Computational Analysis of Antibody Binding Mechanisms to the Omicron RBD of SARS-CoV-2 Spike Protein: Identification of Epitopes and Hotspots for Developing Effective Therapeutic Strategies

The advent of the Omicron strain of SARS-CoV-2 has elicited apprehension regarding its potential influence on the effectiveness of current vaccines and antibody treatments. The present investigation involved the implementation of mutational scanning analyses to examine the impact of Omicron mutations on the binding affinity of four categories of antibodies that target the Omicron receptor binding domain (RBD) of the Spike protein. The study demonstrates that the Omicron variant harbors 23 unique mutations across the RBD regions I, II, III, and IV. Of these mutations, seven are shared between RBD regions I and II, while three are shared among RBD regions I, II, and III. The findings suggest that the mutations exert a noteworthy influence on the antibodies\u27 binding affinity, especially in Class II and Class III antibodies. Among the mutations, those located at positions R346, L452, and F490 appear to have a particularly notable impact. Multiple mutations were detected at positions F375, Y501, and H505 across all sub-variants of Omicron, indicating their potential significance in evading the immune system. The mutations could potentially bear significant ramifications with regards to immune evasion. The research underscores the significance of continuous observation and scrutiny of viral mutations in order to guide the creation of efficacious treatments for novel strains of SARS-CoV-2

Exploring embedding vectors for emotion detection

Textual data nowadays is being generated in vast volumes. With the proliferation of social media and the prevalence of smartphones, short texts have become a prevalent form of information such as news headlines, tweets and text advertisements. Given the huge volume of short texts available, effective and efficient models to detect the emotions from short texts become highly desirable and in some cases fundamental to a range of applications that require emotion understanding of textual content, such as human computer interaction, marketing, e-learning and health. Emotion detection from text has been an important task in Natural Language Processing (NLP) for many years. Many approaches have been based on the emotional words or lexicons in order to detect emotions. While the word embedding vectors like Word2Vec have been successfully employed in many NLP approaches, the word mover’s distance (WMD) is a method introduced recently to calculate the distance between two documents based on the embedded words. This thesis is investigating the ability to detect or classify emotions in sentences using word vectorization and distance measures. Our results confirm the novelty of using Word2Vec and WMD in predicting the emotions in short text. We propose a new methodology based on identifying “idealised” vectors that cap- ture the essence of an emotion; we define these vectors as having the minimal distance (using some metric function) between a vector and the embeddings of the text that contains the relevant emotion (e.g. a tweet, a sentence). We look for these vectors through searching the space of word embeddings using the covariance matrix adap- tation evolution strategy (CMA-ES). Our method produces state of the art results, surpassing classic supervised learning methods

Linguistic Gender Differences in English among Saudi Medical Students in Online Courses

This qualitative research explores gender differences in language use among Saudi medical students in online courses. Employing purposive sampling, the study, aims to comprehend the influence of gender on language patterns and communication styles in online education. Data collection involves online interviews with five teachers and survey questionnaires administered to twenty actively enrolled Saudi medical students. The interviews provide insights into language use patterns in the online learning environment, while surveys offer a broader perspective on student language use. Thematic analysis unveils recurring themes: “courses developed,” “reasons for online courses,” “interaction among male and female students in online courses,” “comfort levels,” “misunderstandings,” and “influence of culture on language use.” Findings reveal gender differences in language use, although some distinctions between male and female students were less pronounced. Both genders acknowledged the significance of supportive language, backchannel cues, and mitigators, contrasting with prior research. Words traditionally associated with one gender, such as ‘pretty,’ and ‘quite’ were deemed essential for both in the digital learning environment. Gender differences became less apparent due to convergence on various language aspects, use of mitigators, such as “I think,” “you know,” highlighting modern education\u27s evolving nature. Saudi culture and societal norms influence language use, emphasizing respect and politeness in online communication. However, individual variations exist, with some students employing more direct language or expressions challenging gender-specific language patterns. The study sheds light on language dynamics in the online classroom, contributing insights into gender differences in language use among Saudi medical students. Recommendations include implementing diverse communication approaches and fostering a culturally sensitive online educational space. Policymakers and educators can leverage these findings to enhance educational practices catering to diverse language preferences in the online classroom

Police powers, legal rights and pre-trial procedures in Saudi Arabia : a comparison with England and Wales

