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Machine learning has been widely used for solving challenging problems in diverse areas. However, to the best of our knowledge, seldom literature has discussed in-depth how machine learning approaches can be used effectively to “hunt” (identify) threats, especially advanced persistent threats (APTs) , in a monitored environment. In this study, we share our past experiences in building machine learning-based threat-hunting models. Several challenges must be considered when a security team attempts to build such models. These challenges include (1) weak signal, (2) imbalanced data sets, (3) lack of high-quality labels, and (4) no storyline. In this study, we propose Fuchikoma and APTEmu to demonstrate how we tackle the above-mentioned challenges. The former is a proof of concept system for demonstrating the ideas behind autonomous threat-hunting. It is a machine learning-based anomaly detection and threat hunting system which leverages natural language processing (NLP) and graph algorithms. The latter is an APT emulator, which emulates the behavior of a well-known APT called APT3, which is the target used in the first round of MITRE ATT&CK Evaluations. APTEmu generates attacks on Windows machines in a virtualized environment, and the captured system events can be further used to train and enhance Fuchikoma’s capabilities. We illustrate the steps and experiments we used to build the models, discuss each model’s effectiveness and limitations of each model, and propose countermeasures and solutions to improve the models. Our evaluation results show that machine learning algorithms can effectively assist threat hunting processes and significantly reduce security analysts’ efforts. Fuchikoma correctly identifies malicious commands and achieves high performance in terms of over 80% True Positive Rate and True Negative Rate and over 60% F3. We believe our proposed approaches provide valuable experiences in the area and shed light on automated threat-hunting research.

Academic evaluation and graduation of students are critical components of an academic information system's (AIS) effectiveness since they allow for the measurement of student learning progress. Additionally, the assessment stating whether the student passed or failed would benefit both the student and teacher by acting as a reference point for future performance suggestions and evaluations. Using Decision Tree C4.5, a comprehensive analysis of the student academic evaluation approach was conducted. Age, gender, public or private high school status, high school department, organization activity, age at high school admission, progress GPA (pGPA), and total GPA (tGPA) were all documented and evaluated from semester 1–4 utilizing three times the graduation criterion periods. The article's scope is confined to undergraduate programs. An accuracy algorithm (AC) with a performance accuracy of 79.60 percent, a true positive rate (TP) of 77.70 percent, and 91 percent quality training data achieved the highest performance accuracy value.

One of the key roles of an orthopedic surgeon treating metastatic bone disease (MBD) is fracture risk prediction. Current widely used impending fracture risk tools such as Mirels scoring lack specificity. Two newer methods of fracture risk prediction, CT-based structural rigidity analysis (CTRA) and finite element analysis (FEA), have each been shown to be more accurate than Mirels. This case series illustrates comparative Mirels, CTRA, and FEA for 8 femurs in 7 subjects. These cases were selected from a much larger data set to portray examples of true positives, true negatives, false positives, and false negatives as defined by CTRA relative to the fracture outcome. Case illustrations demonstrate comparative Mirels and FEA. This series illustrates the use, efficacy, and limitations of these tools. As all current tools have limitations, further work is needed in refining and developing fracture risk prediction.

Objective To analyze the association between radiologists’ performance and image position within a batch in screen reading of mammograms in Norway. Method We described true and false positives and true and false negatives by groups of image positions and batch sizes for 2,937,312 screen readings performed from 2012 to 2018. Mixed-effects models were used to obtain adjusted proportions of true and false positive, true and false negative, sensitivity, and specificity for different image positions. We adjusted for time of day and weekday and included the individual variation between the radiologists as random effects. Time spent reading was included in an additional model to explore a possible mediation effect. Result True and false positives were negatively associated with image position within the batch, while the rates of true and false negatives were positively associated. In the adjusted analyses, the rate of true positives was 4.0 per 1000 (95% CI: 3.8–4.2) readings for image position 10 and 3.9 (95% CI: 3.7–4.1) for image position 60. The rate of true negatives was 94.4% (95% CI: 94.0–94.8) for image position 10 and 94.8% (95% CI: 94.4–95.2) for image position 60. Per 1000 readings, the rate of false negative was 0.60 (95% CI: 0.53–0.67) for image position 10 and 0.62 (95% CI: 0.55–0.69) for image position 60. Conclusion There was a decrease in the radiologists’ sensitivity throughout the batch, and although this effect was small, our results may be clinically relevant at a population level or when multiplying the differences with the number of screen readings for the individual radiologists. Key Points • True and false positive reading scores were negatively associated with image position within a batch. • A decreasing trend of positive scores indicated a beneficial effect of a certain number of screen readings within a batch. • False negative scores increased throughout the batch but the association was not statistically significant.

Objectives Alpha1-antitrypsin deficiency (AATD) is an inherited condition that predisposes individuals to an increased risk of developing lung and liver disease. Even though AATD is one of the most widespread inherited diseases in Caucasian populations, only a minority of affected individuals has been detected. Whereas methods have been validated for AATD testing, there is no universally-established algorithm for the detection and diagnosis of the disorder. In order to compare different methods for diagnosing AATD, we carried out a systematic review of the literature on AATD diagnostic algorithms. Methods Complete biochemical and molecular analyses of 5,352 samples processed in our laboratory were retrospectively studied using each of the selected algorithms. Results When applying the diagnostic algorithms to the same samples, the frequency of False Negatives varied from 1.94 to 12.9%, the frequency of True Negatives was 62.91% for each algorithm and the frequency of True Positives ranged from 24.19 to 35.15%. We, therefore, highlighted some differences among Negative Predictive Values, ranging from 0.83 to 0.97. Accordingly, the sensitivity of each algorithm ranged between 0.61 and 0.95. We also postulated 1.108 g/L as optimal AAT cut-off value, in absence of inflammatory status, which points to the possible presence of genetic AATD. Conclusions The choice of the diagnostic algorithm has a significant impact on the correct diagnosis of AATD, which is essential for appropriate treatment and medical care. The fairly large number of possible false negative diagnoses revealed by the present paper should also warn clinicians of negative results in patients with clinically-suspected AATD.

