Method For Individual Identification Research Articles

Validating the consistency of cSNPs analysis results between DNA and RNA using SNaPshot method

The use of coding region single nucleotide polymorphisms (cSNPs) was recently proposed as a potential method for individual identification because it allows mRNA profiling and DNA typing to be performed concurrently. Nevertheless, availability of this approach still needs some further validation in different aspect. In this study, we have initially selected several SNP loci located in mRNA molecules that were confirmed to be highly expressed in blood. Both coding regions (CDRs) and untranslated regions (UTRs) were taken into consideration during the screening. Genomic DNA (gDNA) and total RNA from venous blood samples were isolated, followed by the synthesis of first-strand complementary DNA (cDNA) using purified RNA samples. Subsequently, the genotypes of these SNPs were respectively determined with gDNA and cDNA by using SNaPshot method. The PCR primers for cDNA were designed to span an intron in order to ensure that the amplification products were not due to the presence of potential DNA contamination. In summary, our study revealed a high consistency of cSNP analysis results between DNA and RNA on capillary electrophoresis platform, which highlighted the potential use of cSNP in forensic investigation.

Standardization and validation of a panel of cross-species microsatellites to individually identify the Asiatic wild dog (Cuon alpinus).

BackgroundThe Asiatic wild dog or dhole (Cuon alpinus) is a highly elusive, monophyletic, forest dwelling, social canid distributed across south and Southeast Asia. Severe pressures from habitat loss, prey depletion, disease, human persecution and interspecific competition resulted in global population decline in dholes. Despite a declining population trend, detailed information on population size, ecology, demography and genetics is lacking. Generating reliable information at landscape level for dholes is challenging due to their secretive behaviour and monomorphic physical features. Recent advances in non-invasive DNA-based tools can be used to monitor populations and individuals across large landscapes. In this paper, we describe standardization and validation of faecal DNA-based methods for individual identification of dholes. We tested this method on 249 field-collected dhole faeces from five protected areas of the central Indian landscape in the state of Maharashtra, India.ResultsWe tested a total of 18 cross-species markers and developed a panel of 12 markers for unambiguous individual identification of dholes. This marker panel identified 101 unique individuals from faecal samples collected across our pilot field study area. These loci showed varied level of amplification success (57–88%), polymorphism (3–9 alleles), heterozygosity (0.23–0.63) and produced a cumulative misidentification rate or PID(unbiased) and PID(sibs) value of 4.7 × 10−10 and 1.5 × 10−4, respectively, indicating a high statistical power in individual discrimination from poor quality samples.ConclusionOur results demonstrated that the selected panel of 12 microsatellite loci can conclusively identify dholes from poor quality, non-invasive biological samples and help in exploring various population parameters. This genetic approach would be useful in dhole population estimation across its range and will help in assessing population trends and other genetic parameters for this elusive, social carnivore.

Ocelot identification through spots

Ocelots are big felines in danger to be extinct but still found in some areas in Mexico, the spots in the body of ocelots make a pattern that is unique to each specimen and can be used for identification purposes. Ecologists are interested in non-intrusive census of these wild felines. In this paper, a method for automatic identification of specific individuals among ocelots is proposed. The proposed method maps each spot to the ellipse that best fits the spot, then keeps only the center of the ellipse and a vector that shows the orientation of the ellipse. Each animal is then characterized by a set of dots and associated vectors, just as human fingerprints are characterized after minutiae has been extracted. The proposed technique identifies individual ocelots by searching for local structures in the corresponding set of dots and associated vectors. According to the experiments, the proposed method outperforms Scale Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), and Oriented fast and Rotated Brief (ORB) based identification systems in the ocelot identification problem.

Adermatoglyphia: Barriers to Biometric Identification and the Need for a Standardized Alternative

Arguably, fingerprinting is the single most widely utilized method for individual identification and authentication (I&A). Dermatoglyphics form a vital portion of mass data collection, biometric scrutiny, and verification. Adermatoglyphia, or simply, loss of fingerprints attributed to a medical cause, represents a taxing situation for such biometric scrutiny systems requiring a fingerprint scan as a mandatory phase in I&A procedure. The scenario can be extremely debilitating for the adermatoglyphia patients, especially when the condition is permanent or irreversible. This article reviews different causes of adermatoglyphia, the challenge it poses to biometric identification, and the potential substitute modalities for fingerprinting technology. These modalities can function as a backup program for biometric surveillance in both medical and non-medical settings under circumstances when the fingerprinting method fails to comply.

