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"Neisseria skkuensis" is a gram-negative coccus that is endemic in the human oral cavity, with only few reports of infection in humans. Herein, we report a case of a male patient in his sixties presenting with infective endocarditis (IE) caused by "N. skkuensis". To our knowledge, this is the second case of IE confirmed using 16S rRNA gene to have been caused by "N. skkuensis". The accurate diagnosis of rare or difficult-to-identify pathogens is a major challenge for clinical microbiological laboratories. Although Neisseria spp. are common in the oral cavity and are often seen in routine tests, identification of their biochemical properties and mass spectrometric analysis are difficult. In this case report, we describe the accurate identification of "N. skkuensis" by 16S rRNA gene sequencing analysis compared to other identification methods. Further cases of "N. skkuensis" are needed to fully evaluate the clinical approach of this detection method.

The present contribution deals with the use of organic colourants in wall paintings, polychrome pottery and painted stone artworks, i.e. painted artworks on inorganic supports. The term organic colourants is referred to the chemical nature of these compounds but not to the way of application; therefore, organic colourants can be dyes, lakes or pigments. The use of organic colourants in wall paintings has been given little attention in the past, perhaps on the assumption that they were rarely used by ancient artists. Recent diagnostic studies, however, brought evidence that their use was not fragmentary; on the contrary, there seems to be continuity in the centuries, at least with regard to the most widely used such as madder, Tyrian purple and indigo. Sources, alteration phenomena, identification methods and analytical evidence is given for the main organic colourants with concern to red, yellow, green, purple and blue hues. Drawbacks and issues are discussed with concern to the identification techniques.

Ensuring the reliability of automatic control systems is directly related to solving diagnostic problems that require the identification of current parameter values in the presence of uncertain impacts or changing operating conditions. This determines the high importance of an effective solution to the problem of parametric identification. Currently, there are many alternatives for solving identification problems, one of which is the joint use of classical identification methods and artificial neural networks. The article proposes a method of systems’ synthesis for identifying the dynamic objects’ parameters based on the complex use of artificial neural networks and recurrent algorithms, which is a parallel-sequential information processing procedure. The efficiency of the recurrent identification algorithm using a neural network is shown. The results of the numerical simulation are presented.

Simple nucleotide matching identification methods are not as accurate as once thought at identifying environmental fungal sequences. This is largely because of incorrect naming and the underrepresentation of various fungal groups in reference datasets, but it is also potentially a reflection of the identification software being used. Here, we explore these issues by examining an environmental metabarcoding dataset of partial large subunit rRNA sequences of Basidiomycota and basal fungi. We employed the simple matching method using the QIIME 2 classifier and the RDP Classifier in conjunction with the latest releases of the SILVA (138.1, 2020) and RDP (11, 2014) reference datasets and then compared the results with a manual phylogenetic binning approach. Of the 71 query sequences tested, 21% and 42% were misidentified using QIIME 2 and the RDP Classifier, respectively. Of these simple matching misidentifications, more than half resulted from the underrepresentation of groups of fungi in the SILVA and RDP reference datasets. More comprehensive reference datasets with fewer misidentified sequences will increase the accuracy of simple matching identifications. However, we argue that the phylogenetic binning approach is a better alternative to simple matching since, in addition to better accuracy, it provides evolutionary information about query sequences.

In the last few decades, there has been a significant increase in interest in developing, constructing, and using structural health monitoring (SHM) systems. The classic monitoring system should, by definition, have, in addition to the diagnostic module, a module responsible for monitoring loads. These loads can be measured with piezoelectric force sensors or indirectly with strain gauges such as resistance strain gauges or FBG sensors. However, this is not always feasible due to how the force is applied or because sensors cannot be mounted. Therefore, methods for identifying excitation forces based on response measurements are often used. This approach is usually cheaper and easier to implement from the measurement side. However, in this approach, it is necessary to use a network of response sensors, whose installation and wiring can cause technological difficulties and modify the results for slender constructions. Moreover, many load identification methods require the use of multiple sensors to identify a single force history. Increasing the number of sensors recording responses improves the numerical conditioning of the method. The proposed article presents the use of contactless measurements carried out with the help of a high-speed camera to identify the forces exiting the object.

