Success Rate Of Identification Research Articles

Ability of dog owners to identify their dogs by smell

Several studies report that olfactory cues play an important role in human life; humans are essentially able to recognize other family members and friends by their odors. Moreover, recent studies report that humans are also able to identify odors of non-conspecifics. The aim of this study was to determine whether dog owners are able to identify their dogs by smell and distinguish the odor of their own dogs from those of other dogs. A total of 53 dog owners (40 females and 13 males of different ages) volunteered to take part in this study. A number of the participants (17) owned 2 dogs; these owners took part in the study twice (i.e., working with only one dog at a time). Sterile gauze pads were used to collect odor samples from the dogs. Each pad was placed in its own sterile glass jar (750 ml) with a twist off lid until the experiment commenced. Participants were asked to identify their own dog´s odor from a line-up of 6 glass containers. This experiment demonstrated that dog owners are capable of identifying their dogs by smell on a significant level. Results of this study additionally suggested that male owners outperformed their female counterparts in the identification process. Moreover, dog owners whose dogs were housed outside had a higher success rate in identification than did participants who kept their dogs indoors with them. The dog owners found it easier to identify dogs that had been neutered, fed dry dog food and bathed less frequently. In general, younger dog owners tended to have more success when attempting to identify their dogs than did their older counterparts.

Data-Dependent Acquisition Ladder for Capillary Electrophoresis Mass Spectrometry-Based Ultrasensitive (Neuro)Proteomics.

Measurement of broad types of proteins from a small number of cells to single cells would help to better understand the nervous system but requires significant leaps in sensitivity in high-resolution mass spectrometry (HRMS). Microanalytical capillary electrophoresis electrospray ionization (CE-ESI) offers a path to ultrasensitive proteomics by integrating scalability with sensitivity. Here, we systematically evaluate performance limitations in this technology to develop a data acquisition strategy with deeper coverage of the neuroproteome from trace amounts of starting materials than traditional dynamic exclusion. During standard data-dependent acquisition (DDA), compact migration challenged the duty cycle of second-stage transitions and redundant targeting of abundant peptide signals lowered their identification success rate. DDA was programmed to progressively exclude a static set of high-intensity peptide signals throughout replicate measurements, essentially forming rungs of a "DDA ladder." The method was tested for ∼500 pg portions of a protein digest from cultured hippocampal (primary) neurons (mouse), which estimated the total amount of protein from a single neuron. The analysis of ∼5 ng of protein digest over all replicates, approximating ∼10 neurons, identified 428 nonredundant proteins (415 quantified), an ∼35% increase over traditional DDA. The identified proteins were enriched in neuronal marker genes and molecular pathways of neurobiological importance. The DDA ladder enhances CE-HRMS sensitivity to single-neuron equivalent amounts of proteins, thus expanding the analytical toolbox of neuroscience.

Fast Identification of Volcanic Tremor and Lahar Signals during the 2009 Redoubt Eruption Using Permutation Entropy and Supervised Machine Learning

Abstract Despite their usefulness for volcano monitoring, emergent seismic signals, such as volcanic tremor or signals generated by lahars, are difficult to identify with confidence in a timely fashion. Machine-learning algorithms offer an objective alternative to traditional methods of identifying such volcanoseismic signals, because they are able to handle quickly large amounts of data, while requiring little input from the user. In this work, we combine permutation entropy and centroid as well as dominant frequency with supervised machine learning to evaluate their potential in identifying volcanic tremor and lahar signals recorded during the 2009 Redoubt volcano eruption. The particular dataset was chosen for the reason that the properties and occurrence times of the volcanoseismic signals during the eruption are well known from previous studies. We find that the selected features can effectively discriminate both types of signals against the seismic background, especially for stations that are near the source. Results show that the identification success rate for volcanic tremor reaches up to 96%, whereas this rate becomes up to 91% for lahar signals. The calculation of the features as well as the application of the machine-learning algorithms is fast, allowing their implementation in the operational environment of a volcano observatory during a volcanic crisis. Finally, the proposed methodology can potentially be used to objectively identify other emergent seismic signals such as tectonic tremor along subduction zones, glacial tremor, or seismic signals generated during landslides.

