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In the medical field, Medical diagnosis using Computed tomography (CT) has become increasingly popular due to their non-invasive approach and quick overall turnaround time. 3D visualization for CT Colonography involves the assessment and diagnosis of a patient to find the presence of cancerous polyps in the colon, by taking a Computed Tomography scan of the patient, and evaluating the reports. This technique reduces evaluation time by allowing the doctors themselves to analyze the CT scans without the need for a radiologist to generate an initial report. This technique also avoids an invasive procedure on the patient. This paper gives the insightsof developing computer aided system for the detection of Polyps in CT Colonography images using the principles of Image Processing and the Deep Learning, specifically an ensemble of Convolutional Neural Networks using GoogleNet architecture and 3D reconstruction of the same. The accuracy achieved by the proposed system for region classification, region 1 polyp detection and region 2 polyp detection are 98.75%, 93.75% and 94.03% respectively, and their F1 Scores are 0.88, 0.82 and 0.84 respectively.

The timely reporting of critical results in radiology is paramount to improved patient outcomes. Artificial intelligence has the ability to improve quality by optimizing clinical radiology workflows. We sought to determine the impact of a United States Food and Drug Administration-approved machine learning (ML) algorithm, meant to mark computed tomography (CT) head examinations pending interpretation as higher probability for intracranial hemorrhage (ICH), on metrics across our healthcare system. We hypothesized that ML is associated with a reduction in report turnaround time (RTAT) and length of stay (LOS) in emergency department (ED) and inpatient populations.An ML algorithm was incorporated across CT scanners at imaging sites in January 2018. RTAT and LOS were derived for reports and patients between July 2017 and December 2017 prior to implementation of ML and compared to those between January 2018 and June 2018 after implementation of ML. A total of 25,658 and 24,996 ED and inpatient cases were evaluated across the entire healthcare system before and after ML, respectively.RTAT decreased from 75 to 69 minutes (P0.001) at all facilities in the healthcare system. At the level 1 trauma center specifically, RTAT decreased from 67 to 59 minutes (P0.001). ED LOS decreased from 471 to 425 minutes (P0.001) for patients without ICH, and from 527 to 491 minutes for those with ICH (P = 0.456). Inpatient LOS decreased from 18.4 to 15.8 days for those without ICH (P = 0.001) and 18.1 to 15.8 days for those with ICH (P = 0.02).We demonstrated that utilization of ML was associated with a statistically significant decrease in RTAT. There was also a significant decrease in LOS for ED patients without ICH, but not for ED patients with ICH. Further evaluation of the impact of such tools on patient care and outcomes is needed.

In the recent past, cloud infrastructure has considerably increased its applicability. This has resulted in effective Big Data processing. Hadoop schedulers are critical components to deliver required levels of efficiency, where MapReduce tasks are assigned to Hadoop nodes by the scheduler. There is a significant challenge in planning the increasing amount of functions and resources in a scalable way. Also, this challenge is further compounded by the potential heterogeneity of the deployed Hadoop. This paper proposes a scheduler that makes scheduling a choice by assessing the entire task group in the job queue. Further, the proposed scheduler uses a new scheduling method based on Real Coded Genetic Algorithm (RCGA). RCGA with MapReduce enables users to create more scalable applications. A higher abstraction is provided in less time. The experimental results indicate that the proposed RCGA scheduler achieves better performance than the existing systems for the following metrics: Execution Time, Total Cost, Resource Utilization, Speedup, Throughput, Scheduling Efficiency, Fairness Relaxation, Scheduling Time, Turnaround Time, CPU Time, Data Locality and Average Node-Locality Ratio.

Living single-cell analysis is vital for cell biology, disease pathology, drug discovery and medical treatment. It is of great significance to reveal the law of creature and to explore the mechanism of serious disease. The conventional single cell analysis focuses on a large number of cells or cell lysis, in order to obtain the average information about cells. Therefore, it fails to analyze the real-time and continuous data of differences between the individual cells, thus limiting the development of many fields, such as biomedical. Nanofluidics based biochemical analysis exhibits advantages over conventional methods in terms of small sample volume, rapid turnaround time, straightforward operation, and efficient processing, which has been widely used in complex operations such as single cell capture, separation and single cell detection. Here we review the recent developments of nanofluidic technologies for single-cell analysis, with emphasis on cell trapping, treatment, and biochemical studies. The potential of nanofluidics-based single-cell analysis is discussed.

The paper presents a method for determining maintenance and spare provisioning costs, for systems containing a non exponential component. The input data of the model are R ( t ) - the reliability function with time of the component, T the duration of the life cycle, cost of a repair, cost of a spare part and the turnaround time. The model is applicable for any form of R ( t ) : Weibull, Normal, expressed by an explicit or implicit set of equations and even in the form of discrete frequency table. The calculations can be performed by a simple Excel worksheet and do not require expensive tools (e.g. a flexible Monte Carlo package): The algorithm requires a few iterations of the dot product of two matrices. The entries of the matrices are calculated from the input values and the results of previous iterations, at M equidistant time points along the life cycle. M determines the accuracy of the calculations. The paper indicates sufficient conditions for M , which ensure a desired accuracy. Numerical examples performed on an Excel worksheet are presented. Since the model is applicable for any R ( t ) , it was applied, for verification purposes for R ( t ) = e − λ t , which has well known accurate results. The comparison confirmed the accuracy of the approximations.

In the current ever-changing cybersecurity scenario, active cyber defense strategies are imperative. In this work, we present a standard testbed to measure the efficacy and efficiency of customized networks while analyzing various parameters during the active attack. The presented testbed can be used for analyzing the network behavior in presence of various types of attacks and can help in fine-tuning the proposed algorithm under observation. The proposed testbed will allow users to design, implement, and evaluate the active cyber defense mechanisms with good library support of nature-inspired and AI-based techniques. Network loads, number of clusters, types of home networks, and number of nodes in each cluster and network can be customized. While using the presented testbed and incorporating active-defense strategies on existing network architectures, users can also design and propose new network architectures for effective and safe operation. In this paper, we propose a unified and standard testbed for cyber defense strategy simulation and bench-marking, which would allow the users to investigate current approaches and compare them with others, while ultimately aiding in the selection of the best approach for a given network security situation. We have compared the network performance in difference scenarios namely, normal, under attack and under attack in presence of NICS-based adaptive defense mechanism and achieved stable experimental results. The experimental results clearly show that the proposed testbed is able to simulate the network conditions effectively with minimum efforts in network configuration. The simulation results of defense mechanisms verified on the proposed testbed got the improvement on almost 80 percent while increasing the turnaround time to 1–2 percent. The applicability of proposed testbed in modern technologies like Fog Computing and Edge Computing is also discussed in this paper.

