Search

Parkinsons disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.

The DC transformers with dual active bridge （DAB） topology is characterized by zero voltage switching （ZVS） and provides the basis for realizing lossless turn-off buffer by directly paralleling buffer capacitors. First， the structure and operation control of IGCT-HDCT （IGCT-based high-capacity DC transformer） are introduced. Then， a black startup strategy based on a soft converter is proposed， which divides the start process into the extended phase shift and single phase shift startup to realize the ZVS of all devices during the black startup of IGCT-HDCT and prevent the surge current on the buffer capacitor. By analyzing the resonance process of the device after shutdown， the relationship between the phase shift and the system parameters in the extended phase shift stage is introduced， and the ZVS characteristics of the device in the single phase shift stage are verified. The transient bias current of the system is eliminated by predictive control and duty ratio design. Finally， the feasibility of the proposed control is verified by a 1.5 kV/2 MW prototype.

As the physicochemical properties of ultrafine bubble systems are governed by their size, it is crucial to determine the size and distribution of such bubble systems. At present, the size or size distribution of nanometer-sized bubbles in suspension is often measured by either dynamic light scattering or the nanoparticle tracking analysis. Both techniques determine the bubble size via the Einstein–Stokes equation based on the theory of the Brownian motion. However, it is not yet clear to which extent the Einstein–Stokes equation is applicable for such ultrafine bubbles. In this work, using atomic molecular dynamics simulation, we evaluate the applicability of the Einstein–Stokes equation for gas nanobubbles with a diameter less than 10 nm, and for a comparative analysis, both vacuum nanobubbles and copper nanoparticles are also considered. The simulation results demonstrate that the diffusion coefficient for rigid nanoparticles in water is found to be highly consistent with the Einstein–Stokes equation, with slight deviation only found for nanoparticle with a radius less than 1 nm. For nanobubbles, including both methane and vacuum nanobubbles, however, large deviation from the Einstein–Stokes equation is found for the bubble radius larger than 3 nm. The deviation is attributed to the deformability of large nanobubbles that leads to a cushioning effect for collision-induced bubble diffusion. [ABSTRACT FROM AUTHOR]

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (

In regions where transmission corridors are badly needed, UHV lines will inevitably be constructed in parallel with other UHV or EHV lines. Hence, the electromagnetic environment characteristics near the corridors are complex and in-depth study should be carried out. In this paper, three models of parallel AC transmission lines are established, and the characteristics of the power frequency electromagnetic field, radio interference and audible noise near the parallel lines are studied. The results show that as the line spacing decreases, the area of strong electromagnetic environment reduces, but the amplitude will increase. The height of the parallel-erected line has more influence on the distribution of the power frequency electromagnetic field than the radio interference and audible noise. Generally, the electromagnetic environment generated by the parallel erection of UHV transmission lines is related to the conductor height and the spacing of parallel lines. When the radio interference and audible noise limits are up to standard, the spacing of parallel erection lines can be appropriately reduced. This study has a certain reference significance for the design and construction of UHV lines in areas being short of corridors.

Transfer of molecular chirality to supramolecular chirality at nanoscale and microscale by chemical self-assembly has been studied intensively for years. However, how such molecular chirality further transfers to the macroscale along the same path remains elusive. Here we reveal how the chirality from molecular level transfers to macroscopic level via self-assembly. We assemble a macrostripe using enantiomeric camphorsulfonic acid (CSA)-doped polyaniline with hierarchical order. The stripe can twist into a single-handed helical ribbon via helical self-motion. A multi-scale chemo-mechanical model is used to elucidate the mechanism underlying its chirality transfer and induction. The molecular origin of this macroscopic helical chirality is verified. Results provide a comprehensive understanding of hierarchical chirality transfer and helical motion in self-assembled materials and even their natural analogues. The stripe exhibits disparate actuation behaviour under stimuli of enantiomeric amines and integrating such chiral perception with helical self-motion may motivate chiral biomimetic studies of smart materials.

The complex phase diagram and bonding nature of the TiAl system make it difficult to accurately describe its various properties and phases by traditional atomistic force fields. Here, we develop a machine learning interatomic potential with a deep neural network method for the TiAlNb ternary alloy based on a dataset built by first-principles calculations. The training set includes bulk elementary metals and intermetallic structures with slab and amorphous configurations. This potential is validated by comparing bulk properties—including lattice constant and elastic constants, surface energies, vacancy formation energies, and stacking fault energies—with their respective density functional theory values. Moreover, our potential could accurately predict the average formation energy and stacking fault energy of γ-TiAl doped with Nb. The tensile properties of γ-TiAl are simulated by our potential and verified by experiments. These results support the applicability of our potential under more practical conditions. [ABSTRACT FROM AUTHOR]

The global effort to combat the COVID-19 pandemic faces ongoing uncertainty with the emergence of Variants of Concern featuring numerous mutations on the Spike (S) protein. In particular, the Omicron Variant is distinguished by 32 mutations, including 10 within its receptor-binding domain (RBD). These mutations significantly impact viral infectivity and the efficacy of vaccines and antibodies currently in use for therapeutic purposes. In our study, we employed structure-based computational saturation mutagenesis approaches to predict the effects of Omicron missense mutations on RBD stability and binding affinity, comparing them to the original Wuhan-Hu-1 strain. Our results predict that mutations such as G431W and P507W induce the most substantial destabilizations in the Wuhan-Hu-1-S/Omicron-S RBD. Notably, we postulate that mutations in the Omicron-S exhibit a higher percentage of enhancing binding affinity compared to Wuhan-S. We found that the mutations at residue positions G447, Y449, F456, F486, and S496 led to significant changes in binding affinity. In summary, our findings may shed light on the widespread prevalence of Omicron mutations in human populations. The Omicron mutations that potentially enhance their affinity for human receptors may facilitate increased viral binding and internalization in infected cells, thereby enhancing infectivity. This informs the development of new neutralizing antibodies capable of targeting Omicron's immune-evading mutations, potentially aiding in the ongoing battle against the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]