Consistent Identification Research Articles

Population-Level Surveillance of Domestic Assaults in the Home Using the National Emergency Medical Services Information System (NEMSIS)

Violence in the home, including partner violence, child abuse, and elder abuse, is pervasive in the United States. An informatics approach allowing automated analysis of administrative data to identify domestic assaults and release timely and localized data would assist preventionists to identify geographic and demographic populations of need and design tailored interventions. This study examines the use of an established national dataset, the NEMSIS 2019, as a potential annual automated data source for domestic assault surveillance. An algorithm was used to identify individuals who utilized emergency medical services (EMS) for a physical assault in a private residence (N = 176,931). Descriptive analyses were conducted to define the identified population and disposition of patients. A logistic regression was performed to predict which characteristics were associated with consistent domestic assault identification by the on-scene EMS clinician and dispatcher. The sample was majority female (52.2%), White (44.7%), urban (85.5%), and 21–29 years old (24.4%). A disproportionate number of those found dead on scene were men (74.5%), and female patients more often refused treatment (57.8%) or were treated and then released against medical advice (58.4%). Domestic assaults against children and seniors had higher odds of being consistently identified by both the dispatcher and EMS clinician than those 21–49, and women had lower odds of consistent identification than men. While a more specific field to identify the type of domestic assault (e.g., intimate partner) would help inform specialized intervention planning, these data indicate an opportunity to systematically track domestic assaults in communities and describe population-specific needs.

Localization of critical isthmus in left atrial macroreentrant tachycardias: insights from peak frequency mapping during sinus rhythm and coronary sinus pacing

Abstract Background Successful ablation of left atrium macroreentrant tachycardia (LAMRT) has advanced significantly, benefiting from an enhanced understanding of reentrant mechanisms and technological innovations. However, challenges like difficult inductions and the presence of multiple coexistent LAMRTs can complicate tachycardia mapping, potentially resulting in recurrences during follow-up. Consequently, there is a growing need for strategies that focus on pinpointing the critical isthmus of conduction (CIC) in LAMRT circuits outside of tachycardia episodes. Peak frequency (PF) mapping, with its ability to discern near-field (NF) from far-field (FF) electrograms (EGMs) in low voltage areas, emerges as a valuable tool for precisely identifying these critical isthmuses. Purpose This study aims to investigate whether the accurate identification of the CIC in LAMRT circuits is achievable through PF mapping during sinus rhythm or coronary sinus pacing. Methods Patients with sustained LAMRT undergoing radiofrequency (RF) ablation were enrolled. Utilizing a 16-pole grid catheter (HD-Grid), bipolar voltage and activation maps were generated during LAMRT. PF maps were retrospectively computed. Voltage, activation, and PF maps were also created during sinus rhythm and coronary sinus pacing (500 ms). Potential CICs (P-CICs) were defined as areas with low voltage (<0.2 mV) and high PF (>250 Hz), covering a surface area of <2 cm2 by adjusting the PF threshold above 250 Hz. Only instances of LAMRT terminated by focal RF lesions (boundary <1.5 cm diameter) were included. Confirmed CICs (C-CICs) were identified at sites where LAMRT termination occurred due to RF application. The localization of P-CICs found during LAMRT, sinus rhythm (SR), and coronary sinus pacing (CSP) mapping, in relation to that of CIC, was analyzed, considering them matching if separated by <10 mm. Results Twenty-four consecutive patients with 26 atrial tachycardias (ATs) were prospectively enrolled, including 18 LAMRTs (270±63 ms) terminated by 2.2±2.9 focal RF lesions after 9.4±8.2 sec of energy delivery. A P-CIC was consistently identified at the tachycardia termination site during LAMRT mapping, with a visual cutoffs discrimination score (1-5) of 4.1±0.7, a mean P-P voltage cutoff <0.22±0.09 mV, and a mean PF cutoff >330±67 Hz in all patients. Importantly, P-CICs found during SR and CSP mapping matched those found during LAMRT mapping in all patients (figure). Conclusion PF mapping demonstrates precision in identifying the critical isthmus in LAMRT circuits during actual tachycardia episodes. Notably, the consistent identification of the CIC through PF mapping during sinus rhythm or coronary sinus pacing exhibits a high correlation with findings during LAMRT mapping. This approach holds promising potential for reducing arrhythmia recurrences, especially in scenarios involving multiple or unmappable LAMRTs.

