Rate Of False Positive Identification Research Articles

In-Depth Analysis of Molecular Network Based on Liquid Chromatography Coupled with Tandem Mass Spectrometry in Natural Products: Importance of Redundant Nodes Discovery.

The identification of molecules within complex mixtures is a major bottleneck in natural products (NPs) research. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has emerged as the main tool for the high-throughput characterization of NPs. The large amount of data sets by LC-MS/MS presents a challenge for data processing and interpretation, and the LC-MS/MS molecular network (MN) is one of the most prominent tools for analyzing large MS/MS data sets, widely used for rapid classification, identification, and structural speculation of unknown compounds. However, the existence of a large number of redundant nodes leads to false-positive results. To solve this problem, we proposed the in-depth analysis of MN. In this study, in-depth analysis of MN of five NPs representing the common structures of saponin, steroid, flavonoid, alkaloid, and phenolic acid revealed the presence of redundant nodes (including other adducts, isotope, and in-source fragmentation) in addition to the normal nodes, which can lead to false-positive identification results. Additionally, the reasons for different redundant nodes are discussed and experimentally verified, and it was found that the impact of redundant nodes can be mitigated by optimizing the experimental conditions and employing Feature-Based Molecular Networking. Furthermore, Ion Identity Molecular Networking can rapidly discover and screen redundant nodes, simplifying the in-depth analysis of MN and improving the network connectivity of structurally related molecules. Finally, a combination formulation of 7 NPs is used as an example to provide a guide for in-depth analysis of MN for comprehensive characterization of complex systems. This study highlights the importance of an in-depth analysis of MN for better understanding and utilization of MS/MS data in complex systems to reduce the false-positive rate of identification by screening and filtering redundant nodes.

Open-set face recognition with maximal entropy and Objectosphere loss

Open-set face recognition characterizes a scenario where unknown individuals, unseen during the training and enrollment stages, appear on operation time. This work concentrates on watchlists, an open-set task that is expected to operate at a low false-positive identification rate and generally includes only a few enrollment samples per identity. We introduce a compact adapter network that benefits from additional negative face images when combined with distinct cost functions, such as Objectosphere Loss (OS) and the proposed Maximal Entropy Loss (MEL). MEL modifies the traditional Cross-Entropy loss in favor of increasing the entropy for negative samples and attaches a penalty to known target classes in pursuance of gallery specialization. The proposed approach adopts pre-trained deep neural networks (DNNs) for face recognition as feature extractors. Then, the adapter network takes deep feature representations and acts as a substitute for the output layer of the pre-trained DNN in exchange for an agile domain adaptation. Promising results have been achieved following open-set protocols for three different datasets: LFW, IJB-C, and UCCS as well as state-of-the-art performance when supplementary negative data is properly selected to fine-tune the adapter network.

Stable Hash Generation for Efficient Privacy-Preserving Face Identification

The development of large-scale facial identification systems that provide privacy protection of the enrolled subjects represents an open challenge. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes appear to be unsuitable for indexing (workload reduction) in biometric identification systems. More precisely, they have been utilized in identification systems performing exhaustive searches, thereby leading to degradations of the computational efficiency. In this work, we propose a privacy-preserving face identification system which utilisers a Product Quantization-based hash look-up table for indexing and retrieval of protected face templates. These face templates are protected through fully homomorphic encryption schemes, thereby guaranteeing high privacy protection of the enrolled subjects. For the best configuration, the experimental evaluation carried out over closed-set and open-set settings shows the feasibility of the proposed technique for the use in large-scale facial identification systems: a workload reduction down to 0.1% of a baseline approach performing an exhaustive search is achieved together with a low pre-selection error rate of less than 1%. In terms of biometric performance, a False Negative Identification Rate (FNIR) in range of 0.0% - 0.2% is obtained for practical False Positive Identification Rate (FPIR) values on the FEI and FERET face databases. In addition, our proposal shows competitive performance on unconstrained databases, e.g., the LFW face database. To the best of the authors’ knowledge, this is the first work presenting a competitive privacy-preserving workload reduction scheme which performs template comparisons in the encrypted domain.

