Vergleich von Fourier-Transformation und der Methode der kleinsten Quadrate harmonischer Funktionen zur Grundwasser-Gezeitenanalyse [Comparison of the Fourier transform and the harmonic least-squares method for analysing groundwater tides]

Eine nachhaltige Nutzung von Grundwasser setzt die Kenntnis hydrogeomechanischer Parameter voraus. Diese werden bisher hauptsächlich durch aufwändige Permeametertests oder Pumpversuche bestimmt. Eine Grundwasser-Gezeitenanalyse bietet die Möglichkeit, Bodenkennwerte anhand von Grundwasserstandsdaten zeit- und kosteneffizient zu ermitteln. Hierfür findet häufg die Fourier-Transformation Anwendung. Mit der Methode der kleinsten Quadrate lassen sich die Amplituden harmonischer Schwingungen ebenfalls bestimmen. Diese Arbeit vergleicht beide Verfahren miteinander, um die Frequenzanalyse von Grundwasserdaten zu verbessern. Außerdem werden daraus Rahmenbedingungen einer Grundwassermessung hinsichtlich der Gezeitenanalyse definiert. Diese umfassen Parameter wie die Messdauer, die Quantisierung der Messdaten sowie Datenlücken. Bei synthetischen Zeitreihen und an einem realen Datensatz werden diese drei Messparameter variiert und ihr Einfluss auf die Genauigkeit der durch beide Regressionsanalyseverfahren bestimmten Amplituden betrachtet. Die Untersuchung zeigt, dass die Fourier-Transformation für die meisten Parametereinstellungen ungenauere Ergebnisse liefert als die Methode der kleinsten Quadrate. Letztere ist insbesondere bei kurzen sowie lückenhaften Zeitreihen der Fourier-Transformation vorzuziehen. Es wird gezeigt, dass eine Standard-Sensorauflösung von 1-5 cm ausreichend, um eine Gezeitenanalyse für den vorliegenden realen Datensatz durchzuführen

Comparing Methods and Defining Practical Requirements for Extracting Harmonic Tidal Components from Groundwater Level Measurements

The groundwater pressure response to the ubiquitous Earth and atmospheric tides provides a largely untapped opportunity to passively characterize and quantify subsurface hydro-geomechanical properties. However, this requires reliable extraction of closely spaced harmonic components with relatively subtle amplitudes but well-known tidal periods from noisy measurements. The minimum requirements for the suitability of existing groundwater records for analysis are unknown. This work systematically tests and compares the ability of two common signal processing methods, the discrete Fourier transform (DFT) and harmonic least squares (HALS), to extract harmonic component properties. First, realistic conditions are simulated by analyzing a large number of synthetic data sets with variable sampling frequencies, record durations, sensor resolutions, noise levels and data gaps. Second, a model of two real-world data sets with different characteristics is validated. The results reveal that HALS outperforms the DFT in all aspects, including the ability to handle data gaps. While there is a clear trade-off between sampling frequency and record duration, sampling rates should not be less than six samples per day and records should not be shorter than 20 days when simultaneously extracting tidal constituents. The accuracy of detection is degraded by increasing noise levels and decreasing sensor resolution. However, a resolution of the same magnitude as the expected component amplitude is sufficient in the absence of excessive noise. The results provide a practical framework to determine the suitability of existing groundwater level records and can optimize future groundwater monitoring strategies to improve passive characterization using tidal signatures

Comparing Methods and Defining Practical Requirements for Extracting Harmonic Tidal Components from Groundwater Level Measurements

The groundwater pressure response to the ubiquitous Earth and atmospheric tides provides a largely untapped opportunity to passively characterize and quantify subsurface hydro-geomechanical properties. However, this requires reliable extraction of closely spaced harmonic components with relatively subtle amplitudes but well-known tidal periods from noisy measurements. The minimum requirements for the suitability of existing groundwater records for analysis are unknown. This work systematically tests and compares the ability of two common signal processing methods, the discrete Fourier transform (DFT) and harmonic least squares (HALS), to extract harmonic component properties. First, realistic conditions are simulated by analyzing a large number of synthetic data sets with variable sampling frequencies, record durations, sensor resolutions, noise levels and data gaps. Second, a model of two real-world data sets with different characteristics is validated. The results reveal that HALS outperforms the DFT in all aspects, including the ability to handle data gaps. While there is a clear trade-off between sampling frequency and record duration, sampling rates should not be less than six samples per day and records should not be shorter than 20 days when simultaneously extracting tidal constituents. The accuracy of detection is degraded by increasing noise levels and decreasing sensor resolution. However, a resolution of the same magnitude as the expected component amplitude is sufficient in the absence of excessive noise. The results provide a practical framework to determine the suitability of existing groundwater level records and can optimize future groundwater monitoring strategies to improve passive characterization using tidal signatures

A meta-analysis of long-term effects of conservation agriculture on maize grain yield under rain-fed conditions

