Individual Data Sets Research Articles

Altered immune response is associated with sex difference in vulnerability to Alzheimer's disease in human prefrontal cortex.

Alzheimer's disease (AD) is a neurodegenerative disorder with a higher risk incidence in females than in males, and there are also differences in AD pathophysiology between sexes. The role of sex in the pathogenesis of AD may be crucial, yet the cellular and molecular basis remains unclear. Here, we performed a comprehensive analysis using four public transcriptome datasets of AD patients and age-matched control individuals in prefrontal cortex, including bulk transcriptome (295 females and 402 males) and single-nucleus RNA sequencing (snRNA-seq) data (224 females and 219 males). We found that the transcriptomic profile in female control was similar to those in AD. To characterize the key features associated with both the pathogenesis of AD and sex difference, we identified a co-expressed gene module that positively correlated with AD, sex, and aging, and was also enriched with immune-associated pathways. Using snRNA-seq datasets, we found that microglia (MG), a resident immune cell in the brain, demonstrated substantial differences in several aspects between sexes, such as an elevated proportion of activated MG, altered transcriptomic profile and cell-cell interaction between MG and other brain cell types in female control. Additionally, genes upregulated in female MG, such as TLR2, MERTK, SPP1, SLA, ACSL1, and FKBP5, had high confidence to be identified as biomarkers to distinguish AD status, and these genes also interacted with some approved drugs for treatment of AD. These findings underscore the altered immune response in female is associated with sex difference in susceptibility to AD, and the necessity of considering sex factors when developing AD biomarkers and therapeutic strategies, providing a scientific basis for further in-depth studies on sex differences in AD.

Lifecycle DoE—The Companion for a Holistic Development Process

Within process development, numerous experimental studies are undertaken to establish, optimize and characterize individual bioprocess unit operations. These studies pursue diverse objectives such as enhancing titer or minimizing impurities. Consequently, Design of Experiment (DoE) studies are planned and analyzed independently from each other, making it challenging to interlink individual data sets to form a comprehensive overview at the conclusion of the development process. This paper elucidates the methodology for constructing a Life-Cycle-DoE (LDoE), which integrates data-driven process knowledge through design augmentations. It delves into the strategy, highlights the challenges encountered and provides solutions for overcoming them. The LDoE approach facilitates the augmentation of an existing model with new experiments in a unified design. It allows for flexible design adaptations as per the requirements of subject matter experts (SME) during process development, concurrently enhancing model predictions by utilizing all available data. The LDoE boasts a broad application spectrum as it consolidates all data generated within bioprocess development into a single file and model. The study demonstrates that the LDoE approach enables a process characterization study (PCS) to be performed solely with development data. Furthermore, it identifies potentially critical process parameters (pCPPs) early, allowing for timely adaptations in process development to address these challenges.

Assessing methods for telluric removal on atmospheric retrievals of high-resolution optical exoplanetary transmission spectra

Recent advancements in ultra-stable ground-based high-resolution spectrographs have propelled ground-based astronomy to the forefront of exoplanet detection and characterisation. However, the resultant transmission and emission spectra of exoplanetary atmospheres are inevitably contaminated by telluric absorption and emission lines due to the light's transmission through the Earth's atmosphere above the observatory. Retrieving accurate atmospheric parameters depends on accurate modelling and removal of this telluric contamination while preserving the faint underlying exoplanet signal. There exist many methods to model telluric contamination, whether directly modelling the Earth's transmission spectrum via radiative transfer modelling, or using a principal component analysis (PCA)-like reconstruction to fit the time-invariant features of a spectrum, and removing these models from the observations. We aimed to assess the efficacy of these various telluric removal methods in preserving the underlying exoplanetary spectra. We compared two of the most common telluric modelling and removal methods molecfit and the PCA-like algorithm SysRem using planetary transmission spectra injected into three high-resolution optical observations taken with ESPRESSO. These planetary signals were injected at orbital periods of P = 2 days and P = 12 days, resulting in differing changes in radial velocity during transit. We then retrieved various injected atmospheric model parameters in order to determine the efficacy of the telluric removal methods, as well as their effect on the transmission spectra of exoplanets with varying orbital architectures. For the close-in, high velocity injected signal, we found that SysRem performed better for species that are also present in the Earth's atmosphere--with accurate and precise retrieval of atmospheric abundances and $T$-$P$ profiles, across each of the datasets. As we moved to slower moving signals at larger orbital separations, for one of the three datasets SysRem dampened the planetary H$_2$O signal, leaving the retrieved abundance value unconstrained. In contrast, the H$_2$O signal was preserved for the telluric modelling method molecfit . However, this behaviour was not ubiquitous across all three of the injected datasets, with another dataset showing a more precise H$_2$O/Fe ratio when preprocessed with SysRem . These conflicts highlight the importance of testing individual high-resolution dataset reduction routines independently to ensure real exoplanetary signals are preserved.

