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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 work was supported by the research group "Ciencia y Cultura del Patrimonio"(HUM-1057) and the Ministerio de Economia y Competitividad in the framework of the I + D research project "Nuevas alternativas al conocimiento de los materiales y los procesos de conservacion y restauracion de obra grafica y patrimonio documental"(REF.MAT2014-58659-P). Funding for open access charge: Universidad de Granada/CBUA., This study offers a new tool to restorers to bleach paper documents: artificial light radiation (ALR). It is a controlled method that is an alternative to other highly aggressive procedures that have been applied to cellulose based media. Its main novelty is its intrinsic lighting characteristics which guarantee uniformity and avoid heating the paper. The results of testing this new tool were compared to those of bleaching with hydrogen peroxide (H2O2). Each of the two methods was applied to five different types of widely used paper: Arches and Somerset (brands serving for engraving and digital printing), Ingres (drawing), press-type (magazines and newspapers) and Bible-type (historical documents). Once characterised by physico-mechanical, chemical and optical parameters (grammage, thickness, specular gloss, optical properties, air permeance, tensile properties, determining the pH of both surface and aqueous extracts), the different papers were bleached by the two methods according to pre-established criteria. Finally, to determine the evolution and effects of the two treatments, the papers were subjected to accelerated ageing., Research group "Ciencia y Cultura del Patrimonio" HUM-1057, Spanish Government MAT2014-58659-P, Universidad de Granada/CBUA

The handling of cultural heritage objects has become a highly debated topic in the last decade. The work and outcomes described in this paper are aimed to provide objective data to assist in making appropriate decisions as to whether or not wearing gloves is appropriate in a given situation. The forensic fingermark development techniques of 1,2-indandione and single metal deposition II were used to investigate the efficacy of handwashing and glove use to improve the information available when deciding whether to use gloves when handling paper objects. It was found that fingermarks did not permeate through polymer glove types but could through cotton gloves. It was also shown that the amounts of observable fingermark residues were greater 5 min after handwashing than if handwashing had not occurred, undermining previous arguments for not wearing gloves if hands could be washed before object handling., Graphical abstract Image 1, Highlights • Fingermarks can be transferred onto paper even after washing hand. • Developed fingermark residues initially increase after handwashing. • Handwashing is not sufficient to prevent fingermark contamination of artifacts. • Fingermark residues can be transferred from surfaces to gloves to paper. • Some antibacterial gels can increase the amount of fingermark residue deposited.

Tissue paper is defined by its physical and mechanical properties, namely: high softness, low grammage, high bulk and high liquid absorption capacity. It is expected that the production of tissue paper will continue to grow, which increases the importance of better understanding the processes involved in its production as well as its optimization [1] . The experimental data presented in this article, are the physical-mechanical characterization of a group of 13 industrial base tissue papers, which were collected at the end of the tissue paper machine on Portuguese factories. These samples vary in grammage, composition and creping [2] , enabling a later evaluation of the crepe type [3] and its relationship with the final properties of the tissue paper.

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

We present a computational framework for stability analysis of systems of coupled linear Partial-Differential Equations (PDEs). The class of PDE systems considered in this paper includes parabolic, elliptic and hyperbolic systems with Dirichlet, Neuman and mixed boundary conditions. The results in this paper apply to systems with a single spatial variable. We exploit a new concept of state for PDE systems which allows us to include the boundary conditions directly in the dynamics of the PDE. The resulting algorithms are implemented in Matlab, tested on several motivating and illustrative examples, and the codes have been posted online. Numerical testing indicates the approach has little or no conservatism for a large class of systems and can analyze systems of up to 20 coupled PDEs.

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.

BACKGROUND: Digital models are considered the new standard in the clinical practice of orthodontics. However, they are difficult to use in the absence of a true scale for chairside use during clinical appointments. Therefore, an easy standardized method with true-scale paper print images would be of great utility. The aim of this study was to correlate and compare measurements obtained manually in true-scale paper printed images of digital models with those obtained digitally in the computer. METHODS: Sixty desktop scanned digital models (30 upper and 30 lower) were analyzed. Ortho Insight 3D software was used for generating the printed images on paper and for measuring the digital models. Intercanine width, interpremolar width (first and second), intermolar width (first), and arch length measures were performed with both methods and were correlated and compared. RESULTS: There was a high correlation between measurements performed with both methods. Values obtained from paper print images were lower and showed statistically significant differences (

