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Glaciers have shaped the Alpine landscape by carving deep valleys and depositing sediments to form overdeepened basins. Understanding these processes provides information on the evolution of the climate and landscape. One such overdeepened structure is the Tannwald Basin (ICDP site 5068_1) north of Lake Constance, which was formed by the Rhine Glacier in several glacial cycles. In order to study these sediments and their seismic properties down to about 160 m depth, we conducted seismic crosshole experiments between three boreholes, obtaining compressional (P) wave data. The P-wave data are generated by a sparker source and recorded by a 24-station hydrophone string. We present the data acquisition and review our approach for future optimization, suggesting a finer time sampling interval and a separate registration of borehole and surface receivers. Travel-time tomography of the P-wave first-arrival picks under geostatistical constraints yields initial subsurface models. The tomograms correlate well with cased-hole sonic logs and the lithology derived from the core of one of the boreholes. These results will be further investigated in future research, which will include full-waveform inversion (FWI) to obtain high-resolution subsurface models.

We present the results of the human genomic small variant calling benchmarking initiative of the German Research Foundation (DFG) funded Next Generation Sequencing Competence Network (NGS-CN) and the German Human Genome-Phenome Archive (GHGA). In this effort, we developed NCBench, a continuous benchmarking platform for the evaluation of small genomic variant callsets in terms of recall, precision, and false positive/negative error patterns. NCBench is implemented as a continuously re-evaluated open-source repository. We show that it is possible to entirely rely on public free infrastructure (Github, Github Actions, Zenodo) in combination with established open-source tools. NCBench is agnostic of the used dataset and can evaluate an arbitrary number of given callsets, while reporting the results in a visual and interactive way. We used NCBench to evaluate over 40 callsets generated by various variant calling pipelines available in the participating groups that were run on three exome datasets from different enrichment kits and at different coverages. While all pipelines achieve high overall quality, subtle systematic differences between callers and datasets exist and are made apparent by NCBench.These insights are useful to improve existing pipelines and develop new workflows. NCBench is meant to be open for the contribution of any given callset. Most importantly, for authors, it will enable the omission of repeated re-implementation of paper-specific variant calling benchmarks for the publication of new tools or pipelines, while readers will benefit from being able to (continuously) observe the performance of tools and pipelines at the time of reading instead of at the time of writing.

We present the results of the human genomic small variant calling benchmarking initiative of the German Research Foundation (DFG) funded Next Generation Sequencing Competence Network (NGS-CN) and the German Human Genome-Phenome Archive (GHGA). In this effort, we developed NCBench, a continuous benchmarking platform for the evaluation of small genomic variant callsets in terms of recall, precision, and false positive/negative error patterns. NCBench is implemented as a continuously re-evaluated open-source repository. We show that it is possible to entirely rely on public free infrastructure (Github, Github Actions, Zenodo) in combination with established open-source tools. NCBench is agnostic of the used dataset and can evaluate an arbitrary number of given callsets, while reporting the results in a visual and interactive way. We used NCBench to evaluate over 40 callsets generated by various variant calling pipelines available in the participating groups that were run on three exome datasets from different enrichment kits and at different coverages. While all pipelines achieve high overall quality, subtle systematic differences between callers and datasets exist and are made apparent by NCBench.These insights are useful to improve existing pipelines and develop new workflows. NCBench is meant to be open for the contribution of any given callset. Most importantly, for authors, it will enable the omission of repeated re-implementation of paper-specific variant calling benchmarks for the publication of new tools or pipelines, while readers will benefit from being able to (continuously) observe the performance of tools and pipelines at the time of reading instead of at the time of writing.

Abstract Background A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling. Methods We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined). Results Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models. Conclusion This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level.

