Major Clusters Research Articles

Green and sustainable AI research: an integrated thematic and topic modeling analysis

This investigation delves into Green AI and Sustainable AI literature through a dual-analytical approach, combining thematic analysis with BERTopic modeling to reveal both broad thematic clusters and nuanced emerging topics. It identifies three major thematic clusters: (1) Responsible AI for Sustainable Development, focusing on integrating sustainability and ethics within AI technologies; (2) Advancements in Green AI for Energy Optimization, centering on energy efficiency; and (3) Big Data-Driven Computational Advances, emphasizing AI’s influence on socio-economic and environmental aspects. Concurrently, BERTopic modeling uncovers five emerging topics: Ethical Eco-Intelligence, Sustainable Neural Computing, Ethical Healthcare Intelligence, AI Learning Quest, and Cognitive AI Innovation, indicating a trend toward embedding ethical and sustainability considerations into AI research. The study reveals novel intersections between Sustainable and Ethical AI and Green Computing, indicating significant research trends and identifying Ethical Healthcare Intelligence and AI Learning Quest as evolving areas within AI’s socio-economic and societal impacts. The study advocates for a unified approach to innovation in AI, promoting environmental sustainability and ethical integrity to foster responsible AI development. This aligns with the Sustainable Development Goals, emphasizing the need for ecological balance, societal welfare, and responsible innovation. This refined focus underscores the critical need for integrating ethical and environmental considerations into the AI development lifecycle, offering insights for future research directions and policy interventions.

Structure of the Hydrothermal System of Thermal Fields Inferred from Geological-Geophysical Modeling: A Case Study of the Kambalny Volcanic Ridge (Kamchatka)

Abstract ––Based on integrated structural and geophysical studies and generalization of geophysical, hydrogeological, mineralogical, geochemical and other data, it is shown that the southern cluster of thermal fields of the Kambalny volcanic ridge (South Kamchatka) is included in the modern hydrothermal system. New data on the structure of the steam hydrotherm discharge area investigated to a depth of about 300 m have been obtained. The structural and hydrodynamic connection between two thermal fields from this group has been established. The Kambalny Ridge geothermal system embracing all major clusters of thermal fields is assumed to have a single deep-seated source of heat supply.

Molecular profiling of Greek native germplasm collection of Rosa canina L. for enhanced fruit extract production: a comprehensive approach utilizing neutral, gene, and exon-based markers

AbstractThe genus Rosa L. is globally distributed and Rosa canina L. is a distinguished member of multifaceted interest. Apart from the traditional uses of R. canina in folk medicine, food and cosmetic industries, or its ornamental applications, its rose hips are renowned for their functional bioactive components. Thus, identifying the genetic diversity within this species is crucial for any plant breeding project. This study employed three molecular markers namely inter simple sequence repeats; (ISSRs), start codon-targeted (SCoTs), and exon-based amplified polymorphisms; (EBAPs) to conduct the first comprehensive genetic analysis of 12 R. canina genotypes. DNA extraction, marker selection, and sequences’ amplification were performed following established protocols. The resulting genetic data were analyzed for polymorphism, diversity indices, and population structure using various statistical methods including principal component analysis (PCA), unweighted pair group method with arithmetic mean (UPGMA) clustering, and STRUCTURE analysis. The ISSR analysis revealed a high level of polymorphism (81.82%) and identified two major clusters in the UPGMA dendrogram. SCoT and EBAP markers also exhibited substantial polymorphism (74.56% and 82.11%, respectively) and formed three distinct clusters. PCA indicated a consistent pattern across markers suggesting reliable genetic grouping. STRUCTURE analysis supported the presence of three genetically uniform subpopulations (K = 3) within the studied R. canina germplasm collection. This study provided a comprehensive genetic characterization of the Greek native R. canina genebank collection. The observed genetic diversity and population structure offered valuable insights for future breeding programs targeting specific R. canina genetic clusters.

Genetic diversity and population structure of Fusarium udum in India and its correlation with pigeonpea wilt incidence.

