Principal Cluster Research Articles

GIS and statistics to analyze scale change consequences on medical desert classification in France

Abstract Background Accessibility is a crucial way to overcome geographical restrictions and provides a path for alleviating the medical desert. Less attention has been paid to the choice of spatial unit in the effect of healthcare multi-accessibility. Inside the OASES European program, the France team proposes to create medical desert classification at the municipality level. The contribution of this study is to highlight the consequences of the choice of spatial scale on spatial distribution and the discrepancies between medical desert classifications. Methods Analyses were carried out in metropolitan France and overseas departments, at the commune scale and two other supra-communal scales used by public policies in France. For each spatial unit, a multidimensional and professional classification has been conducted using a three-step method: scores, principal component analysis and clustering. To demonstrate the presence of spatial dependence and to measure homogeneity between classifications joint count autocorrelation indicators and Theil’s indexes were applied. Results Our results present 7 classes of municipalities, and 5 classes of EPCI or living territories according to their level of healthcare accessibility, the dynamic of supply, and the needs of the population. Spatial representation of the classification demonstrates greater variability at the municipality level than other supra scales. Informative contrasted classes at the municipality level disappeared in favor of the middle class in all accessibility at supra scales. The results demonstrate that spatial dependence between areas at the municipality level reduced the scale effect. Conclusions Spatial approaches have become increasingly popular since they enable decision-makers to identify priority areas and allocate resources. Recognizing the instability across various scales is therefore crucial for thinking about the optimal unit of data aggregation when investigating the medical desert. Key messages • Choice of the scale is a mix between public policy decisions and data availability. • Contiguity constraint is a way to reduce the loss of information after aggregation.

EXPLORING CLIMATIC PATTERNS AMONG BRAZILIAN CAPITALS

This study aims to examine the patterns of climatic variables and delineate climatically homogeneous regions among Brazilian capitals using Principal Component Analysis (PCA) and clustering techniques. Annual climate data from 26 state capitals and the Federal District of Brazil were analyzed, covering the period from 1958 to 2022, representing different regions of the country. The PCA results revealed that the first three principal components accounted for 91.8% of the total data variability. Four climatically homogeneous regions were identified: the East region, the Northeast region, the North region and another region that comprises the rest of the country. Cluster Analysis (CA) also confirmed the formation of four homogeneous groups among municipalities based on climatic variables. The analysis of spatial distribution showed significant variations in annual climate variables between Brazilian capitals, with maximum values observed for maximum and minimum temperatures, precipitation, wind speed, relative humidity, evapotranspiration and soil moisture. These findings contribute to a deeper understanding of climate variability in Brazilian capitals and provide a basis for future studies on climate dynamics and regional impacts. Keywords: Principal Component Analysis, Cluster Analysis, Multivariate Analysis; Biplot

Genetic Characterization of Procambarus clarkii Cultured in Sichuan Province Based on Microsatellite Markers

The Procambarus clarkii production sector in Sichuan Province, China, is experiencing rapid growth. However, the industry faces significant challenges, including on-farm breeding and the widespread “Catch Big, Keep Small” farming practice, which have led to substantial genetic degradation within P. clarkii populations. Moreover, the uncertainty surrounding the origins of breeding stocks poses an additional obstacle to the local selection and cultivation of high-quality juvenile P. clarkii. With the objective of inferring the genetic lineage of cultivated P. clarkii in Sichuan, twelve microsatellite loci were employed to investigate the genetic characters of six P. clarkii populations from Sichuan and two from Hubei Province, China. The results revealed that the Sichuan populations exhibited higher levels of heterozygosity (Ho = 0.549~0.699, He = 0.547~0.607) and genetic diversity than the Hubei populations (Na = 4.00~5.250, PIC = 0.467~0.535). Notably, the DY population located in northeastern Sichuan showed the highest heterozygosity (Ho = 0.699, He = 0.607) and genetic diversity (Na = 5.250, PIC = 0.535) among the eight populations. Population structure, principal coordinate analysis and clustering analysis illuminated a close genetic relationship between the Qionglai population in Sichuan and the Jianli population in Hubei. Additionally, the remaining five Sichuan populations (Luxian, Nanxi, Xingwen, Neijiang and Daying) exhibited strong genetic affinity with the QianJiang population in Hubei, and particularly high genetic exchange may have occurred between Daying and Qianjiang (Fst = 0.001, Nm = 217.141). These results suggest that the primary cultivated P. clarkii populations in Sichuan likely originated from Qianjiang and Jianli counties in Hubei, with Qianjiang contributing a more substantial proportion. The genetic diversity of Sichuan populations was higher than those of some other Chinese P. clarkii farming provinces and even some native populations. Specifically, the Daying population emerges as a potential breeding germplasm source for crayfish in Sichuan. In contrast, the Qionglai population exhibits relatively low genetic diversity, highlighting the need for strategic enhancement through interactions with other populations to promote diversity and resilience. Furthermore, fostering genetic exchange among locally cultivated populations within the southern Sichuan basin is strategic to elevate the quality of P. clarkii germplasm resources.