Front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the rapid and noninvasive recognition of Belgian and Netherlandish Trappist beers against non-Trappist beers. The front-face synchronous fluorescence spectra at wavelength intervals (??) of 30 and 60 nm for 80 bottles of beer, including 41 Trappist and 39 non-Trap-pist beers, were acquired in a 5 × 10 mm fused-quartz cuvette settled in a traditional right-angle sample compartment. The discrimination model was constructed by either principal component analysis (PCA) combined with linear discriminant analysis (LDA) or partial least squares-discriminant analysis (PLS-DA). Both PCA–LDA and PLS-DA models were validated by full (leave-one-out) cross-validation and k-fold cross-validation (k = 5). The PCA–LDA model presents reliable discrimination performance, with the cross-validated sensitivity (true positive rate) and specificity (true negative rate) in the range of 82.9–85.4% and 71.8–76.9%, respectively. The misclassification mainly occurs to a small portion of ambiguous Trappist and non-Trappist samples such as Abbey beers, which are rather similar to Trappist beers.

In recent years, ensemble classification methods have been widely investigated in both industry and literature in the field of machine learning and artificial intelligence. The main advantage of this approach is to benefit from a set of classifiers instead of using a single classifier with the aim of improving the prediction performance, such as accuracy. Selecting the base classifiers and the method for combining them are the most challenging issues in the ensemble classifiers. In this paper, we propose a heterogeneous dynamic ensemble classifier (HDEC) which uses multiple classification algorithms. The main advantage of using heterogeneous algorithms is increasing the diversity among the base classifiers as it is a key point for an ensemble system to be successful. In this method, we first train many classifiers with the original data. Then, they are separated based on their strength in recognizing either positive or negative instances. For doing this, we consider the true positive rate and true negative rate, respectively. In the next step, the classifiers are categorized into two groups according to their efficiency in the mentioned measures. Finally, the outputs of the two groups are compared with each other to generate the final prediction. For evaluating the proposed approach, it has been applied to 12 datasets from the UCI and LIBSVM repositories and calculated two popular prediction performance metrics, including accuracy and geometric mean. The experimental results show the superiority of the proposed approach in comparison to other state-of-the-art methods.

Finding differentially expressed circular RNAs (circRNAs) is instrumental to understanding the molecular basis of phenotypic variation between conditions linked to circRNA-involving mechanisms. To date, several methods have been developed to identify circRNAs, and combining multiple tools is becoming an established approach to improve the detection rate and robustness of results in circRNA studies. However, when using a consensus strategy, it is unclear how circRNA expression estimates should be considered and integrated into downstream analysis, such as differential expression assessment. This work presents a novel solution to test circRNA differential expression using quantifications of multiple algorithms simultaneously. Our approach analyzes multiple tools' circRNA abundance count data within a single framework by leveraging generalized linear mixed models (GLMM), which account for the sample correlation structure within and between the quantification tools. We compared the GLMM approach with three widely used differential expression models, showing its higher sensitivity in detecting and efficiently ranking significant differentially expressed circRNAs. Our strategy is the first to consider combined estimates of multiple circRNA quantification methods, and we propose it as a powerful model to improve circRNA differential expression analysis.

In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster Analysis. Our goal is to find an explanation for each cluster, such that clusters are characterized as precisely and distinctively as possible, i.e., the explanation is fulfilled by as many as possible individuals of the corresponding cluster, true positive cases, and by as few as possible individuals in the remaining clusters, false positive cases. We assume that a dissimilarity between the individuals is given, and propose distance-based explanations, namely those defined by individuals that are close to its so-called prototype. To find the set of prototypes, we address the biobjective optimization problem that maximizes the total number of true positive cases across all clusters and minimizes the total number of false positive cases, while controlling the true positive rate as well as the false positive rate in each cluster. We develop two mathematical optimization models, inspired by classic Location Analysis problems, that differ in the way individuals are allocated to prototypes. We illustrate the explanations provided by these models and their accuracy in both real-life data as well as simulated data.

The human right ventricle is barely monitored during open-chest surgery due to the absence of intraoperative imaging techniques capable of elaborating its complex function. Accordingly, artificial intelligence could not be adopted for this specific task. We recently proposed a video-based approach for the real-time evaluation of the epicardial kinematics to support medical decisions. Here, we employed two supervised machine learning algorithms based on our technique to predict the patients’ outcomes before chest closure. Videos of the beating hearts were acquired before and after pulmonary valve replacement in twelve Tetralogy of Fallot patients and recordings were properly labeled as the “unhealthy” and “healthy” classes. We extracted frequency-domain-related features to train different supervised machine learning models and selected their best characteristics via 10-fold cross-validation and optimization processes. Decision surfaces were built to classify two additional patients having good and unfavorable clinical outcomes. The k-nearest neighbors and support vector machine showed the highest prediction accuracy, the patients’ class was identified with a true positive rate ≥95% and the decision surfaces correctly classified the additional patients in the “healthy” (good outcome) or “unhealthy” (unfavorable outcome) classes. We demonstrated that classifiers employed with our video-based technique may aid cardiac surgeons in decision making before chest closure.

Internet video providers rely on alerting workflows to identify and remedy incidents that can impact users (e.g., outages or buggy players). There is growing evidence for the need for viewership-based analytics---detecting and diagnosing incidents that manifest through changes in viewership patterns but not in other (e.g., QoE) metrics. However, both detection and diagnosis of viewership anomalies is challenging due to the contextual nature of anomalies, non-stationarity of viewership, and complex dependencies between the structure of events and how they impact different subpopulations of viewers. We present Proteas, an alerting framework for video viewership anomalies that tackles these challenges. Proteas builds on key spatiotemporal structural insights. First, across different sub-populations of viewers and days of the week, we find that the shape of the viewership curve remains invariant over multiple weeks, thus enabling anomaly detection. Second, we use the hierarchy of viewership groups to produce compact alerts. Finally, we find that common anomalies manifest with spatiotemporal signatures, which enables us to classify anomalies to produce actionable alerts. We evaluate Proteas using 3 months of real viewership data (including the onset of the COVID-19 pandemic) and show that Proteas is accurate with over 80% True Positive Rate, average precision of over 86% (i.e., few false positives) and doesn't miss any major events. In addition, we find that approximately half of Proteas's alerts refer to events not caught by other alerting workflows, thus adding value to operators' existing toolkit.