Prediction of cardiovascular outcomes by subtle changes in the peripheral immune cell landscape - insights from the LURIC single cell RNA-sequencing study (LuRNA)

Abstract Background and Aims Atherosclerosis and its clinical sequelae, myocardial infarction and stroke, represent the main causes of death worldwide. Although preclinical evidence has suggested the existence of an inflammatory response driving disease, the extend of cellular alterations in human atherosclerosis remains enigmatic. Here, we employ single cell RNA-sequencing (scRNA-seq) on peripheral blood mononucleated cells (PBMCs) in a well-defined cardiovascular risk cohort from the "Ludwigshafen Risk and Cardiovascular Health (LURIC) trial" to define changes in the immune cell landscape in atherosclerotic patients. Methods and Results Of 3317 patients enrolled in the LURIC trial between 1997 and 2000, we selected individuals with stable coronary-artery disease (CAD) (n=31) and healthy patients (no CAD) (n=16) by propensity score matching, adjusted for CRP, NTproBNP, Troponin T, LDL-C, and Cystatin-C, in a case-control design. scRNA-seq was performed by droplet sequencing on PBMCs stored in liquid nitrogen. Individual data sets were integrated and changes between healthy individuals and patients with CAD were evaluated on numeric and transcriptional level. Single cell transcriptomes were annotated using an established CITE-seq reference atlas. While bulk RNA-sequencing of PBMC preparations revealed only minor changes between both conditions, we identified several leukocyte populations on a single cell level with specific transcriptomes that were differentially regulated between both conditions. Interestingly, we found that several subsets of Natural Killer (NK) cells, T cells with an enrichment of proliferation-associated pathways, and a population resembling hematopoietic stem cells were more frequent in patients with CAD, while naïve phenotypes of B and T cells were overrepresented in the absence of CAD. Consistently, most T and B cell populations in patients with CAD were enriched in pathways associated with cell activation, immune receptor signalling, and differentiation. In addition, we detected restricted repertoires of T cell receptor sequences in several adaptive immune cell subtypes from patients with CAD, suggestive of increased cellular clonality and antigen-specificity. Finally, in a comparison of cellular frequencies with cardiovascular outcomes over more than 15 years, several immune cell subsets and cell-type specific transcriptional programs predicted cardiovascular death in this case-control scenario in multi-variate adjusted regression analysis. Notably, addition of scRNA-seq derived parameters was superior to traditional risk prediction with clinical characteristics and soluble biomarkers alone. Conclusion Employing scRNAseq, we demonstrate that atherosclerosis is associated with a profound change in the circulating immune cell landscape even in the absence of measurable differences in inflammatory biomarkers. These findings propose the usage of scRNAseq for the discovery of outcome-relevant cellular biomarkers in the future.

Exploring the Chemopreventive Effect of Medication on Gene Expression Linked to Colorectal Cancer: An Observational and Mendelian Randomization Analysis in Healthy Colon Mucosa