BACKGROUND: Barriers to rapid return of sequencing results can affect the utility of sequence data for infection prevention and control decisions. AIM: To undertake a mixed-methods analysis to identify challenges that sites faced in achieving a rapid turnaround time (TAT) in the COVID-19 Genomics UK Hospital-Onset COVID-19 Infection (COG-UK HOCI) study. METHODS: For the quantitative analysis, timepoints relating to different stages of the sequencing process were extracted from both the COG-UK HOCI study dataset and surveys of study sites. Qualitative data relating to the barriers and facilitators to achieving rapid TATs were included from thematic analysis. FINDINGS: The overall TAT, from sample collection to receipt of sequence report by infection control teams, varied between sites (median 5.1 days, range 3.0-29.0 days). Most variation was seen between reporting of a positive COVID-19 polymerase chain reaction (PCR) result to sequence report generation (median 4.0 days, range 2.3-27.0 days). On deeper analysis, most of this variability was accounted for by differences in the delay between the COVID-19 PCR result and arrival of the sample at the sequencing laboratory (median 20.8 h, range 16.0-88.7 h). Qualitative analyses suggest that closer proximity of sequencing laboratories to diagnostic laboratories, increased staff flexibility and regular transport times facilitated a shorter TAT. CONCLUSION: Integration of pathogen sequencing into diagnostic laboratories may help to improve sequencing TAT to allow sequence data to be of tangible value to infection control practice. Adding a quality control step upstream to increase capacity further down the workflow may also optimize TAT if lower quality samples are removed at an earlier stage.

Next generation sequencing (NGS) is being applied for HLA typing in research and clinical settings. NGS HLA typing has made it feasible to sequence exons, introns and untranslated regions simultaneously, with significantly reduced labor and reagent cost per sample, rapid turnaround time, and improved HLA genotype accuracy. NGS technologies bring challenges for cost-effective computation, data processing and exchange of NGS-based HLA data. To address these challenges, guidelines and specifications such as Genotype List (GL) String, Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING), and Histoimmunogenetics Markup Language (HML) were proposed to streamline and standardize reporting of HLA genotypes. As part of the 17th International HLA and Immunogenetics Workshop (IHIW), we implemented standards and systems for HLA genotype reporting that included GL String, MIRING and HML, and found that misunderstanding or misinterpretations of these standards led to inconsistencies in the reporting of NGS HLA genotyping results. This may be due in part to a historical lack of centralized data reporting standards in the histocompatibility and immunogenetics community. We have worked with software and database developers, clinicians and scientists to address these issues in a collaborative fashion as part of the Data Standard Hackathons (DaSH) for NGS. Here we report several categories of challenges to the consistent exchange of NGS HLA genotyping data we have observed. We hope to address these challenges in future DaSH for NGS efforts.

Introduction The objective of the project was to describe an efficient workflow for quantifying and disseminating tumor imaging metrics essential for assessing tumor response in clinical therapeutic trials. The clinical research utility of integration of the workflow into the electronic health record for radiology reporting was measured before and after the intervention. Materials and Methods A search of institutional clinical trial databases was performed to identify trials with radiology department collaborators. Investigator initiated trials, or those which lacked a standardized or automated system of collaboration with the research team were selected for the study. A web based application integrated in the electronic health record platform, the Quantitative Imaging Analysis Core (QIAC) initiative was established as a divisional resource with institutional support to provide standardized and reproducible imaging metrics across the institution. The turnaround time for radiology reports before (phase 1) and after web based application workflow (phase 2) was measured. During our test period (November 2014 to June 2015), a total of 68 requests with 37 from phase 1 and 31 from phase 2 were analyzed for patients who were enrolled in prospective clinical therapeutic interventional trials. Results The mean turnaround time for generation of quantitative tumor metric results after implementation of the web based QIAC workflow (phase 2) was significantly lower than prior (phase 1) (15.9 ± 21.3 vs 31.7 ± 35.4 hours, p= 0.0005). The mean time from the scan to the preliminary assessment was 19.6 ± 25.6 hours before and significantly reduced to 8.0 ± 9.9 hours with implementation of web based QIAC workflow. Conclusion Implementation of a web based QIAC workflow platform enabled significantly improved turnaround time for quantitative tumor metrics reports and enabled faster access to the reports.

Next-generation sequencing (NGS) has been widely adopted for clinical HLA typing and advanced immunogenetics researches. Current methodologies still face challenges in resolving cis-trans ambiguity involving distant variant positions, and the turnaround time is affected by testing volume and batching. Nanopore sequencing may become a promising addition to the existing options for HLA typing. The technology delivered by the MinION sequencer of Oxford Nanopore Technologies (ONT) can record the ionic current changes during the translocation of DNA/RNA strands through transmembrane pores and translate the signals to sequence reads. It features simple and flexible library preparations, long sequencing reads, portable and affordable sequencing devices, and rapid, real-time sequencing. However, the error rate of the sequencing reads is high and remains a hurdle for its broad application. This review article will provide a brief overview of this technology and then focus on the opportunities and challenges of using nanopore sequencing for high-resolution HLA typing and immunogenetics research.

PURPOSE: A COVID-19 pandemic business continuity plan (BCP) was rapidly developed to protect the Victorian newborn screening (NBS) program. Here, we present the outcomes of our COVID-19 BCP and its impact on the Victorian NBS laboratory service. METHODS: Change management principles were used to develop a BCP that included mapping of NBS processes against staff resources, triaging priorities, technology solutions, supply chain continuity, gap analysis, and supporting maternity service providers. The effect was assessed quantitatively by review of key performance indicator data and qualitatively from staff feedback. RESULTS: A four-stage BCP was implemented. Stage 1 split teams into two, which rotated weekly, onsite (laboratory) and offsite (home). At 20 weeks post-implementation the BCP only progressed to stage 1 and the overall turnaround time was maintained. Staff experience indicated benefits from the review of workflow but noted some social impact associated with the change. CONCLUSION: The preparedness and agility of implementation was based on our focus on the newborn babies and their families, our production system, and a continuous improvement mindset. Both our people and technology infrastructure processes are crucial to this success as we continue to adapt to new challenges.

Circulating cell-free miRNAs (ccf-miRs) have gained significant interest as biomarkers for lung cancer (LC) diagnosis. However, the clinical application of ccf-miRs is mainly limited by time, cost, and expertise-related problems of existing detection strategies. Recently, the development of different point-of-care (POC) approaches offers useful on-site platforms, because these technologies have important features such as portability, rapid turnaround time, minimal sample requirement, and cost-effectiveness. In this review, we discuss different POC approaches for detecting ccf-miRs and highlight the utility of incorporating nanomaterials for enhanced biorecognition and signal transduction, further improving their diagnostic applicability in LC settings.