Individual Cattle Identification Using a Deep Learning Based Framework

Individual cattle identification is required for precision livestock farming. Current methods for individual cattle identification requires either visual, or unique radio frequency, ear tags. We propose a deep learning based framework to identify beef cattle using image sequences unifying the advantages of both CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) network methods. A CNN network was used (Inception-V3) to extract features from a rear-view cattle video dataset and these extracted features were then used to train an LSTM model to capture temporal information and identify each individual animal. A total of 516 rear- view videos of 41 cattle at three time points separated by one month were collected. Our method achieved an accuracy of 88% and 91% for 15-frame and 20-frame video length, respectively. Our approach outperformed the framework that only uses CNN (identification accuracy 57%). Our framework will now be further improved using additional data before integrating the system into on-farm management processes.

Two-dimensional Cd(ii) coordination polymer encapsulated by Tb3+ as a reversible luminescent probe for Fe3.

A two-dimensional luminescent cadmium(ii) coordination polymer, [Cd(modbc)2]n (Cd-P); modbc = 2-methyl-6-oxygen-1,6-dihydro-3,4′-bipyridine-5-carbonitrile, was successfully synthesized by a solvothermal reaction and fully characterized. Cd-P exhibited excellent luminescence emission, and detected Cu2+, Co2+, Fe2+, Hg2+, Ni2+ and Fe3+ ions with high sensitivity and showed good anti-interference performance. After encapsulation of Tb3+ ions in Cd-P, the as-obtained fluorescent functionalized Tb3+@Cd-P maintained distinct chemical stabilities in different pHs and metal salt solutions. Subsequently, we explored the potential application of Tb3+@Cd-P as a probe for Fe3+ ions. A new and convenient method for individual identification of Fe3+ ions by the combination of Cd-P and Tb3+@Cd-P was successfully established. A possible sensing mechanism is discussed in detail.

Amphibian individual identification method applying heat generation by Neel relaxation of iron nanoparticles

Amphibian individual identification method applying heat generation by Neel relaxation of iron nanoparticles

Noninvasive genetic assessment provides evidence of extensive gene flow and possible high movement ability in the African golden wolf

The African golden wolf, Canis anthus, is a newly recognized medium-sized canid species from North Africa, which has remained poorly studied to date. We applied genetic methods for individual identification of non-invasively collected samples (n = 93) of the African golden wolf in Tunisia, assessing their genetic diversity and structure. The mitochondrial control region exhibited high haplotype diversity (Hd = 0.907 ± 0.018) with 15 haplotypes detected among 28 individuals. Similarly, a set of 15 microsatellite loci revealed high genetic diversity at the nuclear level (expected heterozygosity = 0.83 ± 0.04; average number of alleles = 8.30 ± 0.99). The Bayesian model-based clustering method implemented in STRUCTURE did not reveal population structure in African golden wolves within Tunisia. This result was corroborated by the Discriminant Analysis of Principal Components where no clear clusters were observed. Based on seven individuals that were found multiple times among samples, we estimated straight distance movements up to 230 km. Our results provide movement lengths for the species and no evidence for biogeographical structure of genetic diversity within Tunisian golden wolves, probably related to the high dispersal ability of the species, facilitating high gene flow. The evaluation of population diversity and the first information on movement ability provided in this study should be considered as baseline information in the development of a management plan for African golden wolf in Tunisia.

The Individual Identification Method of Wireless Device Based on A Robust Dimensionality Reduction Model of Hybrid Feature Information

With the advent of Internet of things, the number of mobile, and embedded, wearable devices are on the rising nowadays, which make us increasingly faced with the limitations of traditional network security control. Hence, accurately identifying different wireless devices through hybrid information processing method for the Internet of things becomes very important today. To this problem, we design, implement, and evaluate a robust algorithm to identify the wireless device with fingerprint features through integral envelope and Hilbert transform theory based PCA analysis algorithm. Integral envelope theory and Hilbert transform theory was used respectively to process the signals first, then the principal component features can be extracted by PCA analysis algorithm. At last, gray relation classifier was used to identify the signals. We experimentally demonstrate the effectiveness of the proposed algorithm to differentiat between 10 numbers of wireless device with the accuracy in excess of 99%. The approach itself is general and will work with any wireless devices’ recognition.

