Consolidated Data Research Articles

Does intraoperative wound irrigation with diluted povidone-iodine prevent surgical site infection in spine surgery?

This study employed meta-analysis to evaluate whether the application of intraoperative wound irrigation (IOWI) with povidone-iodine (PI) in spine surgery effectively reduces the incidence of postoperative surgical site infection (SSI). The present study was conducted strictly following the methodological guidance provided by the Cochrane Handbook. The protocol of this work was registered with PROSPERO. Two researchers independently conducted electronic searches in Medline via PubMed, Embase, Cochrane Library, and Web of Science. The bias risk of each included study was evaluated by two assessors. We performed statistical analysis on the dataset using STATA software. Fourteen studies involving a total of 6777 patients were included in the present work. The risk of bias of six included randomized controlled trials (RCTs) was considered as low-to-moderate risk, and the quality scores of the eight included retrospective cohort studies were rated as high quality. The results of this meta-analysis indicated a significant difference in the incidence of postoperative SSI between the two groups (RR = 0.29, 95% CI: (0.18, 0.47)). Moreover, patients who underwent IOWI with PI had lower rates of deep and superficial infections after spine surgery compared with the controlled group (superficial infection: RR = 0.28, 95%CI: (0.14, 0.54); Deep infection: RR = 0.24, 95%CI: (0.10, 0.60)). The sensitivity analysis results indicated good robustness and high evidence strength after data consolidation in the overall rate of postoperative SSI and the incidence of deep/superficial infection. IOWI with PI solution during spinal surgery can effectively reduce the incidence of postoperative SSI.

Prospects for the development of cartography through the integration of SLAM technology with GIS technology

Abstract. With the continued development of Simultaneous Localization and Mapping (SLAM) and Geographic Information Systems (GIS) technologies, their application scenarios have become increasingly complex. Combining these technologies can significantly enhance operational efficiency in challenging environments. This paper presents an analysis of existing cases where SLAM and GIS technologies have been integrated, demonstrating that such a merger facilitates the consolidation and complementarity of spatial data. This integration allows robots or systems to simultaneously utilize the global information provided by GIS and the dynamic local data captured by SLAM for a more comprehensive and detailed environmental analysis, which is highly beneficial for the field of cartography. Further research has developed a series of operational procedures for integrating SLAM and GIS, utilizing MATLAB as a tool. This study also reviews several existing technical challenges, including real-time performance and computational capacity, environmental complexity and dynamic changes, and multi-scale data processing, and proposes potential solutions. The paper concludes by predicting that the integration of SLAM and GIS will play a crucial role in areas such as smart city management and disaster emergency response, indicating that this research area will become a hot topic in future cartographic technology.

Skin metastasis of BRCA mutated prostate cancer: A case report and a brief review of literature.

Metastatic castration-resistant prostate cancer has a poor prognosis especially when harboring DNA damage repair gene mutations, nevertheless, in the case of pathogenic BRCA gene mutations, PARPi demonstrated a survival benefit and is a validated treatment. Nowadays, there is no data regarding unusual metastases after these drugs. Cutaneous metastases appear rarely in prostate cancer and were associated with a worse prognosis. Moreover, there are no consolidated data concerning skin tropism of prostate cancer cells, neither in the case of BRCA-associated cancers. Here, we report the case of a patient with a long history of BRCA1-mutated metastatic castration-resistant prostate cancer who developed a skin lesion on the scalp while on his fifth line of systemic therapy with olaparib. After a complete radical surgical excision, the pathology report showed prostate cancer localization. A diagnosis of skin metastasis from prostate cancer was reported. The patient then continued olaparib therapy; after 7 months from excision, he experienced further bone and biochemical progression but not cutaneous progression. A literature review of all reported cases of cutaneous metastasis in prostate cancer was conducted to shed light on the incidence, clinical presentation, diagnosis, treatment, and prognosis of this entity. We also reviewed published cases of skin metastasis in BRCA-associated cancers with an effort to correlate skin involvement with PARPi treatment, BRCAness status, and prognosis.