AimsThe application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve access to high quality diagnostic care in remote settings but is limited by the reliance on signal-based data that are not routinely available. We sought to develop a multilabel automated diagnosis model for electrocardiographic images, more suitable for broader use.Methods and ResultsA total of 2,228,236 12-lead ECGs from 811 municipalities in Minas Gerais, Brazil were sampled into 90% (training):5%(validation):5%(held-out test), and were transformed to ECG images with varying lead locations and formats. We trained a convolutional neural network (CNN) using an EfficientNet-based architecture on ECG images to identify 6 physician-defined clinical labels spanning rhythm and conduction disorders, as well as a hidden label for gender. We trained another CNN for signal-based classification. The image-based model performed well on the held-out test set (average AUROC 0.99, AUPRC 0.68). This was replicated in a distinct test set from Brazil validated by at least two cardiologists (average AUROC 0.99, AUPRC 0.86) as well as an external validation set of 21,785 ECGs from Germany (average AUROC 0.97, AUPRC 0.73), with performance superior to signal-based models. Expert review of 120 out of 120 high confidence false positive predictions on the held-out and external validation sets were confirmed to be true positives with incorrect labels. The model learned generalizable features, confirmed using Gradient-weighted Class Activation Mapping (Grad-CAM).ConclusionWe developed an externally validated model that extends the automated diagnosis of key rhythm and conduction disorders to printed ECGs as well as to the identification of hidden features, allowing the application of AI to ECGs captured across broad settings.

The automated plate assessment system (APAS Independence; Clever Culture System, Bäch, Switzerland) is an automated imaging station linked with interpretive software that detects target colonies of interest on chromogenic media and sorts samples as negative or presumptive positive. We evaluated the accuracy of the APAS to triage methicillin-resistant Staphylococcus aureus (MRSA) and S. aureus cultures using chromogenic medium compared to that by human interpretation. Patient samples collected from the nares on ESwabs were plated onto BD BBL CHROMagar MRSA II and BD BBL CHROMagar Staph aureus and allowed to incubate for 20 to 24 h at 37°C in a non-CO2 incubator. Mauve colonies are suggestive of S. aureus and were confirmed with latex agglutination. Following incubation, samples were first interrogated by APAS before being read by a trained technologist blinded to the APAS interpretation. The triaging by both APAS and the technologists was compared for accuracy. Any discordant results required further analysis by a third reader. Over a 5-month period, 5,913 CHROMagar MRSA cultures were evaluated. Of those, 236 were read as concordantly positive, 5,525 were read as concordantly negative, and 152 required discordant analysis. Positive and negative percent agreements (PPA and NPA, respectively) were 100% and 97.3%, respectively. The APAS detected 5 positive cultures that were missed by manual reading and determined to be true positives. In a separate analysis, 744 CHROMagar Staph aureus plates were read in parallel. Of these, 133 were concordantly positive, 585 were concordantly negative, and 26 required discordant analysis. PPA and NPA were 95.7% and 96.7%, respectively. This study confirmed the high sensitivity of digital image analysis by the APAS Independence such that negative cultures can be reliably reported without technologist intervention (negative predictive values [NPVs] of 100% for CHROMagar MRSA and 99.0% for CHROMagar Staph aureus). Triaging using the APAS Independence may provide great efficiency in a laboratory with high throughput of MRSA and S. aureus surveillance cultures.

Chia oil is a valuable source of omega-3-fatty acids and other nutritional components. However, it is expensive to produce and can therefore be easily adulterated with cheaper oils to improve the profit margins. Spectroscopic methods are becoming more and more common in food fraud detection. The aim of this study was to answer following questions: Is it possible to detect chia oil adulteration by spectroscopic analysis of the oils? Is it possible to identify the adulteration oil? Is it possible to determine the amount of adulteration? Two chia oils from local markets were adulterated with three common food oils, including sunflower, rapeseed and corn oil. Subsequently, six chia oils obtained from different sites in Kenya were adulterated with sunflower oil to check the results. Raman, NIR and fluorescence spectroscopy were applied for the analysis. It was possible to detect the amount of adulterated oils by spectroscopic analysis, with a minimum R2 of 0.95 for the used partial least square regression with a maximum RMSEPrange of 10%. The adulterations of chia oils by rapeseed, sunflower and corn oil were identified by classification with a median true positive rate of 90%. The training accuracies, sensitivity and specificity of the classifications were over 90%. Chia oil B was easier to detect. The adulterated samples were identified with a precision of 97%. All of the classification methods show good results, however SVM were the best. The identification of the adulteration oil was possible, less than 5% of the adulteration oils were difficult to detect. In summary, spectroscopic analysis of chia oils might be a useful tool to identify adulterations.

This paper proposes a practical physical tampering detection mechanism using inexpensive commercial off-the-shelf (COTS) Wi-Fi endpoint devices with a deep neural network (DNN) on channel state information (CSI) in the Wi-Fi signals. Attributed to the DNN that identifies physical tampering events due to the multi-subcarrier characteristics in CSI, our methodology takes effect using only one COTS Wi-Fi endpoint with a single embedded antenna to detect changes in the relative orientation between the Wi-Fi infrastructure and the endpoint, in contrast to previous sophisticated, proprietary approaches. Preliminary results show that our detectors manage to achieve a 95.89% true positive rate (TPR) with no worse than a 4.12% false positive rate (FPR) in detecting physical tampering events.

High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency’s (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis, ~ 765,000 substances). Experimental spectra from EPA’s Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s00216-019-02351-7) contains supplementary material, which is available to authorized users.

To accurately detect defects in patterned fabrics, a novel detection algorithm combining template correction with primitive decomposition (TCPD) method is proposed in this study. First of all, the fabric image is segmented into lattices according to variation regularity. Then, the authors propose an effective anisotropy correction method to reduce the interference of stretching and distortion between lattices. On the basis of the proposed PD method, the corrected lattice is further divided into graphic elements with smaller particle size. The smaller primitives make the boundary of the detection results more accurate. Moreover, a self-supervised threshold selection strategy is presented, which utilises the defect-free regions to obtain threshold. Furthermore, this strategy makes each primitive has corresponding criteria for judging defects. Extensive experiments demonstrate that TCPD method achieves 0.8127 true positive rate, 0.3889 positive predictive value and 0.5261 f value in star-patterned fabrics.

