Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Using artificial neural networks to predict future dryland responses to human and climate disturbances.

ABSTRACT: Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem. Given the potential for currently stabilised dune systems to reactivate under climate change and increased anthropogenic pressures, identifying the role of external disturbances in driving geomorphic response is vitally important. We developed a novel approach, using artificial neural networks (ANNs) applied to time series of historical reactivation-deposition events from the Nebraska Sandhills, to determine the relationship between historic periods of sand deposition in semi-arid grasslands and external climatic conditions, land use pressures and wildfire occurrence. We show that both vegetation growth and sediment re-deposition episodes can be accurately estimated. Sensitivity testing of individual factors shows that localised forcings (overgrazing and wildfire) have a statistically significant impact when the climate is held at present-day conditions. However, the dominant effect is climate-induced drought. Our approach has great potential for estimating future landscape sensitivity to climate and land use scenarios across a wide range of potentially fragile dryland environments.

SUBMITTER: Buckland CE

PROVIDER: S-EPMC6405911 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Artificial neural networks to predict future bone mineral density and bone loss rate in Japanese postmenopausal women.

Project description:ObjectivePredictions of the future bone mineral density and bone loss rate are important to tailor medicine for women with osteoporosis, because of the possible presence of personal risk factors affecting the severity of osteoporosis in the future. We investigated whether it was possible to predict bone mineral density and bone loss rate in the future using artificial neural networks.ResultsA total of 135 women over 50 years old residing in T town of Wakayama Prefecture, Japan were analyzed to establish a statistical model. Artificial neural networks models were constructed using the two variables of bone mineral density and bone loss rate. The multiple correlation coefficients between the actual and measured values for lumbar and femoral bone mineral densities in 2003 showed R2 = 0.929 and R2 = 0.880, respectively, by linear regression analyses, while the values for bone loss rates in lumbar and femoral bone mineral densities were R2 = 0.694 and R2 = 0.609, respectively. Statistical models by artificial neural networks were superior to those by multiple regression analyses. The prediction of future bone mineral density values estimated by artificial neural networks was considered to be useful as a tool to tailor medicine for the early diagnosis of and intervention for women osteoporosis with women.

| S-EPMC5681768 | biostudies-literature

Albedo feedbacks to future climate via climate change impacts on dryland biocrusts.

Project description:Drylands represent the planet's largest terrestrial biome and evidence suggests these landscapes have large potential for creating feedbacks to future climate. Recent studies also indicate that dryland ecosystems are responding markedly to climate change. Biological soil crusts (biocrusts) ‒ soil surface communities of lichens, mosses, and/or cyanobacteria ‒ comprise up to 70% of dryland cover and help govern fundamental ecosystem functions, including soil stabilization and carbon uptake. Drylands are expected to experience significant changes in temperature and precipitation regimes, and such alterations may impact biocrust communities by promoting rapid mortality of foundational species. In turn, biocrust community shifts affect land surface cover and roughness-changes that can dramatically alter albedo. We tested this hypothesis in a full-factorial warming (+4 °C above ambient) and altered precipitation (increased frequency of 1.2 mm monsoon-type watering events) experiment on the Colorado Plateau, USA. We quantified changes in shortwave albedo via multi-angle, solar-reflectance measurements. Warming and watering treatments each led to large increases in albedo (>30%). This increase was driven by biophysical factors related to treatment effects on cyanobacteria cover and soil surface roughness following treatment-induced moss and lichen mortality. A rise in dryland surface albedo may represent a previously unidentified feedback to future climate.

| S-EPMC5345002 | biostudies-literature

Creating artificial human genomes using generative neural networks.

Project description:Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation in the field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the complex distributions of real genomic datasets and generate novel high-quality artificial genomes (AGs) with none to little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by data augmentation to reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and features for solving supervised tasks. Generative models and AGs have the potential to become valuable assets in genetic studies by providing a rich yet compact representation of existing genomes and high-quality, easy-access and anonymous alternatives for private databases.

| S-EPMC7861435 | biostudies-literature

Application of Artificial Neural Networks to Predict the Catalytic Pyrolysis of HDPE Using Non-Isothermal TGA Data.

Project description:This paper presents a comprehensive kinetic study of the catalytic pyrolysis of high-density polyethylene (HDPE) utilizing thermogravimetric analysis (TGA) data. Nine runs with different catalyst (HZSM-5) to polymer mass ratios (0.5, 0.77, and 1.0) were performed at different heating rates (5, 10, and 15 K/min) under nitrogen over the temperature range 303-973 K. Thermograms showed clearly that there was only one main reaction region for the catalytic cracking of HDPE. In addition, while thermogravimetric analysis (TGA) data were shifted towards higher temperatures as the heating rate increased, they were shifted towards lower temperatures and polymer started to degrade at lower temperatures when the catalyst was used. Furthermore, the activation energy of the catalytic pyrolysis of HDPE was obtained using three isoconversional (model-free) models and two non-isoconversional (model-fitting) models. Moreover, a set of 900 input-output experimental TGA data has been predicted by a highly efficient developed artificial neural network (ANN) model. Results showed a very good agreement between the ANN-predicted and experimental values (R2 > 0.999). Besides, A highly-efficient performance of the developed model has been reported for new input data as well.

| S-EPMC7466036 | biostudies-literature

Climate legacies drive the distribution and future restoration potential of dryland forests.