Gait Identification Using Limb Joint Movement and Deep Machine Learning

Person identification is a key problem in the security domain and may be used to automatically identify criminals or missing persons. The traditional face matching approaches adopted by the police and security services across the world have recently been shown to produce a high rate of false positive identification. Alternatively, gait-based person identification has shown to be a convenient method particularly as it can be performed at a distance, without the cooperation of the subject, and is a biometric trait which cannot be easily disguised. In this work, we propose a gait-based person identification approach which uses limb joint motion data and deep machine learning models to identify the individuals. Distinct statistical features are identified and extracted from limb movement using a fixed width sliding window to train a Long Short-Term Memory model. The proposed solution outperforms the existing methods producing 98.87% accuracy when evaluated over unseen samples. In addition, we propose a simple two-stage filtering approach to increase the prediction accuracy up to 100% when identifying individuals from larger sequences of samples. This finding may improve the current solutions in controlled environments such as airports. In the future, this approach may help to overcome the problem of occlusion in gait-based identification, as unlike the existing works, it does not require information regarding the entire body. The study also presents a primary dataset comprising limb joint movement acquired from a diverse range of participants during casual walking captured through two digital goniometers.

A comparison of spectroscopic analysis methods for microplastics: Manual, semi-automated, and automated Fourier transform infrared and Raman techniques

This study was conducted to establish the best practice for microplastic analysis by reducing the time demand and human bias and comparing the characteristics of μ-FTIR and Raman techniques. A manual analysis, semi-automated method, and fully automatic identification method were compared. Fully automated identification took the shortest time to analyze a whole filter paper (Ø25 mm), but its false positive identification rate was 80 ± 15%. The semi-automated analysis using spectrum profiling was suitable for all aspects of microplastic analysis. It was less time consuming than the manual analysis (manual: 6.1 ± 0.8 h, semi-automated: 4.0 ± 0.6 h), and 22 ± 12% more microplastic particles were detected using the semi-automated method compared to the manual analysis due to the reduction in false negative results. Raman microscopy was suitable for small microplastic (>5 μm) identification, although the Raman analysis took nine times longer than the semi-automated analysis.

High confidence copy number variants identified in Holstein dairy cattle from whole genome sequence and genotype array data

Multiple methods to detect copy number variants (CNV) relying on different types of data have been developed and CNV have been shown to have an impact on phenotypes of numerous traits of economic importance in cattle, such as reproduction and immunity. Further improvements in CNV detection are still needed in regard to the trade-off between high-true and low-false positive variant identification rates. Instead of improving single CNV detection methods, variants can be identified in silico with high confidence when multiple methods and datasets are combined. Here, CNV were identified from whole-genome sequences (WGS) and genotype array (GEN) data on 96 Holstein animals. After CNV detection, two sets of high confidence CNV regions (CNVR) were created that contained variants found in both WGS and GEN data following an animal-based (n = 52) and a population-based (n = 36) pipeline. Furthermore, the change in false positive CNV identification rates using different GEN marker densities was evaluated. The population-based approach characterized CNVR, which were more often shared among animals (average 40% more samples per CNVR) and were more often linked to putative functions (48 vs 56% of CNVR) than CNV identified with the animal-based approach. Moreover, false positive identification rates up to 22% were estimated on GEN information. Further research using larger datasets should use a population-wide approach to identify high confidence CNVR.

Issues Related to Face Recognition Accuracy Varying Based on Race and Skin Tone

Face recognition technology has recently become controversial over concerns about possible bias due to accuracy varying based on race or skin tone. We explore three important aspects of face recognition technology related to this controversy. Using two different deep convolutional neural network face matchers, we show that for a fixed decision threshold, the African-American image cohort has a higher false match rate (FMR), and the Caucasian cohort has a higher false nonmatch rate. We present an analysis of the impostor distribution designed to test the premise that darker skin tone causes a higher FMR, and find no clear evidence to support this premise. Finally, we explore how using face recognition for one-to-many identification can have a very low false-negative identification rate and still present concerns related to the false-positive identification rate. Both the ArcFace and VGGFace2 matchers and the MORPH dataset used in our experiments are available to the research community so that others should be able to reproduce or reanalyze our results.