In a study conducted in India, 50 Fusarium isolates were collected from pigeonpea growing regions and extensively examined for their cultural and morphological characteristics. These isolates exhibited significant variations in traits including growth rate, mycelial growth patterns, color, zonation, pigmentation, spore size, and septation. Subsequently, 30 isolates were chosen for pathogenicity testing on eight pigeonpea genotypes. Results showed distinct reactions, with four genotypes displaying differential responses (ICP8858, ICP8859, ICP8862, and BDN-2), while ICP9174 and ICP8863 consistently exhibited resistance and ICP2376 and BAHAR remained susceptible to wilt disease. To study the interaction between Fusarium isolates and pigeonpea host differentials (HDs), an additive main effects and multiplicative interactionanalysis was conducted. The majority of disease incidence variation (75.54%) was attributed to HD effects, while Fusarium isolate effects accounted for only 1.99%. The interaction between Isolates and HDs (I × HD) contributed 21.95% to the total variation, being smaller than HD but larger than I. Based on HD reactions, isolates were classified into nine variants, showing varying distributions across pigeonpea growing states, with variants 2 and 3 being prevalent in several regions. This diversity underscores the need for location-specific wilt-resistant pigeonpea cultivars. Furthermore, genetic analysis of 23 representative isolates, through internal transcribed spacer region of ribosomal DNA and translation elongation factor 1-αgene sequencing, revealed three major clusters: Fusarium udum, Fusarium solani, and Fusarium equiseti. These findings hold potential for developing location-specific wilt-resistant pigeonpea cultivars and enhancing disease management strategies.

Determination of Morpho-Physiological Traits for Assessing Drought Tolerance in Sugarcane.

Drought is a significant constraint to sugarcane productivity. Therefore, understanding how different varieties of sugarcane respond to drought stress can facilitate breeding programs and set up criteria for selecting drought-tolerant varieties. In the present study, we examined eight morpho-physiological traits to distinguish 40 sugarcane genotypes categorized into four groups based on significant differences in cane yield under non-stressed conditions and reduction of cane yield under drought-stressed conditions. The study was conducted during the formative stage in a greenhouse, encompassing both control and drought conditions. Drought treatments resulted in significant changes and differences in the mean values of various morpho-physiological traits. The hierarchical clustering analysis, utilizing stay-green traits such as higher chlorophyll fluorescence ratio (Fv/Fm), leaf chlorophyll content (SPAD), leaf relative water content (RWC), and lower leaf rolling score (LR), leaf drying score (LD), and drought recovery score (DR), successfully grouped 40 sugarcane genotypes into four major clusters, similar to the previously categorized groups. Correlation analysis showed significant relationships among cane yield, reduction of cane yield under drought conditions, and the stay-green traits. Our results demonstrated that morpho-physiological traits contributing to the "stay-green" phenotypes could be useful as selection criteria for drought tolerance in sugarcane.

The Impact of Digital Transformation on International Trade Based on Big Data Analysis Models and Threshold Effects

Currently, the world is experiencing a wave of digitalization, and the digital economy has become a significant engine for the growth of import and export trade. This paper deeply analyzes the impact of the digital economy on import and export trade from dimensions such as technology, society, and institutions. Based on panel data from 42 cities in China’s three major urban clusters (the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei region) from 2011 to 2021, using the mediation effect model, threshold effect model, and K-means clustering algorithm, it examines the impact of digital economic development on import and export trade and its transmission mechanism. The research findings indicate that the development of the digital economy significantly promotes import and export trade; the level of technological innovation plays a mediating role in the relationship between digital economic development and import and export trade; there is a single threshold positive promotion effect of technological innovation level in the process of digital economic development affecting import and export trade; the degree of urbanization exhibits a single threshold effect in the process of digital economic development affecting import and export trade, specifically transitioning from inhibiting to promoting effects once the threshold value of urbanization is surpassed. Furthermore, there is a certain regional heterogeneity in the above conclusions among the three major urban clusters. The research results have certain guiding significance for optimizing the structure of foreign trade, promoting high-quality development of import and export trade, and achieving high-level economic and social development.