We study the problem of automatically detecting if a given multi-class classifier operates outside of its specifications (out-of-specs), i.e. on input data from a different distribution than what it was trained for. This is an important problem to solve on the road towards creating reliable computer vision systems for real-world applications, because the quality of a classifier’s predictions cannot be guaranteed if it operates out-of-specs. Previously proposed methods for out-of-specs detection make decisions on the level of single inputs. This, however, is insufficient to achieve low false positive rate and high false negative rates at the same time. In this work, we describe a new procedure named KS(conf), based on statistical reasoning. Its main component is a classical Kolmogorov–Smirnov test that is applied to the set of predicted confidence values for batches of samples. Working with batches instead of single samples allows increasing the true positive rate without negatively affecting the false positive rate, thereby overcoming a crucial limitation of single sample tests. We show by extensive experiments using a variety of convolutional network architectures and datasets that KS(conf) reliably detects out-of-specs situations even under conditions where other tests fail. It furthermore has a number of properties that make it an excellent candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with any classifier that outputs confidence scores, and requires no a priori knowledge about how the data distribution could change.

Self-report screening instruments for emerging psychosis have the potential to improve early detection efforts by increasing the number of true positives among persons deemed to be at "clinical high risk" of the disorder, but their practical utility depends on their validity across race. This study sought to examine whether a commonly used self-report screening tool for psychosis risk performed equally among black and white youths in its ability to predict clinical high-risk status.Black (N=58) and white (N=50) help-seeking individuals ages 12-25 (61% female) were assessed with the Prime Screen and the Structured Interview for Psychosis-Risk Syndromes (SIPS). A logistic regression model estimated race differences in the strength of the relation between Prime Screen scores and SIPS-defined risk status.Higher Prime Screen scores significantly predicted clinical high-risk status among white (p.01) but not black participants. Among black youths without clinical high risk, self-reported Prime Screen scores more closely resembled scores for youths (black or white) with clinical high risk than scores of white peers who were also without clinical high risk.Results suggest that consideration of race or ethnicity and associated cultural factors is important when screening for clinical high-risk status. Findings support the need to develop culturally valid early psychosis screening tools to promote appropriately tailored early intervention efforts.

The adaptive measurement of change (AMC) refers to the use of computerized adaptive testing (CAT) at multiple occasions to efficiently assess a respondent’s improvement, decline, or sameness from occasion to occasion. Whereas previous AMC research focused on administering the most informative item to a respondent at each stage of testing, the current research proposes the use of Fisher information per time unit as an item selection procedure for AMC. The latter procedure incorporates not only the amount of information provided by a given item but also the expected amount of time required to complete it. In a simulation study, the use of Fisher information per time unit item selection resulted in a lower false positive rate in the majority of conditions studied, and a higher true positive rate in all conditions studied, compared to item selection via Fisher information without accounting for the expected time taken. Future directions of research are suggested.

A Bloom filter is a special case of an artificial neural network with two layers. Traditionally, it is seen as a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, and therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called “autoscaling Bloom filters”, which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. Thus, by relaxing the requirement on perfect true positive rate, the proposed autoscaling Bloom filter addresses the major difficulty of Bloom filters with respect to their scalability. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of its performance and provide a procedure for minimizing its false positive rate., Neural Computing and Applications, 32 (8), ISSN:1433-3058, ISSN:0941-0643

ObjectiveIBD prevalence is estimated to be rising, but no detailed, recent UK data are available. The last reported prevalence estimate in the UK was 0.40% in 2003. We aimed to establish the current, and project future, prevalence in Lothian, Scotland.DesignWe conducted an all-age multiparameter search strategy using inpatient IBD international classification of disease (ICD-10) coding (K50/51)(1997–2018), IBD pathology coding (1990–2018), primary and secondary care prescribing data (2009–2018) and a paediatric registry, (1997–2018) to identify ‘possible’ IBD cases up to 31/08/2018. Diagnoses were manually confirmed through electronic health record review as per Lennard-Jones/Porto criteria. Autoregressive integrated moving average (ARIMA) regression was applied to forecast prevalence to 01/08/2028.ResultsIn total, 24 601 possible IBD cases were identified of which 10 499 were true positives. The point prevalence for IBD in Lothian on 31/08/2018 was 784/100 000 (UC 432/100 000, Crohn’s disease 284/100 000 and IBD unclassified (IBDU) 68/100 000). Capture–recapture methods identified an additional 427 ‘missed’ cases (95% CI 383 to 477) resulting in a ‘true’ prevalence of 832/100 000 (95% CI 827 to 837).Prevalence increased by 4.3% per year between 2008 and 2018 (95% CI +3.7 to +4.9%, p80 years of age.ConclusionsWe report a rigorously validated IBD cohort with all-age point prevalence on 31/08/2018 of 1 in 125, one of the highest worldwide.

Mobile ads are plagued with fraudulent clicks which is a major challenge for the advertising community. Although popular ad networks use many techniques to detect click fraud, they do not protect the client from possible collusion between publishers and ad networks. In addition, ad networks are not able to monitor the user’s activity for click fraud detection once they are redirected to the advertising site after clicking the ad. We propose a new crowdsource-based system called Click Fraud Crowdsourcing (CFC) that collaborates with both advertisers and ad networks in order to protect both parties from any possible click fraudulent acts. The system benefits from both a global view, where it gathers multiple ad requests corresponding to different ad network-publisher-advertiser combinations, and a local view, where it is able to track the users’ engagement in each advertising website. The results demonstrated that our approach offers a lower false positive rate (0.1) when detecting click fraud as opposed to proposed solutions in the literature, while maintaining a high true positive rate (0.9). Furthermore, we propose a new mobile ad charging model that benefits from our system to charge advertisers based on the duration spent in the advertiser’s website.

