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Herein, we describe the development of a paper-based device to detect nucleic acids of pathogens of interest in complex samples using loop-mediated isothermal amplification (LAMP) by producing a colorimetric response visible to the human eye. To demonstrate the utility of this device in emerging public health emergencies, we developed and optimized our device to detect SARS-CoV-2 in human saliva without preprocessing. The resulting device was capable of detecting the virus within 60 min and had an analytical sensitivity of 97% and a specificity of 100% with a limit of detection of 200 genomic copies/μL of patient saliva using image analysis. The device consists of a configurable number of reaction zones constructed of Grade 222 chromatography paper separated by 20 mil polystyrene spacers attached to a Melinex® backing via an ARclean® double-sided adhesive. The resulting device is easily configurable to detect multiple targets and has the potential to detect a variety of pathogens simply by changing the LAMP primer sets.

This chapter mainly focuses on the recent developments made in rapid detection of cancer cells using novel paper-based analytical devices (PADs). Due to their simplicity, portability and biocompatibility, PADs have gained great attention in recent years for rapid detection of biomarkers. Since cancer has become an increasing worldwide concern, there stands an imminent requirement for minimally invasive detection and monitoring techniques. The progress made in paper-based detection of cancer biomarkers has proven to hold great potential for the development of point-of-care (POC) diagnostic devices for early detection of tumor cells. In this chapter, after an introduction on paper diagnosis, we will provide a brief explanation on the fabrication methods used for PADs. Then recently developed sensors based on colorimetric, fluorescence, chemiluminescence (CL), electrochemical, electrochemiluminescence (ECL) and surface-enhanced Raman scattering (SERS) techniques will be elaborated. In the end, current limitations and problems hindering the commercialization of paper-based sensors will be pointed out.

This chapter presents and discusses the most usual methodologies for colorimetric detection on paper-based analytical devices (PADs). State of the art associated with pioneering, popularity, physical aspect, surface modifications, instrumental and instrumental-free colorimetric measurements are carefully presented. Moreover, a side-by-side comparison among the most usual colorimetric detection modes on PADs was performed using a well-known complexometric reaction model. The pros and cons were discussed to show their advantages for chemical analysis.

This chapter describes the concept of paper space. Covered are the primary reasons for using paper space, how to get in/out of paper space, and how to set up title blocks (layouts), viewports and their properties, viewport scale assignments, controlling what is visible in viewports, setting up text and dim styles in viewports, and the annotative concept.

Paper-based microfluidic devices have undergone rapid development and offered a promising low-cost platform for disease diagnostics in poor-resource areas. Recently, paper-hybrid microfluidic devices have attracted much attention and been applied in various low-cost point-of-care testings, due to multiple merits derived from both paper and other substrates. This chapter summarizes the recent progress of low-cost paper and paper-hybrid microfluidic devices for rapid diagnostics of human diseases. The commonly used fabrication techniques are first introduced, and the applications of numerous paper and paper-hybrid microfluidic devices are then elaborated with an emphasis on rapid disease diagnostics in terms of the nature of biomolecules in three major categories, namely, protein-, gene-, and cell-based diagnostics. Both advantages and disadvantages of using these devices are discussed, followed by the perspectives for broad applications in low-cost diagnostics.

Background: Prognostic models combine several prognostic factors to provide an estimate of the likelihood (or risk) of future events in individual patients, conditional on their prognostic factor values. A fundamental part of evaluating prognostic models is undertaking studies to determine whether their predictive performance, such as calibration and discrimination, is reproduced across settings. Systematic reviews and meta-analyses of studies evaluating prognostic models’ performance are a necessary step for selection of models for clinical practice and for testing the underlying assumption that their use will improve outcomes, including patient's reassurance and optimal future planning. Methods: In this paper, we highlight key concepts in evaluating the certainty of evidence regarding the calibration of prognostic models. Results and Conclusion: Four concepts are key to evaluating the certainty of evidence on prognostic models’ performance regarding calibration. The first concept is that the inference regarding calibration may take one of two forms: deciding whether one is rating certainty that a model's performance is satisfactory or, instead, unsatisfactory, in either case defining the threshold for satisfactory (or unsatisfactory) model performance. Second, inconsistency is the critical GRADE domain to deciding whether we are rating certainty in the model performance being satisfactory or unsatisfactory. Third, depending on whether one is rating certainty in satisfactory or unsatisfactory performance, different patterns of inconsistency of results across studies will inform ratings of certainty of evidence. Fourth, exploring the distribution of point estimates of observed to expected ratio across individual studies, and its determinants, will bear on the need for and direction of future research. © 2021

Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efficacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.

The One Health concept that human, animal, plant, environmental, and ecosystem health are linked provides a framework for examining and addressing complex health challenges. This framework can be represented as a multi-dimensional matrix that can be used as a tool to identify upstream drivers of disease potential in a concise, systematic, and comprehensive way. The matrix can involve up to four dimensions depending on users' needs. This paper describes and illustrates how the matrix tool might be used to facilitate systems thinking, enabling the development of effective and equitable public policies. The multidimensional One Health matrix tool will be used to examine, as an example, global human and animal fecal wastes. The fecal wastes are analyzed at the microbial and population levels over a timeframe of years. Political, social, and economic factors are part of the matrix and will be examined as well. The One Health matrix tool illustrates how foodborne illnesses, food insecurity, antimicrobial resistance, and climate change are inter-related. Understanding these inter-relationships is essential to develop the public policies needed to achieve many of the United Nations' Sustainable Development Goals.