The exercise of police powers is subject to rules and guidelines, and the event of police powers has occasioned considerable controversy since the inception of the 'new police'. On the one hand, the police clearly need powers to stop people on the street if they are suspected of a crime, to enter people's houses if they suspect that they are hiding stolen goods or firearms and to arrest people they suspect of a crime. They need to be able to interview suspects in the police station and may have to hold suspects in cells. On the other hand, individual citizens need to be able to carry on with their everyday lives without risking being stopped on the streets, having their homes ransacked by the police and being arrested and taken to the police station. Suspects must be protected from torture, brutality and the extraction of false confessions. Special protection may be afforded to vulnerable groups such as the young and mentally ill. Legislation on police powers, therefore, must balance conflicting needs.Saudi Arabia the Stop, Arrest, Detention and Custody Regulation (SADC) was set up in 1983. The regulation provided powers relating to stop and search, arrest, detention. interviewing, and the investigation of crimes It seeks to protect suspects from the abuse of such powers by granting to suspects certain rights and protections. In practice, however, the balance between the use of the powers and suspects' rights is different. The police appear to exceed their powers as they provided and the safeguards are ignored.Therefore, the question is, how do the pre-trial procedures work in practice? No research has been done to examine the pre-trial process in practice in Saudi Arabia.Data collection for the study as carried out using three methods: questionnaire, observation and documentary data from police files.In this research variations have been found between the official regulation and actual police practice

Impact of Work Hour on Employee Social Wellbeing in Northern Ireland

This paper aims to explore the relationship that exists between the amount of hour’s employees in Northern Ireland, work per week, and their social wellbeing represented by the level of happiness and Health status. This study made use ofsecondary data adapted from the 2009 Northern Ireland Life and Times Survey (NILT). Also, the study provides an analytic account of factors that determine the number of hours spent at work. The paper indicates that in Northern Ireland, there is no significant correlation between the amount of hours employee spent at work and their social wellbeing. Also, the study found that other factors such as: care for someone at home, sex, employed or self-employed, employment status, thinking about work, and socio-economic group significantly predicts the amount of time an employee in Northern Ireland spends at work. The study concluded that the relationship between work hour and employee social wellbeing varies across countries and cultures

A mixed methods study of factors influencing health managers acceptance of eHealth services in the Kingdom of Saudi Arabia.

The Kingdom of Saudi Arabia (KSA) is a country with one of the largest land masses and most difficult geographical terrain in the Middle East. The accessibility of advanced health services, especially for people in rural areas, has been considered one of the main health challenges. Health services across the country are accessible through three categories of providers. The Ministry of Health (MOH), which is the dominant health provider, is responsible for 60% of all health services and facilities. The private health sector and other government-run health authorities are the providers for the remaining 40%. Many initiatives to embrace technology in healthcare were launched by the MOH to advance the level of acceptance. One of the initiatives was the ambitious National eHealth Strategy, which was launched in 2011 to govern eHealth projects across the country, and to set consistent standards, policies, and procedures for the practice activities. This study was sponsored by the MOH as part of a bigger plan to involve stakeholders in the digital transformation. The overall aim of this doctoral research was to explore the factors that influence health managers' acceptance of eHealth services in KSA. The 1st phase was a systematic review (SR): based on a PRISMA-P guided protocol published with CRD Prospero, five databases were searched for studies published between 1993 and 2017. One reviewer performed the search; two reviewers screened the titles and abstracts. Exclusions were recorded with reasons. Tools appropriate to study design were applied independently by two reviewers to assess the quality of included studies. After duplicates were removed, 110 papers were screened and 15 studies met the inclusion criteria. From these 15 papers, 39 factors were identified as influencing varying levels of eHealth adoption and acceptance in KSA. Lack of studies on the views of health managers and limited studies from only a few geographical settings were also identified as knowledge gaps. The 2nd phase was a survey: an online questionnaire in both Arabic and English language was designed around the Unified Theory of Acceptance and Use of Technology (UTAUT) model determinants. Professionals with a health managerial role from multiple disciplines - such as health professions, administration, and health IT - were invited to take part in the study. Ethical approval had been gained. Participation links were distributed across a range of social media platforms. SPSS v25 was used for data analysis. Findings from the 2nd phase survey showed the significance (p < 0.05) of Performance Expectancy and Social Influence moderated by age to the Behavioural Intention of health managers as well as the Performance Expectancy and Facilitating Conditions to the actual Use Behaviour. Some ambiguous results need further investigations. The 3rd phase consisted of a mixture of face-to-face and telephone in-depth interviews with 21 health managers from Aseer province, KSA. Four umbrella domains were derived from the UTAUT model. The pre-defined themes from phases 1 and 2 were explored and mapped against the domains. Ethical approval had been gained. Microsoft Excel and NVivo were used for the data analysis. Through the interviews, ambiguity in the previous phase was clarified and the most influential factors based on the views of health managers in Aseer province, KSA, were identified. Three domains out of four showed significance: Performance Expectancy, Social Influence, and Facilitating Conditions. This mixed methods research design presented across three phases was adopted with the findings from each phase informing the next. Overall, the research confirmed the influence of the same factors on health managers' acceptance of eHealth services in KSA and generated original findings. First, by providing evidence that this area has not been previously studied through registering a protocol and publishing a systematic review. Second, by using social media platforms to support a novel recruitment approach for the study. Third, by employing UTAUT as a theoretical framework in both quantitative and qualitative phases. Finally, exploring eHealth practice in Aseer province, a part of KSA that has not previously been explored in the published literature. These original findings draw a clearer picture of the potential challenges faced by health managers in KSA in accepting and using eHealth services. The findings may also work as a foundational basis from which to better prepare other stakeholder groups for accepting eHealth services. By doing so, staff can more effectively utilise health technology interventions as key concepts in making successful and positive transformational and sustainable change to the delivery of healthcare