Automatic individual identification of Saimaa ringed seals

In order to monitor an animal population and to track individual animals in a non‐invasive way, identification of individual animals based on certain distinctive characteristics is necessary. In this study, automatic image‐based individual identification of the endangered Saimaa ringed seal (Phoca hispida saimensis) is considered. Ringed seals have a distinctive permanent pelage pattern that is unique to each individual. This can be used as a basis for the identification process. The authors propose a framework that starts with segmentation of the seal from the background and proceeds to various post‐processing steps to make the pelage pattern more visible and the identification easier. Finally, two existing species independent individual identification methods are compared with a challenging data set of Saimaa ringed seal images. The results show that the segmentation and proposed post‐processing steps increase the identification performance.

The individual identification method of wireless device based on dimensionality reduction and machine learning

The access security of wireless devices is a serious challenge in present wireless network security. Radio frequency (RF) fingerprint recognition technology as an important non-password authentication technology attracts more and more attention, because of its full use of radio frequency characteristics that cannot be imitated to achieve certification. In this paper, a RF fingerprint identification method based on dimensional reduction and machine learning is proposed as a component of intrusion detection for resolving authentication security issues. We compare three kinds of dimensional reduction methods, which are the traditional PCA, RPCA and KPCA. And we take random forests, support vector machine, artificial neural network and grey correlation analysis into consideration to make decisions on the dimensional reduction data. Finally, we obtain the recognition system with the best performance. Using a combination of RPCA and random forests, we achieve 90% classification accuracy is achieved at SNR $$\ge $$ 10 dB when reduced dimension is 76. The proposed method improves wireless device authentication and improves security protection due to the introduction of RF fingerprinting.

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.

A systematic review of discomfort due to toe or ear clipping in laboratory rodents.

Toe clipping and ear clipping (also ear notching or ear punching) are frequently used methods for individual identification of laboratory rodents. These procedures potentially cause severe discomfort, which can reduce animal welfare and distort experimental results. However, no systematic summary of the evidence on this topic currently exists. We conducted a systematic review of the evidence for discomfort due to toe or ear clipping in rodents. The review methodology was pre-specified in a registered review protocol. The population, intervention, control, outcome (PICO) question was: In rodents, what is the effect of toe clipping or ear clipping, compared with no clipping or sham clipping, on welfare-related outcomes? Through a systematic search in PubMed, Embase, Web of Science and grey literature, we identified seven studies on the effect of ear clipping on animal welfare, and five such studies on toe clipping. Studies were included in the review if they contained original data from an in vivo experiment in rodents, assessing the effect of toe clipping or ear clipping on a welfare-related outcome. Case studies and studies applying unsuitable co-interventions were excluded. Study quality was appraised using an extended version of SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE)’s risk of bias tool for animal studies. Study characteristics and outcome measures were highly heterogeneous, and there was an unclear or high risk of bias in all studies. We therefore present a narrative synthesis of the evidence identified. None of the studies reported a sample size calculation. Out of over 60 different outcomes, we found evidence of discomfort due to ear clipping in the form of increased respiratory volume, vocalization and blood pressure. For toe clipping, increased vocalization and decreased motor activity in pups were found, as well as long-term effects in the form of reduced grip strength and swimming ability in adults. In conclusion, there is too little evidence to reliably assess discomfort due to toe or ear clipping, and the quality of the available evidence is uncertain. Adequately powered, high-quality studies reporting reliable, relevant outcome measures are needed to accurately assess the impact of these identification techniques. Until more reliable evidence is available, any effect of toe clipping or ear clipping on animal welfare and study results cannot be confirmed or excluded.

UAV hyperspectral and lidar data and their fusion for arid and semi‐arid land vegetation monitoring

AbstractUnmanned aerial vehicles (UAVs) provide a new research tool to obtain high spatial and temporal resolution imagery at a reduced cost. Rapid advances in miniature sensor technology are leading to greater potentials for ecological research. We demonstrate one of the first applications of UAV lidar and hyperspectral imagery and a fusion method for individual plant species identification and 3D characterization at submeter scales in south‐eastern Arizona, USA. The UAV lidar scanner characterized the individual vegetation canopy structure and bare ground elevation, whereas the hyperspectral sensor provided species‐specific spectral signatures for the dominant and target species at our study area in leaf‐on condition. We hypothesized that the fusion of the two different data sources would perform better than either data type alone in the arid and semi‐arid ecosystems with sparse vegetation. The fusion approach provides 84–89% overall accuracy (kappa values of 0.80–0.86) in target species classification at the canopy scale, leveraging a wide range of target spectral responses in the hyperspectral data and a high point density (50 points/m2) in the lidar data. In comparison, the hyperspectral image classification alone produced 72–76% overall accuracies (kappa values of 0.70 and 0.71). The UAV lidar‐derived digital elevation model (DEM) is also strongly correlated with manned airborne lidar‐derived DEM (R2 = 0.98 and 0.96), but was obtained at a lower cost. The lidar and hyperspectral data as well as the fusion method demonstrated here can be widely applied across a gradient of vegetation and topography to monitor and detect ecological changes at a local scale.