Background The consumption of alcoholic beverages is an integral part of many socio-cultural traditions. The existing methods of compositional analysis and quality control of alcoholic beverages are not satisfying the burgeoning demands of global market due to their high cost and slow turnaround time. Scope and approach The nondestructive instrumental techniques can be applied for quality control, discrimination and authentication of alcoholic beverages. The cost of analysis, reduction in time, environment friendliness, and non-destructive nature of these techniques offers bright prospects for the future. Key findings and conclusions This review aims to highlight the most relevant and current information on the use of nondestructive instrumental techniques for evaluating quality, discrimination and authentication of alcoholic beverages. Moreover, it will serve as a guidance document for large scale production units for implementation of these laboratory-based smart techniques. This article also covers the challenges faced by the beverage industry which needs to be resolved or investigated in future studies.

Highlights • COVID-19 biosafety action plans can maintain stat laboratory quality. • Test volume initially declined, and then increased to surpass pre-pandemic levels. • Turnaround time was maintained, and pre-analytical errors were stable., Objective It is unclear if implementation of biosafety action plans in response to the COVID-19 pandemic has affected laboratory quality metrics. Methods This retrospective study used quality data, including turnaround time (TAT) and number/type of unacceptable specimens from a stat laboratory supporting an outpatient medical clinic serving predominantly elderly cancer patients. Four months of data from the height of the COVID-19 pandemic (March-June 2020) were compared to the same months in 2019. Results March-May 2020 test volumes were decreased compared to 2019. June 2020 test volume was slightly increased compared to 2019. TATs in 2020 were similar/ slightly improved compared to the same months in 2019, due to shortened collect to receive and receive to verify TATs. The number and types of unacceptable specimens were similar in 2020 and 2019. Conclusions Despite the challenges to the system caused by the pandemic, laboratory quality metrics were maintained.

With Graphic Processing Units (GPUs) being widely adopted in data centers to provide computing power, efficient support for GPU multitasking attracts significant attention. The prior GPU multitasking works include spatial multitasking and simultaneous multitasking (SMK). Spatial multitasking allocates GPU resources at the streaming multiprocessor (SM) granularity which is coarse-grained, and SMK runs concurrent kernels on the same SM, therefore is fine-grained. SMK is beneficial to improve GPU resource utilization especially when concurrent kernels have complementary characteristics. However, the main challenge for SMK is the interference among multiple kernels especially the contention on data cache. In this paper, we propose a fair and cache blocking aware warp scheduling (FCBWS) approach to ameliorate the contention on data cache and improve SMK on GPUs. In FCBWS, equal opportunity of issuing instructions is provided to each kernel, and memory pipeline stalls are tried to be avoided by predicting cache blocking. Kernels are extracted from various applications to construct concurrent kernel execution benchmarks. The simulation experiment results show that FCBWS outperforms previous multitasking methods; even compared to the state-of-the-art SMK method, FCBWS can improve system throughput (STP) by 10% on average and reduce average normalized turnaround time (ANTT) by 41% on average.

Unstructured summary Respiratory point-of-care tests (POCT), detecting influenza A, influenza B and respiratory syncytial virus (RSV), were implemented in response to recent RSV outbreaks at a regional haemato-oncology unit in Glasgow. We provide a descriptive study of pre- and post- POCT implementation, which suggests POCT reduce the time taken to receive results and increase diagnostic rates in outpatients. It is likely that the reduction in turn-around-time afforded by POCT also lead to a faster time to antiviral treatment, prompt isolation and a reduction in the number of hospital acquired infections.

Infectious agents, both standard and emerging or engineered, might initiate outbreaks with global impact and/or existential- level threat potential. Such agents might infect human, livestock, exploitable plant cultures or the environment at large. Instead of responding by sampling teams which may also carry out prolonged surveillance, a more effective concept is to pre-process the samples, possibly to nucleic acid extract so as to transport them without biological risks by standard channels, or to process them up to a point and return the results, leaving the samples which only need specialized tests and dedicated equipment. In this context a portable device, allowing multiple routine and crisis -management applications due to its inherent flexibility, is scrutinized against alternatives and possible needs so as to develop a novel standard of surveillance and intervention, meant to facilitate containment by providing faster much more reliable laboratory results at a fraction of older generation mobile nucleic acids analyzers. Additional steps, such as agarose in pills and buffers pre-packaged in expendable syringes with pistons locked by improvised safety features may improve the Complexity Level of the whole system so as to make it appealing to intervention/crisis management personnel, while the instrument proper may double as backup for respective benchtop devices.

Automated negotiation on qualitative issues taking linguistic values poses the challenge of computation of their offer values while generating proposals/counter-proposals. In order to make the offer values computable for the qualitative issues just as quantitative issues, the proposed flexible negotiation strategies (FlexiNegoS+) for e-commerce negotiations, generate a triangular fuzzy number corresponding to each linguistic value of the qualitative issue from which numerical reservation values are identified. JADE based mobile agent named as ‘JADETraderPal’ has been designed and embedded with FlexiNegoS+ which can act on behalf of its owner in bilateral/multilateral single-issue/multi-issue dynamic e-commerce negotiation environments. The negotiation model of FlexiNegoS+ enables JADETraderPal to make adjustable rate of concession during each proposal/counter-proposal depending upon the dynamicity of the e-market. Performance of JADETraderPal and effectiveness of FlexiNegoS+ are evaluated by developing a prototype multi-agent system representing bilateral/multilateral negotiation scenarios with parameters such as turnaround time, deal success rate and traders’ utility. The empirical results prove that JADETraderPal outperforms static agents, improves the rate of reaching consensus and maximizes the utility of its owner. JADETraderPal demonstrated robust performance even in multi-lateral negotiations and is able to increase negotiation success rates and traders’ utility in multi-lateral negotiation scenarios than in bilateral negotiation environments.

