Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity

Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementiaComment: To appear in INTERSPEECH 202

ComLittee: Literature Discovery with Personal Elected Author Committees

In order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help scholars discover relevant papers and authors. Recent approaches have shown the usefulness of highlighting relevant authors while scholars engage in paper discovery. However, these systems do not capture and utilize users' evolving knowledge of authors. We reflect on the design space and introduce ComLittee, a literature discovery system that supports author-centric exploration. In contrast to paper-centric interaction in prior systems, ComLittee's author-centric interaction supports curation of research threads from individual authors, finding new authors and papers with combined signals from a paper recommender and the curated authors' authorship graphs, and understanding them in the context of those signals. In a within-subjects experiment that compares to an author-highlighting approach, we demonstrate how ComLittee leads to a higher efficiency, quality, and novelty in author discovery that also improves paper discovery

Characterizing and Predicting Tasks at Risk in Team Task Management

Collaborative project management involves interacting with various tasks in a shared planning space where members add, assign, complete, and edit project-related tasks to have a shared view of the project’s status. This process directly impacts how individual team members select, prioritize, and organize tasks on which to focus on a daily basis. However, such coordination and task prioritization can become increasingly challenging for individuals working on multiple projects with big teams. Accordingly, tasks could become at risk and eventually not be completed on time, leading to personal or team losses in many situations. To support task-doers in completing their tasks, we conducted a mixed-methods study focusing on Microsoft Planner—a collaborative project management tool—to understand how users manage their tasks in a team setting, what challenges they encounter, and their preferred solutions. Based on the findings from a qualitative survey with 151 participants and our Planner log data analysis, we further developed a task at risk prediction model using various task characteristics and user actions. Our experimental results suggest that a task at risk can be classified with high effectiveness (accuracy of 89%). Our work provides novel insights on how users manage their tasks in team task management tools, what challenges they face, how they perceive a task at risk, and how tasks at risk can be modeled. Such an application can significantly improve the user experience in such tools by providing a personal assistant that helps users prioritize their tasks and pay attention to critical situations.S.M

Investigating Language Ideologies and Attitudes Towards Dubbing Disney Movies into Arabic

This research delves into the analysis of online discussions surrounding the dubbing of Disney movies into two Arabic varieties: Modern Standard Arabic (MSA) and Egyptian Colloquial Arabic (ECA). The objective is to uncover the language ideologies and attitudes taken by Disney fans online. Dubbing Disney movies into these two varieties has sparked numerous debates on social media, particularly on Facebook. The study employed stance-taking to analyze the metalinguistic comments made by Facebook users about the use of these two Arabic varieties in the context of Disney. A Verbal Guise Technique (VGT) experiment was conducted on Disney excerpts to complement the results. The findings provided valuable insights into how Arab social media users’ stances are influenced by their ideologies and attitudes. Lastly, the VGT experiment provided another perspective on how Arabs evaluate MSA and ECA in the context of Disney movies and how they express their indirect language attitudes. The findings showed the differences in language attitudes between Maghreb, Mashreq, and Arab Peninsula countries. Mashreq and Peninsula countries revealed closer language attitudes than those of the Maghreb countries. Moreover, the ECA competes with MSA in prestige and pleasantness. MSA is seen as the thread that connects the Arab World and is adulated for its connection with the Arab heritage, religion, and education. However, it is less favored for its rigidity, unintelligibility, and unnaturalness. Finally, ECA is favored for its simplicity, humor, closeness, familiarity, intimacy, naturalness, and entertainment

Multimodal inductive transfer learning for detection of Alzheimer's dementia and its severity

Copyright © 2020 ISCA Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia

Iron/Copper/Phosphate nanocomposite as antimicrobial, antisnail, and wheat growth-promoting agent