This paper introduces a novel enzymatic fuel cell design that employs cellulose paper-based quasi-2D microfluidic system to supply biofuel to the enzymatic layer. The state of the art nanoarchitectural design, employing carbon nanotube-based papers for the bioelectrodes, allows a single cell to maintain 400 mV for 16 days of continuous operation in glucose solution and reach 1 mA of current output. Stacks of cells connected in series show successful performance using glucose in Gatorade® resulting in stack-cell potential of 1.8 V, employed to power a digital clock for 36 h, continuously. These designs open the possibility for obtaining enzymatic fuel cells that can run small portable devices on easily available ubiquitous liquids while addressing environmental concerns that are prevalent in traditional fuel cells. Keywords: Enzyme, Bioelectrodes, Enzymatic biofuel cells, Microfluidic system, Capillary driven flow, Bucky paper

This document contains supplemental material for the paper [2] . The notations in this document are the same as in [2] . In particular, we first present here the proof of Theorem 1 in [2] . This theorem expresses the locally most powerful unbiased (LMPU) test, which is a general method for local detection, in the presence of known nuisance parameters. Second, we present here the Matlab code of the LMPU and the generalized LMPU for the special case of detection of a small deviation in the frequency of sinusoidal signals, which arises in various signal processing applications.

Data and supplement material of the article “Effectiveness of digital-based interventions for children with mathematical learning difficulties: A meta-analysis” (Benavides-Varela et al.) [1] are presented. Data were collected from studies included in the meta-analysis to evaluate the effects of digital-based interventions for children with mathematical learning difficulties compared to control conditions in group-designed randomized controlled trials. Literature search, inclusion criteria and coding procedure are described. PRISMA flow-chart is reported to summarize the literature search and coding of all the relevant characteristics of the primary studies is made available. This allows other researchers to easily access to the information needed to evaluate the studies and to use these data in future meta-analyses. However, researchers are highly recommended to refer to the original papers in order to check studies suitability to their own criteria. Moreover, in the supplemental material all the information needed to reproduce the meta-analysis results is reported together with the R code syntax. Data and supplemental material are available online ( https://osf.io/ajdnv/ ).

Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid’s operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. This work is financially supported by the Danish project RemoteGRID, which is a ForskEL program under Energinet.dk with grant agreement no. 2016-1-12399.

[EN] In this work, we present a dataset which provides information on the scientific program of a set conferences of Machine Learning. Data were extracted from the IEEE Xplore Digital Library and the official web site of the International Conference on Machine Learning Applications (ICMLA). We include data of four different editions (from 2014 to 2017). Web scrapping techniques were used to mine the data contained in these web sites. The dataset covers 448 papers presented in the conference and every paper contains 6 attributes including information about the thematic session in which they were presented in the conference. The dataset is hosted in the Mendeley Dataset Repository., This work was partially supported by IDEIAGEOCA Research Group of the Universidad Politecnica Salesiana. This work also has been supported by the EU (FEDER) and the Spanish MINECO under grant TIN2015-69175-C4-1-R, and by Generalitat Valenciana PROMETE0112019/018.

The classification of science into disciplines is at the heart of bibliometric analyses. While most classifications systems are implemented at the journal level, their accuracy has been questioned, and paper-level classifications have been considered by many to be more precise. However, few studies investigated the difference between journal and the paper classification systems. This study addresses this gap by comparing the journal- and paper-level classifications for the same set of papers and journals. This isolates the effects of classification precision (i.e., journal- or paper-level) to reveal the extent of paper misclassification. Results show almost half of papers could be misclassified in journal classification systems. Given their importance in the construction and analysis of bibliometric indicators, more attention should be given to the robustness and accuracy of these disciplinary classifications schemes.

