Joint Analysis Research Articles

Source attribution of Listeria monocytogenes in the Netherlands

The aim of this study was to determine the relative contributions of various potential food sources of human listeriosis and to identify source-specific risk factors, at exposure level, for human Listeria monocytogenes (Lm) infection. To achieve this, available Lm isolates from human cases (n = 756) and food/animal sources (n = 950) from national surveillance systems in the Netherlands (2010−2020) were whole genome sequenced. Additionally, questionnaire-based exposure data for human cases was collected. Source attribution analysis was performed using a Random Forest model based on core-genome multilocus sequence typing (cgMLST). Risk factors for human Lm infection of cattle, chicken and seafood origin were determined using beta regression analysis on the cgMLST-based attribution estimates. Results indicated that the 756 human Lm isolates were mainly attributed to cattle (62.3 %), chicken (19.4 %), and seafood (16.9 %). Specifically, fresh meat (86.2 %), including fresh bovine meat (43.7 %) and fresh chicken meat (39.3 %), accounted for most cases. These attributions stemmed from Lm contamination of either the food products or their production environments. Consumption of steak tartare and smoked salmon was associated with an increased risk of human Lm infections attributed to cattle and seafood, respectively, while no specific risk factors for chicken-borne listeriosis were identified. This study indicated that Lm isolates of cattle origin, particularly those from fresh bovine meat and associated production environments, are estimated to be the primary cause of human listeriosis in the Netherlands. This aligns with several other European source attribution studies on Lm. Moreover, the identified risk factors for human Lm infection from cattle (i.e. steak tartare) and seafood (i.e. smoked salmon) clearly indicated their attributable sources. This joint analysis of core genome and epidemiological data provided novel insights into the origins and transmission pathways of human listeriosis.

Combined Transcriptome and Metabolome Analyses Provide New Insights into the Changes in the Flesh Color of Anthocyanins in Strawberry (Fragaria × ananassa (Weston) Duchesne ex Rozier)

Background: Strawberries are bright in color, sweet and sour in taste, and rich in nutrients and flavonoid compounds such as anthocyanins and proanthocyanidins. The synthesis and accumulation of anthocyanins are the decisive factors that make strawberries appear bright red. From the perspective of plant breeding, a change in flesh color is an important goal. Methods: In this study, two strawberry plants with different flesh colors were selected, and transcriptome and metabolome analyses were performed during the color change period (S1) and ripening period (S2). Results: RNA-seq revealed a total of 13,341 differentially expressed genes (DEGs) between and within materials, which were clustered into 5 clusters. A total of 695 metabolites were detected via metabolome analysis, and 243 differentially regulated metabolites (DRMs) were identified. The anthocyanin biosynthesis, starch and sucrose metabolism and glycolysis/gluconeogenesis pathways were determined to be important regulatory pathways for changes in strawberry flesh color through a joint analysis of RNA-seq data and the metabolome. The leucoanthocyanidin reductase (LAR) and chalcone synthase (CHS) gene is a key gene related to anthocyanins, cinnamic acid, and phenylalanine. In addition, through joint RNA-seq and metabolome analyses combined with weighted gene co-expression network analysis (WGCNA), we identified 9 candidate genes related to strawberry flesh color. Conclusions: Our research findings have laid the groundwork for a more comprehensive understanding of the molecular mechanisms governing the color transformation in strawberry flesh. Additionally, we have identified novel genetic resources that can be instrumental in advancing research related to strawberry color change.

Reconstruction of a matrix of genotypic correlations between variants within a gene for joint analysis of imputed and sequenced data

When combining imputed and sequenced data in a single gene-based association analysis, the problem of reconstructing genetic correlation matrices arises. It is related to the fact that for a gene, we know the correlations between genotypes of all imputed variants and the correlations between genotypes of all sequenced variants, but we do not know the correlations between genotypes of variants, one of which is imputed and the other is sequenced. To recover these correlations, we propose an efficient method based on maximising the determinant of the matrix. This method has a number of useful properties and has an analytical solution for our task. Approbation of the proposed method was performed by comparing reconstructed and real correlation matrices constructed on individual genotypes from the UK biobank. Comparison of the results of gene-based association analysis performed by the SKAT, BT and PCA methods on reconstructed and real matrices, using modelled summary statistics and calculated summary statistics on real phenotypes, showed high quality of reconstruction and robustness of the method to different gene structures.

