Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

INTRODUCTION: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health. MATERIALS AND METHODS: This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting. The calculations used the AAindex matrices in parallel to the Hamming distance. The calculations were carried out with different types and values of antigenic escape thresholds, on four data sets. The results were compared using common binary classification metrics. RESULTS: Significant differentiation is shown depending on the data sets used. The best results were demonstrated by all three models for the forecast autumn season of 2022, which were preliminary trained on the February season of the same year (Auroc 0.934; 0.958; 0.956, respectively). The lowest results were obtained for the entire forecast year 2023, they were set up on data from two seasons of 2022 (Aucroc 0.614; 0.658; 0.775). The dependence of the results on the types of thresholds used and their values turned out to be insignificant. The additional use of AAindex matrices did not significantly improve the results of the models without introducing significant deterioration. CONCLUSION: More complex models show better results. When developing cross-immunity models, testing on a variety of data sets is important to make strong claims about their prognostic robustness.

Large-Scale Construction and Analysis of Amorphous Porous Polymer Network Materials.

In recent decades, data-driven methodologies have emerged as irreplaceable tools in materials science, particularly for elucidating structure-property relationships and facilitating the discovery of novel materials. However, despite the rapid development witnessed in other domains, amorphous materials have received relatively less attention in this context. The disordered atomic structure of amorphous materials resulting from irreversible reactions between building blocks has posed a difficulty in structural modeling, leading to a lack of databases that accurately reflect the amorphous nature of these materials. In this work, a database composed of 10,237 porous polymer networks (PPNs) was constructed from self-assembly simulations, resulting in the largest database of PPNs considering their amorphous characteristics. Through the distinct differences observed in comparison with existing databases, we emphasize that carefully considering the structural disorder of PPNs is essential for accurately characterizing their chemical behaviors. Machine learning models trained on the constructed database have confirmed that the macroscopic properties of amorphous PPNs can be predicted solely from the atomic structures of their monomers, implying that the characteristics of previously unseen PPNs can be assessed without the need for additional self-assembly simulations.

Trends and Most-Cited Articles on Hemispheric Surgery: A Bibliometric Analysis and Historical Review.

INTRODUCTION: Hemispheric surgery is a multistep, highly effective, and radical surgical procedure in the treatment of drug-resistant epilepsy due to extensive unilateral hemispheric disease. The procedure ranges from a resective procedure (hemispherectomy) to disconnection (hemispherotomy) and has developed substantially over the last century from morbid to elegant, minimally invasive, and routinely practiced procedures. Bearing in mind the numerous articles that have been published on hemispherectomy and hemispherotomy, we aimed to highlight the top 100 cited and impactful articles to create familiarity with the topic. We anticipate that this will be a helpful guide for clinicians and academics navigating the literature on this subject. METHODS: A Scopus title-based search on the top 100 most-cited articles on "hemispherectomy" and "hemispherotomy" was performed in September 2023 with no restrictions. The top 100 most-cited articles were then retrieved. The article title, first author, first author's specialty, country of origin, first author's institution at the time of publication, journal of publication, year of publication, citation count, and citations per year were collected. The Google Scholar database citation count for each paper was added for correlation and comprehensive coverage. RESULTS: The top 100 most-cited articles were cited 92 times per paper on average. The publication dates ranged from 1949 to 2016. The most frequently cited article "Clinical outcomes of hemispherectomy for epilepsy in childhood and adolescence" with 307 citations was published by A.M. Devlin et al. (2003) in the journal Brain. The USA was the highest publishing country (41 articles). The highest-publishing journal was Neurology. The most prolific first authors were A. Smith, J. Schramm, and J. Villemure, each with four publications. The institution with the most contributions was McGill University and its affiliated Health Centers, with nine publications in total. Neurosurgery was the most common specialty among the first authors. Most of the included studies were cohort studies or case series. CONCLUSION: We identified the top 100 cited articles on hemispherectomy and hemispherotomy using the Scopus database and supplemented our results with Google Scholar. We highlighted the most prominent authors, institutions, countries, journals, and study designs and illuminated the historical development of hemispherectomy and hemispherotomy procedures, in addition to landmark and currently trending papers.

Checklist and key to species of stink bugs (Hemiptera, Heteroptera, Pentatomidae) of Kentucky, United States of America.