The potential of federated learning for self-configuring medical object detection in heterogeneous data distributions

Medical Object Detection (MOD) is a clinically relevant image processing method that locates structures of interest in radiological image data at object-level using bounding boxes. High-performing MOD models necessitate large datasets accurately reflecting the feature distribution of the corresponding problem domain. However, strict privacy regulations protecting patient data often hinder data consolidation, negatively affecting the performance and generalization of MOD models. Federated Learning (FL) offers a solution by enabling model training while the data remain at its original source institution. While existing FL solutions for medical image classification and segmentation demonstrate promising performance, FL for MOD remains largely unexplored. Motivated by this lack of technical solutions, we present an open-source, self-configuring and task-agnostic federated MOD framework. It integrates the FL framework Flower with nnDetection, a state-of-the-art MOD framework and provides several FL aggregation strategies. Furthermore, we evaluate model performance by creating simulated Independent Identically Distributed (IID) and non-IID scenarios, utilizing the publicly available datasets. Additionally, a detailed analysis of the distributions and characteristics of these datasets offers insights into how they can impact performance. Our framework’s implementation demonstrates the feasibility of federated self-configuring MOD in non-IID scenarios and facilitates the development of MOD models trained on large distributed databases.

RNA-Binding Proteins as Novel Effectors in Osteoblasts and Osteoclasts: A Systems Biology Approach to Dissect the Transcriptional Landscape.

Bone health is ensured by the coordinated action of two types of cells-the osteoblasts that build up bone structure and the osteoclasts that resorb the bone. The loss of balance in their action results in pathological conditions such as osteoporosis. Central to this study is a class of RNA-binding proteins (RBPs) that regulates the biogenesis of miRNAs. In turn, miRNAs represent a critical level of regulation of gene expression and thus control multiple cellular and biological processes. The impact of miRNAs on the pathobiology of various multifactorial diseases, including osteoporosis, has been demonstrated. However, the role of RBPs in bone remodeling is yet to be elucidated. The aim of this study is to dissect the transcriptional landscape of genes encoding the compendium of 180 RBPs in bone cells. We developed and applied a multi-modular integrative analysis algorithm. The core methodology is gene expression analysis using the GENEVESTIGATOR platform, which is a database and analysis tool for manually curated and publicly available transcriptomic data sets, and gene network reconstruction using the Ingenuity Pathway Analysis platform. In this work, comparative insights into gene expression patterns of RBPs in osteoblasts and osteoclasts were obtained, resulting in the identification of 24 differentially expressed genes. Furthermore, the regulation patterns upon different treatment conditions revealed 20 genes as being significantly up- or down-regulated. Next, novel gene-gene associations were dissected and gene networks were reconstructed. Additively, a set of osteoblast- and osteoclast-specific gene signatures were identified. The consolidation of data and information gained from each individual analytical module allowed nominating novel promising candidate genes encoding RBPs in osteoblasts and osteoclasts and will significantly enhance the understanding of potential regulatory mechanisms directing intracellular processes in the course of (patho)physiological bone turnover.

Linkability measures to assess the data characteristics for record linkage.