Molecular and Biological Characterization of Transient Antithrombin Deficiency: A New Concept in Congenital Thrombophilia

Antithrombin deficiency, mainly but not exclusively due to SERPINC1 gene variants, is a major thrombophilia that is significantly associated to early-onset venous thromboembolism. The diagnostic algorithm of antithrombin deficiency relies on the classical biochemical-molecular sequence, adopted for all thrombophilic states. Therefore, genetic analysis of SERPINC1 is restricted to cases with confirmed antithrombin deficiency, that is, cases with at least two positive findings by using functional assays or with other relatives carrying this disorder. This strategy has enabled the identification of gene variants in up to 80% of cases with antithrombin deficiency and rendered plenty of both biochemical and genetic knowledge about antithrombin. Furthermore, defects of N-glycosylation underlie a proportion of cases with antithrombin deficiency that is not explained by SERPINC1 variants.Nevertheless, diagnosis of antithrombin deficiency still encloses some uncertain puzzling questions. Although the prevalence of antithrombin deficiency is tremendously low, the use of high-throughput nucleotide sequencing for genetic analysis of SERPINC1 in consecutive patients with thromboembolic events suggests that antithrombin deficiency might be and underestimated disorder that surreptitiously increases thrombotic risk. Additionally, false negative results by using functional methods for antithrombin deficiency screening have been reported, all involving type II deficiencies.The present study aimed to identify gene defects and mechanisms involved in a specific type of antithrombin deficiency that might be elusive to an easy diagnosis to the classic diagnostic strategy. We addressed this aim with an original approach, the selection of cases with at least a positive finding by functional methods that however was not confirmed by a second analysis in other laboratory or in other sample from the same patient: what we have called transient antithrombin deficiency.This work included a total of 444 consecutive unrelated subjects, referred to our centre from more than 20 European hospitals during 23 years (1998-2021), with potential antithrombin deficiency, based on at least one positive functional assay performed at the hospital of origin. At least a new sample from all patients was delivered to our centre, so a new functional assay (a uniform anti-FXa assay for all recruited samples) was carried out for validation.By performing a full clinical, biological and characterization, a genetic defect was observed in 84.6% of 305 cases with constitutive deficiency, those with consistent deficiency in all tested samples: 248 in SERPINC1 and 10 had N-glycosylation defects. These results are fully compatible with those obtained from other large cohorts, supporting the high success rate of identification of a SERPINC1 genetic variant in these patients, and confirmed the relevance of disorders of glycosylation among cases with not SERPINC1 defect. But more interestingly, our study identified a molecular basis explaining antithrombin deficiency in 43.9% of 139 cases who had normal antithrombin activity in at least one sample, what we called transient antithrombin deficiency. These 61 cases, all with thrombosis, had missense SERPINC1 mutations (N=48), with two recurrent mutations (p.Ala416Ser, antithrombin Cambridge II, N=15 and p.Val30Glu, antithrombin Dublin, N=12) or N-glycosylation defects (N=13). Two mechanisms explained transient deficiency: the limitation of current functional methods to detect some variants, and the influence of external factors (conformational stress, generation of thrombin, alcohol intake) on the pathogenic consequences of these mutations.Our study supports that antithrombin deficiency is underestimated and cases with moderate risk of thrombosis may be missed if only functional methods and classical diagnostic algorithms are used. Furthermore, although we must take into consideration that most cases with transient deficiency are explained by acquired deficiency and laboratory mistakes, this work shows new evidences supporting that the pathogenic effect of a gene defect may be modulated by external factors, changing the paradigm of congenital thrombophilia and making it also a transient disorder. DisclosuresNo relevant conflicts of interest to declare.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry.Graphic abstractFrom the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Surfaceome CRISPR screen identifies OLFML3 as a rhinovirus-inducible IFN antagonist

BackgroundRhinoviruses (RVs) cause more than half of common colds and, in some cases, more severe diseases. Functional genomics analyses of RVs using siRNA or genome-wide CRISPR screen uncovered a limited set of host factors, few of which have proven clinical relevance.ResultsHerein, we systematically compare genome-wide CRISPR screen and surface protein-focused CRISPR screen, referred to as surfaceome CRISPR screen, for their efficiencies in identifying RV host factors. We find that surfaceome screen outperforms the genome-wide screen in the success rate of hit identification. Importantly, using the surfaceome screen, we identify olfactomedin-like 3 (OLFML3) as a novel host factor of RV serotypes A and B, including a clinical isolate. We find that OLFML3 is a RV-inducible suppressor of the innate immune response and that OLFML3 antagonizes type I interferon (IFN) signaling in a SOCS3-dependent manner.ConclusionOur study suggests that RV-induced OLFML3 expression is an important mechanism for RV to hijack the immune system and underscores surfaceome CRISPR screen in identifying viral host factors.