Use of fever detection in combination with thoracic ultrasonography to identify respiratory disease, and compare treatments of antimicrobials and NSAID: a randomised study in dairy calves

BackgroundThis study explored the combined use of fever detection and thoracic ultrasonography to identify calves with early onset bovine respiratory disease (BRD). Thoracic ultrasonography was then used to assess the...

ECG Biometric Recognition: Template-Free Approaches Based on Deep Learning.

Biometric technologies offer much convenience over the conventional approaches to identity recognition, but security and privacy concerns also accompany their applications. In this paper, an electrocardiogram (ECG)-based identification scheme is proposed to relieve such concerns. With the help of a deep learning (DL) technique, the identity of an unknown beat bundle can be determined without the need for biometric template construction. Thus, the disclosure of the physiological and pathological condition of an individual from his/her stolen templates will no longer be possible. Furthermore, the problem of being vulnerable to unregistered subjects in this DL-based recognition system is also addressed. Experiments with real and synthesized ECGs are used to illustrate the efficacy of the proposed scheme. An identification rate of 97.84% for the 200 registered subjects with a false-positive identification rate of 0.69% under the attack of 1,000 synthesized single-lead ECGs was achieved.

Performance evaluation of an anomaly-detection algorithm for keystroke-typing based insider detection

Keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user's legitimacy, and the performances of these classifications should be confirmed to identify the most promising research direction. However, classification research contains several experiments with different conditions such as datasets and methodologies. This study aims to benchmark the algorithms to the same dataset and features to equally measure all performances. Using a dataset that contains the typing rhythm of 51 subjects, we implement and evaluate 15 classifiers measured by F1-measure, which is the harmonic mean of a false-negative identification rate and false-positive identification rate. We also develop a methodology to process the typing data. By considering a case in which the model will reject the outsider, we tested the algorithms on an open set. Additionally, we tested different parameters in random forest and k nearest neighbors classifications to achieve better results and explore the cause of their high performance. We also tested the dataset on one-class classification and explained the results of the experiment. The top-performing classifier achieves an F1-measure rate of 92% while using the normalized typing data of 50 subjects to train and the remaining data to test. The results, along with the normalization methodology, constitute a benchmark for comparing the classifiers and measuring the performance of keystroke dynamics for insider detection.

A comparative analysis of similarity distance measure functions for biocryptic authentication in cloud databases

Biometric recognition identifies an individual with the help of biological traits of humans like fingerprint, iris, voice etc. Estimation of similarity between the biometric images stored in cloud databases needs a closer attention due to its essentiality. To improve the privacy concerns, the proposed system encrypts the biometric data via feature extracted data hashing with the private key. The minutiae feature extraction procedure adequately removes edge detected features from the sensed data. The keys generated are stored in the cloud database. This work focuses on the comparison of the various distance similarity measure functions used for matching the stored template key with the query key. It has been found that by comparing with many existing similarity distance measures Minkowski distance method performs better in terms of accuracy, recall and F-score. Since the work involves fingerprint as the biometric trait, the performance is measured using false matching rate, false non-matching rate, false positive identification rate, false negative identification rate, accuracy, recall, F-score. The implementation has been done with MATLAB.

Treefrog lateral line as a mean of individual identification through visual and software assisted methodologies

