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Sea level rise (SLR) poses a significant threat to coastal regions worldwide, particularly affecting over 60 million people living below 10 m above sea level along the African coast. This study analyzes the spatio-temporal trends of sea level anomaly (SLA) and its components (thermosteric, halosteric and ocean mass) in the Eastern Tropical Atlantic Ocean (ETAO) from 1993 to 2022. The SLA trend for the ETAO, derived from satellite altimetry, is 3.52 ± 0.47 mm/year, similar to the global average of 3.56 ± 0.67 mm/year. Of the three upwelling regions, the Gulf of Guinea (GoG) shows the highest regional trend of 3.42 ± 0.12 mm/year. Using the ARMORD3D dataset, a positive thermosteric sea level trend of 0.88 ± 0.04 mm/year is observed, particularly in the equatorial and southern Atlantic regions. The steric component drives the interannual SLA variability, while the ocean mass component dominates the long-term trends, as confirmed by the GRACE and GRACE-FO missions for 2002-2022. For those two decades, the total SLR from altimetry amounts to 3.80 ± 0.8 mm/year, whilst the steric component is reduced to only 0.19 ± 0.05 mm/year, leaving a residual increase in the ETAO of 3.69 ± 0.5 mm/year. The independent mass change from GRACE amounts to 2.78 ± 0.6 mm/year for this region, which just closes the sea level budget within present uncertainty levels. Spatial analysis of the steric components indicates a warming along the equatorial African coast including the GoG and a freshening near Angola. Strong correlations with regional climate factors, particularly the Tropical South Atlantic Index, highlight the influence of persistent climate modes. These findings underscore the urgent need for mitigation and adaptation strategies to SLR in the ETAO, especially for densely populated coastal communities.

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(1) Background: The basophil activation test (BAT) is a functional whole blood-based ex vivo assay to quantify basophil activation after allergen exposure by flow cytometry. One of the most important prerequisites for the use of the BAT in the routine clinical diagnosis of allergies is a reliable, standardized and reproducible data analysis workflow. (2) Methods: We re-analyzed a public mass cytometry dataset from peanut (PN) allergic patients (n = 6) and healthy controls (n = 3) with our binning approach "pattern recognition of immune cells" (PRI). Our approach enabled a comprehensive analysis of the dataset, evaluating 30 markers to achieve optimal basophil identification and activation through multi-parametric analysis and visualization. (3) Results: We found FcεRIα/CD32 (FcγRII) as a new marker couple to identify basophils and kept CD63 as an activation marker to establish a modified BAT in combination with our PRI analysis approach. Based on this, we developed an algorithm for automated raw data processing, which enables direct data analysis and the intuitive visualization of the test results including controls and allergen stimulations. Furthermore, we discovered that the expression pattern of CD32 correlated with FcεRIα, anticorrelated with CD63 and was detectable in both the re-analyzed public dataset and our own flow cytometric results. (4) Conclusions: Our improved BAT, combined with our PRI procedure (bin-BAT), provides a reliable test with a fully reproducible analysis. The advanced bin-BAT enabled the development of an automated workflow with an intuitive visualization to discriminate allergic patients from non-allergic individuals.

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The majority of patients undergoing exome or genome sequencing receive a nondiagnostic result. Periodic reanalysis is known to increase diagnostic yield from exome sequencing, yet laboratory reanalysis practices are obscure. We sought to define the landscape of exome and genome reanalysis across clinical laboratories. Genetic testing registries were queried to identify eligible clinical genetic laboratories offering exome and/or genome sequencing in the United States. A survey administered to lab representatives investigated reanalysis offerings, policies, perceived uptake, bioinformatic steps, and billing options. The analysis consisted of descriptive statistics. Survey data were collected from 30 of 32 eligible laboratories (93%), comprising 28 exome products and 13 genome products. Reanalysis was widely available for both exomes (n = 27/28, 96%) and genomes (n = 12/13, 92%). Most participating laboratories required ordering providers to initiate reanalysis (n = 24/28, 86%). Most respondents estimated providers initiated reanalysis in less than 10% of all exomes (n = 12/22) or genomes (n = 6/9) sequenced. The approach to reanalysis varied greatly by laboratory. Laboratory approaches to exome and genome reanalysis are highly variable and typically require provider initiation. This could contribute to low reanalysis uptake and increased administrative burden on providers. Further work should emphasize development of clinical exome and genome reanalysis standards.