Stink bugs (Heteroptera: Pentatomidae) have received a lot of attention as there are many economically important pest species. However, the status of species richness, distribution, and taxonomy remain overlooked and outdated in Kentucky (USA). Having such information at a regional scale is crucial to allow the development of suitable pest management and conservation programs. Here, the stink bug fauna of Kentucky was examined from museum specimens, literature, and public online repositories. Overall, 42 species in 28 genera and three subfamilies (Asopinae, Podopinae, and Pentatominae) are listed from Kentucky. Thirteen species are new records for Kentucky, 10 species are considered to be of economic importance and eight are strict predators. Pictures of species are provided along with the first key for the identification of the stink bug species of Kentucky.

Current Applications and Future Implications of Artificial Intelligence in Spine Surgery and Research: A Narrative Review and Commentary.

STUDY DESIGN: Narrative review. OBJECTIVES: Artificial intelligence (AI) is being increasingly applied to the domain of spine surgery. We present a review of AI in spine surgery, including its use across all stages of the perioperative process and applications for research. We also provide commentary regarding future ethical considerations of AI use and how it may affect surgeon-industry relations. METHODS: We conducted a comprehensive literature review of peer-reviewed articles that examined applications of AI during the pre-, intra-, or postoperative spine surgery process. We also discussed the relationship among AI, spine industry partners, and surgeons. RESULTS: Preoperatively, AI has been mainly applied to image analysis, patient diagnosis and stratification, decision-making. Intraoperatively, AI has been used to aid image guidance and navigation. Postoperatively, AI has been used for outcomes prediction and analysis. AI can enable curation and analysis of huge datasets that can enhance research efforts. Large amounts of data are being accrued by industry sources for use by their AI platforms, though the inner workings of these datasets or algorithms are not well known. CONCLUSIONS: AI has found numerous uses in the pre-, intra-, or postoperative spine surgery process, and the applications of AI continue to grow. The clinical applications and benefits of AI will continue to be more fully realized, but so will certain ethical considerations. Making industry-sponsored databases open source, or at least somehow available to the public, will help alleviate potential biases and obscurities between surgeons and industry and will benefit patient care.

Bioinformatics in Russia: history and present-day landscape.

Bioinformatics has become an interdisciplinary subject due to its universal role in molecular biology research. The current status of Russia's bioinformatics research in Russia is not known. Here, we review the history of bioinformatics in Russia, present the current landscape, and highlight future directions and challenges. Bioinformatics research in Russia is driven by four major industries: information technology, pharmaceuticals, biotechnology, and agriculture. Over the past three decades, despite a delayed start, the field has gained momentum, especially in protein and nucleic acid research. Dedicated and shared centers for genomics, proteomics, and bioinformatics are active in different regions of Russia. Present-day bioinformatics in Russia is characterized by research issues related to genetics, metagenomics, OMICs, medical informatics, computational biology, environmental informatics, and structural bioinformatics. Notable developments are in the fields of software (tools, algorithms, and pipelines), use of high computation power (e.g. by the Siberian Supercomputer Center), and large-scale sequencing projects (the sequencing of 100 000 human genomes). Government funding is increasing, policies are being changed, and a National Genomic Information Database is being established. An increased focus on eukaryotic genome sequencing, the development of a common place for developers and researchers to share tools and data, and the use of biological modeling, machine learning, and biostatistics are key areas for future focus. Universities and research institutes have started to implement bioinformatics modules. A critical mass of bioinformaticians is essential to catch up with the global pace in the discipline.

Development and validation of a geographic search filter for MEDLINE (PubMed) to identify studies about Germany.

While geographic search filters exist, few of them are validated and there are currently none that focus on Germany. We aimed to develop and validate a highly sensitive geographic search filter for MEDLINE (PubMed) that identifies studies about Germany. First, using the relative recall method, we created a gold standard set of studies about Germany, dividing it into 'development' and 'testing' sets. Next, candidate search terms were identified using (i) term frequency analyses in the 'development set' and a random set of MEDLINE records; and (ii) a list of German geographic locations, compiled by our team. Then, we iteratively created the filter, evaluating it against the 'development' and 'testing' sets. To validate the filter, we conducted a number of case studies (CSs) and a simulation study. For this validation we used systematic reviews (SRs) that had included studies about Germany but did not restrict their search strategy geographically. When applying the filter to the original search strategies of the 17 SRs eligible for CSs, the median precision was 2.64% (interquartile range [IQR]: 1.34%-6.88%) versus 0.16% (IQR: 0.10%-0.49%) without the filter. The median number-needed-to-read (NNR) decreased from 625 (IQR: 211-1042) to 38 (IQR: 15-76). The filter achieved 100% sensitivity in 13 CSs, 85.71% in 2 CSs and 87.50% and 80% in the remaining 2 CSs. In a simulation study, the filter demonstrated an overall sensitivity of 97.19% and NNR of 42. The filter reliably identifies studies about Germany, enhancing screening efficiency and can be applied in evidence syntheses focusing on Germany.