Project description:Knowing the extent and environmental drivers of forests is key to successfully restore degraded ecosystems, and to mitigate climate change and desertification impacts using tree planting. Water availability is the main limiting factor for the development of forests in drylands, yet the importance of groundwater resources and palaeoclimate as drivers of their current distribution has been neglected. Here we report that mid-Holocene climates and aquifer trends are key predictors of the distribution of dryland forests worldwide. We also updated the global extent of dryland forests to 1,283 million hectares and showed that failing to consider past climates and aquifers has resulted in ignoring or misplacing up to 130 million hectares of forests in drylands. Our findings highlight the importance of a wetter past and well-preserved aquifers to explain the current distribution of dryland forests, and can guide restoration actions by avoiding unsuitable areas for tree establishment in a drier world.

| S-EPMC7613308 | biostudies-literature

Measuring context dependency in birdsong using artificial neural networks.

Project description:Context dependency is a key feature in sequential structures of human language, which requires reference between words far apart in the produced sequence. Assessing how long the past context has an effect on the current status provides crucial information to understand the mechanism for complex sequential behaviors. Birdsongs serve as a representative model for studying the context dependency in sequential signals produced by non-human animals, while previous reports were upper-bounded by methodological limitations. Here, we newly estimated the context dependency in birdsongs in a more scalable way using a modern neural-network-based language model whose accessible context length is sufficiently long. The detected context dependency was beyond the order of traditional Markovian models of birdsong, but was consistent with previous experimental investigations. We also studied the relation between the assumed/auto-detected vocabulary size of birdsong (i.e., fine- vs. coarse-grained syllable classifications) and the context dependency. It turned out that the larger vocabulary (or the more fine-grained classification) is assumed, the shorter context dependency is detected.

| S-EPMC8746767 | biostudies-literature

Inverse design of porous materials using artificial neural networks.

Project description:Generating optimal nanomaterials using artificial neural networks can potentially lead to a notable revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. Here, we have implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 121 crystalline porous materials. Our neural network takes in inputs in the form of energy and material dimensions, and we show that zeolites with a user-desired range of 4 kJ/mol methane heat of adsorption can be reliably produced using our neural network. The fine-tuning of user-desired capability can potentially accelerate materials development as it demonstrates a successful case of inverse design of porous materials.

| S-EPMC6941911 | biostudies-literature

Modeling transcriptomic age using knowledge-primed artificial neural networks.

Project description:The development of 'age clocks', machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design. The clock, an artificial neural network constructed according to well-described biological pathways, allows the prediction of age from gene expression data of skin tissue with high accuracy, while at the same time capturing and revealing aging states of the pathways driving the prediction. The model recapitulates known associations of aging gene knockdowns in simulation experiments and demonstrates its utility in deciphering the main pathways by which accelerated aging conditions such as Hutchinson-Gilford progeria syndrome, as well as pro-longevity interventions like caloric restriction, exert their effects.

| S-EPMC8169742 | biostudies-literature

Identifying threshold responses of Australian dryland rivers to future hydroclimatic change.

Project description:Rivers provide crucial ecosystem services in water-stressed drylands. Australian dryland rivers are geomorphologically diverse, ranging from through-going, single channels to discontinuous, multi-channelled systems, yet we have limited understanding of their sensitivity to future hydroclimatic changes. Here, we characterise for the first time the geomorphology of 29 dryland rivers with catchments across a humid to arid gradient covering >1,800,000 km2 of continental eastern and central Australia. Statistical separation of five specific dominantly alluvial river types and quantification of their present-day catchment hydroclimates enables identification of potential thresholds of change. Projected aridity increases across eastern Australia by 2070 (RCP4.5) will result in ~80% of the dryland rivers crossing a threshold from one type to another, manifesting in major geomorphological changes. Dramatic cases will see currently through-going rivers (e.g. Murrumbidgee, Macintyre) experience step changes towards greater discontinuity, characterised by pronounced downstream declines in channel size and local termination. Expanding our approach to include other river styles (e.g. mixed bedrock-alluvial) would allow similar analyses of dryland rivers globally where hydroclimate is an important driver of change. Early identification of dryland river responses to future hydroclimatic change has far-reaching implications for the ~2 billion people that live in drylands and rely on riverine ecosystem services.

| S-EPMC7171090 | biostudies-literature

Nanophotonic particle simulation and inverse design using artificial neural networks.

Project description:We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical.

| S-EPMC5983917 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data