114 research outputs found
Improving the predictions of ML-corrected climate models with novelty detection
While previous works have shown that machine learning (ML) can improve the
prediction accuracy of coarse-grid climate models, these ML-augmented methods
are more vulnerable to irregular inputs than the traditional physics-based
models they rely on. Because ML-predicted corrections feed back into the
climate model's base physics, the ML-corrected model regularly produces out of
sample data, which can cause model instability and frequent crashes. This work
shows that adding semi-supervised novelty detection to identify out-of-sample
data and disable the ML-correction accordingly stabilizes simulations and
sharply improves the quality of predictions. We design an augmented climate
model with a one-class support vector machine (OCSVM) novelty detector that
provides better temperature and precipitation forecasts in a year-long
simulation than either a baseline (no-ML) or a standard ML-corrected run. By
improving the accuracy of coarse-grid climate models, this work helps make
accurate climate models accessible to researchers without massive computational
resources.Comment: Appearing at Tackling Climate Change with Machine Learning Workshop
at NeurIPS 202
Insulin upstream factor 1 and a novel ubiquitous factor bind to the human islet amyloid polypeptide/amylin gene promoter
Machine-learned climate model corrections from a global storm-resolving model
Due to computational constraints, running global climate models (GCMs) for
many years requires a lower spatial grid resolution ( km) than is
optimal for accurately resolving important physical processes. Such processes
are approximated in GCMs via subgrid parameterizations, which contribute
significantly to the uncertainty in GCM predictions. One approach to improving
the accuracy of a coarse-grid global climate model is to add machine-learned
state-dependent corrections at each simulation timestep, such that the climate
model evolves more like a high-resolution global storm-resolving model (GSRM).
We train neural networks to learn the state-dependent temperature, humidity,
and radiative flux corrections needed to nudge a 200 km coarse-grid climate
model to the evolution of a 3~km fine-grid GSRM. When these corrective ML
models are coupled to a year-long coarse-grid climate simulation, the time-mean
spatial pattern errors are reduced by 6-25% for land surface temperature and
9-25% for land surface precipitation with respect to a no-ML baseline
simulation. The ML-corrected simulations develop other biases in climate and
circulation that differ from, but have comparable amplitude to, the baseline
simulation
Emulating Fast Processes in Climate Models
Cloud microphysical parameterizations in atmospheric models describe the
formation and evolution of clouds and precipitation, a central weather and
climate process. Cloud-associated latent heating is a primary driver of large
and small-scale circulations throughout the global atmosphere, and clouds have
important interactions with atmospheric radiation. Clouds are ubiquitous,
diverse, and can change rapidly. In this work, we build the first emulator of
an entire cloud microphysical parameterization, including fast phase changes.
The emulator performs well in offline and online (i.e. when coupled to the rest
of the atmospheric model) tests, but shows some developing biases in
Antarctica. Sensitivity tests demonstrate that these successes require careful
modeling of the mixed discrete-continuous output as well as the input-output
structure of the underlying code and physical process.Comment: Accepted at the Machine Learning and the Physical Sciences Workshop
at the 36th conference on Neural Information Processing Systems (NeurIPS)
December 3, 202
Vitamin D pathway gene polymorphisms, diet, and risk of postmenopausal breast cancer: a nested case-control study
INTRODUCTION: Vitamin D receptor (VDR) polymorphisms have been inconsistently associated with breast cancer risk. Whether risk is influenced by polymorphisms in other vitamin D metabolism genes and whether calcium or vitamin D intake modifies risk by genotype have not been evaluated. METHODS: We conducted a nested case-control study within the Cancer Prevention Study II Nutrition Cohort of associations between breast cancer and four VDR single-nucleotide polymorphisms (SNPs), Bsm1,Apa1,Taq1, and Fok1, a poly(A) microsatellite, and associated haplotypes (baTL and BAtS). We also examined one SNP in the 24-hydroxylase gene (CYP24A1) and two in the vitamin D-binding protein (group-specific component [GC]) gene. Participants completed a questionnaire on diet and medical history at baseline in 1992. This study includes 500 postmenopausal breast cancer cases and 500 controls matched by age, race/ethnicity, and date of blood collection. RESULTS: Incident breast cancer was not associated with any genotype examined. However, women with the Bsm1 bb SNP who consumed greater than the median intake of total calcium (β₯902 mg/day) had lower odds of breast cancer compared to women with the Bb or BB genotype and less than the median calcium intake (odds ratio 0.61, 95% confidence interval 0.38 to 0.96; p(interaction )= 0.01). Similar interactions were observed for Taq1 (T allele) and the poly(A) (LL) repeat. CONCLUSION: We found no overall association between selected vitamin D pathway genes and postmenopausal breast cancer risk. However, certain VDR gene polymorphisms were associated with lower risk in women consuming high levels of calcium, suggesting that dietary factors may modify associations by VDR genotype
Genetic analysis of the vitamin D receptor gene in two epithelial cancers: melanoma and breast cancer case-control studies
<p>Abstract</p> <p>Background</p> <p>Vitamin D serum levels have been found to be related to sun exposure and diet, together with cell differentiation, growth control and consequently, cancer risk. Vitamin D receptor (<it>VDR</it>) genotypes may influence cancer risk; however, no epidemiological studies in sporadic breast cancer (BC) or malignant melanoma (MM) have been performed in a southern European population. In this study, the <it>VDR </it>gene has been evaluated in two epithelial cancers BC and MM.</p> <p>Methods</p> <p>We have conducted an analysis in 549 consecutive and non-related sporadic BC cases and 556 controls, all from the Spanish population, and 283 MM cases and 245 controls. Genotyping analyses were carried out on four putatively functional SNPs within the <it>VDR </it>gene.</p> <p>Results</p> <p>An association with the minor allele A of the non-synonymous SNP rs2228570 (rs10735810, <it>Fok</it>I, Met1Thr) was observed for BC, with an estimated odds ratio (OR) of 1.26 (95% CI = 1.02β1.57; p = 0.036). The synonymous variant rs731236 (<it>Taq</it>I) appeared to be associated with protection from BC (OR = 0.80, 95%CI = 0.64β0.99; p = 0.047). No statistically significant associations with MM were observed for any SNP. Nevertheless, sub-group analyses revealed an association between rs2228570 (<it>FokI</it>) and absence of childhood sunburns (OR = 0.65, p = 0.003), between the 3'utr SNP rs739837 (<it>Bgl</it>I) and fair skin (OR = 1.31, p = 0.048), and between the promoter SNP rs4516035 and the more aggressive tumour location in head-neck and trunk (OR = 1.54, p = 0.020).</p> <p>Conclusion</p> <p>In summary, we observed associations between SNPs in the <it>VDR </it>gene and BC risk, and a comprehensive analysis using clinical and tumour characteristics as outcome variables has revealed potential associations with MM. These associations required confirmation in independent studies.</p
Vitamin D receptor gene polymorphisms are associated with breast cancer risk in a UK Caucasian population
There is increasing evidence that vitamin D can protect against breast cancer. The actions of vitamin D are mediated via the vitamin D receptor (VDR). We have investigated whether polymorphisms in the VDR gene are associated with altered breast cancer risk in a UK Caucasian population. We recruited 241 women following a negative screening mammogram and 181 women with known breast cancer. The VDR polymorphism BsmI, an intronic 3β² gene variant, was significantly associated with increased breast cancer risk: odds ratio bb vs BB genotype = 2.32 (95% CI, 1.23β4.39). The BsmI polymorphism was in linkage disequilibrium with a candidate translational control site, the variable length poly (A) sequence in the 3β² untranslated region. Thus, the βLβ poly (A) variant was also associated with a similar breast cancer risk. A 5β² VDR gene variant, FokI, was not associated with breast cancer risk. Further investigations into the mechanisms of interactions of the VDR with other environmental and/or genetic influences to alter breast cancer risk may lead to a new understanding of the role of vitamin D in the control of cellular and developmental pathways. Β© 2001 Cancer Research Campaign http://www.bjcancer.co
- β¦