The Prominence of Artificial Intelligence in COVID-19

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.Comment: 63 pages, 3 tables, 17 figure

A multi-omics analysis of bone morphogenetic protein 5 (BMP5) mRNA expression and clinical prognostic outcomes in different cancers using bioinformatics approaches

Cumulative studies have provided controversial evidence for the prognostic values of bone morphogenetic protein 5 (BMP5) in different types of cancers such as colon, breast, lung, bladder, and ovarian cancer. To address the inconsistent correlation of BMP5 expression with patient survival and molecular function of BMP5 in relation to cancer progression, we performed a systematic study to determine whether BMP5 could be used as a prognostic marker in human cancers. BMP5 expression and prognostic values were assessed using different bioinformatics tools such as ONCOMINE, GENT, TCGA, GEPIA, UALCAN, PrognoScan, PROGgene V2 server, and Kaplan–Meier Plotter. In addition, we used cBioPortal database for the identification and analysis of BMP5 mutations, copy number alterations, altered expression, and protein–protein interaction (PPI). We found that BMP5 is frequently down-regulated in our queried cancer types. Use of prognostic analysis showed negative association of BMP5 down-regulation with four types of cancer except for ovarian cancer. The highest mutation was found in the R321*/Q amino acid of BMP5 corresponding to colorectal and breast cancer whereas the alteration frequency was higher in lung squamous carcinoma datasets (>4%). In PPI analysis, we found 31 protein partners of BMP5, among which 11 showed significant co-expression (p-value 1). Pathway analysis of differentially co-expressed genes with BMP5 in breast, lung, colon, bladder and ovarian cancers revealed the BMP5-correlated pathways. Collectively, this data-driven study demonstrates the correlation of BMP5 expression with patient survival and identifies the involvement of BMP5 pathways that may serve as targets of a novel biomarker for various types of cancers in human

Smart electronic textile‐based wearable supercapacitors

Abstract: Electronic textiles (e‐textiles) have drawn significant attention from the scientific and engineering community as lightweight and comfortable next‐generation wearable devices due to their ability to interface with the human body, and continuously monitor, collect, and communicate various physiological parameters. However, one of the major challenges for the commercialization and further growth of e‐textiles is the lack of compatible power supply units. Thin and flexible supercapacitors (SCs), among various energy storage systems, are gaining consideration due to their salient features including excellent lifetime, lightweight, and high‐power density. Textile‐based SCs are thus an exciting energy storage solution to power smart gadgets integrated into clothing. Here, materials, fabrications, and characterization strategies for textile‐based SCs are reviewed. The recent progress of textile‐based SCs is then summarized in terms of their electrochemical performances, followed by the discussion on key parameters for their wearable electronics applications, including washability, flexibility, and scalability. Finally, the perspectives on their research and technological prospects to facilitate an essential step towards moving from laboratory‐based flexible and wearable SCs to industrial‐scale mass production are presented

Coronavirus disease 2019 and future pandemics: Impacts on livestock health and production and possible mitigation measures

The World Health Organization declared coronavirus disease 2019 (COVID-19) a pandemic on March 11, 2020. COVID-19, the current global health emergency, is wreaking havoc on human health systems and, to a lesser degree, on animals globally. The outbreak has continued since the first report of COVID-19 in China in December 2019, and the second and third waves of the outbreak have already begun in several countries. COVID-19 is expected to have adverse effects on crop production, food security, integrated pest control, tourism, the car industry, and other sectors of the global economy. COVID-19 induces a range of effects in livestock that is reflected economically since human health and livelihood are intertwined with animal health. We summarize the potentially harmful effects of COVID-19 on livestock and possible mitigation steps in response to this global outbreak. Mitigation of the negative effects of COVID-19 and future pandemics on livestock requires the implementation of current guidelines

The effect of surface treatments and graphene-based modifications on mechanical properties of natural jute fiber composites: A review

Natural fiber reinforced composites (FRC) are of great interests, because of their biodegradability, recyclability, and environmental benefits over synthetic FRC. Natural jute FRC could provide an environmentally sustainable, light weight, and cost-effective alternative to synthetic FRC. However, the application of natural jute FRC is limited because of their poor mechanical and interfacial properties. Graphene and its derivatives could potentially be applied to modify jute fiber surface for manufacturing natural FRC with excellent mechanical properties, and lower environmental impacts. Here, we review the physical and chemical treatments, and graphene-based modifications of jute fibers, and their effect on mechanical properties of jute FRC. We introduce jute fiber structure, chemical compositions, and their potential applications first. We then provide an overview of various surface treatments used to improve mechanical properties of jute FRC. We discuss and compare various graphene derivative-based surface modifications of jute fibers, and their impact on the performance of FRC. Finally, we provide our future perspective on graphene-based jute fibers research to enable next generation strong and sustainable FRC for high performance engineering applications without conferring environmental problems

Highly scalable, sensitive and ultraflexible Graphene‐based wearable E‐Textiles sensor for bio‐signal detection

Abstract: Graphene‐based wearable electronic textiles (e‐textiles) show promise for next‐generation personalized healthcare applications due to their non‐invasive nature. However, the poor performance, less comfort, and higher material cost limit their wide applications. Here a simple and scalable production method of producing graphene‐based electro‐conductive yarn that is further embroidered to realize piezoresistive sensors is reported. The multilayer structures improved the conductivity of the piezoresistive sensors, exhibiting good sensitivity with high response and recovery speed. Additionally, the potential applications of such wearable, ultraflexible and machine‐washable piezoresistive sensors as pressure and breathing sensors are demonstrated. This will be an important step toward realizing multifunctional applications of wearable e‐textiles for next‐generation personalized healthcare applications

Rapid Localization and Mapping Method Based on Adaptive Particle Filters.

With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an inertial measurement device; (c) selection of the best candidate particles by observing at each timestamp the match rate (also called particle weight) of the local map (with the real-time distances to the objects) and the distances of the particles to the corresponding chunks of the global map; (d) averaging the selected particles to derive the estimated position, and, finally, using a resampling method on the particles to ensure the reliability of the position estimation. The performance of the newly proposed technique is investigated on different sequences of the Kitti and Pandaset raw data with different environmental setups, weather conditions, and seasonal changes. The obtained results validate the performance of the proposed approach in terms of speed and representativeness of the feature extraction for real-time localization in comparison with other state-of-the-art methods