Employees’ work ability during returning to work with common mental disorders: a mixed methods study

Abstract Background A main goal during the return to work (RTW) process after a long-term sickness absence due to common mental disorders (CMDs), is to restore and maintain an employees’ work ability to enable sustained work participation. To date, surprisingly little research has examined the concept of work ability during the RTW process with CMDs. To better understand how to restore employees’ work ability during RTW with CMDs, both quantitative and qualitative research is needed. The goal of this mixed methods study is to expand the understanding of restoring work ability during the RTW process with CMDs. Methods In this mixed methods follow-up study of N = 286 participants, work ability was quantitatively assessed with the Work Ability Score (WAS, range 0-10) at five measurement points (0, 6, 12, 18 and 30 months) during 30 months of follow-up. In a sub-sample, the qualitative work ability experiences of N = 32 participants were jointly analysed with their quantitative work ability ratings at 6 and 12 months. Results In the joint analysis, three groups of qualitative work ability experiences together with their respective quantitative work ability ratings emerged: Employees with (1) poor work ability (WAS 0-3), who did not RTW yet and described great difficulties in coping with everyday life; (2) moderate work ability (WAS 4-6), who mainly did RTW, but still showed a certain level of fragility; and (3) good to very good work ability (WAS 7-10), who mainly returned within 12 months and reported many individual and work accommodations to restore and maintain their work ability. Conclusions The joint evaluation of work ability ratings and experiences allowed a deeper understanding of restoring work ability during the RTW process. This knowledge may help occupational health professionals and other key RTW stakeholders to provide more tailored support during the RTW process and to promote a sustainable RTW for employees with CMDs. Key messages • Restoring and maintaining the work ability of employees with common mental disorders during the return to work process is crucial for their sustained work participation. • Achieving a good to very good work ability helps returning employees with common mental disorders to ‘stay and thrive’ at work again.

Genome-wide association study of CMR image-based aortic stiffness in 45,789 individuals identifies loci linked with cardiovascular diseases

Abstract Background The use of artificial intelligence on large-scale cardiac magnetic resonance (CMR) imaging data has enabled detailed characterisation of cardiac shape and function for use in genome-wide association studies (GWAS), leading to improved understanding of genetic aetiology of cardiovascular diseases (CVDs). Aortic stiffness is associated with increased risk of cardiovascular events in observational studies. Here, we present a novel CMR-based pressure-independent measure of aortic stiffness, identify genetic associations and explore links with CVDs. Purpose Identify genetic contributions to pressure-independent aortic stiffness and links to CVDs using large-scale GWAS analysis on the UK Biobank cohort. Methods A pre-trained neural network was used to segment CMR images of participants in the UK Biobank to quantify ascending aorta diameter, which together with contemporaneous systolic and diastolic blood pressure measurements (SBP, DBP, respectively) were used to calculate a pressure-independent measure of aortic stiffness (β0). The measurement assumes an exponential relationship between pressure and aortic diameter. We then performed a GWAS on β0 in 45,789 participants of European ancestry without CVDs. A multi-trait-based conditional & joint analysis (mtCOJO) was performed to estimate genetic effects independent of SBP, DBP and aortic area. We mapped the independent lead variants associated with aortic stiffness at P < 5x10-8 to nearest gene, and characterised shared genetic association across CVDs using Open Targets Platform database. Results The GWAS identified 17 independent lead variants, including 6 that remained significant in the conditional analysis. Four of these lead variants, not previously reported in GWAS of other aortic traits, were located near CTD-2203A3.1, MCF2L, CFDP1 and CDH13 genes (red annotations in the Figure). MCF2L, CFDP1 and CDH13 have been associated with coronary artery disease, and CFDP1 with myocardial infarction and coronary atherosclerosis in previous studies. Conclusion A GWAS of CMR-derived aortic stiffness identified 4 loci distinct from other aortic traits that also associate with other CVDs, suggesting genetic links between aortic stiffness and CVDs. Further analyses are required to explore biological mechanisms underlying these genetic associations.Aortic Stiffness Manhattan Plot