The quality of geothermal carbonate reservoirs is controlled by, for instance, depositional environment, lithology, diagenesis, karstification, fracture networks, and tectonic deformation. Carbonatic rock formations are thus often extremely heterogeneous, and reservoir parameters and their spatial distribution difficult to predict. Using a 3D seismic dataset combined with well data from Munich, Germany, we demonstrate how a comprehensive seismic attribute analysis can significantly improve the understanding of a complex carbonate reservoir. We deliver an improved reservoir model concept and identify possible exploitation targets within the Upper Jurassic carbonates. We use seismic attributes and different carbonate lithologies from well logs to identify parameter correlations. From this, we obtain a supervised neural-network-based 3D lithology model of the geothermal reservoir. Furthermore, we compare fracture orientations measured in seismic (ant-tracking analysis) and well scale (image log analysis) to address scalability. Our results show that, for example, acoustic impedance is suitable to identify reefs and karst-related dolines, and sweetness proves useful to analyse the internal reef architecture, whereas frequency- and phase-related attributes allow the detection of karst. In addition, reef edges, dolines, and fractures, associated with high permeabilities, are characterized by strong phase changes. Fractures are also identified using variance and ant tracking. Morphological characteristics, like dolines, are captured using the shape index. Regarding the diagenetic evolution of the reservoir and the corresponding lithology distribution, we show that the Upper Jurassic carbonate reservoir experienced a complex evolution, consisting of at least three dolomitization phases, two karstification phases, and a phase of tectonic deformation. We observe spatial trends in the degree of dolomitization and show that it is mainly facies-controlled and that karstification is facies- and fault-controlled. Karstification improves porosity and permeability, whereas dolomitization can either increase or decrease porosity. Therefore, reservoir zones should be exploited that experienced only weak diagenetic alteration, i.e. the dolomitic limestone in the upper part of the Upper Jurassic carbonates. Regarding the fracture scalability across seismic and well scales, we note that a general scalability is, due to a combination of methodological limitations and geological reasons, not possible. Nevertheless, both methods provide an improved understanding of the fracture system and possible fluid pathways. By integrating all the results, we are able to improve and adapt recent reservoir concepts, to outline the different phases of the reservoir's structural and diagenetic evolution, and to identify high-quality reservoir zones in the Munich area. These are located southeast at the Ottobrunn Fault and north of the Munich Fault close to the Nymphenburg Fault.

Mountainous grassland soils are considered one of the most unique biological hotspots, rich in organic carbon (OC). At the same time, they are exposed to great threats, as climate warming is more pronounced in mountainous regions than in lowland areas. In this study, we assessed the effect of simulated warming (+1K, +2K, and + 3 K) on OC stocks and soil structure in grassland soils of the Northern Limestone Alps in Germany by translocating plant-soil mesocosms from high- (1260 m a.s.l., Rendzic Phaeozem) and mid- (860 m a. s. l., Haplic Cambisol) to low-elevation (600 m a.s.l). Plant-soil mesocosms were exposed to both extensive and intensive grassland management practices. Four years after translocation, we observed a rapid decrease of topsoil SOC stocks under intensive (−1.0 t C ha yr−1) and extensive management (-2.2 t C ha yr−1), under the highest temperature increase. Intensive management with about 1 t C ha−1 yr−1 higher manure C return than extensive management (1.6 vs. 0.8 t C ha−1 yr−1 intensive and extensive, respectively) may explain the difference in SOC losses between different management treatments. Under both management practices, the loss of SOC was mainly associated with a decrease of large macroaggregates, at both management practices. In addition, different aggregate specific OC loss rates resulted in an altered distribution of OC among the aggregate size classes. Our study provides evidence that simulated climate change induced a rapid and substantial decline of SOC in mountainous, OC-rich grassland soils, which may be attributed to decreased physical OC protection within large macroaggregates. Optimized grassland management in form of increased application of organic fertilizers could only partially offset the SOC loss by improved formation of small macroaggregates.

Abstract Background Aquaporins (AQPs) have been recognized to promote tumor progression, invasion, and metastasis and are therefore recognized as promising targets for novel anti-cancer therapies. Potentially relevant AQPs in distinct cancer entities can be determined by a comprehensive expression analysis of the 13 human AQPs. Methods We analyzed the presence of all AQP transcripts in 576 different normal lung and non-small cell lung cancer (NSCLC) samples using microarray data and validated our findings by qRT-PCR and immunohistochemistry. Results Variable expression of several AQPs (AQP1, -3, -4, and -5) was found in NSCLC and normal lung tissues. Furthermore, we identified remarkable differences between NSCLC subtypes in regard to AQP1, -3 and -4 expression. Higher transcript and protein levels of AQP4 in well-differentiated lung adenocarcinomas suggested an association with a more favourable prognosis. Beyond water transport, data mining of co-expressed genes indicated an involvement of AQP4 in cell-cell signalling, cellular movement and lipid metabolism, and underlined the association of AQP4 to important physiological functions in benign lung tissue. Conclusions Our findings accentuate the need to identify functional differences and redundancies of active AQPs in normal and tumor cells in order to assess their value as promising drug targets.

Abstract Background The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing. Results For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios. Conclusions We present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.