Accurate record linkage (RL) enables consolidation and de-duplication of data from disparate datasets, resulting in more comprehensive and complete patient data. However, conducting RL with low quality or unfit data can waste institutional resources on poor linkage results. We aim to evaluate data linkability to enhance the effectiveness of record linkage. We describe a systematic approach using data fitness ("linkability") measures, defined as metrics that characterize the availability, discriminatory power, and distribution of potential variables for RL. We used the isolation forest algorithm to detect abnormal linkability values from 188 sites in Indiana and Colorado, and manually reviewed the data to understand the cause of anomalies. We calculated 10 linkability metrics for 11 potential linkage variables (LVs) across 188 sites for a total of 20680 linkability metrics. Potential LVs such as first name, last name, date of birth, and sex have low missing data rates, while Social Security Number vary widely in completeness among all sites. We investigated anomalous linkability values to identify the cause of many records having identical values in certain LVs, issues with placeholder values disguising data missingness, and orphan records. The fitness of a variable for RL is determined by its availability and its discriminatory power to uniquely identify individuals. These results highlight the need for awareness of placeholder values, which inform the selection of variables and methods to optimize RL performance. Evaluating linkability measures using the isolation forest algorithm to highlight anomalous findings can help identify fitness-for-use issues that must be addressed before initiating the RL process to ensure high-quality linkage outcomes.

Efficacy of Different Doses and Forms of the GLP-1 Receptor Agonist Semaglutide in Weight Reduction Among Non-diabetic Obese or Overweight Populations.

Disorders linked to increased body weight are on the rise and obesity is a global epidemic associated with a rising risk for developing comorbidities, such as hypertension or type 2 diabetes. There is a significant need to develop a multimodal approach targeting obesity within clinical medicine. Pharmacological options to produce weight loss have been a popular research area and the novel glucagon-like Peptide-1 receptor agonists (GLP-1 RA) are highly effective glycemic control agents that have shown a substantial weight loss effect. This systematic review explores the efficacy of semaglutide, a GLP-1 RA agent, in a non-diabetic population, looking at endpoints of changes in weight and waist circumference and the percentage of patients achieving a clinically effective weight loss of at least 5%. This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A comprehensive search was undertaken to find applicable papers using three databases, including PubMed, PubMed Central, and Cochrane Library. The included articles were narrowed down from an initial pool of 423 papers using filters, automation tools, inclusion/exclusion criteria, and quality appraisal tools. In this systematic review, we have analyzed 10 high-quality studies published in the last five years, including nine randomized control trials (RCTs) and a retrospective cohort study. The aim was to combine the results of these studies, encompassing 6623 participants, to showcase the effectiveness of GLP-1 RAs in the non-diabetic obese or overweight population. The consolidated data from the literature in this systematic review endorses the use of semaglutide as a highly efficient weight-reducing agent, contributing positive insight to both clinicians and researchers in the field of obesity treatment.

Colorectal Leiomyosarcoma: Demographics Patterns, Treatment Characteristics, and Survival Analysis in the U.S. Population.

Colorectal leiomyosarcoma (CR-LMS) is a rare neoplasm arising from smooth muscle cells. It accounts for less than 0.1% of all colorectal malignancies. In this population-based study, we aim to understand the demographics, treatment characteristics, and pathologic factors associated with survival in CR-LMS. Data from the SEER Program (2000-2018) were analyzed using SEER*Stat and SPSS. Statistical methods included descriptive analysis, Kaplan-Meier survival curves, log-rank tests, and Cox proportional hazards regression to assess the impact of various factors on disease-specific and overall survival. A total of 191 cases of CR-LMS were identified. Most patients were 60-69years of age (median: 64years) and Caucasian (78%). There was nearly the same distribution in sex (M:F ratio; 1:1.2). The overall 5-year observed survival was 50.3% (95% C.I., 46.3-54.2). The 5-year disease-specific survival (DSS) was 66.1% (95% C.I., 62.0-70.1). The 5-year overall survival after resection was 60.8% (95% C.I., 56.3-65.3). Multivariable analysis identified grades III and IV (p = 0.028) as negative predictors of overall survival. Regional spread and distant stage are negative predictors of overall survival (p < 0.01). Our data reveals that colorectal leiomyosarcoma (CR-LMS) often presents in patients around 64years old with advanced stages and poor differentiation. Key adverse prognostic factors include older age, high tumor grade, large tumor size, and distant metastases, with surgical resection showing the best survival outcomes. To improve outcomes, further research and consolidation of data are essential for developing targeted therapies and comprehensive guidelines.