Gene expression appears altered in apparently normal tissue surrounding tumor tissue. The observed biological alterations in the tumor microenvironment play a crucial role in cancer development and are named the cancer field effect (FE). A robust set of overexpressed FE genes in tissue surrounding colorectal cancer (CRC) tumor were identified in previous studies. Our study aimed to investigate the influence of common medication intake and modifiable risk factors on FE gene expression using a colonic mucosa sample dataset of healthy individuals (BarcUVa-Seq). We applied expression enrichment analysis of the FE genes for each studied medication and factor. Both observational and instrumental (Mendelian randomization) analysis were conducted, and the results were validated using independent datasets. The findings from the observational and instrumental analyses consistently showed that medication intake, especially metformin, considerably downregulated the FE genes. Chemopreventive effects were also noted for antihypertensive drugs targeting the renin–angiotensin system. Conversely, benzodiazepines usage might upregulate FE genes, thus fostering a tumor-promoting microenvironment. In contrast, the findings from the observational and instrumental analyses on modifiable risk factors showed some discrepancies. The instrumental results indicated that obesity and smoking might promote a tumor-favorable microenvironment. These findings offer insights into the biological mechanisms through which risk factors might influence CRC development and highlight the potential chemopreventive roles of metformin and antihypertensive drugs in CRC risk.

Constrained cosmological simulations of the Local Group using Bayesian hierarchical field-level inference

We present a novel approach based on Bayesian field-level inference that provides representative initial conditions for simulation of the Local Group (LG) of galaxies and its neighbourhood, constrained by present-day observations. We extended the Bayesian Origin Reconstruction from Galaxies ( algorithm with a multi-resolution approach, allowing us to reach the smaller scales needed to apply the constraints. Our data model simultaneously accounts for observations of mass tracers within the dark haloes of the Milky Way (MW) and M31, for their observed separation and relative velocity, and for the quiet surrounding Hubble flow, represented by the positions and velocities of 31 galaxies at distances between one and four megaparsec. Our approach delivers representative posterior samples of realisations that are statistically and simultaneously consistent with all of these observations, leading to significantly tighter mass constraints than found if the individual datasets are considered separately. In particular, we estimate the virial masses of the MW and M31 to be $ (M_ 200c /M_ and $12.33 respectively, their sum to be $ 200c /M_ and the enclosed mass within spheres of radius $R$ to be $ (M(R)/M_ and $12.96 for $R=1$ Mpc and $3$ Mpc, respectively. The M31-MW orbit is nearly radial for most of our realisations, and most of them feature a dark matter sheet aligning approximately with the supergalactic plane, despite the surrounding density field not being used explicitly as a constraint. High-resolution, high-fidelity resimulations from initial conditions identified using the approximate simulations of our inference scheme continue to satisfy the observational constraints, demonstrating a route to future high-resolution, full-physics simulations of ensembles of LG look-alikes, all of which closely mirror the observed properties of the real system and its immediate environment.

Individual Tree Crown Delineation Using Airborne LiDAR Data and Aerial Imagery in the Taiga–Tundra Ecotone

The circumpolar Taiga–Tundra Ecotone significantly influences the feedback mechanism of global climate change. Achieving large-scale individual tree crown (ITC) extraction in the transition zone is crucial for estimating vegetation biomass in the transition zone and studying plants’ response to climate change. This study employed aerial images and airborne LiDAR data covering several typical transitional zone regions in northern Finland to explore the ITC delineation method based on deep learning. First, this study developed an improved multi-scale ITC delineation method to enable the semi-automatic assembly of the ITC sample collection. This approach led to the creation of an individual tree dataset containing over 20,000 trees in the transitional zone. Then, this study explored the ITC delineation method using the Mask R-CNN model. The accuracies of the Mask R-CNN model were compared with two traditional ITC delineation methods: the improved multi-scale ITC delineation method and the local maxima clustering method based on point cloud distribution. For trees with a height greater than 1.3 m, the Mask R-CNN model achieved an overall recall rate (Ar) of 96.60%. Compared to the two conventional ITC delineation methods, the Ar of Mask R-CNN showed an increase of 1.99 and 5.52 points in percentage, respectively, indicating that the Mask R-CNN model can significantly improve the accuracy of ITC delineation. These results highlight the potential of Mask R-CNN in extracting low trees with relatively small crowns in transitional zones using high-resolution aerial imagery and low-density airborne point cloud data for the first time.