BackgroundEcological research often requires monitoring of a specific individual over an extended period of time. To enable non-invasive re-identification, consistent external marking is required. Treefrogs possess lateral lines for crypticity. While these patterns decrease predator detection, they also are individual specific patterns. In this study, we tested the use of lateral lines in captive and wild populations of Dryophytes japonicus as natural markers for individual identification. For the purpose of the study, the results of visual and software assisted identifications were compared.ResultsIn normalized laboratory conditions, a visual individual identification method resulted in a 0.00 rate of false-negative identification (RFNI) and a 0.0068 rate of false-positive identification (RFPI), whereas Wild-ID resulted in RFNI = 0.25 and RFNI = 0.00. In the wild, female and male data sets were tested. For both data sets, visual identification resulted in RFNI and RFPI of 0.00, whereas the RFNI was 1.0 and RFPI was 0.00 with Wild-ID. Wild-ID did not perform as well as visual identification methods and had low scores for matching photographs. The matching scores were significantly correlated with the continuity of the type of camera used in the field.ConclusionsWe provide clear methodological guidelines for photographic identification of D. japonicus using their lateral lines. We also recommend the use of Wild-ID as a supplemental tool rather the principal identification method when analyzing large datasets.

An update on sORFs.org: a repository of small ORFs identified by ribosome profiling.

sORFs.org (http://www.sorfs.org) is a public repository of small open reading frames (sORFs) identified by ribosome profiling (RIBO-seq). This update elaborates on the major improvements implemented since its initial release. sORFs.org now additionally supports three more species (zebrafish, rat and Caenorhabditis elegans) and currently includes 78 RIBO-seq datasets, a vast increase compared to the three that were processed in the initial release. Therefore, a novel pipeline was constructed that also enables sORF detection in RIBO-seq datasets comprising solely elongating RIBO-seq data while previously, matching initiating RIBO-seq data was necessary to delineate the sORFs. Furthermore, a novel noise filtering algorithm was designed, able to distinguish sORFs with true ribosomal activity from simulated noise, consequently reducing the false positive identification rate. The inclusion of other species also led to the development of an inner BLAST pipeline, assessing sequence similarity between sORFs in the repository. Building on the proof of concept model in the initial release of sORFs.org, a full PRIDE-ReSpin pipeline was now released, reprocessing publicly available MS-based proteomics PRIDE datasets, reporting on true translation events. Next to reporting those identified peptides, sORFs.org allows visual inspection of the annotated spectra within the Lorikeet MS/MS viewer, thus enabling detailed manual inspection and interpretation.

Bloom filter‐based search structures for indexing and retrieving iris‐codes

Large-scale biometric deployments are becoming ubiquitous. The computational workload of the conventional retrieval method, requiring 1:N comparisons in the identification mode, is impractical for such systems. In recent years, many approaches for efficient biometric identification were proposed, but their scalability is often questionable. Furthermore, the lack of a unified methodology for biometric workload reduction reporting often makes a direct benchmark or a thorough evaluation of the proposed schemes cumbersome. We present an iris indexing scheme based on Bloom filters and binary search trees. With a statistical model, the system is shown to be theoretically scalable for arbitrarily many enrollees. We evaluate this system on a combined database from several publicly available datasets, containing a total of 11,936 iris images from 1477 instances. In an open-set identification scenario, the system maintains the biometric performance of an iris-code 1:N baseline – a true positive identification rate of approximately 98% measured at 0.1% false positive identification rate, at only 10% of the baseline workload. In a proof-of-concept multi-iris indexing experiment, the true positive identification rate is further increased to over 99%, without additional workload costs. Lastly, we define several prerequisites necessary for a transparent and comprehensive methodology of biometric workload reduction results dissemination.

Quantification of 3D curvature in the iliac crest: Ontogeny and implications for sex determination in juveniles.

Sex-specific differences in the shape of the iliac crest have been reported based on quantification of simplified curvature using two-dimensional variables or qualitative identification of faint/marked S-shaped curvature. An objective and quantitative approach for the evaluation of iliac crest curvature in juveniles was developed. Using three-dimensional data, ontogenetic variation in iliac curvature was investigated in relation to size and chronological age and its accuracy for sex estimation among juveniles was evaluated. Three-dimensional geometric morphometric landmarks were collected from virtual models of 53 surface-scanned ilia and used to: (1) quantify differences between male and female shape, and (2) to calculate a measure of sinuosity by extracting linear measurements relative to a plane constructed in virtual space. Males were found to exhibit on average more pronounced curvature of the iliac crest than females in equivalent age groups, and the difference between sexes was more marked on the posterior section of the bone than on the anterior section. Classification accuracy was higher for measurements of sinuosity relative to a three-dimensional plane (62-87%) than for landmarks describing crest shape (64-74%). An increased likelihood of correct male identification (reduced rate of false positive identification) was observed in older children (>5 years). This reflects divergence of male and female iliac crest shape with size (or age), and the development of a unique, more curved iliac crest shape in males. The results suggest more conspicuous male trait expression than female trait expression.