How accurately can all recorded locations of 97 knife homicides in one year be forecast across all 4835 Lower Layer Super Output Areas (LSOAs) of London, based only upon all 3506 known locations of nonfatal knife injury assaults in the preceding year? All recorded “knife crimes” in the Metropolitan Police Service (MPS) area of London in the financial year 2016–2017 (year 1) were manually reviewed to identify all 3506 reported locations of knife-enabled (KE) but nonfatal injuries, as distinct from other events digitally coded as “knife crimes”, such as displaying, threatening with, or carrying knives. All KE homicides in 2017–2018 (year 2) were then added to the database. Each KE injury assault in year 1 was classified for occurrence in one of London’s 4835 LSOAs. The total N of such crimes within each LSOA was summed across all records to divide all LSOAs into seven categories of frequency of KE injury assaults in 2016/2017: 0, 1, 2, 3, 4, 5, and 6 or more. We then divided the N of LSOAs in each category in 2016/2017 (year 1) into the total N of knife homicides in 2017/2018 (year 2). False positives, false negatives, and percentage of homicides targeted were calculated for six different targeting plans. Over two thirds (69%) of KE homicides in 2017/2018 (year 2) occurred in just 67 (1.4%) of all 4835 LSOAs, comprising 3.3% of the 2048 LSOAs that had had one or more of the 3506 KE injury assaults mapped in 2016/2017 (year 1). The proportion of LSOAS with a KE homicide in year 2 was higher where there were higher numbers of KE injury assaults per LSOA in year 1. Among LSOAs with zero assaults in year 1, only 1% had a KE homicide in year 2. Among LSOAs with six or more KE injury assaults in year 1, 15% had a KE homicide in year 2. While the risk of homicide was 1400% higher in the hottest spots of knife assaults than in the coolest spots, the absolute number of year two KE homicides in those hottest areas was only 6% of the total. All LSOAs with one or more year 1 KE assaults had three times as much KE homicide risk than LSOAs with no year 1 KE assaults. Targeting all 2048 areas that had one or more year 1 KE assaults would have yielded a true positive rate of over 3% and covered 69% of actual homicide locations. The true negative rate for 2781 areas that would not have been targeted based on having no year 1 KE injuries would have been 99%. Predicting which local areas are most likely to suffer knife-enabled homicides, based only on recent nonfatal knife injuries, can pinpoint risks of homicide in local areas that are up to 1400% higher than in most local areas, offering a range of strategies for resource allocation.

Hamstring strain injury (HSI) is one of the most prevalent and severe injury in professional soccer. The purpose was to analyze and compare the predictive ability of a range of machine learning techniques to select the best performing injury risk factor model to identify professional soccer players at high risk of HSIs. A total of 96 male professional soccer players underwent a pre-season screening evaluation that included a large number of individual, psychological and neuromuscular measurements. Injury surveillance was prospectively employed to capture all the HSI occurring in the 2013/2014 season. There were 18 HSIs. Injury distribution was 55.6% dominant leg and 44.4% non-dominant leg. The model generated by the SmooteBoostM1 technique with a cost-sensitive ADTree as the base classifier reported the best evaluation criteria (area under the receiver operating characteristic curve score=0.837, true positive rate=77.8%, true negative rate=83.8%) and hence was considered the best for predicting HSI. The prediction model showed moderate to high accuracy for identifying professional soccer players at risk of HSI during pre-season screenings. Therefore, the model developed might help coaches, physical trainers and medical practitioners in the decision-making process for injury prevention.

Clinical genomic tests increasingly use a next-generation sequencing (NGS) platform due in part to the high fidelity of variant calls, yet rare errors are still possible. In germline DNA screening, failure to correct such errors could have serious consequences for patients, who may follow an unwarranted screening or surgical management path. It has been suggested that routine orthogonal confirmation by Sanger sequencing is required to verify NGS results, especially low-confidence positives with depressed allele fraction ( 15,000 samples. Licensed reviewers manually inspected both raw and processed data at the batch, sample, and variant levels, including raw NGS read pileups. Of ambiguous variant calls with 99% (n = 1701) as true positives (enriched for long insertions or deletions and homopolymers) or true negatives (often conspicuous NGS artifacts), with the remaining

Orthogonal confirmation of next-generation sequencing (NGS)-detected germline variants is standard practice, although published studies have suggested that confirmation of the highest-quality calls may not always be necessary. The key question is how laboratories can establish criteria that consistently identify those NGS calls that require confirmation. Most prior studies addressing this question have had limitations: they have been generally of small scale, omitted statistical justification, and explored limited aspects of underlying data. The rigorous definition of criteria that separate high-accuracy NGS calls from those that may or may not be true remains a crucial issue. We analyzed five reference samples and over 80.000 patient specimens from two laboratories. Quality metrics were examined for approximately 200.000 NGS calls with orthogonal data, including 1662 false positives. A classification algorithm used these data to identify a battery of criteria that flag 100% of false positives as requiring confirmation (CI lower bound, 98.5% to 99.8%, depending on variant type) while minimizing the number of flagged true positives. These criteria identify false positives that the previously published criteria miss. Sampling analysis showed that smaller data sets resulted in less effective criteria. Our methodology for determining test- and laboratory-specific criteria can be generalized into a practical approach that can be used by laboratories to reduce the cost and time burdens of confirmation without affecting clinical accuracy.

The Iowa Newborn Screening (NBS) Program began screening for very long-chain acyl-CoA dehydrogenase deficiency (VLCAD) in 2003. Untreated VLCAD can lead to liver failure, heart failure, and death. Current confirmatory testing recommendations by the American College of Medical Genetics (ACMG) for VLCAD list molecular and functional analysis (i.e., fibroblast fatty acid oxidation probe) as optional. This can lead to misclassification of VLCAD carriers as false positives. Iowa implemented a comprehensive VLCAD confirmatory testing algorithm at the beginning of 2016 that included both molecular and fibroblast analysis. Here, we compare the historic multi-algorithmic confirmatory testing protocol (2005–2016) to this comprehensive protocol (2016–2017). A metabolic specialist reviewed all medical records and NBS data for each out-of-range VLCAD that fell in each testing period. During the comprehensive testing period, 48,651 specimens were screened. Thirteen individuals with out-of-range C14:1 results were classified as follows after review: ten carriers, zero true positives, zero false positives, zero lost to follow-up, and four unable to assess carrier status. During the variable testing period, a total of 486,566 specimens were screened. Eighty-five individuals with out-of-range C14:1 were classified as follows: 45 carriers, two true positives, four false positives, four lost to follow-up, and 30 unable to assess carrier status. Our findings suggest that many out-of-range VLCAD cases that do not receive molecular confirmatory testing could be carriers mistakenly classified as false positives. We recommend comprehensive molecular and functional testing for all children with out-of-range VLCAD NBS results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12687-019-00409-8) contains supplementary material, which is available to authorized users.