The power of citizen science to advance fungal conservation.

Fungal conservation is gaining momentum globally, but many challenges remain. To advance further, more data are needed on fungal diversity across space and time. Fundamental information regarding population sizes, trends, and geographic ranges is also critical to accurately assess the extinction risk of individual species. However, obtaining these data is particularly difficult for fungi due to their immense diversity, complex and problematic taxonomy, and cryptic nature. This paper explores how citizen science (CS) projects can be lever-aged to advance fungal conservation efforts. We present several examples of past and ongoing CS-based projects to record and monitor fungal diversity. These include projects that are part of broad collecting schemes, those that provide participants with targeted sampling methods, and those whereby participants collect environmental samples from which fungi can be obtained. We also examine challenges and solutions for how such projects can capture fungal diversity, estimate species absences, broaden participation, improve data curation, and translate resulting data into actionable conservation measures. Finally, we close the paper with a call for professional mycologists to engage with amateurs and local communities, presenting a framework to determine whether a given project would likely benefit from participation by citizen scientists.

Employing machine learning for advanced gap imputation in solar power generation databases.

This research evaluates the application of advanced machine learning algorithms, specifically Random Forest and Gradient Boosting, for the imputation of missing data in solar energy generation databases and their impact on the size of green hydrogen production systems. The study demonstrates that the Random Forest model notably excels in harnessing solar data to optimize hydrogen production, achieving superior prediction accuracy with mean absolute error (MAE) of 0.0364, mean squared error (MSE) of 0.0097, root mean squared error (RMSE) of 0.0985, and a coefficient of determination (R2) of 0.9779. These metrics surpass those obtained from baseline models including linear regression and recurrent neural networks, highlighting the potential of accurate imputation to significantly enhance the efficiency and output of renewable energy systems. The findings advocate for the integration of robust data imputation methods in the design and operation of photovoltaic systems, contributing to the reliability and sustainability of energy resource management. Furthermore, this research makes significant contributions by showcasing the comparative performance of traditional machine learning models in handling data gaps, emphasizing the practical implications of data imputation on optimizing hydrogen production systems. By providing a detailed analysis and validation of the imputation models, this work offers valuable insights for future advancements in renewable energy technology.

Using active learning methodologies to teach sequence analysis and molecular phylogeny.

The great development of high-throughput molecular biology techniques and the consequent generation of massive data have made Bioinformatics essential for undergraduate Bioscience students. The importance of this scientific discipline is evidenced by the huge number of specialized publications, tools, and databases available. Training in Bioinformatics equips undergraduates with transferable skills that can be applied in all fields of Biology, such as programming abilities, data analysis, database management, biological knowledge, statistics, problem solving, and interdisciplinary collaboration. Over the past decade, there has been a notable increase in the number of higher education institutions worldwide that have adopted a competency-based curricula. This approach places a significant emphasis on the actions and skills that students are expected to develop, rather than merely focusing on the information, they are required to memorize. In this educational context, the use of active learning strategies has been demonstrated to enhance student comprehension and competency development. This paper describes the implementation of an active learning approach in a hands-on lesson performed by undergraduate students of Biology at the University of Malaga (Spain). Its main objective is to introduce students to molecular databases and information search systems on genes, proteins, and phylogeny. This is achieved within the framework of a smart campus, which integrates technological and sustainable resources to promote a positive and productive learning environment for the university community. This work presents the content and procedure of this practical activity, as well as the evaluation method and the results of a student survey to assess their opinions.

Integrating machine learning to advance epitope mapping.