We report a surprising phenomenon about identifying differentially expressed genes (DEGs) from population-level RNA-seq data: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates (FDRs). Via permutation analysis on an immunotherapy RNA-seq dataset, we observed that DESeq2 and edgeR identified even more DEGs after samples’ condition labels were randomly permuted. Motivated by this, we evaluated six DEG identification methods (DESeq2, edgeR, limma-voom, NOISeq, dearseq, and the Wilcoxon rank-sum test) on population-level RNA-seq datasets. We found that the FDR control was often failed by the three popular parametric methods—DESeq2, edgeR, and limma-voom— and the new non-parametric method dearseq. In particular, the actual FDRs of DESeq2 and edgeR sometimes exceeded 20% when the target FDR threshold was only 5%. Although NOISeq, a non-parametric method used by GTEx, controlled the FDR better than the other four methods did, its power was much lower than that of the Wilcoxon rank-sum test, a classic nonparametric test that consistently controlled the FDR and achieved good power in our evaluation. Based on these results, for population-level RNA-seq studies, we recommend the Wilcoxon rank-sum test.

When testing botanical ingredients of herbal medicines and dietary supplements, the complexity of botanical matrixes often requires the use of orthogonal methods to establish identification procedures suitable for quality control purposes. Genomic-based botanical identification methods are evolving and emerging as useful quality control tools to complement traditional morphological and chemical identification methods. Species-specific polymerase chain reaction methods are being evaluated for botanical quality control and as a cost-effective approach to identify and discriminate between closely related botanical species. This paper describes orthogonal identification of Panax ginseng, P. quinquefolius, and P. notoginseng materials in commerce as an example of the development and validation of a set of species-specific polymerase chain reaction methods to establish botanical identity in ginseng roots. This work also explored the possibility of extending the application of species-specific polymerase chain reaction methods to provide species identity information for processed materials, such as steamed roots and hydroalcoholic extracts, and showed success with this approach. Finally, the paper provides recommendations for an out-of-specification investigation of samples that may pass some of the orthogonal tests and fail others.

The unending outburst of COVID-19 has reinforced the necessity of SARS-CoV-2 identification approaches for the prevention of infection transmission and the proper care of severe and critical patients. As there is no cure, a prompt and reliable diagnosis of SARS-CoV2 is vital to counter the spread and to provide adequate care and treatment for the infection. Currently, RT-PCR is a gold standard detection method for the qualitative and quantitative detection of viral nucleic acids. Besides, enzyme-linked immunosorbent assay is also a primarily used method for qualitative estimation of viral load. However, almost all the detection methods have their pros and cons in terms of specificity, accuracy, sensitivity, cost, time consumption, the need for sophisticated laboratories, and the requirement of skilled technical experts to carry out the detection tests. Thus, it is suggested to integrate different techniques to enhance the detection efficiency and accurateness for SARS-CoV2. This review focuses on preliminary, pre-confirmatory, and confirmatory methods of detection such as imaging techniques (chest-X-ray and chest- computed tomography), nucleic acid detection methods, serological assay methods, and viral culture and identification methods that are currently being employed to detect the presence of SARS-CoV-2 infection along with recent detection method and applicability for COVID-19.

Streptococcus suis is an emerging zoonotic bacterium causing septicemia and meningitis in humans. Due to rapid disease progression, high mortality rate, and many underdiagnosed cases by time-consuming routine identification methods, alternative diagnostic testing is essential. Among 29 broadly accepted S. suis serotypes, serotypes 2 and 14 are high prevalent, however, many PCR assays showed an inability to differentiate serotype 2 from 1/2, and 1 from 14. In this study, we developed and validated a new multiplex PCR assay that facilitates the identification of only the 29 true serotypes of S. suis and simultaneously differentiates serotypes 1, 1/2, 2, and 14 within a single reaction. Importantly, the multiplex PCR could detect S. suis directly from positive hemocultures and CSF. The results revealed high sensitivity, specificity, and 100% accuracy with almost perfect agreement (κ = 1.0) compared to culture and serotyping methods. Direct detection enables a decrease in overall diagnosis time, rapid and efficient treatment, reduced fatality rates, and proficient disease control. This multiplex PCR offers a rapid, easy, and cost-effective method that can be applied in a routine laboratory. Furthermore, it is promising for developing point-of-care testing (POCT) for S. suis detection in the future.

