147 research outputs found
Magnetism of novel rare-earth-free intermetallic compounds
Rare-earth-free magnets have drawn lots of interest because of their low cost, and the production is not limited by the shortage of rare-earth elements. This dissertation focuses on three rare-earth-free materials, Fe-Co-Ti alloys, Fe-Ni-B alloys, and Co-Si. All of them are synthesized by arc melting followed by melt-spinning. Fe3+xCo3−xTi2 (x = 0, 2, 3) alloys exhibit hexagonal crystal structures and show non-collinear spin structures according to neutron diffraction. The magnetic moments have projections on both the c-axis and basal plane, and the corresponding misalignment angle exhibits a nonlinear decrease with x, which we explain as a micromagnetic effect caused by Fe-Co site disorder. To increase the magnetic anisotropy of Fe2Ni alloy, we dope boron into Fe2Ni and analyze the structure with X-ray diffraction, which shows face center cubic or body center cubic structure according to different temperatures. Magnetic analysis with magnetometer shows that the presence of boron dramatically increases the anisotropy of Fe-Ni-B alloy. Neutron powder diffraction is employed to investigate the magnetism and spin structure in single-phase B20 Co1.043Si0.957. The magnetic contributions to the neutron powder diffraction data measured in zero fields are consistent with the helical order among the allowed spin structures derived from group theory. The magnitude of the magnetic moment is larger than the bulk magnetization determined from magnetometry, indicating the formation of a helical spin phase and the associated conical states in high magnetic fields.
Advisor: Xiaoshan X
A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction
While linear mixed model (LMM) has shown a competitive performance in
correcting spurious associations raised by population stratification, family
structures, and cryptic relatedness, more challenges are still to be addressed
regarding the complex structure of genotypic and phenotypic data. For example,
geneticists have discovered that some clusters of phenotypes are more
co-expressed than others. Hence, a joint analysis that can utilize such
relatedness information in a heterogeneous data set is crucial for genetic
modeling.
We proposed the sparse graph-structured linear mixed model (sGLMM) that can
incorporate the relatedness information from traits in a dataset with
confounding correction. Our method is capable of uncovering the genetic
associations of a large number of phenotypes together while considering the
relatedness of these phenotypes. Through extensive simulation experiments, we
show that the proposed model outperforms other existing approaches and can
model correlation from both population structure and shared signals. Further,
we validate the effectiveness of sGLMM in the real-world genomic dataset on two
different species from plants and humans. In Arabidopsis thaliana data, sGLMM
behaves better than all other baseline models for 63.4% traits. We also discuss
the potential causal genetic variation of Human Alzheimer's disease discovered
by our model and justify some of the most important genetic loci.Comment: Code available at https://github.com/YeWenting/sGLM
Deep Learning for Genomics: A Concise Overview
Advancements in genomic research such as high-throughput sequencing
techniques have driven modern genomic studies into "big data" disciplines. This
data explosion is constantly challenging conventional methods used in genomics.
In parallel with the urgent demand for robust algorithms, deep learning has
succeeded in a variety of fields such as vision, speech, and text processing.
Yet genomics entails unique challenges to deep learning since we are expecting
from deep learning a superhuman intelligence that explores beyond our knowledge
to interpret the genome. A powerful deep learning model should rely on
insightful utilization of task-specific knowledge. In this paper, we briefly
discuss the strengths of different deep learning models from a genomic
perspective so as to fit each particular task with a proper deep architecture,
and remark on practical considerations of developing modern deep learning
architectures for genomics. We also provide a concise review of deep learning
applications in various aspects of genomic research, as well as pointing out
potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning
Application
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