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Tropospheric ozone affects human health, ecosystems, and climate change. Previous studies on Tropospheric Column Ozone (TCO) have primarily concentrated on specific regions or global geographic divisions. This has led to insufficient exploration of the spatiotemporal characteristics and influencing factors of TCO in global and rational subregions. In this study, TCO is calculated using the Modern Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data and corrected using satellite data. Cluster analysis is conducted to explore the temporal characteristics of TCO variations in different regions. The results show that the global TCO is basically distributed latitudinally, with higher TCO in the northern hemisphere, which is related to atmospheric circulation, radiation, stratospheric transport, and the distribution of ozone precursors. Between 1980 and 2020, the global average annual TCO showed an increasing trend at 0.09 DU yr-1 due to rising anthropogenic emissions of ozone precursors (NOx at 589547.86 t yr-1 and NMVOC at 1070818.24 t yr-1), increasing tropopause height (-0.10 hPa yr-1), and the enhanced ozone flux at the tropopause (0.22 ppbv m s-2 yr-1). Cluster analysis reveals different trends in TCO changes across regions. The ocean south of 60°S and parts of West Antarctica (Region 2), the region from 30°N to 60°N and the western oceanic region of 30°S (Region 3), and the region from the equator to 60°S and the region north of 60°N (Region 5) exhibit increasing trends (with rates of 0.08 DU yr-1, 0.07 DU yr-1, and 0.11 DU yr-1, respectively), linked to the enhanced ozone flux at the tropopause, the rising tropopause height and increasing ozone p precursors. Conversely, the decreasing TCO trends in the equatorial Pacific (Region 1) and East Antarctica (Region 4) (with rates of -0.01 DU yr-1 and -0.02 DU yr-1) may be related to increased cloudiness and weakened photochemical reactions.

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Rapid urbanization and industrialization have intensified air pollution, posing severe health risks and necessitating accurate PM2.5 predictions for effective urban air quality management. This study distinguishes itself by utilizing high-resolution ERA5 reanalysis data for a grid-based spatial analysis of Istanbul, Türkiye, a densely populated city with diverse pollutant sources. It assesses the predictive accuracy of advanced machine learning (ML) models-Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGB), Random Forest (RF), and Nonlinear Autoregressive with Exogenous Inputs (NARX). Notably, it introduces genetic algorithm optimization for the NARX model to enhance its performance. The models were trained on hourly PM2.5 concentrations from twenty monitoring stations across 2020-2021. Istanbul was divided into seven regions based on ERA5 grid distributions to examine PM2.5 spatial variability. Seventeen input variables from ERA5, including meteorological, land cover, and vegetation parameters, were analyzed using the Neighborhood Component Analysis (NCA) method to identify the most predictive variables. Comparative analysis showed that while all models provided valuable insights (RF > LGB > XGB > MLR), the NARX model outperformed them, particularly with the complex dataset used. The NARX model achieved a high R-value (0.89), low RMSE (5.24 µg/m³), and low MAE (2.94 µg/m³). It performed best in autumn and winter, with the highest accuracy in Region-1 (R-value 0.94) and the lowest in Region-5 (R-value 0.75). This study's success in a complex urban setting with limited monitoring underscores the robustness of the NARX model and the methodology's potential for global application in similar urban contexts. By addressing temporal and spatial variability in air quality predictions, this research sets a new benchmark and highlights the importance of advanced data analysis techniques for developing targeted pollution control strategies and public health policies.