Ubiquitin-Specific Protease 15 Plays an Important Role in Controlling the Tolerance to Salt, Drought and Abscisic Acid in Arabidopsis thaliana

Ubiquitin-specific proteases (UBPs), the largest subfamily of deubiquitinating enzymes (DUBs), are critical for plant growth and development as well as abiotic-stress responses. In this study, we discovered that the expression of the ubiquitin-specific protease 15 (UBP15) gene of the gene ubiquitin-specific protease 15 (UBP15) was induced by salt, mannitol and abscisic acid (ABA) treatments. Further research revealed that UBP15 is involved in modulation of salt, drought tolerance and ABA signaling during seed germination, early seedling development, post-germination root growth or adult-plant stage. Enrichment analysis showed that many genes related to abiotic stresses and metabolic pathways were altered in the ubp15-1 mutant. Through the joint analysis of the quantitative real-time polymerase chain reaction (qRT-PCR) and differentially-expressed gene relationship network, we found that UBP15 may mainly regulate salt-stress tolerance by modulating the dwarf and delayed flowering 1 (DDF1) pathway through a cascade reaction. In the regulation of drought-stress responses, ring domain ligase1 (RGLG1) may be a direct substrate of UBP15. Moreover, we cannot exclude the possibility that UBP15 acts in a feed-forward loop mechanism in the regulation of drought-stress responses via ethylene response factor 53 (ERF53) and its ubiquitin (Ub) ligase RGLG1. In ABA signal transduction, UBP15 may play a role in at least three aspects of the ABA signaling pathway: ABA synthesis, stomatal closure regulated by ABA signaling, and transcription factors in the ABA pathway. Taken together, our results suggest that UBP15 is involved in salt, osmotic, and drought-stress tolerance and the ABA signaling pathway by directly regulating the stability of key substrates or indirectly affecting the expression of genes related to abiotic stresses in Arabidopsis thaliana. Our research provides new germplasm resources for stress-resistant crops cultivation. These results demonstrate that UBP15 is a key regulator of salt, drought and ABA tolerance in Arabidopsis.

The genetic architecture of exercise-induced ventricular ectopy

Abstract Background Previously, we have shown that the frequency of exercise-induced premature ventricular contractions (PVCs) during and after exercise are associated with increased risk of major adverse cardiovascular events in asymptomatic individuals from the UK Biobank study1. The underlying mechanisms are, however, unclear. Several electrocardiographic markers associated with arrhythmogenesis have a known hereditary component, but the genetic underpinnings of exercise-induced ventricular ectopy have not been investigated. Purpose Our aim was to explore the genetic basis of premature ventricular ectopy during and after exercise and unveil plausible candidate genes and biological mechanisms. Methods We conducted a genome-wide association study (GWAS) for PVC frequency during exercise and recovery in >44,000 asymptomatic individuals of European ancestry without known cardiovascular disease from the UK Biobank study. As the distribution of PVC frequency is highly skewed (e.g. absence of negative values and excess of zero values), we first applied Gaussian transformation. Simulation studies were performed to justify that this approach would lead to valid inference. In the GWAS model, we included sex, age, UK Biobank genotyping array, and the first 10 genetic principal components as covariates. Statistical power was boosted by joint analysis of exercise and recovery PVC traits using multi-trait analysis of GWAS (MTAG)2. Genetic risk scores were constructed in independent samples from UK Biobank (N=341,948) and were tested for association with heart failure and life-threatening ventricular arrhythmias. Results We identified 4 loci for PVC frequency during exercise, and 1 locus for PVC frequency during recovery (Picture 1). Candidate genes included CRIM1, FLNC, and BAG3 for which mutations have been linked in the pathogenesis of (arrhythmogenic) cardiomyopathy. Subjects in the top 10% of the genetic risk scores had a significantly higher risk of heart failure and life-threatening ventricular arrhythmias compared to the bottom 90%, (odds ratio (OR) (95% confidence interval): 1.14 (1.08 – 1.20), p < 0.001; and 1.13 (1.02 – 1.24), p < 0.001, respectively). Conclusions Ventricular ectopic burdens during exercise and recovery have a significant heritable component. Candidate genes highlight a possible shared genetic background with inherited cardiomyopathy and a genetic risk score was associated with heart failure and ventricular arrhythmias. These findings substantial advance our understanding of the genetic basis of exercise-induced ventricular ectopy in a population-based cohort without cardiovascular disease.Miami plot of ventricular ectopic burden