Streamflow drought forecasting is a key element of contemporary drought early warning systems (DEWS). The term streamflow drought forecasting (not streamflow forecasting), however, has created confusion within the scientific hydrometeorological community as well as in operational weather and water management services. Streamflow drought forecasting requires an additional step, which is the application of a drought identification method to the forecasted streamflow time series. The way streamflow drought is identified is the main reason for this misperception. The purpose of this study, therefore, is to provide a comprehensive overview of the differences between different drought identification approaches to identify droughts in European rivers, including an analysis of both historical drought and implications for forecasting. Streamflow data were obtained from the LISFLOOD hydrological model forced with gridded meteorological observations (known as LISFLOOD-Simulation Forced with Observed, SFO). The same model fed with seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts system 5 (ECMWF SEAS 5) was used to obtain the forecasted streamflow. Streamflow droughts were analyzed using the daily and monthly variable threshold methods (VTD and VTM, respectively), the daily and monthly fixed threshold methods (FTD and FTM, respectively), and the Standardized Streamflow Index (SSI). Our results clearly show that streamflow droughts derived from different approaches deviate from each other in their characteristics, which also vary in different climate regions across Europe. The daily threshold methods (FTD and VTD) identify 25 %–50 % more drought events than the monthly threshold methods (FTM and VTM), and accordingly the average drought duration is longer for the monthly than for the daily threshold methods. The FTD and FTM, in general, identify drought occurrences earlier in the year than the VTD and VTM. In addition, the droughts obtained with the VTM and FTM approaches also have higher drought deficit volumes (about 25 %–30 %) than the VTD and FTD approaches. Overall, the characteristics of SSI-1 drought are close to what is being identified by the VTM. The different outcome obtained with the drought identification methods illustrated with the historical analysis is also found in drought forecasting, as documented for the 2003 drought across Europe and for the Rhine River specifically. In the end, there is no unique hydrological drought definition (identification method) that fits all purposes, and hence developers of DEWS and end-users should clearly agree in the co-design phase upon a sharp definition of which type of streamflow drought is required to be forecasted for a specific application.

During the last decade, exponential growth in energy production by Photovoltaic systems (PVS) has been observed. Although very promising concerning energy production, PVS are often prone to faults that arise either due to environmental conditions or to the quality of materials used for their manufacturing and handling during installation. If these faults are left untreated, a risk arises both to the operation of the system itself (risk of destruction) and to its very ability to produce energy reliably. This paper discusses methods for fault detection and identification on the DC side of the photovoltaic systems. The methods are studied for their ability to identify various fault types as well as their complexity and limitations.

Among many available biometrics identification methods, finger-vein recognition has an advantage that is difficult to counterfeit, as finger veins are located under the skin, and high user convenience as a non-invasive image capturing device is used for recognition. However, blurring can occur when acquiring finger-vein images, and such blur can be mainly categorized into three types. First, skin scattering blur due to light scattering in the skin layer, second, optical blur occurs due to lens focus mismatching, and third, motion blur exists due to finger movements. Blurred images generated in these kinds of blur can significantly reduce finger-vein recognition performance. Therefore, restoration of blurred finger-vein images is necessary. Most of the previous studies have addressed the restoration method of skin scattering blurred images and some of the studies have addressed the restoration method of optically blurred images. However, there has been no research on restoration methods of motion blurred finger-vein images that can occur in actual environments. To address this problem, this study proposes a new method for improving the finger-vein recognition performance by restoring motion blurred finger-vein images using a modified deblur generative adversarial network (modified DeblurGAN). Based on an experiment conducted using two open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger-vein database and Hong Kong Polytechnic University finger-image database version 1, the proposed method demonstrates outstanding performance that is better than those obtained using state-of-the-art methods.