Objective To describe the system-wide implementation of integrated electronic prior authorization (ePA) and its impact within a centralized prior authorization (PA) department in an academic health system. Setting Prescription drugs have been among the fastest growing areas of health care and continue to advance; therefore, costs associated with medications are continually rising. Pharmacy benefit managers implement tools such as PAs in an effort to manage costs. PAs are often viewed as inconvenient and irritating by both patients and clinical staff. Practice description The pharmacy ambulatory clinical care center (PAC3), a centralized PA department, provides PA services to some clinics within the system. Since 2014, the system has worked to optimize the manual (by telephone and fax) PA process. However, it is still a time-consuming, multistep process. Practice innovation A resolution to the PA problems that health systems face is ePA, which involves processing PAs electronically through an online portal or integrated within an electronic health record. The system began the implementation process with the selection of an ePA vendor, followed by implementation within the electronic health record. Once the software was ready, the system took a phased rollout approach and implemented ePA in a few specialty and primary care clinics at a time. Evaluation An average of 24% of PAs processed by PAC3 were completed by ePA. The system saw a 25% increase in the number of PAs processed per month by 1 PA coordinator full-time equivalent. In addition, a 62% decrease in the PA turnaround time allowed patients to receive medications faster. Results Not applicable. Conclusion ePA offers several benefits to the system including decreased turnaround time. This allows patients to receive medications and start therapy earlier. The ePA process also increased efficiency, allowing PAC3 to centrally manage PAs for additional clinics, thereby decreasing the workload in the clinic.

OBJECTIVE To detail the development of an electronic report that graphically conveys all relevant information from targeted prostate biopsy. METHODS The Urology Integrated Diagnostic Report (Uro-IDR) is based on a published framework (RadPath) which enables the compilation of diagnostic data from urology, radiology, and pathology. Each component of the Uro-IDR is generated by the contributing clinician, is assembled in one document, and provides correlation of the 3 inputs at a glance. Upon completion, the Uro-IDR is automatically linked to the electronic medical record as an interactive file and can also be downloaded for offline sharing as a PDF. RESULTS At our institution, 1638 individual Uro-IDRs were generated between June 2016 and April 2019. There were 5715 views of these documents via the EMR. The average turnaround time for the creation of an individual report decreased from nearly 8 days at the time of its launch to 2 days after 6 months of use. The average time for report generation was 22 seconds for the pathologist and 69 seconds for the radiologist. An instructive video is linked to this article. CONCLUSION The Uro-IDR has proven to be a feasible, efficient, clinically useful form to concisely transmit key information about targeted prostate biopsy to both clinicians and patients.

Summary Background Publication of research in highly regarded journals is a laborious journey that requires a considerable amount of effort and time. The objective of this article is to obtain insight into the time required for articles to be accepted and published in the top six plastic surgery journals and to study the effects of article characteristics on their publication time. Methods A comprehensive review of all articles published by six scientific plastic surgery journals during a one-year period (2018) was performed. Time taken from submission to acceptance, online, and in-print publication was abstracted and analyzed. Results A total of 1141 articles were reviewed. The median total time (TT) from submission to in-print publication was 10.3 months (IQR 8–12.6), with a median time of 4.6 months (3–6.8) from submission to acceptance and 5.4 months (4.2–6.3) from acceptance to publication. TT varied among journals, ranging from 7.2 months for Aesthetic Plastic Surgery to 16.1 months for Microsurgery. The articles were available online after a median of 21 days from their acceptance date. When comparing our results to publication times in 2005, most journals showed a decrease in their total publication time. Conclusion Our study highlights the time taken for publication and the differences between plastic surgery journals and study characteristics. Most journals have demonstrated a quicker turnaround time during the last 13 years. Online publication has greatly decreased the time between acceptance and in-print publication. We advocate for improving certain areas of this timeline, but this improvement should not be at the expense of review quality.

The paper deals with the airlines diversion management as this entails a complex decision-making process that comes into action during the adverse events, preventing the execution of the regular flight operations. Diverting from the intended route can be caused by several reasons both when the safety of the flight or the wellbeing of the passengers is concerned. The most frequent reasons to divert a flight are the repugnant weather, medical emergency or the technical fault. A flight diversion can severely disrupt the passenger’s comfort, jeopardize the fleet or the crew planning and is generally accompanied with the significant expenses. The intention of every airline in a case of a diversion is to assure the fastest turnaround time and/or provide the passengers with the highest level of services to mitigate the negative effects. A wrong selection of an alternate airport, that is not having enough capacity, personnel or material resources to handle a flight diversion in a timely manner can negatively influence the airline in several ways. Both strategic and tactical diversion management process is a collaborative decision-making procedure, heavily dependent on the base of the information available, where the main actors are not only flight crew and the airline Operational Control Centre but also the Air Traffic Controller, airport operators and the local service providers. Airline strategic diversion planning shall comprehend a well-defined data set and its data quality procedures shall ensure the information accuracy in the pre-tactical phase. The consequent real time data processing can enhance the decision-making process in a tactical phase of the flight diversion. The data and information required comprise of available airport capacities and other relevant ground resources needed to handle a diverted flight. As stated, such data and information are mostly tactically critical, therefore continuous monitoring and updating shall be ensured. The paper suggests that the overall data management is managed via a local Airport Operations Center, which will assure provision of actual airport diversion capabilities (incl. weather situation) needed for accommodation of an affected flight.

Decide when and where allocate incoming ships at a container terminal is among the main problem confronted by port operators. This problem often called " the Berth Allocation Problem (BAP)".In this work, we realize an analytical study on the major planning models presented in the literature for the berth allocation problem, namely dynamic and discrete berth allocation problem model (DDBAP), dynamic and continuous berth allocation problem model (DCBAP) and dynamic and hybrid berth allocation problem model (DHBAP). The objective of this study is determine which model among the mentioned above can be used as a standard model for all type of terminal layouts and which of them ensure satisfactory outcome at level of minimization of ships turnaround time in the port. A commercial solver (CPLEX) as well as an efficient sailfish-based algorithm (SFA) are used to solve each model. Finally, computational experiences and comparison have been presented to demonstrate the most suited planning model.

Quality practices in forensic science are often in tension with providing timely results. When the Costa Rican Department of Forensic Sciences ballistics unit achieved accreditation under ISO/IEC 17020:2012 in 2018, the unit started experiencing slower turnaround times. With a view to expediting the resolution of forensic examination processes, the unit undertook a Lean Six Sigma project with a five-phase problem-solving methodology. The unit began to use data and process tools to transform its current state. The completion of all phases showed that two main aspects were preventing the laboratory from fulfilling the customers’ expectations: standardization and constraints. This project took 6 months and improvements were seen through the reduction of the number of pending cases with a backlog of more than 3 months by 97% and the turnaround time from 4 months to 1 month. Through this project, leadership identified an effective methodology, creating a positive impact on customers’ expectations.