Examining the local Universe isotropy with galaxy cluster velocity dispersion scaling relations

In standard cosmology, the Universe is assumed to be statistically homogeneous and isotropic. This assumption suggests that the expansion rate of the Universe, as measured by the Hubble parameter, should be the same in all directions. However, our recent study based on galaxy clusters finds an apparent angular variation of approximately $9<!PCT!>$ in the Hubble constant, $H_0$, across the sky. In the study, the authors utilised galaxy cluster scaling relations between various cosmology-dependent cluster properties and a cosmology-independent property, i.e. the temperature of the intracluster gas ($T$). A position-dependent systematic bias of $T$ measurements can, in principle, result in an overestimation of apparent $H_0$ variations. Therefore, it is crucial to confirm or exclude this possibility. In this work, we search for directional $T$ measurement biases by examining the relationship between the member galaxy velocity dispersion and gas temperature ($ v -T$) of galaxy clusters. Both measurements are independent of any cosmological assumptions and do not suffer from the same potential systematic biases. Additionally, we search for apparent $H_0$ angular variations independently of $T$ by analysing the relations between the X-ray luminosity and Sunyaev-Zeldovich signal with the velocity dispersion, $L_ X v $ and $Y_ SZ v To study the angular variation of scaling relation parameters, we determined the latter for different sky patches across the extra-galactic sky. We constrained the possible directional $T$ bias using the $ v -T$ relation, as well as the apparent $H_0$ variations using the $L_ X v $ and $Y_ SZ v $ relations. We utilised Monte Carlo simulations of isotropic cluster samples to quantify the statistical significance of any observed anisotropies. We calculated and rigorously took into account a correlation of $L_ X $ and $Y_ SZ $ residuals. No significant directional $T$ measurement biases are found from the $ v -T$ anisotropy study. The probability that the previously observed $H_0$ anisotropy is caused by a directional $T$ bias is only $0.002<!PCT!>$. On the other hand, from the joint analysis of the $L_ X v $ and $Y_ SZ v $ relations, the maximum variation of $H_0$ is found in the direction of $(295^ with a statistical significance of $3.64 fully consistent with our previous results. Our findings, based on the analysis of new scaling relations utilising a completely independent cluster property, $ v $, strongly corroborate the previously detected anisotropy of galaxy cluster scaling relations. The underlying cause, for example, $H_0$ angular variation or large-scale bulk flows of matter, remains to be identified.

Columnar jointing in Paleoproterozoic carbon-bearing rocks (Onega Basin, NW Russia): implications for carbon migration in sedimentary rocks