Pollution and health risk assessments related to heavy metals on three prominent beaches in Makkah Province, Kingdom of Saudi Arabia: Concerning levels of cadmium pollution.

Beach pollution can impact the health of people living in or visiting coastal areas. The primary goal of this research was to investigate the presence of heavy metal pollutants and associated health risks in three distinct coastal cities, Jeddah, Al-Lith, and Al-Qunfudhah, located along Saudi Arabia's Red Sea coast. Forty-three soil samples were collected from different locations, heavy metals including Fe, Zn, Mn, Cu, Cd, and Pb were isolated, and analyzed using inductively coupled plasma optical emission spectroscopy. Various metrics such as pollution indices (PI), integrated pollution indices (IPI), enrichment factors (EF), daily dose averages (ADD), hazard quotients (HQ), and hazard indices (HI), as well as principal component analysis (PCA) and cluster analysis were employed to evaluate the environmental impacts and health risks posed by these heavy metals. The results revealed that Fe, Pb, Cu, Mn, and Zn concentrations in beach zones were below their respective background levels, while concentrations of Cd exceeded corresponding background levels. PCA revealed the highest levels of Pb, Fe, and Mn on Al-Qundudah beach, while Cd and Cu were highest on Al-Lith beach, and Zn was highest on Jeddah beach. PI values highlighted extremely high levels of Cd pollution on all designated beaches. The findings provide a foundational basis for further investigations into health problems potentially related to Cd contamination, such as chronic kidney disease (CKD), osteomalacia, and osteoporosis.

Exploring disparities and similarities in daily travel mode choices between electric vehicle owners and internal combustion engine vehicle owners

ABSTRACT This study proposes a novel instrument for measuring private car owners’ travel-related lifestyle (PCTRL) based on a survey (N = 2045) conducted in China’s four first-tier cities and ten quasi first-tier cities. By employing principal component analysis (PCA) and cluster analysis, the study classifies electric vhehicle (EV) owners and internal combustion engine vehicle (ICEV) owners into five distinct PCTRL segments, respectively. The findings reveal that only 66% of EV owners have similar PCTRLs to 67% of ICEV owners, with the most notable disparity being the higher likelihood of EV owners choosing private cars for their daily commutes. Furthermore, a random forest model was developed to predict the daily activity-based travel mode choices behavior of both EV and ICEV owners. The findings offer valuable insights into understanding the behavior of EV and ICEV owners in their daily travel mode choices and help formulating more effective traffic management strategies.

Developing driving cycles for garbage trucks to estimate fuel consumption

Driving cycle offers a comprehensive understanding of operational features throughout the waste collection and transportation (C&T) process, which is critical for optimizing waste management aiming at reducing energy use and environmental pollution. In this study, the Micro-trip method, combined with principal component and cluster analysis, was adopted to divide the C&T process into five operational phases. Considering diverse waste types and C&T modes, four driving cycles were synthesized for garbage trucks. The average speeds of garbage truck cycles are considerably lower, coupled with prolonged idle durations, marking a substantial deviation from standard cycles. Additionally, the OpMode-bin method was employed to estimate fuel consumption for both garbage truck cycles and standard cycles. The results reveal that using standard cycles generally results in an underestimation of fuel consumption by 20 % to 30 %, which highlights the necessity of developing specific driving cycles to ensure accurate fuel use and emission quantification for garbage trucks.