Self-Identification of Cable-Driven Exoskeleton Based on Asynchronous Iterative Method

Abstract The upper limb rehabilitation exoskeleton with cable-driven parallel structure has the advantages of light weight and large payload, etc. However, due to the non-rigid nature of the actuating cables and the different body shape of the wearer, the geometric parameters of the exoskeleton have a large error. The parameter identification of cable-driven exoskeleton is of great significance. An asynchronous self-identification method for the upper limb seven degree-of-freedom (DOF) cable-driven exoskeleton was proposed and used in a wearable multi-redundant exoskeleton. Asynchronous iteration eliminates the accumulation of joint errors. High identification reliability is achieved by selecting proper identification parameters and optimizing error model.With the method, the geometric parameters of the exoskeleton can be identified by using exoskeleton joint angle and cable length data. The experiment verifies that the success rate of parameter identification for different wearers is in line with expectations, and the control precision and stability of the prototype are greatly improved after parameter identification.

Design of and Experiment with Seedling Selection System for Automatic Transplanter for Vegetable Plug Seedlings

In the process of vegetable plug seedling cultivation, packaging, and transportation, there may be missing, unhealthy or injured seedlings in the tray, which results in a missed planting or a low seedling survival rate after automatic transplanting. In this study, a seedling selection system with the function of seedlings identification, week seedlings elimination, and missing seedlings supplement was developed for an automatic transplanter. A plug seedling identification system based on a machine vision was used to detect vegetable plug seedlings based on the area characteristics of plug seedlings, stem leaves and plug bodies. The identification results were transmitted to a programmable logic controller (PLC), which controlled a nozzle to eliminate the unqualified seedlings from the conveyor belt lattice. When the empty conveyor belt lattice reaches the seedling throwing funnel, the rear conveyor belt lattice with the plug seedling is accelerated to ensure the continuity of seedlings supply. The adaptive fuzzy PID control algorithm was used to control the stepper motor of the conveyor belt to realize accurate seedling conveying and a seedling supplement. Using 30 days pepper plug seedlings as experimental seedlings, a comparative field experiment was carried out to evaluate the performance of the seedling selection system. The results showed that when the seedling selection system was turned on and the seedling extracting frequencies were 60, 80, and 100 plants/min, the success rates of plug seedling identification were 98.84%, 98.38%, and 96.99%, and the robust seedling rates were 98.05%, 97.78%, and 95.83%. The robust seedling rates were increased by 15.64%, 16.07%, and 13.89%, respectively, in contrast to turning off the seedling selection system.

Comparative Study of Parathyroid Identification Techniques Using Zuckerkandl's Tubercle Versus Parathyroid Arterial Supply as Intra-Operative Markers.

Thyroid surgeons should be able to identify factors that prevent parathyroid damage. The aim of the study was (i) to compare the effectiveness of using Zuckerkandl's Tubercle (ZT) versus superior thyroid artery(STA) and inferior parathyroid artery(ITA) as markers for identification of superior and inferior parathyroid glands and (ii) to demonstrate a series of detailed, logical and orderly operative steps to identify ZT during thyroidectomy operation. This 1-year prospective observational study was carried out in the Department of Otolaryngology in a tertiary medical institute. Out of 36 cases of thyroidectomy, parathyroid identification in Group A was based on STA and ITA and in Group B was based on ZT. The surgical steps, parathyroid location, preservation and its anatomical relations were noted. The mean age in Group A and Group B was 38.8years and 44.9years respectively with 77.4 and 62.5% SPT identified above the intersection of RLN and ITA respectively. On left side 62.5% SPT were located at 2 o'clock position and 50% at 10 o'clock location in right side. In Group A, 60.7% of IPT glands were related close to ITA while in Group B itwas 44.4%. Group B reported a higher rate of successful identification and preservation (93.75%). ZT greatly improved the reliability for localising and preserving the parathyroid glands during thyroidectomy. SPT is usually found to lie cranial to ZT, above the intersection of RLN & ITA and behind RLN whereas IPT is variable and lies below the intersection.