Precise and consistent genotype identification for essential oil yield and ursolic acid in a multi-year assessment of basil

In the current study, sixteen basil genotypes in India were analyzed over three years using the AMMI stability model. The study years explained the bulk of the variation in all nine variables. The vast amount of data required for this inquiry hinders plant breeding processes. For basil's two most critical features, ursolic acid and essential oil yield, we found that genotypes 6, 11, and 12 were the best and most consistent options for practically every analyzed property. Furthermore, there were substantial + ve and -ve correlations among the major characteristics that drove stable line selection. Trait associations and stability-related factors both influence stable genotype selection in basil. Based on the results, genotypes 6, 11, and 12 may be recommended for large-area cultivation.

Automatic Identification of Hate Speech - A Case-Study of alt-Right YouTube Videos.

Identifying hate speech (HS) is a central concern within online contexts. Current methods are insufficient for efficient preemptive HS identification. In this study, we present the results of an analysis of automatic HS identification applied to popular alt-right YouTube videos. This essay describes methodological challenges of automatic HS detection. The case study concerns data on a formative segment of contemporary radical right discourse. Our purpose is twofold. (1) To outline an interdisciplinary mixed-methods approach for using automated identification of HS. This bridges the gap between technical research on the one hand (such as machine learning, deep learning, and natural language processing, NLP) and traditional empirical research on the other. Regarding alt-right discourse and HS, we ask: (2) What are the challenges in identifying HS in popular alt-right YouTube videos? The results indicate that effective and consistent identification of HS communication necessitates qualitative interventions to avoid arbitrary or misleading applications. Binary approaches of hate/non-hate speech tend to force the rationale for designating content as HS. A context-sensitive qualitative approach can remedy this by bringing into focus the indirect character of these communications. The results should interest researchers within social sciences and the humanities adopting automatic sentiment analysis and for those analysing HS and radical right discourse. Automatic identification or moderation of HS cannot account for an evolving context of indirect signification. This study exemplifies a process whereby automatic hate speech identification could be utilised effectively. Several methodological steps are needed for a useful outcome, with both technical quantitative processing and qualitative analysis being vital to achieve meaningful results. With regard to the alt-right YouTube material, the main challenge is indirect framing. Identification demands orientation in the broader discursive context and the adaptation towards indirect expressions renders moderation and suppression ethically and legally precarious.

Role of a Pharmacist in Postdischarge Care for Patients With Kidney Disease: A Scoping Review.

The objective was to explore and describe the role of pharmacists in providing postdischarge care to patients with kidney disease. PubMed, Embase (Elsevier), CINAHL (Ebscohost), Web of Science Core Collection, and Scopus were searched on January 30, 2023. Publication date limits were not included. Search terms were identified based on 3 concepts: kidney disease, pharmacy services, and patient discharge. Experimental, quasi-experimental, observational, and qualitative studies, or study protocols, describing the pharmacist's role in providing postdischarge care for patients with kidney disease, excluding kidney transplant recipients, were eligible. Six unique interventions were described in 10 studies meeting inclusion criteria. Four interventions targeted patients with acute kidney injury (AKI) during hospitalization and 2 evaluated patients with pre-existing chronic kidney disease. Pharmacists were a multidisciplinary care team (MDCT) member in 5 interventions and were the sole provider in 1. Roles commonly identified include medication review, medication reconciliation, medication action plan formation, kidney function assessment, drug dose adjustments, and disease education. Some studies showed improvements in diagnostic coding, laboratory monitoring, medication therapy problem (MTP) resolution, and patient education; prevention of hospital readmission was inconsistent. Limitations include lack of standardized reporting of kidney disease, transitions of care processes, and differences in outcomes evaluated. This review identifies potential roles of a pharmacist as part of a postdischarge MDCT for patients with varying degrees of kidney disease. The pharmacist's role in providing postdischarge care to patients with kidney disease is inconsistent. Multidisciplinary care teams including a pharmacist provided consistent identification and resolution of MTPs, improved patient education, and increased self-awareness of diagnosis.