Integrative taxonomic revision of the grasshopper genera Parapetasia Bolívar, 1884, and Loveridgacris Rehn, 1954 (Orthoptera, Pyrgomorphidae), with description of a new species of Loveridgacris

The taxonomic status of the Pyrgomorphid genera Parapetasia Bolívar, 1884, and Loveridgacris Rehn, 1954 is complex and challenging. Here, we use a combination of morphological, distributional, and genetic data to revise the two genera and provide new information on their diversity. We describe a new species, Loveridgacris tectiferussp. nov., from Tanzania and formally resurrect the status of Parapetasia rammei as a valid species within Parapetasia, resulting in two species in Parapetasia (P. femorata and P. rammei) and two in Loveridgacris (L. impotens and L. tectiferussp. nov.). We also sequenced the COI and 16S genes of 10 Pyrgomorphidae species and provided the first phylogeny of the group. Our data show that all species are clearly distinct and represent molecular operational taxonomic units (mOTUs), with the exceptions of L. impotens and L. tectiferussp. nov., which are morphologically clearly distinct but for which the concatenated sequence alignments of the two individual gene datasets (COI and 16S) do not provide sufficient information. In addition, high interspecific distances were found between Parapetasia and Loveridgacris. Moreover, the complete mitogenomes of L. impotens and L. tectiferussp. nov. were sequenced using next-generation sequencing technology. The total lengths of the assembled mitogenomes were 15,592 bp and 15,737 bp, representing 13 protein-coding genes, 22 transfer RNA genes, two ribosomal RNA genes and one D-loop region, respectively. To aid in identification, we present a key for the two genera, including a key to species. This study provides insights into the morphology, distribution, and phylogeny of Pyrgomorphidae in Africa.

A Reproducibility Focused Meta-Analysis Method for Single-Cell Transcriptomic Case-Control Studies Uncovers Robust Differentially Expressed Genes.

Here we systematically studied the reproducibility of DEGs in previously published Alzheimer's Disease (AD), Parkinson's Disease (PD), and COVID-19 scRNA-seq studies. We found that while transcriptional scores created from differentially expressed genes (DEGs) in individual PD and COVID-19 datasets had moderate predictive power for the case control status of other datasets (mean AUC=0.77 and 0.75, respectively), genes from individual AD datasets had poor predictive power (mean AUC=0.68). We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets. The meta-analysis genes had improved predictive power (AUCs of 0.88, 0.91, and 0.78, respectively). By multiple other metrics, specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. The DEGs revealed known and novel biological pathways, and we validate the BCAT1 gene as down-regulated in oligodendrocytes in an AD mouse model. Our analyses show that for heterogeneous diseases, DEGs of individual studies often have low reproducibility, but combining information across multiple datasets promotes the rigorous discovery of reproducible DEGs.

The Distribution of Cervical Transformation Zone and Its Impact on Colposcopic Diagnosis: A Multicenter Study in China.

The value of the transformation zone (TZ) is often overlooked in clinical settings. This study aims to assess TZ distribution, associated factors, and its impact on colposcopic diagnosis. χ2 tests were used to analyze demographics, clinical history, and tissue samples to examine the differences in TZ distribution. Factors affecting the TZ were explored using logistic regression, and diagnostic indicators were calculated. A total of 5,302 individual datasets were finally included. TZ1, TZ2, and TZ3 accounted for 31.6%, 38.5%, and 30.0%, respectively. Age is the most important factor that influences the location of the TZ. The proportion of TZ3 steadily increased with age, comprising over 55% in women over 50. The colposcopic diagnostic performance shows that high-grade squamous intraepithelial lesion or worse (HSIL+) sensitivity of TZ3 (58.1%, 95% confidence interval [CI] = 52.9-63.4) is significantly lower than that of TZ1 (69.8%, 95% CI = 65.5-74.1) and TZ2 (73.2%, 95% CI = 69.7-76.8). The HSIL+ specificity of TZ3 (96.3, 95% CI = 95.3-97.4) was higher than that of TZ1 (96.3, 95% CI = 95.2-97.3) and TZ2 (92.5, 95% CI = 91.1-93.9). The HSIL+ positive predictive value (81.3%, 95% CI = 76.4-86.2) and negative predictive value (89.3%, 95% CI = 87.6-90.9) for TZ3 are high, with no significant differences when compared with TZ1 and TZ2. Age predominantly influences TZ location, with TZ3 being most frequently found in women over 50. While TZ3 poses a higher risk of missed diagnosis during colposcopy, it remains clinically valuable in identifying diseased and nondiseased status. Increasing colposcopists' awareness of TZ importance is needed in clinical practice.