Molecular profiling of rice (Oryza sativa L.) genotypes using trait-based SNP markers linked to yield under drought condition

Drought is one of the major abiotic stress factors affecting the growth and production of rice globally and it can cause an estimated yield loss up to 90% in rice. With the increasing population around the globe, a comprehensive approach for mitigating drought stress should be taken to develop drought-tolerant rice varieties to meet future food demands. One hundred and eighty-two rice genotypes were evaluated for six critical gene/QTLs associated with drought tolerance using SNP marker data. The results revealed that 172 genotypes carry at least one gene/QTL for drought tolerance. The six gene/QTLs, namely, DTY1.1, qDTY2.2, qDTY3.1, qDTY3.2, qDTY4.1 and qDTY12.1were found in 103, 26, 90, 25, 27 and 45 genotypes corresponding to 57, 49, 14, 14, 15 and 25% of the total screened genotypes, respectively. Remarkably, five genotypes (RL-32, RL-105, RL-110, RL-142 and RL-158) possessed a unique combination three major genes/QTLs and three genotypes (RL-21, RL-41 and RL-188) possessed a unique combination four major gene/QTLs for drought tolerance. Furthermore, both cluster and populationn structure analyses revealedd the distribution of the genotypes into two major clusters. The genotypes carrying valuable gene/QTLs either in single or combination, hold immense potential for deployment in drought tolerance rice breeding programs.

Recombinational exchange of M-fibril and T-pilus genes generates extensive cell surface diversity in the global group A Streptococcus population.

Among genes present in all group A streptococci (GAS), those encoding M-fibril and T-pilus proteins display the highest levels of sequence diversity, giving rise to the two primary serological typing schemes historically used to define strain. A new genotyping scheme for the pilin adhesin and backbone genes is developed and, when combined with emm typing, provides an account of the global GAS strain population. Cluster analysis based on nucleotide sequence similarity assigns most T-serotypes to discrete pilin backbone sequence clusters, yet the established T-types correspond to only half the clusters. The major pilin adhesin and backbone sequence clusters yield 98 unique combinations, defined as "pilin types." Numerous horizontal transfer events that involve pilin or emm genes generate extensive antigenic and functional diversity on the bacterial cell surface and lead to the emergence of new strains. Inferred pilin genotypes applied to a meta-analysis of global population-based collections of pharyngitis and impetigo isolates reveal highly significant associations between pilin genotypes and GAS infection at distinct ecological niches, consistent with a role for pilin gene products in adaptive evolution. Integration of emm and pilin typing into open-access online tools (pubmlst.org) ensures broad utility for end-users wanting to determine the architecture of M-fibril and T-pilus genes from genome assemblies.IMPORTANCEPrecision in defining the variant forms of infectious agents is critical to understanding their population biology and the epidemiology of associated diseases. Group A Streptococcus (GAS) is a global pathogen that causes a wide range of diseases and displays a highly diverse cell surface due to the antigenic heterogeneity of M-fibril and T-pilus proteins which also act as virulence factors of varied functions. emm genotyping is well-established and highly utilized, but there is no counterpart for pilin genes. A global GAS collection provides the basis for a comprehensive pilin typing scheme, and online tools for determining emm and pilin genotypes are developed. Application of these tools reveals the expansion of structural-functional diversity among GAS via horizontal gene transfer, as evidenced by unique combinations of surface protein genes. Pilin and emm genotype correlations with superficial throat vs skin infection provide new insights on the molecular determinants underlying key ecological and epidemiological trends.