A bias-correction modeling method of Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements

Modeling of Hammerstein–Wiener nonlinear systems has received a lot of attention in the signal processing community. However, all existing model identification methods may fail to provide a consistent parameter estimate for Errors-In-Variables (EIV) Hammerstein–Wiener systems, where both input–output data are contaminated by measurement white noises. In this paper, a bias-correction Least-Squares (LS) algorithm for consistent identification of EIV Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements is proposed. Firstly, the analytic expression for the estimated bias of the LS algorithm using noisy measurements for EIV Hammerstein–Wiener systems with polynomial nonlinearities is derived, which is caused by the correlation between the input–output signals and measurement noises. Secondly, a consistent estimation method for the bias-correcting term, including a recursive step and a cross-validation step based on the available noisy measurements only, is then proposed to estimate the unknown terms of noises variances and noise-free measurements in the estimated bias. The effectiveness of the proposed algorithm is demonstrated through a simulated example and a robot arm system.

The usefulness of the MALDI-TOF MS technique in the determination of dairy samples' microbial composition: comparison of the new EXS 2600 system with MALDI Biotyper platform.

This study compared the EXS 2600 system with the MALDI Biotyper for identifying microorganisms in dairy samples. Of the 196 bacterial isolates from milk, whey, buttermilk, cream, and dairy wastewater, the species and genus consistent identification between two systems showed 74% and 99%, respectively. However, the level of species identification rate exhibited a difference, which was higher in Zybio than in Bruker-76.0% and 66.8%, respectively. Notably, the EXS 2600 system performed better with certain yeast species and H. alvei, while the Biotyper excelled with Pseudomonas bacteria. Unique microbial compositions were found in 85% of dairy samples, with whey and buttermilk having the highest diversity. This research highlights the EXS 2600's potential as a reliable dairy microbial identification tool and underscores the need for a more diverse and comprehensive spectral database, despite the database's focus on clinical applications (as announced).

On the derivation of the delta formulation for decision value

This paper contains decision analytical approaches, conditions and models for the quantification of decision values for built environment systems. Specifically, (1) delta formulations of objective functions for decision value quantification are introduced, (2) conditions for decision value are identified and (3) action value analysis formulations are further developed. The delta objective functions are formulated with differences in utility, cost, and probabilities for consistent decision identification by expected utility and value. The delta formulations facilitate the direct calculation of action and information values and the explication of conditions for a positive decision value. Action value objective functions are derived in delta formulation for the action types of utility and system state actions and with action implementation states and action uncertainty models. The delta formulations are exemplified for predicted information and predicted actions values. The paper closes with a synthesis and discussion of decision values and with findings encompassing a computational effort reduction and the identification of predicted information value mechanisms.

Prevalence, risk factors and impact of delirium in adult inpatients in a tertiary care hospital: A point prevalence study.

To describe the point prevalence, risk factors and possible outcomes of delirium in inpatients. A cross-sectional point prevalence study. Delirium is an acute brain syndrome that negatively affects patients, healthcare professionals and institutions alike; it is common in inpatient settings and is preventable in about one third of cases. Although guidelines recommend systematic screening and assessment, delirium is often unrecognised, undiagnosed and uncoded. There is a lack of valid data on this patient safety indicator in German-speaking countries. The study was conducted in a tertiary care hospital in Switzerland on 5 July 2022. Specially trained registered nurses collected data from all patients meeting the inclusion criteria using CAM, ICDSC or mCAM-ED. Data were analysed descriptively with stratification by delirium status, setting and surgery. The point prevalence across all settings was 6.9% (27/390), with large variation between settings: ICU 28.6% (4/14), IMC 28.0% (7/25), wards 4.6% (15/326) and ED 4% (1/25). Surgical patients were almost twice as likely to be affected as medical patients (8.9% vs. 4.8%). Patients with delirium differed most clearly from those without by having a larger number of ICD-10 F-diagnoses, a larger number of medications and higher age, which are known risk factors. Moreover, those with delirium had more missed diagnoses, increased mortality, more adverse events and higher costs. A significant number of patients experienced delirium and adverse clinical outcomes. Missed delirium diagnoses may further jeopardise patient safety and result in lost revenue. It remains unclear to what extent the risk factors and effects of delirium are causal and what determinants underlie missed diagnoses. Consistent identification of high-risk patients and treatment settings with elevated risk, accompanied by the implementation of effective preventive and management strategies, is critical to addressing delirium.

