Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach

Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this article, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: (1) cST-ML captures the dynamics of traffic prediction tasks using variational inference, and to better capture the temporal uncertainties within tasks, cST-ML performs as a rolling window within each task; (2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic-related features; (3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models especially when obvious traffic dynamics and temporal uncertainties are presented.

Quantitative comparison of SARS-CoV-2 nucleic acid amplification test and antigen testing algorithms: a decision analysis simulation model

Background Antigen tests for SARS-CoV-2 offer advantages over nucleic acid amplification tests (NAATs, such as RT-PCR), including lower cost and rapid return of results, but show reduced sensitivity. Public health organizations recommend different strategies for utilizing NAATs and antigen tests. We sought to create a framework for the quantitative comparison of these recommended strategies based on their expected performance. Methods We utilized a decision analysis approach to simulate the expected outcomes of six testing algorithms analogous to strategies recommended by public health organizations. Each algorithm was simulated 50,000 times in a population of 100,000 persons seeking testing. Primary outcomes were number of missed cases, number of false-positive diagnoses, and total test volumes. Outcome medians and 95% uncertainty ranges (URs) were reported. Results Algorithms that use NAATs to confirm all negative antigen results minimized missed cases but required high NAAT capacity: 92,200 (95% UR: 91,200-93,200) tests (in addition to 100,000 antigen tests) at 10% prevalence. Selective use of NAATs to confirm antigen results when discordant with symptom status (e.g., symptomatic persons with negative antigen results) resulted in the most efficient use of NAATs, with 25 NAATs (95% UR: 13-57) needed to detect one additional case compared to exclusive use of antigen tests. Conclusions No single SARS-CoV-2 testing algorithm is likely to be optimal across settings with different levels of prevalence and for all programmatic priorities. This analysis provides a framework for selecting setting-specific strategies to achieve acceptable balances and trade-offs between programmatic priorities and resource constraints.

Testing Algorithms for Identifying Source Confusion in the H i-MaNGA Survey

Astronomical observations of neutral atomic hydrogen (H i) are an important tracer of several key processes of galaxy evolution, but face significant difficulties with terrestrial telescopes. Among these is source confusion, or the inability to distinguish between emission from multiple nearby sources separated by distances smaller than the telescope’s spatial resolution. Confusion can compromise the data for the primary target if the flux from the secondary galaxy is sufficient. This paper presents an assessment of the confusion-flagging methods of the H i-MaNGA survey, using higher-resolution H i data from the Westorbork Synthesis Radio Telescope-Apertif survey. We find that removing potentially confused observations using a confusion probability metric—calculated from the relationship between galaxy color, surface brightness, and H i content—successfully eliminates all significantly confused observations in our sample, although roughly half of the eliminated observations are not significantly confused.

Direct oral anticoagulant (DOAC) interference in hemostasis assays

Direct oral anticoagulants (DOACs) are a group of direct coagulation factor inhibitors including both direct thrombin inhibitors and direct factor Xa inhibitors. These medications may cause hemostasis assay interference by falsely increasing or decreasing measured values, depending on the analyte. Considering the potential for DOAC interference in a variety of hemostasis assays is essential to avoid erroneous interpretation of results. Preanalytic strategies to avoid DOAC interference include selecting alternatives to clot-based hemostasis assays in patients taking DOACs when possible and sample collection timed when the patient is off anticoagulant therapy or at the expected drug trough. Clinical laboratories may also provide educational materials that clearly describe possible interferences from DOAC, develop testing algorithms to aid in detection of DOAC in submitted samples, use DOAC-neutralizing agents to remove DOACs before continuing with testing, and write interpretive comments that explain the effects of DOAC interference in hemostasis tests. Using a combination of the described strategies will aid physicians and laboratorians in correctly interpreting hemostasis and thrombosis laboratory tests in the presence of DOACs.

A review paper on memory fault models and test algorithms

Testing embedded memories in a chip can be very challenging due to their high-density nature and manufactured using very deep submicron (VDSM) technologies. In this review paper, functional fault models which may exist in the memory are described, in terms of their definition and detection requirement. Several memory testing algorithms that are used in memory built-in self-test (BIST) are discussed, in terms of test operation sequences, fault detection ability, and also test complexity. From the studies, it shows that tests with 22 N of complexity such as March SS and March AB are needed to detect all static unlinked or simple faults within the memory cells. The N in the algorithm complexity refers to Nx*Ny*Nz whereby Nx represents the number of rows, Ny represents the number of columns and Nz represents the number of banks. This paper also looks into optimization and further improvement that can be achieved on existing March test algorithms to increase the fault coverage or to reduce the test complexity.

Current Developments in Diagnostic Assays for Laboratory Confirmation and Investigation of Botulism

Detection of botulinum neurotoxin or isolation of the toxin producing organism is required for the laboratory confirmation of botulism in clinical specimens. In an effort to reduce animal testing required by the gold standard method of botulinum neurotoxin detection, the mouse bioassay, many technologies have been developed to detect and characterize the causative agent of botulism. Recent advancements in these technologies have led to improvements in technical performance of diagnostic assays; however, many emerging assays have not been validated for the detection of all serotypes in complex clinical and environmental matrices. Improvements to culture protocols, endopeptidase-based assays, and a variety of immunological and molecular methods have provided laboratories with a variety of testing options to evaluate and incorporate into their testing algorithms. While significant advances have been made to improve these assays, additional work is necessary to evaluate these methods in various clinical matrices and to establish standardized criteria for data analysis and interpretation.

SARS-CoV-2 supply shortages and TB diagnostics: Current issues requiring immediate solutions

The SARS-CoV-2 pandemic has strained manufacturing capacity worldwide resulting in significant shortages of laboratory supplies both directly and indirectly. Such shortages include probe-based kits for detection of the M. tuberculosis complex from positive liquid broth cultures. These shortages and possible loss of this particular assay have consequences for laboratory testing algorithms and public health in the United States. As there are no FDA approved, commercially available options that currently exist which could immediately fill this gap, laboratories must identify alternatives and plan for modifying current testing algorithms to accommodate this change.

Comparison of SARS-CoV-2 antigen electrochemiluminescence immunoassay to RT-PCR assay for laboratory diagnosis of COVID-19 in Peshawar

Objectives Antigen based rapid diagnostic tests possesses a potential to be utilized along with Gold standard methods to detect Covid-19 infection to cope with the demand of testing. The aim of this study was to determine diagnostic accuracy of electrochemiluminescence based automated antigen detection immunoassay comparing with molecular based test RT-PCR (Covid-19). Methods It was a cross-sectional study conducted in RMI Peshawar, from 1st April 2021 till 30th April 2021. The study comprised 170 individuals who were suspected of having Covid-19. Nasopharyngeal samples taken from suspected individuals were analyzed by RT-PCR and automated antigen test (Elecsys SARS-CoV-2 Antigen) simultaneously. The correlation of SARS-CoV-2 antigen with PCR positive and negative cases was analyzed for specificity, sensitivity respectively. Results The ECLIA based Elecsys antigen test (Roche) revealed overall sensitivity 72%, specificity 95% and accuracy of 94.9%. Sensitivity of antigen test progressively declined from 94.3% in Ct <25 to 70.8% in Ct 26–29 and then to 47.2% in Ct 30–35. Conclusions Based on the findings of our study we conclude that automated antigen testing (Elecsys SARS-CoV-2 Antigen) cannot replace molecular based testing like RT PCR. Elecsys SARS-CoV-2 Ag test should be used complementary to RT-PCR in testing algorithms. Frequent testing strategy should be adopted while using automated antigen testing to overcome its limitation in individuals with low viral loads.