The architecture of the system of intelligent analysis of competencies for updating university educational programs

The problem of updating educational programs and educational content is closely related to the need to adapt them to the changing conditions of regional labor markets. The solution to the problem is achieved through the collection and analysis of data on the competence requirements of employers for the qualifications of specialists (the data itself can be extracted from job advertisements on open resources on the Internet). The article presents a method of using search robots and text data processing modules to obtain formalized descriptions of competence requirements and compare them by degree of similarity with competence descriptions from educational programs. For a comparative analysis of competency requirements, a clustering method was developed using fuzzy logic and a neural network. The architecture of the collection and intelligent analysis system includes search crawlers, a module for parsing text data with cleaning from information noise, a data consolidation module, a keyword dictionary generation module, a module for synthesizing competency vectors, a clustering module, a distributed registry of formalized descriptions of competencies, etc. The results of the comparative analysis are crucial for assessing the compliance of competencies of specialists trained in regional universities with the requirements of employers in the regions. The assessment allows to develop recommendations for updating educational programs and educational content of an educational institution, as well as to carry out adaptive adjustment of information and educational environment components of higher educational institutions.

Enhancing Healthcare Informatics: Integrating Category Theory Reasoning into OMOP-CDM Ontology Model.

The task of managing diverse electronic health records requires the consolidation of data from different sources to facilitate clinical research and decision-making support, with the emergence of the Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) as a standard relational database schema for structuring health records from different sources. Working with ontologies is strongly associated with reasoners. Implementing them over expansive and intricate Ontologies can pose computational challenges, potentially resulting in slow performance. In this paper, we propose the implementation of a new reasoner based on categorical logic over a translation of OMOP-CDM into an ontology model. This enables enhancements to the efficiency and scalability of implementing such models.

Association of information technology ability, workplace social engagement, and successful ageing: validation of a short measure with three African samples

This study examined the association of workplace social engagement (WSE) and information technology ability (ITA) with successful ageing and validated a brief scale measuring WSE. The interaction of WSE and ITA on successful ageing was also assessed. A cross-sectional design was adopted, and the participants were 1186 older adults living in Kenya (n = 350), Nigeria (n = 260), and Ghana (n = 576). Pearson’s correlation and factor analyses of two datasets (i.e., waves 1 and 2) from the sample were utilised to validate the WSE scale. Hierarchical linear regression analyses with relevant sensitivity analyses were utilised to assess the associations with wave 2 data. The WSE scale produced satisfactory psychometric properties (i.e., reliability and validity) as a unidimensional measure. WSE and ITA were positively associated with successful ageing in Kenya and Ghana and in the consolidated data. The interaction between WSE and ITA was positively associated with successful ageing and its domains (i.e., illness avoidance, functioning, and engagement with life) in Kenya, Ghana, and consolidated data. At higher ITA or the use of information technologies, WSE is less strongly associated with successful ageing. WSE is more strongly associated with successful ageing only at moderate ITA.

Information Communication Technology's Influence on Exchange Rate

Investigated in this study is the influence of information communication technology (ICT) on the exchange rate in Zambia from 2010 to 2021. The research methods employed involve using descriptive, exploratory, and experimental designs. The results are reported focusing on ICT acquisition, usage, and production in relation to the performance of the exchange rate: imports of ICTs from the Zambia Revenue Authority (ZRA) represented the uptake while the performance of the exchange rate from the Bank of Zambia represented actual volatilities. It was further discovered that the ICTs influence movements in financial transactions by increasing the quantum of transactions. The impact on the movements of currencies advertently affects the exchange rate causing volatility. The role of ICTs in the amalgamation of markets, trade linkages, and openness, is key. Conclusively, ICTs influence the exchange rate: inherent in productive means and permeate consumptive aspects of ICTs and financial transactions. The main limitation arose from data collection on ICT software and services due to lack of a consolidated data capturing information system.

