ethicalabs/Research-Intent-Collab huggingface dataset:
Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets

The primary objective of this review is to investigate the effectiveness of machine learning and deep learning methodologies in the context of extracting adverse drug events (ADEs) from clinical benchmark datasets. We conduct an in-depth analysis, aiming to compare the merits and drawbacks of both machine learning and deep learning techniques, particularly within the framework of named-entity recognition (NER) and relation classification (RC) tasks related to ADE extraction. Additionally, our focus extends to the examination of specific features and their impact on the overall performance of these methodologies. In a broader perspective, our research extends to ADE extraction from various sources, including biomedical literature, social media data, and drug labels, removing the limitation to exclusively machine learning or deep learning methods.We conducted an extensive literature review on PubMed using the query "(((machine learning [Medical Subject Headings (MeSH) Terms]) OR (deep learning [MeSH Terms])) AND (adverse drug event [MeSH Terms])) AND (extraction)", and supplemented this with a snowballing approach to review 275 references sourced from retrieved articles.In our analysis, we included twelve articles for review. For the NER task, deep learning models outperformed machine learning models. In the RC task, gradient Boosting, multilayer perceptron and random forest models excelled. The Bidirectional Encoder Representations from Transformers (BERT) model consistently achieved the best performance in the end-to-end task. Future efforts in the end-to-end task should prioritize improving NER accuracy, especially for 'ADE' and 'Reason'.These findings hold significant implications for advancing the field of ADE extraction and pharmacovigilance, ultimately contributing to improved drug safety monitoring and healthcare outcomes.
Expert panel survey among occupational health and safety professionals in Denmark for prevention and handling of musculoskeletal disorders at workplaces

Abstract   Occupational health and safety (OHS) professionals have a key role in supporting the health and work ability of employees, including preventing and handling musculoskeletal disorders (MSDs) at workplaces. MSDs are the leading cause of work disability, productivity loss and sickness absence in Europe. This may be due to limited consensus on use of effective OHS practices as facilitation of evidence-based practices increases quality of provided services. This study explored consensus of OHS professionals’ practices and examined OHS professionals’ request for development of evidence-based guidelines for prevention and handling of MSDs at workplaces. This was done by 1) field observations and interviews with OHS professionals working with ergonomics or MSDs at workplaces, 2) development and pilot testing of a panel survey, 3) a three-round expert panel survey and 4) workshop with OHS stakeholders within the OHS organisations in Denmark. The findings indicate limited consensus of OHS practices and a request for development of practice- and evidence-based guidelines for prevention and handling of work-related MSDs in Denmark. The study also presents an end user involving process for increased uptake and implementation of guidelines.
Detection of ultra-high energy cosmic ray air showers by Cosmic Ray Air Fluorescence Fresnel lens Telescope for next generation

In the future, ultra-high energy cosmic ray (UHECR) observatory will be expanded due to the small flux. Then, cost reduction is useful strategy to realize a huge scale observatory. For this purpose, we are developing a simple structure cosmic ray detector named as Cosmic Ray Air Fluorescence Fresnel-lens Telescope (CRAFFT). We deployed CRAFFT detectors at the Telescope Array site and performed a test observation. We have successfully observed UHECR air showers. We will report the status and the result of the test observation.
Artificial intelligence-driven integration of multi-omics and radiomics: A new hope for precision cancer diagnosis and prognosis.

Despite advances in cancer diagnosis and treatment, the disease remains a major health challenge. Integrating multi-omics, radiomics, and artificial intelligence has improved detection, prognosis, and treatment monitoring. Molecular multi-omics provides insights into tumor biology, while radiomics extracts imaging features for outcome prediction. Liquid biopsy and circulating tumor DNA aid early detection and personalized therapy. Artificial intelligence-driven models integrate data to identify biomarkers and guide precision oncology. Despite challenges like cost and data integration, future advancements aim to enhance resolution, scalability, and non-invasive diagnostics. This mini-review explores these methodologies, their clinical impact, and their potential in personalized cancer treatment.
Genome Instability-related MiRNAs Predict Survival, Immune Landscape, and Immunotherapy Responses In Gastric Cancer