Clustering is the most widely used unsupervised machine learning technique, having extensive applications in statistical analysis. We have multiple clustering algorithms available in theory and many more implementations available in practice. A bunch of literatures can be found focusing on the quality of clustering algorithms using various internal and external evaluation techniques. The motivation behind this work is the scarcity of literatures dealing with performance of clustering algorithms in terms of turnaround time. This paper summarizes the experimental analysis conducted on the performance of multiple clustering algorithms based on cardinality and dimensionality. The analysis is performed in R, which is a free and open source programming language mainly used for statistical computing. This work evaluates nine key algorithms coming under partitioning, hierarchical, density-based and model-based clustering approaches using different social media data sets. We captured performance trends of these algorithms in terms of turnaround time by varying the cardinality and dimensionality parameters of the data sets. Based on our experiments, CLARA, CLARANS, and k-means algorithms demonstrate best performances with varying cardinality. It is also observed that changes in dimensionality do not impact hierarchical clustering approaches whereas there is a positive influence on the execution time for partitioning, density-based and model-based clustering approaches.

Background/Purpose Access, cost and quality are limiting parameters of any healthcare delivery system. RNTCP (Revised National Tuberculosis Control Program) has largely addressed the access and cost issues, however the quality of care is a major hurdle in TB care today. Methods We propose using an evidence based method of quality improvement principles to address many quality issues ranging from delayed turnaround time in testing, to low patient satisfaction, and slow private sector engagement. Results We propose a 5 step approach to learning and conducting quality improvement at the district level. Step 1: Form a team and define the problem Step 2: Develop baseline data Step 3: Create a process map Step 4: Bring a change through a PDSA Plan-Do-Study-Act cycle Step 5: Prepare run charts. Conclusion We cannot expect a different result by doing the same thing over and over again. This holds particularly true for the TB program in India. A major paradigm shift is necessary if we wish to achieve TB Free within our lifetimes. A shift from quality assurance to quality improvement offers this hope for change and TB elimination.

The goal of the study was to adapt our Middle East respiratory syndrome coronavirus (MERS-CoV) lab-developed test (LDT) to 3 "Sample to Result" (S2R) systems: BD MAX (BD), ELITe InGenius (ELITechGroup), and ARIES (Luminex). The BD MAX and InGenius system allowed use of lab-developed primers and TaqMan probes, while ARIES required conversion to MultiCode primers for melting curve analysis. Each device required ≤1 day of training and assay optimization. No discordant results were noted after analysis of 32 External Quality Control (EQC) samples. On a 10-fold dilution series of a MERS-CoV-positive EQC sample, InGenius obtained the highest detection rate. Laboratory technicians rated the ARIES as the user-friendliest. It also required the least hands-on time. BD MAX had the lowest turnaround time and highest throughput. While each device had distinguishing system properties with associated (dis)advantages, the 3 S2R systems were comparable in terms of assay development and validation. ispartof: DIAGNOSTIC MICROBIOLOGY AND INFECTIOUS DISEASE vol:94 issue:4 pages:349-354 ispartof: location:United States status: published

The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015-2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 hours) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument calibration signals. A large asphalt parking lot near the Western Basin of Lake Erie was spectrally characterized and was determined to be a suitable correction target. Due to the HSI deployment aboard an aircraft, it is able to provide unique insights into water quality conditions not offered by space-based solutions. Aircraft can operate under cloud cover and flight paths can be chosen and changed on-demand, allowing for far more flexibility than space-based platforms. The HSI is also able to collect data at a high spatial resolution (~1 m), allowing for the monitoring of small water bodies, the ability to detect small patches of surface scum, and the capability to monitor the proximity of blooms to targets of interest such as water intakes. With this new rapid turnaround time, airborne data can serve as a complementary monitoring tool to existing satellite platforms, targeting critical areas and responding to bloom events on-demand.

Bloodstream infections (BSIs) are serious infections associated with high rates of morbidity and mortality. Every hour delay in initiation of an effective antibiotic increases mortality due to sepsis by 7%. Turnaround time (TAT) for conventional blood cultures takes 48 h, forcing physicians to streamline therapy by exposing patients to broad-spectrum antimicrobials. Our objective was (1) to evaluate the accuracy and TAT of an optimized workflow combining direct matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and in-house real-time polymerase chain reaction (PCR) for bacterial identification and antimicrobial resistance profiling directly from positive blood bottles for diagnosing bloodstream infections and (2) to verify the effect of reporting results to medical staff. A total of 103 BSI episodes from 91 patients admitted to three hospitals in São Paulo, Brazil were included. TAT from molecular versus conventional methods was measured and compared. Our protocol showed an overall agreement of 93.5% for genus and 78.5% for species identification; 74.2% for methicillin resistance detection, 89.2% for extended-spectrum β-lactamase profiling, 77.8% for metallo-β-lactamase profiling, and 100% for carbapenemase profile and vancomycin-resistance detection when compared with conventional testing. TAT of molecular sample processing according to our protocol was 38 h shorter than conventional methods. Antimicrobial interventions were possible in 27 BSI episodes. Antimicrobial discontinuation was achieved in 12 BSI episodes while escalation of therapy occurred in 15 episodes. Antimicrobial therapy was inadequate in three (12%) BSI episodes diagnosed using results of molecular testing. Our in-house rapid protocol for identifying both bacteria and antimicrobial resistance provided rapid and accurate results, having good agreement with conventional testing results. These results could contribute to faster antimicrobial therapy interventions in BSI episodes. Keywords: Bloodstream infections, Molecular diagnosis, Molecular panels, PCR, Mass spectrometry

Interferences during the boarding procedure are one of the main reasons of delay and increased turnaround time, becoming a relevant problem for airlines. Observations of the boarding process and questionnaires inside the aircraft revealed three main bottlenecks during the boarding process: (1) Hand luggage: Storage space is not sufficient and/or not used efficiently; (2) Preparation: Passengers are not well prepared for the boarding process; and (3) Communication: Audio announcements are unclear and unfocused. By translating these bottlenecks as possibilities for improvement, solutions were designed for the airport and aircraft interiors to reduce boarding time and improve the passenger boarding experience. Concepts ranged from an app to scan your hand luggage at home and make a reservation for overhead bin space; to a redesigned waiting area to help passengers prepare for boarding; to new boarding methods and redesigned aircraft seats. In this paper, several design concepts are presented in more detail.

Inherited cardiomyopathies (ICs) are a major cause of heart disease. Given their marked clinical and genetic heterogeneity, the content and clinical utility of IC multi-gene panels has been the topic of continuous debate. Our genetics diagnostic laboratory has been providing clinical diagnostic testing for ICs since 2012. We began by testing nine genes and expanded our panel by fivefold in 2015. Here, we describe the implementation of a cost-effective next-generation sequencing (NGS)-based assay for testing of IC genes, including a protocol that minimizes the amount of Sanger sequencing required to confirm variants identified by NGS, which reduces the cost and time of testing. The NGS assay was developed for the simultaneous analysis of 45 IC genes and was assessed for the impact of panel expansion on variant detection, turnaround time, and cost of testing in a cohort of 993 patients. The assay led to a considerable reduction in test cost and turnaround time. However, only a marginal increase was observed in the diagnostic yield, whereas the rate of inconclusive findings increased considerably. These findings suggest that the ongoing evaluation of gene content and monitoring of clinical utility for multi-gene tests are essential to achieve maximum clinical utility of multi-gene tests in a publicly funded health care setting.

