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We trace the specific star formation rate (sSFR) of massive star-forming galaxies (greater than or similar to 10(10)M(circle dot)) from z similar to 2 to 7. Our method is substantially different from previous analyses, as it does not rely on direct estimates of star formation rate, but on the differential evolution of the galaxy stellar mass function (SMF). We show the reliability of this approach by means of semianalytical and hydrodynamical cosmological simulations. We then apply it to real data, using the SMFs derived in the COSMOS and CANDELS fields. We find that the sSFR is proportional to (1 + z)(1.1) (+/-) (0.2) at z > 2, in agreement with other observations but in tension with the steeper evolution predicted by simulations from z similar to 4 to 2. We investigate the impact of several sources of observational bias, which, however, cannot account for this discrepancy. Although the SMF of high-redshift galaxies is still affected by significant errors, we show that future large-area surveys will substantially reduce them, making our method an effective tool to probe the massive end of the main sequence of star-forming galaxies.
Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms.
PRI: Re-analysis of a public mass cytometry dataset reveals patterns of effective tumor treatments
(2022)
Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4(+)T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.
Episodes of environmental stability and instability may be equally important for African hominin speciation, dispersal, and cultural innovation. Three examples of a change from stable to unstable environmental conditions are presented on three different time scales: (1) the Mid Holocene (MH) wet dry transition in the Chew Bahir basin (Southern Ethiopian Rift; between 11 ka and 4 ka), (2) the MIS 5-4 transition in the Naivasha basin (Central Kenya Rift; between 160 ka and 50 ka), and (3) the Early Mid Pleistocene Transition (EMPT) in the Olorgesailie basin (Southern Kenya Rift; between 1.25 Ma and 0.4 Ma). A probabilistic age modeling technique is used to determine the timing of these transitions, taking into account possible abrupt changes in the sedimentation rate including episodes of no deposition (hiatuses). Interestingly, the stable-unstable conditions identified in the three records are always associated with an orbitally-induced decrease of insolation: the descending portion of the 800 kyr cycle during the EMPT, declining eccentricity after the 115 ka maximum at the MIS 5-4 transition, and after similar to 10 ka. This observation contributes to an evidence-based discussion of the possible mechanisms causing the switching between environmental stability and instability in Eastern Africa at three different orbital time scales (10,000 to 1,000,000 years) during the Cenozoic. This in turn may lead to great insights into the environmental changes occurring at the same time as hominin speciation, brain expansion, dispersal out of Africa, and cultural innovations and may provide key evidence to build new hypotheses regarding the causes of early human evolution. (C) 2015 Elsevier Ltd. All rights reserved.
Phenology has emerged as key indicator of the biological impacts of climate change, yet the role of functional traits constraining variation in herbaceous species' phenology has received little attention. Botanical gardens are ideal places in which to investigate large numbers of species growing under common climate conditions. We ask whether interspecific variation in plant phenology is influenced by differences in functional traits. We recorded onset, end, duration and intensity of initial growth, leafing out, leaf senescence, flowering and fruiting for 212 species across five botanical gardens in Germany. We measured functional traits, including plant height, absolute and specific leaf area, leaf dry matter content, leaf carbon and nitrogen content and seed mass and accounted for species' relatedness. Closely related species showed greater similarities in timing of phenological events than expected by chance, but species' traits had a high degree of explanatory power, pointing to paramount importance of species' life-history strategies. Taller plants showed later timing of initial growth, and flowered, fruited and underwent leaf senescence later. Large-leaved species had shorter flowering and fruiting durations. Taller, large-leaved species differ in their phenology and are more competitive than smaller, small-leaved species. We assume climate warming will change plant communities' competitive hierarchies with consequences for biodiversity.
Twenty-two scientists met at Krobielowice, Poland, to discuss the impact of the social environment, spatial proximity, migration, poverty, but also psychological factors such as body perception and satisfaction, and social stressors such as elite sports, and teenage pregnancies, on child and adolescent growth. The data analysis included linear mixed effects models with different random effects, Monte Carlo analyses, and network simulations. The work stressed the importance of the peer group, but also included historic material, some considerations about body proportions, and growth in chronic liver, and congenital heart disease.
HPI Future SOC Lab
(2024)
The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners.
The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies.
This technical report presents results of research projects executed in 2020. Selected projects have presented their results on April 21st and November 10th 2020 at the Future SOC Lab Day events.