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Given the inherent complexities of bioanalysis, the role of incurred sample reanalysis (ISR) is increasingly appreciated in regulatory bioanalysis. Incurred sample reanalysis has evolved as an integral part of an assay to ensure method reproducibility. The current regulatory ISR guidelines do not provide clarity regarding ISR assessment for chiral drugs comprising enantiomers. Because chiral assays evaluate two enantiomers, there are additional complexities associated with the ISR data generation and interpretation. Based on the current literature, the practices for conducting ISR in chiral methods were reviewed and assessed. While ISR was conducted in chiral methods for both enantiomers using the acceptance criteria prescribed for non-chiral methods, there may be a need to streamline the nuances of ISR data interpretation and define the ISR requirements for chiral methods. The article provides perspectives on the ISR of enantiomeric drugs, including strategy development, by providing various hypothetical scenarios and possible considerations for defining ISR evaluation for chiral assays.

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Hüsler-Reiss vectors and Brown-Resnick fields are popular models in multivariate and spatial extreme-value theory, respectively, and are widely used in applications. We provide analytical formulas for the correlation between powers of the components of the bivariate Hüsler-Reiss vector, extend these to the case of the Brown-Resnick field, and thoroughly study the properties of the resulting dependence measure. The use of correlation is justified by spatial risk theory, while power transforms are insightful when taking correlation as dependence measure, and are moreover very suited damage functions for weather events such as wind extremes or floods. This makes our theoretical results worthwhile for, e.g., actuarial applications. We finally perform a case study involving insured losses from extreme wind speeds in Germany, and obtain valuable conclusions for the insurance industry.

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Introduction: The HVTN 105 vaccine clinical trial tested four combinations of two immunogens - the DNA vaccine DNA-HIV-PT123, and the protein vaccine AIDSVAX B/E. All combinations induced substantial antibody and CD4+ T cell responses in many participants. We have now re-examined the intracellular cytokine staining flow cytometry data using the high-resolution SWIFT clustering algorithm, which is very effective for enumerating rare populations such as antigen-responsive T cells, and also determined correlations between the antibody and T cell responses. Methods: Flow cytometry samples across all the analysis batches were registered using the swiftReg registration tool, which reduces batch variation without compromising biological variation. Registered data were clustered using the SWIFT algorithm, and cluster template competition was used to identify clusters of antigen-responsive T cells and to separate these from constitutive cytokine producing cell clusters. Results: Registration strongly reduced batch variation among batches analyzed across several months. This in-depth clustering analysis identified a greater proportion of responders than the original analysis. A subset of antigen-responsive clusters producing IL-21 was identified. The cytokine patterns in each vaccine group were related to the type of vaccine - protein antigens tended to induce more cells producing IL-2 but not IFN-γ, whereas DNA vaccines tended to induce more IL-2+ IFN-γ+ CD4 T cells. Several significant correlations were identified between specific antibody responses and antigen-responsive T cell clusters. The best correlations were not necessarily observed with the strongest antibody or T cell responses. Conclusion: In the complex HVTN105 dataset, alternative analysis methods increased sensitivity of the detection of antigen-specific T cells; increased the number of identified vaccine responders; identified a small IL-21-producing T cell population; and demonstrated significant correlations between specific T cell populations and serum antibody responses. Multiple analysis strategies may be valuable for extracting the most information from large, complex studies.

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Mass spectrometry proteomics data are typically evaluated against publicly available annotated sequences, but the proteogenomics approach is a useful alternative. A single genome is commonly utilized in custom proteomic and proteogenomic data analysis. We pose the question of whether utilizing numerous different genome assemblies in a search database would be beneficial. We reanalyzed raw data from the exoprotein fraction of four reference Enterobacterial Repetitive Intergenic Consensus (ERIC) I-IV genotypes of the honey bee bacterial pathogen Paenibacillus larvae and evaluated them against three reference databases (from NCBI-protein, RefSeq, and UniProt) together with an array of protein sequences generated by six-frame direct translation of 15 genome assemblies from GenBank. The wide search yielded 453 protein hits/groups, which UpSet analysis categorized into 50 groups based on the success of protein identification by the 18 database components. Nine hits that were not identified by a unique peptide were not considered for marker selection, which discarded the only protein that was not identified by the reference databases. We propose that the variability in successful identifications between genome assemblies is useful for marker mining. The results suggest that various strains of P. larvae can exhibit specific traits that set them apart from the established genotypes ERIC I-V.

