Developing soil water potential time series using the Montana Mesonet’s in-situ sensor data and lab-measured soil characteristics

As a headwaters state, changes in water across Montana’s landscape can have major implications for nearly two-thirds of the land area of the Conterminous United States to which its two major river basins drain. Monitoring watersheds in Montana provides important metrics for drought detection, agricultural management, and other natural resource management decisions. The Montana Mesonet, a wireless network of 79 meteorological, soil moisture, and groundwater monitoring stations, was created in 2016 to provide monitoring data. This network is supported through federal, state, tribal and private partnerships. Each station records measures at 15-minute intervals, including: soil moisture, soil-water conductivity, precipitation, relative humidity, temperature, wind speed and direction, solar radiation, and groundwater levels. Developing information about soil characteristics will put sensor data into a context applicable for land management decisions. As such, this project-in-progress focuses on the development of soil water retention curves and soil texture characterizations to enhance data collected by the Montana Mesonet. To assess soil characteristics, triplicate soil cores were collected at each station site at four depths. These cores are used for two lab analyses. Soil water retention curves were developed across a moisture gradient using tensiometers, balances, and potentiometers. Lab-measured soil water retention curves allow in-situ soil moisture time series to be converted to soil water potential, a biologically meaningful variable used to assess plant water stress. So far, approximately one-third of sites have soil-water retention curves and soil-water-potential time series generated at all depths. Soil texture will be determined by assessing the particle size distribution of a sample using the integral suspension pressure (ISP) method. The addition of these soil characteristics to the extensive Mesonet data will allow for site-specific historic, near real-time, and forecasted modeling of plant-available water, groundwater recharge, calculations of drought indices, and other practical applications to inform management decisions

Gate control of low-temperature spin dynamics in two-dimensional hole systems

We have investigated spin and carrier dynamics of resident holes in high-mobility two-dimensional hole systems in GaAs/Al$_{0.3}$Ga$_{0.7}$As single quantum wells at temperatures down to 400 mK. Time-resolved Faraday and Kerr rotation, as well as time-resolved photoluminescence spectroscopy are utilized in our study. We observe long-lived hole spin dynamics that are strongly temperature dependent, indicating that in-plane localization is crucial for hole spin coherence. By applying a gate voltage, we are able to tune the observed hole g factor by more than 50 percent. Calculations of the hole g tensor as a function of the applied bias show excellent agreement with our experimental findings.Comment: 8 pages, 7 figure

3D-Guided Face Manipulation of 2D Images for the Prediction of Post-Operative Outcome after Cranio-Maxillofacial Surgery

Cranio-maxillofacial surgery often alters the aesthetics of the face which can be a heavy burden for patients to decide whether or not to undergo surgery. Today, physicians can predict the post-operative face using surgery planning tools to support the patient\u27s decision-making. While these planning tools allow a simulation of the post-operative face, the facial texture must usually be captured by another 3D texture scan and subsequently mapped on the simulated face. This approach often results in face predictions that do not appear realistic or lively looking and are therefore ill-suited to guide the patient\u27s decision-making. Instead, we propose a method using a generative adversarial network to modify a facial image according to a 3D soft-tissue estimation of the post-operative face. To circumvent the lack of available data pairs between pre- and post-operative measurements we propose a semi-supervised training strategy using cycle losses that only requires paired open-source data of images and 3D surfaces of the face\u27s shape. After training on "in-the-wild" images we show that our model can realistically manipulate local regions of a face in a 2D image based on a modified 3D shape. We then test our model on four clinical examples where we predict the post-operative face according to a 3D soft-tissue prediction of surgery outcome, which was simulated by a surgery planning tool. As a result, we aim to demonstrate the potential of our approach to predict realistic post-operative images of faces without the need of paired clinical data, physical models, or 3D texture scans

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis

DeepWAS: multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS

A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

The genetic variant rs72824905-G (minor allele) in the PLCG2 gene was previously associated with a reduced Alzheimer's disease risk (AD). The role of PLCG2 in immune system signaling suggests it may also protect against other neurodegenerative diseases and possibly associates with longevity. We studied the effect of the rs72824905-G on seven neurodegenerative diseases and longevity, using 53,627 patients, 3,516 long-lived individuals and 149,290 study-matched controls. We replicated the association of rs72824905-G with reduced AD risk and we found an association with reduced risk of dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). We did not find evidence for an effect on Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) risks, despite adequate sample sizes. Conversely, the rs72824905-G allele was associated with increased likelihood of longevity. By-proxy analyses in the UK Biobank supported the associations with both dementia and longevity. Concluding, rs72824905-G has a protective effect against multiple neurodegenerative diseases indicating shared aspects of disease etiology. Our findings merit studying the PLC?2 pathway as drug-target