In this paper, we introduce a framework for processing genetics and genomics literature, based on ontologies and lexical resources from the biomedical domain. The main objective is to support the diagnosis process that is done by medical geneticists who extract knowledge from published works. We constructed a pipeline that gathers several genetics- and genomics-related resources and applies natural language processing techniques, which include named entity recognition and relation extraction. Working on a corpus created from PubMed abstracts, we built a knowledge database that can be used for processing medical records written in Spanish. Given a medical record from Uruguayan healthcare patients, we show how we can map it to the database and perform graph queries for relevant knowledge paths. The framework is not an end user application, but an extensible processing structure to be leveraged by external applications, enabling software developers to streamline incorporation of the extracted knowledge. Keywords: Controlled vocabulary, Natural language processing, Genomics, Automated pattern recognition, Publications, Medical records

This data article contains data related to the research article entitled, “Solving the multi-vehicle multi-covering tour problem” (Pham et al., 2017) [4]. All data of this article was generated from instances kroA100, kroB100, kroC100, kroD100, kroA200, and kroB200 from TSPLIB. It can be downloaded from public repository. This data can be used as benchmarks for the covering tour problem (CTP) variants, such as m-CTP-p, m-CTP, mm-CTP-p, mm-CTP, mm-CTP-o, mm-CTP-wo. We tested our algorithm on these data and results are shown in Pham et al. (2017) [4].

This paper describes how SHARE (Sharing Hosted Autonomous Research Environments) satisfies the criteria of the Elsevier 2011 Executable Paper Grand Challenge. This challenge aims at disseminating the use of systems that provide reviewers and fellow scientists a convenient way to reproduce computational results of research papers. This can involve among others the calculation of a number, the plotting of a diagram, the automatic proof of a theorem or the interactive transformation of various inputs into a complex output document. Besides reproducing the literate results, readers of an executable paper should also be able to explore the result space by entering different input parameters than the ones reported in the original text. SHARE is a web portal that enables academics to create, share, and access remote virtual machines that can be cited from research papers. By deploying in SHARE a copy of the required operating system as well as all the relevant software and data, authors can make a conventional paper fully reproducible and interactive. Shared virtual machines can also contain the original paper text— when desirable even with embedded computations. This paper shows the concrete potential of SHARE-based articles by means of an example virtual machine that is based on a conventional research article published by Elsevier recently. More generally, it demonstrates how SHARE has supported the publication workflow of a journal special issue and various workshop proceedings. Finally, it clarifies how the SHARE architecture supports among others the Elsevier challenge's licensing and scalability requirements without domain specific restrictions.

The Workshop on Advances in Knowledge Extraction and Re-engineering of Software (WAKERS) was held at the Stellenbosch Institute for Advanced Studies (STIAS) in Stellenbosch, South Africa from January 23rd until January 26th, 2017. The workshop was specifically designed to bring together researchers and practitioners from the software reengineering and knowledge extraction communities in order to foster collaboration between these research communities.

Technology is improving day-by-day and so is the usage of mobile devices. Every activity that would involve manual and paper transactions can now be completed in seconds using your fingertips. On one hand, life has become fairly convenient with the help of mobile devices, whereas on the other hand security of the data and the transactions occurring in the process have been under continuous threat. This paper, re-evaluates the different policies and procedures used for preserving the privacy of sensitive data and device location. Policy languages have been very vital in the mobile environments as they can be extended/used significantly for sending/receiving any data. In the mobile environment users always go to service providers to access various services. Hence, communications between the service providers and mobile handsets needs to be secured. Also, the data access control needs to be in place. A section of this paper will review the communication paths and channels and their related access criteria. This paper is a contribution to the mobile domain, showing the possible attacks related to privacy and the various mechanisms used to preserve the end-user privacy. In addition, it also gives a comparison of the different privacy preserving methods in mobile environments to provide guidance to the readers. Finally, the paper summarizes future research challenges in the area of privacy preservation. This paper examines the 'where' problem and in particular, examines tradeoffs between enforcing location security at a device vs. enforcing location security at an edge location server. This paper also sketches an implementation of location security solution at both the device and the edge location server and presents detailed experiments using real mobility and user profile data sets collected from multiple data sources (taxicabs, Smartphones).

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We propose an intelligent sensor system based on a new sensing methodology, relying also on 3D map reconstruction techniques, for computing with high precision, in real-time and without human intervention the parameters needed for stream-flow computa- tion: water levels, morphology of the streams of all potentially flooded areas by each controlled stream. The collected data will be continuously transmitted, through a communication infrastructure, to software agents designed to compute the stream-flow and to quantify the spatial distribution of flood risk for each controlled watershed. The computed risks, together with other data coming from other sources (barometric sensors, camera operators of public organizations, emergency agencies, private citizens), will be analyzed by a diagnostic decision system implementing a risk-alert scheduling strategy. This system will be able to diagnose the health state of the controlled environment and to define specialized alarm levels for each potentially interested area that will be used to alert all citizens (ubiquity) locally present (alerting locality).

