicrobial stewardship, and 11 used vancomycin for empirical coverage in late-onset-sepsis evaluations. We detected significant variations in the diagnostic criteria for urinary tract infection and ventilator-associated pneumonia.

OBJECTIVES/GOALS: Childhood food allergy (FA) is a chronic condition that can profoundly impact patient and caregiver quality of life (QoL). Risk factors for impaired FA-related QoL remain largely unknown. This study aims to estimate latent profiles of caregiver attitudes and behaviors, and examine if group membership correlates with established QoL outcomes. METHODS/STUDY POPULATION: This project is subsumed under the FORWARD study (5R01AI130348-04), a multisite longitudinal cohort study conducted at four major pediatric medical centers. Eligible participants include caregivers of children ages 12 years and under who identify as Black or White and have at least one physician-diagnosed FA. Participants complete intake surveys, annual clinical visits, and quarterly surveys administered via REDCap. As of November 2022, 413 Non-Hispanic Black and 486 Non-Hispanic White participants were enrolled. RESULTS/ANTICIPATED RESULTS: We anticipate that specific latent profiles will emerge based on caregiver-reported attitudes and behaviors. We predict these profiles will demonstrate configural, metric, and scalar invariance across key sociodemographic strata (e.g., child race/ethnicity, household income, caregiver educational attainment, and child sex). Relatedly, we anticipate that sociodemographic factors, such as race, sex, ethnicity, parental education, and income, will significantly predict group membership. Finally, we predict that latent profile membership will be associated with different levels of quality of life, as assessed by the parental food allergy-related quality of life questionnaire (FAQLQ-PF-10) and other validated measures of FA-related psychosocial burden. DISCUSSION/SIGNIFICANCE: Given the psychosocial burden associated with FA, and limited mental health resources for patients and families, it is essential to improve understanding of how caregiver attitudes influence behavior and disease management outcomes. In so doing, we will inform the development and implementation of tailored interventions for those at highest risk.