The Accuracy of Arrest Data in the National Incident-Based Reporting System (NIBRS)

This study compared NIBRS arrest data in a statewide sample with arrest and summons data on the same cases collected directly from law enforcement agencies (LEAs). NIBRS matched LEA data in 84.1% of cases. However, 5.8% of LEA arrests and 52.9% of LEA summons were false negatives, that is, they were incorrectly represented as not cleared by arrest in NIBRS. False negatives were more likely when more than 1 day elapsed between incident and arrest and when the crimes were sexual assault or intimidation. False negatives were less likely in small LEAs (for summons) Recommendations are presented for improving accuracy.

Can In-Line Iodine Value Predictions (NitFomTM) Be Used for Early Classification of Pork Belly Firmness?

Commercial technologies for assessing meat quality may be useful for performing early in-line belly firmness classification. This study used 207 pork carcasses to measure predicted iodine value (IV) at the clear plate region of the carcass with an in-line near-infrared probe (NitFomTM), calculated IV of belly fat using wet chemistry methods, determined the belly bend angle (an objective method to measure belly firmness), and took dimensional belly measurements. A regression analysis revealed that NitFomTM predicted IV (R2 = 0.40) and belly fat calculated IV (R2 = 0.52) separately contributed to the partial variation of belly bend angle. By testing different NitFomTM IV classification thresholds, classifying soft bellies in the 15th percentile resulted in 5.31% false negatives, 5.31% false positives, and 89.38% correctly classified soft and firm bellies. Similar results were observed when the classification was based on belly fat IV calculated from chemically analyzed fatty acid composition. By reducing the level of stringency on the percentile of the classification threshold, an increase in false positives and decrease in false negatives was observed. This study suggests the IV predicted using the NitFomTM may be useful for early in-line presorting of carcasses based on expected belly firmness, which could optimize profitability by allocating carcasses to specific cutout specifications.

In silico evaluation of the impact of the Omicron variant on the sensitivity of RT-qPCR assays for SARS-CoV-2 detection using whole genome sequencing

Background The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VoC) Omicron (B.1.1.529) has rapidly spread around the world presenting a new threat to global public human health. Due to the large number of mutations possessed by Omicron, concerns have emerged over potentially reduced diagnostic accuracy of reverse transcription polymerase chain reaction (RT-qPCR), the gold standard diagnostic test for SARS-CoV-2. Here, we aimed to assess the impact of Omicron on the integrity and sensitivity of RT-qPCR assays used for coronavirus disease-2019 (COVID-19) diagnosis via in silico analysis employing whole genome sequencing data and evaluated the potential for false negatives or test failure due to mismatches between primers/probes and viral genome. Methods In silico sensitivity of 12 RT-qPCR tests (containing 30 primers and probe sets) developed for detection of SARS-CoV-2 reported by the World Health Organization (WHO) or available in the literature, was assessed for use in detecting SARS-CoV-2 Omicron BA.1 and BA.2 sublineages, obtained after removing redundancy from publicly available genomes from National Center for Biotechnology Information (NCBI) and Global Initiative on Sharing Avian Influenza Data (GISAID) databases. The mismatches between the amplicon regions of the SARS-CoV-2 Omicron VoC and primers and probe sets were evaluated, and the clustering analysis of the corresponding amplicon sequences was carried out. Results From the 232 representative SARS-CoV-2 BA.1 Omicron sublineage genomes analyzed, 229 showed substitutions at the forward primer annealing site for assay China-CDC N, 226 showed mismatches in the reverse primer annealing site for assay Thai N, and all 232 had substitution at the 3’ end of the reverse primer annealing site for assay HKUniv RdRp/Hel. Therefore, the lowest sensitivity was observed for assay ChinaCDC N, Thai N and HKUniv RdRp/Hel for SARS-CoV-2 BA.1 sublineage genomes. For 5 SARS-CoV-2 BA.2 Omicron sublineage genomes, false negative results were observed for assays ChinaCDC N, Thai N, HKUniv RdRp/Hel, SigmAldr S5, SigmAldr S6 and HKUniv S. Conclusion In this study, we observed three (25%) assays (ChinaCDC N, Thai N, and HKUniv RdRp/Hel) demonstrated potential for false negatives for the SARS-CoV-2 Omicron BA.1 sublineage, while four (33.3%) assays (ChinaCDC N, Thai N, HKUniv RdRp/Hel, HKUniv S, SigmAldr S5 and SigmAldr S6) demonstrated potential false negative results for the for SARS-CoV-2 Omicron BA.2 sublineage, which also has the potential for Spike (S) gene dropout despite lacking 69-70 deletion in the S gene. Further, amplicon clustering and additional substitutions analysis along with the sensitivity analysis could be used for modification and development of RT-qPCR assays for detection of SARS-CoV-2 Omicron VoC lineages.