Deployment of A Data Consolidation System in an Aeronautical Administration: Application to the Rva/City of Kananga in the Democratic Republic of Congo

This article examines the specific challenges encountered by the aeronautical administration of the Airway Authority of the City of Kananga in the Democratic Republic of Congo in the management and consolidation of its aeronautical data, as well as possible solutions to remedy them. We present the different stages of the system deployment process, highlighting the technical, organizational and operational aspects to consider. Finally, the study provides an analysis of the results obtained after the implementation of the data consolidation system, highlighting the benefits and possible remaining challenges.

Towards Millions of Database Transmission Services in the Cloud

Alibaba relies on its robust database infrastructure to facilitate realtime data access and ensure business continuity despite regional disruptions. To address these operational imperatives, Alibaba developed the Data Transmission Service (DTS), which has become critical for internal applications and public cloud services alike. This paper presents a comprehensive study of the architectural innovations, resource scheduling mechanisms, and performance optimization strategies that have been implemented within DTS to tackle the significant challenges of cross-network, heterogeneous data transmission in a cost-effective manner. We explore the novel Any-to-Any (A2A) architecture, which simplifies the complexity of data paths between diverse databases and mitigates network connectivity issues, thereby significantly reducing development overhead. Additionally, we examine a dynamic network bandwidth scheduling algorithm that effectively maintains Service-Level Objectives (SLOs), complemented by a serverless mechanism that ensures efficient resource utilization. Furthermore, DTS utilizes advanced strategies such as transaction dependency tracking, hot data consolidation, and batching to enhance synchronization performance and efficiency. DTS has distilled the lessons learned from years of serving our customer base and currently supports nearly 1 million public cloud instances annually. Our evaluation results show that DTS can effectively and efficiently handle real-time data transmission in both experimental and production environments.

Clear Cell Papillary Renal Cell Carcinoma: Case Series with Review of the Literature

Abstract Clear cell papillary renal cell carcinoma (CCPRCC) was included as a separate entity in the World Health Organisation classification of renal tumours in 2016. Immuno-histopathological and genetic characteristics are much known, but still, clinical features and long-term follow-up require more consolidated data. We report three cases of CCPRCC, detected in different clinical settings. The first case was co-incidentally diagnosed on routine imaging, in a follow-up case of open cystolithotripsy. The second case presented with haematuria under evaluation; and the third case presented with mild thrombocytopenia without anaemia or a positive Coombs test. All the cases revealed characteristic histopathological examination and immunohistochemistry. None of the patients showed tumour recurrence/metastases on follow-up. The study holds importance as it presents CCPRCC cases in different clinical scenarios, describes a rarely reported case of thrombocytopenia without associated anaemia/positive Coombs test, and emphasises the need to consider CCPRCC in relevant clinico-radiological settings and immuno-histopathological features, due to its indolent nature and consequently better prognosis.