VIKOR is one of the well known Multiple Criteria Decision Aid methods, used frequently and considered to produce robust results. However, it seems that it is not immune to the rank reversal phenomenon. The latter is a well studied phenomenon that occurs sometimes in many such methods and is the cause of often severe criticism. This short paper provides such an example (and a counter-example) in the case of a fuzzy version of VIKOR.

Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus., Graphical Abstract ga1

The interest in and use of high-power ultrasound in food processing have been known for several years, especially to disrupt cells and disperse aggregated materials, for enzyme inactivation, and the induction of oxidation reactions, among others. In the General Assembly of the OIV in Geneva in 2019, the Resolution OENO 616-2019 was approved. In this resolution, the use of high-power ultrasound to stimulate the extraction of grape compounds during prefermentation maceration, after destemming and crushing, was accepted as an enological practice. The application of this technology opens a whole new scenario for improving and/or accelerating some winemaking processes. This technology has been especially used in red wine winemaking processes, but in this chapter, the different possibilities it presents for white wine elaboration will be covered and discussed.

Background: Operating schools safely under pandemic conditions is a widespread policy goal. We analyse the effectiveness of classroom cohorting, i.e., the decomposition of classrooms into smaller isolated units, in inhibiting the spread of SARS-CoV-2 in European secondary schools and compare different cohorting strategies. Methods: Using real-world network data on 12,291 adolescents collected in classrooms in England, Germany, the Netherlands, and Sweden in 2010/2011, we apply agent-based simulations to compare the effect of forming cohorts randomly to network-based cohorting. Network-based cohorting attempts to allocate out-of-school contacts to the same cohort to prevent cross-cohort infection more effectively. We consider explicitly minimizing out-of-school cross-cohort contacts, approximating this information-heavy optimization strategy by chained nominations of contacts, and dividing classrooms by gender. We also compare the effect of instructing cohorts in-person every second week to daily but separate in-person instruction of both cohorts. Findings: We find that cohorting reduces the spread of SARS-CoV-2 in classrooms. Relative to random cohorting, network-based strategies further reduce infections and quarantines when transmission dynamics are strong. In particular, network-based cohorting inhibits superspreading in classrooms. Cohorting that explicitly minimizes cross-cohort contacts is most effective, but approximation based on chained nominations and classroom division by gender also outperform random cohorting. Every-second-week instruction in-person contains outbreaks more effectively than daily in-person instruction of both cohorts. Interpretation: Cohorting of school classes can curb SARS-CoV-2 outbreaks in the school context. Factoring in out-of-school contacts can achieve a more effective separation of cohorts. Network-based cohorting reduces the risk of outbreaks in schools and can prevent superspreading events. Funding: None.

Clinical studies with time to event endpoints typically report the median follow-up (i.e., censoring) time for the subjects in the trial, alongside the median time to event. The reason for this is to provide information about the opportunity for subjects in the study to experience the event of interest (Betensky, 2015 [1]). The median follow-up time is often calculated from the Kaplan-Meier estimate for time to censoring. In most clinical studies, the censoring time is a composite measure, defined as the minimum of time to drop-out from the study and time to administrative end of study. The time to drop-out component may or may not be observed; it is observed only if drop-out occurs before the event and the end of the study. However, the time to end of study is observed for each subject, as it is the time from entry to the study to the calendar date that is administratively set as the end of the study. It is known even for subjects who have the event prior to the end of the study. This decomposition of the censoring time into a time that is itself potentially censored and a time that is fully observed raises the interesting question of whether estimation of the censoring distribution could be improved through a decoupling of these times. We demonstrate in simulations that consideration of censoring in this way yields reduced variability under some circumstances and should be used in practice. We illustrate these concepts through application to a meningioma study.

Within the past few decades, the application of light scattering techniques to a broad range of scientific disciplines has increased significantly, especially in the field of analytical chemistry. The resulting interest in and use of light scattering methods suggests the need for an easily understood introduction and review of material for those new to the method as well as for current users in need of a refresher. In many respects, the theory and its applications may appear so overwhelming for many studying the field for the first time, that they rarely can spend the time just needed to understand the basic measurements and their interpretations. A variety of applications in analytical chemistry especially have resulted in a greater understanding of many of the macromolecular processes themselves from molar mass distributions, to the macromolecular interactions responsible for aggregation processes, to determinations of structure and function. The use of such analytical processes to obtain a better understanding of nanoparticle structure and function has become almost universal., Graphical abstract Image 1, Highlights • Deriving the distribution of fractionated macromolecules by light scattering. • Deriving the distribution of fractionated nanoparticles by light scattering. • Deriving the masses of fractionated macromolecules from their scattered light. • Deriving the size of nanoparticles from the angular variation of scattered light. • Deriving bacterial size and structure by light scattering.

Background Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1. Methods This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance. Findings Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted. Interpretation The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available. Funding This study was supported by the National Key Research and Development Program (2017YFC1001800).