When analysts evaluate performance assessments, they often use modern measurement theory models to identify raters who frequently give ratings that are different from what would be expected, given the quality of the performance. To detect problematic scoring patterns, two rater fit statistics, the infit and outfit mean square error ( MSE) statistics are routinely used. However, the interpretation of these statistics is not straightforward. A common practice is that researchers employ established rule-of-thumb critical values to interpret infit and outfit MSE statistics. Unfortunately, prior studies have shown that these rule-of-thumb values may not be appropriate in many empirical situations. Parametric bootstrapped critical values for infit and outfit MSE statistics provide a promising alternative approach to identifying item and person misfit in item response theory (IRT) analyses. However, researchers have not examined the performance of this approach for detecting rater misfit. In this study, we illustrate a bootstrap procedure that researchers can use to identify critical values for infit and outfit MSE statistics, and we used a simulation study to assess the false-positive and true-positive rates of these two statistics. We observed that the false-positive rates were highly inflated, and the true-positive rates were relatively low. Thus, we proposed an iterative parametric bootstrap procedure to overcome these limitations. The results indicated that using the iterative procedure to establish 95% critical values of infit and outfit MSE statistics had better-controlled false-positive rates and higher true-positive rates compared to using traditional parametric bootstrap procedure and rule-of-thumb critical values.

Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the true positive rate for a biomarker combination at a prespecified, clinically acceptable false positive rate is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the true positive rate while constraining the false positive rate. Theoretical results demonstrate desirable properties of biomarker combinations produced by the new method. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with alternative methods for constructing biomarker combinations., 37 pages (including appendices)

Blood culture tests have a major role in rapid and accurate diagnosis and selection of optimal antibiotic treatment for infectious diseases. The true-positive and contamination rates of blood cultures are performance indicators for the practice of blood culture. However, the optimal number of blood cultures has not been adequately elucidated. We aimed to assess the validity of the number of blood cultures obtained in the hospital. We conducted a retrospective study of blood cultures collected from hospitalized patients between 2010 and 2014 at a tertiary care hospital in Japan and assessed the validity of the number of blood cultures per 1,000 admissions as an indicator for the optimal practice of obtaining blood culture, considering the average length of stay. We also investigated the validity of true-positive and contamination rates of blood cultures in the hospital. The number of blood cultures in this study ranged from 28.6 to 56 per 1,000 patient-days and from 404.4 to 894.6 per 1,000 admissions. The median true-positive and contamination rates of blood cultures per year was 10.1% and 2.8%, respectively. The median contamination rate of blood cultures in a community-acquired group was significantly higher than that in a hospital-acquired group (4.1% vs. 1.9%, p = 0.029). We conclude that there is a possibility that the number of blood cultures per 1,000 admissions is a more appropriate factor to evaluate than per patient-days. Moreover, the true-positive and contamination rates of blood cultures in our hospital are valid.

Reject option is introduced in classification tasks to prevent potential misclassifications. Although optimization of error-reject trade-off has been widely investigated, it is shown that error rate itself is not an appropriate performance measure, when misclassification costs are unequal or class distributions are imbalanced. ROC analysis is proposed as an alternative approach to performance evaluation in terms of true positives (TP) and false positives (FP). Considering classification with reject option, we need to represent the tradeoff between TP, FP and rejection rates. In this paper, we propose 3D ROC analysis to determine the optimal rejection threshold as an analogy to decision threshold optimization in 2D ROC curves. We have demonstrated our proposed method with Naive Bayes classifier on Heart Disease dataset and validated the efficiency of the method on multiple datasets from UCI Machine Learning Repository. Our experiments reveal that classification with optimized rejection threshold significantly improves true positive rates in biomedical datasets. Furthermore, false positive rates remain the same with rejection rates below 10% on average. © 2021, Ismail Saritas. All rights reserved.

AIMS: The aim of this study is to assess the value of ultrasound in the differential diagnosis of orbital lesions. METHODS: One hundred and thirty-eight patients with clinical features of an orbital mass were examined by orbital ultrasound prior to undergoing surgery, from January 2000 to January 2017. All patients underwent excisional or incisional orbital biopsy. The results of orbital ultrasonography were compared with the final histological diagnosis. RESULTS: Orbital lesions were localized by ultrasonography in 133/138 cases (96.4%); the false-negative rate of orbital echography was 3.6% (5/138). The nature of the orbital lesions was correctly determined by ultrasonography in 54.3% of the cases (75/138) preoperatively (true positives). In 58/138 (42%) patients, there was no correspondence between the ultrasound diagnosis and the histological diagnosis (false positives). The sensitivity of orbital ultrasonography for the detection of an orbital mass was 93.75% (CI 87.87–99.63%), while the specificity yielded no meaningful result (CI 0.00–60.24%). Moreover, the specificity of orbital ultrasonography to identify a malignant tumor falls within a CI of 0–62.72%. CONCLUSIONS: Orbital ultrasonography is a rapid and noninvasive test that is highly sensitive in displaying an orbital mass; however, the specificity in the differential diagnosis of orbital lesions is not meaningful, particularly if malignancy is suspected. The assessment of orbital diseases requires multiple diagnostic approaches to balance the strengths and weaknesses of each method.

