An Updated Review on Rheumatoid Arthritis (RA): Epidemiology, Pathophysiology, Diagnosis, and the Current Approaches for Its Treatment

Rheumatoid arthritis (RA) is a systemic self-inflicted inflammatory disease that primarily affects middle-aged women. Globally, 1% of people live with RA. This review aims to provide updated information on the different aspects of RA, including its epidemiology, pathophysiology, diagnosis, treatment, and management. A web-based literature search was conducted through various databases, including PubMed, Google Scholar, and Science Direct, to identify the most relevant studies. Epidemiological studies have suggested that the prevalence and occurrence of RA have remained inconsistent across geographical areas in different periods. Many factors such as age, gender, inheritances, and environmental exposure can contribute to the severity of the disease. The acute form of RA usually presents with pain, and if left untreated, it can result in joint deformities and influence a patient’s quality of life (QoL). RA diagnosis is usually based on the manifestation of pain with inflammation. Currently, many therapeutic strategies are available for the cure of RA. The management of daily routine activities is required with treatment to curtail the damage, avoid future deformities, and ultimately minimize the aching trouble of the patient

Robotic motion planning in unknown dynamic environments: existing approaches and challenges

Path planning with obstacles avoidance in dynamic environments is a crucial issue in robotics. Numerous approaches have been suggested for the navigation of mobile robots with moving obstacles. In this paper, about 50 articles have been reviewed and briefly described to offer an outline of the research progress in motion planning of mobile robot approaches in dynamic environments for the last five years. The benefits and drawbacks of each article are also explained. These papers are classified based on their issues into ten groups which are: stability, efficiency, smooth path, run time, path length, accuracy, safety, future prediction (uncertainties), control, and less computation cost. Finally, some scope and challenging topics are presented based on the papers mentioned

A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa

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A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa.

The progression of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Africa has so far been heterogeneous, and the full impact is not yet well understood. In this study, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished after the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1, and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Mortality of emergency abdominal surgery in high-, middle- and low-income countries

Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low- or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI). Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression. Results: Data were obtained for 10 745 patients from 357 centres in 58 countries; 6538 were from high-, 2889 from middle- and 1318 from low-HDI settings. The overall mortality rate was 1⋅6 per cent at 24 h (high 1⋅1 per cent, middle 1⋅9 per cent, low 3⋅4 per cent; P < 0⋅001), increasing to 5⋅4 per cent by 30 days (high 4⋅5 per cent, middle 6⋅0 per cent, low 8⋅6 per cent; P < 0⋅001). Of the 578 patients who died, 404 (69⋅9 per cent) did so between 24 h and 30 days following surgery (high 74⋅2 per cent, middle 68⋅8 per cent, low 60⋅5 per cent). After adjustment, 30-day mortality remained higher in middle-income (odds ratio (OR) 2⋅78, 95 per cent c.i. 1⋅84 to 4⋅20) and low-income (OR 2⋅97, 1⋅84 to 4⋅81) countries. Surgical safety checklist use was less frequent in low- and middle-income countries, but when used was associated with reduced mortality at 30 days. Conclusion: Mortality is three times higher in low- compared with high-HDI countries even when adjusted for prognostic factors. Patient safety factors may have an important role. Registration number: NCT02179112 (http://www.clinicaltrials.gov)

FPGA SoC-Based Reliable Systems for AI Applications in Automotive Industry

This thesis targets two-level reliability in Driver Monitoring Systems for effective vehicle safety by addressing chip-level functional and image-level system reliability with the following contributions. Aiming to address chip-level functional reliability, this thesis proposes LFTSM - a Lightweight and Fully Testable Single-Event Upsets (SEUs) Mitigation System for SRAM-based Xilinx SoC FPGAs that combines a Xilinx internal configuration repair mechanism with an external scrubber in the processor cores. Existing SEU mitigation techniques, such as Triple Modular Redundancy and configuration scrubbing have high resource overheads, limited testability, or use resources that are susceptible to SEUs themselves. In comparison, LFTSM achieves reliability in resource-intensive applications with less than 1% resource overhead on XC7Z020 FPGA, widest fault coverage, and full testing control as per Automotive Safety Integrity Level, defined by ISO 26262. The proposed system achieves the lowest resource utilization in comparison to the existing solutions in literature, without the need for external memories or third-party tools. This thesis validates LFTSM through controlled fault injection with complete control over the number and locations of error injections in the configuration memory, achieving detection of upsets within 8ms and correction of single-bit and multi-bit upsets in a few milliseconds for XC7Z020 device. JPEG images transmitted over noisy channels render image reconstruction impossible and consequently, lead to severe degradation of CNN object detection performance on these corrupted JPEG images. Existing error-resilient techniques are often complex, offer no parameterization, have limited hardware implementation, or they need architectural changes. Aiming to address this image-level system reliability, this thesis proposes EPHJEG system - a novel FPGA-based error-resilient parameterizable and reconfigurable JPEG encoder core that enhances error resilience in JPEG images while improving CNN-based object detection performance on these images. The system is validated through controlled JPEG image corruptions. Results show a 3x improvement in relative robustness for COCO-C and Pinochle card datasets with the Faster-RCNN model. Additionally, this thesis contributes by exploring the EPHJEG error-resilient design space to identify efficient parameters that achieve adequate JPEG error resilience while maintaining a balance between reliability and overhead. At last, this thesis contributes by presenting a unique method that uses Restart Markers to selectively compress regions of interest (ROI), reducing the image file size for efficient storage and bandwidth. Results show that ROI-based selective encoding effectively reduces image file size. The FPGA SoC-based reliability systems presented in this thesis are successfully integrated and tested with industrial resource-intensive automotive applications. The conclusion highlights key findings and proposes future research areas