Priority Preemptive scheduling algorithm is a popular among various other algorithms for scheduling CPU, however it leads to the problem of starvation which happens when processes with lower priority are not given any chance of CPU utilization due to continuous CPU usage by processes with higher priorities. To eradicate this problem here, we have proposed a new algorithm for scheduling CPU which we call it as Modified Priority Preemptive Scheduling Algorithm based on a new approach where we are executing priority Preemptive scheduling in a round robin fashion taking the time quanta equal to the shortest burst time of all the available processes. On analyzing the results, it is observed that modified Preemptive algorithm not only solves the problem of starvation but also enhances the performance of regular Priority Preemptive algorithm by lowering the mean turnaround time and mean waiting time.

The rapid spread of cloud computing during the past few years has increased the need to improve cloud services. The infrastructure-as-a-service (IaaS) model is among the highly challenging cloud computing models that has been subject to many optimization proposals. In particular, the scheduling algorithms followed by IaaS resource management systems to allocate virtual machine requests to physical cloud severs play an important role in the performance of the entire cloud. This paper proposes a distributed scheduling algorithm for IaaS cloud computing services by imitating the locust behavior in nature. The simulation results show that the proposed Locust algorithm reduces the average turnaround time of requests without compromising the server utilization.

Cloud computing offers a pool of virtual resources to users on demand. Among the main challenge facing cloud computing is job scheduling which is an NP-hard problem. Usually, scheduling should consider job priority, where high-priority jobs should be immediately allocated required resources, whereas low-priority jobs can wait. In this paper, we propose a scheduling algorithm that imitates the parasitic behaviour of the cuckoo bird. Cuckoos reproduce by exploiting the nests of other birds with eggs similar to their own. We have evaluated the proposed cuckoo scheduling algorithm under varying number of nodes and percentage of high priority jobs. The experimental results demonstrate the superiority of our proposed algorithm over the benchmarks in terms of average CPU utilization and average turnaround time for each type of job.

The combination of viable heuristic attributes with statistical measurements presents significant challenges in industrial maintenance for complex assets under through-life service contracts. Techniques to obtain and process heuristic attributes raise numerous uncertainties which often go undefined and unmitigated. A holistic view of these uncertainties may improve decision-making capabilities and reduce maintenance costs and turnaround time. It is therefore necessary to identify and rank factors that influence uncertainties originating from challenges in the above context. This, along with an identification of who contributes to such challenges and current practice to handle them, sets the focus for this study.\ud \ud The influence of 32 categorised factors on uncertainty is assessed through a questionnaire completed by nine experienced maintenance managers from a leading defence company. The pedigree approach is applied to score validity of respondents’ answers according to their experience and job role to normalise scores. Results are discussed in interviews with respondents along with current practice in and ways to improve uncertainty assessment. Scores are weighted through the Analytical Hierarchy Process (AHP) in order to identify the most influential factors on uncertainty in maintenance. The analysis revealed that these include: intellectual property rights (IPR), maintainer performance, quality of information, resistance to change, stakeholder communication and technology integration. These are verified with 40 practitioners from various industrial backgrounds. From the interviews, it is deemed that a holistic view of heuristic and statistical attributes ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle.

Objectives Culture-based assays are currently the reference standard for drug susceptibility testing for Mycobacterium tuberculosis. They provide good sensitivity and specificity but are time consuming. The objective of this study was to evaluate whether whole genome sequencing (WGS), combined with software tools for data analysis, can replace routine culture-based assays for drug susceptibility testing of M. tuberculosis. Methods M. tuberculosis cultures sent to the Finnish mycobacterial reference laboratory in 2014 (n = 211) were phenotypically tested by Mycobacteria Growth Indicator Tube (MGIT) for first-line drug susceptibilities. WGS was performed for all isolates using the Illumina MiSeq system, and data were analysed using five software tools (PhyResSE, Mykrobe Predictor, TB Profiler, TGS-TB and KvarQ). Diagnostic time and reagent costs were estimated for both methods. Results The sensitivity of the five software tools to predict any resistance among strains was almost identical, ranging from 74% to 80%, and specificity was more than 95% for all software tools except for TGS-TB. The sensitivity and specificity to predict resistance to individual drugs varied considerably among the software tools. Reagent costs for MGIT and WGS were €26 and €143 per isolate respectively. Turnaround time for MGIT was 19 days (range 10–50 days) for first-line drugs, and turnaround time for WGS was estimated to be 5 days (range 3–7 days). Conclusions WGS could be used as a prescreening assay for drug susceptibility testing with confirmation of resistant strains by MGIT. The functionality and ease of use of the software tools need to be improved.

This study aims to examine the effects of maintenance service firms’ resources and capabilities on maintenance performance, measured by the turnaround time of medical devices (TAT). The theoretical framework used in this study is based on the resource-based view (RBV) and agency theory (AT). The hypotheses were tested using information on 764 medical devices and 60 maintenance service providers, resulting in 1403 maintenance transactions. As such, our data sample is significantly larger than those used in previous studies in this area. For this study we used a variation of the proportional hazards model, the conditional frailty gap time hazards model. The empirical analysis of this research is among the first to quantify how the performance of maintenance providers is affected by the core constructs of both the resource-based view and agency theory, using statistical evidence.

Next-generation sequencing (NGS)-based mutation panels profile multiple genes simultaneously, allowing the reporting of numerous genes while saving labor and resources. However, one drawback of using NGS is that the turnaround time is often longer than conventional single gene tests. This delay can be problematic if molecular results are required to guide therapy in patients with clinically aggressive diseases, such as acute myeloid leukemia. To overcome this limitation, we developed a novel custom platform designated as Ultra-rapid Reporting of GENomic Targets (URGENTseq), an integrated solution that includes workflow optimization and an innovative custom bioinformatics pipeline to provide targeted NGS results on fresh peripheral blood and bone marrow samples within an actionable time period. URGENTseq was validated for clinical use by determining mutant allelic frequency and minimum coverage in silico to achieve 100% concordance for all positive and negative calls between the URGENTseq and conventional sequencing approach. URGENTseq enables the reporting of selected genes useful for immediate diagnosis (CALR, CSF3R, JAK2, KRAS, MPL, NPM1, NRAS, SF3B1) and treatment decisions (IDH1, IDH2) in hematologic malignancies within 48 hours of specimen collection. In addition, we summarize the molecular findings of the first 272 clinical test results performed using the URGENTseq platform.