The Rapid Antigen Detection Test for SARS-CoV-2 Underestimates the Identification of COVID-19 Positive Cases and Compromises the Diagnosis of the SARS-CoV-2 (K417N/T, E484K, and N501Y) Variants

Timely detection of severe acute respiratory syndrome due to coronavirus 2 (SARS-CoV-2) by reverse transcription quantitative polymerase chain reaction (RT-qPCR) has been the gold- strategy for identifying positive cases during the current pandemic. However, faster and less expensive methodologies are also applied for the massive diagnosis of COVID-19. In this way, the rapid antigen test (RAT) is widely used. However, it is necessary to evaluate its detection efficiency considering the current pandemic context with the circulation of new viral variants. In this study, we evaluated the sensitivity and specificity of RAT (SD BIOSENSOR, South Korea), widely used for testing and SARS-CoV-2 diagnosis in Santiago of Chile. The RAT showed a 90% (amplification range of 20 ≤ Cq <25) and 10% (amplification range of 25 ≤ Cq <30) of positive SARS-CoV-2 cases identified previously by RT-qPCR. Importantly, a 0% detection was obtained for samples within a Cq value>30. In SARS-CoV-2 variant detection, RAT had a 42.8% detection sensitivity in samples with RT-qPCR amplification range 20 ≤ Cq <25 containing the single nucleotide polymorphisms (SNP) K417N/T, N501Y and E484K, associated with beta or gamma SARS-CoV-2 variants. This study alerts for the special attention that must be paid for the use of RAT at a massive diagnosis level, especially in the current scenario of appearance of several new SARS-CoV-2 variants which could generate false negatives and the compromise of possible viral outbreaks.

Medical diagnosis of COVID-19 using blood tests and machine learning

Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2), colloquially known as Coronavirus surfaced in late 2019 and is an extremely dangerous disease. RT-PCR (Reverse transcription Polymerase Chain Reaction) tests are extensively used in COVID-19 diagnosis. However, they are prone to a lot of false negatives and erroneous results. Hence, alternate methods are being researched and discovered for the detection of this infectious disease. We diagnose and forecast COVID-19 with the help of routine blood tests and Artificial Intelligence in this paper. The COVID-19 patient dataset was obtained from Israelita Albert Einstein Hospital, Brazil. Logistic regression, random forest, k nearest neighbours and Xgboost were the classifiers used for prediction. Since the dataset was extremely unbalanced, a technique called SMOTE was used to perform oversampling. Random forest obtained optimal results with an accuracy of 92%. The most important parameters according to the study were leukocytes, eosinophils, platelets and monocytes. This preliminary COVID-19 detection can be utilised in conjunction with RT-PCR testing to improve sensitivity, as well as in further pandemic outbreaks.

Timely Reporting of Heavy Hitters Using External Memory

Given an input stream S of size N , a ɸ-heavy hitter is an item that occurs at least ɸN times in S . The problem of finding heavy-hitters is extensively studied in the database literature. We study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = ɸ N-th occurrence (and hence it becomes a heavy hitter). We call this the Timely Event Detection ( TED ) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams with a low reporting threshold (high sensitivity). Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space (Ω (N) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes). We show how to adapt heavy-hitters algorithms to external memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable tradeoff between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead. We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device’s random I/O throughput, i.e., ≈100K observations per second.

Reliable hypotheses testing in animal social network analyses: global index, index of interactions and residual regression

Animal social network analyses (ASNA) have led to a foundational shift in our understanding of animal sociality that transcends the disciplinary boundaries of genetics, spatial movements, epidemiology, information transmission, evolution, species assemblages and conservation. However, some analytical protocols (i.e., permutation tests) used in ASNA have recently been called into question due to the unacceptable rates of false negatives (type I error) and false positives (type II error) they generate in statistical hypothesis testing. Here, we show that these rates are related to the way in which observation heterogeneity is accounted for in association indices. To solve this issue, we propose a method termed the "global index" (GI) that consists of computing the average of individual associations indices per unit of time. In addition, we developed an "index of interactions" (II) that allows the use of the GI approach for directed behaviours. Our simulations show that GI: 1) returns more reasonable rates of false negatives and positives, with or without observational biases in the collected data, 2) can be applied to both directed and undirected behaviours, 3) can be applied to focal sampling, scan sampling or "gambit of the group" data collection protocols, and 4) can be applied to first- and second-order social network measures. Finally, we provide a method to control for non-social biological confounding factors using linear regression residuals. By providing a reliable approach for a wide range of scenarios, we propose a novel methodology in ASNA with the aim of better understanding social interactions from a mechanistic, ecological and evolutionary perspective.