Data Mining of Job Requirements in Online Job Advertisements Using Machine Learning and SDCA Logistic Regression

There are currently many job portals offering job positions in the form of job advertisements. In this article, we are proposing an approach to mine data from job advertisements on job portals. Mainly, it would concern job requirements mining from individual job advertisements. Our proposed system consists of a data mining module, a machine learning module, and a postprocessing module. The machine learning module is based on the SDCA logistic regression. The postprocessing module includes several approaches to increase the success rate of the job requirements identification. The proposed system was verified on 20 most searched IT job positions from the selected job portal. In total, 9971 job advertisements were analyzed. Our system’s verification is finding all job requirements in 80% of analyzed advertisements. The detected job requirements were also compared with the Open Skills database. Based on this database and the extension of IT job positions with other typical job skills, we created a list of the most frequent job skills in selected IT job positions. The main contribution is the development of a universal system to detect job requirements in job advertisements. The proposed approach can be used not only for IT positions, but also for various job positions. The presented data mining module can also be used for various job portals.

Identification of Species by Combining Molecular and Morphological Data Using Convolutional Neural Networks.

Integrative taxonomy is central to modern taxonomy and systematic biology, including behavior, niche preference, distribution, morphological analysis, and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting, and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network [MMNet]) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently available alternative methods when tested with 10 independent data sets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species), and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy ($>$98%) in four data sets including closely related species from the same genus. The average accuracy of two modest subgenomic (single nucleotide polymorphism) data sets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image vs. gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multimodal information for integrative taxonomy, such as image, audio, video, 3D scanning, and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring, and conservation of biodiversity. [Convolutional neural network; deep learning; integrative taxonomy; single nucleotide polymorphism; species identification.].

SELEX: Critical factors and optimization strategies for successful aptamer selection.

Within the last decade, the application range of aptamers in biochemistry and medicine has expanded rapidly. More than just a replacement for antibodies, these intrinsically structured RNA- or DNA-oligonucleotides show great potential for utilization in diagnostics, specific drug delivery, and treatment of certain medical conditions. However, what is analyzed less frequently is the process of aptamer identification known as systematic evolution of ligands by exponential enrichment (SELEX) and the functional mechanisms that lie at its core. SELEX involves numerous singular processes, each of which contributes to the success or failure of aptamer generation. In this review, critical steps during aptamer selection are discussed in-depth, and specific problems are presented along with potential solutions. The discussed aspects include the size and molecule type of the selected target, the nature and stringency of the selection process, the amplification step with its possible PCR bias, the efficient regeneration of RNA or single-stranded DNA, and the different sequencing procedures and screening assays currently available. Finally, useful quality control steps and their role within SELEX are presented. By understanding the mechanisms through which aptamer selection is influenced, the design of more efficient SELEX procedures leading to a higher success rate in aptamer identification is enabled.

Identifikasi Golongan Darah O dengan Metode Absorpsi Elusi pada Sampel Darah Kering yang Terdapat pada substrat Kain Jeans dalam Waktu dan Lingkungan Berbeda

This research aims to determine the time limit needed and the environmental conditions that provide the highest success rate in identifying dried blood samples on the fabric of stretch denim, ramie denim, black coated denim, and dry denim jeans using the elution absorption method. Also, to evaluate the effect of the fabric fabric, environmental condition, time, and interactions regarding the success rate of blood group identification. The study was conducted using 3 replications for each type of jeans fabric, which was carried out by exposing blood to a jeans fabric, leaving it in an indoor and outdoor environment for up to 336 hours (14 days), then continued by making a red blood cell suspension for comparison, and examining blood groups using the absorption-elution method. From the results obtained, it is known that the blood exposed in the jeans fabric that being placed indoor for up to 336 hours (14 days) can still be identified and has a success percentage rate of up to 100%. Meanwhile, the blood exposed on the jeans fabric that was placed outdoor could no longer be identified after 264 hours (11 days). Based on the result of this study, it can be concluded that the exposure time of 366 hours (14 days) for the indoor environment can still be identified properly, while the exposure time of 264 hours (11 days) for dry blood samples in the outdoor environment can no longer be identified. The indoor environment gives a better percentage of success than the outdoor environment in identifying blood types from dry blood samples. The best types of substrates that can store blood at an exposure time of up to 336 hours (14 days) are dry denim, black coated denim, and stretch denim.