Identifying epitopes, or the segments of a protein that bind to antibodies, is critical for the development of a variety of immunotherapeutics and diagnostics. In vaccine design, the intent is to identify the minimal epitope of an antigen that can elicit an immune response and avoid off-target effects. For prognostics and diagnostics, the epitope-antibody interaction is exploited to measure antigens associated with disease outcomes. Experimental methods such as X-ray crystallography, cryo-electron microscopy, and peptide arrays are used widely to map epitopes but vary in accuracy, throughput, cost, and feasibility. By comparing machine learning epitope mapping tools, we discuss the importance of data selection, feature design, and algorithm choice in determining the specificity and prediction accuracy of an algorithm. This review discusses limitations of current methods and the potential for machine learning to deepen interpretation and increase feasibility of these methods. We also propose how machine learning can be employed to refine epitope prediction to address the apparent promiscuity of polyreactive antibodies and the challenge of defining conformational epitopes. We highlight the impact of machine learning on our current understanding of epitopes and its potential to guide the design of therapeutic interventions with more predictable outcomes.

Contextualizing toxic elements in the diet: a case for integration of toxic element data into food databases.

Food composition data plays a key role in the practice of nutrition. However, nutrition professionals may currently lack the resources they need to integrate information about toxic elements - such as arsenic, cadmium, and lead - in food into the advice they give consumers. Geographic, sociocultural, and individual factors may impact not only the toxic element content of food, but also how the balance between potentially toxic and health-promoting components of food must be weighed. Better integration and contextualization of toxic element data into key food databases could allow for more nuanced, comprehensive nutrition guidance.

4th DNA Forensic Symposium: Challenges and future directions in Africa.

The 4th Forensic DNA Symposium in Africa underscored the critical role of regional collaboration in advancing forensic sciences, with a particular focus on forensic DNA examinations, databases, and humanitarian initiatives. The symposium aimed to assess the current forensic DNA capabilities across African countries and develop strategies to expand and better utilize DNA platforms. Key findings from the symposium highlight the necessity for enhanced cooperation among African nations to build robust forensic DNA databases and improve data-sharing mechanisms. The symposium also identified significant gaps in current capabilities and the need to develop legal frameworks, infrastructure, and expertise to support forensic initiatives. Moving forward, these findings suggest a strategic focus on capacity building, establishing standardized procedures, and implementing sustainable forensic practices across the continent. Champions were nominated by attending delegates to lead their respective countries in the implementation of these strategies, marking a critical step towards strengthening forensic science in Africa and addressing the pressing challenges related to crime and humanitarian efforts.

Cyber catalogue and revision of the nematode genus Enchodelus (Dorylaimida, Nordiidae).

Background: The genus Enchodelus is an intriguing free-living dorylaimid nematode taxon. Its representatives display a distinct distributional pattern as they are mainly spread in high altitudinal enclaves of the Northern Hemisphere, being often associated with mosses and cliff vegetation. Although their feeding habits have not been studied with experimental protocols, it is traditionally assumed that they are omnivorous.The genus Enchodelus has not been recently revised; descriptions of many 'old species' (that have been described long ago and have not been reported since their original discovery) are of poor quality, hardly discoverable and do not conform to the nowadays taxonomical standards. Thus, a comprehensive compilation and analysis of their literature data is indispensable to provide new insights into the taxonomy of the genus and to elucidate its evolutionary relationships. New information: This contribution provides a cyber catalogue of all Enchodelus species, 28 in total. It compiles available information from the key European Research Infrastructures, such as TreatmentBank, Swiss Institute of Bioinformatics Literature Services (SIBiLS), the Catalogue of Life (CoL), Global Biodiversity Information Facility (GBIF), European Nucleotide Archive (ENA) and Biodiversity Literature Repository (BLR). Data about their distribution (geographical records and habitats) are incorporated too and all brought together. It is completed with discussion and notes for some species, along with information on species distributions and microhabitats. Here, all available information on Enchodelus species is brought together. This will contribute to a more complete assessment of species diversity and distribution and support further biogeographical and ecological research.Besides, type material Enchodelusvestibulifer Altherr, 1952, deposited in the Museo Cantonale di Storia Naturale di Lugano (Switzerland), is re-examined and the species is considered as incertae sedis. Further, a new species of the genus found in Caucasus, Georgia is described after its morphological and molecular study; also morphological and molecular data for E.macrodorus (de Man, 1880) Thorne, 1939, the type species of the genus, collected from Spain are provided.

Single-Cell Transcriptomics Applied in Plants.