Abstract         Backgroud  Increasing evidence suggests that microRNAs (miRNAs) are involved in genome instability (GI) and drive the occurrence of tumors. However, the role of GI-related miRNAs in gastric cancer (GC) remains largely unknown. Herein, we developed a novel GI-related miRNA signature (GIMiSig) and further investigated its role in prognosis, the immune landscape, and immunotherapy responses in GC patients. Methods  An analysis of somatic mutation data on 434 gastric cancer cases from The Cancer Genome Atlas (TCGA) database was performed, thereby generating genome stability (GS) and GI groups. By detecting differentially expressed miRNAs between the GS and GI groups that were associated with overall survival, 8 miRNAs were identified and used to construct the GIMiSig.  Results The  GIMiSig showed high accuracy in detecting GC patients. Using GIMiSig to stratify the patients into high- and low-risk subgroups to predict survival outperformed the use of regular clinical features such as age, gender, or disease stage. Patients with low risk had a more favorable survival time than those with high risk. More importantly, the high-risk patients were associated with decreased UBQLN4 expression, higher accumulation of immune cells, lower Titin (TTN) mutation frequency, worse immunotherapy efficacy, and cancer-associated pathways. Conversely, the low-risk patients were characterized by UBQLN4 overexpression, lower fraction of immune cells, higher TTN mutation frequency, better response to immunotherapy, and GI-related pathways. Conclusion  In summary, we constructed a novel GIMiSig that could stratify GC patients into distinct risk groups that have different survival outcomes and immunotherapy efficacy. The results may provide new clues for improving GC outcomes.
Predicting the Employees’ Organizational Citizenship Behavior Components with respect to Managers’ Spiritual Leadership

This research aims to investigate the role ofmanagers’ spiritual leadership components in predicting and explaining theemployees’ organizational citizenship behavior components. In terms of purpose,this is an "applied" research. Due to the nature of the research, thedata-collection method, the method used in this research is "descriptivesurvey". Population of this study includes the employees of IranshahrUniversity of Medical Sciences in 2013 which they were 280 people. 162 peopleof them were selected as sample using ratio stratified sampling methods.Research datawascollected by spiritual leadership questionnaire organizational citizenshipbehavior questionnaireandwas analyzed by Pearson correlation test, multiple regression and Spsssoftware.Theresults showed that the level of managers’ spiritual leadershipand employees’ organizational citizenshipbehavior was moderate. The highest positive and significant value is betweenthe components of organizational commitment to politeness and consideration (r=0.377).
Constrained Multiview Representation for Self-Supervised Contrastive Learning

11 pages, 9 figures, 2 algorithms
Photonic neural networks and optics-informed deep learning fundamentals

The recent explosive compute growth, mainly fueled by the boost of artificial intelligence (AI) and deep neural networks (DNNs), is currently instigating the demand for a novel computing paradigm that can overcome the insurmountable barriers imposed by conventional electronic computing architectures. Photonic neural networks (PNNs) implemented on silicon integration platforms stand out as a promising candidate to endow neural network (NN) hardware, offering the potential for energy efficient and ultra-fast computations through the utilization of the unique primitives of photonics, i.e., energy efficiency, THz bandwidth, and low-latency. Thus far, several demonstrations have revealed the huge potential of PNNs in performing both linear and non-linear NN operations at unparalleled speed and energy consumption metrics. Transforming this potential into a tangible reality for deep learning (DL) applications requires, however, a deep understanding of the basic PNN principles, requirements, and challenges across all constituent architectural, technological, and training aspects. In this Tutorial, we, initially, review the principles of DNNs along with their fundamental building blocks, analyzing also the key mathematical operations needed for their computation in photonic hardware. Then, we investigate, through an intuitive mathematical analysis, the interdependence of bit precision and energy efficiency in analog photonic circuitry, discussing the opportunities and challenges of PNNs. Followingly, a performance overview of PNN architectures, weight technologies, and activation functions is presented, summarizing their impact in speed, scalability, and power consumption. Finally, we provide a holistic overview of the optics-informed NN training framework that incorporates the physical properties of photonic building blocks into the training process in order to improve the NN classification accuracy and effectively elevate neuromorphic photonic hardware into high-performance DL computational settings.
Introduction Of Spatial Database Spatial Data Types, Spatial Indexing, Geographic Information Systems Gis &amp; Query Structure

This paper covers the overview of Spatial Database System which offers Spatial Data Structure along with the Spatial Datatypes. It is most commonly used in Geographic Information Systems (GIS) and other applications. Many applications operate on spatial data which include lines, points, regions and polygons etc. Spatial data are large in size and complex in structure and relationship hence spatial indexes are required to retrieve the desired result-set from the large dataset in optimum timeframe  https://journalnx.com/journal-article/20150676
A Core set of patient-reported outcome measures to measure quality of life in obesity treatment research