Navigating Global Research Trends on Seafoods-Related Foodborne Diseases: A Decadal Bibliometric Analysis

Ensuring seafood safety has become a critical area of focus due to its strong association with foodborne diseases. Despite the critical nature of this field, an in-depth understanding of global research trends in this field remains limited. Our study conducted an extensive bibliometric analysis to track the evolution and thematic focus of research on seafood safety from 2013 to 2023. Utilizing Scopus for publication and citation data, along with VOSviewer for visualizing keyword networks, this study highlights key developments and scholarly efforts aimed at mitigating risks associated with seafoods consumption. The analysis pinpoints the United States, China, the United Kingdom, France, South Korea and Malaysia as leading contributors. Their extensive research outputs and collaborations have markedly shaped the field's evolution. The overall positive trajectory in research volume experienced a minor setback in 2022, suggesting a complex and dynamic research landscape. "Food Control" is identified as a principal journal for the dissemination of research works, anchoring a dynamic co-citation network that fosters extensive international dialogue. We categorize seven principal research clusters: epidemiology, molecular detections, microbial resistance, bacteriophage applications, toxin detection methodologies, predictive modelling and biofilm studies. These clusters encompass the scopes and the breadth of current investigations. This decade-long review highlights the necessity for cohesive global research strategies to enhance the knowledge and control of foodborne pathogens in seafoods. This strategy is critical for improving food safety measures and safeguarding public health globally.

Marine macroalgae Chaetomorpha aerea as a dietary supplement: Optimizing immunity and resistance to Edwardsiella tarda in tilapia (Oreochromis mossambicus)

The intensification of aquaculture has led to a rise in fish infections, necessitating the search for alternative antibiotics. In this context, our study investigated the effects of dietary supplementation with Chaetomorpha aerea, a filamentous green algae, on the immune health and resistance to infections in tilapia (Oreochromis mossambicus). Diets containing varying concentrations of C. aerea (0, 1, 2, 5, and 10 g/kg) were prepared and administered to the fish for 30 days, followed by a challenge with Edwardsiella tarda to evaluate survival rates. The results were significant. The diet containing 5 g/kg of C. aerea (group T3) brought about substantial improvements in hematological parameters, including increases in red blood cell count (RBC), hematocrit (Hct), and hemoglobin (Hb). The T3 group exhibited a robust immune response, with higher lysozyme and ceruloplasmin activity in immunological assays. LBP gene expression was significantly elevated in the spleen and thymus of fish in the T3 group, which correlated with higher survival after bacterial challenge compared to the control group. Principal Component Analysis (PCA) and cluster analysis confirmed that the 5 g/kg concentration stood out for maximizing immunological benefits without compromising the overall health of the fish. These findings highlight the robust immune response in the T3 group, a key finding of our study. We conclude that supplementation with C. aerea represents a promising and sustainable alternative in the formulation of diets for tilapia, contributing to improved health and resistance to diseases. Future studies are recommended to explore its application in other species and development stages, in addition to evaluating other health biomarkers.

Thermoelectric Materials: A Scientometric Analysis of Recent Advancements and Future Research Directions

This scientometric study looks at the current trend in thermoelectric materials research and explores the evolving domain of thermoelectric materials research using a combination of bibliometric and scientometric methodologies. The analysis examines global research trends from a dataset of over 37,739 research articles, focusing on thematic evolution, annual growth rates, and significant contributions. Six principal research clusters were identified, encompassing energy conversion, material synthesis and nanostructures (the most prominent cluster), computational modeling and material properties, measurement and characterization, material performance enhancement, and material processing and microstructure. Each cluster highlights a critical aspect of the field, reflecting its broad scope and depth. The key findings reveal a marked annual increase in research output, highlighting the growing global importance of thermoelectric materials in sustainable energy solutions. This is especially evident in the significant contributions from China and the USA, emphasizing their leadership in the field. The study also highlights the collaborative nature of thermoelectric research, showing the impact of global partnerships and the synergistic effects of international collaboration in advancing the field. Overall, this analysis provides a comprehensive overview of the thermoelectric materials research landscape over the past decade, offering insights into trends, geographic contributions, collaborative networks, and research growth. The findings underscore thermoelectric materials’ vital role in addressing global energy challenges, highlighting recent advancements and industrial applications for energy efficiency and sustainability.

