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Abstract We study classical closed bosonic strings probing the near-horizon region of a non-extremal black hole and show that this corresponds to understanding string theory in the Carroll regime. This is done by first performing a Carroll expansion and then a near-horizon expansion of a closed relativistic string, subsequently showing that they agree. Concretely, we expand the phase space action in powers of c 2, where c is the speed of light, assuming that the target space admits a string Carroll expansion (where two directions are singled out) and show that there exist two different Carroll strings: a magnetic and an electric string. The magnetic string has a Lorentzian worldsheet, whereas the worldsheet of the electric string is Carrollian. The geometry near the horizon of a four-dimensional (4D) Schwarzschild black hole takes the form of a string Carroll expansion (a 2D Rindler space fibred over a 2-sphere). We show that the solution space of relativistic strings near the horizon bifurcates and the two sectors precisely match with the magnetic/electric Carroll strings with an appropriate target space. Magnetic Carroll strings near a black hole shrink to a point on the two-sphere and either follow null geodesics or turn into folded strings on the 2D Rindler spacetime. Electric Carroll strings wrap the two-sphere and follow a massive geodesic in the Rindler space. Finally, we show that 4D non-extremal Kerr and Reissner-Nordström black holes also admit string Carroll expansions near their outer horizons, indicating that our formulation extends to generic non-extremal black holes.

Abstract Sequence-based genetic testing identifies causative variants in ~ 50% of individuals with developmental and epileptic encephalopathies (DEEs). Aberrant changes in DNA methylation are implicated in various neurodevelopmental disorders but remain unstudied in DEEs. We interrogate the diagnostic utility of genome-wide DNA methylation array analysis on peripheral blood samples from 582 individuals with genetically unsolved DEEs. We identify rare differentially methylated regions (DMRs) and explanatory episignatures to uncover causative and candidate genetic etiologies in 12 individuals. Using long-read sequencing, we identify DNA variants underlying rare DMRs, including one balanced translocation, three CG-rich repeat expansions, and four copy number variants. We also identify pathogenic variants associated with episignatures. Finally, we refine the CHD2 episignature using an 850 K methylation array and bisulfite sequencing to investigate potential insights into CHD2 pathophysiology. Our study demonstrates the diagnostic yield of genome-wide DNA methylation analysis to identify causal and candidate variants as 2% (12/582) for unsolved DEE cases.

As per the universal principles of usability and sustainable development goal defined for reducing inequality, each individual will have special abilities and challenges. Individuals with visual challenges have trouble interacting with digital platforms. In order to achieve inclusivity, there is a need to integrate universal accessibility into the web portals which are used as the most popular digital platforms. This study mainly focuses on the requirements of visually impaired users. The research work discusses the proposed approach for a telemedicine web platform by integrating voice navigation system. With this system, users can orally interact with the platform by using defined commands. Users can also receive the audio feedback from the computer for the specific command. The proposed speech recognition engine is implemented using deep learning models and tested on various browsers. The engine captures the commands in the form of user inputs and generates the proper audio feedback after executing the commands. Users with visual impairment have been involved in the evaluation by allowing them to interact on the telemedicine platform with verbal commands. The evaluation questions have been asked after each interaction to capture the response time, accuracy, experience and satisfaction. The outcome of the evaluation shows that individuals are showing significant progress in accessing required information and navigating the web pages on their choice of browsers. The study also observed that speech recognition engine along with speech grammar, acoustic model and synthesis has improved the usage of the system for all types of users. Ultimately, integrating voice navigation into web platforms provides a satisfactory experience to the users and improves accessibility, inclusivity and reduces inequality.

ABSTRACT Habitat loss is one of the primary drivers of large felid decline. The leopard (Panthera pardus), a generalist large felid species, has the behavioural and dietary flexibility to exploit different habitat types of varying human influence. Understanding habitat selection in a shared landscape is critical for the development of conservation strategies and managing negative human–leopard interactions. The development of conservation policy requires data on large spatial scales, which is mostly lacking, especially within shared landscapes in India. This study aims to determine habitat selection by Indian leopards and the anthropogenic, environmental, and climatic variables contributing to this selection. Leopard occurrence records were obtained from an occupancy survey conducted in the five administrative districts in Karnataka (28,375 km2). 267 randomly selected 30 km2 grids were each walked for 10 km and all leopard signs were recorded. Environment variables were chosen to reflect land use, climatic, topographic, and human disturbances that could affect habitat selection at a resolution of 0.1 km2. The mean ensemble model was projected to the state of Karnataka. Habitat selection predicted by the ensemble model was driven by proximity to forest cover and rocky outcrops, higher precipitation, and negatively by distance to cropland and roads. Protected Areas and Reserved Forests in the study covered 47% of the predicted habitat, while 25% is within human‐use areas such as human habitation and croplands. This study predicts that half of the habitat selected by leopards is outside of protected areas and reserved forests. The selection of human‐use areas is predicted because of the availability of cover from irrigated croplands and the proximity to natural cover that provides refuge. Livestock density did not drive large‐scale habitat selection. The preservation of natural cover and rocky outcrops that provide refuge between protected areas is paramount for leopard conservation.