The lack of standardization in patient-reported outcome measures (PROMs) has made measurement and comparison of quality of life (QoL) outcomes in research focused on obesity treatment challenging. This study reports on the results of the second and third global multidisciplinary Standardizing Quality of life measures in Obesity Treatment (S.Q.O.T.) consensus meetings, where a core set of PROMs to measure nine previously selected patient-reported outcomes (PROs) in obesity treatment research was established.The S.Q.O.T. II online and S.Q.O.T. III face-to-face hybrid consensus meetings were held in October 2021 and May 2022. The meetings were led by an independent moderator specializing in PRO measurement. Nominal group techniques, Delphi exercises, and anonymous voting were used to select the most suitable PROMs by consensus. The meetings were attended by 28 and 27 participants, respectively, including a geographically diverse selection of people living with obesity (PLWO) and experts from various disciplines.Out of 24 PROs and 16 PROMs identified in the first S.Q.O.T. consensus meeting, the following nine PROs and three PROMs were selected via consensus: BODY-Q (physical function, physical symptoms, psychological function, social function, eating behavior, and body image), IWQOL-Lite (self-esteem), and QOLOS (excess skin). No PROM was selected to measure stigma as existing PROMs deemed to be inadequate.A core set of PROMs to measure QoL in research focused on obesity treatment has been selected incorporating patients' and experts' opinions. This core set should serve as a minimum to use in obesity research studies and can be combined with clinical parameters.
The Mediating Role of Organizational Culture in the Effect of School Managers' Authentic Leadership Behaviors on Teachers' Organizational Citizenship Behavior

The purpose of this research is to examine the mediating role of organizational culture in the effect of school managers' authentic leadership behaviors on teachers' organizational citizenship behaviors. The research was conducted with 300 teachers working in the Pendik district of Istanbul province in the 2020-2021 Academic Year. The research used the authentic leadership scale, organizational culture scale, and organizational citizenship level scale. According to the research findings, school managers' authentic leadership behavior, teachers' organizational citizenship behavior, and organizational culture levels were high. Authentic leadership's organizational citizenship behavior is 8%; It was found that it affects organizational culture by 61%. It has been concluded that organizational culture has a fully mediating role in the effect of authentic leadership on organizational citizenship behavior. It is recommended that managers be given the training to develop authentic leadership skills.
AutoParams: An Automated Web-Based Tool To Generate Force Field Parameters for Molecular Dynamics Simulations

The growth of machine learning as a predictive tool in biochemical research has led to an increased need for large-scale datasets. Certain research questions benefit from molecular dynamics simulations to observe the motions and conformations of molecules over time, however contemporary methods rely on forcefields which describe sets of common biomolecules. Unusual molecules, such as nucleotide analogues, functionalized carbohydrates, and modified amino acids are often ill-described in standard forcefields, requiring the development of custom parameters for each unique molecule. While these parameters may be created by individual users, the process is time-consuming and can introduce errors that may not be immediately apparent. We present an open-source automated parameter generation service, AutoParams, which requires minimal input from the user and creates useful forcefield parameter sets for most molecules, particularly those that combine molecular types (ex: a carbohydrate functionalized with a benzene). It can be straightforwardly linked to any charge generation program, and currently has interfaces to PsiRESP and TeraChem, and is available via GitHub and as a Docker container. It includes error checking and testing protocols to ensure the parameters will be sufficient for subsequent molecular dynamics simulations, and streamlines the creation of force field databases.
Ethical Considerations in Explainable Artificial Intelligence: Transparency and Accountability in AI Decision-Making

In many industries, AI has emerged as a powerful tool, which has raised valid concerns over the opaqueness of algorithms used in decision making processes. This paper aims to investigate the ethical considerations surrounding AXI with a focus on critical industries including; healthcare, banking and criminal justice. The main question is focused on how to effectively address the problem of realizing the applications of AI techniques as well as the demands for the interpretability, explainability, and audibility of AI decisions while promoting justice, accountability, and privacy. In a similar manner, XAI approaches are assessed, prototyped within this study, and case studies were conducted to explain how XAI could be implemented. It also entails consultations with the stakeholders in order to identify some of the issues and goals that they may have regarding the use of AI in activities such as transparency and accountability. The study’s implications indicate that XAI has the potential to improve AI governance to become more transparent and fair in applying AI technologies through eliminating risks and algorithm bias, as well as strengthening the level of trust of all interested parties. Recognizing the lack of congruency between technology adoption and its ethical implications in literature, this research will help in the progression of proper implementation policies of AI solutions.
'But for The Nicety of Knocking and Requesting a Right of Entry': Surveillance Law and Privacy Rights in South Africa

As communications tools expand beyond that of the traditional fixed line telephone, so too do the tools for monitoring those communications. Fuelled by dual needs to protect the privacy rights of individuals, as well as monitor the activities of criminals using the communications networks, governments around the world are toning their surveillance laws in accordance with technological developments and constitutional necessity. In the South African context, the clash of rights inherent in this activity warrants an examination of the continued constitutional status of the Interception and Monitoring Prohibition Act of 1992, in light of recent proposals by the Law Commission to amend its provisions. It is argued that whilst the target of such a law justifies its existence, the reach of its ambit potentially displaces its ongoing constitutional validity.
Computational approaches for drug repositioning and repurposing to combat SARS-CoV-2 infection