Factors predicting mortality in hospitalised HIV-negative children with lower-chest-wall indrawing pneumonia and implications for management.

In 2012, the World Health Organization revised treatment guidelines for childhood pneumonia with lower chest wall indrawing (LCWI) but no 'danger signs', to recommend home-based treatment. We analysed data from children hospitalized with LCWI pneumonia in the Pneumonia Etiology Research for Child Health (PERCH) study to identify sub-groups with high odds of mortality, who might continue to benefit from hospital management but may not be admitted by staff implementing the 2012 guidelines. We compare the proportion of deaths identified using the criteria in the 2012 guidelines, and the proportion of deaths identified using an alternative set of criteria from our model. PERCH enrolled a cohort of 2189 HIV-negative children aged 2-59 months who were admitted to hospital with LCWI pneumonia (without obvious cyanosis, inability to feed, vomiting, convulsions, lethargy or head nodding) between 2011-2014 in Kenya, Zambia, South Africa, Mali, The Gambia, Bangladesh, and Thailand. We analysed risk factors for mortality among these cases using predictive logistic regression. Malnutrition was defined as mid-upper-arm circumference <125mm or weight-for-age z-score <-2. Among 2189 cases, 76 (3·6%) died. Mortality was associated with oxygen saturation <92% (aOR 3·33, 1·99-5·99), HIV negative but exposed status (4·59, 1·81-11·7), moderate or severe malnutrition (6·85, 3·22-14·6) and younger age (infants compared to children 12-59 months old, OR 2·03, 95%CI 1·05-3·93). At least one of three risk factors: hypoxaemia, HIV exposure, or malnutrition identified 807 children in this population, 40% of LCWI pneumonia cases and identified 86% of the children who died in hospital (65/76). Risk factors identified using the 2012 WHO treatment guidelines identified 66% of the children who died in hospital (n = 50/76). Although it focuses on treatment failure in hospital, this study supports the proposal for better risk stratification of children with LCWI pneumonia. Those who have hypoxaemia, any malnutrition or those who were born to HIV positive mothers, experience poorer outcomes than other children with LCWI pneumonia. Consistent identification of these risk factors should be prioritised and children with at least one of these risk factors should not be managed in the community.

Characterizing the absolute sound levels of ambient noise in a water tank

The underwater acoustic measurement tank used at Brigham Young University is affected by several external sound sources which, although easily identified or hypothesized about on a superficial level, must be investigated in order to determine their specific effects on data collection. Therefore, our goal is to characterize the absolute sound levels of these external acoustic sources. The measurements are conducted using B&amp;K and TC4038 hydrophones. Data are collected over frequency bands within the “flat” range of these hydrophones, facilitating the consistent identification of ambient sounds across several bands. Measurements are taken at various points of the tank in order to determine to what extent tank location affects the ambient sound. Data in Volts are converted to Pascals by applying sensitivity values. To evaluate the accuracy of levels obtained, the same method is applied to the signal from a known source and compared to the levels obtained via a deconvolution approach. The conclusions of this study are applicable in obtaining sound levels in our own tank and in other environments (e.g., aquariums). [Undergraduate Research supported by the College of Physical and Mathematical Sciences, Brigham Young University]

Spatial extent drives patterns of relative climate change sensitivity for freshwater fishes of the United States

AbstractAssessing the sensitivity of freshwater species to climate change is an essential component of prioritizing conservation efforts for threatened freshwater ecosystems and organisms. Sensitivity to climate change can be systematically evaluated for multiple species using geographic attributes such as range size and climate niche breadth, and using species traits associated with climate change sensitivity. These systematic evaluations produce relative rankings of species sensitivity to aid conservation prioritization and to identify relatively sensitive species that may otherwise be understudied or overlooked. Due in part to biogeographic constraints, species assemblages change across regions and spatial extents; yet, the degree to which spatial factors influence relative rankings of species sensitivity is unclear. The spatial extent of multispecies analyses may alter relative rankings of species climate sensitivity; alternatively, relative climate sensitivity may be conserved among spatial scales, resulting in consistent identification of sensitive species among regions and spatial extents. We investigated how spatial extent influences our understanding of relative climate sensitivity for 137 native freshwater fishes of the United States that were representative of taxonomic, trait, and geographic diversity. Using publicly available occurrence data from the Global Biodiversity Information Facility, we calculated a systematic, geographically derived index of climate change sensitivity for study species at national and regional extents, including within four major hydrologic subregions of the United States. We examined the effects of spatial extent on the relative ranking of climate sensitivity among species, and we explored relationships among climate sensitivity, species traits, and conservation status at regional and national extents. We found that climate sensitivity rankings of species were influenced by spatial extent in some specific instances, but that relative rankings were largely conserved across spatial scales. However, correlations among geographically derived climate sensitivity rankings and species traits associated with climate sensitivity were variable across scales and regions, suggesting that links between geographic rarity and species traits may be scale‐dependent in some cases. Finally, we found few associations between climate sensitivity and current conservation status among species. Systematic approaches to quantifying climate sensitivity may offer an opportunity to identify sensitive but overlooked species for pre‐listing actions such as monitoring or conservation agreements.