ABSTRACT Columnar jointing occurs commonly in igneous rocks and rarely in sedimentary rocks. We report the results of combined field and petrological studies, Raman spectroscopy and column geometry analysis of columnar jointing in Paleoproterozoic carbon-bearing rocks, Eastern Fennoscandian Shield, NW Russia. The columnar rocks occur near the contact with the saucer-shaped dolerite sill emplaced in the unconsolidated organic rich sediments. Columnar jointing aureole (1 to 7 m) and column architecture strongly depends on the contact morphology. Columnar jointing is caused by the contraction of organic-rich sediments due to generation and further release of gas and oil rich fluid. The results of integrated studies indicate that fluidization driving the jointing is associated with not only the dehydration, but also with the mobilization of gas and oil rich fluid due to the thermal maturation of organic matter. Columnar jointing is associated with intensive fluid circulation and devolatilization evidenced by the numerous veins, globules, and vugs filled with migrated carbonaceous matter and secondary minerals in the columnar rocks as well as high vesicularity of dolerites near the contact. The redistribution of the mobile elements (K, Rb, LREE) along with the significant enrichment of S near the contact also indicates the intensive fluid circulation. The study contributes to understanding of mechanism of columnar jointing in sedimentary rocks and formation the fracture networks in organic-rich sediments.

Mechanism analysis of dual wavelength laser welding AlN-PC joint based on microtexture treatment

High-strength interfacial bonding between ceramics and transparent polymers is hardly to be achieved, especially in the field of optical packaging of biochips. This paper proposes a novel laser welding method that enhances the joint strength between aluminum nitride (AlN) ceramic and polycarbonate (PC) by laser surface microtexturing. The study investigates the influence of welding methods and microtextures on welding effectiveness, elucidating the bonding mechanisms of the joints. The obtained results showed that dual wavelength laser welding effectively achieves the connection between AlN ceramic and PC. Laser microtexturing increases surface roughness and contact area, leading to stronger mechanical interlocking and overall joint strength. The highest shear strength reached up to 14.66 MPa with longitudinal grooves microtexture. The laser microtexturing improves the hydrophilicity of the AlN surface, facilitating more effective bonding between the materials. The bonding mechanisms at the joint interface include both mechanical interlocking and chemical bonding. Microtexturing improves the stress distribution at the joint interface. The formation of C-O-Al chemical bonds at the interface effectively enhances the joint strength. Longitudinal grooves microtexture exhibit a higher substrate fracture rate due to stress concentration and capillary pores. In contrast, staggered square holes microtexture provide superior mechanical performance through uniform stress distribution and robust mechanical interlocking.

Recombinant leptins affect lipid metabolism in hepatocytes of Cynoglossus semilaevis

Leptin is a peptide hormone primarily produced by adipose tissue, which plays a crucial role in regulating energy balance by controlling appetite and energy expenditure. To better understand the intricate physiological functions of leptin in hepatocytes in tongue sole (Cynoglossus semilaevis), this study presents an integrated transcriptomic and metabolomic analysis of tongue sole hepatocytes following stimulation with lepA and lepB. Comparative analysis identified 2971 and 2900 upregulated differentially expressed genes (DEGs) and 1895 and 1821 downregulated DEGs in response to lepA and lepB stimulation, respectively. Notably, 5706 genes were commonly regulated by both stimulations, suggesting overlapping functional roles of these two leptin proteins. GO and KEGG enrichment analysis indicated significant enrichment in immune response (JAK-STAT signaling pathway, NF-κB signaling pathway, etc.) and lipid metabolism pathways, such as fatty acid synthesis, with similar findings for lepB. Metabolomic analysis demonstrated clear separation between treatment and control groups, with 34 medium- to long-chain fatty acids and numerous differentially expressed metabolites (DEMs) identified. KEGG analysis of the metabolome paralleled the transcriptomic findings, with shared pathways in lipid metabolism and immune function. Finally, joint analysis highlighted co-enrichment in linoleic acid metabolism and leishmaniasis pathways. Detailed mechanistic insights revealed the activation of JAK-STAT and NF-κB signaling pathways, modulation of arachidonic acid metabolism, and the influence on the MAPK signaling pathway. Additionally, lepA uniquely co-enriched in the fatty acid synthesis pathway, with specific gene regulation affecting the synthesis of various fatty acids. The study concluded that lepA and lepB exerted significant regulatory effects on lipid metabolism and immune responses in tongue sole hepatocytes through complex molecular networks. To our current knowledge, this represents the first direct evidence of leptin's role in immune function within teleost fish and offers valuable insights into the molecular mechanisms of lipid metabolism underlying the actions of lepA and lepB.