The Box and Block Test (BBT) is a reliable outcome measure in upper limb rehabilitation. The present work aims to automate the test of unilateral manual dexterity of a patient's hand. A box consisting of 80 blocks was divided into two partitions. A motion tracking sensor (MPU6050) was placed on a wrist to obtain the orientation of hand movement. Thirty-four healthy participants were chosen for the test. Each participant completed both traditional and automated versions of BBT within a one-week interval, with each session lasting 60 seconds. A colour sensor at the box monitored block movements across the partitions. A Liquid Crystal Display (LCD) and a 4-digit 7-segment display provided feedback on the number of cubes moved and timing information, respectively. An independent t-test revealed a non-significant difference between traditional BBT and automated BBT. Concurrent validity was established by correlating the results of the automated BBT with the traditional BBT. A positive correlation (r= 0.94) was found between traditional and automated BBT. This validates the automated BBT report with the same result as traditional BBT. Automated BBT can assist a broad range of patients in independently tracking the progress of their hand injuries or conditions without needing a clinician. This can be conveniently done from the comfort of their homes. HighlightsLack of fine motor skills and dexterity is a primary concern affecting the person’s daily living activity; hence, the automated box and block test (BBT) is valid for assessing hand dexterity quantitatively.The traditional BBT is an old and golden test used in dexterity assessment with neurological patients, which only provides outcomes in the form of box count, which is insufficient for hand assessment.The need for this work arises from the limitations of the traditional BBT, which only provides a basic outcome in the form of box counts, insufficient for comprehensive hand dexterity assessment, particularly in neurological patients.Automated BBT is developed, which reduces the clinician’s burden during assessment and makes the test foolproof by adding a mechanism to capture hand trajectory during block movements.Along with the block count, the accelerometer gives hand orientation during the test process, which helps the clinician further analyze the results, such as motion pattern analysis, temporal analysis and comparative analysis.The independent t-test between traditional BBT and automated BBT, p = 0.109, was observed with a 0.95 confidence interval. Levene’s test revealed no significant difference between the scores of the traditional and automated BBT (t = 2.03; p = 0.109), also between dominant and non-dominant hand (t = 1.99; p = 0.74). A positive Pearson’s correlation (r = 0.94, p

Oncogenic fusions formed through chromosomal rearrangements are hallmarks of childhood cancer that define cancer subtype, predict outcome, persist through treatment, and can be ideal therapeutic targets. However, mechanistic understanding of the etiology of oncogenic fusions remains elusive. Here we report a comprehensive detection of 272 oncogenic fusion gene pairs by using tumor transcriptome sequencing data from 5190 childhood cancer patients. We identify diverse factors, including translation frame, protein domain, splicing, and gene length, that shape the formation of oncogenic fusions. Our mathematical modeling reveals a strong link between differential selection pressure and clinical outcome in CBFB-MYH11. We discover 4 oncogenic fusions, including RUNX1-RUNX1T1, TCF3-PBX1, CBFA2T3-GLIS2, and KMT2A-AFDN, with promoter-hijacking-like features that may offer alternative strategies for therapeutic targeting. We uncover extensive alternative splicing in oncogenic fusions including KMT2A-MLLT3, KMT2A-MLLT10, C11orf95-RELA, NUP98-NSD1, KMT2A-AFDN and ETV6-RUNX1. We discover neo splice sites in 18 oncogenic fusion gene pairs and demonstrate that such splice sites confer therapeutic vulnerability for etiology-based genome editing. Our study reveals general principles on the etiology of oncogenic fusions in childhood cancer and suggests profound clinical implications including etiology-based risk stratification and genome-editing-based therapeutics.

Background: Ellagic acid (EA) is a natural polyphenolic compound recognized for bioactive and pharmacologic properties that is found abundantly in various plant groups, particularly eudicotyledons. In the current study a novel, reliable, and cost-effective bioanalytical method was developed for quantifying EA in Wistar rat plasma using RP-HPLC. Methods: Zorbax SB C18 (5μ, 4.6150 mm) and Ascentis C18 (5μ, 1004.6 mm) columns were used in the stationary phase, while the mobile phase consisted of water with 0.1 % formic acid (A) and acetonitrile (ACN) with 0.08% formic acid (B). Results: Optimized parameters were as follows: temperature, 30°C; flow rate, 1.0 ml/min; and PDA detectors, 254 nm. The method exhibited high linearity (r2 = 0.9993) between 5 and 300 μg/ml. Precision, both intra- and inter-day, was within acceptable limits (relative standard deviation >2%), and the mean recovery was 99.73%. The LOD and LOQ were 1.564 ± 0.026 μg/ml and 5.015 ± 0.025 μg/ml, respectively. Stability tests, including short- and long-term evaluations, demonstrated stability under various conditions. Conclusion: The developed method met the necessary criteria and holds promise for application in clinical laboratories for assessing EA levels, either alone or with analytes.