Antimicrobial resistance is a recognized global challenge. Tools for bacterial detection can combat antimicrobial resistance by facilitating evidence-based antibiotic prescribing, thus avoiding their overprescription, which contributes to the spread of resistance. Unfortunately, traditional culture-based identification methods take at least a day, while emerging alternatives are limited by high cost and a requirement for skilled operators. Moreover, photodynamic inactivation of bacteria promoted by photosensitisers could be considered as one of the most promising strategies in the fight against multidrug resistance pathogens. In this context, carbon dots (CDs) have been identified as a promising class of photosensitiser nanomaterials for the specific detection and inactivation of different bacterial species. CDs possess exceptional and tuneable chemical and photoelectric properties that make them excellent candidates for antibacterial theranostic applications, such as great chemical stability, high water solubility, low toxicity and excellent biocompatibility. In this review, we will summarize the most recent advances on the use of CDs as antimicrobial agents, including the most commonly used methodologies for CD and CD/composites syntheses and their antibacterial properties in both in vitro and in vivo models developed in the last 3 years.

We compared methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) captured by culture-based surveillance and MRSA septicemia hospitalizations captured by administrative coding using statewide hospital discharge data in Connecticut from 2010 to 2018. Observed discrepancies between identification methods suggest administrative coding is inappropriate for assessing trends in MRSA BSIs.

Knowledge of dynamic parameters of mechanical systems is required in different applications, particularly in the simulation and control problems. In this paper, the standard identification methods are discussed and a new methodology for identification of inertial parameters is raised when the closed chain has low mobility. The methodology includes formulating a symbolic model based on the transfer of inertial properties and a reduction using dynamic contribution indices based on CAD approximations. The new method is applied to the front suspension of an electrical vehicle. After applying the procedure, a model with few parameters that allows accurately reproducing the dynamic behaviour of the system is obtained. A novel methodology has been developed that allows the identification of dynamic parameters in low mobility mechanical systems.

Background Clostridium septicum aortitis is a lethal infection. C. septicum has a strong association with an underlying malignancy, most commonly in the colon. Aim Early identification methods and management strategies of C. Septicum infection. Materials and methods We present a 64-year-old man with aortic aneurysm and C. septicum bacteremia with unknown malignancy who passed away on the fourth day of hospitalization despite emergent endovascular intervention. Computed tomography showed periaortic gas which is the hallmark of infection. Discussion This case report highlights the need of prompt surgical treatment and its different modalities along with the early use of appropriate antibiotics due to the rapid spread of infection associated with high fatality. The authors also discuss the association of C. septicum aortitis with underlying occult malignancies. Conclusion Delay in identification and treatment of C. Septicum is associated with very high mortality rates.

ObjectiveTo synthetize the state of the art of methods for identifying candidate technologies for disinvestment and propose an evidence-based framework for executing this task.MethodsAn interpretative review was conducted. A systematic literature search was performed to identify secondary or tertiary research related to disinvestment initiatives and/or any type of research that specifically described one or more methods for identifying potential candidates technologies, services, or practices for disinvestment. An iterative and critical analysis of the methods described alongside the disinvestment initiatives was performed.ResultsSeventeen systematic reviews on disinvestment or related terms (health technology reassessment or medical reversal) were retrieved and methods of 45 disinvestment initiatives were compared. On the basis of this evidence, we proposed a new framework for identifying these technologies based on the wide definition of evidence provided by Lomas et al. The framework comprises seven basic approaches, eleven triggers and thirteen methods for applying these triggers, which were grouped in embedded and ad hoc methods.ConclusionsAlthough identification methods have been described in the literature and tested in different contexts, the proliferation of terms and concepts used to describe this process creates considerable confusion. The proposed framework is a rigorous and flexible tool that could guide the implementation of strategies for identifying potential candidates for disinvestment.

Objective:To characterize nontuberculous mycobacteria (NTM) associated with case clusters at 3 medical facilities.Design:Retrospective cohort study using molecular typing of patient and water isolates.Setting:Veterans Affairs Medical Centers (VAMCs).Methods:Isolation and identification of NTM from clinical and water samples using culture, MALDI-TOF, and gene population sequencing to determine species and genetic relatedness. Clinical data were abstracted from electronic health records.Results:An identical strain of Mycobacterium conceptionense was isolated from 41 patients at VA Medical Centers (VAMCs A, B, and D), and from VAMC A’s ICU ice machine. Isolates were initially identified as other NTM species within the M. fortuitum clade. Sequencing analyses revealed that they were identical M. conceptionense strains. Overall, 7 patients (17%) met the criteria for pulmonary or nonpulmonary infection with NTM, and 13 of 41 (32%) were treated with effective antimicrobials regardless of infection or colonization status. Separately, a M. mucogenicum patient strain from VAMC A matched a strain isolated from a VAMC B ICU ice machine. VAMC C, in a different state, had a 4-patient cluster with Mycobacterium porcinum. Strains were identical to those isolated from sink-water samples at this facility.Conclusion:NTM from hospital water systems are found in hospitalized patients, often during workup for other infections, making attribution of NTM infection problematic. Variable NTM identification methods and changing taxonomy create challenges for epidemiologic investigation and linkage to environmental sources.