DNA barcoding aids in generating a preliminary checklist of the lichens and allied fungi of Calvert Island, British Columbia: Results from the 2018 Hakai Terrestrial BioBlitz.

Bioblitzes are a tool for the rapid appraisal of biodiversity and are particularly useful in remote and understudied regions and for understudied taxa. Lichens are an example of an often overlooked group, despite being widespread in virtually all terrestrial ecosystems and having many important ecological functions. We report the lichens and allied fungi collected during the 2018 terrestrial bioblitz conducted on Calvert Island on the Central Coast of British Columbia, Canada. We identified 449 specimens belonging to 189 species in 85 genera, increasing the total number of species known from Calvert Island to 194, and generated Internal Transcribed Spacer (ITS) sequences for 215 specimens from 121 species. Bryoriafurcellata, Chaenothecopsislecanactidis and C.nigripunctata were collected for the first time in British Columbia. We also found Pseudocyphellariarainierensis, which is listed as Special Concern on the federal Species at Risk Act, and other rarely reported species in British Columbia including Opegraphasphaerophoricola, Protomicarealimosa, Raesaeneniahuuskonenii and Sareadifformis. We demonstrate that DNA barcoding improves the scope and accuracy of expert-led bioblitzes by facilitating the detection of cryptic species and allowing for consistent identification of chemically and morphologically overlapping taxa. Despite the spatial and temporal limitations of our study, the results highlight the value of intact forest ecosystems on the Central Coast of British Columbia for lichen biodiversity, education and conservation.

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research.

Shape-constrained deformable brain segmentation: Methods and quantitative validation

MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.

The Impact of Patterns in Linkage Disequilibrium and Sequencing Quality on the Imprint of Balancing Selection.

Regions under balancing selection are characterized by dense polymorphisms and multiple persistent haplotypes, along with other sequence complexities. Successful identification of these patterns depends on both the statistical approach and the quality of sequencing. To address this challenge, at first, a new statistical method called LD-ABF was developed, employing efficient Bayesian techniques to effectively test for balancing selection. LD-ABF demonstrated the most robust detection of selection in a variety of simulation scenarios, compared against a range of existing tests/tools (Tajima's D, HKA, Dng, BetaScan, and BalLerMix). Furthermore, the impact of the quality of sequencing on detection of balancing selection was explored, as well, using: (i) SNP genotyping and exome data, (ii) targeted high-resolution HLA genotyping (IHIW), and (iii) whole-genome long-read sequencing data (Pangenome). In the analysis of SNP genotyping and exome data, we identified known targets and 38 new selection signatures in genes not previously linked to balancing selection. To further investigate the impact of sequencing quality on detection of balancing selection, a detailed investigation of the MHC was performed with high-resolution HLA typing data. Higher quality sequencing revealed the HLA-DQ genes consistently demonstrated strong selection signatures otherwise not observed from the sparser SNP array and exome data. The HLA-DQ selection signature was also replicated in the Pangenome samples using considerably less samples but, with high-quality long-read sequence data. The improved statistical method, coupled with higher quality sequencing, leads to more consistent identification of selection and enhanced localization of variants under selection, particularly in complex regions.

Seed classification with random forest models.

