Response of a Stretched String Subjected to a Moving Mass

In this paper, the dynamics of a taut horizontal string with a constant velocity moving mass including its rotary inertia, is modelled. The equation of motion is solved using Galerkin’s approach, employing appropriate comparison functions. A discontinuity or jump in the trajectory of the mass has been established when the mass is about to leave the string. The consideration of rotary inertia in the model is found to affect the spatial location of the jump in the trajectory of the moving mass

Cross-Domain Transfer Learning for Domain Adaptation in Autism Spectrum Disorder Diagnosis

A cross-domain transfer learning approach is introduced to address the challenges of diagnosing individuals with Autism Spectrum Disorder (ASD) using small-scale fMRI datasets. Vision Transformer (ViT) and TinyViT models pre-trained on the ImageNet, were employed to transfer knowledge from the natural image domain to the brain imaging domain. The models were fine-tuned on ABIDE and CMI-HBN, using a teacher student framework with knowledge distillation loss. Experimental results demonstrated that our method outperformed previous studies, ViT models, and CNN-based models. Our approach achieved competitive performance (F-1 score 78.72%) with a much smaller parameter size. This study highlights the effectiveness of cross-domain transfer learning in medical applications, particularly for scenarios with small datasets. It suggests that pre-trained models can be leveraged to improve diagnostic accuracy for neuro-developmental disorders such as ASD. The findings indicate that the features learned from natural images can be adapted to fMRI data using the proposed method, potentially providing a reliable and efficient approach to diagnosingautism

Hydrogels: A Comprehensive Review of Structure, Properties, and Multifaceted Applications

Hydrogels, three-dimensional networks of hydrophilic polymers capable of absorbing and retaining significant amounts of water, have transitioned from laboratory curiosities to cornerstone materials in modern technology and medicine. Their unique biocompatibility, tunable physical and chemical properties, and stimuli-responsive nature have made them indispensable in fields ranging from drug delivery and tissue engineering to soft robotics and sustainable agriculture. This review provides a systematic overview of hydrogel classification, cross-linking mechanisms, and fundamental properties. We then delve into their advanced applications across biomedical and non-biomedical sectors, highlighting recent breakthroughs. Finally, we discuss the current challenges and future perspectives for the next generation of "smart" hydrogel systems. Keywords: Hydrogel, Cross-linking, Stimuli-responsive, Biomaterial, Drug Delivery, Tissue Engineering, Soft Robotics, Agriculture

Identification of critical brain regions for autism diagnosis from fMRI data using explainable AI: an observational analysis of the ABIDE dataset

BACKGROUND: Diagnosis of autism spectrum disorder (ASD) significantly improves quality of life, yet current diagnostic practices rely on subjective behavioural assessments. While machine learning models using functional Magnetic Resonance Imaging (fMRI) show promise for objective diagnosis, existing approaches have critical limitations. High-accuracy models often neglect interpretability, increasing clinical distrust, while interpretable approaches frequently suffer from low accuracy and lack neuroscientific validation of identified biomarkers. Furthermore, no studies have systematically benchmarked which interpretability methods are most effective for fMRI data, hindering the translation of Artificial intelligence (AI) findings into clinical practice. METHODS: In this observational study, we used the Autism Brain Imaging Data Exchange I (ABIDE I) dataset comprising 884 participants (408 with ASD; 476 controls with typical development) aged 7-64 years from five countries (Belgium, Germany, Ireland, the Netherlands, and the USA) across 17 international sites after applying mean framewise displacement (FD) filtering (\u3e0.2 mm). Data were collected at the respective sites prior to the release of the ABIDE I dataset in August 2012. We described an explainable deep learning (DL) pipeline using a Stacked Sparse Autoencoder (SSAE) with a softmax classifier, trained on functional connectivity data. We systematically benchmarked seven interpretability methods using the Remove And Retrain (ROAR) technique and cross-validated our findings across three preprocessing pipelines. Critically, we validated identified biomarkers against independent neuroscientific literature from genetic, neuroanatomical, and functional studies. FINDINGS: Filtering head movement (FD \u3e 0.2 mm) increased classification accuracy from 91% to 98.2% (F1-score: 0.97), achieving state-of-the-art performance. ROAR analysis revealed gradient-based methods, particularly Integrated Gradients, as most reliable for fMRI interpretation. The model consistently identified visual processing regions (calcarine sulcus, cuneus) as critical for ASD classification. These findings aligned with independent genetic studies and neuroimaging research, confirming our model captured genuine neurobiological ASD markers rather than overfitting to dataset artifacts. INTERPRETATION: We addressed three key research gaps by developing a highly accurate, interpretable ASD classification model, establishing a benchmarking framework for interpretability methods in the fMRI modality, and validating our identified biomarkers against established neuroscience literature. The consistent importance of visual processing regions suggests a fundamental biomarker potentially present across the ASD spectrum, offering new directions for diagnostic biomarker research, though translation to individual-level clinical applications requires further development. FUNDING: This work was supported by the Engineering and Physical Sciences Research Council, UK [EP/W524554/1]

Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data

Early diagnosis and intervention for Autism Spectrum Disorder (ASD) has been shown to significantly improve the quality of life of autistic individuals. However, diagnostics methods for ASD rely on assessments based on clinical presentation that are prone to bias and can be challenging to arrive at an early diagnosis. There is a need for objective biomarkers of ASD which can help improve diagnostic accuracy. Deep learning (DL) has achieved outstanding performance in diagnosing diseases and conditions from medical imaging data. Extensive research has been conducted on creating models that classify ASD using resting-state functional Magnetic Resonance Imaging (fMRI) data. However, existing models lack interpretability. This research aims to improve the accuracy and interpretability of ASD diagnosis by creating a DL model that can not only accurately classify ASD but also provide explainable insights into its working. The dataset used is a preprocessed version of the Autism Brain Imaging Data Exchange (ABIDE) with 884 samples. Our findings show a model that can accurately classify ASD and highlight critical brain regions differing between ASD and typical controls, with potential implications for early diagnosis and understanding of the neural basis of ASD. These findings are validated by studies in the literature that use different datasets and modalities, confirming that the model actually learned characteristics of ASD and not just the dataset. This study advances the field of explainable AI in medical imaging by providing a robust and interpretable model, thereby contributing to a future with objective and reliable ASD diagnostics

True Generalized Microdontia and Hypodontia with Spondyloepiphyseal Dysplasia

Spondyloepiphyseal dysplasia (SED) is a descriptive term used for group of inherited disorders of bone growth resulting in short stature, skeletal abnormalities, and problems with hearing and vision. SED have three major forms, SED congenital, pseudoachondroplastic SED, and SED tarda. SED tarda is milder than SED congenita. True generalized microdontia is a rare condition in which all the teeth are abnormally small. This is a report of a rare case having SED with generalized microdontia in a 26-year-old patient

Engineering design of vertical test stand cryostat

Under Indian Institutions and Fermilab collaboration, Raja Ramanna Centre for Advanced Technology and Fermi National Accelerator Laboratory are jointly developing 2K Vertical Test Stand (VTS) cryostats for testing SCRF cavities at 2K. The VTS cryostat has been designed for a large testing aperture of 86.36 cm for testing of 325 MHz Spoke resonators, 650 MHz and 1.3 GHz multi-cell SCRF cavities for Fermilab's Project-X. Units will be installed at Fermilab and RRCAT and used to test cavities for Project-X. A VTS cryostat comprises of liquid helium (LHe) vessel with internal magnetic shield, top insert plate equipped with cavity support stand and radiation shield, liquid nitrogen (LN{sub 2}) shield and vacuum vessel with external magnetic shield. The engineering design and analysis of VTS cryostat has been carried out using ASME B&PV Code and Finite Element Analysis. Design of internal and external magnetic shields was performed to limit the magnetic field inside LHe vessel at the cavity surface <1 {micro}T. Thermal analysis for LN{sub 2} shield has been performed to check the effectiveness of LN{sub 2} cooling and for compliance with ASME piping code allowable stresses

Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study

Background We hypothesised that host-response biomarkers of viral infections might contribute to early identification of individuals infected with SARS-CoV-2, which is critical to breaking the chains of transmission. We aimed to evaluate the diagnostic accuracy of existing candidate whole-blood transcriptomic signatures for viral infection to predict positivity of nasopharyngeal SARS-CoV-2 PCR testing.Methods We did a nested case-control diagnostic accuracy study among a prospective cohort of health-care workers (aged ≥18 years) at St Bartholomew’s Hospital (London, UK) undergoing weekly blood and nasopharyngeal swab sampling for whole-blood RNA sequencing and SARS-CoV-2 PCR testing, when fit to attend work. We identified candidate blood transcriptomic signatures for viral infection through a systematic literature search. We searched MEDLINE for articles published between database inception and Oct 12, 2020, using comprehensive MeSH and keyword terms for “viral infection”, “transcriptome”, “biomarker”, and “blood”. We reconstructed signature scores in blood RNA sequencing data and evaluated their diagnostic accuracy for contemporaneous SARS-CoV-2 infection, compared with the gold standard of SARS-CoV-2 PCR testing, by quantifying the area under the receiver operating characteristic curve (AUROC), sensitivities, and specificities at a standardised Z score of at least 2 based on the distribution of signature scores in test-negative controls. We used pairwise DeLong tests compared with the most discriminating signature to identify the subset of best performing biomarkers. We evaluated associations between signature expression, viral load (using PCR cycle thresholds), and symptom status visually and using Spearman rank correlation. The primary outcome was the AUROC for discriminating between samples from participants who tested negative throughout the study (test-negative controls) and samples from participants with PCR-confirmed SARS-CoV-2 infection (test-positive participants) during their first week of PCR positivity.Findings We identified 20 candidate blood transcriptomic signatures of viral infection from 18 studies and evaluated their accuracy among 169 blood RNA samples from 96 participants over 24 weeks. Participants were recruited between March 23 and March 31, 2020. 114 samples were from 41 participants with SARS-CoV-2 infection, and 55 samples were from 55 test-negative controls. The median age of participants was 36 years (IQR 27–47) and 69 (72%) of 96 were women. Signatures had little overlap of component genes, but were mostly correlated as components of type I interferon responses. A single blood transcript for IFI27 provided the highest accuracy for discriminating between test-negative controls and test-positive individuals at the time of their first positive SARS-CoV-2 PCR result, with AUROC of 0·95 (95% CI 0·91–0·99), sensitivity 0·84 (0·70–0·93), and specificity 0·95 (0·85–0·98) at a predefined threshold (Z score >2). The transcript performed equally well in individuals with and without symptoms. Three other candidate signatures (including two to 48 transcripts) had statistically equivalent discrimination to IFI27 (AUROCs 0·91–0·95).Interpretation Our findings support further urgent evaluation and development of blood IFI27 transcripts as a biomarker for early phase SARS-CoV-2 infection for screening individuals at high risk of infection, such as contacts of index cases, to facilitate early case isolation and early use of antiviral treatments as they emerge

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK

Background A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. Methods This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. Findings Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0–75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4–97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8–80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3–4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. Interpretation ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK.

BACKGROUND: A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. METHODS: This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. FINDINGS: Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0-75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4-97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8-80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3-4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. INTERPRETATION: ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials. FUNDING: UK Research and Innovation, National Institutes for Health Research (NIHR), Coalition for Epidemic Preparedness Innovations, Bill & Melinda Gates Foundation, Lemann Foundation, Rede D'Or, Brava and Telles Foundation, NIHR Oxford Biomedical Research Centre, Thames Valley and South Midland's NIHR Clinical Research Network, and AstraZeneca