57 research outputs found
Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors
The efficient and economical exploitation of polymers with high thermal
conductivity is essential to solve the issue of heat dissipation in organic
devices. Currently, the experimental preparation of functional thermal
conductivity polymers remains a trial and error process due to the
multi-degrees of freedom during the synthesis and characterization process. In
this work, we have proposed a high-throughput screening framework for polymer
chains with high thermal conductivity via interpretable machine learning and
physical-feature engineering. The polymer thermal conductivity datasets for
training were first collected by molecular dynamics simulation. Inspired by the
drug-like small molecule representation and molecular force field, 320 polymer
monomer descriptors were calculated and the 20 optimized descriptors with
physical meaning were extracted by hierarchical down-selection. All the machine
learning models achieve a prediction accuracy R2 greater than 0.80, which is
superior to that of represented by traditional graph descriptors. Further, the
cross-sectional area and dihedral stiffness descriptors were identified for
positive/negative contribution to thermal conductivity, and 107 promising
polymer structures with thermal conductivity greater than 20.00 W/mK were
obtained. Mathematical formulas for predicting the polymer thermal conductivity
were also constructed by using symbolic regression. The high thermal
conductivity polymer structures are mostly {\pi}-conjugated, whose overlapping
p-orbitals enable easily to maintain strong chain stiffness and large group
velocities. The proposed data-driven framework should facilitate the
theoretical and experimental design of polymers with desirable properties
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Oncogenic R132 IDH1 Mutations Limit NADPH for De Novo Lipogenesis through (D)2-Hydroxyglutarate Production in Fibrosarcoma Sells.
Neomorphic mutations in NADP-dependent isocitrate dehydrogenases (IDH1 and IDH2) contribute to tumorigenesis in several cancers. Although significant research has focused on the hypermethylation phenotypes associated with (D)2-hydroxyglutarate (D2HG) accumulation, the metabolic consequences of these mutations may also provide therapeutic opportunities. Here we apply flux-based approaches to genetically engineered cell lines with an endogenous IDH1 mutation to examine the metabolic impacts of increased D2HG production and altered IDH flux as a function of IDH1 mutation or expression. D2HG synthesis in IDH1-mutant cells consumes NADPH at rates similar to de novo lipogenesis. IDH1-mutant cells exhibit increased dependence on exogenous lipid sources for in vitro growth, as removal of medium lipids slows growth more dramatically in IDH1-mutant cells compared with those expressing wild-type or enzymatically inactive alleles. NADPH regeneration may be limiting for lipogenesis and potentially redox homeostasis in IDH1-mutant cells, highlighting critical links between cellular biosynthesis and redox metabolism
Enhancing thermoelectric properties of isotope graphene nanoribbons via machine learning guided manipulation of disordered antidots and interfaces
Structural manipulation at the nanoscale breaks the intrinsic correlations
among different energy carrier transport properties, achieving high
thermoelectric performance. However, the coupled multifunctional (phonon and
electron) transport in the design of nanomaterials makes the optimization of
thermoelectric properties challenging. Machine learning brings convenience to
the design of nanostructures with large degree of freedom. Herein, we conducted
comprehensive thermoelectric optimization of isotopic armchair graphene
nanoribbons (AGNRs) with antidots and interfaces by combining Green's function
approach with machine learning algorithms. The optimal AGNR with ZT of 0.894 by
manipulating antidots was obtained at the interfaces of the aperiodic isotope
superlattices, which is 5.69 times larger than that of the pristine structure.
The proposed optimal structure via machine learning provides physical insights
that the carbon-13 atoms tend to form a continuous interface barrier
perpendicular to the carrier transport direction to suppress the propagation of
phonons through isotope AGNRs. The antidot effect is more effective than
isotope substitution in improving the thermoelectric properties of AGNRs. The
proposed approach coupling energy carrier transport property analysis with
machine learning algorithms offers highly efficient guidance on enhancing the
thermoelectric properties of low-dimensional nanomaterials, as well as to
explore and gain non-intuitive physical insights
L2hgdh Deficiency Accumulates l-2-Hydroxyglutarate with Progressive Leukoencephalopathy and Neurodegeneration
l-2-Hydroxyglutarate aciduria (L-2-HGA) is an autosomal recessive neurometabolic disorder caused by a mutation in the l-2-hydroxyglutarate dehydrogenase (L2HGDH) gene. In this study, we generated L2hgdh knockout (KO) mice and observed a robust increase of l-2-hydroxyglutarate (L-2-HG) levels in multiple tissues. The highest levels of L-2-HG were observed in the brain and testis, with a corresponding increase in histone methylation in these tissues. L2hgdh KO mice exhibit white matter abnormalities, extensive gliosis, microglia-mediated neuroinflammation, and an expansion of oligodendrocyte progenitor cells (OPCs). Moreover, L2hgdh deficiency leads to impaired adult hippocampal neurogenesis and late-onset neurodegeneration in mouse brains. Our data provide in vivo evidence that L2hgdh mutation leads to L-2-HG accumulation, leukoencephalopathy, and neurodegeneration in mice, thereby offering new insights into the pathophysiology of L-2-HGA in humans
D-2-hydroxyglutarate is essential for maintaining oncogenic property of mutant IDH-containing cancer cells but dispensable for cell growth
Cancer-associated isocitrate dehydrogenase (IDH) 1 and 2 mutations gain a new activity of reducing α-KG to produce D-2-hydroxyglutarate (D-2-HG), which is proposed to function as an oncometabolite by inhibiting α-KG dependent dioxygenases. We investigated the function of D-2-HG in tumorigenesis using IDH1 and IDH2 mutant cancer cell lines. Inhibition of D-2-HG production either by specific deletion of the mutant IDH1-R132C allele or overexpression of D-2-hydroxyglutarate dehydrogenase (D2HGDH) increases α-KG and related metabolites, restores the activity of some α-KG-dependent dioxygenases, and selectively alters gene expression. Ablation of D-2-HG production has no significant effect on cell proliferation and migration, but strongly inhibits anchorage independent growth in vitro and tumor growth in xenografted mouse models. Our study identifies a new activity of oncometabolite D-2-HG in promoting tumorigenesis
Minute-cadence Observations of the LAMOST Fields with the TMTS: III. Statistic Study of the Flare Stars from the First Two Years
Tsinghua University-Ma Huateng Telescopes for Survey (TMTS) aims to detect
fast-evolving transients in the Universe, which has led to the discovery of
thousands of short-period variables and eclipsing binaries since 2020. In this
paper, we present the observed properties of 125 flare stars identified by the
TMTS within the first two years, with an attempt to constrain their eruption
physics. As expected, most of these flares were recorded in late-type red stars
with > 2.0 mag, however, the flares associated with
bluer stars tend to be on average more energetic and have broader profiles. The
peak flux (F_peak) of the flare is found to depend strongly on the equivalent
duration (ED) of the energy release, i.e., , which is consistent with results derived from the Kepler
and Evryscope samples. This relation is likely related to the magnetic loop
emission, while -- for the more popular non-thermal electron heating model -- a
specific time evolution may be required to generate this relation. We notice
that flares produced by hotter stars have a flatter relation compared to that from cooler stars. This is related to the
statistical discrepancy in light-curve shape of flare events with different
colors. In spectra from LAMOST, we find that flare stars have apparently
stronger H alpha emission than inactive stars, especially at the low
temperature end, suggesting that chromospheric activity plays an important role
in producing flares. On the other hand, the subclass having frequent flares are
found to show H alpha emission of similar strength in their spectra to that
recorded with only a single flare but similar effective temperature, implying
that the chromospheric activity may not be the only trigger for eruptions.Comment: 17 pages, 15 figures, 2 tables, refereed version. For associated data
files, see https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/523/219
WT1 Recruits TET2 to Regulate Its Target Gene Expression and Suppress Leukemia Cell Proliferation
The TET2 DNA dioxygenase regulates cell identity and suppresses tumorigenesis by modulating DNA methylation and expression of a large number of genes. How TET2, like most other chromatin modifying enzymes, is recruited to specific genomic sites is unknown. Here we report that WT1, a sequence-specific transcription factor, is mutated in a mutually exclusive manner with TET2, IDH1 and IDH2 in acute myeloid leukemia (AML). WT1 physically interacts with and recruits TET2 to its target genes to activate their expression. The interaction between WT1 and TET2 is disrupted by multiple AML-derived TET2 mutations. TET2 suppresses leukemia cell proliferation and colony formation in a manner dependent on WT1. These results provide a mechanism for targeting TET2 to specific DNA sequence in the genome. Our results also provide an explanation for the mutual exclusivity of WT1 and TET2 mutations in AML and suggest an IDH1/2-TET2-WT1 pathway in suppressing AML
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