To improve forest conservation monitoring, we developed a protocol to automatically count and identify the seeds of plant species with minimal resource requirements, making the process more efficient and less dependent on human operators. Seeds from six North American conifer tree species were separated from leaf litter and imaged on a flatbed scanner. In the most successful species-classification approach, an ImageJ macro automatically extracted measurements for random forest classification in the software R. The method allows for good classification accuracy, and the same process can be used to train the model on other species. This protocol is an adaptable tool for efficient and consistent identification of seed species or potentially other objects. Automated seed classification is efficient and inexpensive, making it a practical solution that enhances the feasibility of large-scale monitoring projects in conservation biology.

Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

Person and suspicious activity detection is a major challenge for image-based surveillance systems. However, the accuracy of person detection is affected by several factors, such as the presence of the person, his trajectory, posture, complex background, and object distortion. In this work, we developed a person-focused dataset that includes various behaviors of students in an educational institution, such as cheating, theft of lab equipment, fights, and threatening situations. This dataset ensures consistent and standardized identification annotations for individuals, making it suitable for detection, tracking, and behavioral analysis of individuals. In addition, we have increased the detection accuracy through an improved architecture called YOLOv5 and introduced an efficient method for detecting global and local anomalous behaviors. This method extracts motion features that accurately describe the person’s movement, speed, and direction. To evaluate the effectiveness of our proposed approach, we validated it against our proposed database and publicly available benchmark datasets. Our method achieves state-of-the-art detection accuracy, namely 96.12%, with an error rate of 6.68% compared to existing methods. The empirical results show a significant improvement in anomalous activity detection. Our paper concludes with a summary and a discussion of possible future research directions.

New records of Provanna (Gastropoda, Provannidae) from the Costa Rica Margin and an identification key for the genus.

Consistent species identification is foundational to biological research and requires coordination among a diversity of researchers and institutions. However, such consistency may be hindered for rare organisms where specimens, identification resources, and taxonomic experts are few. This is often the case for deep-sea taxonomic groups. For example, the deep-sea gastropod genus Provanna Dall, 1918 is common at chemosynthetic sites throughout the world's oceans, yet no formal guide to these species has yet been produced. Recent exploration has recovered new specimens of Provanna from hydrocarbon seeps off the Pacific Costa Rica Margin. The current work assesses the species identity of these specimens using shell morphology, radular morphology, and genetic barcoding (mitochondrial CO1 and nuclear H3). Records of occurrence for P.laevis Warén & Ponder, 1991, P.ios Warén & Bouchet, 1986, and P.pacifica Warén & Bouchet, 1986 are herein presented from the Costa Rica Margin. A critical taxonomic review of the 29 extant species within this genus was conducted and their genetic, morphological, and biogeographical distinction assessed. In this review, genetic and morphological support was found for nearly all current species delineations except for P.glabraOkutani et al., 1992, syn. nov. and P.laevis, syn. nov., which are herein synonymized to P.laevis, and for P.ios, syn. nov. and P.goniata Warén & Bouchet, 1986, syn. nov., which are synonymized to P.ios. Finally, the first species identification key for the extant species in this genus is presented. This work clarifies the taxonomy and systematics of this deep-sea gastropod genus and contributes a novel polytomous key for use in future research.

Разработка вооружения ОКБ-43 в период эвакуации: к постановке вопроса

The paper outlines the engineering activity of the Special Design Bureau No. 43 (OKB-43, Leningrad) during its 1941-1945 evacuation to the Mari El Republic (Yoshkar-Ola City). The relevance of the research is explained by the absence of special generalizing works on this topic and the need to introduce into scientific practice the materials of state and departmental archives, which had not been published before and were declassified only due to the application of the authors in 2023. Consistent identification and study of the materials and also collating documents with the preserved hardware specimens enable comprehending the contribution of this design organization to the development of the USSR military technology in the 1940s as well as forming a view of the place and importance of that period in the OKB-43 activity. To address the specified task, it was necessary to identify and analyze the main development lines of the 1940s compared to the 1930s, the previous stage in the organization activity. In particular, the reasons for the change in the type of the experimental design works of the bureau were indicated. The conducted research disclosed and systematized a list of the major development works of the period. Prominent among the accomplished projects are cannonry, attachments of weapons for the fortified areas, anti-aerial and marine attachments, small arms, machinegun mounts, mobile personal and collective armored protection equipment, etc. The conclusion is drawn about the significance of this stage for the general development and activity of the OKB-43 in creating airborne guns and weaponry of the USSR