A first determination of the strong coupling αS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha _S$$\\end{document} at approximate N3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO order in a global PDF fit

We present the first determination of the value of the strong coupling via a simultaneous global fit of the proton parton distribution functions (PDFs) at approximate N3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO (aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO) order in QCD. This makes use of the MSHT global PDF fitting framework, and in particular the recent theoretical advances t l;;mhat allow a PDF fit to now be performed at this order. The value of the strong coupling is found to be αS(MZ2)(aN3LO)=0.1170±0.0016\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha _{S}(M_Z^2)(\\mathrm{aN^3LO}) = 0.1170 \\pm 0.0016$$\\end{document}. This is in excellent agreement with the NNLO value of αS(MZ2)(NNLO)=0.1171±0.0014\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\alpha _{S}(M_Z^2)(\ extrm{NNLO}) = 0.1171 \\pm 0.0014$$\\end{document}, indicating that good perturbative convergence has been found. The resulting uncertainties, calculated using the MSHT dynamic tolerance procedure, are somewhat larger, but more accurate, at aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO, due to the missing higher order theoretical uncertainties that are included at this order, but not at NNLO. We in addition present a detailed breakdown of the individual dataset sensitivity to the value of the strong coupling, with special focus on the impact of fitting dijet rather than inclusive jet data. This choice is found to have a non-negligible impact, but with overall good consistency found, especially at aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO.

A-228 Evaluation of an Equation for Low-Density Lipoproteins-Triglycerides in Predicting ASCVD Risk in the UK Biobank Population

Abstract Background Low-density lipoprotein-cholesterol (LDL-C) is the main univariate risk marker recommended for initial atherosclerotic cardiovascular disease (ASCVD) risk stratification. LDL, however, transports other constituents that can serve as pro-atherogenic risk markers. Like LDL-C, the triglyceride content of LDL (LDL-TG) can also be estimated from the lipid panel. Using data from UK Biobank (UKB), we compared LDL-C to LDL-TG for predicting future ASCVD events. Methods A UKB dataset (N=270,436) of primary prevention individuals not on lipid lowering therapy was used for analysis. LDL-C was calculated by the following equations: Friedewald, Extended Martin-Hopkins, Sampson-NIH, and Enhanced Sampson-NIH. LDL-TG was calculated by our newly devised equation (eLDL-TG = TG/38.46 + nonHDL-C/5.75 + (TG/nonHDL-C)/0.103 + (1/HDL-C)/0.0041 - 2.95)), which was derived from a dyslipidemic cohort (N=40,202) analyzed by the beta-quantification reference method. Results Although there was only a modest comparison between eLDL-TG and measured LDL-TG (R2=0.601, slope=0.611), eLDL-TG appeared to be a better risk marker than LDL-C in UKB. When the population was divided into quintiles and analyzed by survival curve analysis, eLDL-TG was better than LDL-C in segregating patients into low- and high-risk groups (Figure). Similar findings on the relative value of LDL-TG over other risk markers were also found when analyzed by ROC analysis (AUC: eLDL-TG>HDL-C>remnant-cholesterol>NonHDL-C>TG>apoB>Sampson-NIH-LDL-C). By Cox-proportional hazard analysis eLDL-TG showed alongside apoB and NonHDL-C the strongest association with future ASCVD events. Discordance analysis showed that when stratified as LDL-TG/LDL-C as High/Low there was an 26% increased risk for ASCVD, and when as High/High showed an 40% increased risk for ASCVD. Conclusions Like LDL-C, LDL-TG levels can also be estimated from the standard lipid panel. If our findings hold in other populations, the superiority of LDL-TG over LDL-C as a risk marker suggest that the reporting of calculated LDL-TG along with LDL-C could be a new strategy for improving ASCVD risk stratification.

Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon

This study proposes a novel methodology to employ discrete point spectra as input variable for digital mapping of soil organic carbon (SOC). Accordingly, two SOC modeling approaches were used in three agricultural sites in Czech Republic: i) machine learning (ML) including partial least square regression (PLSR), cubist, random forest (RF), and support vector regression (SVR), and ii) regression kriging (RK) by the combination of ordinary kriging (OK) and PLSR (PLSR-K), cubist (cubist-K), RF (RF-K), and SVR (SVR-K). Models were developed on environmental predictor covariates (EPCs) and thirty genetic algorithms (GA)-selected visible, near-infrared, and shortwave-infrared (VNIR–SWIR) wavelengths spectra, individually and combined. Thirty rasters were then created using interpolation of the selected spectra and served as the input variables – with and without EPCs – to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach: iii) kriging using OK of the measured and ML-predicted SOC. The impact of employing selected wavelengths’ spectra and EPCs on models' performance was investigated using independent test samples and the uncertainty associated with the produced maps. Using interpolated spectra as the only input variable yielded a relatively acceptable accuracy (Nová Ves: RMSE = 0.19%, Údrnice: RMSE = 0.12%, Klučov: RMSE = 0.13%). In comparison, the interpolated spectra coupled with EPCs enhanced the results. Regarding the uncertainty, however, the ML-based SOC maps were more reliable, than RK-based ones. Furthermore, maps produced using both spectra and EPCs showed less uncertainty than those constructed on the individual datasets.

Blending gauge, multi-satellite and atmospheric reanalysis precipitation products to facilitate drought monitoring

Long-term, continuous, and highly accurate precipitation estimation is crucial for reliable drought monitoring. Precipitation estimation based on gauge measurements, satellite retrieval, and reanalysis datasets exhibits heterogeneous uncertainties across different regions of mainland China. This study introduces two modifiable weighting schemes (Cheng-Kling-Gupta Efficiency (CKGE) weighted-ensemble model (CWEM) and Bayesian Model Averaging (BMA)), utilizing posterior probabilities generated by BMA and the performance of CKGE, to merge 7 monthly precipitation datasets into new weighted precipitation (BMA Ensemble Precipitation (BMAEP) and CKGE Weighted-Ensemble Precipitation (CWEP)) using various quantity combination schemes. Subsequently, the precision and drought monitoring utility of the weighted precipitation are evaluated and compared with the representative fused precipitation product Multi-Source Weighted-Ensemble Precipitation (MSWEP) in mainland China using gauged data. The amalgamated results demonstrate that, compared to individual datasets, certain weighted precipitation schemes exhibit superiority over MSWEP. In particular, BMAEP-2P demonstrates superior performance in the composite index CKGE, achieving a value of 0.828. CWEP-4P exhibits a higher correlation coefficient (CC), reaching 0.905. CWEP-2P excels in terms of relative bias (BIAS) and root mean square error (RMSE), with values of 0.579 % and 20.755 mm, respectively. Furthermore, BMAEP or CWEP performs optimally in drought monitoring applications across all sub-regions of mainland China at different time scales (1, 3, 6, 12 and 24 months), with the average of the highest values of CC and probability of detection (POD) reached 0.919 and 0.844, respectively. Further contribution analysis reveals CPC as the dominant factor contributing to the greatest improvement in the performance (excluding CKGE, CC_SPEI1, CC_SPEI24 and POD_SPEI24) of the fusion models, among which it boosted MERRA2′s performance on POD_SPEI6 by 9.41 %. In conclusion, the merging schemes based on CWEM and BMA methods effectively generate a new precipitation dataset, integrating information from multiple products into drought monitoring applications.

Integrated immuno-transcriptomic analysis of ovarian cancer identifies a four-chemokine-dominated subtype with antitumor immune-active phenotype and favorable prognosis.

