Quantifying Period Uncertainty in X-ray Pulsars with Poisson-Limited Data

There have been significant developments in the period estimation tools and methods for analysing high energy pulsars in the past few decades. However, these tools lack well-standardised methods for calculating uncertainties in period estimation and other recovered parameters for Poisson--dominated data. Error estimation is important for assigning confidence intervals to the models we study, but due to their high computational cost, errors in the pulsar periods were largely ignored in the past. Furthermore, existing literature has often employed semi-analytical techniques that lack rigorous mathematical foundations or exhibit a predominant emphasis on the analysis of white noise and time series data. We present results from our numerical and analytical study of the error distribution of the recovered parameters of high energy pulsar data using the $Z_n^2$ method. We comprehensively formalise the measure of error for the generic pulsar period with much higher reliability than some common methods. Our error estimation method becomes more reliable and robust when observing pulsars for few kilo-seconds, especially for typical pulsars with periods ranging from a few milliseconds to a few seconds. We have verified our results with observations of the \emph{Crab} pulsar, as well as a large set of simulated pulsars. Our codes are publicly available for use.Comment: 18 pages, 23 figures, pre-prin

Online Detection of AI-Generated Images

With advancements in AI-generated images coming on a continuous basis, it is increasingly difficult to distinguish traditionally-sourced images (e.g., photos, artwork) from AI-generated ones. Previous detection methods study the generalization from a single generator to another in isolation. However, in reality, new generators are released on a streaming basis. We study generalization in this setting, training on N models and testing on the next (N+k), following the historical release dates of well-known generation methods. Furthermore, images increasingly consist of both real and generated components, for example through image inpainting. Thus, we extend this approach to pixel prediction, demonstrating strong performance using automatically-generated inpainted data. In addition, for settings where commercial models are not publicly available for automatic data generation, we evaluate if pixel detectors can be trained solely on whole synthetic images.Comment: ICCV DeepFake Analysis and Detection Workshop, 202

Teledentistry: A New Evolution in Dentistry

Teledentistry is a new field that combines telecommunication to advanced dental care. Most of the dentists are not aware about the goals, advantages of teledentistry and how it can be used to improve the delivery of oral healthcare and lower its costs. It also has the potential to eliminate the disparities in oral health care between rural and urban communities. In dentistry, it can be used by specialists in various branches and can serve the general dentist too. Some barriers still exist for teledentistry practice, which includes legal, educational and insurance issues. In spite of these barriers, telemedicine and teledentistry have showed tremendous growth in recent years . This article places emphasis on how teledentistry can be a cost effective answer to the dentists and their patients

Phytochemical Investigation and Pharmacological Evaluation of Solanum xanthocarpum Endowed with their potential Activity

The study was done to assess the in-vitro antibacterial potential of various extracts was studied and compared with ciprofloxacin as the standard and shows significant action against E. coli, B. substilis S. aureus, S. pyrogenes, P. aeruginosa, and S. typhi. Anti-fungal potential of the aqueous extract also studied using miconazole as standard and shows significant action against A. niger and C. albicans. Anthelmintic potential of the aqueous and ethanolic extracts was also studied on earthworms, Eudrillus eugeniae using albendazole as standard and shows moderate activity. In the present study in-vitro free radical scavenging activity of whole plant material performed. Various crude extracts of S. xanthocarpum was prepared by successive maceration process using various solvents such as; chloroform, petroleum ether (60-80o), acetone, ethanol and distilled water. Each one extract have been chosen to study the free radical inhibitory activity by DPPH radical scavenging method. The preliminary phytochemical screening of extracts showed that sterols, alkaloids, glycosides, tannins, saponins, phenolic compounds, carbohydrates and proteins were present in the plant. Petroleum ether, chloroform, acetone, ethanol and distilled water extracts showed 52.69, 46.15, 21.08, 52.72 and 44.35 % respectively compared to standard ascorbic acid. Acetone extract showed poor inhibition of DPPH radical compared to standard and other extracts also

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

A numerical study on stress mitigation in through-thickness tailored bi-adhesive single-lap joints

No abstract available

Performance of quantitative CT parameters in assessment of disease severity in COPD: A prospective study

Background: Both emphysematous destruction of lung parenchyma and airway remodeling is thought to contribute to airflow limitation in cases of chronic obstructive pulmonary disease (COPD). Objective: To evaluate the value of quantitative computed tomography (QCT) parameters of emphysema and airway disease with disease severity in patients with COPD. Materials and Methods: We prospectively studied 50 patients with COPD, which included nonsmokers and patients with different degrees of cumulative smoking exposure. Three QCT parameters namely LAA% (low attenuation area percentage), WA% (Wall area percentage), and pi10 were calculated as per the standard technique. Forced expiratory volume in 1 s (FEV1), BODE score, and MMRC dyspnea scale were used as measures of disease severity. Results: FEV1 was inversely and significantly associated with all three QCT parameters. Receiver operated characteristic curves in prediction of GOLD class 3 COPD yielded cut-off values of 12.2, 61.45, and 3.5 for LAA%, WA%, and pi10, respectively, with high sensitivities and specificities. In multiple linear regression model, however, only LAA% proved to be significantly associated with FEV1, BODE, and dyspnea. Conclusion: QCT indices of both emphysema and airway disease influence FEV1, dyspnea, and BODE score in patients with COPD. Emphysema, however, appears to be more closely related to disease severity

The Science of Cariostatic Action of Cheese against Dental Caries

Dental caries, often known as tooth decay or cavities, is a common oral health issue that has a significant impact on overall health. Cheese, a dairy product, has received attention in terms of nutrition due to its potential benefits for reducing tooth cavities. This narrative review examines the nutritional benefits of cheese in the context of avoiding dental caries, as well as its nutritional profile, mechanisms of action, and relevant advice for incorporating cheese into a caries-preventive diet

Transcriptomic analysis of immune cells in a multi-ethnic cohort of systemic lupus erythematosus patients identifies ethnicity- and disease-specific expression signatures.

Systemic lupus erythematosus (SLE) is an autoimmune disease in which outcomes vary among different racial groups. We leverage cell-sorted RNA-seq data (CD14+ monocytes, B cells, CD4+ T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four-tier approach including unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning to identify SLE subgroups within this multiethnic cohort. K-means clustering on each cell-type resulted in three clusters for CD4 and CD14, and two for B and NK cells. To understand the identified clusters, correlation analysis revealed significant positive associations between the clusters and clinical parameters including disease activity as well as ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across cells were involved in SLE or other autoimmune-related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Finally, random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White individuals. The results from these analyses will help stratify patients based on their gene expression signatures to enable SLE precision medicine