The impact of debris flow is not only limited to the source area but it is also significantly governed by the runout. It is evident in many parts of the world, and it is also a usual phenomenon in the Himalayan environment. Even though a significant body of scientific work was concentrated on landslide susceptibility in this region, studies focused on the debris flow runout assessment are still scarce. Therefore, this study aims to develop a debris flow susceptibility assessment in the Kalimpong region (Himalaya, India) by integrating the initiation and runout areas. Additionally, the changes in exposed elements for different susceptibility zones are analysed between 2006 and 2018. The methodological framework considers two different approaches: the modelling of debris flow initiation based on the logistic regression method and the assessment of the runout using the Flow-R model. Regarding the debris flow initiation, the logistic regression model comprises eleven conditioning factors and shows an area under the receiver operating characteristic curve of 0.88. For the runout assessment, 139 models are simulated for selecting Flow-R parameters. The best combination of the Holmgren exponent of 4, the travel angle of 5° and the velocity of 13 m s− 1 produced the model with a true positive rate of 86.6% for the whole area. The final debris flow susceptibility model is generated by integrating the initiation and the runout areas. Lastly, a substantial change of buildings (+18%), roads (+27%) and agricultural land (−12%) can be found within the debris flow susceptible areas between 2006 and 2018.

Among the thousands of existing rice varieties, aromatic rice has increasingly attracted consumer’s preference in recent years. Within aromatic rice, Basmati, cultivated in some regions in Pakistan and India, is highly demanded. Other aromatic rice, cultivated in specific regions, for instance in Thailand (commonly referred to as Jasmine Thai rice), are also highly appreciated by consumers. In this work, the elemental profiles of commercially available rice samples (17 Basmati, 11 Thai, and 7 Long Grain rice) were determined by energy-dispersive X-ray fluorescence (ED-XRF) spectroscopy. The mass fractions of P, Cl, S, K, Fe, Cu, and Zn were significantly different (95% confidence interval) between Basmati and Thai rice and between Thai and Long Grain rice; only Cl, S, and Zn were significantly different between Basmati and Long Grain rice. Multivariate evaluation of the results combining soft independent modelling by class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA) allowed the correct classification (true positives) of 94.1, 85.6, and 100% of the Basmati, Long Grain, and Thai rice, respectively. The specificity (true negatives) of Basmati, Long Grain, and Thai was 94.4, 82.1, and 100%, respectively.

Funder: University of Cambridge, Research question How accurately can all locations of 44 fatal collisions in 1 year be forecasted across 1403 micro-areas in Toronto, based upon locations of all 1482 non-fatal collisions in the preceding 4 years? Data All 1482 non-fatal traffic collisions from 2008 through 2011 and all 44 fatal traffic collisions in 2012 in the City of Toronto, Ontario, were geocoded from public records to 1403 micro-areas called ‘hexagonal tessellations’. Methods The total number of non-fatal traffic collisions in Period 1 (2008 through 2011) was summed within each micro-area. The areas were then classified into seven categories of frequency of non-fatal collisions: 0, 1, 2, 3, 4, 5, and 6 or more. We then divided the number of micro-areas in each category in Period 1 into the total number of fatal traffic collisions in each category in Period 2 (2012). The sensitivity and specificity of forecasting fatal collision risk based on prior non-fatal collisions were then calculated for five different targeting strategies. Findings The micro-locations of 70.5% of fatal collisions in Period 2 had experienced at least 1 non-fatal collision in Period 1. In micro-areas that had zero non-fatal collisions during Period 1, only 1.7% had a fatal collision in Period 2. Across all areas, the probability of a fatal collision in the area during Period 2 increased with the number of non-fatal collisions in Period 1, with 6 or more non-fatal collisions in Period 1 yielding a risk of fatal collision in Period 2 that was 8.7 times higher than in areas with no non-fatal collisions. This pattern is evidence that targeting 25% of micro-areas effectively could cut total traffic fatalities in a given year by up to 50%. Conclusion Highly elevated risks of traffic fatalities can be forecasted based on prior non-fatal collisions, targeting a smaller portion of the city for more concentrated investment in saving lives.

Simple Summary Collar-mounted activity monitors using battery-powered accelerometers can continuously and accurately analyze specific canine behaviors and activity levels. These include normal behaviors and those that are indicators of disease conditions such as scratching, inappetence, excessive weight, or osteoarthritis. Algorithms used to analyze activity data are validated by video recordings of specific canine behaviors, which were used to label accelerometer data. The study described here was noteworthy for the large volume of data collected from more than 2500 dogs in clinical and real-world home settings. The accelerometer data were analyzed by a machine learning methodology, whereby algorithms were continually updated as additional data were acquired. The study determined that algorithms from the accelerometer data detected eating and drinking behaviors with a high degree of accuracy. Accurate detection of other behaviors such as licking, petting, rubbing, scratching, and sniffing was also demonstrated. The study confirmed that activity monitors using validated algorithms can accurately detect important health-related canine behaviors via a collar-mounted accelerometer. The validated algorithms have widespread practical benefits when used in commercially available canine activity monitors. Abstract Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices’ position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.

Recently it has been shown, that it is possible to detect malware based on the memory access patterns produced before executions reaches its Entry Point. In this paper, we investigate the usefulness of memory access patterns over time, i.e to what extent can machine learning algorithm trained on “old” data, detect new malware samples, that was not part of the training set and how does this performance change over time. During our experiments, we found that machine learning models trained on memory access patterns of older samples can provide both high accuracy and a high true positive rate for the period from several months to almost a year from the update of the model. We also perform a substantial analysis of our findings that may aid researchers who work with malware and Big Data. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Summary Background Carbapenems are a family of end line antibiotics with increasing levels of resistance that are a cause for concern. Aim To ascertain whether the CPE screening programme employed in an acute tertiary hospital is fit for purpose. Method We outlined the current working algorithm employed using a universal screening programme over a 26-month screening period. Rectal swabs are cultured on arrival. Those with suspicious growth are further investigated using NG-Carba 5 lateral flow tests and Vitek 2.0 sensitivity cards. These practices were compared with NHS guidelines. Findings & Conclusions In all, 53 true positives were detected from 45 patients since the screening was implemented in early 2018 (46 OXA-48, 6 KPC, 1 NDM). As the rate of screening increased, the number of positive screens decreased over time. There were a lot of similarities between the HSE guidelines and the published NHS CPE toolkit. It was evident that there is no standard practice being employed across all hospitals. Comparing the MUH to national guidelines it appears to be quicker and more effective with universal screening in place at reducing the potential contacts and identifying carriers. Cost analysis indicates that the need to confirm all positive strains in a reference lab is costly, unnecessary and time consuming. There are adequate confirmatory tests available in-house for routine positive screens. It was concluded that infection prevention and control are key to identifying and controlling possible outbreaks in a hospital setting.