Ovarian cancer (OV) is a heterogeneous disease but has traditionally been treated as an immunologically cold malignancy. The relationship between the immune-active cancer phenotype typified by a T helper 1 (Th-1) immune response and clinical outcome in OV remains uncertain. A cohort-scale compendium of transcriptomic data from 2850 OV samples from 19 individual datasets was compiled for integrative immuno-transcriptomic analysis. The immunological constant of rejection was used as a metric to assess the Th-1/cytotoxic response orientation and investigate the clinical-biological significance of immune polarization towards a Th-1 immune response. Single-cell RNA sequencing data from 39 OV samples were analyzed to elucidate the variability of the immune microenvironment, and immunohistochemical validation was performed on 39 samples from the Harbin Medical University Cancer Hospital. Our results demonstrated the prognostic significance of a Th-1/cytotoxic immune profile within the tumor microenvironment (TME) using the immunological constant of rejection classification to OV samples. Specifically, patients with tumors expressing high levels of ICR markers showed significantly improved survival. A gene panel consisting of four chemokines (CXCL9, CXCL10, CXCL11 and CXCL13) was identified as critical players in mediating the establishment of an active T-cell-inflamed antitumor phenotype. This 4-chemokine signature, which was extensively validated in external multicenter cohorts through transcriptomic profiling and in an independent in-house cohort through immunohistochemistry, introduced a novel immune classification in OV and identified a chemokine-dominated subtype associated with an active antitumor immune phenotype and favorable prognosis. Single-cell transcriptomic analysis revealed that chemokine-dominated tumors increase CXCR3 + NK and T cell recruitment to the TME primarily through the overexpression of macrophage-derived CXCL9/10/11. This study provides new insights into understanding immune heterogeneity within the TME and paves the way for tailoring appropriate therapeutic interventions for patients with differing immune profiles.

Detecting Poisoning Attacks in Collaborative IDSs of Vehicular Networks Using XAI and Shapley Value

Machine learning-based Intrusion Detection Systems (IDSs) for vehicle networks can collaborate to enhance their performance by sharing crucial decisions when individual datasets lack diversity, which hinders effective model training. However, such collaborative coalitions are vulnerable to poisoning attacks, where certain members tamper with training data, leading to wrong predictions by the IDSs. The current solutions to this problem have the following issues: 1) They require accessing the training dataset of IDSs, which raises critical privacy concerns; 2) Their heavy reliance on voting mechanisms may exclude clients and fail to detect malicious coalition members when attackers form the majority coalition; and 3) The verification process can potentially expose sensitive information, posing privacy risks. To address these issues and improve the resilience of collaborative IDSs against poisoning attacks, we propose a novel approach that combines XAI (Explainable AI) technology with Shapley value from cooperative game theory. XAI justifies IDS decisions, while the Shapley value identifies poisoning attacks in training datasets by capturing contradictions between explanations and decisions. We tested our implementation using a publicly available dataset and various machine learning models (e.g., RFC, SVM and LSTM). Our results prove highly promising in detecting poisoning attacks and overcoming the flaws in existing solutions.

A probability model for estimating age in young individuals relative to key legal thresholds: 15, 18 or 21-year.

Age estimations are relevant for pre-trial detention, sentencing in criminal cases and as part of the evaluation in asylum processes to protect the rights and privileges of minors. No current method can determine an exact chronological age due to individual variations in biological development. This study seeks to develop a validated statistical model for estimating an age relative to key legal thresholds (15, 18, and 21years) based on a skeletal (CT-clavicle, radiography-hand/wrist or MR-knee) and tooth (radiography-third molar) developmental stages. The whole model is based on 34 scientific studies, divided into examinations of the hand/wrist (15 studies), clavicle (5 studies), distal femur (4 studies), and third molars (10 studies). In total, data from approximately 27,000 individuals have been incorporated and the model has subsequently been validated with data from 5,000 individuals. The core framework of the model is built upon transition analysis and is further developed by a combination of a type of parametric bootstrapping and Bayesian theory. Validation of the model includes testing the models on independent datasets of individuals with known ages and shows a high precision with separate populations aligning closely with the model's predictions. The practical use of the complex statistical model requires a user-friendly tool to provide probabilities together with the margin of error. The assessment based on the model forms the medical component for the overall evaluation of an individual's age.

ConvexAdam: Self-Configuring Dual-Optimisation-Based 3D Multitask Medical Image Registration.