Analysis of Coupling Coordination Degree of Digital Economy Industry Synergistic Development—Empirical Evidence Based on Urban Agglomerations in the Northern Region of China

With the rise of the national strategic position of Beijing-Tianjin-Hebei city cluster, Central Plains city cluster and Shandong Peninsula city cluster, inter-city cluster collaboration has become particularly important, and scientific evaluation of the level of digital economy industry synergistic development of major northern city clusters is conducive to cracking the dilemma of industrial synergy of major northern city clusters, and forming the pattern of high-quality integrated development. The data related to digital infrastructure, digital industrialization and industrial digitization of the Beijing-Tianjin-Hebei city cluster, the Central Plains city cluster and the Shandong Peninsula city cluster from 2013 to 2021 are selected, and the coupled synergy model is applied to evaluate the level of collaborative development of digital economy industries in major northern city clusters based on the analysis of digital economy industry development in major northern city clusters. The study found that: the coupling and coordination degree of digital economy industry of the three major northern city clusters from 2013 to 2021 has been improving, and the digital economy industry of the Beijing-Tianjin-Hebei city cluster will reach the intermediate level of coordination in 2021; in terms of the spatial synergy of the digital economy industry of the three city clusters, the synergy of the digital economy industry among the three clusters has reached the level of just barely dislocation, but the degree of industrial digitization synergy among the three clusters has obviously lags behind the level of digital infrastructure synergy and industrial digitalization synergy. Based on this, it puts forward countermeasures and suggestions to accelerate the top-level design of digital economy industry synergistic development in the major northern city clusters and to establish institutional mechanisms to support the synergistic development of digital economy industry in the cities.

The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review

Artificial intelligence (AI) technologies are increasingly being applied to the shipping industry to advance its development. In this study, 476 articles published in the Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI) of the Web of Science Core Collection from 2001 to 2022 were collected, and bibliometric methods were applied to conduct a systematic literature of the field of AI technology applications in the shipping industry. The review commences with an annual publication trend analysis, which shows that research in the field has been growing rapidly in recent years. This is followed by a statistical analysis of journals and a collaborative network analysis to identify the most productive journals, countries, institutions, and authors. The keyword “co-occurrence analysis” is then utilized to identify major research clusters, as well as hot research directions in the field, providing directions for future research in the field. Finally, based on the results of the keyword co-occurrence analysis and the content analysis of the papers published in recent years, the research gaps in AIS data applications, ship trajectory, and anomaly detection, as well as the possible future research directions, are discussed. The findings indicate that AIS data in the future research direction are mainly reflected in the analysis of ship behavior and AIS data repair. Ship trajectory in the future research direction is mainly reflected in the deep learning-based method research and the discussion of ship trajectory classification. Anomaly detection in the future research direction is mainly reflected in the application of deep learning technology in ship anomaly detection and improving the efficiency of ship anomaly detection. These insights offer guidance for researchers’ future investigations in this area. In addition, we discuss the implications of research in the field of shipping AI from both theoretical and practical perspectives. Overall, this review can help researchers understand the status and development trend of the application field of AI technology in shipping, correctly grasp the research direction and methodology, and promote the further development of the field.

Abstract LB098: Deep plasma proteome characterization in two independent clinical cohorts identifies clusters of biomarkers separating benign and malign tumors in women with suspicion of ovarian cancer