LemurFaceID: a face recognition system to facilitate individual identification of lemurs

BackgroundLong-term research of known individuals is critical for understanding the demographic and evolutionary processes that influence natural populations. Current methods for individual identification of many animals include capture and tagging techniques and/or researcher knowledge of natural variation in individual phenotypes. These methods can be costly, time-consuming, and may be impractical for larger-scale, population-level studies. Accordingly, for many animal lineages, long-term research projects are often limited to only a few taxa. Lemurs, a mammalian lineage endemic to Madagascar, are no exception. Long-term data needed to address evolutionary questions are lacking for many species. This is, at least in part, due to difficulties collecting consistent data on known individuals over long periods of time. Here, we present a new method for individual identification of lemurs (LemurFaceID). LemurFaceID is a computer-assisted facial recognition system that can be used to identify individual lemurs based on photographs.ResultsLemurFaceID was developed using patch-wise Multiscale Local Binary Pattern features and modified facial image normalization techniques to reduce the effects of facial hair and variation in ambient lighting on identification. We trained and tested our system using images from wild red-bellied lemurs (Eulemur rubriventer) collected in Ranomafana National Park, Madagascar. Across 100 trials, with different partitions of training and test sets, we demonstrate that the LemurFaceID can achieve 98.7% ± 1.81% accuracy (using 2-query image fusion) in correctly identifying individual lemurs.ConclusionsOur results suggest that human facial recognition techniques can be modified for identification of individual lemurs based on variation in facial patterns. LemurFaceID was able to identify individual lemurs based on photographs of wild individuals with a relatively high degree of accuracy. This technology would remove many limitations of traditional methods for individual identification. Once optimized, our system can facilitate long-term research of known individuals by providing a rapid, cost-effective, and accurate method for individual identification.

上半身骨格情報の教師付きデータを用いた個人識別に関する研究

Production age Popularity of service robots is expected with decreasing population. There is a guide robot as one of service robots. The guiding robot can conduct guidance that is more personalized by identifying individuals. Therefore, personal identification is a function required for the guide robot. Personal identification has some skeletal information of the whole body acquired using Kinect v2. However, since the distance between the guide robot and the guidance target is short, even if Kinect v2 is installed in the guide robot, it is not possible to acquire skeletal information of the whole body. Therefore, the authors proposed an individual identification method using human upper body skeleton information. In this paper, we describe the effectiveness confirmation by using the actual data on the proposed method.

WEVOTE: Weighted Voting Taxonomic Identification Method of Microbial Sequences.

BackgroundMetagenome shotgun sequencing presents opportunities to identify organisms that may prevent or promote disease. The analysis of sample diversity is achieved by taxonomic identification of metagenomic reads followed by generating an abundance profile. Numerous tools have been developed based on different design principles. Tools achieving high precision can lack sensitivity in some applications. Conversely, tools with high sensitivity can suffer from low precision and require long computation time.MethodsIn this paper, we present WEVOTE (WEighted VOting Taxonomic idEntification), a method that classifies metagenome shotgun sequencing DNA reads based on an ensemble of existing methods using k-mer-based, marker-based, and naive-similarity based approaches. Our evaluation on fourteen benchmarking datasets shows that WEVOTE improves the classification precision by reducing false positive annotations while preserving a high level of sensitivity.ConclusionsWEVOTE is an efficient and automated tool that combines multiple individual taxonomic identification methods to produce more precise and sensitive microbial profiles. WEVOTE is developed primarily to identify reads generated by MetaGenome Shotgun sequencing. It is expandable and has the potential to incorporate additional tools to produce a more accurate taxonomic profile. WEVOTE was implemented using C++ and shell scripting and is available at www.github.com/aametwally/WEVOTE.