BANKING SUPERVISION THROUGH SUPTECH

Victor Ababii E-mail: ababii.victor@gmail.com PhD student, Academy of Economic Studies of Moldova Head of Division, Reporting and Statistics Department, National Bank of Moldova Chișinău, Republic of Moldova https://orcid.org/0009-0008-9573-0204 Abstract: The banking system requires continuous development to enhance stability and integrity of the financial sector by implementing the latest technological tools. Maintaining a sustainable financial system, resilient to the shocks generated by the digital age and able to adapt and prevent emerging cyber risks remains a priority for supervisory institutions, in response to financial supervision and risk management framework through leveraging state-of-the-art technology for better governance and control. SupTech's primary objective is to promote inclusion in the financial sector by focusing on governance management and digital financing. It`s goal is to support the development of an inclusive financial system built on a robust digital infrastructure that enhances efficiency, transparency, and resilience. As technological innovations advance, financial services have to evolve, requiring financial institutions to adapt their surveillance technologies (SupTech) to safeguard citizen interests. Establishing both normative and technical SupTech frameworks helps supervisory authorities to eliminate gaps in internal processes, streamline workflows, fortify risk management, protect consumers, and maintain financial system stability amid ongoing market changes. Implementing a SupTech framework involves several stages: diagnosing the institution's current state, defining the purpose, vision, and objectives of SupTech, identifying key drivers and adaptive capabilities, evaluating necessary actions, and detailing steps for successful implementation. A thorough diagnosis of the supervisory institution involves IT solutions that facilitate continuous bank monitoring and offsite supervision. This diagnostic analysis includes: (1) Data products – identifying the position and trends to reach the level of AI-augment business intelligence tools; (2) Analytics – aiming to implement analytics tools perspectives; (3) Access controls - with the position on the agency - wide access; (4) Storage – oriented towards the use target of big data tools; (5) Validation and processing – aiming at the use of advanced data processing; (6) Collection – using the latest AI-based collection trends or alternative data sources. Configuring the SupTech framework involves transforming vulnerabilities into opportunities. For example, automating data flows can address deficiencies in manual data handling, risk assessment, and report generation. Implementing a data warehouse can solve challenges related to data consolidation, multiple analysis tools, system integration complexity, and data correlation. SupTech aims to provide supervisory authorities with reliable, high-quality data, aligning with best practices in governance and data management. This ensures consistency in the supervisory process and resilience to technological advancements. Digitizing surveillance processes is crucial for operational efficiency, with advanced analytics and business intelligence solutions enhancing surveillance information and decision-making. Keywords: SupTech, Fintech, Regtech, risks, data model. JEL classification: G21

Developing Student Character Education Through Islamic Boarding School Culture In Islamic Elementary Schools

Many problems occur in the world of education, especially moral (character) crises among students, some of which are violence between students, promiscuity, free sex, and drugs. School participation is really needed to deal with the problems that occur, and teachers are the main spearheads in the process of forming student character at school. This research aims to develop character education through Islamic boarding school culture in Islamic elementary schools. This type of research is qualitative research using a phenomenological approach with the aim of describing, understanding, and interpreting the phenomena that occur. This research was conducted at an Islamic school located on Sumenep Island. The reason the researcher conducted research at an Islamic elementary school was that this school was unique in implementing Islamic boarding school culture into the school environment. The research subjects used a purposive sampling technique because the target informants met the research objective criteria. The informants in this research were school principals, religious teachers, and students. Data collection techniques in this research used semi-structured interviews and non-participant observation. Data analysis in this research uses Miles and Huberman's theory, which includes data collection, data consolidation, data display, and drawing conclusions or data verification. The research results show that Islamic boarding schools are one of the educational institutions in Indonesia that contribute to developing the character of students in the form of culture and can be applied at the elementary, middle, and high school levels by internalizing the process of instilling attitudes within oneself through coaching and guidance. Additionally, development.

Applicability of selected Brighton Collaboration case definitions in low-resource settings: A prospective hospital-based active surveillance in Addis Ababa, Ethiopia