Fifteen strains of Bacillus spp. (three B. cereus strains, one B. subtilis, B. macerans, B. pumilus, and B. mycoides strains, and eight unknown isolates from the food-industry) were identified on species level with three designed pairs of primers for B. cereus, B. licheniformis, and B. subtilis. Primers designed for B. licheniformis (BlichF and BlichR) and B. subtilis (BsubF and BsubR) allowed specific species identification, whereas the designed pair of primers for B. cereus (BcerF2 and BcerR2) showed specificity for B. cereus sensu lato, because both B. cereus strains and B. mycoides gave positive reaction. Bacilli identification was also carried out with routine API method with unsatisfactory results. The (GTG)₅-PCR method was used for strain characterization, enabling bacilli classification into separate clusters according to their taxonomic designations.

The popularity of the German Shepherd breed and its use in many areas of human life creates the need to search for new identification methods to establish ownership. The identification methods available, such as chipping and marking, have several disadvantages. The method of dogs’ identification by rhinarium’s imprint is comparatively cheap and reliable. Papillary patterns reflected on rhinarium’s imprint are relatively constant, and have common and particular signs of difference, which make it possible to distinguish the impression of one dog from another. Dogs of the same breed have a standardized exterior, that is, similarity in external characters, which suggests that there are similarities between common and particular characteristics within the same breed. It is found that imprints of the rhinarium of German shepherd breed dogs, obtained from dogs of different origin, housing and feeding conditions, have common features of papillary patterns peculiar to the given breed. The presented results of the comparison of skin patterns of the rhinarium show that this breed is characterized by a straight, straight round-pointed and V-shaped forms of the nose symmetry line, the majority of the representatives of the breed have prints heavily spotted with lines and, as a rule, have 2 branches of the line of symmetry, which proves the presence of breed characteristics and allows for the use of prints in forensic and forensic veterinary identification of dogs.

Background: A requirement of current good manufacturing practices for dietary supplements is that manufacturers must identify their dietary ingredients. DNA-based methods can provide species-level authentication that may sometimes be difficult to achieve using conventional morphological and chemical analysis methods. However, because of varying levels of DNA degradation in botanical materials, many commercial tests fail to generate consistent test results across all types of botanical materials. AOAC published guidelines for validation of botanical identification methods and proposed probability of identification (POI) as a method performance parameter. However, few DNA-based botanical authentication methods in the literature follow these guidelines and evaluate POI. Objective: To provide a targeted PCR method validation example that follows AOAC guidelines for validation of botanical identification methods. Methods: Using Matricaria chamomilla (chamomile) as an example, we performed a single-laboratory validation for a targeted PCR method that aimed to identify both raw and processed chamomile materials. The performance parameters of the test were evaluated by carrying out an inclusivity/exclusivity study and a Specified Superior Test Material/Specified Inferior Test Material study to demonstrate that the method’s POI meets industry requirements. Results: The chamomile samples were identified by the method and achieved a POI greater than 0.9 with respect to all types of chamomile botanical materials. Conclusions: The method was validated for DNA-based identification of raw and processed chamomile materials, such as sterilized powders and extracts. Highlights: This work will provide insight for laboratories and manufacturers that aim to develop and validate DNA-based botanical identification methods.

The application of business management systems and cooperative organizations faces the challenges of the development of business dynamics due to the development of science and technology. The use of information technology in all business actors is a form of adaptation to anticipate business development that aims to increase competitiveness and win competition. Cooperative is a business that still has obstacles in optimizing the use of IT to manage businesses in providing services to members. Therefore, through the use of the cooperative organization's business identification methods, the analysis of the guidelines for the use of cooperative online systems, training and outreach, and evaluating the implementation of activities. In this case, the community service activities can provide results that have an impact on optimizing the online cooperative system in improving services and incomes of members and cooperatives.