Abstract Early detection is essential for ovarian cancer survival but today’s biomarkers are not specific enough to clear benign from malignant tumors nor sensitive enough to be used in screening. Here we have used the proximity extension assay (PEA) Olink Explore HT to characterize 5416 plasma proteins. For a subset of the women, we have also characterized the transcriptional landscape in corresponding tumor tissue using total RNA-sequencing on the Illumina NovaSeq 6000 instrument. The plasma samples were from two independent Swedish clinical cohorts (N1 = 171, N2 = 233) and tumor RNA was extracted from 112 of the samples from the second cohort. Each cohort consisted of women who after suspicion of ovarian cancer were surgically diagnosed with either benign or malign tumors. All plasma samples were collected at time of diagnosis, before commencement of treatment. In the plasma protein analyses, the first cohort was used as a discovery and the second as replication. In the discovery cohort, we found a total of 328 plasma proteins differing significantly between women with benign tumors and; early stage (I-II), late stage (III-IV) or any stage(I-IV) malignant tumors. These associations were examined in the replication cohort, and were all found to have similar foldchange between women with benign and malignant tumors. 99.7% (327/328) differed significantly after adjustment for multiple hypothesis testing. The 328 associations corresponded to a total of 192 proteins out of which 26 (13.5%) were found to have a significant correlation with RNA-expression in the corresponding tumor. A correlation network analysis based on the 192 proteins identified 5 major clusters, consisting of 18 to 59 protein-protein couplings. 80% of these clusters contained proteins that are annotated with up to five major KEGG cancer pathways and several of these contained proteins correlating significantly with the tumor RNA-SEQ data. All except one cluster contained one or two proteins that are either currently approved FDA drug targets or listed as druggable in DrugBank by specific compounds. Ovarian cancer has a low 5-year survival and eludes the current treatment regimens if discovered late. By applying high-throughput proteomics in two independent cohorts, we identified a large number of affected proteins out of which only a few have corresponding changes in tumor RNA-expression. We also identified large highly co-regulated networks of proteins, suggesting large downstream systemic effects which could be a contributing factor to the difficulties in finding efficient treatments. Among these proteins, there are several druggable targets, however, not all the associated compounds are currently primarily used as anti-cancer drugs, highlighting the possibility of drug re-purposing for future use against ovarian cancer. Citation Format: Stefan Enroth, Mikaela Moskov, Julia Lindberg Hedlund, Svetlana Popova, Simn K. Forsberg, Klev Diamanti, Maria Lycke, Emma Ivansson, Anna Tolf, Karin Sundfeldt, Karin Stålberg, Ulf Gyllensten. Deep plasma proteome characterization in two independent clinical cohorts identifies clusters of biomarkers separating benign and malign tumors in women with suspicion of ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB098.

Single-cell ‘omic profiles of human aortic endothelial cells in vitro and human atherosclerotic lesions ex vivo reveal heterogeneity of endothelial subtype and response to activating perturbations

Heterogeneity in endothelial cell (EC) sub-phenotypes is becoming increasingly appreciated in atherosclerosis progression. Still, studies quantifying EC heterogeneity across whole transcriptomes and epigenomes in both in vitro and in vivo models are lacking. Multiomic profiling concurrently measuring transcriptomes and accessible chromatin in the same single cells was performed on six distinct primary cultures of human aortic ECs (HAECs) exposed to activating environments characteristic of the atherosclerotic microenvironment in vitro. Meta-analysis of single-cell transcriptomes across 17 human ex vivo arterial specimens was performed and two computational approaches quantitatively evaluated the similarity in molecular profiles between heterogeneous in vitro and ex vivo cell profiles. HAEC cultures were reproducibly populated by four major clusters with distinct pathway enrichment profiles and modest heterogeneous responses: EC1-angiogenic, EC2-proliferative, EC3-activated/mesenchymal-like, and EC4-mesenchymal. Quantitative comparisons between in vitro and ex vivo transcriptomes confirmed EC1 and EC2 as most canonically EC-like, and EC4 as most mesenchymal with minimal effects elicited by siERG and IL1B. Lastly, accessible chromatin regions unique to EC2 and EC4 were most enriched for coronary artery disease (CAD)-associated single-nucleotide polymorphisms from Genome Wide Association Studies (GWAS), suggesting that these cell phenotypes harbor CAD-modulating mechanisms. Primary EC cultures contain markedly heterogeneous cell subtypes defined by their molecular profiles. Surprisingly, the perturbations used here only modestly shifted cells between subpopulations, suggesting relatively stable molecular phenotypes in culture. Identifying consistently heterogeneous EC subpopulations between in vitro and ex vivo models should pave the way for improving in vitro systems while enabling the mechanisms governing heterogeneous cell state decisions.

Single-cell 'omic profiles of human aortic endothelial cells in vitro and human atherosclerotic lesions ex vivo reveal heterogeneity of endothelial subtype and response to activating perturbations.

