AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks

Patients increasingly turn to search engines and online content before, or in place of, talking with a health professional. Low quality health information, which is common on the internet, presents risks to the patient in the form of misinformation and a possibly poorer relationship with their physician. To address this, the DISCERN criteria (developed at University of Oxford) are used to evaluate the quality of online health information. However, patients are unlikely to take the time to apply these criteria to the health websites they visit. We built an automated implementation of the DISCERN instrument (Brief version) using machine learning models. We compared the performance of a traditional model (Random Forest) with that of a hierarchical encoder attention-based neural network (HEA) model using two language embeddings, BERT and BioBERT. The HEA BERT and BioBERT models achieved average F1-macro scores across all criteria of 0.75 and 0.74, respectively, outperforming the Random Forest model (average F1-macro = 0.69). Overall, the neural network based models achieved 81% and 86% average accuracy at 100% and 80% coverage, respectively, compared to 94% manual rating accuracy. The attention mechanism implemented in the HEA architectures not only provided 'model explainability' by identifying reasonable supporting sentences for the documents fulfilling the Brief DISCERN criteria, but also boosted F1 performance by 0.05 compared to the same architecture without an attention mechanism. Our research suggests that it is feasible to automate online health information quality assessment, which is an important step towards empowering patients to become informed partners in the healthcare process

How do the top 40 business schools in the UK understand, teach and implement KM in their teaching?

Purpose: The emergence of “knowledge economies” brings along new lenses to organizational management and behaviour. One of the key concepts at the heart of this new wave is knowledge management (KM). The purpose of this paper is to scrutinize how KM is taught and discussed within the context of business schools around the UK. Design/methodology/approach: The general research question is: how do top 40 business schools in the UK understand, teach and implement KM in their teaching? To answer this question, the author reviewed the curriculums of leading schools and contacted all schools to collect more information and data. Findings: The study reveals that KM has yet to carve a self-standing place for itself within taught programmes in UK business schools. Research limitations/implications: The study’s methodological design can explore the relevance of KM as a term, but it can only provide limited perspective into how this complex and multidimensional concept is operationalized in business schools’ curriculums. Moreover, the capacity of business schools to frame KM holistically is beyond the scope of this research. Practical implications: Framing KM discourse within the relevant academic literature, this paper outlines that, while KM is being scrutinized as a research topic, interest in KM has yet to be translated into a widespread integration of KM as a taught skill within business schools. Originality/value: The study is considered as one of the first attempts to investigate how KM is understood, taught and implemented in teaching and curriculum design within the UK business schools

Challenges and Opportunities of E-learning Networks in Africa

Allam Ahmed and Williams E. Nwagwu examine global networks for e-learning networks, with particular interest on the characteristics of the structures adopted by African countries to participate in the new educational strategy, and how these structures are moderated by Africa’s peculiar social and political characteristics. They look at the challenges and opportunities that e-learning networks face in Africa, and then finally suggest how the challenges can be met, in addition to also how the opportunities can be utilized

Digital publishing and the new era of digital divide

The scarcity of literature in educational institutions is a serious problem in different parts of the world, particularly in developing countries where there is a real need for better access to information. It is vitally important that the technological gap between developing and developed countries is narrowed, and scientific journals have a key role to play in ensuring that this takes place. This paper aims to assess and evaluate Digital Publishing (Open Access Publishing and Open Source Models) as a proposed solution to avoid restrictions on accessing scientific knowledge, particularly in the developing countries. More importantly, the paper outlines opportunities and challenges of open-access publishing for the developing countries. However, oftentimes there are mismatches between what the ‘donor’ countries can reasonably offer and what the developing countries can implement

Neural networks versus Logistic regression for 30 days all-cause readmission prediction

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1)explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2)to examine the additive value of patients' hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95%CI, 0.640-0.646). We conclude that data from patient timelines improve 30 day readmission prediction for neural network-based models, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets

Smart KM Model: the integrated knowledge management revolutionary approach for organisational excellence

