1,606 research outputs found
Prevalence and factors associated with underweight children: a population-based subnational analysis from Pakistan
No full textObjectives This study aims to determine the prevalence of and factors associated with underweight children under the age of 5 in Punjab, Pakistan. Design We analysed cross-sectional household-level subnationally representative Multiple Indicator Cluster Survey. Settings Punjab province, Pakistan. Participants 24 042 children under 5 years of age. Data analysis Multilevel multivariate logistic regression analysis. Results Prevalence of moderately and severely underweight children was found to be (33.3% and 11.3%, respectively). Multivariate multilevel logistic regression results show that as the child grows older the likelihood of the child being underweight increases significantly (eg, children between 12 and 23 months are one and half times more likely to be underweight, whereas children between the ages of 36 and 47 months are two and a half times more likely to be underweight). Gender was found to be another significant factor contributing to underweight prevalence among children under the age of 5. The likelihood of a girl child being underweight is more than that of a boy child being underweight (OR 0.92, 95% CI 0.8 to 1.0). Similarly, a child whose birth order is three or more is two times more likely to be underweight (OR 1.96, 95% CI 1.5 to 2.5) relative to a child of a lower birth order. Moreover, diarrhoea also significantly increases the likelihood of the child being underweight (OR 1.31, 95% CI 1.1 to 1.5). Child size is another determinant for underweight prevalence among children under 5, for example, a child with a size smaller than average at the time of birth is 2.7 times more likely to be moderately underweight than a child with an average or larger than average size at the time of birth. Conclusion Rigorous community-based interventions should be developed and executed throughout the province to improve this grave situation of underweight prevalence in Punjab. Mother’s education should be uplifted by providing them formal education and providing awareness about the importance of proper nutrition for children
The Complexity of Human Walking: A Knee Osteoarthritis Study
Get PDFThis study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space
sj-docx-1-cath-10.1177_10760296211068487 - Supplemental material for Thrombosis with Thrombocytopenia Syndrome After Administration of AZD1222 or Ad26.COV2.S Vaccine for COVID-19: A Systematic Review
No full textSupplemental material, sj-docx-1-cath-10.1177_10760296211068487 for Thrombosis with Thrombocytopenia Syndrome After Administration of AZD1222 or Ad26.COV2.S Vaccine for COVID-19: A Systematic Review by Usama Waqar, Shaheer Ahmed, Syed M.H.Ali Gardezi, Muhammad Sarmad Tahir, Zain ul Abidin, Ali Hussain, Natasha Ali and Syed Faisal Mahmood in Clinical and Applied Thrombosis/Hemostasis</p
sj-xlsx-2-cath-10.1177_10760296211068487 - Supplemental material for Thrombosis with Thrombocytopenia Syndrome After Administration of AZD1222 or Ad26.COV2.S Vaccine for COVID-19: A Systematic Review
No full textSupplemental material, sj-xlsx-2-cath-10.1177_10760296211068487 for Thrombosis with Thrombocytopenia Syndrome After Administration of AZD1222 or Ad26.COV2.S Vaccine for COVID-19: A Systematic Review by Usama Waqar, Shaheer Ahmed, Syed M.H.Ali Gardezi, Muhammad Sarmad Tahir, Zain ul Abidin, Ali Hussain, Natasha Ali and Syed Faisal Mahmood in Clinical and Applied Thrombosis/Hemostasis</p
Infopreneurship from the Perspective of Great Infopreneurs: an interview with Dan Poynter
No full textDan Poynter is author of more than 130 books, has been a publisher since 1969, and is a Certified Speaking Professional (CSP). He is an evangelist for books, an ombudsman for authors, an advocate for publishers, and the godfather to thousands of successfully published books. In this interview Poynter offers his point of view towards infopreneurship in context
Do cross modal systems leverage semantic relationships?
No full textCurrent cross modal retrieval systems are evaluated using R@K measure which does not leverage semantic relationships rather strictly follows the manually marked image text query pairs. Therefore, current systems do not generalize well for the unseen data in the wild. To handle this, we propose a new measure SemanticMap to evaluate the performance of cross modal systems. Our proposed measure evaluates the semantic similarity between the image and text representations in the latent embedding space. We also propose a novel cross modal retrieval system using a single stream network for bidirectional retrieval. The proposed system is based on a deep neural network trained using extended center loss, minimizing the distance of image and text descriptions in the latent space from the class centers. In our system, the text descriptions are also encoded as images which enabled us to use single stream network for both text and images. To the best of our knowledge, our work is the first of its kind in terms of employing a single stream network for cross modal retrieval systems. The proposed system is evaluated on two publicly available datasets including MSCOCO and Flickr30K and has shown comparable results to the current state-of-the-art methods
Improving Perception of Usability Through User Interface Design Patterns to Optimize Information Architecture for Cognitive Benefits and User Satisfaction in Massive Open Online Courses
No full textThis study explores the impact of user interface design patterns on usability, cognitive load, and user satisfaction for Massive Open Online Courses using small-screen devices. An empirical approach was adopted, involving 232 university students who voluntarily participated in the experiment. Prototypes of three well-known Massive Open Online Courses platforms (i.e., Coursera, Udemy, and edX) were developed to assess how various user interface design patterns influence user experience. The findings revealed that the aesthetic design of Coursera, including color scheme, content organization, was perceived as the most visually appealing, while Udemy received higher ratings for its typography, i.e, font size, type, and button shape. Coursera also outperformed the other platforms in terms of navigation (e.g., tab navigation, hamburger menu, drop-down, floating action button, listview), customization features (e.g., search filters, font, and background settings), and feedback mechanisms (e.g., toast messages, error alerts, progress indicators, confirmation prompts, and system status updates). Overall, participants reported higher satisfaction with Coursera, and its interface was associated with a lower cognitive load compared to Udemy and edX. These results underscore the importance of thoughtful user interface design in enhancing usability and reducing cognitive effort in mobile learning applications
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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A Multi-resolution Hard Attention Model to Select Regions of Interest on Whole Pathology Slide Images
No full textWith drastic improvements in the performance of neural networks and computer vision algorithms, deep learning-based imaging analysis models have been applied to a wide variety of fields. Along with this expansion, many applications increasingly involve very large inputs, which directly affects memory usage and the scaling of model architectures. Two of such challenges in the field include: 1) efficiently processing very high resolution images, and 2) detecting and classifying tiny objects relative to the size of the image. Both of these challenges are especially salient in the field of computational pathology, where most of the workflow involves processing high resolution images of scanned whole pathology slides on the scale of gigapixels. While rapid progress has been made in the past few years in cancer diagnosis, subtyping, and survival prediction using whole pathology slide images (WSIs), these methods involve patching and processing the entire WSI or at least the segmented tissue region at the highest resolution. However, many problems in computational pathology only require making a decision based on identifying small regions of interest (ROI) that make up a tiny proportion of the WSI, such as identifying cancer metastasis, identifying diseased glomeruli, etc. Additionally, WSIs come in the format of a multi-resolution image pyramid, yet most current methods only examine the slide at a fixed (usually a very high) resolution, without taking advantage of the multi-resolution data. To address these challenges, we propose a hard-attention method trained with reinforcement learning that identifies ROIs by selectively processing the WSI in a sequential top-down approach: examining the slide at lower resolutions and selectively zooming into patches that are likely to contain ROIs. We apply our method to the task of identifying glomeruli in kidney biopsies and show that it significantly lowers the proportion of the WSI sampled at high resolutions while maintaining high coverage of the structures of interest. Our method has the potential to reduce the time and cost in computational settings as well as to integrate into the traditional pathology workflow, with a higher expected impact in more resource-constrained settings
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Combining Foundation Models in Computational Pathology: Unlocking Multi-Representational Insights
No full textFoundation models have revolutionized computational pathology, enabling impressive results on tasks involving the classification of gigapixel whole-slide images (WSIs). However, no single foundation model consistently excels across all clinical scenarios. Given that these models differ substantially in their self-supervised training strategies, architectures, and data distributions, each model captures distinct morphological and structural features from histopathological slides. Leveraging multiple foundation models through patch-level feature fusion offers a promising approach to integrate their complementary strengths, potentially improving model robustness and generalization.
In this work, we present what is, to the best of our knowledge, one of the first and most comprehensive investigations of patch-level feature fusion using multiple foundation models. We systematically evaluate fusing three state-of-the-art pathology foundation models—UNI, Virchow, and GigaPath—across 11 established pathology tasks, 8 distinct fusion strategies, all possible encoder combinations, and various latent-space dimensionalities to thoroughly assess robustness. Since clinicians and researchers typically lack advance knowledge of which foundation model will perform best on unseen data, we adopt the average single-model performance as a practically relevant baseline for evaluating fusion methods. Our analysis demonstrates that a novel MLP-based fusion operator consistently surpasses this baseline in 132 out of 176 experiments across four multiple-instance learning (MIL) frameworks.
We further investigate factors influencing fusion effectiveness, finding that learned, parametric fusion operators typically outperform simpler, non-parametric methods predominantly studied in prior work. Additionally, we find that careful tuning of latent dimensionality can yield further performance gains, particularly for challenging multi-class subtyping tasks. Compared to conventional ensembles (aggregating final predictions), we discover that deep patch-level fusion is especially beneficial for multi-class diagnostic scenarios, whereas simpler ensembles may suffice for binary molecular biomarker tasks. Overall, this thesis provides valuable methodological insights and demonstrates the potential of multi-encoder patch-level fusion as a practical strategy for improving computational pathology systems.Computer Scienc
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