Characterization of Pasture Production Systems in Makueni County of Kenya

Livestock feed is a key factor influencing animal production and productivity as evidenced by the increased demand for animal source foods (ASFs) to feed a growing human population in Kenya. However, there exists untapped potential of pasture commercialization and hence the need to harness the social, economic and environmental benefits in the ASALs for overall rural development. A study was conducted to characterize the existing pasture production systems in Makueni County. A purposive random sampling of 300 respondents drawn from 3 Sub-counties and 12 wards was conducted in Makueni County in January, 2021. The study aimed to characterize existing pasture production systems using a structured questionnaire. Data were analyzed using descriptive and inferential statistics. Multivariate statistical techniques; principal Component Analysis (PCA) and Cluster Analysis (CA) were used to determine whether or not there were significant differences in the pasture production systems in Makueni County. Results of the study showed that majority (97%) of the farmers were small scale farmers (SSFs) who dedicated less than 5 acres of their land to pasture production. The mean age of small- scale farmers was 52 years compared to 55 years for the large-scale farmers (LSFs). Most of the households were male headed (83%). Most of the SSFs household heads had primary level of education level or lower while most of the large-scale household heads had secondary education and above. Majority (35%) of SSFs owned the land under pasture without a title while most of the LSFs had a title. The land under pasture for SSFs was about 2 acres with an average of 102 bales per season while LSFs had about 23 acres under pasture and produced about 1,762 bales per season. Majority (92%) grew local grass varieties and sold their pasture in form of a bale. 58% of SSFs sited NGOs as their main source of grass seeds while LSFs mainly sourced from agrovets Results of PCA revealed that 6 of the 17 components had eigen values greater than 1 and accounted for 58% of the total variance. Based on Euclidian distance, six clusters were determined using the agglomeration schedule. ANOVA analysis of the six profiles were estimated to have p-values of 0.000, suggesting the existence of significance difference between cluster 1 to 6 in relation to the 6 profiles and hence concluding the existence of variations in pasture production systems in Makueni County. Development strategies should focus on knowledge of and improved access to grass seeds to farmers as well as development of standards of the mode of sale of pasture.

The Jan Sjödin faba bean mutant collection: morphological and molecular characterization.

Plant mutagenesis creates novel alleles, thereby increasing genetic and phenotypic diversity. The availability of the faba bean (Vicia faba L.) reference genome and a growing set of additional genomic resources has increased the scientific and practical value of mutant collections. We aimed to genotype and morphologically phenotype a historical faba bean mutant collection developed and characterized by Jan Sjödin (1934-2023) over half a century ago in order to increase its value to researchers. The collection was genotyped using high-throughput single-primer enrichment technology (SPET) assays. We used 11,073 informative single nucleotide polymorphism (SNP) markers spanning the faba bean genome to genotype 52 mutant lines along with the background line, cv. Primus. A range of flower, seed, leaf, and stipule mutations were observed. The analysis of population structure revealed a shallow structure with no major subpopulations. Principal component and cluster analyses revealed, to a minor extent, that the mutants clustered by their phenotype. The mutants' phenotypic variation and shallow structure indicate that the Sjödin faba bean collection has the potential to play a significant role in faba bean breeding and in genetic and functional studies.