The seminar aims to introduce the newly developed Knowledge Management Model (SMART KM) which presents a unique integrated solution for the highly debated subject. SMART KM ensures that knowledge management becomes part of the organisational culture through the appropriate integration with number of organisation facets such as strategy, management systems, ICT and support services. SMART KM is a revolutionary model being driven by best-in-class KM practices from a number of industries in addition to well-founded theories. The framework contain number of business components which supports knowledge flow throughout the organisations which can be tailored to achieve the organisation specific goals and objectives in alignment with the adopted operating model. Throughout the seminar, number of key issues facing organisations in implementing KM initiatives will be introduced and you would also be able to realise the design principles supporting SMART KM and how it can be used to develop fit-for-purpose KM Framework

Remineralization of artificially demineralized dentin in vitro

Objectives: Based on new concepts on the pathogenesis of dental caries, the management of carious lesions in proximity to the dental pulp has been largely changed. Ultraconservative excavation or removal is usually advised to avoid pulpal damage. The remaining demineralized tissue near the pulp, however, comes with challenges. One such challenge is to remineralize the residual lesion, for example using lining or restorative materials. In the present study, we tried to compare the remineralization activity of two different materials, calcium hydroxide and glass hybrid, on artificial residual lesions, in a pulpal fluid simulation system in vitro. Methods: On human dentin discs (n=20), artificial residual lesions (median mineral loss ΔZ [25th/75th percentiles]=1643 [1301/1858] vol%×µm) were chemically induced using demineralization solution without bacterial invasion. The dentin discs were divided into five parts, one of them served as baseline sample. The remaining four parts were used as experimental groups, with each being covered with a different material or material combination (n=20/group): Flowable composite (CO) without liner (control), setting and non-setting calcium hydroxide covered with flowable composite (CH-S, CH-NS), glass hybrid (GH). Samples were mounted in a dual-chamber device allowing pulpal fluid simulation. Pulpal surfaces were subjected to simulated pulpal fluid at 2.94 kPa, while coronal surfaces were exposed to artificial saliva, and once weekly rinsed with 200 ppm NaF. Solutions were changed regularly every 14 day. After 12 weeks, mineral loss differences compared to baseline were evaluated using transversal microradiography. Fluoride and strontium concentrations in exemplary samples were analyzed using Field Emission Electron Probe Micro-Analyzer (FE-EPMA). Results: Mineral gain in CO (negative control) was ΔΔZ=372 (115/501) vol%×µm. This was not significantly different from CH-S (ΔΔZ=317 [229/919] vol%×µm) or CH-NS (ΔΔZ=292 [130/579] vol%×µm), (p>0.05/Wilcoxon-test), but while mineral gain was significantly higher in GH (ΔΔZ=1044 [751/1264] vol%×µm, p<0.001). GH samples showed fluoride and strontium enrichments deep into the dentin. Such enrichment was not found in CO or CH samples. Conclusion: Within the limitations of this study, glass hybrid, but not calcium hydroxide provided coronal remineralization of residual carious lesions. Clinical relevance: Glass hybrids might provide additional remineralization of residual carious lesions.Ziel: Basierend auf neuen Konzepten zur Pathogenese der Dentalkaries hat sich das Management kariöser Läsionen in der Nähe der Zahnpulpa weitgehend geändert. In der Regel wird zu einer ultrakonservativen Exkavation oder Entfernung geraten, um Schäden an der Pulpa zu vermeiden. Das verbleibende demineralisierte Gewebe in der Nähe der Pulpa bringt jedoch Herausforderungen mit sich, u.a. die Remineralisierung der verbleibenden Läsion, zum Beispiel durch Liner- oder Restaurationsmaterialien. In der vorliegenden Studie haben wir die Remineralisierungsaktivität zwei verschiedener Materialien, Calciumhydroxid und Glashybrid, auf künstlichen Residualläsionen in vitro verglichen. Methoden: Auf humanen Dentinscheiben (n=20) wurden künstliche Residualläsionen (medianer Mineralverlust ΔZ [25./75. Perzentile] =1643 [1301/1858] Vol.%×µm) chemisch induziert. Die Dentinscheiben wurden in fünf Teile geteilt, von denen einer als Ausgangsprobe diente. Die restlichen vier Teile dienten als Versuchsgruppen, wobei jede mit einem anderen Material oder einer anderen Materialkombination beschichtet wurde (n=20/Gruppe): Fließfähiges Komposit (CO) ohne Liner (Kontrolle), abbindendes und nicht abbindendes Kalziumhydroxid, bedeckt mit fließfähigem Komposit (CH-S, CH-NS), Glashybrid (GH). Die Proben wurden in einem Zweikammergerät montiert, das eine Simulation von Pulpaflüssigkeit ermöglichte. Die Pulpaoberflächen wurden der simulierten Pulpaflüssigkeit bei 2,94 kPa ausgesetzt, während die koronalen Oberflächen einem künstlichen Speichel ausgesetzt und einmal wöchentlich mit 200 ppm NaF gespült wurden. Die Lösungen wurden regelmäßig alle 14 Tage gewechselt. Nach 12 Wochen wurden die Unterschiede im Mineralverlust im Vergleich zur Ausgangsprobe mittels transversaler Mikroradiographie ausgewertet. Die Fluorid- und Strontiumkonzentrationen in exemplarischen Proben wurden mittels Field Emission Electron Probe Micro-Analyzer (FE-EPMA) analysiert. Ergebnisse: Der Mineralgewinn in CO (Negativkontrolle) betrug ΔΔZ=372 (115/501) Vol.%×µm, dies war nicht signifikant unterschiedlich zu CH-S (ΔΔZ=317 [229/919] Vol.%×µm) oder CH-NS (ΔΔZ=292 [130/579] Vol.%×µm), (p>0.05/Wilcoxon-Test), aber während der Mineralgewinn in GH signifikant höher war (ΔΔZ=1044 [751/1264] Vol.%×µm, p<0.001). GH-Proben zeigten Fluorid- und Strontiumanreicherungen tief im Dentin. Eine solche Anreicherung wurde in CO- oder CH-Proben nicht gefunden. Schlussfolgerung: Innerhalb der Grenzen dieser Studie bewirkte Glashybrid, nicht aber Calciumhydroxid, eine koronale Remineralisierung von kariösen Residualläsionen. Klinische Relevanz: Glashybride könnten eine zusätzliche Remineralisierung von kariösen Residualläsionen bewirken