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PURPOSE: We investigated the contribution of genomic data reanalysis to the diagnostic yield of dystonia patients who remained undiagnosed after prior genome sequencing. METHODS: Probands with heterogeneous dystonia phenotypes who underwent initial genome sequencing (GS) analysis in 2019 were included in the reanalysis, which was performed through gene-specific discovery collaborations and systematic genomic data reanalysis. RESULTS: Initial GS analysis in 2019 (n = 111) identified a molecular diagnosis in 11.7 % (13/111) of cases. Reanalysis between 2020 and 2023 increased the diagnostic yield by 7.2 % (8/111); 3.6 % (4/111) through focused gene-specific clinical correlation collaborative efforts [VPS16 (two probands), AOPEP and POLG], and 3.6 % (4/111) by systematic reanalysis completed in 2023 [NUS1 (two probands) and DDX3X variants, and a microdeletion encompassing VPS16]. Seven of these patients had a high phenotype-based dystonia score ≥3. Notable unverified findings in four additional cases included suspicious variants of uncertain significance in FBXL4 and EIF2AK2, and potential phenotypic expansion associated with SLC2A1 and TREX1 variants. CONCLUSION: GS data reanalysis increased the diagnostic yield from 11.7 % to 18.9 %, with potential extension up to 22.5 %. While optimal timing for diagnostic reanalysis remains to be determined, this study demonstrates that periodic re-interrogation of dystonia GS datasets can provide additional genetic diagnoses, which may have significant implications for patients and their families.

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This work presents the design and implementation of an operational infrastructure for the monitoring of atmospheric parameters at sea through GNSS meteorology sensors installed on liners operating in the north-west Mediterranean Sea. A measurement system, capable of operationally and continuously providing the values of surface parameters, is implemented together with software procedures based on a float-PPP approach for estimating zenith path delay (ZPD) values. The values continuously registered over a three year period (2020-2022) from this infrastructure are compared with the data from a numerical meteorological reanalysis model (MERRA-2). The results clearly prove the ability of the system to estimate the ZPD from ship-based GNSS-meteo equipment, with the accuracy evaluated in terms of correlation and root mean square error reaching values between 0.94 and 0.65 and between 18.4 and 42.9 mm, these extreme values being from the best and worst performing installations, respectively. This offers a new perspective on the operational exploitation of GNSS signals over sea areas in climate and operational meteorological applications.

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Body temperature is universally recognized as a dominant driver of biological performance. Although the critical distinction between the temperature of an organism and its surrounding habitat has long been recognized, it remains common practice to assume that trends in air temperature-collected via remote sensing or weather stations-are diagnostic of trends in animal temperature and thus of spatiotemporal patterns of physiological stress and mortality risk. Here, by analysing long-term trends recorded by biomimetic temperature sensors designed to emulate intertidal mussel temperature across the US Pacific Coast, we show that trends in maximal organismal temperature ('organismal climatologies') during aerial exposure can differ substantially from those exhibited by co-located environmental data products. Specifically, using linear regression to compare maximal organismal and environmental (air temperature) climatologies, we show that not only are the magnitudes of body and air temperature markedly different, as expected, but so are their temporal trends at both local and biogeographic scales, with some sites showing significant decadal-scale increases in organismal temperature despite reductions in air temperature, or vice versa. The idiosyncratic relationship between the spatiotemporal patterns of organismal and air temperatures suggests that environmental climatology cannot be statistically corrected to serve as an accurate proxy for organismal climatology. Finally, using quantile regression, we show that spatiotemporal trends vary across the distribution of organismal temperature, with extremes shifting in different directions and at different rates than average metrics. Overall, our results highlight the importance of quantifying changes in the entire distribution of temperature to better predict biological performance and dispel the notion that raw or 'corrected' environmental (and specially air temperature) climatologies can be used to predict organismal temperature trends. Hence, despite their widespread coverage and availability, the severe limitations of environmental climatologies suggest that their role in conservation and management policy should be carefully considered.

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Transcriptomic profiling became a standard approach to quantify a cell state, which led to the accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here, we present Phantasus: a user-friendly web application for interactive gene expression analysis which provides a streamlined access to more than 96,000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing, and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed online at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under an MIT license.

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Pulmonary fibrosis (PF) is a severe pulmonary disease with limited available therapeutic choices. Recent evidence increasingly points to abnormal lipid metabolism as a critical factor in PF pathogenesis. Our latest research identifies the dysregulation of low-density lipoprotein (LDL) is a new risk factor for PF, contributing to alveolar epithelial and endothelial cell damage, and fibroblast activation. In this study, we first integrative summarize the published literature about lipid metabolite changes found in PF, including phospholipids, glycolipids, steroids, fatty acids, triglycerides, and lipoproteins. We then reanalyze two single-cell RNA-sequencing (scRNA-seq) datasets of PF, and the corresponding lipid metabolomic genes responsible for these lipids' biosynthesis, catabolism, transport, and modification processes are uncovered. Intriguingly, we found that macrophage is the most active cell type in lipid metabolism, with almost all lipid metabolic genes being altered in macrophages of PF. In type 2 alveolar epithelial cells, lipid metabolic differentially expressed genes (DEGs) are primarily associated with the cytidine diphosphate diacylglycerol pathway, cholesterol metabolism, and triglyceride synthesis. Endothelial cells are partly responsible for sphingomyelin, phosphatidylcholine, and phosphatidylethanolamines reprogramming as their metabolic genes are dysregulated in PF. Fibroblasts may contribute to abnormal cholesterol, phosphatidylcholine, and phosphatidylethanolamine metabolism in PF. Therefore, the reprogrammed lipid profiles in PF may be attributed to the aberrant expression of lipid metabolic genes in different cell types. Taken together, these insights underscore the potential of targeting lipid metabolism in developing innovative therapeutic strategies, potentially leading to extended overall survival in individuals affected by PF.

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Despite the significant advances in understanding the genetic architecture of epilepsy, many patients do not receive a molecular diagnosis after genomic testing. Re-analysing existing genomic data has emerged as a potent method to increase diagnostic yields-providing the benefits of genomic-enabled medicine to more individuals afflicted with a range of different conditions. The primary drivers for these new diagnoses are the discovery of novel gene-disease and variants-disease relationships; however, most decisions to trigger re-analysis are based on the passage of time rather than the accumulation of new knowledge. To explore how our understanding of a specific condition changes and how this impacts re-analysis of genomic data from epilepsy patients, we developed Vigelint. This approach combines the information from PanelApp and ClinVar to characterise how the clinically relevant genes and causative variants available to laboratories change over time, and this approach to five clinical-grade epilepsy panels. Applying the Vigelint pipeline to these panels revealed highly variable patterns in new, clinically relevant knowledge becoming publicly available. This variability indicates that a more dynamic approach to re-analysis may benefit the diagnosis and treatment of epilepsy patients. Moreover, this work suggests that Vigelint can provide empirical data to guide more nuanced, condition-specific approaches to re-analysis.