IntroductionStandardizing case definitions for priority vaccine safety conditions facilitates uniform evaluation and consolidation of data obtained from different settings. The Brighton Collaboration case definitions (BCCD) were created to support this harmonization and enable classification from level 1 (most certain) to level 5 (not a case) of certainty. Assessing the performance of BCCD in practice is critical, particularly in resource-limited settings, where many new vaccines may be introduced without prior monitoring in high-income countries. We assessed the performance of BCCD in Addis Ababa, Ethiopia, as applicable to COVID-19 and other vaccines. MethodsActive surveillance was conducted at Tikur Anbessa Specialized Hospital, the largest referral hospital in Ethiopia. During June 1, 2022–May 31, 2023, three trained physicians prospectively identified patients eligible for COVID-19 vaccination (regardless of vaccine receipt) who presented with one or more of eleven pre-specified adverse events of special interest (AESI) from the emergency department and inpatient wards. Standardized data collection forms were used to capture patient information and assign level of certainty (LOC), regardless of vaccination status for COVID-19. We conducted descriptive analysis to characterize cases and the LOCs reached for each AESI. ResultsWe detected 203 AESI cases. The most detected conditions were thrombosis and thromboembolism (n = 100, 49 %) and generalized convulsions (n = 38, 19 %). Ninety-six percent of the cases were confirmed at levels 1–3 (n = 187) or level 5 (n = 9) LOC. Non-classifiable (level 4) cases were observed for pericarditis (n = 2), encephalitis (n = 2), myelitis (n = 2), and generalized convulsion (n = 1). ConclusionThe BCCD were successfully applied in > 95 % of cases in a large referral hospital in Ethiopia, with generalized convulsion, pericarditis, and encephalomyelitis as the exceptions. We recommend further evaluation in other low-resource settings, particularly in rural or non-referral hospitals, to gain additional insights into performance of these definitions for revision or adaptation, as needed.

TORO Indexer: a PyTorch-based indexing algorithm for kilohertz serial crystallography.

Serial crystallography (SX) involves combining observations from a very large number of diffraction patterns coming from crystals in random orientations. To compile a complete data set, these patterns must be indexed (i.e. their orientation determined), integrated and merged. Introduced here is TORO (Torch-powered robust optimization) Indexer, a robust and adaptable indexing algorithm developed using the PyTorch framework. TORO is capable of operating on graphics processing units (GPUs), central processing units (CPUs) and other hardware accelerators supported by PyTorch, ensuring compatibility with a wide variety of computational setups. In tests, TORO outpaces existing solutions, indexing thousands of frames per second when running on GPUs, which positions it as an attractive candidate to produce real-time indexing and user feedback. The algorithm streamlines some of the ideas introduced by previous indexers like DIALS real-space grid search [Gildea, Waterman, Parkhurst, Axford, Sutton, Stuart, Sauter, Evans & Winter (2014). Acta Cryst. D70, 2652-2666] and XGandalf [Gevorkov, Yefanov, Barty, White, Mariani, Brehm, Tolstikova, Grigat & Chapman (2019). Acta Cryst. A75, 694-704] and refines them using faster and principled robust optimization techniques which result in a concise code base consisting of less than 500 lines. On the basis of evaluations across four proteins, TORO consistently matches, and in certain instances outperforms, established algorithms such as XGandalf and MOSFLM [Powell (1999). Acta Cryst. D55, 1690-1695], occasionally amplifying the quality of the consolidated data while achieving superior indexing speed. The inherent modularity of TORO and the versatility of PyTorch code bases facilitate its deployment into a wide array of architectures, software platforms and bespoke applications, highlighting its prospective significance in SX.

An Updated Review of the Marine Ornamental Fish Trade in the European Union

Wild-caught fish from coral reefs, one of the most threatened ecosystems on the planet, continue to supply the marine aquarium trade. Despite customs and veterinary checks during imports, comprehensive data on this global industry remain scarce. This study provides consolidated data on the largest import market by value, the European Union (EU): a 24-million-euro annual trade value, detailing the main exporting and importing countries, as well as the species and families of the 26 million specimens imported between 2014 and 2021. A watchlist alert system based on the number of specimens traded, import trends, and vulnerability index according to FishBase and the IUCN Red List conservation status is presented, providing key information on which species should require closer scrutiny by authorities. While the European TRAde Control and Expert System (TRACES) electronically monitors the movement of live animals to respond quickly to biosecurity risks, one-third of marine ornamental fish imported lack species-level information. With minor adjustments, TRACES holds the potential to significantly enhance data granularity and the monitoring of wildlife trade, with marine ornamental fish being an interesting case study to validate this approach.