Conscious Brain Mind-Controlled Cybonthitic Cyborg Bionic-Leg -- V2

Lower limb amputations affect about 28.9 million people worldwide, influencing normal human functions, we are developing a conscious brain mind-controlled Cybonthitic cyborg bionic-leg to provide a professional solution for this problem, which is classified as restricted knee movement, short-term solution, limited pressure bearing, unspecific analog reading of EMG; Because the output voltage measured in nano-volts, resulting in unspecific knee movement. The functionality of these modern gadgets is still limited due to a lack of neuromuscular control (i.e. For movement creation, control relies on human efferent neural signals to peripheral muscles). Electromyographic (EMG) or myoelectric signals are neuromuscular control signals that can be recorded from muscles for our engineering goals. We worked on a sophisticated prosthetic knee design with a 100-degree angle of motion. We also used a specific type of coiled spring to absorb abrupt or unexpected motion force. In addition, we amplified the EMG output from (Nano-Voltage) to (Milli-Voltage) using customized instrumentation amplifiers (operational amplifiers). We used a full-wave rectifier to convert AC to DC, as a consequence of these procedures, sine-wave output voltage measures in millivolts, and the spring constant indicates the most force for every 1cm. Von mises Stress analysis shows bearing as 3000N is the maximum load for the design. Detecting the edge of a stairwell using the first derivative. The benefit of a system that controls the prosthetic limb is activated by the patient's own EMG impulses, rather than sensors linked to the body

Attention-based Multi-task Learning for Base Editor Outcome Prediction

Human genetic diseases often arise from point mutations, emphasizing the critical need for precise genome editing techniques. Among these, base editing stands out as it allows targeted alterations at the single nucleotide level. However, its clinical application is hindered by low editing efficiency and unintended mutations, necessitating extensive trial-and-error experimentation in the laboratory. To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence. We further propose a multi-task learning schema to jointly learn multiple base editors (i.e. variants) at once. Our model's predictions consistently demonstrated a strong correlation with the actual experimental results on multiple datasets and base editor variants. These results provide further validation for the models' capacity to enhance and accelerate the process of refining base editing designs