This article analyzes the appropriateness of a text summarization system, COMPENDIUM, for generating abstracts of biomedical papers. Two approaches are suggested: an extractive (COMPENDIUM E), which only selects and extracts the most relevant sentences of the documents, and an abstractive-oriented one (COMPENDIUM E–A), thus facing also the challenge of abstractive summarization. This novel strategy combines extractive information, with some pieces of information of the article that have been previously compressed or fused. Specifically, in this article, we want to study: i) whether COMPENDIUM produces good summaries in the biomedical domain; ii) which summarization approach is more suitable; and iii) the opinion of real users towards automatic summaries. Therefore, two types of evaluation were performed: quantitative and qualitative, for evaluating both the information contained in the summaries, as well as the user satisfaction. Results show that extractive and abstractive-oriented summaries perform similarly as far as the information they contain, so both approaches are able to keep the relevant information of the source documents, but the latter is more appropriate from a human perspective, when a user satisfaction assessment is carried out. This also confirms the suitability of our suggested approach for generating summaries following an abstractive-oriented paradigm. This research was partially supported by the FPI grant (BES-2007-16268) and the project grants TEXT-MESS (TIN2006-15265-C06-01), TEXT-MESS 2.0 (TIN2009-13391-C04) and LEGOLANG (TIN2012-31224) from the Spanish Government. It has been also funded by the Valencian Government (grant no. PROMETEO/2009/119 and ACOMP/2011/001).

In a recent paper [H.-E. Hwang, P. Han, Opt. Commun. 282 (2009) 351] a speckle based metrology system is proposed which it is claimed provides significant advantages over existing systems. In this paper, we show that the discussion presented in [H.-E. Hwang, P. Han, Opt. Commun. 282 (2009) 351] is deficient, and that several of the statements made are incorrect and/or misleading.

The aim of this article is to outline the issues involved in the application of machine vision to the automatic extraction and registration of watermarks from continuous web paper. The correct identification and localization of watermarks are key issues in paper manufacturing. As well as requiring the position of the watermark for defect detection and classification, it is necessary to insure its position on the paper prior to the cutting process. Two paper types are discussed, with and without laid and chain lines (these lines appear as a complex periodic background to the watermark and further complicate the segmentation process). We will examine both morphological and Fourier approaches to the watermark segmentation process, concentrating specifically on those images with complex backgrounds. Finally we detail a system design suitable for real-time implementation.

A project that piloted the secure electronic preparation of examination papers ran during the first semester of the academic year 1998/99 at the University of Salford. The examination papers were transferred between the participants (lecturers, administrators and external examiners) using secure electronic mail. Security was provided by a managed public key infrastructure. Users were profiled and interviewed in order to determine the likely success of further roll out within the institution, as well as the user friendliness of the existing paper-based and proposed electronic systems. The project found that, while the technology worked for some participants, others had severe problems with installation of the software and failed to grasp key concepts. There appear to be formidable obstacles to extending the system to cover the whole university, including compatibility of equipment and the reliability of the network infrastructure.

Today's real-time systems are characterized by managing large volumes of data. Efficient database management algorithms for accessing and manipulating data are required to satisfy timing constraints of supported applications. Real-time database systems involve a new research area investigating possible ways of applying database systems technology to real-time systems. Management of real-time information through a database system requires the integration of concepts from both real-time systems and database systems. Some new criteria need to be developed to involve timing constraints of real-time applications in many database systems design issues, such as transaction/query processing, data buffering, CPU, and IO scheduling. In this paper, a basic understanding of the issues in real-time database systems is provided and the research efforts in this area are introduced. Different approaches to various problems of real-time database systems are briefly described, and possible future research directions are discussed. © 1995.

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.

Interest in brown adipose tissue remains high a decade after it was determined to be present outside of the neonatal period. In vivo imaging, however, has remained a challenge due to the lack of a imaging modality suitable for large healthy-volunteer studies, post-prandial investigations and vulnerable groups, such as children. Infrared thermography is increasingly accepted as a valid, non-invasive and flexible alternative but there is a wide approach to analysis between different groups. Defining the region of interest with anatomical borders rather than using a simple polygon may have advantages in terms of consistency but makes image analysis slower, limiting some applications. Our novel semi-automated method, using a custom-built graphical user interface, allows an 86% improvement in speed of image analysis (54.9 (38.3–71.4) seconds/image) without increases in variation between analysers or with repeated analysis. The improved efficiency demonstrated makes feasible larger studies, longer imaging periods or increased image acquisition frequency, providing an opportunity to study novel features of brown adipose tissue function., Graphical abstract Image 1, Highlights • Brown adipose tissue is a key heat-generating tissue but is difficult to measure. • Thermal imaging can measure brown adipose tissue response without radiation. • A semi-automated approach increases image analysis efficiency. • Thermal video analysis and imaging over longer periods is now feasible.

For research domains such as life sciences, which pursue fundamental scientific understanding and applications intended for immediate use, academic entrepreneurship has played a pivotal role in commercialization. This paper presents an evaluation method of researchers related to user-inspired fundamental research, using global databases of startup finances and academic research papers of "startup readiness." Case studies of startups related to biopharmaceutical research topics suggest that the biopharmaceutical field has rich opportunities stemming from scientific research, commercialization, and entrepreneurship. This evaluation method sorts specific industry segments by which financing activities are active, and by which related growing research topics attract increased academic attention. We constructed networks of author citation and co-authorship from paper citation networks related to research topics in industry segments in the biopharmaceutical domain. Results obtained across all research topics we surveyed demonstrated that authors in the top 10% of degree centrality ranking in both networks are far more likely to be startup participants than other authors. Our computational approach might provide convenient, dynamic, global, and real-time understanding of the “startup readiness” of researchers working with research topics for which academic attention is emerging in actively financed biopharmaceutical fields., Business; Computer science; Pharmaceutical science; Biotechnology; Startup readiness; Research-based startups; Paper citation networks; Co-authorship networks; Academic entrepreneurship; Startup finances; Venture capital; User-inspired fundamental research; Technological innovation; Biopharmaceuticals.

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

Highlights • Near miss events provide opportunities yet limit resources for incident learning. • Prioritization frameworks can triage near miss events. • Event occurrence, detection and harm can inform prioritization. • Objectively prioritizing events can focus a program’s efforts for incident learning., Introduction The term near miss implies the aversion of a harm event but often there is a lack of evidence when establishing a link between a failure in process and potential harm. The focus of this study was to use reported incident data to inform a prioritization framework for the triage of near miss events in a radiation therapy program. Materials and Methods Actual and near miss events during the study period were categorized using thematic analysis based on incident types. Near miss were characterized based upon their potential to result in harm to the patient using the concepts of failure modes and Analytic Hierarchy Process (AHP) theory. Near miss events were assessed for occurrence, detection and the potential impact and then assigned a summative normalized score reflecting prioritization recommendations, the normalized 10 point score (NTPS). Results 107 events were reported within the study timeframe. 65% of event type categories (n = 20) were attributed to near misses. 107 total events we analyzed using the framework with a maximum NTPS of 4 achieved across all event types. Of the 47 actual events 100% received a NTPS of 3 or greater. Of the 60 near miss invents 47% received an NTPS less than or equal to 1. Finally 15% of near miss events received a NTPS of 3 or greater. Conclusions Near miss events provide a unique opportunity for learning however, can yield a great deal of data potentially limiting the resources for effective incident learning. A FMEA and AHP based prioritization framework for the triage of near miss events, including the likelihood of occurrence, probability of the event to go undetected and the potential impact if the incident did occur, allows for the optimal focus of programmatic resources in the analysis of these events.

With the development of computing and information processing techniques, artificial intelligence (AI) has been extensively applied in education. Artificial intelligence in education (AIEd) opens new opportunities, potentials, and challenges in educational practices. In its short history, AIEd has been undergoing several paradigmatic shifts, which are characterized into three paradigms in this position paper: AI-directed, learner-as-recipient, AI-supported, learner-as-collaborator, and AI-empowered, learner-as-leader. In three paradigms, AI techniques are used to address educational and learning issues in varied ways. AI is used to represent knowledge models and direct cognitive learning while learners are recipients of AI service in Paradigm One; AI is used to support learning while learners work as collaborators with AI in Paradigm Two; AI is used to empower learning while learners take agency to learn in Paradigm Three. Overall, the development trend of AIEd has been developing to empower learner agency and personalization, enable learners to reflect on learning and inform AI systems to adapt accordingly, and lead to an iterative development of the learner-centered, data-driven, personalized learning.

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.

This paper presents a novel evaluation approach for optical coherence tomography (OCT) image analysis of pharmaceutical solid dosage forms based on deep convolutional neural networks (CNNs). As a proof of concept, CNNs were applied to image data from both, in- and at-line OCT implementations, monitoring film-coated tablets as well as single- and multi-layered pellets. CNN results were compared against results from established algorithms based on ellipse-fitting, as well as to human-annotated ground truth data. Performance benchmarks used include, efficiency (computation speed), sensitivity (number of detections from a defined test set) and accuracy (deviation from the reference method). The results were validated by comparing the output of several algorithms to data manually annotated by human experts and microscopy images of cross-sectional cuts of the same dosage forms as a reference method. In order to guarantee comparability for all results, the algorithms were executed on the same hardware. Since modern OCT systems must operate under real-time conditions in order to be implemented in-line into manufacturing lines, the necessary steps are discussed on how to achieve this goal without sacrificing the algorithmic performance and how to tailor a deep CNN to cope with the high amount of image noise and alterations in object appearance. The developed deep learning approach outperforms static algorithms currently available in pharma applications with respect to performance benchmarks, and represents the next level in real time evaluation of challenging industrial OCT image data., Graphical abstract Unlabelled Image

Collaborative approaches in health, such as One Health (OH), are promising; nevertheless, several authors point at persistent challenges for designing and implementing OH initiatives. Among other challenges, OH practitioners struggle in their efforts to collaborate across disciplines and domains. This paper aims to provide insights into the existing challenges for designing and implementing OH initiatives, their causes and solutions, and points out strategic solutions with the potential to solve practical challenges. A systematic literature search was performed for emerging challenges and proposed solutions in the process of conducting OH initiatives. Next, a thematic and a causal analysis were performed to unravel challenges and their causes. Finally, solutions were discriminated on whether they were only recommended, or implemented as a proof-of-principle. The 56 included papers describe 21 challenges endured by OH initiatives that relate to different themes (policy and funding; education and training; surveillance; multi-actor, multi-domain, and multi-level collaborations; and evidence). These challenges occur in three different phases: the acquisition of sufficient conditions to start an initiative, its execution, and its monitoring and evaluation. The findings indicate that individual challenges share overlapping causes and crosscutting causal relations. Accordingly, solutions for the successful performance of OH initiatives should be implemented to tackle simultaneously different types of challenges occurring in different phases. Still, promoting collaboration between the wide diversity of stakeholders, as a fundamental aspect in the OH approach, is still by far the most challenging factor in performing OH initiatives. Causes for that are the difficulties in promoting meaningful and equal participation from diverse actors. Solutions proposed for this challenge focused on guiding stakeholders to think and collaborate beyond their professional and cultural silos to generate knowledge co-creation and innovative methodologies and frameworks. Finally, the biggest knowledge gap identified, in terms of proposed solutions, was for monitoring and evaluating OH initiatives. This highlights the need for future research on evaluation methods and tools specific for the OH approach, to provide credible evidence on its added value. When considering challenges endured by former OH initiatives and the proposed solutions for these challenges, practitioners should be able to plan and structure such initiatives in a more successful way, through the strategic pre-consideration of solutions or simply by avoiding known barriers. Keywords: One health, Challenges, Design, Implementation, Interdisciplinary collaboration, Transdisciplinary research, strategic solutions

This short paper presents an HPC enhanced Agent Based Model (ABM) developed with the aim of quantitatively estimating the strategies for accelerating emergency mass evacuations, like tsunami evacuation. In order to facilitate inclusion of various influencing factors, such as localized congestion, multi-modes, pedestrian vehicle interactions, fallen debris from damaged buildings, visibility, etc., which demand detailed models, the developed system includes a 1mx1m resolution model of environment, and agents capable of perceiving and autonomously interact with this high resolution environment and visible agents. In order to meet the computational demand of large scale simulations with complex agents, a scalable high performance extension was implemented. Short introductions to the agent based model and the HPC extension are presented in this paper. In order to demonstrate the scalability of the system, both in problem size and computational capability, a 588 km2 region with 10 million agents is simulated in K computer. It is demonstrated that the system has high strong scalability up to 2048 computing nodes, which is equivalent to 16,384 CPU cores., Transportation Research Procedia, 34, ISSN:2352-1465, International Symposium of Transport Simulation (ISTS’18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS’18) Emerging Transport Technologies for Next Generation Mobility

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).

Despite appealing characteristics for the clinical trials setting, Bayesian inference methods remain scarcely used, especially in randomized controlled clinical trials (RCT). This is particularly true when dealing with a survival endpoint, likely due to the additional complexities to model specifications. We propose to use Bayesian inference to estimate the treatment effect in this setting, using a proportional hazards (PH) model for right-censored data. Implementation of such an estimation process is illustrated on two working examples from cancer RCTs, the ALLOZITHRO and the CLL7-SA trials, both originally analyzed using a frequentist approach. In these two different settings, we show that Bayesian sequential analyses can provide early insight on treatment effect in RCTs. Relying on posterior distributions and predictive posterior probabilities, we find that Bayesian sequential analyses of the ALLOZITHRO trial, which was terminated early due to an unanticipated deleterious effect of the intervention on survival, allow quantifying early that the treatment effect was opposite to what was expected. Then, incorporating historical data in the sequential analyses of the CLL7-SA trial would have allowed the treatment effect to be closer to the protocol hypothesis. These post-hoc results give grounds to advocate for a wider use of Bayesian approaches in RCTs, including those with right-censored endpoints, as informative decision tools.

We introduce a Bayesian optimization method for estimating the maximum tolerated dose in this article. A number of parametric model-based methods have been proposed to estimate the maximum tolerated dose; however, parametric model-based methods need an assumption that dose-toxicity relationships follow specific theoretical models. This assumption potentially leads to suboptimal dose selections if the dose-toxicity curve is misspecified. Our proposed method is based on a Bayesian optimization framework for finding a global optimizer of unknown functions that are expensive to evaluate while using very few function evaluations. It models dose-toxicity relationships with a nonparametric model; therefore, a more flexible estimation can be realized compared with existing parametric model-based methods. Also, most existing methods rely on point estimates of dose-toxicity curves in their dose selections. In contrast, our proposed method exploits a probabilistic model for an unknown function to determine the next dose candidate without ignoring the uncertainty of posterior while imposing some dose-escalation limitations. We investigate the operating characteristics of our proposed method by comparing them with those of the Bayesian-based continual reassessment method and two different nonparametric methods. Simulation results suggest that our proposed method works successfully in terms of selections of the maximum tolerated dose correctly and safe dose allocations.

Summary: Background: Detailed evaluation of bile duct (BD) is main focus during endoscopic ultrasound (EUS). The aim of this study was to develop a system for EUS BD scanning augmentation. Methods: The scanning was divided into 4 stations. We developed a station classification model and a BD segmentation model with 10681 images and 2529 images, respectively. 1704 images and 667 images were applied to classification and segmentation internal validation. For classification and segmentation video validation, 264 and 517 videos clips were used. For man-machine contest, an independent data set contained 120 images was applied. 799 images from other two hospitals were used for external validation. A crossover study was conducted to evaluate the system effect on reducing difficulty in ultrasound images interpretation. Findings: For classification, the model achieved an accuracy of 93.3% in image set and 90.1% in video set. For segmentation, the model had a dice of 0.77 in image set, sensitivity of 89.48% and specificity of 82.3% in video set. For external validation, the model achieved 82.6% accuracy in classification. In man-machine contest, the models achieved 88.3% accuracy in classification and 0.72 dice in BD segmentation, which is comparable to that of expert. In the crossover study, trainees’ accuracy improved from 60.8% to 76.3% (P

Since the past decade, the use of nanomaterials, especially nano-molecular imprinted (nano-MIP) systems, production and equipment integration are rapidly improving due to affinity sensors. This chapter focuses on the nanobiosensors usability of imprinted nanopolymers for various purposes. It is important to assess potential applications for several areas such as environmental, food safety, medicine, etc. In addition, latest trend is minimally invasive or non-invasive wearable and implantable biomedical applications for continuous and real-time monitored systems. This chapter presents an overview of nano-MIP sensors and classification of nanopolymers and discusses basic properties and potential applications. © 2021 Elsevier Inc.

The application of X-ray microtomography for quantitative structural analysis of pharmaceutical multi-particulate systems was demonstrated for commercial capsules, each containing approximately 300 formulated ibuprofen pellets. The implementation of a marker-supported watershed transformation enabled the reliable segmentation of the pellet population for the 3D analysis of individual pellets. Isolated translation- and rotation-invariant object cross-sections expanded the applicability to additional 2D image analysis techniques. The full structural characterisation gave access to over 200 features quantifying aspects of the pellets' size, shape, porosity, surface and orientation. The extracted features were assessed using a ReliefF feature selection method and a supervised Support Vector Machine learning algorithm to build a model for the detection of broken pellets within each capsule. Data of three features from distinct structure-related categories were used to build classification models with an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets. This approach to extract quantitative information on particle quality attributes combined with advanced data analysis strategies has clear potential to directly inform manufacturing processes, accelerating development and optimisation., Graphical abstract Unlabelled Image, Highlights • Coupling micro-XRT analysis with feature selection and machine learning for advanced pharmaceutical product characterisation. • Information on particle 3D-orientation were utilised to extract translation- and rotation-invariant object cross-sections. • Successful extraction of over 200 quantitative pellet descriptors linked to size, shape, porosity, surface and orientation. • Sensitivity analysis and ReliefF feature selection approach to identify predictive features for pellet classification. • Feature-based binary SVM classification model for the detection of broken pellets within the formulated system.

In product development, it is crucial to choose the appropriate drug manufacturing route accurately and timely and to ensure that the technique selected is suitable for achieving the desired product quality. Guided by the QbD principles, the pharmaceutical industry is currently transitioning from batch to continuous manufacturing. In this context, process understanding and prediction are becoming even more important. With regard to hot melt extrusion, the process setup, optimization and scale-up in early stages of product development are particularly challenging due to poor process understanding, complex product-process relationship and a small amount of premix available for extensive experimental studies. Hence, automated, quick and reliable process setup and scale-up requires simulation tools that are accurate enough to capture the process and determine the product-process relationships. To this end, the effect of process settings on the degradation of the active pharmaceutical ingredient (API) in a lab-scale Leistritz ZSE12 extruder was investigated. As part of the presented study, the limitations of traditional process analysis using integral process values were investigated, together with the potential that simulations may have in predicting the process performance and the product quality. The results of our investigation indicate that the average melt temperatures and the exposure times in specific zones along the screw configuration correlate well with the API degradation values and can be used as potent process design criteria to simplify the process development., Graphical abstract Unlabelled Image

Graphical Abstract The prediction model of oral disintegrating tablets formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques were established. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20) to build prediction model.Image, graphical abstract, Oral disintegrating tablets (ODTs) are a novel dosage form that can be dissolved on the tongue within 3 min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product.

The International Classification of Diseases (ICD) relies on clinical features and lags behind the current understanding of the molecular specificity of disease pathobiology, necessitating approaches that incorporate growing biomedical data for classifying diseases to meet the needs of precision medicine. Our analysis revealed that the heterogeneous molecular diversity of disease chapters and the blurred boundary between disease categories in ICD should be further investigated. Here, we propose a new classification of diseases (NCD) by developing an algorithm that predicts the additional categories of a disease by integrating multiple networks consisting of disease phenotypes and their molecular profiles. With statistical validations from phenotype-genotype associations and interactome networks, we demonstrate that NCD improves disease specificity owing to its overlapping categories and polyhierarchical structure. Furthermore, NCD captures the molecular diversity of diseases and defines clearer boundaries in terms of both phenotypic similarity and molecular associations, establishing a rational strategy to reform disease taxonomy., Highlights • The International Classification of Diseases (ICD) lags behind the current molecular characteristics of disease. • We quantified the limitations (specificity and blurred boundary) of ICD with integrated phenotypic and molecular profiles. • An integrative disease network integrating phenotypic and genotypic profiles proposes a refined disease category framework. Disease taxonomy is one of the foundations of medical science and healthcare solutions. The most widely used disease taxonomy in clinical settings is the International Classification of Diseases (ICD), a system established >100 years ago and maintained by the World Health Organization to track disease incidence. It is well recognized that ICD, which is based on clinical observations, largely lags behind the molecular achievements of this medical big data era. We quantified the limitations of ICD using integrated phenotypic and molecular profiles and proposed a refined disease taxonomy with possible applications for precision medicine.