Independent and joint influence of depression and advanced lung cancer inflammation index on mortality among individuals with chronic kidney disease.

The combined effect of depression and nutritional-inflammatory status on mortality in the chronic kidney disease (CKD) population is unclear. We prospectively analyzed 3,934 (weighted population: 22,611,423) CKD participants from the National Health and Nutrition Examination Survey (2007-2018). Depression and nutritional-inflammatory status were assessed with Patient Health Questionnaire 9 (PHQ-9) and Advanced Lung Cancer Inflammation Index (ALI), respectively. Weighted multivariate COX regression models, restricted cubic splines (RCS) models, and stratified analyses were used to investigate the association of PHQ-9 scores and ALI with all-cause mortality. During a median follow-up of 5.8 years (interquartile range 3.4-8.6 years), a total of 985 patients died (25.0%). Each point increase in a patient's PHQ-9 score increased the risk of all-cause mortality by 4% (HR, 1.04; 95% CI, 1.02-1.06; p < 0.001), in the full adjusted model. However, an increase in ALI levels was associated with a decreased risk. HRs (95% CI) of 0.76 (0.65-0.90), 0.70 (0.57-0.86), and 0.51 (0.41-0.64) in the Q2, Q3, and Q4 of ALI compared with the Q1 of ALI, respectively. In addition, the joint analysis showed that CKD patients without depression and with higher ALI were associated with a reduced risk of all-cause mortality. Namely, patients in the highest ALI group (Q4) without depression had the lowest risk (HR, 0.32; 95% CI, 0.21-0.48). Furthermore, this combined effect was consistent across all subgroups, and no significant interaction was found (p > 0.05 for interaction). In a nationally representative sample of US patients with CKD, coexisting depression and poorer nutrition-inflammation were associated with a significantly increased risk of all-cause mortality.

Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.

Rice breeding programs globally have worked to release increasingly productive and climate-smart cultivars, but the genetic gains have been limited for some reasons. One is the capacity for field phenotyping, which presents elevated costs and an unclear approach to defining the number and allocation of multi-environmental trials (MET). To address this challenge, we used soil information and ten years of historical weather data from the USA rice belt, which was translated into rice response based on the rice cardinal temperatures and crop stages. Next, we eliminated those highly correlated Environmental Covariates (ECs) (>0.95) and applied a supervised algorithm for feature selection using two years of data (2021-22) and 25 genotypes evaluated for grain yield in 18 representative locations in the Southern USA. To test the trials' optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. Finally, we weigh the trial's allocation considering the counties' economic importance and the environmental group to which they belong. Our findings show that eight ECs explained 58% of grain yield variation across sites and 53% of the observed genotype-by-environment interaction. Moreover, it is possible to reduce 28% the number of locations without significant loss in accuracy. Furthermore, the US Rice belt comprises four clusters, with economic importance varying from 13 to 45%. These results will help us better allocate trials in advance and reduce costs without penalizing accuracy.

Third-order intrinsic alignment of SDSS BOSS LOWZ galaxies

Cosmic shear is a powerful probe of cosmology, but it is affected by the intrinsic alignment (IA) of galaxy shapes with the large-scale structure. Upcoming surveys such as Euclid and Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require an accurate understanding of IA, particularly for higher-order cosmic shear statistics that are vital for extracting the most cosmological information. In this paper, we report the first detection of third-order IA correlations using the LOWZ galaxy sample from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS). We compare our measurements with predictions from the MICE cosmological simulation and an analytical model inspired by the Non-linear Linear Alignment (NLA) model and informed by second-order correlations. We also explore the dependence of the third-order correlation on the galaxies' luminosity. We find that the amplitude $A_ IA $ of the IA signal is non-zero at the $4.7 level for scales between Mpc Mpc Mpc Mpc $ the inferred $A_ IA $ agrees both with the prediction from the simulation and estimates from second-order statistics within 1sigma but deviations arise at smaller scales. Our results demonstrate the feasibility of measuring third-order IA correlations and using them for constraining IA models. The agreement between second- and third-order IA constraints also opens the opportunity for a consistent joint analysis and IA self-calibration, promising tighter parameter constraints for upcoming cosmological surveys.