Screening of Ecotypes and Construction of Evaluation System for Drought Resistance during Seed Germination in Kudouzi (Sophora alopecuroides)

Drought is a major limiting factor in the cultivation of Sophora alopecuroides in China’s arid and semi-arid regions. This study aimed to identify drought-resistant S. alopecuroides ecotypes and explore key drought tolerance indicators during germination by simulating drought conditions with a 10% PEG-6000 solution, using pure water as a control. Determination of seven germination indicators for S. alopecuroides, including germination rate (GR), germination energy (GE), germination index (GI), vigor index (VI), promptness index (PI), fresh weight (FW), and dry weight (DW), was conducted. Principal component analysis (PCA), membership function, cluster analysis, and linear regression were employed to comprehensively evaluate the drought resistance of thirty-five S. alopecuroides ecotypes. The results showed that drought stress caused reductions in six of the seven indicators across all ecotypes, except for DW, compared to the control. Correlation analysis revealed varying relationships among the indicators, with most showing significant or highly significant correlations. PCA reduced the seven indicators to two independent comprehensive factors, with a cumulative contribution rate of 83.99%. Based on the D-value and cluster analysis, the thirty-five ecotypes were ranked for drought resistance and classified into four categories. The top five drought-tolerant genotypes during the germination stage were identified as Yutian, Alar, Jinghe, Baoding, and Guyuan. Moreover, the stepwise regression model was established and demonstrated that GR, GE, PI, FW, and DW are key indicators for screening and identifying drought-resistant S. alopecuroides ecotypes. This study offers a comprehensive and reliable method for evaluating drought resistance in S. alopecuroides ecotypes and provides a reference for selecting ecotypes for artificial cultivation in Northwestern China.

Applications of different forms of nitrogen fertilizers affect soil bacterial community but not core ARGs profile.

The objective of this study was to investigate the impact of various chemical nitrogen fertilizers on the profile of antibiotic resistance genes (ARGs) in soil. A microcosm experiment was conducted with four treatments, including CK (control with no nitrogen), AN (ammonium nitrogen), NN (nitrate nitrogen), and ON (urea nitrogen), and the abundance of ARGs was assessed over a 30-day period using a metagenomic sequencing approach. The levels of core ARGs varied between 0.16 and 0.22 copies per cell across different treatments over time. The abundance of core ARGs in the ON treatment closely resembled that of the CK treatment, suggesting that environmentally friendly nitrogen fertilizers, particularly those in controlled release formulations, may be preferable. The core ARG abundance in the AN and NN treatments exhibited noticeable fluctuations over time. Overall, chemical nitrogen fertilizers had minimal effects on the core ARG profile as determined by principal component analysis and clustering analyses. Conversely, distinct and significant changes in bacterial communities were observed with the use of different nitrogen fertilizers. However, the influence of nitrogen fertilizers on the core ARGs is limited due to the unaffected potential bacterial hosts. Nitrogen-cycling-related genes (NCRGs), such as those involved in nitrogen-fixing (nifK, nifD, nifH) and denitrification (narG, napA, nirK, norB, nosZ) processes, exhibit a positive correlation with ARGs (rosA, mexF, bacA, vanS), indicating a potential risk of ARG proliferation during intense denitrification activities. This study indicates that the application of chemical nitrogen has a minimal effect on the abundance of ARGs in soil, thereby alleviating concerns regarding the potential accumulation of ARGs due to the use of chemical nitrogen fertilizers.

Clinical correlates of lifetime and current comorbidity patterns in autoimmune and inflammatory diseases

BackgroundAutoimmune and inflammatory diseases (AIDs) are a heterogeneous group of disorders with diverse etiopathogenic mechanisms. This study explores the potential utility of family history, together with present and past comorbidities, in identifying distinct etiopathogenic subgroups. This approach may facilitate more accurate diagnosis, prognosis and personalized therapy. MethodsWe performed a multiple correspondence analysis on patients' comorbidities, followed by hierarchical principal component clustering of clinical data from 48 healthy volunteers and 327 patients with at least one of 19 selected AIDs included in the TRANSIMMUNOM cross-sectional study. ResultsWe identified three distinct clusters characterized by: 1) the absence of comorbidities, 2) polyautoimmunity, and 3) polyinflammation. These clusters were further distinguished by specific comorbidities and biological parameters. Autoantibodies, allergies, and viral infections characterized the polyautoimmunity cluster, while older age, BMI, depression, cancer, hypertension, periodontal disease, and dyslipidemia characterized the polyinflammation cluster. Rheumatoid arthritis patients were distributed across all three clusters. They had higher DAS28 and prevalence of extra-articular manifestations when belonging to the polyinflammation and polyautoimmunity clusters, and also lower ACPA and RF seropositivity and higher pain scores within the polyinflammation cluster. We developed a model allowing to classify AID patients into comorbidity clusters. ConclusionsIn this study, we have uncovered three distinct comorbidity profiles among AID patients. These profiles suggest the presence of distinct etiopathogenic mechanisms underlying these subgroups. Validation, longitudinal stability assessment, and exploration of their impact on therapy efficacy are needed for a comprehensive understanding of their potential role in personalized medicine.