In a rational model, some terms of the information vector are correlated with the noise, which makes the traditional least squares based iterative algorithms biased. In order to overcome th...

Refrigeration of raw milk favours the growth of psychrotrophic bacteria (psychrotrophs) that can produce spoilage enzymes, which may deteriorate final dairy products. The psychrotrophs from raw milk across four seasons were identified using culture-dependent and culture-independent approaches. Both 16S rDNA high-throughput sequencing (HTS) and MALDI-TOF MS showed the Pseudomonas genus to predominate; however, predominant species differed between the two identification methods. There was no geographical trend in microbiota, but a seasonal variation was evident. The use of HTS and MALDI-TOF MS was complementary in describing the psychrotrophic bacterial diversity of raw milk and provides an understanding of the raw milk microflora that may influence milk quality. This is the first report to compare data obtained from 16S rDNA high-throughput sequencing and MALDI-TOF analysis to assess the psychrotrophic microbial quality of refrigerated raw milk in New Zealand.

Diatoms are among the most commonly used bioindicators. Correct taxonomic identifications are critical to their use as bioindicators because closely related diatom species can respond diffe...

Face recognition is one of current biometrics identification methods, that based on the measuring to one of human biological characteristics and utilize them to recognize individuals. these characteristics which are called biometric they are hard to fake because they identify a person by measuring one of its biological characteristics such as (finger print, iris print and face print). With the rapid improvement of mobile technologies that happen in last decade face recognition process can make using mobile phone, this paper explains the building of mobile face recognition system using Eigen face approach, Experimental results have been tested on a local data-set that has been created to analyze the efficiency of the application in various cases including different illumination conditions, variation of view, and orientation, the recognition rate of the application when testing on Galaxy Grand Prime + was 78.4. while The recognition rate when testing on Galaxy Note 5 was 82.4. The accuracy of this application can reach to 100% if we use camera with high accuracy and on good light condition.

Fragile X syndrome (FXS) is a neurogenetic syndrome characterized by cognitive impairments and high rates of autism spectrum disorder (ASD). FXS is often highlighted as a model for exploring pathways of symptom expression in ASD due to the high prevalence of ASD symptoms in this population and the known single-gene cause of FXS. Early vocalization features-including volubility, complexity, duration, and pitch-have shown promise in detecting ASD in idiopathic ASD populations but have yet to be extensively studied in a population with a known genetic cause for ASD such as FXS. Investigating early trajectories of these features in FXS may inform our limited knowledge of potential mechanisms that predict later social communication outcomes. The present study addresses this need by presenting preliminary findings which (a) characterize early vocalization features in FXS relative to low-risk controls (LRC) and (b) test the specificity of associations between these features and language and ASD outcomes. We coded vocalization features during a standardized child-examiner interaction for 39 nine-month-olds (22 FXS, 17 LRC) whose clinical outcomes were assessed at 24 months. Our results provide preliminary evidence that within FXS, associations between vocalization features and 24-month language outcomes may diverge from those observed in LRC, and that vocalization features may be associated with later ASD symptoms. These findings provide a starting point for more research exploring these features as potential early markers of ASD in FXS, which in turn may lead to improved early identification methods, treatment approaches, and overall well-being of individuals with ASD. Autism Res2019. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Although vocal features of 9-month-olds with FXS did not differ from those of low-risk controls, several features were associated with later language and ASD outcomes at 24 months in FXS. These preliminary results suggest acoustic data may be related to clinical outcomes in FXS and potentially other high-risk populations. Further characterizing these associations may facilitate understanding of biological mechanisms and risk factors associated with social communication development and ASD.

Criminal and victim identification based on crime scene images is an important part of forensic investigation. Criminals usually avoid identification by covering their faces and tattoos in the evidence images, which are taken in uncontrolled environments. Existing identification methods, which make use of biometric traits, such as vein, skin mark, height, skin color, weight, race, etc., are considered for solving this problem. The soft biometric traits, including skin color, gender, height, weight and race, provide useful information but not distinctive enough. Veins and skin marks are limited to high resolution images and some body sites may neither have enough skin marks nor clear veins. Terrorists and rioters tend to expose their wrists in a gesture of triumph, greeting or salute, while paedophiles usually show them when touching victims. However, wrists were neglected by the biometric community for forensic applications. In this paper, a wrist identification algorithm, which includes skin segmentation, key point localization, image to template alignment, large feature set extraction, and classification, is proposed. The proposed algorithm is evaluated on NTU-Wrist-Image-Database-v1, which consists of 3945 images from 731 different wrists, including 205 pairs of wrist images collected from the Internet, taken under uneven illuminations with different poses and resolutions. The experimental results show that wrist is a useful clue for criminal and victim identification. Keywords: biometrics, criminal and victim identification, forensics, wrist.

