Gas Detection Using Portable Deep-UV Absorption Spectrophotometry: A Review.

Several gas molecules of environmental and domestic significance exhibit a strong deep-UV absorption. Therefore, a sensitive and a selective gas detector based on this unique molecular property (i.e., absorption at a specific wavelength) can be developed using deep-UV absorption spectrophotometry. UV absorption spectrometry provides a highly sensitive, reliable, self-referenced, and selective approach for gas sensing. This review article addresses the recent progress in the application of deep-UV absorption for gas sensing owing to its inherent features and tremendous potentials. Applications, advancements, and challenges related to UV emission sources, gas cells, and UV photodetectors are assessed and compared. We present the relevant theoretical aspects and challenges associated with the development of portable sensitive spectrophotometer. Finally, the applications of UV absorption spectrometry for ozone, NO2, SO2, and aromatic organic compounds during the last decades are discussed and compared. A portable UV absorption spectrophotometer can be developed by using LEDs, hollow core waveguides (HCW), and UV photodetectors (i.e., photodiodes). LED provides a portable UV emission source with low power input, low-intensity drifts, low cost, and ease of alignment. It is a quasi-chromatic UV source and covers the absorption band of molecules without optical filters for absorbance measurement of a target analyte. HCWs can be applied as a miniature gas cell for guiding UV radiation for measurement of low gas concentrations. Photodiodes, on the other hand, offer a portable UV photodetector with excellent spectral selectivity with visible rejection, minimal dark current, linearity, and resistance against UV-aging.

Identifying the role of vision transformer for skin cancer-A scoping review.

Introduction: Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance. Objective: This scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection. Methods: The review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included. Results and discussions: The review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.

Longitudinal analysis of behavioral factors and techniques used to identify vaccine hesitancy among Twitter users: Scoping review.

While vaccines have played a pivotal role in the fight against infectious diseases, individuals engage in online resources to find vaccine-related support and information. The benefits and consequences of these online peers are unclear and mainly cause a behavioral shift in user sentiment toward vaccination. This scoping review aims to identify the community and individual factors that longitudinally influence public behavior toward vaccination. The secondary aim is to gain insight into techniques and methodologies used to extract these factors from Twitter data. We followed PRISMA-ScR guidelines to search various online repositories. From this search process, a total of 28 most relevant articles out of 705 relevant studies. Three main themes emerged including individual and community factors influencing public attitude toward vaccination, and techniques employed to identify these factors. Anti-vax, Pro-vax, and neutral are the major communities, while misinformation, vaccine campaign, and user demographics are the common individual factors assessed during this reviewing process. Twitter user sentiment (positive, negative, and neutral) and emotions (fear, trust, sadness) were also discussed to identify the intentions to accept or refuse vaccines. SVM, LDA, BERT are the techniques used for topic modeling, while Louvain, NodeXL, and Infomap algorithms are used for community detection. This research is notable for being the first systematic review that emphasizes the dearth of longitudinal studies and the methodological and underlying practical constraints underpinning the lucrative implementation of an explainable and longitudinal behavior analysis system. Moreover, new possible research directions are suggested for the researchers to perform accurate human behavior analysis.

Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine.

Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.

A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection.

Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based variations in virus type add new challenges for the research and practitioners to combat. COVID-19-infected patients comprise trenchant radiographic visual features, including dry cough, fever, dyspnea, fatigue, etc. Chest X-ray is considered a simple and non-invasive clinical adjutant that performs a key role in the identification of these ocular responses related to COVID-19 infection. Nevertheless, the defined availability of proficient radiologists to understand the X-ray images and the elusive aspects of disease radiographic replies to remnant the biggest bottlenecks in manual diagnosis. To address these issues, the proposed research study presents a hybrid deep learning model for the accurate diagnosing of Delta-type COVID-19 infection using X-ray images. This hybrid model comprises visual geometry group 16 (VGG16) and a support vector machine (SVM), where the VGG16 is accustomed to the identification process, while the SVM is used for the severity-based analysis of the infected people. An overall accuracy rate of 97.37% is recorded for the assumed model. Other performance metrics such as the area under the curve (AUC), precision, F-score, misclassification rate, and confusion matrix are used for validation and analysis purposes. Finally, the applicability of the presumed model is assimilated with other relevant techniques. The high identification rates shine the applicability of the formulated hybrid model in the targeted research domain.

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing.

Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

BnaA02.NIP6;1a encodes a boron transporter required for plant development under boron deficiency in Brassica napus.

Boron (B) is an essential micronutrient for the plant normal growth. In Arabidopsis, NIP6;1 is a boric acid channel required for the proper distribution of boric acid, especially in the nodal regions of shoots. BnaA02.NIP6;1a, a homologous gene of AtNIP6;1 in Brassica napus, was reported to play a key role in B transport activity. However, little is known about the other functions of BnaA02.NIP6;1a in Brassica napus. In this study, we found that BnaA02.NIP6; 1a was localized in both plasma membrane and cytoplasm, which was different from that in Arabidopsis. The transgenic Arabidopsis plant containing a BnaA02.NIP6;1a promoter driven GUS reporter gene displayed strong GUS activity in roots, stems, leaves, especially in buds and open flowers, which are different from the expression pattern from its homologous gene in Arabidopsis. Silencing BnaA02.NIP6;1a repressed vegetative growth under B-deficient condition in Brassica napus. More importantly, knockdown of BnaA02.NIP6;1a in rapeseed resulted in the reduction of boron accumulation in the flower under boron deficiency and lead to severe sterility, which has not yet been reported before. Furthermore, nip6;1 mutant in Arabidopsis only showed the loss of apical dominance phenotype under boron deficiency at reproductive stage, whereas BnaA02.NIP6;1 RNAi lines exhibited large amounts of abnormal development of the inflorescence as compared with the wild type under boron limitation. Taken together, our results demonstrate that BnaA02.NIP6;1a encodes a boron transporter required for plant development under boron deficiency in Brassica napus, which shows its novel and diverse function in rapeseed compared with model plant Arabidopsis.