Heterogeneity in endothelial cell (EC) sub-phenotypes is becoming increasingly appreciated in atherosclerosis progression. Still, studies quantifying EC heterogeneity across whole transcriptomes and epigenomes in both in vitro and in vivo models are lacking. Multiomic profiling concurrently measuring transcriptomes and accessible chromatin in the same single cells was performed on six distinct primary cultures of human aortic ECs (HAECs) exposed to activating environments characteristic of the atherosclerotic microenvironment in vitro. Meta-analysis of single-cell transcriptomes across 17 human ex vivo arterial specimens was performed and two computational approaches quantitatively evaluated the similarity in molecular profiles between heterogeneous in vitro and ex vivo cell profiles. HAEC cultures were reproducibly populated by four major clusters with distinct pathway enrichment profiles and modest heterogeneous responses: EC1-angiogenic, EC2-proliferative, EC3-activated/mesenchymal-like, and EC4-mesenchymal. Quantitative comparisons between in vitro and ex vivo transcriptomes confirmed EC1 and EC2 as most canonically EC-like, and EC4 as most mesenchymal with minimal effects elicited by siERG and IL1B. Lastly, accessible chromatin regions unique to EC2 and EC4 were most enriched for coronary artery disease (CAD)-associated single-nucleotide polymorphisms from Genome Wide Association Studies (GWAS), suggesting that these cell phenotypes harbor CAD-modulating mechanisms. Primary EC cultures contain markedly heterogeneous cell subtypes defined by their molecular profiles. Surprisingly, the perturbations used here only modestly shifted cells between subpopulations, suggesting relatively stable molecular phenotypes in culture. Identifying consistently heterogeneous EC subpopulations between in vitro and ex vivo models should pave the way for improving in vitro systems while enabling the mechanisms governing heterogeneous cell state decisions.

Vocalizations of the Pekin duck (Anas platyrhynchos domesticus): how stimuli, sex, and social groups affect their vocal repertoire

Pekin ducks are exposed to stressors such as heat stress, enteric pathogens, mycotoxins, and other environmental stressors. We know from wild bird literature that birds communicate through vocalizations. We hypothesized that Pekin ducks would have a diverse repertoire that is affected by the sex, social group, and specific stimuli. We utilized adult Pekin ducks to develop a vocal repertoire. We placed 1 to 4 ducks of varying sexes into a sound chamber with various stimuli used to encourage new vocalizations. Birds were recorded for 20 min with several variations of number and sexes of ducks. Once the ducks were recorded each vocalization that was clipped was named based on a predetermined naming system. We characterized the vocal system of the ducks under each stimulus and social treatment in 4 ways: overall call rates, call diversity, call repertoire, and call spectral properties. In all cases, normality of residuals and homogeneity of variances for GLM and ANOVA models were confirmed using Proc Univariate (SAS v9.4) where a p ≤ 0.05 was considered significant. We found that Pekin ducks produce up to 16 different vocalizations. The treatments had a significant effect on the overall rate of calls given by the ducks (ANOVA: F6,31 = 8.55, p < 0.0001). Ducks produced the most calls by far when someone was sitting in the chamber with them (30.04 ± 4.45 calls/min). For call diversity, we found that there was a significant main effect of hen number (F218 = 12.21, p = 0.0004) but no main effect of drake number (F3,18 = 3.04, p = 0.0555). Cluster analyses indicated that certain types of calls were given under specific conditions. There were generally 6 major clusters of vocal repertoires (R-square = 0.899, Cubic Clustering Criterion = 9.30). Our results suggest that Pekin ducks are affected by the types of stimuli and social environment in how much they vocalize and in the properties of the calls they use. In addition, males and females differ somewhat in the repertoire of the calls they use, and in the spectral properties of their calls.

Genetic variation and structure of endemic and endangered wild celery (Kelussia odoratissima Mozaff.) quantified using novel microsatellite markers developed by next-generation sequencing.