Summary There is increasing demand for access to rapid microbiological testing, with a view to improving clinical outcomes. The possibility of rapid testing has been facilitated by development of cartridge-based random access molecular technologies that are now widely available. Whether the expense of cartridge-based assays is justified in terms of clinical or laboratory cost savings is controversial. This prospective study evaluated the impact of the Biofire FilmArray Respiratory Panel (‘FilmArray’), a cartridge-based random access molecular test, compared with standard batched molecular testing using an ‘in-house’ respiratory polymerase chain reaction (PCR) on laboratory and health service outcomes for adult patients at a tertiary-level adult hospital in Melbourne, Australia. Laboratory result turnaround time was significantly reduced with the FilmArray (median 4.4 h) compared to a standard validated in-house respiratory PCR assay (median 21.6 h, p < 0.0001) and there was a significant increase in diagnostic yield with the Filmarray (71/124, 57.3%) compared to in-house PCR (79/200; 39.5%; p = 0.002). Despite improved result turnaround time and increased diagnostic yield from testing, there was no corresponding reduction in hospital length of stay or use of isolation beds. Although cartridge-based molecular testing reduced turnaround time to result for respiratory pathogen testing, it did not impact on health service outcomes such as hospital length of stay. Further work is warranted to determine whether cartridge-based tests at the point of care can improve clinical and health service impacts.

The production of skim milk powder (SMP) has increased among Irish Dairy processors in recent years. SMP has nutritional benefits and functional properties, including high calcium and potassium, a low fat content, excellent gelation, emulsification and foaming properties. As Irish dairy exports are increasing year on year, the requirement to intensify process optimisation is heightened. This study investigated the analytical test levels applicable to the regulatory requirements for the quality and safety assurance in the production of SMP across a 12 month period in two large Irish milk processing plants. The efficacy of the testing strategies and turnaround time for SMP for both plants were examined and compared. The results of this study demonstrate excess testing at 64% and 36% respectively, over the regulatory requirements for both companies. The sediment and density tests demonstrated the highest out of specification excess test results for each plant respectively. This is the first study to our knowledge on optimising sampling regimes in a dairy processing setting and the outcomes of this study propose an overview to the current SMP testing strategies for improvement in process optimisation for any milk product, focusing on the process in order to optimise overall production levels and to address analytical test levels, out of specification results and laboratory turn-around time (TAT).

Piece-rate compensation schemes, where workers are paid for each completed task regardless of the time spent on it, are common in practice. Detecting a potential negative impact on firm performance associated with their use adds to the literature on challenges of piece-rate compensation schemes. We study a radiology workflow platform that connects off-site radiologists with hospitals. These radiologists select tasks from a common pool, and the service level is characterized by meeting priority-specific turnaround time targets. However, imbalances between pay and workload of different tasks could result in higher priority tasks with low pay relative to workload receiving poorer service than low priority tasks. Using a large dataset, we investigate whether low priority tasks with a high pay-to-workload ratio have a shorter turnaround time. Then, using the same approach, we investigate whether having many low priority tasks with high pay-to-workload increases the turnaround time and probability of delay of higher priority tasks. We show that turnaround time is decreasing in pay-to-workload for lower priority tasks, whereas it is increasing in workload for high priority tasks. More importantly, we find evidence of a spillover effect: Having many economically attractive tasks with low priority can lead to longer turnaround times for higher priority tasks, increasing the likelihood that those tasks are delayed. Our results suggest that organizations where workers have task discretion from a common pool need to carefully align their piece-rate compensation scheme with the workload of each task. Imbalances may lead to a degradation in the system service level provided to time-sensitive customers.

Background: Molecular testing (PCR) is the recommended method for the diagnosis of coronavirus disease 19 (COVID-19). In low-resource settings (LRS), the availability and public health impact of these tests is constrained. Despite lower sensitivity, antigen detection rapid diagnostic tests (Ag-RDTs) could provide improved access at lower costs and quicker turnaround-time (TAT). We evaluated the optimal use of Ag-RDTs to increase testing access within TAT and reduce the cost and the number of cases missed in LRS. Methods: We modeled estimated COVID-19 testing demand coverage based on current PCR capacity in three different epidemic phases across five African countries. We then modelled five additional testing strategies that utilized a combination of PCR and Ag-RDT. For each strategy, epidemic phase and country, we estimated the total number of correct test results expected within a 48hr-TAT, the number of positive cases missed, corresponding costs ($12/PCR, $6/Ag-RDT), and the incremental cost-effectiveness ratios. Findings: Across all countries and phase of epidemic, there was insufficient PCR capacity to meet the calculated required testing demand. The strategy limited to current PCR capacity was not considered cost-effective. Saturating testing demand with Ag-RDT was considered robustly cost-effective in every epidemic phase ($5-$7/additional person with a correct test result within 48hr-TAT and $50-$1000/additional case missed averted) Interpretation: Inclusion of Ag-RDT in testing strategies is cost-effective and critical in increasing timely testing access in countries with low PCR capacity. Funding: Supported by the Government of the Netherlands in the context of the ACT-Accelerator and USAID. Declaration of Interest: None to declare.

Background: The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Methods: In this study, we developed a machine learning-based framework for predicting COVID-19 positive test status relying only on readily available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we divided the patient data into a training set (patient encounters through April 13, 2020) and a test set (patient encounters from April 13, 2020 through June 2, 2020). We trained our machine learning models on the training set and evaluated model performance on the test set. Findings: We trained and tested multiple types of machine learning models, achieving an area under the curve of 0·75 in the test set. Feature importance analyses highlighted serum calcium levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we identified an optimal probability threshold for patient screening and developed a single decision tree model that provided an operable method for stratifying sub-populations. Interpretation: Overall, this study provides a proof-of-concept that COVID-19 status prediction models can be developed using only baseline data. Our machine learning models can be adapted to a variety of global pandemic scenarios, as the resulting prediction could complement existing tests to enhance screening and pandemic containment workflows. Funding: Icahn School of Medicine at Mount Sinai, New York, NY. Declaration of Interests: J.F. is an employ of Outco Inc. All other authors declare no competing financial interests. Ethics Approval Statement: This study utilized de-identified data extracted from the electronic health record and as such was considered nonhuman subject research. Therefore, this study was exempted from the Mount Sinai IRB review and approval process. All analyses were carried out in accordance with relevant guidelines and regulations.

