Complex-Valued K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Large-Scale Hybrid Functional Enabled Ab Initio Molecular Dynamics Simulations within Plane Waves.

K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition for hybrid functional electronic structure calculations. Real-valued K-means clustering for accelerating the ISDF decomposition has been demonstrated for large-scale hybrid functional enabled ab initio molecular dynamics (hybrid AIMD) simulations within plane-wave basis sets where the Kohn-Sham orbitals are real-valued. However, it is unclear whether such K-means clustering works for complex-valued Kohn-Sham orbitals. Here, we propose an improved weight function defined as the sum of the square modulus of complex-valued Kohn-Sham orbitals in K-means clustering for hybrid AIMD simulations. Numerical results demonstrate that the K-means algorithm with a new weight function yields smoother and more delocalized interpolation sampling points, resulting in smoother energy potential, smaller energy drift, and longer time steps for hybrid AIMD simulations compared to the previous weight function used in the real-valued K-means algorithm. In particular, we find that this improved algorithm can obtain more accurate oxygen-oxygen radial distribution functions in liquid water molecules and a more accurate power spectrum in crystal silicon dioxide compared to the previous K-means algorithm. Finally, we describe a massively parallel implementation of this ISDF decomposition to accelerate large-scale complex-valued hybrid AIMD simulations containing thousands of atoms (2,744 atoms), which can scale up to 5,504 CPU cores on modern supercomputers.

Machine Learning K-Means Clustering in Interpolative Separable Density Fitting Algorithm: Advancing Accurate and Efficient Cubic-Scaling Density Functional Perturbation Theory Calculations within Plane Waves.

Density functional perturbation theory (DFPT) is a crucial tool for accurately describing lattice dynamics. The adaptively compressed polarizability (ACP) method reduces the computational complexity of DFPT calculations from O(N4) to O(N3) by combining the interpolative separable density fitting (ISDF) algorithm. However, the conventional QR factorization with column pivoting (QRCP) algorithm, used for selecting the interpolation points in ISDF, not only incurs a high cubic-scaling computational cost, O(N3), but also leads to suboptimal convergence. This convergence issue is particularly pronounced when considering the complex interplay between the external potential and atomic displacement in ACP-based DFPT calculations. Here, we present a machine learning K-means clustering algorithm to select the interpolation points in ISDF, which offers a more efficient quadratic-scaling O(N2) alternative to the computationally intensive cubic-scaling O(N3) QRCP algorithm. We implement this efficient K-means-based ISDF algorithm to accelerate plane-wave DFPT calculations in KSSOLV, which is a MATLAB toolbox for performing Kohn-Sham density functional theory calculations within plane waves. We demonstrate that this K-means algorithm not only offers comparable accuracy to QRCP in ISDF but also achieves better convergence for ACP-based DFPT calculations. In particular, K-means can remarkably reduce the computational cost of selecting the interpolation points by nearly 2 orders of magnitude compared to QRCP in ISDF.

High-Throughput Screening of Rattling-Induced Ultralow Lattice Thermal Conductivity in Semiconductors.

Thermoelectric (TE) materials with rattling model show ultralow lattice thermal conductivity for high-efficient energy conversion between heat and electricity. In this work, by analysis of the key spirit of the rattling model, we propose an efficient empirical descriptor to realize the high-throughput screening of ultralow thermal conductivity in a series of semiconductors. This descriptor extracts the structural information of rattling atoms whose bond lengths with all the nearest neighboring atoms are larger than the sum of corresponding covalent radiuses. We obtain 1171 candidates from the Materials Project (MP) Database that contains more than 100 000 materials. Combining the empirical equation of high-throughput computation with a machine learning algorithm, we compute the approximate lattice thermal conductivities (κL) and find the κL values of 532 materials are less than 2.0 W m-1 K-1 at 300 K, which can be regarded as the criteria of ultralow κL in general. In particular, we demonstrate that halide double perovskites structures show ultralow κL, which provides valuable references for promising low κL materials in future experiments. In order to further verify our computational results, we calculate accurate κL for Rb2SnBr6 and CsCu3O2 as candidates with the low lattice thermal conductivity by solving the phonon Boltzmann transport equation. In particular, we demonstrate that Rb2SnBr6 has the lowest κL value of 0.1 W m-1 K-1 at 300 K of all known thermal conductivity materials with the rattling model so far.

Realizing Effective Cubic-Scaling Coulomb Hole Plus Screened Exchange Approximation in Periodic Systems via Interpolative Separable Density Fitting with a Plane-Wave Basis Set.

The GW approximation is an effective way to accurately describe the single-electron excitations of molecules and the quasiparticle energies of solids. However, a perceived drawback of the GW calculations is their high computational cost and large memory usage, which limit their applications to large systems. Herein, we demonstrate an accurate and effective low-rank approximation to accelerate non-self-consistent GW (G0W0) calculations under the static Coulomb hole plus screened exchange (COHSEX) approximation for periodic systems. Our approach is to adopt the interpolative separable density fitting (ISDF) decomposition and Cauchy's integral to construct low-rank representations of the dielectric matrix ϵ and self-energy matrix Σ. This approach reduces the number of floating point operations from O(Ne4) to O(Ne3) and requires a much smaller memory footprint. Two methods are used to select the interpolation points in ISDF, including the standard QR factorization with column pivoting (QRCP) procedure and the machine learning K-means clustering (K-means) algorithm. We demonstrate that these two methods can yield similar accuracy for both molecules and solids at much lower computational cost. In particular, K-means clustering can significantly reduce the computational cost of selecting the interpolation points by an order of magnitude compared to QRCP, resulting in an overall speedup factor of about ten times ISDF accelerated the static COHSEX calculations compared with conventional COHSEX approximation.

Machine Learning K-Means Clustering Algorithm for Interpolative Separable Density Fitting to Accelerate Hybrid Functional Calculations with Numerical Atomic Orbitals.

The interpolative separable density fitting (ISDF) is an efficient and accurate low-rank decomposition method to reduce the high computational cost and memory usage of the Hartree-Fock exchange (HFX) calculations with numerical atomic orbitals (NAOs). In this work, we present a machine learning K-means clustering algorithm to select the interpolation points in ISDF, which offers a much cheaper alternative to the expensive QR factorization with column pivoting (QRCP) procedure. We implement this K-means-based ISDF decomposition to accelerate hybrid functional calculations with NAOs in the HONPAS package. We demonstrate that this method can yield a similar accuracy for both molecules and solids at a much lower computational cost. In particular, K-means can remarkably reduce the computational cost of selecting the interpolation points by nearly two orders of magnitude compared to QRCP, resulting in a speedup of ∼10 times for ISDF-based HFX calculations.

Landform and lithospheric development contribute to the assembly of mountain floras in China.

Although it is well documented that mountains tend to exhibit high biodiversity, how geological processes affect the assemblage of montane floras is a matter of ongoing research. Here, we explore landform-specific differences among montane floras based on a dataset comprising 17,576 angiosperm species representing 140 Chinese mountain floras, which we define as the collection of all angiosperm species growing on a specific mountain. Our results show that igneous bedrock (granitic and karst-granitic landforms) is correlated with higher species richness and phylogenetic overdispersion, while the opposite is true for sedimentary bedrock (karst, Danxia, and desert landforms), which is correlated with phylogenetic clustering. Furthermore, we show that landform type was the primary determinant of the assembly of evolutionarily older species within floras, while climate was a greater determinant for younger species. Our study indicates that landform type not only affects montane species richness, but also contributes to the composition of montane floras. To explain the assembly and differentiation of mountain floras, we propose the 'floristic geo-lithology hypothesis', which highlights the role of bedrock and landform processes in montane floristic assembly and provides insights for future research on speciation, migration, and biodiversity in montane regions.

Study of Brain Structure in HIV Vertically Infected Adolescents.

Neuroimaging studies have focused mainly on human immunodeficiency virus (HIV)-infected adults or younger children, showing abnormal brain structures. In this study, we used voxel-based morphometry to investigate the brain integrity of HIV vertically infected adolescents. Twenty-five HIV vertically infected (HIV+) adolescents and 33 HIV-exposed, but uninfected (HIV-) and demographically matched controls participated in this study. T1 high-resolution anatomical magnetic resonance imaging images were obtained and segmented into gray matter (GM) and white matter (WM) segments. Then, population templates were derived from the entire imaging dataset using the diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) technique. Between-group GM and WM maps were contrasted using independent two-sample t-tests, with age and sex as nuisance regressors of no interest. Significant effects were identified using voxel-wise p < .001 and cluster-level p < .05 with a family-wise error correction. Whole brain volume between the groups did not demonstrate a significant difference. Relative to HIV- controls, the HIV+ adolescents demonstrated less GM in the bilateral cerebellum, right pallidum, right calcarine, left anterior cingulate cortex (ACC), and right superior occipital lobe. HIV+ adolescents also demonstrated less WM volume in the bilateral cerebellum, right brainstem, and left occipital lobe. Furthermore, the volume of the ACC was positively correlated with the Mini-Mental State Examination (MMSE) and the CD4 cell counts in the HIV+ adolescents. The age of highly active antiretroviral therapy (HAART) onset was positively correlated with GM volume in the right temporal lobe, left occipital lobe, and left precentral gyrus. In HIV+ adolescents, a pattern of less WM density and altered GM and WM volume suggests that early HIV infection combined with neurotoxicity effect of early HAART, a lack of viral control may have a significant effect on the brain structural integrity. The process of corpus callosum formation in the corpus callosum and the frontal WM is more susceptible to HIV infection. Altered ACC integrity may represent a promising biomarker of cognitive dysfunction following HIV infection.

Asymptomatic Human Immunodeficiency Virus Vertical Transmitted Adolescents' Brain Functional Changes: Based on Resting-State Functional Magnetic Resonance Imaging.

Perinatal HIV-infected (PHIV+) adolescents survive longer with the use of readily found combination antiretroviral therapy (cART); however, they still have the risk of developing cognitive deficits. The article aims to explore the brain functional changes in asymptomatic PHIV+ adolescents with cART based on the resting-state functional magnetic resonance imaging (rs-fMRI). rs-fMRI was performed on 20 PHIV+ adolescents and 28 PHIV- controls to evaluate the regional homogeneity (ReHo) in different brain regions by calculating the Kendall harmonious coefficient. Montreal cognitive assessment and laboratory studies (nadir CD4+ T cell counts) were also performed on all the subjects to evaluate their cognitive and immune status. Thirteen PHIV+ adolescents and 22 PHIV- controls were enrolled. There was a significant difference of ReHo values in PHIV+ adolescents compared to PHIV- controls, the areas with increased ReHo values include bilateral precentral/postcentral gyrus and right middle temporal pole. Also, the areas with decreased ReHo values locate in right putamen/pallidum/insula, left caudate/putamen/insula, right superior temporal pole/insula, right caudate/putamen, bilateral anterior cingulate cortex, and left inferior temporal pole. Furthermore, age, cognitive scores, and laboratory studies (nadir CD4+ T cell counts) did not show any significant correlation with altered ReHo values of brain regions neither in PHIV+ groups nor in PHIV- control groups. Among PHIV+ adolescents, brain areas with increased ReHo values were mainly located in the central somatic motor-sensory cortex, which might be related to the compensatory mechanism, whereas brain areas with decreased ReHo values were mainly focused on corticostriatal pathway, which might be associated with abnormal dopamine consumption. Thus, rs-fMRI could demonstrate the brain functional changes in resting state of asymptomatic PHIV+ adolescents.

Structural Covariance of Gray Matter Volume in HIV Vertically Infected Adolescents.

Human immunodeficiency virus (HIV) infection significantly affect neurodevelopmental and behavioral outcomes. We investigated whether alterations of gray matter organization and structural covariance networks with vertical HIV infection adolescents exist, by using the GAT toolbox. MRI data were analysed from 25 HIV vertically infected adolescents and 33 HIV-exposed-uninfected control participants. The gray matter volume (GMV) was calculated, and structural brain networks were reconstructed from gray matter co-variance. Gray matter losses were pronounced in anterior cingulate cortex (ACC), right pallidum, right occipital lobe, inferior parietal lobe, and bilateral cerebellum crus. The global brain network measures were not significantly different between the groups; however, the nodal alterations were most pronounced in frontal, temporal, basal ganglia, cerebellum, and temporal lobes. Brain hubs in the HIV-infected subjects increased in number and tended to shift to sensorimotor and temporal areas. In the HIV-infected subjects, decreased GMVs in ACC and bilateral cerebellum were related to lower Mini-Mental State Examination scores; the CD4 counts were positively related to the GMVs in ACC and sensorimotor areas. These findings suggest that focally reduced gray matter, disrupted nodal profiles of structural wirings, and a shift in hub distribution may represent neuroanatomical biomarkers of HIV infection on the developing brain.

Altered Activation in Cerebellum Contralateral to Unilateral Thalamotomy May Mediate Tremor Suppression in Parkinson's Disease: A Short-Term Regional Homogeneity fMRI Study.

BACKGROUND: Ventral intermediate nucleus thalamotomy is an effective treatment for Parkinson's disease tremor. However, its mechanism is still unclear. PURPOSE: We used resting-state fMRI to investigate short-term ReHo changes after unilateral thalamotomy in tremor-dominant PD, and to speculate about its possible mechanism on tremor suppression. METHODS: 26 patients and 31 healthy subjects (HS) were recruited. Patients were divided into two groups according to right- (rPD) and left-side (lPD) thalamotomy. Tremor was assessed using the 7-item scale from the Unified Parkinson's disease rating scale motor score (mUPDRS). Patients were scanned using resting state fMRI after 12h withdrawal of medication, both preoperatively (PDpre) and 7- day postoperatively (PDpost), whereas healthy subjects were scanned once. The regions associated with tremor and altered ReHo due to thalamic ablation were examined. RESULTS: The impact of unilateral VIM thalamotomy was characterized in the frontal, parietal, temporal regions, basal ganglia, thalamus, and cerebellum. Compared with PDpre, significantly reduced ReHo was found in the left cerebellum in patients with rPDpost, and slightly decreased ReHo in the cerebellum vermis in patients with lPDpost, which was significantly higher than HS. We demonstrated a positive correlation between the ReHo values in the cerebellum (in rPD, peak coordinate [-12, -54, -21], R = 0.64, P = 0.0025, and peak coordinate [-9, -54, -18], R = 0.71, P = 0.0025; in lPD, peak coordinate [3, -45, -15], R = 0.71, P = 0.004) in the pre-surgical condition, changes of ReHo induced by thalamotomy (in rPD, R = 0.63, P = 0.021, R = 0.6, P = 0.009; in lPD, R = 0.58, P = 0.028) and tremor scores contralateral to the surgical side, respectively. CONCLUSION: The specific area that may be associated with PD tremor and altered ReHo due to thalamic ablation is the cerebellum. The neural basis underlying thalamotomy is complex; cerebellum involvement is far beyond cerebello-thalamic tract breakage.

Altered spontaneous brain activity in patients with acute spinal cord injury revealed by resting-state functional MRI.

BACKGROUND: Previous neuroimaging studies have provided evidence of structural and functional reorganization of brain in patients with chronic spinal cord injury (SCI). However, it remains unknown whether the spontaneous brain activity changes in acute SCI. In this study, we investigated intrinsic brain activity in acute SCI patients using a regional homogeneity (ReHo) analysis based on resting-state functional magnetic resonance imaging. METHODS: A total of 15 patients with acute SCI and 16 healthy controls participated in the study. The ReHo value was used to evaluate spontaneous brain activity, and voxel-wise comparisons of ReHo were performed to identify brain regions with altered spontaneous brain activity between groups. We also assessed the associations between ReHo and the clinical scores in brain regions showing changed spontaneous brain activity. RESULTS: Compared with the controls, the acute SCI patients showed decreased ReHo in the bilateral primary motor cortex/primary somatosensory cortex, bilateral supplementary motor area/dorsal lateral prefrontal cortex, right inferior frontal gyrus, bilateral dorsal anterior cingulate cortex and bilateral caudate; and increased ReHo in bilateral precuneus, the left inferior parietal lobe, the left brainstem/hippocampus, the left cingulate motor area, bilateral insula, bilateral thalamus and bilateral cerebellum. The average ReHo values of the left thalamus and right insula were negatively correlated with the international standards for the neurological classification of spinal cord injury motor scores. CONCLUSION: Our findings indicate that acute distant neuronal damage has an immediate impact on spontaneous brain activity. In acute SCI patients, the ReHo was prominently altered in brain regions involved in motor execution and cognitive control, default mode network, and which are associated with sensorimotor compensatory reorganization. Abnormal ReHo values in the left thalamus and right insula could serve as potential biomarkers for assessment of neuronal damage and the prediction of clinical outcomes in acute SCI.

Identification of cisplatin-resistance associated genes through proteomic analysis of human ovarian cancer cells and a cisplatin-resistant subline.

Chemoresistance to cancer therapy is a major obstacle to the effective treatment of human cancers with cisplatin (DDP), but the mechanisms of cisplatin-resistance are not clear. In this study, we established a cisplatin- resistant human ovarian cancer cell line (COC1/DDP) and identified differentially expressed proteins related to cisplatin resistance. The proteomic expression profiles in COC1 before and after DDP treatment were examined using 2-dimensional electrophoresis technology. Differentially expressed proteins were identified using matrix- assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and high performance liquid chromatography-electrospray tandem MS (NanoUPLC-ESI-MS/MS). 5 protein spots, for cytokeratin 9, keratin 1, deoxyuridine triphosphatase (dUTPase), aarF domain containing kinase 4 (ADCK 4) and cofilin1, were identified to be significantly changed in COC1/DDP compared with its parental cells. The expression of these five proteins was further validated by quantitative PCR and Western blotting, confirming the results of proteomic analysis. Further research on these proteins may help to identify novel resistant biomarkers or reveal the mechanism of cisplatin-resistance in human ovarian cancers.