Geometric morphometric analysis of spore shapes improves identification of fungi

Morphology of organisms is an essential source of evidence for taxonomic decisions and understanding of ecology and evolutionary history. The geometric structure (i.e., numeric description of shape) provides richer and mathematically different information about an organism's morphology than linear measurements. A little is known on how these two sources of morphological information (shape vs. size) contribute to the identification of organisms when implied simultaneously. This study hypothesized that combining geometric information on the outline with linear measurements results in better species identification than either evidence alone can provide. As a test system for our research, we used the microscopic spores of fungi from the genus Subulicystidium (Agaricomycetes, Basidiomycota). We analyzed 2D spore shape data via elliptic Fourier and principal component analyses. Using flexible discriminant analysis, we achieved the highest species identification success rate for a combination of shape and size descriptors (64.7%). The shape descriptors alone predicted species slightly better than size descriptors (61.5% vs. 59.1%). We conclude that adding geometric information on the outline to linear measurements improves the identification of the organisms. Despite the high relevance of spore traits for the taxonomy of fungi, they were previously rarely analyzed with the tools of geometric morphometrics. Therefore, we supplement our study with an open access protocol for digitizing and summarizing fungal spores' shape and size information. We propagate a broader use of geometric morphometric analysis for microscopic propagules of fungi and other organisms.

The Effect of Wall Type and Environmental Conditions toward Blood Type Identification Success Rate in Identifying Criminal Evident

Findings of blood at the crime scene (TKP) can provide important information in criminal cases such as homicide. Blood findings at crime scenes are usually blood spots or traces of blood that can be found on various substrates. This study aims to determine the type of wall substrate and environmental conditions that have the ability to preserve blood better, prove by success rate in identifying blood type in ABO system until 336 hour of exposure. In this study, blood samples were exposed to three variations of wall substrates, namely plastered walls, plastered and painted walls, plastered and oil-painted walls; and in two different environmental conditions (indoor and outdoor). The method used to identify blood type in this study was absorption elution. First, blood samples from substrate was transferred to a gauze by NaCl 0,98%. Then a confirmation test was carried out to ensure that the gauze sample contains a blood sample. Positive results of the confirmation test were indicated by a change in the color of the sample to bluish green when it is dripped with H2O2 and Leuco Malachite Green (LMG). The positive sample was then dripped with antiserum to determine the blood type. The success of identification of blood groups was indicated by the agglutination in the samples that were dripped with antisera A, because the blood samples used in this study are group A. The results showed that the success rate of blood group identification on the three types of substrates in the indoor environment for 336 hours was 100%. Meanwhile, in the outdoor environment, only blood samples exposed to plastered wall substrate and wall paint for 264 hours could be identified for their blood type. It can be concluded that the three types of wall substrates have the same ability to preserve blood samples for up to 336 hours of exposure, but the environmental conditions that give the best percentage of successful blood group identification were in indoor conditions. For further research is recommended to focus on external environmental factors that have the most influence on the success of blood group identification in dried blood samples.

How Interpersonal Distance Between Avatar and Human Influences Facial Affect Recognition in Immersive Virtual Reality.

Purpose: The purpose of this study was to determine the optimal interpersonal distance (IPD) between humans and affective avatars in facial affect recognition in immersive virtual reality (IVR). The ideal IPD is the one in which the humans show the highest number of hits and the shortest reaction times in recognizing the emotions displayed by avatars. The results should help design future therapies to remedy facial affect recognition deficits.Methods: A group of 39 healthy volunteers participated in an experiment in which participants were shown 65 dynamic faces in IVR and had to identify six basic emotions plus neutral expression presented by the avatars. We decided to limit the experiment to five different distances: D1 (35 cm), D2 (55 cm), D3 (75 cm), D4 (95 cm), and D5 (115 cm), all belonging to the intimate and personal interpersonal spaces. Of the total of 65 faces, 13 faces were presented for each of the included distances. The views were shown at different angles: 50% in frontal view, 25% from the right profile, and 25% from the left profile. The order of appearance of the faces presented to each participant was randomized.Results: The overall success rate in facial emotion identification was 90.33%, being D3 the IPD with the best overall emotional recognition hits, although statistically significant differences could not be found between the IPDs. Consistent with results obtained in previous studies, identification rates for negative emotions were higher with increasing IPD, whereas the recognition task improved for positive emotions when IPD was closer. In addition, the study revealed irregular behavior in the facial detection of the emotion surprise.Conclusions: IVR allows us to reliably assess facial emotion recognition using dynamic avatars as all the IPDs tested showed to be effective. However, no statistically significant differences in facial emotion recognition were found among the different IPDs.

Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations

Individual characterization of subjects based on their functional connectome (FC), termed “FC fingerprinting”, has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

The possibility of molecular genetic identity testing of an unidentified body from the second world war

Background: In the current forensic scenario, molecular genetic analyzes are used to identify the remains of victims of natural and man-made disasters, emergencies, and war conflicts. Aim: This study aimed to summarize forensic molecular genetic examinations conducted at the RCFME for identifying the victims of the Second World War in the Russian Federation. This was conducted to assess the prospects of using DNA identification analysis for unidentified remains of bodies buried in 19391945. Material and methods: We studied 30 expert reports from 2007 to 2021 of 171 skeletal bodies from exhumed graves. The total number of objects studied was 421 (bone fragments and teeth, from 1 to 5 objects from 1 body). Results: Using the remains, genotypes of 167 people were established. The success rate of evidence-based molecular genetic identification was 98% (171 cases). Of these, positive identification was noted for 10 body remains and negative identification for 157 remains. Four skeletal bodies (2%) were deemed unsuitable for research. In most cases, indirect identification schemes were used a comparative study with putative relatives by biostatistical analysis of allelic variants of polymorphic loci of autosomal DNA as well as by direct comparative analysis of the polymorphism at the Y-chromosome DNA and mitochondrial DNA. Conclusions: Molecular genetic studies are highly effective for identifying the remains of victims of natural and man-made disasters, military conflicts, and other emergencies. The success of molecular genetic studies regarding the identification of victims of the Second World War conducted at the RCFME clearly demonstrate these possibilities. Simultaneously, it should be emphasized that this type of expertise falls to the category of rather complex molecular genetic studies, which requires the use of expanded panels of analytical test systems as well as the use of special methodological techniques and hardware and software.

Chimera Spectrum Diagnostics for Peptides Using Two-Dimensional Partial Covariance Mass Spectrometry.

The rate of successful identification of peptide sequences by tandem mass spectrometry (MS/MS) is adversely affected by the common occurrence of co-isolation and co-fragmentation of two or more isobaric or isomeric parent ions. This results in so-called `chimera spectra’, which feature peaks of the fragment ions from more than a single precursor ion. The totality of the fragment ion peaks in chimera spectra cannot be assigned to a single peptide sequence, which contradicts a fundamental assumption of the standard automated MS/MS spectra analysis tools, such as protein database search engines. This calls for a diagnostic method able to identify chimera spectra to single out the cases where this assumption is not valid. Here, we demonstrate that, within the recently developed two-dimensional partial covariance mass spectrometry (2D-PC-MS), it is possible to reliably identify chimera spectra directly from the two-dimensional fragment ion spectrum, irrespective of whether the co-isolated peptide ions are isobaric up to a finite mass accuracy or isomeric. We introduce ‘3-57 chimera tag’ technique for chimera spectrum diagnostics based on 2D-PC-MS and perform numerical simulations to examine its efficiency. We experimentally demonstrate the detection of a mixture of two isomeric parent ions, even under conditions when one isomeric peptide is at one five-hundredth of the molar concentration of the second isomer.

Application of Embedded Web Server in the Design of Ship Network Monitoring System

The frequent cross-ship communication has raised the complexity of ship network, therefore raised the requirements of monitoring system on the complicated local network on board. The traditional monitoring system is expensive and can hardly monitor cross-OS local network. Aimed at the specifications of ship network, this paper puts forward using embedded web server to design monitoring system. Through designing monitoring layer with LibPcap, designing application layer with CGI and designing user layer with JavaScript and REST, this paper finish the complete monitoring system and high improves the performance on latency and threat identification success rate.