Here is presented a survey of the most common cattle identification methods comparing advantages, disadvantages and costs in the utilization of a new Identification model that uses tags with Radio Frequency Identification Devices (RFID). From there, a RFID system of evaluation is proposed for management automated. When the herd pass through a monitored portal, it can be obtained the immediate identification of each animal. Three tests were performed to verify the better position of the RFID reader in order to determine a setting for it and verify that the settings selected for the assembly of the system remains adequate. The use of this system gives the producer an electronic identification, facilitating the management, identification and control of the herd.

Meat authentication and meat adulteration have become a growing global concern leading to consumers’ demand for accurate meat identification methods. Since existing methods for meat identification are cumbersome and require specialized equipment and/or training, we developed a specific colorimetric detection method that can rapidly identify horse meat as few as 0.1% contamination in xenogeneic meat sources within 1 h with no false-positive signals observed. Specificity analysis revealed no cross-reactivity with 14 other animal species. Therefore, a rapid on-site survey based on our method can be set up to identify undeclared meat in the food supply chain and contributes to an approach on food analysis by meeting the demands for quick and easy-to-perform analytical methods.

Fusarium graminearum species complex is the major Fusarium head blight disease pathogen in worldwide. Fusarium head blight disease lead to damages on small grain cereals. The identification of the F. graminearum species complex is important for improving disease control and management. Traditional identification methods such as macroscopy and microscopy analysis need to be supported by biochemical and genetics assays. Thus, DNA sequencing-based methods is one of the most preferred, reliable, low priced in the identification methods. In the study, 54 F. graminearum isolates obtained from diseased wheat, barley and corn fields in Turkey and Iran were identified by morphological characteristics and then characterized by species-specific SCAR marker. β-tubulin, Tef1-α, 28s rDNA and Histone H3 genes amplified, purified and then sequenced. The merged multiloci length was obtained as last of all 2215 bp. These sequencing results was used to multiloci genotyping assays. Last of all, 20 isolates were determined as F. graminearum sensu stricto by multiloci genotyping analysis. Remaining isolates were identified as F. asiaticum or Fusarium sp. . The findings are important in terms of revealing the first-time identification in Turkish and Iranian isolates as F. graminearum species complex members by amplification four (β-tubulin, Tef1-α, 28s rDNA and Histone H3) highly conserved DNA regions.

Social housing of laboratory rodents is recommended whenever possible to encourage natural behavior and social dynamics. Several identification methods are used to distinguish rodents from one another. One of the most common means of identifying mice is ear punching. The effect of ear punching for identification or genotyping on the welfare of mice remains a concern, because this method negatively affects welfare in other species. To assess the influence of ear punching on the welfare of mice, we implanted telemetry units in 6 female Swiss–Webster mice and monitored heart rate, body temperature, and activity after various routine procedures. The physiologic and behavioral responses to restraint (by scruffing) only, restraint and ear punching, and routine handling for husbandry were evaluated. The mean heart rate of mice after receiving an ear punch was significantly higher than baseline values at 30 min after the procedure, and the mean body temperature was significantly increased over baseline for at least 1 h. The heart rate, body temperature, and activity levels of mice after scruffing only and routine handling did not differ from baseline values. The proportion of time mice spent head grooming, a potentially nocifensive behavior, was increased immediately after ear punching and began to decline by 60 min. We show that the physiologic stress of mice receiving an ear punch was greater than that from restraint (scruffing) alone, whereas behavioral indices of pain were unchanged, suggesting that ear punching causes a transient response in mice.