Spatially seamless and temporally continuous assessment on compound flood risk in Hong Kong

Compound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.

MAPbrain: a multi-omics atlas of the primate brain.

The brain is the central hub of the entire nervous system. Its development is a lifelong process guided by a genetic blueprint. Understanding how genes influence brain development is critical for deciphering the formation of human cognitive functions and the underlying mechanisms of neurological disorders. Recent advances in multi-omics techniques have now made it possible to explore these aspects comprehensively. However, integrating and analyzing extensive multi-omics data presents significant challenges. Here, we introduced MAPbrain (http://bigdata.ibp.ac.cn/mapBRAIN/), a multi-omics atlas of the primate brain. This repository integrates and normalizes both our own lab's published data and publicly available multi-omics data, encompassing 21 million brain cells from 38 key brain regions and 436 sub-regions across embryonic and adult stages, with 164 time points in humans and non-human primates. MAPbrain offers a unique, robust, and interactive platform that includes transcriptomics, epigenomics, and spatial transcriptomics data, facilitating a comprehensive exploration of brain development. The platform enables the exploration of cell type- and time point-specific markers, gene expression comparison between brain regions and species, joint analyses across transcriptome and epigenome, and navigation of cell types across species, brain regions, and development stages. Additionally, MAPbrain provides an online integration module for users to navigate and analyze their own data within the platform.

Open Geospatial Engine: A Cloud-based Spatiotemporal Computing Platform

Abstract. The quantity and diversity of Earth spatiotemporal big data have significantly increased in recent years, providing the potential to comprehensively analyse complex spatiotemporal problems from new perspectives. However, integrating, managing, and applying multi-source heterogeneous Earth spatiotemporal big data remains a challenge. To address this issue, this study proposes a cloud-based spatiotemporal computing platform, the Open Geospatial Engine (OGE), for the unified organization and joint analysis of Earth spatiotemporal big data in multiple dimensions and scales. The framework of this platform comprises three modules: data management, computing engine, and service interface. The data management module adopts the GeoCube model to effectively integrate and manage multi-source heterogeneous spatiotemporal data through multi-dimensional aligned tiles. The computing engine module seamlessly maps the GeoCube model to the cloud environment by extending Spark RDD, making the GeoCube model capable of high-performance distributed computing. Following OGC specifications, the service interface module integrates and shares data, operators, and spatiotemporal analysis models through extensible APIs in an interactive development environment. Combined with a series of high-performance optimization techniques, OGE simplifies data queries and the construction of complex analytical applications by integrating these three modules. The applicability of OGE is demonstrated by case studies involving multi-dimensional queries and joint analysis of long-time-series and heterogeneous spatiotemporal data.

MSIreg: an R package for unsupervised coregistration of mass spectrometry and H&E images.

Joint analysis of mass spectrometry images (MS images) and microscopy images of hematoxylin and eosin (H&E) stained tissues assists pathologists in characterizing the morphological structure of the tissues, and in performing diagnosis. Unfortunately, the analysis is undermined by substantial differences between these modalities in terms of aspect ratios, spatial resolution, number of channels in each image, as well as by large global or small local elastic spatial deformations of one image with respect to the other. Therefore, accurate coregistration of the images is a critical pre-requisite for their joint interpretation. We introduce MSIreg, an open-source R package for coregistration of MSI and H&E images. MSIreg is designed for high-dimensional MSI experiments where each spatial location is represented by thousands of mass features. Unlike most existing coregistration methods, MSIreg implements a landmark free workflow, and quantitative metrics for performance evaluation. We evaluate the performance of MSIreg on six case studies, including coregistration of contiguous tissues with large deformations, as well as simultaneous coregistration of 29 tissue microarray cores. The R package, installation instructions, and fully reproducible vignettes describing methods and Case Studies are are available open-source under the GPL-3.0 license at https://github.com/sslakkimsetty/msireg/.