Kelussia odoratissima Mozaff. (Apiaceae) is a native plant that has been traditionally consumed in Iran's food and pharmaceutical industries. Overharvesting of the taxon, especially at the beginning of the growing season, due to its considerable medicinal and economic value, is believed to be the main reason for the extirpating of this plant. The consequences of the severe anthropogenic impacts on the genetic diversity of populations are poorly known. In order to investigate the level of genetic variation and patterns of the genetic structure of K. odoratissima, we developed novel microsatellite markers using the 454 Roche next-generation sequencing (NGS) platform for the first time. Out of 1,165 microsatellite markers bioinformatically confirmed, twenty-five were tested, of which 23 were used to screen genetic variation across 12 natural populations. Our results showed that the average number of alleles per locus and the polymorphic information content (PIC) were 10.87 (range 7 to 27), and 0.81 (range 0.67 to 0.94), respectively. The mean observed and expected heterozygosities (± SD) across all populations were 0.80 ± 0.31 and 0.72 ± 0.14, respectively. The average pairwise FST among the populations was 0.37 (range 0.04 to 0.81). Bayesian and distance-based clustering, and principal coordinate analyses revealed at least four major genetic clusters. Although high level of structure can be explained by landscape topography and geographic distance, presence of admixed populations can be associated to seed or pollen dispersal. Contrary to expectations, the high level of genetic variation and lack of inbreeding suggest that overexploitation has not yet significantly purged the allelic variability within the natural populations in protected areas.

Preliminary selection of wild male kiwifruit (Actinidia callosa var. strigillosa C. F. Liang) accessions in North Sikkim, India, for future breeding programmes

ABSTRACT The flower morphology and phenology of 10 healthy, profusely flowering wild male kiwifruit (Actinidia callosa var. strigillosa C. F. Liang) accessions growing in North Sikkim district in Sikkim state, Northeast India, were analysed in 2021–2022. There was distinct variation among the accessions in the number of flowers per inflorescence, number of sepals and petals, colour of the sepals, arrangement of the petals, and average number of stamens, as well as the timing and duration of flowering. Accessions SKNFF-04 and SKNLC-21 were superior in terms of pollinating traits, with the highest average numbers of flowers and stamens per vine and the longest duration of flowering. Principal component analysis (PCA) revealed that the first and second principal components (PC1 and PC2) had eigenvalues greater than 1 and together explained 95.13% of the variation. Hierarchical cluster analysis categorised the accessions into two major clusters: Cluster I with eight accessions and Cluster II with the two superior accessions. The presence of the pleiochasium inflorescence type among all the observed accessions is of particular significance, as it can increase the efficacy of male selection in kiwifruit breeding programmes. This research sets the stage for further investigations into the potential advantages and unique characteristics of wild kiwifruit germplasm.

Knowledge mapping of immunotherapy for breast cancer: A bibliometric analysis from 2013 to 2022

ABSTRACT Breast cancer is the leading cause of cancer-related death among women globally. Immunotherapy has emerged as a major milestone in contemporary oncology. This study aims to conduct a bibliometric analysis in the field of immunotherapy for breast cancer, providing a comprehensive overview of the current research status, identifying trends and hotspots in research topics. We searched and retrieved data from the Web of Science Core Collection, and performed a bibliometric analysis of publications on immunotherapy for breast cancer from 2013 to 2022. Current status and hotspots were evaluated by co-occurrence analysis using VOSviewer. Evolution and bursts of knowledge base were assessed by co-citation analysis using CiteSpace. Thematic evolution by bibliometrix package was used to discover keywords trends. The attribution and collaboration of countries/regions, institutions and authors were also explored. A total of 7,975 publications were included. In co-occurrence analysis of keywords, 6 major clusters were revealed: tumor microenvironment, prognosis biomarker, immune checkpoints, novel drug delivery methods, immune cells and therapeutic approaches. The top three most frequently mentioned keywords were tumor microenvironment, triple-negative breast cancer, and programmed cell death ligand 1. The most productive country, institution and author were the USA (2926 publications), the University of Texas MD Anderson Cancer Center (219 publications), and Sherene Loi (28 publications), respectively. There has been a rapid growth in studies on immunotherapy for breast cancer worldwide. This research area has gained increasing attention from different countries and institutions. With the rising incidence of breast cancer, immunotherapy represents a research field of significant clinical value and potential.