The advancement of emerging technologies is fundamentally reshaping the landscape of brain tumor diagnosis and treatment planning, with a notable emphasis on precise segmentation techniques to enable early intervention. Traditional manual segmentation methods encounter substantial challenges stemming from inherent noise and intensity variations in medical imaging data. Consequently, there has been a paradigm shift towards leveraging deep learning methodologies, particularly UNet++, to address these issues. The proposed methodology integrates EfficientNetB7 as the encoder component of the UNet++ architecture. This integration harnesses the power of transfer learning, specifically leveraging pre-trained weights from the AdvProp dataset, to facilitate knowledge transfer for improved performance in segmentation tasks. UNet++’s decoder module facilitates multi-scale feature fusion and enhances mask refinement through the incorporation of skip connections. The segmentation head of the architecture generates pixel-wise masks corresponding to different segmentation classes. In our experimental setup, we utilize the Kaggle lower-grade gliomas (LGG) dataset comprising 110 patient datasets for both training and evaluation purposes. The model demonstrates remarkable performance enhancements, achieving a Dice coefficient of 0.9387 and a mean Intersection over Union (IoU) of 0.9123, outperforming previous methodologies. Particularly noteworthy is the model’s ability to accurately delineate tumor boundaries and identify tumor regions, underscoring its potential for enhancing clinical decision-making and ultimately improving patient outcomes.

Deraining of images plays a pivotal role in computer vision by addressing the challenges posed by rain, enhancing visibility, and refining image quality by eliminating rain streaks. Traditional methods often fall short of effectively handling intricate rain patterns, resulting in incomplete removal. In this paper, we propose an innovative deep learning-based deraining model leveraging a modified residual UNet and a multiscale attention-guided convolutional neural network module as a discriminator within a conditional generative adversarial network framework. The proposed approach introduces custom hyperparameters and a tailored loss function to facilitate the efficient removal of rain streaks from images. Evaluation on both synthetic and real-world datasets showcases superior performance, as indicated by improved image evaluation metrics such as PSNR, SSIM, and NIQE. The effectiveness of our model extends to improving both rainy and foggy images. We also conducted a comparative analysis of computational complexity in terms of running time, GFLOPs, and no. of parameters against other state-of-the-art methods to demonstrate our model’s superiority.

In order to achieve quality education as a defined one of the sustainable goals, it is necessary to provide information about the education system according to the stakeholders’ requirements. The process to obtain the information about university/institute is a critical stage in the academic journey of prospective students who are seeking information about the specific courses which makes that university/institute unique. This process begins with exploration to general information about universities through websites, rankings, and brochures from various sources. Most of the time, information available on different sources leads to discrepancies and influences student’s decisions. By addressing inquiries promptly and providing valuable information, universities can guide individuals in making informed choices about their academic future. To address this, the chatbot application is the most effective tool to be implemented and make it functional on university’s functional website. A chatbot is an artificially intelligent tool which can interact with humans and can mimic a conversation. This tool can be implemented using advanced Natural Language Processing (NLP) models to provide the pre-defined answers to the student’s queries. Chatbot is very helpful for query resolution during the counseling process of the institute as it will provide official/uniform information and can be accessed $24\times 7$ . Therefore, the aim of this research work was to implement a chatbot using various NLP models and compare them to identify best one. In this work, five chatbot models were implemented using neural networks, TF-IDF vectorization, sequential modeling and pattern matching. From the results, it was observed that neural network-related models had better accuracy than TF-IDF and pattern matching model, and sequential modeling is the most accurate model because it prevents over-fitting. Furthermore, a chatbot having any kind of optimizer can improve the result and it is most important that pattern matching, and semantic analysis should be the parts of a chatbot for real time scenarios.

Otoscopy is a diagnostic procedure to visualize the external ear canal and eardrum, facilitating the detection of various ear pathologies and conditions. Timely otoscopy image classification offers significant advantages, including early detection, reduced patient anxiety, and personalized treatment plans. This paper introduces a novel OTONet framework specifically tailored for otoscopy image classification. It leverages octave 3D convolution and a combination of feature and region-focus modules to create an accurate and robust classification system capable of distinguishing between various otoscopic conditions. This architecture is designed to efficiently capture and process the spatial and feature information present in otoscopy images. Using a public otoscopy dataset, OTONet has reached a classification accuracy of 99.3% and an F1 score of 99.4% across 11 classes of ear conditions. A comparative analysis demonstrates that OTONet surpasses other established machine learning models, including ResNet50, ResNet50v2, VGG16, Dense-Net169, and ConvNeXtTiny, across various evaluation metrics. The research’s contribution to improved diagnostic accuracy reduced human error, expedited diagnostics, and its potential for telemedicine applications.

It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of laboratory-based science, clinical trials and qualitative research that were presented during the 2023 European Respiratory Society International Congress within the sessions from the five groups of Assembly 1 (Respiratory Clinical Care and Physiology). Selected presentations are summarised from a wide range of topics: clinical problems, rehabilitation and chronic care, general practice and primary care, electronic/mobile health (e-health/m-health), clinical respiratory physiology, exercise and functional imaging.