We present the results of the human genomic small variant calling benchmarking initiative of the German Research Foundation (DFG) funded Next Generation Sequencing Competence Network (NGS-CN) and the German Human Genome-Phenome Archive (GHGA). In this effort, we developed NCBench, a continuous benchmarking platform for the evaluation of small genomic variant callsets in terms of recall, precision, and false positive/negative error patterns. NCBench is implemented as a continuously re-evaluated open-source repository. We show that it is possible to entirely rely on public free infrastructure (Github, Github Actions, Zenodo) in combination with established open-source tools. NCBench is agnostic of the used dataset and can evaluate an arbitrary number of given callsets, while reporting the results in a visual and interactive way. We used NCBench to evaluate over 40 callsets generated by various variant calling pipelines available in the participating groups that were run on three exome datasets from different enrichment kits and at different coverages. While all pipelines achieve high overall quality, subtle systematic differences between callers and datasets exist and are made apparent by NCBench.These insights are useful to improve existing pipelines and develop new workflows. NCBench is meant to be open for the contribution of any given callset. Most importantly, for authors, it will enable the omission of repeated re-implementation of paper-specific variant calling benchmarks for the publication of new tools or pipelines, while readers will benefit from being able to (continuously) observe the performance of tools and pipelines at the time of reading instead of at the time of writing.

Abstract Background The requirement of a large amount of high-quality RNA is a major limiting factor for microarray experiments using biopsies. An average microarray experiment requires 10–100 μg of RNA. However, due to their small size, most biopsies do not yield this amount. Several different approaches for RNA amplification in vitro have been described and applied for microarray studies. In most of these, systematic analyses of the potential bias introduced by the enzymatic modifications are lacking. Results We examined the sources of error introduced by the T7 RNA polymerase based RNA amplification method through hybridisation studies on microarrays and performed statistical analysis of the parameters that need to be evaluated prior to routine laboratory use. The results demonstrate that amplification of the RNA has no systematic influence on the outcome of the microarray experiment. Although variations in differential expression between amplified and total RNA hybridisations can be observed, RNA amplification is reproducible, and there is no evidence that it introduces a large systematic bias. Conclusions Our results underline the utility of the T7 based RNA amplification for use in microarray experiments provided that all samples under study are equally treated.

The subsurface dissolution of soluble rocks can affect areas over a long period of time and pose a severe hazard. We show the benefits of a combined approach using P-wave and SH-wave reflection seismics, electrical resistivity tomography, transient electromagnetics, and gravimetry for a better understanding of the dissolution process. The study area, “Esperstedter Ried” in northern Thuringia, Germany, located south of the Kyffhäuser hills, is a large inland salt marsh that developed due to dissolution of soluble rocks at approximately 300 m depth. We were able to locate buried dissolution structures and zones, faults and fractures, and potential fluid pathways, aquifers, and aquitards based on seismic and electromagnetic surveys. Further improvement of the model was accomplished by analyzing gravimetry data that indicates dissolution-induced mass movement, as shown by local minima of the Bouguer anomaly for the Esperstedter Ried. Forward modeling of the gravimetry data, in combination with the seismic results, delivered a cross section through the inland salt marsh from north to south. We conclude that tectonic movements during the Tertiary, which led to the uplift of the Kyffhäuser hills and the formation of faults parallel and perpendicular to the low mountain range, were the initial trigger for subsurface dissolution. The faults and the fractured Triassic and lower Tertiary deposits serve as fluid pathways for groundwater to leach the deep Permian Zechstein deposits, since dissolution and erosional processes are more intense near faults. The artesian-confined saltwater rises towards the surface along the faults and fracture networks, and it formed the inland salt marsh over time. In the past, dissolution of the Zechstein formations formed several, now buried, sagging and collapse structures, and, since the entire region is affected by recent sinkhole development, dissolution is still ongoing. From the results of this study, we suggest that the combined geophysical investigation of areas prone to subsurface dissolution can improve the knowledge of control factors, hazardous areas, and thus local dissolution processes.

Aim of study: Detecting possible small-scale soil effects on height growth of single trees in monospecific stands of three important tree species (Abies alba, Fagus sylvatica, and Picea abies). Area of study: 37 mature stands along an ecological gradient in Southern Germany from the cold and wet “optimal niche zone” to warmer and drier niche zones, including gravelly soils with poor water supply. Material and methods: Measurement of achieved height and age of 15 to 20 sample trees per stand. Estimation of the available water capacity of the soil (AWC) in close proximity to sample trees based on soil texture following the German soil survey guidelines. Examining height growth depending on niche zone and AWC. Main results: On sites (stand level) with the lowest water regime, height growth increased significantly with AWC of microsites. The estimated effect on height growth over the whole range of AWC values was almost 8 m at those sites. In contrast, the effect was negative on optimal sites. For intermediate and marginal sites, the effect was positive, albeit not significant for marginal sites. Research highlights: To our knowledge this is the first study about small-scale effects of AWC on height growth of single trees in temperate European forests. Small-scale soil variability should be considered in future scientific studies and practical evaluation, involving single tree performance at stands with low water regime. This seems particularly important in genetic environmental associations studies and in the process of selecting trees for breeding purposes in such stands.