Analysis of free amino acids and evaluation of the quality of gallnut honeys from different geographical areas

AbstractGallnut honey, a typical Chinese medicinal commodity, is produced from the nectar of Rhus chinensis Mill. Gallnut honey is very popular with consumers because of its health effects and nutritional value, but its specific nutritional composition and medicinal components are not clear. Free amino acids are important nutritional components of honey, and can provide relevant information about the honey, such as geographical and botanical origin, as well as adulterations. The aim of this paper was to explore the free amino acid characteristics of gallnut honeys in different areas. 18 free amino acids from 17 places in China were determined by automatic amino acid analyser analysis, and the geographical characteristics were studied by principal component analysis and cluster analysis. Results showed that gallnut honey is rich in free amino acids. A total of 12 types of free amino acids were detected in all honey-producing areas, and the total amino acid content was 0.132–0.292 g/100 g. Due to its free amino acid content gallnut honey has high nutritional supplement potential, and exhibits good pharmacodynamic activity and special taste characteristics. Flavour-related free amino acids were primarily aromatic free amino acids and umami free amino acids. Pharmacodynamic free amino acids accounted for 61.22–83.85%. Results also showed that free amino acids contribute to the geographical tracing of honey, and samples of gallnut honey at different altitudes can be distinguished by cluster analysis.

Exploring critical quality indicators and developing a non-destructive detection method using near-infrared spectroscopy for sea bass (Lateolabrax japonicus) quality evaluation

In this study, chemometrics were employed to explore the relationship between sensory evaluation and physicochemical indicators of sea bass (Lateolabrax japonicus). Through principal component analysis, cluster analysis, and Pearson correlation analysis, three pivotal indicators were identified: protein content, b* value, and condition factor. Leveraging the grey relational analysis, weights were assigned to these three core quality indicators, resulting in a comprehensive sea bass quality evaluation model: Y = 0.911 × protein (g/100 g) + 0.742 × b* + 0.747 × condition factor. Moreover, near-infrared spectroscopy combined with chemometrics were employed to evaluate the quality of sea bass. The different origins of sea bass were accurately distinguished using orthogonal partial least squares discriminant analysis. The partial least squares regression model was constructed for predicting the critical quality indicator, protein content, with R2P of 0.926. This study offers new insights for developing rapid, economical, and reliable methods for assessing aquatic product quality.

The proximate composition of vegetables enriched by incorporation of municipal solid waste into fertilizers and its impacts on environment and human health

The recent over production of municipal solid waste (MSW) poses a significant threat to both the ecosystem and human health. Utilizing MSW for agricultural purposes has emerged as a promising strategy to reduce solid waste disposal while simultaneously increasing soil fertility. To explore this potential solution further, an experiment was designed to assess the impact of varying concentrations of MSW (25%, 50%, and 75%) on the proximate composition of 15 different vegetable species. The experiment, conducted between 2018 and 2019, involved treating soil with different levels of solid waste and analyzing the proximate components, such as crude protein, dry matter, crude fiber, crude fat, and moisture content, in the 15 selected crops. The results indicate that the application of 25% MSW significantly increased the levels of crude protein, crude fiber, dry matter, and fat in Spinacia oleracea, Solanum tuberosum, Solanum melongena, and Abelmoschus esculentus. Conversely, the addition of 75% MSW notably elevated the moisture and ash content in Cucumis sativus. Correlation and scatter matrix analyses were conducted to elucidate the relationships between the protein, fiber, dry matter, ash, and fat contents. Principal component analysis and clustering confirmed the substantial impact of Treatment_1 (25% MSW) and Treatment_3 (75% MSW) on the proximate composition of the aforementioned vegetables, leading to their categorization into distinct groups. Our study highlights the efficacy of using 25% MSW to enhance the proximate composition and nutritional value of vegetables. Nonetheless, further research is warranted to investigate the mineral, antioxidant, vitamin, and heavy metal contents in the soil over an extended period of MSW application.

Morphological variation of endemic Gymnocypris chilianensis (Cyprinidae) collected from three inland water systems in Qilian Mountains, China

To explore the morphological differences among different geographic populations of Gymnocypris chilianensis, 29 measurable traits and 13 landmarks on 191 individuals of 10 geographic populations were collected from three different inland water systems, i.e., Shiyang River, Heihe River, and Shule River, in Qilian Mountains, China. Multivariate statistical methods were used to compare morphological differences among the populations, such as principal component analysis, cluster analysis, discriminant analysis, and visual analysis of landmark data. The results showed that: (1) There were significant morphological differences between groups of G. chilianensis. The Shiyang River basin differs significantly from populations in the other two basins. (2) The inter-population differences were mainly reflected in the length of the trunk and tail, and the difference between trunk and caudal fin had the most obvious characteristics, proving the tail being the main direction of variation. This research provides an effective theoretical basis for the fine management and accurate protection of G. chilianensis germplasm resources.

Loss of synovial tissue macrophage homeostasis precedes rheumatoid arthritis clinical onset.

This study performed an in-depth investigation into the myeloid cellular landscape in the synovium of patients with rheumatoid arthritis (RA), "individuals at risk" of RA, and healthy controls (HC). Flow cytometric analysis demonstrated the presence of a CD40-expressing CD206+CD163+ macrophage population dominating the inflamed RA synovium, associated with disease activity and treatment response. In-depth RNA sequencing and metabolic analysis demonstrated that this macrophage population is transcriptionally distinct, displaying unique inflammatory and tissue-resident gene signatures, has a stable bioenergetic profile, and regulates stromal cell responses. Single-cell RNA sequencing profiling of 67,908 RA and HC synovial tissue cells identified nine transcriptionally distinct macrophage clusters. IL-1B+CCL20+ and SPP1+MT2A+ are the principal macrophage clusters in RA synovium, displaying heightened CD40 gene expression, capable of shaping stromal cell responses, and are importantly enriched before disease onset. Combined, these findings identify the presence of an early pathogenic myeloid signature that shapes the RA joint microenvironment and represents a unique opportunity for early diagnosis and therapeutic intervention.

Boosting resilience and yields in water-stressed sunflower through coordinated irrigation scheduling and silica gel applications

Sunflower (Helianthus annuus L.) is a globally significant oilseed crop, yet its yields may suffer due to water deficiency. A field experiment was conducted through two summer seasons (2021 and 2022) at the agricultural research station, Luxor, Egypt, to study the effects of irrigation regime, cultivar selection, and silica gel application on sunflower growth and yield parameters. The experiment utilized a split-split plot design with three irrigation levels based on field capacity (85 %, 70 %, 55 %), three sunflower cultivars (Sakha-53, Giza-102, and Giza-120), and two Silica gel treatments (0 and 300 kg/ha). Analysis of variance revealed significant effects of irrigation (P < 0.001) on all measured traits except 100-seed weight. Utilizing various cultivars also revealed substantial influence, affecting most traits consistently across the two seasons. Silica gel application positively impacted head diameter, 100-seed weight, oil content, and oil yield (P < 0.05). Furthermore, treatments combining moderate irrigation with silica gel application exhibited the highest water productivity, with values reaching 1.35 kg/m³ in the first season and 1.38 kg/m³ in the second season, indicating the synergistic effects of improved water management and silica gel on crop water use efficiency. Multivariate analysis of principal component analysis and clustering heatmap revealed that treatment combinations such as I2S1 (moderate irrigation with silica gel) optimized sunflower productivity, particularly for seed yield and oil content, reinforcing the importance of tailored agronomic practices for maximizing crop performance under varying environmental conditions. These findings underscore the significance of integrated agronomic approaches in enhancing sunflower productivity while promoting sustainable crop management practices.