A study of how R2P and the problems with the implementation of that principle

This study is about the principle “Responsibility to Protect” and its implementation in several countries. The purpose of this study is to examine why the implementation of “Responsibility to Protect” has been used differently in similar situations. To limit my study I have chosen to examine two countries that have two different results on similar issues. I have chosen to study the conflict/civil war in Libya and Syria. The reason why I have chosen these countries is because these conflicts have similar causes and similar history, but how the international community responded differently and has argued various. This study will also highlight and observe the difficulties that the principle faces. For example the veto right in the united nation security council, and also the principle of sovereignty. At the end of this study I discuss the principle in general, what I think about it and how the principle “Responsibility to Protect” can be improved itself and develop to be more powerful and fulfill its purpose, to Protect civilians from genocide, crime against humanity and towards war crime

Development of a motion planning and obstacle avoidance algorithm using adaptive neuro fuzzy inference system for mobile robot navigation

The autonomous navigation of robots is one of the most significant issues about robotics because of its difficulty and dynamism. This is because it relies on environmental situations such as the interface between themselves, individuals or any unexpected changes within the surroundings. It is necessary that the trajectory to the robots’ destination be calculated online, and throughout motion, to enable the robot to respond to variations within the environment. However, the essential difficulty in solving this issue may obstruct a sufficiently quick solution from being calculated online, given sensible calculation resources. These come from high dimensions of the exploration of space, geometrical and kinematic features of the obstacles. Especially their velocities, uncertainty, cost function to be improved, and the robot’s dynamic and kinematic model, This research focuses on the existing drawbacks and inefficiencies of the available path planning approaches within unknown dynamic environments. These drawbacks can be categorized as the problem encountered in this research into four categories, including inability to plan under uncertainty of dynamic environments, non- optimality, failure in crowded complex situations, and predicting the obstacle velocity vector. In this research, a new sensor-based online approach was proposed for generating a collision-free trajectory for differential-drive wheeled mobile robots, which could be applied to an unknown dynamic environment, in which the obstacles are moving and their speed profiles are not pre-identified. This approach depends on future predictive behaviour to predict the obstacles’ future route and priority behaviour to make decisions about the best navigation to reach the destination safely. This approach employs several intelligent techniques to improve the performance of the planner in terms of the quality of the resulted path, runtimes of the planner, ability to solve complex problems effectively and capability of planning in unknown dynamic environments. Firstly, a new sensor-based online approach is planned to reach the first and second objective of the research. This comprises planning in unknown dynamic environments and predicting the obstacle’s velocity vector in order to find safe and fast reactive trajectories. This is particularly true in unforeseen environments that contain both static and dynamic obstacles. After this, the third objective of the research is planning in a crowded complex situation to evaluate the risk of collision between the robot and the obstacle’s trajectory using a fuzzy logic controller. This would allow the FLC to generate a local path for an obstacle avoidance system unique to mobile robot navigation in dynamic environments. Finally, the last objective is to improve the optimality of the new approach using a robust Machine Learning strategy. An adaptive neuro-fuzzy inference system (ANFIS) was designed which constructs and optimizes a fuzzy logic controller using a given dataset of input/output variables in order for the mobile robot to learn. This depends on the previous outcomes to generate a short path with a low runtime for an obstacle avoidance system unique to mobile robot navigation in dynamic environments. The proposed multilayer decision approach successfully guides the robot in uncertain and ever-changing surroundings. It also efficiently predicts the obstacles’ velocity vector. The designed multilayer decision-based fuzzy logic model effectively solves the path planning queries in crowded and complex situations without any failure. Finally, the proposed ANFIS generated FLC successfully improves the optimality and reduces runtime rates of the proposed FLC planner. The present algorithm exhibits attractive features such as high optimality, high stability, low running cost and zero failure rates. The failure rate were zero for all test problems. The average path length for all test environments is 16.51 with standard deviation of 0.49 which gives an average optimality rate of 89.79%. The average runtime is 4.74 (standard deviation is 0.26)