Transcriptional and functional profiling identifies inflammation and endothelial-to-mesenchymal transition as potential drivers for phenotypic heterogeneity within a cohort of endothelial colony forming cells

BackgroundEndothelial colony-forming cells (ECFCs) derived from patients can be used to investigate pathogenic mechanisms of vascular diseases like von Willebrand disease. Considerable phenotypic heterogeneity has been observed between ECFC clones derived from healthy donors. This heterogeneity needs to be well understood in order to use ECFCs as endothelial models for disease. ObjectivesTherefore, we aimed to determine phenotypic and gene expression differences between control ECFCs. MethodsA total of 34 ECFC clones derived from 16 healthy controls were analyzed. The transcriptome of a selection of ECFC clones (n = 15) was analyzed by bulk RNA sequencing and gene set enrichment analysis. Gene expression was measured in all ECFC clones by quantitative polymerase chain reaction. Phenotypic profiling was performed and migration speed of the ECFCs was measured using confocal microscopy, followed by automated quantification of cell morphometrics and migration speed. ResultsThrough hierarchical clustering of RNA expression profiles, we could distinguish 2 major clusters within the ECFC cohort. Major differences were associated with proliferation and migration in cluster 1 and inflammation and endothelial-to-mesenchymal transition in cluster 2. Phenotypic profiling showed significantly more and smaller ECFCs in cluster 1, which contained more and longer Weibel–Palade bodies. Migration speed in cluster 1 was also significantly higher. ConclusionWe observed a range of different RNA expression patterns between ECFC clones, mostly associated with inflammation and clear differences in Weibel–Palade body count and structure. We developed a quantitative polymerase chain reaction panel that can be used for the characterization of ECFC clones, which is essential for the correct analysis of pathogenic mechanisms in vascular disorders.

Bibliometric Methods in HRM: Contribution and Utility

This paper offers an overview of the usefulness of bibliographic analysis and conducts a retrospective review of the Human Resource Management Journal (HRMJ). The first part of the paper explains that bibliographic methods are not fully democratized in management and have just started their modest ascent in HRM. In the second part, the paper provides a retrospective review (1990–2022) of HRMJ articles (n = 855), which constitutes one of the two general objectives of bibliometric analysis. The results revealed that bibliometrics analysis significantly contributes to the HRM body of knowledge, especially given that this methodology is barely used in HRM research. Conversely, the retrospective review has identified trends in performance analysis, such as co-authorship internationalization in HRMJ articles that were unclear until 2011. We also highlighted the existence of six major clusters: HRM and organizational behavior (cluster 1), HR practices and policies (cluster 2), HRM as a strategy (cluster 3), HRM and performance (cluster 4), international HRM (cluster 5), and HRM roles and contingencies (cluster 6). Insights into potential trajectories for HRMJ scope are provided.

A bibliometric analysis of gastric cancer liver metastases: advances in mechanisms of occurrence and treatment options.

Gastric cancer (GC) is the fifth most commonly diagnosed cancer worldwide, and its poor prognosis is predominantly attributed to distant metastasis. The liver is the primary site of GC metastasis. However, there is no universally approved treatment regimen for liver metastasis in GC. The aim of this article is to review the current research status and trends of liver metastasis of GC worldwide. The authors utilized the Web of Science Core Collection database to identify articles on liver metastasis from GC published between 2000 and 2022. The authors used bibliometric methods to analyze authors, institutions, countries, journals, and references through CiteSpace and VOSviewer. A total of 1003 articles were included in this study. Japan published the most articles in the field, followed by China. Nagoya University is the leading institution in the field of liver metastases in GC. Yasuhiro Kodera from Japan has made significant achievements in this area. The authors identified GC to be the most influential journal in this field. Using cluster analysis, the keywords were divided into four major clusters:(1) the molecular mechanism of GC liver metastasis, (2) prognosis, (3) liver resection, and (4) chemotherapy. Our study systematically summarizes the results of GC liver metastasis research from 2000 to 2022 and describes and predicts research hotspots and trends on a global scale. Research on the molecular mechanisms of GC liver metastasis will become a hot topic in the future, and the expansion of the surgical treatment scope and the advancement of translational therapy will benefit more patients.