Abstract Background Nonsyndromic cleft lip with/without cleft palate (nsCL/P) is a congenital malformation of multifactorial etiology. Research has identified >40 genome‐wide significant risk loci, which explain less than 40% of nsCL/P heritability. Studies show that some of the hidden heritability is explained by rare penetrant variants. Methods To identify new candidate genes, we searched for highly penetrant de novo variants (DNVs) in 50 nsCL/P patient/parent‐trios with a low polygenic risk for the phenotype (discovery). We prioritized DNV‐carrying candidate genes from the discovery for resequencing in independent cohorts of 1010 nsCL/P patients of diverse ethnicities and 1574 population‐matched controls (replication). Segregation analyses and rare variant association in the replication cohort, in combination with additional data (genome‐wide association data, expression, protein–protein‐interactions), were used for final prioritization. Conclusion In the discovery step, 60 DNVs were identified in 60 genes, including a variant in the established nsCL/P risk gene CDH1. Re‐sequencing of 32 prioritized genes led to the identification of 373 rare, likely pathogenic variants. Finally, MDN1 and PAXIP1 were prioritized as top candidates. Our findings demonstrate that DNV detection, including polygenic risk score analysis, is a powerful tool for identifying nsCL/P candidate genes, which can also be applied to other multifactorial congenital malformations.

Biological and artificial microswimmers often navigate channels under external flow, where in many biomicroswimmers the active upstream motion is oscillatory. Here the authors demonstrate that regular, controllable, and reproducible oscillatory rheotaxis can be observed in artificial microswimmers.

Abstract There are many successful geothermal projects that exploit the Upper Jurassic aquifer at 2–3 km depth in the German Molasse Basin. However, up to now, only P-wave seismic exploration has been carried out. In an experiment in the Greater Munich area, we recorded S-waves that were generated by the conventional P-wave seismic survey, using 3C receivers. From this, we built a 3D volume of P- to S-converted (PS) waves using the asymptotic conversion point approach. By combining the P-volume and the resulting PS-seismic volume, we were able to derive the spatial distribution of the vp/vs ratio of both the Molasse overburden and the Upper Jurassic reservoir. We found that the vp/vs ratios for the Molasse units range from 2.0 to 2.3 with a median of 2.15, which is much higher than previously assumed. This raises the depth of hypocenters of induced earthquakes in surrounding geothermal wells. The vp/vs ratios found in the Upper Jurassic vary laterally between 1.5 and 2.2. Since no boreholes are available for verification, we test our results against an independently derived facies classification of the conventional 3D seismic volume and found it correlates well. Furthermore, we see that low vp/vs ratios correlate with high vp and vs velocities. We interpret the latter as dolomitized rocks, which are connected with enhanced permeability in the reservoir. We conclude that 3C registration of conventional P-wave surveys is worthwhile.

Simple Summary: Bone marrow (myelo-) fibrosis in myeloproliferative neoplasms is associated with poor prognosis and treatment failure emphasizing the importance of investigating novel treatment approaches outside the JAK pathway. This study investigates the role of neuropilin 2 (NRP2) in myelofibrosis and bone formation, identifies the type of stromal cells expressing and upregulating NRP2 and outcomes when NRP2 is lost. Our results suggest that NRP2 is an interesting molecular druggable target in myeloproliferative neoplasm. Bone marrow fibrosis in myeloproliferative neoplasm (MPN), myelodysplastic syndromes (MDS), MPN/MDS overlap syndromes and acute myeloid leukemia (AML) is associated with poor prognosis and early treatment failure. Myelofibrosis (MF) is accompanied by reprogramming of multipotent bone marrow mesenchymal stromal cells (MSC) into osteoid and fiber-producing stromal cells. We demonstrate NRP2 and osteolineage marker NCAM1 (neural cell adhesion molecule 1) expression within the endosteal niche in normal bone marrow and aberrantly in MPN, MDS MPN/MDS overlap syndromes and AML (n = 99), as assessed by immunohistochemistry. Increased and diffuse expression in mesenchymal stromal cells and osteoblasts correlates with high MF grade in MPN (p < 0.05 for NRP2 and NCAM1). Single cell RNA sequencing (scRNAseq) re-analysis demonstrated NRP2 expression in endothelial cells and partial co-expression of NRP2 and NCAM1 in normal MSC and osteoblasts. Potential ligands included transforming growth factor β1 (TGFB1) from osteoblasts and megakaryocytes. Murine ThPO and JAK2V617F myelofibrosis models showed co-expression of Nrp2 and Ncam1 in osteolineage cells, while fibrosis-promoting MSC only express Nrp2. In vitro experiments with MC3T3-E1 pre-osteoblasts and analysis of Nrp2−/− mouse femurs suggest that Nrp2 is functionally involved in osteogenesis. In summary, NRP2 represents a potential novel druggable target in patients with myelofibrosis. [ABSTRACT FROM AUTHOR]