Conservation agriculture involves reduced tillage, permanent soil cover and crop rotations to enhance soil fertility and to supply food from a dwindling land resource. Recently, conservation agriculture has been promoted in Southern Africa, mainly for maize-based farming systems. However, maize yields under rain-fed conditions are often variable. There is therefore a need to identify factors that influence crop yield under conservation agriculture and rain-fed conditions. Here, we studied maize grain yield data from experiments lasting 5 years and more under rain-fed conditions. We assessed the effect of long-term tillage and residue retention on maize grain yield under contrasting soil textures, nitrogen input and climate. Yield variability was measured by stability analysis. Our results show an increase in maize yield over time with conservation agriculture practices that include rotation and high input use in low rainfall areas. But we observed no difference in system stability under those conditions. We observed a strong relationship between maize grain yield and annual rainfall. Our meta-analysis gave the following findings: (1) 92% of the data show that mulch cover in high rainfall areas leads to lower yields due to waterlogging; (2) 85% of data show that soil texture is important in the temporal development of conservation agriculture effects, improved yields are likely on well-drained soils; (3) 73% of the data show that conservation agriculture practices require high inputs especially N for improved yield; (4) 63% of data show that increased yields are obtained with rotation but calculations often do not include the variations in rainfall within and between seasons; (5) 56% of the data show that reduced tillage with no mulch cover leads to lower yields in semi-arid areas; and (6) when adequate fertiliser is available, rainfall is the most important determinant of yield in southern Africa. It is clear from our results that conservation agriculture needs to be targeted and adapted to specific biophysical conditions for improved impact

Chapter 4 Geographic information system spatial data structures, models, and case studies

This chapter provides a basic overview of geographic information systems (GISs) as well as a summary of basic concepts encountered with GISs. Specifically, it touches on the various spatial data structures and models used by GISs to represent geographical information. First, general concepts related to information organization and data structure are briefly described and related to the different ways of representing real-world geographical data and information in GISs. Second, different perspectives on information organization are discussed, including different types of spatial relationships processed by GISs as well as the underlying information organization structure within GISs. Finally, the concept of data is investigated, as well as the purpose of databases with respect to GISs, including the various methods of modeling real-world data, relationships, and processes into databases

Meta-analysis of gene–environment-wide association scans accounting for education level identifies additional loci for refractive error

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/Myopia is the most common human eye disorder and it results from complex genetic and environmental causes. The rapidly increasing prevalence of myopia poses a major public health challenge. Here, the CREAM consortium performs a joint meta-analysis to test single-nucleotide polymorphism (SNP) main effects and SNP × education interaction effects on refractive error in 40,036 adults from 25 studies of European ancestry and 10,315 adults from 9 studies of Asian ancestry. In European ancestry individuals, we identify six novel loci (FAM150B-ACP1, LINC00340, FBN1, DIS3L-MAP2K1, ARID2-SNAT1 and SLC14A2) associated with refractive error. In Asian populations, three genome-wide significant loci AREG, GABRR1 and PDE10A also exhibit strong interactions with education (P<8.5 × 10(-5)), whereas the interactions are less evident in Europeans. The discovery of these loci represents an important advance in understanding how gene and environment interactions contribute to the heterogeneity of myopia

Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis

Genetic association studies have identified 21 loci associated with atopic dermatitis risk predominantly in populations of European ancestry. To identify further susceptibility loci for this common, complex skin disease, we performed a meta-analysis of >15 million genetic variants in 21,399 cases and 95,464 controls from populations of European, African, Japanese and Latino ancestry, followed by replication in 32,059 cases and 228,628 controls from 18 studies. We identified ten new risk loci, bringing the total number of known atopic dermatitis risk loci to 31 (with new secondary signals at four of these loci). Notably, the new loci include candidate genes with roles in the regulation of innate host defenses and T cell function, underscoring the important contribution of (auto)immune mechanisms to atopic dermatitis pathogenesis

Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer's disease etiopathogenesis

Alzheimer’s disease (AD) is the most common form of dementia, currently affecting 35 million people worldwide. Apolipoprotein E (APOE) ε4 allele is the major risk factor for sporadic, late-onset AD (LOAD), which comprises over 95% of AD cases, increasing the risk of AD 4-12 fold. Despite this, the role of APOE in AD pathogenesis is still a mystery. Aiming for a better understanding of APOE-specific effects, the ADAPTED consortium analysed and integrated publicly available data of multiple OMICS technologies from both plasma and brain stratified by APOE haplotype (APOE2, APOE3 and APOE4). Combining genome-wide association studies (GWAS) with differential mRNA and protein expression analyses and single-nuclei transcriptomics, we identified genes and pathways contributing to AD in both APOE dependent and independent fashion. Interestingly, we characterised a set of biomarkers showing plasma and brain consistent protein profiles and opposite trends in APOE2 and APOE4 AD cases that could constitute screening tools for a disease that lacks specific blood biomarkers. Beside the identification of APOE-specific signatures, our findings advocate that this novel approach, based on the concordance across OMIC layers and tissues, is an effective strategy for overcoming the limitations of often underpowered single-OMICS studies

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms