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Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P< 5 x 10(-8). In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.
Management intensity modifies soil properties, e.g., organic carbon (C-org) concentrations and soil pH with potential feedbacks on plant diversity. These changes might influence microbial P concentrations (P-mic) in soil representing an important component of the Pcycle. Our objectives were to elucidate whether abiotic and biotic variables controlling P-mic concentrations in soil are the same for forests and grasslands, and to assess the effect of region and management on P-mic concentrations in forest and grassland soils as mediated by the controlling variables. In three regions of Germany, Schwabische Alb, Hanich-Dun, and Schorfheide-Chorin, we studied forest and grassland plots (each n=150) differing in plant diversity and land-use intensity. In contrast to controls of microbial biomass carbon (C-mic), P-mic was strongly influenced by soil pH, which in turn affected phosphorus (P) availability and thus microbial Puptake in forest and grassland soils. Furthermore, P-mic concentrations in forest and grassland soils increased with increasing plant diversity. Using structural equation models, we could show that soil C-org is the profound driver of plant diversity effects on P-mic in grasslands. For both forest and grassland, we found regional differences in P-mic attributable to differing environmental conditions (pH, soil moisture). Forest management and tree species showed no effect on P-mic due to a lack of effects on controlling variables (e.g., C-org). We also did not find management effects in grassland soils which might be caused by either compensation of differently directed effects across sites or by legacy effects of former fertilization constraining the relevance of actual practices. We conclude that variables controlling P-mic or C-mic in soil differ in part and that regional differences in controlling variables are more important for P-mic in soil than those induced by management.
In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated firstly from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE-algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and get again promising results. The thermally inducedaccuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems. errors can be estimated with 1-2 micrometer
In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated firstly from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE-algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and get again promising results. The thermally induced errors can be estimated with 1-2${mu m}$ accuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems.
Plain Language Summary Cassini flew through the gap between Saturn and its rings for 22 times before plunging into the atmosphere of Saturn, ending its 20-year mission. The radio and plasma waves instrument on board Cassini helped quantify the dust hazard in this previously unexplored region. The measured density of large dust particles was much lower than expected, allowing high-value science observations during the subsequent Grand Finale orbits.
We compiled global occurrence data sets of 13 congeneric sexual and apomictic species pairs, and used principal components analysis (PCA) and kernel smoothers to compare changes in climatic niche optima, breadths and unfilling/expansion between native and alien ranges. Niche change metrics were compared between sexual and apomictic species. All 26 species showed changes in niche optima and/or breadth and 14 species significantly expanded their climatic niches. However, we found no effect of the reproductive system on niche dynamics. Instead, species with narrower native niches showed higher rates of niche expansion in the alien ranges. Our results suggest that niche shifts are frequent in plant invasions but evolutionary potential may not be of major importance for such shifts. Niche dynamics rather appear to be driven by changes of the realized niche without adaptive change of the fundamental climatic niche.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
Amonchquite dyke, in the vicinity of Loch Roag, Lewis, Outer Hebrides has an unusually enriched chemistry, and contains a unique assemblage of megacrysts and xenoliths from the lithosphere of the Hebridean craton. A Ar-40/Ar-39 plateau age of 45.2 +/- 0.2 Ma (2 sigma) of a phlogopite megacryst from the dyke overlaps an earlier reported K-Ar age, and confirms that the British Palaeogene Igneous Province extended into the Eocene. Similar late low-volume melts were erupted in the Eocene and Oligocene in West and East Greenland, suggesting that such late-stage magmatic rejuvenescence is a widespread feature across the North Atlantic Igneous Province.
We used single-molecule FRET in combination with other biophysical methods and molecular simulations to investigate the effect of temperature on the dimensions of unfolded proteins. With singlemolecule FRET, this question can be addressed even under nearnative conditions, where most molecules are folded, allowing us to probe a wide range of denaturant concentrations and temperatures. We find a compaction of the unfolded state of a small cold shock protein with increasing temperature in both the presence and the absence of denaturant, with good agreement between the results from single-molecule FRET and dynamic light scattering. Although dissociation of denaturant from the polypeptide chain with increasing temperature accounts for part of the compaction, the results indicate an important role for additional temperaturedependent interactions within the unfolded chain. The observation of a collapse of a similar extent in the extremely hydrophilic, intrinsically disordered protein prothymosin suggests that the hydrophobic effect is not the sole source of the underlying interactions. Circular dichroism spectroscopy and replica exchange molecular dynamics simulations in explicit water show changes in secondary structure content with increasing temperature and suggest a contribution of intramolecular hydrogen bonding to unfolded state collapse.
Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother–child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P < 5 Â 10 À8 . In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.
X-ray free electron lasers (XFELs) enable unprecedented new ways to study the electronic structure and dynamics of transition metal systems. L-edge absorption spectroscopy is a powerful technique for such studies and the feasibility of this method at XFELs for solutions and solids has been demonstrated. However, the required x-ray bandwidth is an order of magnitude narrower than that of self-amplified spontaneous emission (SASE), and additional monochromatization is needed. Here we compare L-edge x-ray absorption spectroscopy (XAS) of a prototypical transition metal system based on monochromatizing the SASE radiation of the linac coherent light source (LCLS) with a new technique based on self-seeding of LCLS. We demonstrate how L-edge XAS can be performed using the self-seeding scheme without the need of an additional beam line monochromator. We show how the spectral shape and pulse energy depend on the undulator setup and how this affects the x-ray spectroscopy measurements. (C) 2016 Optical Society of America
L-edge spectroscopy of 3d transition metals provides important electronic structure information and has been used in many fields. However, the use of this method for studying dilute aqueous systems, such as metalloenzymes, has not been prevalent because of severe radiation damage and the lack of suitable detection systems. Here we present spectra from a dilute Mn aqueous solution using a high-transmission zone-plate spectrometer at the Linac Coherent Light Source (LCLS). The spectrometer has been optimized for discriminating the Mn L-edge signal from the overwhelming 0 K-edge background that arises from water and protein itself, and the ultrashort LCLS X-ray pulses can outrun X-ray induced damage. We show that the deviations of the partial-fluorescence yield-detected spectra from the true absorption can be well modeled using the state-dependence of the fluorescence yield, and discuss implications for the application of our concept to biological samples.
Thermally driven chemistry as well as materials’ functionality are determined by the potential energy surface of a systems electronic ground state. This makes the potential energy surface a central and powerful concept in physics, chemistry and materials science. However, direct experimental access to the potential energy surface locally around atomic centers and to its long-range structure are lacking. Here we demonstrate how sub-natural linewidth resonant inelastic soft x-ray scattering at vibrational resolution is utilized to determine ground state potential energy surfaces locally and detect long-range changes of the potentials that are driven by local modifications. We show how the general concept is applicable not only to small isolated molecules such as O2 but also to strongly interacting systems such as the hydrogen bond network in liquid water. The weak perturbation to the potential energy surface through hydrogen bonding is observed as a trend towards softening of the ground state potential around the coordinating atom. The instrumental developments in high resolution resonant inelastic soft x-ray scattering are currently accelerating and will enable broad application of the presented approach. With this multidimensional potential energy surfaces that characterize collective phenomena such as (bio)molecular function or high-temperature superconductivity will become accessible in near future.
The thermal behavior of poly(methoxydiethylenglycol acrylate) (PMDEGA) is studied in thin hydrogel films on solid supports and is compared with the behavior in aqueous solution. The PMDEGA hydrogel film thickness is varied from 2 to 422 nm. Initially, these films are homogenous, as measured with optical microscopy, atomic force microscopy, X-ray reflectivity, and grazing-incidence small-angle X-ray scattering (GISAXS). However, they tend to de-wet when stored under ambient conditions. Along the surface normal, no long-ranged correlations between substrate and film surface are detected with GISAXS, due to the high mobility of the polymer at room temperature. The swelling of the hydrogel films as a function of the water vapor pressure and the temperature are probed for saturated water vapor pressures between 2,380 and 3,170 Pa. While the swelling capability is found to increase with water vapor pressure, swelling in dependence on the temperature revealed a collapse phase transition of a lower critical solution temperature type. The transition temperature decreases from 40.6 A degrees C to 36.6 A degrees C with increasing film thickness, but is independent of the thickness for very thin films below a thickness of 40 nm. The observed transition temperature range compares well with the cloud points observed in dilute (0.1 wt.%) and semi-dilute (5 wt.%) solution which decrease from 45 A degrees C to 39 A degrees C with increasing concentration.
A 2-fold thermoresponsive diblock copolymer PSPP430-b-PNIPAM(200) consisting of a zwitterionic polysulfobetaine (PSPP) block and a nonionic poly(N-isopropylacrylamide) (PNIPAM) block is prepared by successive RAFT polymerizations. In aqueous solution, the corresponding homopolymers PSPP and PNIPAM feature both upper and lower critical solution temperature (UCST and LCST) behavior, respectively. The diblock copolymer exhibits thermally induced "schizophrenic" aggregation behavior in aqueous solutions. Moreover, the ion sensitivity of the, cloud point of the zwitterionic PSPP block to both the ionic strength and the nature of the salt offers the possibility to create switchable systems which respond sensitively to changes of the temperature and of the electrolyte type and concentration. The diblock copolymer solutions in D2O are investigated by means of turbidimetry and small-angle neutron scattering (SANS) with respect to the phase behavior and the self-assembled structures in dependence on temperature and electrolyte content. Marked, differences of the aggregation below the UCST-type and above the LCST-type transition are observed. The addition of a small amount of NaBr (0.004 M) does not affect the overall behavior, and only the UCST-type transition and aggregate structures are slightly altered, reflecting the well-known ion sensitivity of the zwitterionic PSPP block.
Pak choi (Brassica rapa subsp. chinensis) is rich in secondary metabolites and contains numerous antioxidants, including flavonoids; hydroxycinnamic acids; carotenoids; chlorophylls; and glucosinolates, which can be hydrolyzed to epithionitriles, nitriles, or isothiocyanates. Here, we investigate the effect of reduced exposure to ultraviolet B (UVB) and UV (UVA and UVB) light at four different developmental stages of pak choi. We found that both the plant morphology and secondary metabolite profiles were affected by reduced exposure to UVB and UV, depending on the plant’s developmental stage. In detail, mature 15- and 30-leaf plants had higher concentrations of flavonoids, hydroxycinnamic acids, carotenoids, and chlorophylls, whereas sprouts contained high concentrations of glucosinolates and their hydrolysis products. Dry weights and leaf areas increased as a result of reduced UVB and low UV. For the flavonoids and hydroxycinnamic acids in 30-leaf plants, less complex compounds were favored, for example, sinapic acid acylated kaempferol triglycoside instead of the corresponding tetraglycoside. Moreover, also in 30-leaf plants, zeaxanthin, a carotenoid linked to protection during photosynthesis, was increased under low UV conditions. Interestingly, most glucosinolates were not affected by reduced UVB and low UV conditions. However, this study underlines the importance of 4-(methylsulfinyl)butyl glucosinolate in response to UVA and UVB exposure. Further, reduced UVB and low UV conditions resulted in higher concentrations of glucosinolate-derived nitriles. In conclusion, exposure to low doses of UVB and UV from the early to late developmental stages did not result in overall lower concentrations of plant secondary metabolites.
Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe
(2020)
Subsurface contamination due to excessive nutrient surpluses is a persistent and widespread problem in agricultural areas across Europe. The vulnerability of a particular location to pollution from reactive solutes, such as nitrate, is determined by the interplay between hydrologic transport and biogeochemical transformations. Current studies on the controls of subsurface vulnerability do not consider the transient behaviour of transport dynamics in the root zone. Here, using state-of-the-art hydrologic simulations driven by observed hydroclimatic forcing, we demonstrate the strong spatiotemporal heterogeneity of hydrologic transport dynamics and reveal that these dynamics are primarily controlled by the hydroclimatic gradient of the aridity index across Europe. Contrasting the space-time dynamics of transport times with reactive timescales of denitrification in soil indicate that similar to 75% of the cultivated areas across Europe are potentially vulnerable to nitrate leaching for at least onethird of the year. We find that neglecting the transient nature of transport and reaction timescale results in a great underestimation of the extent of vulnerable regions by almost 50%. Therefore, future vulnerability and risk assessment studies must account for the transient behaviour of transport and biogeochemical transformation processes.
Based on diblock copolymers, a pair of "schizophrenic" micellar systems is designed by combining a nonionic and thermoresponsive block with a zwitterionic block, which is thermoresponsive and salt-sensitive. The nonionic block is poly(N-isopropylacrylamide) (PNIPAM) or poly(N-isopropylmethacrylamide) (PNIPMAM) and exhibits a lower critical solution temperature (LCST) behavior in aqueous solution. The zwitterionic block is a polysulfobetaine, i.e., poly(4((3-methacrylamidopropyl)dimethylammonio)butane-1-sulfonate) (PSBP), and has an upper critical solution temperature (UCST) behavior with the clearing point decreasing with increasing salt concentration. The PSBP-b-PNIPAM and PSBP-b-PNIPMAM diblock copolymers are prepared by successive reversible addition-fragmentation chain transfer (RAFT) polymerizations. The PSBP block is chosen such that the clearing point of the homopolymer is significantly higher in pure water than the cloud point of PNIPAM or PNIPMAM. Using turbidimetry, H-1 NMR, and small-angle neutron scattering, we investigate the overall phase behavior as well as the structure and interaction between the micelles and the intermediate phase, both in salt-free D2O and in 0.004 M NaBr in D2O in a wide temperature range. We find that PSBP-b-PNIPAM at 50 g L-1 in salt-free D2O is turbid in the entire temperature range. It forms spherical micelles below the cloud point of PNIPAM and cylindrical micelles above. Similar behavior is observed for PSBP-b-PNIPMAM at 50 g L-1 in salt-free D2O with a slight and smooth increase of the light transmission below the cloud point of PNIPMAM and an abrupt decrease above. Upon addition of 0.004 M NaBr, the UCST-type cloud point of the PSBP-block is notably decreased, and an intermediate regime is encountered below the cloud point of PNIPMAM, where the light transmission is slightly enhanced. In this regime, the polymer solution exhibits behavior typical for polyelectrolyte solutions. Thus, double thermosensitive and salt-sensitive behavior with "schizophrenic" micelle formation is found, and the width of the intermediate regime, where both blocks are hydrophilic, can be tuned by the addition of electrolyte.
We introduce azobenzene-functionalized polyelectrolyte multilayers as efficient, inexpensive optoacoustic transducers for hyper-sound strain waves in the GHz range. By picosecond transient reflectivity measurements we study the creation of nanoscale strain waves, their reflection from interfaces, damping by scattering from nanoparticles and propagation in soft and hard adjacent materials like polymer layers, quartz and mica. The amplitude of the generated strain ε ∼ 5 × 10−4 is calibrated by ultrafast X-ray diffraction.
The configuration and dynamic behavior of O-allyl-S-methyl-N-(acridin-9-yl)iminothiocarbonate (1) and its S- allyl-O-methyl regioisomer (2) were studied using quantum chemical calculations and by applying a novel graphical method to scatter maps obtained from MD simulations for evaluation of an NOE-weighted internuclear distance (r(NOE)). Energy calculations indicated that the Z configuration was predominant for each compound and, further, this was supported both by the calculated chemical shifts and the rNOE. Both N-inversion- and rotation-type transition-state structures were also calculated for the E/Z isomerization process, the results indicating that the preferred interconversion mechanism for 1 is N-inversion, but contrastingly, interconversion via rotation is equally as probable as N-inversion for 2. This supports the notion that one or the other or both pathways can be active and each system needs to be assessed on a case- by-case basis. Copyright (c) 2005 John Wiley & Sons, Ltd
Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe
(2020)
Subsurface contamination due to excessive nutrient surpluses is a persistent and widespread problem in agricultural areas across Europe. The vulnerability of a particular location to pollution from reactive solutes, such as nitrate, is determined by the interplay between hydrologic transport and biogeochemical transformations. Current studies on the controls of subsurface vulnerability do not consider the transient behaviour of transport dynamics in the root zone. Here, using state-of-the-art hydrologic simulations driven by observed hydroclimatic forcing, we demonstrate the strong spatiotemporal heterogeneity of hydrologic transport dynamics and reveal that these dynamics are primarily controlled by the hydroclimatic gradient of the aridity index across Europe. Contrasting the space-time dynamics of transport times with reactive timescales of denitrification in soil indicate that similar to 75% of the cultivated areas across Europe are potentially vulnerable to nitrate leaching for at least onethird of the year. We find that neglecting the transient nature of transport and reaction timescale results in a great underestimation of the extent of vulnerable regions by almost 50%. Therefore, future vulnerability and risk assessment studies must account for the transient behaviour of transport and biogeochemical transformation processes.
Ground-penetrating radar (GPR) is a method that can provide detailed information about the near subsurface in sedimentary and carbonate environments.
The classical interpretation of GPR data (e.g., based on manual feature selection) often is labor-intensive and limited by the experience of the intercally used for seismic interpretation, can provide faster, more repeatable, and less biased interpretations. We have recorded a 3D GPD data set collected across a paleokarst breccia pipe in the Billefjorden area on Spitsbergen, Svalbard. After performing advanced processing, we compare the results of a classical GPR interpretation to the results of an attribute-based classification.
Our attribute classification incorporates a selection of dip and textural attributes as the input for a k-means clustering approach. Similar to the results of the classical interpretation, the resulting classes differentiate between undisturbed strata and breccias or fault zones.
The classes also reveal details inside the breccia pipe that are not discerned in the classical fer that the intrapipe GPR facies result from subtle differences, such as breccia lithology, clast size, or pore-space filling.
FIBER
(2021)
Objectives:
The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames.
Materials and Methods:
FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models.
Results:
Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case.
Conclusion:
FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.