Abstract Background One of the current challenges is to secure wheat crop production to meet the increasing global food demand and to face the increase in its purchasing power. Therefore, the current study aimed to exploit a new synthesized nanocomposite to enhance wheat growth under both normal and drought regime. The effectiveness of this nanocomposite in improving the microbiological quality of irrigation water and inhibiting the snail’s growth was also assessed. Results Upon the employed one-step synthesis process, a spherical Fe/Cu/P nanocomposite was obtained with a mean particle size of 4.35 ± 1.524 nm. Cu2+, Fe2+, and P4+ were detected in the dried nanocomposite at 14.533 ± 0.176, 5.200 ± 0.208, and 34.167 ± 0.203 mg/ml concentration, respectively. This nanocomposite was found to exert antibacterial activity against Escherichia coli and Salmonella typhi. It caused good inhibition percent against Fusarium oxysporum (43.5 ± 1.47%) and reduced both its germination rate and germination efficiency. The lethal concentration 50 (LC50) of this nanocomposite against Lanistes carinatus snails was 76 ppm. The treated snails showed disturbance in their feeding habit and reached the prevention state. Significant histological changes were observed in snail digestive tract and male and female gonads. Drought stress on wheat’s growth was mitigated in response to 100 and 300 ppm treatments. An increase in all assessed growth parameters was reported, mainly in the case of 100 ppm treatment under both standard and drought regimes. Compared to control plants, this stimulative effect was accompanied by a 2.12-fold rise in mitotic index and a 3.2-fold increase in total chromosomal abnormalities. Conclusion The finding of the current study could be employed to mitigate the effect of drought stress on wheat growth and to enhance the microbiological quality of irrigation water. This is due to the increased efficacy of the newly synthesized Fe/Cu/P nanocomposite against bacteria, fungi, and snails. This methodology exhibits potential for promoting sustainable wheat growth and water resource conservation

Corporate Governance and Earnings Management Nexus: Evidence from the UK and Egypt Using Neural Networks

Using conventional regressions and Generalized Regression Neural Networks (GRNN), we examine the relationship between Corporate Governance (CG) and Earnings Management (EM). We also examine whether governance quality moderates the association between EM and CG for a sample of British and Egyptian companies. Our findings show that: (a) UK firms are likely to have lower levels of EM if they: have smaller boards, are dominated by independent outside directors, and have a low percentage of female directors; (b) Egyptian firms are likely to have lower levels of EM if they: have larger boards, are dominated by independent outside directors, and have a low percentage of female directors; (c) The governance quality (control of corruption) has a significant hidden effect on EM. Since our results provide empirical evidence that the board of directors plays a vital role in mitigating EM, these findings might lead to an improvement in the credibility of financial statements for investors in both the UK and Egypt. As policy implications, our findings inform regulators and policy-makers that corruption has a very strong hidden effect on EM and that they can deter EM by controlling the corruption level in their countries

The clinical musculoskeletal ultrasonography: Egyptian guidelines for structured musculoskeletal ultrasound scanning and reporting

Abstract Background The aim of this work is to set up the standards for performing musculoskeletal ultrasound scans and reporting as an additional procedure in the rheumatology setting. We used two rounds of the Delphi approach to get the consensus on a musculoskeletal ultrasound reporting. Results Fifteen expert panels had completed the two rounds of surveys. After the end of round two, eighteen recommendations distributed upon eight domains were released. The percentage of the agreement on the recommendations was 93.3 to 100 %. All eighteen key questions were answered at the end of the second round with agreement. Conclusion A musculoskeletal ultrasound report template has been developed by this study, based on outcomes of a Delphi process, by an international participants’ panel. All domains met the 80% voting threshold set in this work. The reporting template can be used for both clinical research as well as standard practice to provide guidance and standardize the musculoskeletal ultrasound reporting

COVID-19 and Diabetes Mellitus: A Complex Interplay

COVID-19 pandemic, which caused by the newly emerged severe acute respiratory syndrome coronavirus-2 (SARS- CoV-2), puts the entire world in an unprecedented crisis, leaving behind huge human losses and serious socio-economical damages. The clinical spectrum of COVID-19 varies from asymptomatic to multi-organ manifestations. Diabetes mellitus (DM) is a chronic inflammatory condition, which associated with metabolic and vascular abnormalities, increases the risk for SARS-CoV-2 infection, severity and mortality. Due to global prevalence, DM effect on COVID-19 outcomes as well as the potential mechanisms by which DM modulates the host-viral interactions and host-immune responses are discussed in this review. This review also highlights the effects of anti-diabetic drugs on treatment of SARS-CoV-2 infection and vice versa

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population