Issues of COVID 19 Screening with Machine Learning Algorithm and Data Sets Availability

There is a need to wear a mask during the coronavirus outbreak to efficiently deter the transmission of COVID-19 virus. In these instances, traditional facial screening technologies obsolete for monitoring of group entry at Airports, shopping malls, railway stations, etc. It is, therefore, vital to boost the efficiency of screening. This paper addresses the machine learning algorithm for contactless face screening systems in group participation, social interaction, school management, mall entry management, and market resumption scenarios in the case of COVID- 19. A method to screen entry with masks are developed using machine learning, which depends on various face specimens that were discussed here. The second fold discussion in this paper is that previously there are not many freely accessible masked face-databases. To this end, various forms of masked face data sets are identified, namely MFDD, Real MFRD, and Simulated MFRD. Such data sets became widely accessible to businesses and academics, based on which specific apps may be built on masked faces. The mathematical model, with the code was given. The availability and issues of the above data sets were discussed for the benefit of researchers.

Implication of High Variance in Germplasm Characteristics

The beauty of conserving germplasm is the securement of genetic resources with numerous important traits, which could be utilized whenever they need to be incorporated into current cultivars. However, it would not be as useful as expected if proper information was not given to breeders and researchers. In this study, we demonstrated that there is a large variation, both among and within germplasm, using a low-cost image-based phenotyping method; this could be valuable for improving gene bank screening systems and for crop breeding. Using the image analyses of 507 accessions of buckwheat, we identified a wide range of variations per trait between germplasm accessions and within an accession. Since this implies a similarity with other important agronomic traits, we suggest that the variance of the presented traits should be checked and provided for better germplasm enhancement.

Designing and Evaluating an Avatar for On-Person Screening

In aviation security, avatars are generic human figures that are used to display alarms provided by on-person screening systems. One critical feature of these avatars is that they provide no body detail unique to an individual traveler. However, the generic nature of these avatars leaves few landmarks that can be used to map the location of an alarm on the avatar to a passenger. We manipulated two features of an avatar, body detail and grid lines, to create 6 avatars to investigate how design influences estimation of target location. Body detail was manipulated at three levels: no joints, some joints, and direct outline of the passenger. Grid lines were manipulated at two levels: grid lines or no grid lines. The results of the study showed that security screeners were nearly 20% closer to the true target location when the avatar featured landmarks that can be found on a typical passenger.

High Throughput Screening for Drug Discovery and Virus Detection

Background: High throughput screening systems are automated labs for the analysis of many biochemical substances in the drug discovery and virus detection process. This paper was motivated by the problem of automating testing for viruses and new drugs using high throughput screening systems. The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the turn of 2019-2020 presented extradentary challenges to public health. Existing approaches to test viruses and new drugs do not use optimal schedules and are not efficient. Objective: The scheduling of activities performed by various resources in a high throughput screening system affects its efficiency, throughput, operations cost, and quality of screening. This study aims to minimize the total screening (flow) time and ensure the consistency and quality of screening. Methods: This paper develops innovative mixed integer models that efficiently compute optimal schedules for screening many microplates to identify new drugs and determine whether samples contain viruses. The methods integrate job-shop and cyclic scheduling. Experiments are conducted for a drug discovery process of screening an enzymatic assay and a general process of detecting SARS-CoV-2. Results: The method developed in this article can reduce screening time by as much as 91.67%. Conclusion: The optimal schedules for high throughput screening systems greatly reduce the total flow time and can be computed efficiently to help discover new drugs and detect viruses.

Current and Prospective Radiation Detection Systems, Screening Infrastructure and Interpretive Algorithms for the Non-Intrusive Screening of Shipping Container Cargo: A Review

The non-intrusive screening of shipping containers at national borders serves as a prominent and vital component in deterring and detecting the illicit transportation of radioactive and/or nuclear materials which could be used for malicious and highly damaging purposes. Screening systems for this purpose must be designed to efficiently detect and identify material that could be used to fabricate radiological dispersal or improvised nuclear explosive devices, while having minimal impact on the flow of cargo and also being affordable for widespread implementation. As part of current screening systems, shipping containers, offloaded from increasingly large cargo ships, are driven through radiation portal monitors comprising plastic scintillators for gamma detection and separate, typically 3He-based, neutron detectors. Such polyvinyl-toluene plastic-based scintillators enable screening systems to meet detection sensitivity standards owing to their economical manufacturing in large sizes, producing high-geometric-efficiency detectors. However, their poor energy resolution fundamentally limits the screening system to making binary “source” or “no source” decisions. To surpass the current capabilities, future generations of shipping container screening systems should be capable of rapid radionuclide identification, activity estimation and source localisation, without inhibiting container transportation. This review considers the physical properties of screening systems (including detector materials, sizes and positions) as well as the data collection and processing algorithms they employ to identify illicit radioactive or nuclear materials. The future aim is to surpass the current capabilities by developing advanced screening systems capable of characterising radioactive or nuclear materials that may be concealed within shipping containers.

Assessing the Performance of Artificial Intelligence Systems for the Screening of Diabetic Retinopathy: A Systematic Review and Meta-Analysis

Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and one of the leading causes of blindness globally. Due to the progressive nature of the disease, earlier detection and timely treatment can lead to substantial reductions in the incidence of irreversible vision-loss. Artificial intelligence (AI) screening systems have offered clinically acceptable and quicker results in detecting diabetic retinopathy from retinal fundus and optical coherence tomography (OCT) images. Thus, this systematic review and meta-analysis of relevant investigations was performed to document the performance of AI screening systems that were applied to fundus and OCT images of patients from diverse geographic locations including North America, Europe, Africa, Asia, and Australia. A systematic literature search on Medline, Global Health, and PubMed was performed and studies published between October 2015 and January 2020 were included. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and AI-based investigations were mandatory for studies inclusion. The abstracts, titles, and full-texts of potentially eligible studies were screened against inclusion and exclusion criteria. Twenty-one studies were included in this systematic review; 18 met inclusion criteria for the meta-analysis. The pooled sensitivity of the evaluated AI screening systems in detecting diabetic retinopathy was 0.93 (95% CI: 0.92-0.94) and the specificity was 0.88 (95% CI: 0.86-0.89). The included studies detailed training and external validation datasets, criteria for diabetic retinopathy case ascertainment, imaging modalities, DR-grading scales, and compared AI results to those of human graders (e.g., ophthalmologists, retinal specialists, trained nurses, and other healthcare providers) as a reference standard. The findings of this study showed that the majority AI screening systems demonstrated clinically acceptable levels of sensitivity and specificity for detecting referable diabetic retinopathy from retinal fundus and OCT photographs. Further improvement depends on the continual development of novel algorithms with large and gradable sets of images for training and validation. If cost-effectiveness ratios can be optimized, AI can become a financially sustainable and clinically effective intervention that can be incorporated into the healthcare systems of low-to-middle income countries (LMICs) and geographically remote locations. Combining screening technologies with treatment interventions such as anti-VEGF therapy, acellular capillary laser treatment, and vitreoretinal surgery can lead to substantial reductions in the incidence of irreversible vision-loss due to proliferative diabetic retinopathy.

Longitudinal Analysis (1994–2020) of Prevalence and Trends of Underweight in Polish Children

Nutritional status disorders are a worldwide problem. Approximately 5.9 million children under the age of five die each year, and 45% of these deaths are related to malnutrition. The aim of the study was to analyse the prevalence of underweight children aged between 6 and 7 years old, living in the Gdańsk, Poland, in the years 1994–2020. The anthropometric parameters of 67,842 children were analysed. BMI (Body Mass Index) value <5 percentile (pc) was defined as underweight. The BMI value was compared to the WHO (World Health Organization) centile charts and the OLAF (research project PL0080) national reference charts. The prevalence of underweight children in relation to the WHO charts was 1.9%; underweight status was found to be more significant in the group of boys (2.1%) than the group of girls (1.7%) (p < 0.001). According to the OLAF centile charts, the underweight figure among all of the study population was 2.1% and no statistical significance between boys (2.1%) and girls (2.0%) was found (p = 0.670). The occurrence of underweight indviduals in the studied group slightly increased in the years 1994–2020. We found a statistically significant increasing linear trend in the analysis of underweight children in our group (p < 0.001), in group of boys (p < 0.001), but not girls (WHO p = 0.603; OLAF p = 0.787). This points to the need to conduct regular screening systems for children and adolescents.