Activity and polymorphisms of butyrylcholinesterase in a Polish population

Butyrylcholinesterase (BChE) activity assay and inhibitor phenotyping can help to identify individuals at risk of prolonged paralysis following the administration of neuromuscular blocking agents, like succinylcholine, pesticides and nerve agents.In this study, the activity of BChE and its sensitivity to inhibition by dibucaine and fluoride was evaluated in 1200 Polish healthy individuals. In addition, molecular analysis of all exons, exon-intron boundaries and the 3′UTR sequence of the BCHE gene was performed in a group of 72 subjects with abnormal BChE activity (<2000 U/L and >5745 U/L) or with DN (Dibucaine Number) or FN (Fluoride-Number) values outside the reference range (DN < 78 and FN < lower than wild type).In a studied group, BChE activity range was similar to those observed in other populations. BChE activity screening allowed to detect UA and UF phenotypes in 26 (2.2%) and 15 (1.2%) individuals, respectively. Observed UA or UF phenotypes were confirmed by direct sequencing and heterozygous c.293A > G or c.1253G > T substitutions were identified in all cases. Nine out of 18 (50%) individuals with BChE activity below 2000 U/L had a mutation in 5′UTR (32G/A), intron 2 (c.1518-121T/C) or exon 4 (c.1699G/A; the K variant mutation). Majority of the individuals with BChE activity ≥6000 U/L were wild type.To summarize, the range of BChE activity in a Polish population is similar to those observed in other countries. We conclude that the BChE phenotyping assay is a reliable method for identification of individuals with the UA and UF genotypes.

Development of novel tetra- and trinucleotide microsatellite markers for giant grouper Epinephelus lanceolatus using 454 pyrosequencing.

Giant grouper (Epinephelus lanceolatus) is a commercially important species, but its wild population has recently been classified as vulnerable. This species has significant potential for use in aquaculture, though a greater understanding of population genetics is necessary for selective breeding programs to minimize kinship for genetically healthy individuals. High-throughput pyrosequencing of genomic DNA was used to identify and characterize novel tetra- and trinucleotide microsatellite markers in giant grouper from Sabah, Malaysia. In total, of 62,763 sequences containing simple sequence repeats (SSRs) were obtained, and 78 SSR loci were selected to possibly contain tetra- and trinucleotide repeats. Of these loci, 16 had tetra- and 8 had trinucleotide repeats, all of which exhibited polymorphisms within easily genotyped regions. A total of 143 alleles were identified with an average of 5.94 alleles per locus, with mean observed and expected heterozygosities of 0.648 and 0.620, respectively. Among of them, 15 microsatellite markers were identified without null alleles and with Hardy-Weinberg equilibrium. These alleles showed a combined non-exclusion probability of 0.01138. The probability of individual identification (PID) value combined with in descending order 12 microsatellite markers was 0.00008, which strongly suggests that the use of the microsatellite markers developed in this study in various combinations would result in a high resolution method for parentage analysis and individual identification. These markers could be used to establish a broodstock management program for giant grouper and to provide a foundation for genetic studies such as population structure, parentage analysis, and kinship selection.

Detecting nuisance species using NGST: Methodology shortcomings and possible application in ballast water monitoring

Detecting the presence of potential invasive species in ballast water is a priority for preventing their spread into new environments. Next generation sequencing technologies are being increasingly used for exploring and assessing biodiversity from environmental samples. Here we apply high throughput sequencing from DNA extracted from ballast water (BW) samples employing two different platforms, Ion Torrent and 454, and compare the putative species catalogues from the resulting Operational Taxonomic Units (OTU). Water samples were taken from the RV Polastern ballast tank in five different days between the second and the twentieth navigation day. Pronounced decrease of oxygen concentration and increase of temperature occurred in the BW during this time, coincident with a progressively higher proportion of unassigned OTU and short reads indicating DNA degradation. Discrepancy between platforms for species catalogues was consistent with previously published bias in AT-rich sequences for Ion Torrent platform. Some putative species detected from the two platforms increased in frequency during the Polarstern travel, which suggests they were alive and therefore tolerant to adverse conditions. OTU assigned to the highly invasive red alga Polysiphonia have been detected at low but increasing frequency from the two platforms. Although in this moment NGST could not replace current methods of sampling, sorting and individual taxonomic identification of BW biota, it has potential as an exploratory methodology especially for detecting scarce species.