Through the early establishment of a DH-IC, planning for a surge in SARS-CoV-2 cases in New Hampshire was highly efficient, building on prior institutional preparations for the monitoring of new, emerging infections. The proactive and visionary work of molecular diagnostic teams in the DH clinical laboratories resulted in us being able to validate an array of new diagnostic test platforms for SARS-CoV-2, to meet the turnaround time and sensitivity needs of the regional population that we serve. We used our existing regional laboratory infrastructure to coordinate testing strategies, reagents, and supplies. A respectful and supportive relationship with the New Hampshire state laboratory maintained a consistently fast SARS-CoV-2 test result turnaround time, statewide. We met the challenges head-on and were not overwhelmed.

Purpose: Efficient interventional radiology (IR) procedure room utilization relies on various healthcare professionals performing clinical and nonclinical tasks to minimize room downtime. The coronavirus-19 (COVID) pandemic offers insight into the importance of staff ratios in improving room utilization, by evaluating the effect of a relative increase in support staffing in the setting of reduced physician staffing and reduced overall case volume. Materials and Methods: In March 2020, as a result of a department-wide COVID disaster management plan, a 4-angiosuite IR department in a 900-bed tertiary care center experienced reduced case volume by approximately 1/3, matched by decreased physician staffing (3 to 2 attendings). IR technologist and nurse staffing remained unchanged. Normal staffing resumed June 2020. A prospective database of event times related to room workflow was reviewed for case volume, room turnaround time, and first case delay time before and after volume and staff changes. Turnaround times greater than 2 hours were excluded to correct for gaps in schedule and the prolonged turnaround associated with COVID-positive patient cases. Wilcoxon rank-sum two-sample tests were used to evaluate the differences between time periods. All P values reported are two-sided. Analysis was done in RStudio 1.2.5033 (RStudio Team, Boston, MA). Results: Under standard operation, the median values for room turnaround time, case start delay, weekly case volume, and total case hours were 43.0 minutes, 30.0 minutes, 64 cases, and 44.4 hours, respectively. Under the COVID disaster management plan, a 32.6% (P < 0.001, 95% C.I. 23.3%, 41.9%) estimated increase was observed for room turnaround times, and a 53% (P < 0.001, 95% C.I. 36.7%, 70.0%) estimated increase was observed in case start delay;despite a 29.7% (P < 0.001, 95% C.I. 20.3%, 42.2%) decrease in case number and 29.5% (P = 0.009, 95% C.I. 13.1%, 43.2%) estimated decrease in total case hours. Conclusions: Paradoxically, a relative increase in staffing did not improve measured metrics for room utilization, despite a stable attending physician-to-case ratio. Rather, findings may be allegorical to Parkinson’s law suggesting that work expands so as to fill the time available for its completion.

Highlights • RT-LAMP coupled with CRISPR-Cas12 provides a sensitive and specific virus detection platform. • iSCAN sensitivity and specificity are comparable with RT-qPCR. • iSCAN is a 1 h detection module that can help in testing in low resource areas. • iSCAN can be developed as a one-pot assay. • iSCAN reagents can be produced locally and deployed for SARS-CoV2 detection., The COVID-19 pandemic caused by SARS-CoV-2 affects all aspects of human life. Detection platforms that are efficient, rapid, accurate, specific, sensitive, and user friendly are urgently needed to manage and control the spread of SARS-CoV-2. RT-qPCR based methods are the gold standard for SARS-CoV-2 detection. However, these methods require trained personnel, sophisticated infrastructure, and a long turnaround time, thereby limiting their usefulness. Reverse transcription-loop-mediated isothermal amplification (RT-LAMP), a one-step nucleic acid amplification method conducted at a single temperature, has been used for colorimetric virus detection. CRISPR-Cas12 and CRISPR-Cas13 systems, which possess collateral activity against ssDNA and RNA, respectively, have also been harnessed for virus detection. Here, we built an efficient, rapid, specific, sensitive, user-friendly SARS-CoV-2 detection module that combines the robust virus amplification of RT-LAMP with the specific detection ability of SARS-CoV-2 by CRISPR-Cas12. Furthermore, we combined the RT-LAMP-CRISPR-Cas12 module with lateral flow cells to enable highly efficient point-of-care SARS-CoV-2 detection. Our iSCAN SARS-CoV-2 detection module, which exhibits the critical features of a robust molecular diagnostic device, should facilitate the effective management and control of COVID-19.

Early diagnosis of SARS-CoV-2 infected patients is essential to control the dynamics of the COVID-19 pandemic. We develop a rapid and accurate one-step multiplex TaqMan probe-based real-time RT-PCR assay, along with a computational tool to systematically analyse the data. Our assay could detect to a limit of 15 copies of SARS-CoV-2 transcripts—based on experiments performed by spiking total human RNA with in vitro synthesized viral transcripts. The assay was evaluated by performing 184 validations for the SARS-CoV-2 Nucleocapsid gene and human RNase P as an internal control reference gene with dilutions ranging from 1-100 ng for human RNA on a cohort of 26 clinical samples. 5 of 26 patients were confirmed to be infected with SARS-CoV-2, while 21 tested negative, consistent with the standards. The accuracy of the assay was found to be 100% sensitive and 100% specific based on the 26 clinical samples that need to be further verified using a large number of clinical samples. In summary, we present a rapid, easy to implement real-time PCR based assay with automated analysis using a novel COVID qPCR Analyzer tool with graphical user interface (GUI) to analyze the raw qRT-PCR data in an unbiased manner at a cost of under $3 per reaction and turnaround time of less than 2h, to enable in-house SARS-CoV-2 testing across laboratories., Infectious disease; Microbial genomics; Genomics; Molecular biology; Virology; Diagnostics; SARS-CoV-2; Real-time RT- PCR; COVID-19 detection assay; COVID qPCR analyzer; Graphical user interface (GUI)

The exponential increase in technological complexity of modern engineering systems necessitates rigorous and accurate maintenance planning to determine optimum equipment availability and turnaround time whilst allowing for overruns and unforeseen costs. Quality and availability of quantitative data, as well as qualitative expert opinion and experience expose uncertainties that can result in under or over estimation of the above factors. Uncertainty quantification in complex engineering systems should consider inter-connected components and associated processes from a combination of quantitative and qualitative (compound) perspectives. This paper presents a framework to quantify and aggregate compound uncertainties and to be assessed against a predetermined acceptable level of uncertainty. This will provide maintenance planners with a confident, comprehensive view of parameters surrounding the above factors to improve decision making capabilities. The framework was validated by assessing individual and compound uncertainties in a bespoke heat exchanger test rig comprised of subsystem modules interact in a non-linear manner, as well as subjective opinions and actions of operators. The results demonstrate the framework’s ability to effectively quantify these factors with an indication of their impact on the system. Future work will include further validation with more complex case studies and development of methods to forecast the quantified uncertainty through the in-service phase of an asset’s life cycle.