Single-cell RNA sequencing (scRNA-seq) is a high-tech method for characterizing the expression patterns of heterogeneous cells in the same tissue and has changed our evaluation of biological systems by increasing the number of individual cells analyzed. However, the full potential of scRNA-seq, particularly in plant science, has not yet been elucidated. To explore the utilization of scRNA-seq technology in plants, we firstly conducted a comprehensive review of significant scRNA-seq findings in the past few years. Secondly, we introduced the research and applications of scRNA-seq technology to plant tissues in recent years, primarily focusing on model plants, crops, and wood. We then offered five databases that could facilitate the identification of distinct expression marker genes for various cell types. Finally, we analyzed the potential problems, challenges, and directions for applying scRNA-seq in plants, with the aim of providing a theoretical foundation for the better use of this technique in future plant research.

Evaluation of the Use of Cell Lines in Studies of Selenium-Dependent Glutathione Peroxidase 2 (GPX2) Involvement in Colorectal Cancer.

Hydroperoxides (ROOHs) are known as damaging agents capable of mediating mutation, while a role as signaling agents through oxidation of protein sulfhydryls that can alter cancer-related pathways has gained traction. Glutathione peroxidase 2 (GPX2) is an antioxidant enzyme that reduces ROOHs at the expense of glutathione (GSH). GPX2 is noted for a tendency of large increases or decreases in expression levels during tumorigenesis that leads to investigators focusing on its role in cancer. However, GPX2 is only one component of multiple enzyme families that metabolize ROOH, and GPX2 levels are often very low in the context of these other ROOH-reducing activities. Colorectal cancer (CRC) was selected as a case study for examining GPX2 function, as colorectal tissues and cancers are sites where GPX2 is highly expressed. A case can be made for a significant impact of changes in expression levels. There is also a link between GPX2 and NADPH oxidase 1 (NOX1) from earlier studies that is seldom addressed and is discussed, presenting data on a unique association in colon and CRC. Tumor-derived cell lines are quite commonly used for pre-clinical studies involving the role of GPX2 in CRC. Generally, selection for this type of work is limited to identifying cell lines based on high and low GPX2 expression with the standard research scheme of overexpression in low-expressing lines and suppression in high-expressing lines to identify impacted pathways. This overlooks CRC subtypes among cell lines involving a wide range of gene expression profiles and a variety of driver mutation differences, along with a large difference in GPX2 expression levels. A trend for low and high GPX2 expressing cell lines to segregate into different CRC subclasses, indicated in this report, suggests that choices based solely on GPX2 levels may provide misleading and conflicting results by disregarding other properties of cell lines and failing to factor in differences in potential protein targets of ROOHs. CRC and cell line classification schemes are presented here that were intended to assist workers in performing pre-clinical studies but are largely unnoted in studies on GPX2 and CRC. Studies are often initiated on the premise that the transition from normal to CRC is associated with upregulation of GPX2. This is probably correct. However, the source normal cells for CRC could be almost any colon cell type, some with very high GPX2 levels. These factors are addressed in this study.

Concomitant Administration of Psychotropic and Prostate Cancer Drugs: A Pharmacoepidemiologic Study Using Drug-Drug Interaction Databases.

Prostate cancer (PC) represents the second most common diagnosed cancer in men. The burden of diagnosis and long-term treatment may frequently cause psychiatric disorders in patients, particularly depression. The most common PC treatment option is androgen deprivation therapy (ADT), which may be associated with taxane chemotherapy. In patients with both PC and psychiatric disorders, polypharmacy is frequently present, which increases the risk of drug-drug interactions (DDIs) and drug-related adverse effects. Therefore, this study aimed to conduct a pharmacoepidemiologic study of the concomitant administration of PC drugs and psychotropics using three drug interaction databases (Lexicomp®, drugs.com®, and Medscape®). This study assayed 4320 drug-drug combinations (DDCs) and identified 814 DDIs, out of which 405 (49.63%) were pharmacokinetic (PK) interactions and 411 (50.37%) were pharmacodynamic (PD) interactions. The most common PK interactions were based on CYP3A4 induction (n = 275, 67.90%), while the most common PD interactions were based on additive torsadogenicity (n = 391, 95.13%). Proposed measures for managing the identified DDIs included dose adjustments, drug substitutions, supplementary agents, parameters monitoring, or simply the avoidance of a given DDC. A significant heterogenicity was observed between the selected drug interaction databases, which can be mitigated by cross-referencing multiple databases in clinical practice.