Drug repositioning (also referred to as drug repurposing) is the method of exploring novel therapeutic indications for Food and Drug Administration-approved clinically implemented drugs. The unique strategy of drug repositioning is used to boost the drug development process since drug discovery is an expensive, arduous, cumbersome, and high-risk procedure. Recently, several pharmaceutical firms have used the drug repositioning technique in their drug discovery and development programs to develop new medications based on the identification of new therapeutic targets. This technique is extremely effective, saves time, is comparatively economical, and has a low chance of failure. Developing appropriate treatment measures to inhibit the spread of Coronavirus disease-2019 (COVID-19) is currently a top priority. As a result, several studies were conducted to build novel therapeutic molecules using diverse strategies of drug repurposing to discover drug candidates against COVID-19 infection that can act as substantial inhibitors against virus particles. By implementing virtual screening of drug libraries, it is possible to identify potential drugs through drug repurposing. A molecular docking approach and calculation of binding free energy are used to estimate binding affinity and drug–receptor interactions. Drug-repurposing methodologies can be divided into three categories: target-oriented, drug-oriented, and disease-oriented, based on the gathered data about the various physicochemical, pharmacokinetic and pharmacological features of a drug candidate. Using computational methods such as homology modeling and molecular similarity, this methodology aids in determining the binding interaction of drug molecules with the target protein of the virus. In this book chapter, we explore a typical set of currently utilized computational techniques for identifying repurposable drug molecules for COVID-19, as well as their supporting databases. We also assess promising drugs anticipated by computational approaches to drugs currently being evaluated in clinical trials. Moreover, we also examine the takeaways from the evaluated research efforts, such as how to competently combine bioinformatics tools with experimental work and suggest a fully integrated drug-repurposing approach to combat the deadly COVID-19 infection.
Using high performance computing for unrelated parallel machine scheduling with sequence-dependent setup times: Development and computational evaluation of a parallel branch-and-price algorithm

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Alleviating Catastrophic Forgetting in Continual Learning

Machine learning has enjoyed rapid and substantial advances in the past few years. However, machine learning models cannot learn continually as we humans do. Humans are continual learners, meaning they can accumulate knowledge, use the previous knowledge to learn from new experiences better, and retain knowledge from previous experiences. In contrast, current machine learning models learn in an isolated manner meaning there is no notion of time (e.g., past or present) in their closed-world learning. The goal of continual learning is to mimic the learning mechanism of humans for machines with significant impacts on the machine learning community. However, this is a challenging problem since current machine learning systems suffer from the catastrophic forgetting problem, meaning they cannot preserve their learned knowledge. Catastrophic forgetting happens mainly because the model is trained sequentially over evolving data distributions. Consequently, the representations the model has learned for previous data will change to adapt to the new data, and the new representations are no longer adequate for the past data. While the recent progress in continual learning is encouraging, our understanding of the catastrophic forgetting problem is still limited. This dissertation aims to understand the continual learning problem better and fill this knowledge gap by studying the theoretical and practical implications of the catastrophic forgetting problem for deep learning models. We will study the catastrophic forgetting problem from various perspectives and show that the optimization, training regime, loss landscape, and architectures of neural networks all play a significant role in alleviating the forgetting. We then use the gained insights to develop continual agents that are more robust to catastrophic forgetting.
ByteSeries

Monitoring large-scale and complex systems often generates high-dimensional and highly dynamic time series data. In such a scenario, massive metadata has to be maintained to support efficient querying, whose large footprint poses great challenges to in-memory databases. In this paper, we present ByteSeries, an in-memory time series database that is designed specifically for large-scale monitoring systems to manage high-dimensional time series. We start with an analysis of the production data and workload at ByteDance's metric monitoring system, which contains over 10 billion time series dimensions. The observation of high overhead of metadata management in high-dimensional time series data calls for a rethink of time series database systems. Byte-Series's memory structure employs the novel Compressed Inverted Index to effectively compress metadata while maintaining high efficiency for multi-dimensional queries. In addition, an algorithm is proposed to effectively convert data into compressed form without sacrificing the data ingestion throughput. We experimentally evaluate ByteSeries by comparing it with ByteDance's original production system, tsdc, as well as two open-source systems, namely Gorilla and Prometheus. We show that ByteSeries significantly improves over ByteDance's original production system by 1) reducing the memory footprint of metadata by 60% and the whole memory consumption by 50%, and 2) speeding up multi-dimensional queries by 1.8x-10.7x.