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Probabilistic seismic-hazard analysis (PSHA) is the current tool of the trade used to estimate the future seismic demands at a site of interest. A modern PSHA represents a complex framework that combines different models with numerous inputs. It is important to understand and assess the impact of these inputs on the model output in a quantitative way. Sensitivity analysis is a valuable tool for quantifying changes of a model output as inputs are perturbed, identifying critical input parameters, and obtaining insight about the model behavior. Differential sensitivity analysis relies on calculating first-order partial derivatives of the model output with respect to its inputs; however, obtaining the derivatives of complex models can be challenging.
In this study, we show how differential sensitivity analysis of a complex framework such as PSHA can be carried out using algorithmic/automatic differentiation (AD). AD has already been successfully applied for sensitivity analyses in various domains such as oceanography and aerodynamics. First, we demonstrate the feasibility of the AD methodology by comparing AD-derived sensitivities with analytically derived sensitivities for a basic case of PSHA using a simple ground-motion prediction equation. Second, we derive sensitivities via AD for a more complex PSHA study using a stochastic simulation approach for the prediction of ground motions. The presented approach is general enough to accommodate more advanced PSHA studies of greater complexity.
Response spectra are of fundamental importance in earthquake engineering and represent a standard measure in seismic design for the assessment of structural performance. However, unlike Fourier spectral amplitudes, the relationship of response spectral amplitudes to seismological source, path, and site characteristics is not immediately obvious and might even be considered counterintuitive for high oscillator frequencies. The understanding of this relationship is nevertheless important for seismic-hazard analysis. The purpose of the present study is to comprehensively characterize the variation of response spectral amplitudes due to perturbations of the causative seismological parameters. This is done by calculating the absolute parameter sensitivities (sensitivity coefficients) defined as the partial derivatives of the model output with respect to its input parameters. To derive sensitivities, we apply algorithmic differentiation (AD). This powerful approach is extensively used for sensitivity analysis of complex models in meteorology or aerodynamics. To the best of our knowledge, AD has not been explored yet in the seismic-hazard context. Within the present study, AD was successfully implemented for a proven and extensively applied simulation program for response spectra (Stochastic Method SIMulation [SMSIM]) using the TAPENADE AD tool. We assess the effects and importance of input parameter perturbations on the shape of response spectra for different regional stochastic models in a quantitative way. Additionally, we perform sensitivity analysis regarding adjustment issues of groundmotion prediction equations.
In this pilot study, we describe a high-pressure incubation system allowing multiple subsampling of a pressurized culture without decompression. The system was tested using one piezophilic (Photobacterium profundum), one piezotolerant (Colwellia maris) bacterial strain and a decompressed sample from the Mediterranean deep sea (3044 m) determining bacterial community composition, protein production (BPP) and cell multiplication rates (BCM) up to 27 MPa. The results showed elevation of BPP at high pressure was by a factor of 1.5 +/- 1.4 and 3.9 +/- 2.3 for P. profundum and C. maris, respectively, compared to ambient-pressure treatments and by a factor of 6.9 +/- 3.8 fold in the field samples. In P. profundum and C. maris, BCM at high pressure was elevated (3.1 +/- 1.5 and 2.9 +/- 1.7 fold, respectively) compared to the ambient-pressure treatments. After 3 days of incubation at 27 MPa, the natural bacterial deep-sea community was dominated by one phylum of the genus Exiguobacterium, indicating the rapid selection of piezotolerant bacteria. In future studies, our novel incubation system could be part of an isopiestic pressure chain, allowing more accurate measurement of bacterial activity rates which is important both for modeling and for predicting the efficiency of the oceanic carbon pump.
BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. METHODS: Ninety detoxified, medication-free, alcohol-dependent patients and 96 age-and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. RESULTS: Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. CONCLUSIONS: These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.
Signaling trough p53is a major cellular stress response mechanism and increases upon nutrient stresses such as starvation. Here, we show in a human hepatoma cell line that starvation leads to robust nuclear p53 stabilization. Using BioID, we determine the cytoplasmic p53 interaction network within the immediate-early starvation response and show that p53 is dissociated from several metabolic enzymes and the kinase PAK2 for which direct binding with the p53 DNA-binding domain was confirmed with NMR studies. Furthermore, proteomics after p53 immunoprecipitation (RIME) uncovered the nuclear interactome under prolonged starvation, where we confirmed the novel p53 interactors SORBS1 (insulin receptor signaling) and UGP2 (glycogen synthesis). Finally, transcriptomics after p53 re-expression revealed a distinct starvation-specific transcriptome response and suggested previously unknown nutrient-dependent p53 target genes. Together, our complementary approaches delineate several nodes of the p53 signaling cascade upon starvation, shedding new light on the mechanisms of p53 as nutrient stress sensor. Given the central role of p53 in cancer biology and the beneficial effects of fasting in cancer treatment, the identified interaction partners and networks could pinpoint novel pharmacologic targets to fine-tune p53 activity.
Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.
Massive stars, at least similar to 10 times more massive than the Sun, have two key properties that make them the main drivers of evolution of star clusters, galaxies, and the Universe as a whole. On the one hand, the outer layers of massive stars are so hot that they produce most of the ionizing ultraviolet radiation of galaxies; in fact, the first massive stars helped to re-ionize the Universe after its Dark Ages. Another important property of massive stars are the strong stellar winds and outflows they produce. This mass loss, and finally the explosion of a massive star as a supernova or a gamma-ray burst, provide a significant input of mechanical and radiative energy into the interstellar space. These two properties together make massive stars one of the most important cosmic engines: they trigger the star formation and enrich the interstellar medium with heavy elements, that ultimately leads to formation of Earth-like rocky planets and the development of complex life. The study of massive star winds is thus a truly multidisciplinary field and has a wide impact on different areas of astronomy. In recent years observational and theoretical evidences have been growing that these winds are not smooth and homogeneous as previously assumed, but rather populated by dense "clumps". The presence of these structures dramatically affects the mass loss rates derived from the study of stellar winds. Clump properties in isolated stars are nowadays inferred mostly through indirect methods (i.e., spectroscopic observations of line profiles in various wavelength regimes, and their analysis based on tailored, inhomogeneous wind models). The limited characterization of the clump physical properties (mass, size) obtained so far have led to large uncertainties in the mass loss rates from massive stars. Such uncertainties limit our understanding of the role of massive star winds in galactic and cosmic evolution. Supergiant high mass X-ray binaries (SgXBs) are among the brightest X-ray sources in the sky. A large number of them consist of a neutron star accreting from the wind of a massive companion and producing a powerful X-ray source. The characteristics of the stellar wind together with the complex interactions between the compact object and the donor star determine the observed X-ray output from all these systems. Consequently, the use of SgXBs for studies of massive stars is only possible when the physics of the stellar winds, the compact objects, and accretion mechanisms are combined together and confronted with observations. This detailed review summarises the current knowledge on the theory and observations of winds from massive stars, as well as on observations and accretion processes in wind-fed high mass X-ray binaries. The aim is to combine in the near future all available theoretical diagnostics and observational measurements to achieve a unified picture of massive star winds in isolated objects and in binary systems.
Climate change has a major impact on arctic and boreal terrestrial ecosystems as warming leads to northward treeline shifts, inducing consequences for heterotrophic organisms associated with the plant taxa. To unravel ecological dependencies, we address how long-term climatic changes have shaped the co-occurrence of plants and fungi across selected sites in Siberia. We investigated sedimentary ancient DNA from five lakes spanning the last 47,000 years, using the ITS1 marker for fungi and the chloroplast P6 loop marker for vegetation metabarcoding. We obtained 706 unique fungal operational taxonomic units (OTUs) and 243 taxa for the plants. We show higher OTU numbers in dry forest tundra as well as boreal forests compared to wet southern tundra. The most abundant fungal taxa in our dataset are Pseudeurotiaceae, Mortierella, Sordariomyceta, Exophiala, Oidiodendron, Protoventuria, Candida vartiovaarae, Pseudeurotium, Gryganskiella fimbricystis, and Tricho-sporiella cerebriformis. The overall fungal composition is explained by the plant composition as revealed by redundancy analysis. The fungal functional groups show antagonistic relationships in their climate susceptibility. The advance of woody taxa in response to past warming led to an increase in the abun-dance of mycorrhizae, lichens, and parasites, while yeast and saprotroph distribution declined. We also show co-occurrences between Salicaceae, Larix, and Alnus and their associated pathogens and detect higher mycorrhizal fungus diversity with the presence of Pinaceae. Under future warming, we can expect feedbacks between fungus composition and plant diversity changes which will affect forest advance, species diversity, and ecosystem stability in arctic regions.
Although temporal heterogeneity is a well-accepted driver of biodiversity, effects of interannual variation in land-use intensity (LUI) have not been addressed yet. Additionally, responses to land use can differ greatly among different organisms; therefore, overall effects of land-use on total local biodiversity are hardly known. To test for effects of LUI (quantified as the combined intensity of fertilization, grazing, and mowing) and interannual variation in LUI (SD in LUI across time), we introduce a unique measure of whole-ecosystem biodiversity, multidiversity. This synthesizes individual diversity measures across up to 49 taxonomic groups of plants, animals, fungi, and bacteria from 150 grasslands. Multidiversity declined with increasing LUI among grasslands, particularly for rarer species and aboveground organisms, whereas common species and belowground groups were less sensitive. However, a high level of interannual variation in LUI increased overall multidiversity at low LUI and was even more beneficial for rarer species because it slowed the rate at which the multidiversity of rare species declined with increasing LUI. In more intensively managed grasslands, the diversity of rarer species was, on average, 18% of the maximum diversity across all grasslands when LUI was static over time but increased to 31% of the maximum when LUI changed maximally over time. In addition to decreasing overall LUI, we suggest varying LUI across years as a complementary strategy to promote biodiversity conservation.
Interpretation of conformational effects on 2-endo-norborneol by natural chemical shielding analysis
(2005)
This paper represents an extension of our work on the H-1 and C-13 NMR chemical shifts of norbornane and 2-endo- norborneol. NCS-NBO analysis was employed to probe contributions of bond orbitals and orbitals of lone pairs to nuclear shielding in conformers of the alcohol generated by rotation of the C-O bond. Variations in H-1 and C-13 chemical shifts with the dihedral angle are discussed in terms of Lewis and non-Lewis partitioning and their respective importance is evaluated. In addition to hyperconjugation of the lone pair in a p orbital of oxygen that was previously reported, a sizable participation of the lone pair which is in an sp orbital is also observed and their combined effect dominates the carbon chemical shifts of the C-1-C-2-OH and C-3-C-2-OH fragments. Both lone pairs on oxygen also contribute to localized, though-space effects on nuclei in the vicinity, these effects answering for the largest deviations in hydrogen chemical shifts on rotation around the C-O bond. On the other hand, for conformers in which nonbonded repulsions lead to distortions in the molecular framework, variations in chemical shifts may be attributed to angular effects