Artificial Neural Networks (ANN) have been proven applicable for updating finite-element (FE) baseline model and structural damage assessment. Most ANN-based damage identification methods use natural frequencies and mode shapes as input layer, limiting their application to quantifying single symmetrical damage in small structures. However, getting higher modal information of a structure is a crucial challenge in practice. As of late, researchers began utilizing mode shape derivatives as input layer in ANN to defeat the challenges for damage assessment in real-life structures. This study, therefore, proposes an ANN-based damage assessment method that employs the change in the first mode shape slope (CFMSS) damage index (DI) as input layer in ANN. For single-damage scenarios, the CFMSS-based DI has been able to detect, locate, and quantify the damage. For multiple-damage scenarios, the DI and corresponding stiffness reduction (SR) are fit as input and output layers, respectively, in ANN to measure the damage severity. Structural damage intensity is indicated as rate of decrease in story stiffness compared to baseline model. The efficiency of the proposed damage identification method is demonstrated through a nine-story numerical shear frame model and an experimental test on a three-story steel shear frame model.

Combining detailed temporal and spatial catch data, including catch per unit effort, with a high-resolution microsatellite genetic baseline facilitated the development of stock-specific coastal migration models for the four largest Atlantic salmon (Salmo salar) populations, Målselv, Alta, Tana and Kola rivers, contributing to the Barents Sea mixed-stock fishery. Målselv salmon displayed a restricted coastal movement with 85% of the fish captured within 20 km of their natal river. Kola salmon also demonstrated limited coastal movements in Norwegian waters, with most (> 90%) caught in eastern Finnmark. Multi-sea-winter (MSW) Alta salmon were caught west of Alta fjord across a broader stretch of coast while one-sea-winter (1SW) fish migrated more extensively along the coast prior to river entry. Tana salmon, however, were detected over a broad expanse (600 km) of the North-Norwegian coast. For all populations MSW salmon dominating catches earlier in the season (May–June) while 1SW fish were more common from July to August. This study provides an example of how traditional catch and effort information may be combined with genetic methods to obtain insights into spatial and temporal changes in Atlantic salmon catch composition and their associated migration patterns in a mixed-stock coastal fishery.

The impacts of microplastics on aquatic ecosystems and biota are gaining attention globally. Although microplastics have been widely detected in biota, there currently are few standardized detection and identification methods. The present study developed a novel one-step digestion method which was evaluated with mussel and fish samples. This method employed nitric acid and hydrogen peroxide (HNO3 :H2 O2 = 4:1 by volume) as digestion reagents, which completely digested biota samples 0.05) were observed for all tested polymers. Fourier transform infrared spectral analyses demonstrated that the method did not degrade any of the polymers except for polyethylene terephthalate. The method was demonstrated with mussel and fish samples collected from the Pearl River delta, south China, and was able to recover microplastics effectively. Overall, the present method is time-saving and easy to operate, with low procedural cross-contamination. The properties of microplastics recovered by the present method remained largely intact, greatly benefiting subsequent qualitative and quantitative analyses. Environ Toxicol Chem 2019;38:1400-1408. © 2019 SETAC.

The article focuses on the basics of criminalistics identification. The authors emphasize that the emergence of the method of criminalistics identification was initiated by the need to obtain reliable knowledge about the crime event and all its components. The need to recreate a picture of the past event and establish all the objects involved in it, and as a result, its correct legal assessment from the standpoint of current legislation, initiates the use of reliable methods based on the knowledge of the natural and exact sciences by the subjects of investigation. The role and place of the considered method in identification methods, as well as the impact of the achievements of scientific and technical progress on the improvement of identification technologies are shown. It is noted that this method, as a reliable tool of knowledge, along with the investigating crimes, is increasingly being used in other spheres of human activity. A special place belongs to him in the work of modern information systems.

Malware homology identification is important in attacking event tracing, emergency response scheme generation, and event trend prediction. Current malware homology identification methods still rely on manual analysis, which is inefficient and cannot respond quickly to the outbreak of attack events. In response to these problems, we propose a new malware homology identification method from a gene perspective. A malware gene is represented by the subgraph, which can describe the homology of malware families. We extract the key subgraph from the function dependency graph as the malware gene by selecting the key application programming interface (API) and using the community partition algorithm. Then, we encode the gene and design a frequent subgraph mining algorithm to find the common genes between malware families. Finally, we use the family genes to guide the identification of malware based on homology. We evaluate our method with a public dataset, and the experiment results show that the accuracy of malware classification reaches 97% with high efficiency.