IntroductionThe condition onset flag (COF) variable was introduced into hospitalization coding practice in 2008 to distinguish ICD coded diagnoses as either preexisting, new or status unknown. For datasets with multiple hospitalizations per participant, the COF enables identification of diagnoses that may have arisen in between admissions thereby enriching datasets considerably. However, datasets preceding 2008 lack equivalent depth, potentially leading to data waste. Objectives and Approach The aim of this study was to determine if a lookback period could accurately identify new onset conditions in datasets coded prior to 2008. Outcomes identified in an index admission and not present in the lookback period were classified as a new condition. Three different lookback periods entailing the preceding 3, 2, and immediately previous admission were investigated. Accuracy was determined by applying this procedure to datasets in which COF were assigned by coders and comparing agreement between the lookback procedure and the COF. Codes examined were myocardial infarction, pulmonary embolism and pneumonia. Sensitivity analyses excluded indexed admissions where a lookback period was not possible. True positives were those conditions correctly identified by the lookback periods when compared to that classified using the COF. The crude incidence rates were also determined. Results248,042 hospital records from 12,355 patients on chronic kidney replacement therapy were analysed. Lookback Period 1 provided the highest true positive rates for all 3 complications, ranging from 83% to 92%. Sensitivity analyses excluding patients with no previous separations, demonstrated a true positive percentage from 98% to 100%. Lookback period 3 true positive rates were the lowest (77.2-78.3%). The crude incidence calculated for first-time myocardial infarction using Lookback Period 1 was 25 events per 1000 patient years, consistent with current literature. Rates calculated using the COF variable was more than 3-fold lower. This finding was similar when evaluating other clinical outcomes. Conclusion / ImplicationsThe Lookback Period 1 method accurately identifies in-hospital complications compared with the current method using the COF variable and is a means of identifying new hospital complications in health administrative datasets predating the availability of COF, thereby enriching existing datasets and minimising data waste.

The main objective of this paper is the monitoring of daily activity patterns of fattening pigs at different locations in the housing environment using UHF-RFID. Four hundred fattening pigs were equipped with UHF-RFID ear tags and monitored during the fattening period for about four months. The RFID antennas were installed at the feeding troughs, playing devices and drinkers. A validation phase for each of these locations was carried out prior to the first data collection. The sensitivity (true positive rate) of the UHF-RFID system was about 80% at the feeding trough and the playing device and about 60% at the drinkers. The mean of the daily visiting time of all pigs at the trough was about 55 min. The mean visiting duration at the playing device was about 38 min and at the drinkers about 9 min. The visiting times of the pigs showed a high intra- and inter-variability. It was observed that the average visit duration at the feeding trough decreases over the course of a fattening period but increases at the playing device. A documentation of visiting times of animals is possible utilizing RFID systems, allowing a higher data density than video or direct observations.

Bitter pit has been described as one of the most important physiological disorders of apple fruit whose symptoms appear late in the season and during storage and which can cause high economic losses. The negative relationship between bitter pit and mineral contents in fruit led to develop prediction models. However, these models are based on mineral content close to harvest and can only provide valuable information at harvest. Aim of this study was to assess for three years the accuracy of different bitter pit prediction methods along fruit development, based on either Ca content in fruit (at 90 days before harvest and at harvest) or induction of symptoms (Mg infiltration, ethephon dip, passive method) at three levels: the overall accuracy, the accuracy of bitter pit incidence prediction (true positive rate), and the accuracy of non-bitter pit prediction (true negative rate). These three accuracies were considered together to know the real prediction risk of each method. From the results obtained, we proposed and validated, for other two years more, a detailed protocol to predict bitter pit which incorporated fruitlet analysis at 90 DBH, in order to gain prediction time, and the passive method at 40 and 20 DBH, in order to reduce the proportion of negative samples that were incorrectly classified. info:eu-repo/semantics/acceptedVersion

The objective of this study was to investigate three different calving detection systems in order to assess and compare their efficiency. The study was conducted at a large-scale dairy farm involving 54 Holstein-Friesian dairy cows and heifers. Animals were fitted with multiple devices: a rumination measuring device (Ruminact® (RA)), an intravaginal thermometer (Vel'Phone® (VP)) and a tail movement sensor (Moocall® (MC)) 5 to 7 days before expected calving and were removed after parturition. RA detects rumination time (RT) and calculates it in 2-hr intervals. VP detects a decrease in vaginal temperature that might indicate calving within 48 hr and the drop in temperature resulting from the expulsion of the device at calving (EXP message). MC detected increased tail movements and if they persisted for one hour, 1HA message was sent. If they continued during the subsequent hour, then 2HA message was sent. Messages sent by MC within 4 hr before calving (C4) were selected retrospectively as true positives for the prediction of calving, using the significant changes in RT as a baseline. All other messages were categorized as false positive. The mean value of RT decreased in a non-significant manner between interval -22 and -4 before calving. Significant decrease of RT was detectable between the two intervals of -4 and -2 before calving (24.7 ± 18.6 min/2 hr and 14.0 ± 13.0 min/2 hr, respectively). There was no significant difference between RT of primiparous and multiparous animals. EXP messages were accurate (positive predictive value 100%) indicators of the onset of calving. We received on average 12.7 ± 15.2 messages/animal (11.0 ± 10.1 and 16.6 ± 22.2 for cows and heifers, respectively). Positive predictive value was 12.6%. The number of false-positive messages was significantly higher in heifers. All three automatic systems could be used in a large-scale farm environment.

Two tests for covid-19 antibodies developed by the drug companies Roche and Abbott are “highly specific” but one was found to have lower sensitivity than was previously reported by the company, evaluation by Public Health England has shown. Abbott had reported that its assays had a sensitivity of 100% (the true positive rate) 14 days after symptom onset. However, PHE found that it had a sensitivity of 93.4% at 14 days, respectively. PHE published the reports after experts called for transparency in relation to the evaluation results, so that any claims could be fully scrutinised.1 Now that the evaluations have been made public, experts have said it is not clear whether these tests are any better than other available antibody tests and emphasised that the evaluations did not meet the standard needed for accreditation. Roche’s Anti-SARS-CoV-2 serology assay was tested between 5 and 7 May by the …