Protective effect of Tecoma stans (L.) Juss.ex Kunth in CFA-induced arthritic rats

Ethnopharmacological relevanceTecoma stans (L.) Juss.ex Kunth (Bignoniaceae) is mainly found in tropical and subtropical regions of Africa and Asia. The leaves, flowers, roots, and bark are used to treat various aliments includes, skin infections, kidney problems, intestinal disorders, jaundice, toothaches, joint pain and repair cracked bones, antidotes for snake, scorpion, and rat bites. Aim of the studyThe objective of the study is to assess the anti-arthritic properties of T. stans leaf using Complete Freund's adjuvant (CFA)-induced rat. Materials and methodThe ethanol extract of T. stans leaf (ETSL) was subjected toGas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS) analysis for the identification of potential bioactive. The anti-arthritic activity was carried out by administering CFA (0.1 ml) into the sub-plantar surface of the right hind paw. The experimental animals were treated with indomethacin (10 mg/kg) and ETSL (250, 500 mg/kg) once a day orally for fourteen days. The arthritic parameters and hematological and biochemical parameters were evaluated using standard kit reagents. The levels of pro-inflammatory cytokines and inflammatory mediators were measured in blood serum. Antioxidant parameters were assessed in homogenized liver and joint tissues. Radiological and histopathological analysis of joint was performed. A computational molecular docking investigation of the phytoconstituents was conducted against COX-2, IL-1β, IL-6, and TNF-α receptors. ResultsThe ETSL at 500 mg/kg demonstrated significant (p < 0.01) restoration of arthritic parameters, hematological and biochemical indices and oxidative stress in CFA-induced rats which was further supported by radiological histological examination. In addition, there was significant (p < 0.05) reduction observed in pro-inflammatory cytokines, inflammatory mediators and up-regulation of anti-inflammatory cytokines in the treated group. Verbascoside was found to exhibit better biding affinities −10.4, −7.4, −7 and −6.2 kcal/mol against COX-2, IL-1β, TNF-α, and IL-6 respectively, confirmed through in silico study. ConclusionsThe observed outcome suggests that ETSL at a dosage of 500 mg/kg demonstrated notable anti-arthritic effects by suppressing pro-inflammatory cytokines and oxidative stress biomarkers. This effect could potentially be attributed to the presence of bioactive verbascoside identified in the LC-MS analysis.

Evaluation and identification of high yielding and stable cowpea genotypes using GGE biplot and joint regression analysis for varietal development in East and Southern Africa

Malawi, Mozambique, and Tanzania's cowpea breeding programs have been collaborating on pre-breeding trials in different locations, focusing on key traits such as grain yield. However, there is a lack of information regarding the nature of genotype-environment interaction patterns among these environments. The study was conducted to assess and identify high-yielding and stable cowpea genotypes for the development of improved varieties in these three countries. Thirty genotypes were evaluated in ten environments using an incomplete block design with three replications. Grain yield data was analyzed using genotype (G) × genotype-environment interaction (GGE) biplot and joint regression analysis. The joint analysis of variance showed highly significant differences (P < 0.001) for the main effects of genotypes and environment, as well as their interaction effects. This suggested that the environments assessed were distinct and that the genotypes exhibited varying performances in response to environmental variations. The environmental indices showed that only four environments were favorable for high grain yield, and overall, Guariba was the topmost yielder (1973.5 kg ha−1) across all environments. The analysis identified IT10K-817-7 and MWcp08 as superior and stable genotypes, making them highly desirable and potentially recommended across test environments for their expected ability to achieve economically viable yields. Overall, Chitedze Research Station in Malawi and Nametil Research Station in Mozambique were both discriminating and representative, indicating that they are good test environments for selecting best-performing genotypes that can perform well across a wide range of conditions. These environments can be recommended as the best environments for future cowpea genotype evaluations targeting the release of varieties in the three countries (Malawi, Mozambique and Tanzania) where the METs were carried out.