Registration of medical image data requires methods that can align anatomical structures precisely while applying smooth and plausible transformations. Ideally, these methods should furthermore operate quickly and apply to a wide variety of tasks. Deep learning-based image registration methods usually entail an elaborate learning procedure with the need for extensive training data. However, they often struggle with versatility when aiming to apply the same approach across various anatomical regions and different imaging modalities. In this work, we present a method that extracts semantic or hand-crafted image features and uses a coupled convex optimisation followed by Adam-based instance optimisation for multitask medical image registration. We make use of pre-trained semantic feature extraction models for the individual datasets and combine them with our fast dual optimisation procedure for deformation field computation. Furthermore, we propose a very fast automatic hyperparameter selection procedure that explores many settings and ranks them on validation data to provide a self-configuring image registration framework. With our approach, we can align image data for various tasks with little learning. We conduct experiments on all available Learn2Reg challenge datasets and obtain results that are to be positioned in the upper ranks of the challenge leaderboards. github.com/multimodallearning/convexAdam.

Salinity and Stratification at the Sea Ice Edge (SASSIE): an oceanographic field campaign in the Beaufort Sea

Abstract. As our planet warms, Arctic sea ice coverage continues to decline, resulting in complex feedbacks with the climate system. The core objective of NASA's Salinity and Stratification at the Sea Ice Edge (SASSIE) mission is to understand how ocean salinity and near-surface stratification affect upper-ocean heat content and thus sea ice freeze and melt. SASSIE specifically focuses on the formation of Arctic Sea ice in autumn. The SASSIE field campaign in 2022 collected detailed observations of upper-ocean properties and meteorology near the sea ice edge in the Beaufort Sea using ship-based and piloted and drifting assets. The observations collected during SASSIE include vertical profiles of stratification up to the sea surface, air–sea fluxes, and ancillary measurements that are being used to better understand the role of salinity in coupled Arctic air–sea–ice processes. This publication provides a detailed overview of the activities during the 2022 SASSIE campaign and presents the publicly available datasets generated by this mission (available at https://podaac.jpl.nasa.gov/SASSIE, last access: 29 May 2024; DOIs for individual datasets in the “Data availability” section), introducing an accompanying repository that highlights the numerical routines used to generate the figures shown in this work.

Patient lived experiences of functioning and disability following lumbar discectomy: a secondary analysis of qualitative data

BackgroundKnowledge of patient lived experiences of functioning and disability is limited. This study aims to address the gap in the literature by exploring patient lived experiences of functioning and disability following lumbar discectomy.MethodA secondary analysis, reported in line with the Standards for Reporting Qualitative Research, was conducted of qualitative data exploring patient journeys following lumbar discectomy surgery (DiscJourn). Adult patients (≥ 16 years) undergoing elective or emergency primary lumbar discectomy were recruited from one National Health Service secondary care centre in the UK. Semi-structured interviews were conducted at 1–3 weeks and 1-year post surgery. Participants who completed both semi-structured interviews were eligible for the secondary analysis. Transcripts from the semi-structured interviews were analysed using interpretative phenomenological analysis (IPA). IPA involved two independent reviewers identifying themes for individual data sets followed by an iterative process involving the wider research team to identify overarching themes that represented the whole date set. Subthemes generated from the IPA were mapped against the International Classification of Functioning, Disability and Health (ICF) framework at the level of chapters, in order to ascertain the ICF’s utility in capturing experiences of functioning and disability. Strategies to enhance trustworthiness of data analysis included blind coding, peer examination and debrief, declaration of pre-conceived beliefs and active reflexivity throughout the study.ResultsNine participants met the eligibility criteria and their interview transcripts were analysed. Patient lived experiences of functioning and disability were captured by three overarching themes: Immediate impact following surgery, Multiple roads to recovery over 1 year, and Functioning influenced by personal loci of control. Each theme consisted of three subthemes which were subsequently mapped onto the ICF. Three subthemes mapped to the ICF’s body component, 1 to activity and participation and 3 to environment. Two subthemes themes did not map onto the ICF.ConclusionFindings provide valuable insights into patient experiences of functioning and disability following lumbar discectomy. Convergence in experiences of functioning and disability were identified immediately following surgery. Divergence in such experiences were identified with regards to the roads to recovery over 1 year and the individuals’ locus of control. Findings build on the body of literature exploring patients functioning and disability following discectomy and make recommendations for future research and clinical practice.