False identification of other microorganisms as <i>Staphylococcus aureus</i> in Southern Nigeria

Purpose: Staphylococcus aureus is the causative agent of many infections and the advent MRSA has drawn much attention to it. However, some organisms have been noted to be wrongly identified as S. aureus through phenotypic identifications leading to wrong treatment of infections. This study is therefore undertaken to evaluate the rate of false identification of other organisms as S. aureus in Southern Nigeria.Methods: 507 microorganisms which have been previously identified as S. aureus in 8 States in Southern Nigeria through characteristic morphology on blood agar, Gram staining, growth and fermentation on Mannitol Salt Agar and coagulase formation were collected. All the isolates were identified in this study through sequencing of 16S rRNA and detection of spa gene. The percentages of true and false identities were determined.Results: Of the 507 isolates previously identified as S. aureus, only 54 (11 %) were confirmed as S. aureus while the rest were coagulase negative Staphylococci (85 % misidentification rate), Bacillus sp. (12 % misidentification rate), and Brevibacterium sp. (3 % misidentification rate).Conclusion: A high rate of false positive identification of S. aureus which could lead to the misuse of antibiotics in emergency situation has been identified in this study. The use of standard methods for the identification of S. aureus at all times is highly recommended.Keywords: Coagulase staphylococci differentiation, misidentification of S. aureus, phenotypic identifications, Southern Nigeria

MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition.

With recent advances in mass spectrometry, there is an increased interest in data-independent acquisition (DIA) techniques for metabolomics. With DIA technique, all metabolite ions are sequentially selected and isolated using a wide window to generate multiplexed MS/MS spectra. Therefore, DIA strategy enables a continuous and unbiased acquisition of all metabolites and increases the data dimensionality, but presents a challenge to data analysis due to the loss of the direct link between precursor ion and fragment ions. However, very few DIA data processing methods are developed for metabolomics application. Here, we developed a new DIA data analysis approach, namely, MetDIA, for targeted extraction of metabolites from multiplexed MS/MS spectra generated using DIA technique. MetDIA approach considers each metabolite in the spectral library as an analysis target. Ion chromatograms for each metabolite (both precursor ion and fragment ions) and MS(2) spectra are readily detected, extracted, and scored for metabolite identification, referred as metabolite-centric identification. A minimum metabolite-centric identification score responsible for 1% false positive rate of identification is determined as 0.8 using fully (13)C labeled biological extracts. Finally, the comparisons of our MetDIA method with data-dependent acquisition (DDA) method demonstrated that MetDIA could significantly detect more metabolites in biological samples, and is more accurate and sensitive for metabolite identifications. The MetDIA program and the metabolite spectral library is freely available on the Internet.

Evaluation of trace analyte identification in complex matrices by low-resolution gas chromatography – Mass spectrometry through signal simulation

The identification of trace levels of compounds in complex matrices by conventional low-resolution gas chromatography hyphenated with mass spectrometry is based in the comparison of retention times and abundance ratios of characteristic mass spectrum fragments of analyte peaks from calibrators with sample peaks. Statistically sound criteria for the comparison of these parameters were developed based on the normal distribution of retention times and the simulation of possible non-normal distribution of correlated abundances ratios. The confidence level used to set the statistical maximum and minimum limits of parameters defines the true positive rates of identifications. The false positive rate of identification was estimated from worst-case signal noise models. The estimated true and false positive identifications rate from one retention time and two correlated ratios of three fragments abundances were combined using simple Bayes' statistics to estimate the probability of compound identification being correct designated examination uncertainty. Models of the variation of examination uncertainty with analyte quantity allowed the estimation of the Limit of Examination as the lowest quantity that produced “Extremely strong” evidences of compound presence. User friendly MS-Excel files are made available to allow the easy application of developed approach in routine and research laboratories. The developed approach was successfully applied to the identification of chlorpyrifos-methyl and malathion in QuEChERS method extracts of vegetables with high water content for which the estimated Limit of Examination is 0.14mgkg−1 and 0.23mgkg−1 respectively.