Risk factors associated with tick infestations on equids in Khyber Pakhtunkhwa, Pakistan, with notes on Rickettsia massiliae detection.

BACKGROUND: Studies on ticks infesting equids are lacking in various parts of the world, including Khyber Pakhtunkhwa (KP), Pakistan. The aim of this study was to investigate the diversity of ticks infesting equids, associated risk factors and rickettsial detection in ticks from equids in KP. METHODS: Inspection of 404 equid hosts from November 2018 to October 2019 resulted in the collection of 550 ticks. Data on tick-associated risk factors were collected from equid owners by means of a questionnaire. After morphological identification, partial DNA sequences of the tick mitochondrial 16S rRNA gene were used for taxonomic confirmation of species. Partial sequences of the gltA and ompA genes were used for Rickettsia detection in ticks. RESULTS: A total of 550 tick specimens were collected on 324 (80.2%) of the equids inspected, of which 161 were horses (50%), 145 (45%) were donkeys and 18 were mules (5%). The ticks were identified as belonging to the following five species: Rhipicephalus microplus (341 specimens, 62% of the total ticks), Rh. haemaphysaloides (126, 23%), Rh. turanicus (39, 7%), Rh. sanguineus (s.l.) (33, 6%) and Hyalomma anatolicum (11, 2%). The most prevalent tick life stage was adult females (279, 51%) followed by adult males (186, 34%) and nymphs (85, 15%). Higher tick infestations were observed on male equids (relative risk [RR] 0.7432, P < 0.0005) and adult equids (RR 1.268, P < 0.0020). Ticks were frequently attached to the axial region of horses (55, 21%), sternum of donkeys (44, 21%) and belly of mules (19, 23%) (P < 0.04). Temporal patterns of tick infestation in association with temperature and humidity were highly significant (P < 0.05). Risk factors, such as animal housing (P < 0.0003), living management (P < 0.006), grazing type (P < 0.01) and location in hilly areas (P < 0.02), significantly enhanced the chances for tick infestation. Tick species analyzed in this study were phylogenetically related to species from Afghanistan, China, South Africa and Taiwan. Partial sequences of the gltA and ompA genes obtained from Rh. microplus and Rh. haemaphysaloides were 100% identical to the spotted fever group pathogen Rickettsia massiliae. CONCLUSIONS: Equids exposed to significant risk factors were infected by one or more of at least five tick species in KP, Pakistan, and some of the ticks harbored the human pathogen R. massiliae.

Low-volume PEEK gas cell for BTEX detection using portable deep-UV absorption spectrophotometry.

Monitoring of indoor air quality by detecting individual airborne pollutant is essential for maintaining a healthy indoor environment. UV absorption spectrophotometry coupled with gas chromatography offers a reliable, self-referenced and non-destructive technique for the identification and detection of gas molecules. This paper presents a deep-UV absorption spectrophotometer coupled with a micro gas-chromatography (µGC) for the detection of benzene, toluene, ethylbenzene and xylenes (BTEX). The spectrophotometer was developed using a low-volume gas cell made of PolyEther Ether Ketone (PEEK) polymer tube, connected with a portable deep-UV LED and photomultiplier tube. The performance of the detection unit was evaluated with different concentrations of toluene (5-100 ppm) in nitrogen and a sensitivity of 107.1 µAU/ppm with a limit of detection of 1.41 ppm was obtained. The detector was incorporated into a micro gas-chromatography setup and high quality chromatograms, having all the peaks separated with good repeatability were obtained for BTEX molecules. The deep-UV absorption spectrophotometer has low-volume, low-cost, and ease of development and integration. While demonstrated for BTEX in a nitrogen carrier gas, the spectrometer has the potential to be applied to chromatographic analysis of different analytes in gas or liquid media.

Development of a Toluene Detector Based on Deep UV Absorption Spectrophotometry Using Glass and Aluminum Capillary Tube Gas Cells with a LED Source.

A simple deep-ultraviolet (UV) absorption spectrophotometer based on ultraviolet light-emitting diode (UV LED) was developed for the detection of air-borne toluene with a good sensitivity. A fiber-coupled deep UV-LED was employed as a light source, and a spectrometer was used as a detector with a gas cell in between. 3D printed opto-fluidics connectors were designed to integrate the gas flow with UV light. Two types of hollow core waveguides (HCW) were tested as gas cells: a glass capillary tube with aluminum-coated inner walls and an aluminum capillary tube. The setup was tested for different toluene concentrations (10⁻100 ppm), and a linear relationship was observed with sensitivities of 0.20 mA·U/ppm and 0.32 mA·U/ppm for the glass and aluminum HCWs, respectively. The corresponding limits of detection were found to be 8.1 ppm and 12.4 ppm, respectively.

Design, synthesis, and biological evaluation of a novel class of fluorescein-based N-glycosylamines.

A series of fluorescein-based N-glycosylamines was synthesized from the corresponding fluorescein amine and a partially protected d-glucose. The physiochemical investigation of these compounds by spectral and morphological studies reveals their gelation potential. The exclusive localization of fluorescence in the cytoplasm through cell imaging studies reveals the anti-cancer potentials of N-glycosylamines.