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Introduction: Ciliopathies with major skeletal involvement embrace a group of heterogeneous disorders caused by pathogenic variants in a group of diverse genes. A narrow thorax with shortening of long bones inspires a clinical entity underlined by dysfunction of primary cilia. Currently, more than 23 genes are listed in the OMIM database corresponding to this clinical entity: WDR19/34/35/60, IFT43/52/80/81/140/172, DYNC2LI1, TTC21B, DYNLT2B, EVC2, EVC, INTU, NEK1, CEP120, DYNC2H1, KIAA0586, SRTD1, KIAA0753, and SRTD12. Recently, individuals with biallelic loss-of-function variants in GRK2 are shown to demonstrate a phenotype compatible with Jeune syndrome. Experimental evidence has shown that impaired function of GRK2 compromises cilia-based signaling of Hedgehog pathway as well as Wnt signaling, while cilia morphology remains intact. Hence, GRK2 is now considered an essential protein in regulation of the skeletogenesis. Case Presentation: We presented a female infant born to a consanguineous marriage who was found to have a biallelic p.R474* alteration in GRK2 in reanalysis of the whole-exome sequencing (WES) data. The patient was exhibiting major clinical features of Jeune syndrome, such as shortened long bones, ribs, and narrow thorax. Discussion: Our reanalysis of WES data revealed a likely pathogenic biallelic variant in the GRK2 which is probably responsible for the Jeune syndrome phenotype in the patient. Hence, our report supports the recently discovered association of GRK2 loss-of-function variants with Jeune syndrome phenotype and emphasizes the significance of reanalysis of WES data, notably in patients with phenotypes suggestive of a such discernible Mendelian disorder.

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Previous research on real-time sentence processing in German has shown that listeners use the morphological marking of accusative case on a sentence-initial noun phrase to not only interpret the current argument as the object and patient, but also to predict a plausible agent. So far, less is known about the use of case marking to predict the semantic role of upcoming arguments after the subject/agent has been encountered. In the present study, we examined the use of case marking for argument interpretation in transitive as well as ditransitive structures. We aimed to control for multiple factors that could have influenced processing in previous studies, including the animacy of arguments, world knowledge, and the perceptibility of the case cue. Our results from eye- and mouse-tracking indicate that the exploitation of the first case cue that enables the interpretation of the unfolding sentence is influenced by (i) the strength of argument order expectation and (ii) the perceptual salience of the case cue. PsycINFO code: 2720 Linguistics & Language & Speech.

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Upward lightning (UL) has become a major threat to the growing number of wind turbines producing renewable electricity. It can be much more destructive than downward lightning due to the large charge transfer involved in the discharge process. Ground-truth lightning current measurements indicate that less than 50% of UL could be detected by lightning location systems (LLS). UL is expected to be the dominant lightning type during the cold season. However, current standards for assessing the risk of lightning at wind turbines mainly consider summer lightning, which is derived from LLS. This study assesses the risk of LLS-detectable and LLS-undetectable UL at wind turbines using direct UL measurements at instrumented towers. These are linked to meteorological data using random forests. The meteorological drivers for the absence/occurrence of UL are found from these models. In a second step, the results of the tower-trained models are extended to a larger study area (central and northern Germany). The tower-trained models for LLS-detectable lightning are independently verified at wind turbine sites in this area and found to reliably diagnose this type of UL. Risk maps based on cold season case study events show that high probabilities in the study area coincide with actual UL flashes. This lends credibility to the application of the model to all UL types, increasing both risk and affected areas.

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The study addresses the characteristics of a climate service targeting tourists and discusses the evaluation of its products with a particular focus on the thermal stress information. Furthermore, an assessment of the impact of input data on the accuracy and relevance of the thermal stress product is presented. The thermal stress is expressed through UTCI (Universal Thermal Climate Index) and it is computed from UERRA regional reanalysis and E-OBS gridded dataset, for summer season during 2011-2018. The analysis targets 10 cities with different characteristics located in Romania and Italy. It focuses on the impact of three temperature-related input data (instantaneous temperature at 12:00 UTC, daily maximum and daily mean temperature) on the thermal stress intensity. The results show that differences up to 4 days in the pronounced thermal stress category may appear when employing daily maximum temperature compared to the use 12:00 UTC instantaneous temperature, while the use of daily mean temperature leads to strong underestimation of thermal stress in this category. The findings are of interest in defining the technical choices of products to be incorporated in a climate service for tourism in order to assure a good user uptake.