Reliable Physical Unclonable Functions Using Data Retention Voltage of SRAM Cells

Physical unclonable functions (PUFs) are circuits that produce outputs determined by random physical variations from fabrication. The PUF studied in this paper utilizes the variation sensitivity of static random access memory (SRAM) data retention voltage (DRV), the minimum voltage at which each cell can retain state. Prior work shows that DRV can uniquely identify circuit instances with 28% greater success than SRAM power-up states that are used in PUFs [1]. However, DRV is highly sensitive to temperature, and until now this makes it unreliable and unsuitable for use in a PUF. In this paper, we enable DRV PUFs by proposing a DRV-based hash function that is insensitive to temperature. The new hash function, denoted DRV-based hashing (DH), is reliable across temperatures because it utilizes the temperature-insensitive ordering of DRVs across cells, instead of using the DRVs in absolute terms. To evaluate the security and performance of the DRV PUF, we use DRV measurements from commercially available SRAM chips, and use data from a novel DRV prediction algorithm. The prediction algorithm uses machine learning for fast and accurate simulation-free estimation of any cell's DRV, and the prediction error in comparison to circuit simulation has a standard deviation of 0.35 mV. We demonstrate the DRV PUF using two applications-secret key generation and identification. In secret key generation, we introduce a new circuit-level reliability knob as an alternative to error correcting codes. In the identification application, our approach is compared to prior work and shown to result in a smaller false-positive identification rate for any desired true-positive identification rate.

Differential specificity of selective culture media for enumeration of pathogenic vibrios: Advantages and limitations of multi-plating methods

Plating environmental samples on vibrio-selective chromogenic media is a commonly used technique that allows one to quickly estimate concentrations of putative vibrio pathogens or to isolate them for further study. Although this approach is convenient, its usefulness depends directly on how well the procedure selects against false positives. We tested whether a chromogenic medium, CHROMagar Vibrio (CaV), used alone (single-plating) or in combination (double-plating) with a traditional medium thiosulfate-citrate-bile-salts (TCBS), could improve the discrimination among three pathogenic vibrio species (Vibrio cholerae, Vibrio parahaemolyticus, and Vibrio vulnificus) and thereby decrease the number of false-positive colonies that must be screened by molecular methods. Assays were conducted on water samples from two estuarine environments (one subtropical, one tropical) in a variety of seasonal conditions. The results of the double-plating method were confirmed by PCR and 16S rRNA sequencing. Our data indicate that there is no significant difference in the false-positive rate between CaV and TCBS when using a single-plating technique, but determining color changes on the two media sequentially (double-plating) reduced the rate of false positive identification in most cases. The improvement achieved was about two-fold on average, but varied greatly (from 0- to 5-fold) and depended on the sampling time and location. The double-plating method was most effective for V. vulnificus in warm months, when overall V. vulnificus abundance is high (false positive rates as low as 2%, n=178). Similar results were obtained for V. cholerae (minimum false positive rate of 16%, n=146). In contrast, the false positive rate for V. parahaemolyticus was always high (minimum of 59%, n=109). Sequence analysis of false-positive isolates indicated that the majority of confounding isolates are from the Vibrionaceae family, however, members of distantly related bacterial groups were also able to grow on vibrio-selective media, even when using the double-plating method. In conclusion, the double-plating assay is a simple means to increase the efficiency of identifying pathogenic vibrios in aquatic environments and to reduce the number of molecular assays required for identity confirmation. However, the high spatial and temporal variability in the performance of the media mean that molecular approaches are still essential to obtain the most accurate vibrio abundance estimates from environmental samples.