Real Time Vehicle License Plate Recognition on Mobile Devices

Automatic license plate recognition is useful in many contexts such as parking control, law enforcement and vehicle background checking. The high cost and low portability of commercial systems makes them inaccessible to the majority of end users. However, current mobile devices now have processors and cameras that make image processing and recognition applications feasible on them. This thesis investigates high accuracy real-time license plate recognition on a smartphone, taking into account device limitations. It first explores how, using the minimal image processing and simple configurable heuristics based on plate geometry, license plates and their characters can be detected in an image. Then, using minimal training data, it shows that a character recognition package can achieve high levels of accuracy. This approach accurately recognized 99 percent of plates appearing in a test set of videos of vehicles with New Zealand license plates

Patient Satisfaction Visiting the Dental Clinics, Faculty of Dentistry, Najran University

Objectives: To find out the level of patient satisfaction visiting the dental clinic of faculty of dentistry, Najran University, Saudi Arabia. Providing quality in health care is very important and it can be assessed by evaluating the patient satisfaction seeking dental care. Materials and Method: A cross sectional study was conducted at the dental clinics of faculty of dentistry at Najran University. A simple random sampling technique was used. A questionnaire consisting of twenty two questions was used.Results: A total of 160 patients participated in this study. The highest proportion (40%)of the patient belonged to the age group of 18-25 years. The most common reason to visit a dentist was found to be for restoration of teeth(35%).Majority of the participants (93.75%) said that they would recommend the treatment provided at the clinics, College of dentistry to others.Conclusion: The survey showed that most of the patients were satisfied with the dentist-patient domain. Keywords: dentist, patient, satisfaction, dental clinics, Najran Universit

Classification of Cyber-Attack using Adaboost Regression Classifier and Securing the Network

In recent years, with adverse development of technology leads to several security breaches. To withstand those security threats and breaches especially for cyber attacks resources are optimized with improved network lifetime. Those security challenges are lead to confidentiality, privacy, integrity and availability. To prevent cyberattacks artificial intelligence-based technology is evolved. To adopt appropriate cybersecurity wireless communication systems are intended to withstand threats and challenges. This paper, presented a deep learning-based classification technique for cyber attack detection. Deep learning structure involved in attack detection with proposed AdaBoost Regression Classifier (ABRC). The proposed ABRC with deep learning involved in estimation of attacks in the network security with deep learning structure.&nbsp; The proposed classifier model is involved in estimation of threats. The developed algorithm integrates AdaBoost and Regression classifier for threat detection and classification. The performance analysis expressed that proposed ABRC exhibits significant performance for cyber-attack detection than the existing deep learning technique

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility