bluuebunny/crossref_metadata_2025 huggingface dataset:
Restoration of the focal parameters for an extreme-power laser pulse with ponderomotively scattered proton spectra by using a neural network algorithm
Authors: Bukharskii N. D. , Vais O. E. , Korneev Ph. A. , Bychenkov V. Yu.
Published: December 2022

A neural network-based approach is proposed both for reconstructing the focal spot intensity profile and for estimating the peak intensity of a high-power tightly focused laser pulse using the angular energy distributions of protons accelerated by the pulse from rarefied gases. For these purposes, we use a convolutional neural network architecture. Training and testing datasets are calculated using the test particle method, with the laser description in the form of Stratton–Chu integrals, which model laser pulses focused by an off-axis parabolic mirror down to the diffraction limit. To demonstrate the power and robustness of this method, we discuss the reconstruction of axially symmetric intensity profiles for laser pulses with intensities and focal diameters in the ranges of 1021–1023 W cm−2 and ∼(1–4)λ, respectively. This approach has prospects for implementation at higher intensities and with asymmetric laser beams, and it can provide a valuable diagnostic method for emerging extremely intense laser facilities.
A fractal power-law approach for investigating the interactions between reservoir microstructure and gas migration
Authors: Ye Dayu , Liu Guannan , Zhou Jianhua , Yang Tingting , Cheng Gaojie , Fan Shudi
Published: December 2022

As one of the most significant clean energy sources, the migration characteristics and extraction processes of coalbed methane (CBM) have been extensively studied. The structural distribution and evolutionary characteristics of reservoir fractures, as the main conduit for gas migration, significantly affect the permeability and gas production. However, few models have been able to quantitatively and accurately explore reservoir micro–macro interactions under coupled thermal-fluid–solid effects. This work develops a new highly coupled model based on the widely adopted power-law function to quantify reservoir thermal conduction effect, gas pressure evolution, reservoir deformation, in situ stress, the adsorption–desorption effect, and reservoir microstructure evolution. Three parameters are adopted to quantitatively characterize the reservoir structure: (1) fracture power index αf (to characterize the fracture density), (2) fracture length ratio rf (to characterize the fracture size), and (3) the maximum fracture length l. The results demonstrate that the fractal network is a special kind of network in the power-law length distribution. The proposed power-law seepage model is able to accurately characterize the evolution of reservoir microstructure and the impact of microevolution on extraction under multi-field coupling effects, compared to the traditional power-law model. The proposed model can provide a good theoretical and practical support for the study of CBM migration and extraction.
Influence of nucleation on cavitation inception in tip leakage flows
Authors: Russell P. S. , Barbaca L. , Venning J. A. , Pearce B. W. , Brandner P. A.
Published: December 2022

Cavitation in a tip leakage flow is experimentally investigated in a cavitation tunnel using a stationary hydrofoil analogy. The experiments were performed for different tip clearances (τ=gap height/maximum profile thickness) and hydrofoil incidences (α). The chord-based Reynolds number remained fixed at Re=3×106. The influence of nucleation on both inception and developed cavitation is evaluated by performing tests with two populations of freestream nuclei: a low concentration with strong critical tensions for activation and a high concentration with weak critical tensions. These populations represent the extremes that would be expected in practical tip leakage flows. Cavitation was characterized using high-speed imaging and acoustic measurements. Following a survey of developed cavitation topology for a range τ and α values, α=6° was selected for further investigation of cavitation inception as it demonstrated a rich variety of physical processes. From the acoustic measurements, the worst performance in terms of cavitation inception was observed at an intermediate gap height of around τ=0.6–0.8 for the “strong water” case. Broadly, cavitation and inception is intermittent when nuclei are sparse, becoming continuous as additional nuclei are introduced. While a continuous cavity in the seeded flow resulted in a higher baseline acoustic signature, sparse populations allow the leakage vortex to sustain tension, which can result in extremely loud incipient events. Optimization of gap height will, therefore, depend on the expected nuclei population during operation.
Rheology of dilute granular gas mixtures where the grains interact via a square shoulder and well potential
Authors: Yoshii Kiwamu , Takada Satoshi , Kurosawa Kosuke , Pöschel Thorsten
Published: December 2022

We develop the rheology of a dilute granular gas mixture. Motivated by the interaction of charged granular particles, we assume that the grains interact via a square shoulder and well potential. Employing a kinetic theory, we compute the temperature and the shear viscosity as a function of the shear rate. Numerical simulations confirm our results are above the critical shear rate. At a shear rate below a critical value, clustering of the particles occurs.
Vapor concentration and temperature field measurement of an evaporating sessile drop by tomographic laser absorption spectroscopy
Authors: Chen Xiaoliang , Zhu Ning , Wang Weitian , Wang Zhenhai , Wakata Yuki , Chao Xing
Published: December 2022

Drop evaporation is a ubiquitous phenomenon that has been studied for over a century. However, the surrounding gas-phase field including the temperature and vapor concentration distribution is not sufficiently studied experimentally. In this paper, a sensor based on tunable laser absorption spectroscopy is designed to study the vapor-phase temperature and concentration distribution of evaporating sessile drops, and data processing method involving data pre-processing and tomographic reconstruction is proposed to realize high-precision, spatially resolved measurement, which was realized by scanning the mechanical galvanometer in the horizontal direction. With free-knot splines smoothing and “denucleated” onion-peeling algorithm, temperature and H2O concentration distributions surrounding the evaporated drop at three different substrate plate temperatures are observed. The concentration and temperature in close vicinity to the gas–liquid interface are reconstructed accurately despite the high-gradient changes. A spatial resolution of under 100 μm with a temporal resolution of 10 s has been realized. Quantitative depiction of the temperature and concentration fields shows evidence of convection and indicates that while the concentration level sharply peaks at the interface, temperature in the close vicinity to the drop shows flattening or even dipping trends. The in situ laser measurement results are validated against contact measurement, theoretical prediction with saturated vapor pressure, and model simulation of COMSOL. Uncertainties have been evaluated based on both repeated measurements and model prediction of input uncertainty propagation. Temperature and concentration measurement uncertainties are estimated to be <1.5% and <3.5%, respectively, even though all experiments were performed in open air with non-negligible buoyancy-induced convection.
Numerical investigation on the droplet entrainment from gas sheared liquid film of hydroxyl-terminated polybutadiene/paraffin fuels
Authors: Liu Xue-li , Hu Song-qi , Wang Yin , Liu Lin-lin , Zhang Yan
Published: December 2022

Hydroxyl-terminated polybutadiene (HTPB)/paraffin fuels applied in hybrid rocket motors enjoy high regression rates and excellent mechanical properties; however, the special microscopic structure of the fuels leads to more complex droplet entrainment mechanisms. The two-dimensional heterogeneous HTPB/paraffin fuels geometry model was established by a dynamic packing algorithm. Dynamic evolution characteristics of a liquid film disturbed by the interaction between HTPB and paraffin were investigated by numerical calculation, and the controlling mechanism of droplet entrainment in a hot shear flow was explored. The results showed that the motion of high-strength HTPB fragments in the liquid film distorted the structure of interfacial wave and generated uncommon rough waves with an averaged wavelength of up to 9.05 mm, for the fuel containing 60 wt. % paraffin. Additionally, there were three pathways of droplet entrainment, i.e., rupturing of paraffin-coated HTPB filaments, splashing at the crest of paraffin wavelets, and the breakup of paraffin ligaments. However, periodic interfacial wavelets were generated for the fuel with a 30 wt. % content of paraffin due to the damping of HTPB, and most of the entrained droplets were generated when the exposed molten paraffin flowed to both ends of the fuel slab. The diameter of entrained droplets was in the range of 0.02–0.74 mm, and it was shown to be positively correlated with the paraffin content, the temperature of shear flow, and the mass flux of shear flow.
A lattice Boltzmann investigation of the saturated pool boiling heat transfer on micro-cavity/fin surfaces
Authors: Lou A. Qin , Wang Haoyuan , Li Ling
Published: December 2022

Saturated pool boiling heat transfer on micro-cavity and micro-fin surfaces is examined by a mesoscopic phase change lattice Boltzmann method. The important interfacial processes and boiling heat transfer performance are explored concerning the effects of micro-structure configurations, specifically fin and cavity, and micro-structure parameters, including fin/cavity shape, height, length, and spacing between fins/cavities. It is discovered that both the micro-cavity and micro-fin surfaces are conducive to bubble nucleation and can enhance nucleate boiling heat transfer (NBHT) when compared with the smooth surface. By comparing fin and cavity surfaces, it is found that micro-cavity is more conducive to bubble nucleation, whereas micro-fin is more conducive to bubble departure. As a result, micro-cavity surface has a higher NBHT while a micro-fin surface has a higher critical heat flux (CHF). The saturated pool boiling heat transmission is significantly influenced by the micro-structure parameters as well, i.e., the boiling on the rectangular cavity/fin surfaces has an earlier nucleation time while that on the conical surfaces has a faster bubble escape speed. The mass of residual bubble left over after the bubble department increases with cavity/fin height, which leads to the advance of CHF. On the other hand, the CHF increases as the distance between micro-structures. Additionally, with the increase in micro-structure length, the CHF increases for the micro-cavity surface whereas decreases for the micro-fin surface. Finally, a series of fitting equations between CHF and the micro-structure parameters are presented and an improved hybrid surface is developed based on the theoretical predictions.
An experiment generates a specified mean strained rate turbulent flow: Dynamics of particles
Authors: Hassanian R. , Helgadóttir Á. , Bouhlali L. , Riedel M.
Published: December 2022

This study aimed to simulate straining turbulent flow empirically, having direct similarities with vast naturally occurring flows and engineering applications. The flow was generated in 100<Reλ<500 and seeded with passive and inertial particles. Lagrangian particle tracking and particle image velocimetry were employed to extract the dynamics of particle statistics and flow features, respectively. The studies for axisymmetric straining turbulent flow reported that the strain rate, flow geometry, and gravity affect particle statistics. To practically investigate mentioned effects in the literature, we present the behavior of both passive and inertial particles from the novel experiment conducted on initially homogeneous turbulence undergoing a sudden axisymmetric expansion. We represent the result with two different mean strains and Reynolds–Taylor microscales. However, this study, in contrast to the previous studies, considers the fields of inertial particles in the presence of gravity. The result discloses that the novel designed and conducted experiments simulated the flow satisfactorily. Then, the particle behavior in such flow showed the effectiveness of the flow distortion on particle dynamics such as velocity root mean square and Reynolds stress. Straining turbulence flow is subject to many industrial applications and physics studies, such as stagnation points, external flow around an airfoil, internal flow in changeable cross section pipe, expansion in the engine mixing chamber, and leading edge erosion. This study's conclusion could apply constructively to these areas.
Blockage coefficient of cylindrical blocker and diffusion resistance of membrane channels
Authors: Skvortsov A. T. , Dagdug L. , Berezhkovskii A. M. , Bezrukov S. M.
Published: December 2022

This study deals with potential flow of ideal fluid in an infinite cylindrical tube in the presence of a blocking object. The blockage effect of the object on the flow can be characterized by a lump parameter, blockage coefficient, which accounts for the object shape and size. For a cylindrical blocking object, analytical results for the blockage coefficient are known only in three limiting cases: for a long thin cylinder and for small and large blocking disks. We propose a simple analytical expression for the blockage coefficient of a cylindrical blocker of arbitrary length and radius that reduces to the known asymptotic results in the corresponding limits.
An emerging global picture of heavy fermion physics
Authors: Yang Yi-feng
Published: December 2022

Recent progresses using state-of-the-art experimental techniques have motivated a number of new insights on heavy fermion physics. This article gives a brief summary of the author’s research along this direction. We discuss five major topics including: (1) development of phase coherence and two-stage hybridization; (2) two-fluid behavior and hidden universal scaling; (3) quantum phase transitions and fractionalized heavy fermion liquid; (4) quantum critical superconductivity; (5) material-specific properties. These cover the most essential parts of heavy fermion physics and lead to an emerging global picture beyond conventional theories based on mean-field or local approximations.
Nondestructive measurement of anisotropic elastic constants of selective laser melted 316L based on a tri-mode ultrasonic method
Authors: Yuan Maodan , Dai Anbang , Ma Jiatao , Chen Yan , Ji Xuanrong
Published: December 2022

Selective laser melting (SLM) technology can rapidly form metal parts with complex geometry, but the internal microstructure and material properties are significantly different from those of traditionally manufactured parts, such as obvious anisotropy and inhomogeneity. This paper proposed a tri-mode ultrasonic system to nondestructively measure the anisotropic elastic constants of 316L stainless steel prepared by SLM. Firstly, the quantitative relationship between ultrasonic wave velocities and anisotropic elastic constants was derived for elliptical anisotropic materials. The anisotropic elastic constants and Thomsen anisotropic parameters can be determined by measuring four ultrasonic wave velocities

V

11

,

V

33

,

V

12

, and

V

13

. Therefore, a tri-mode ultrasonic transducer was designed and fabricated to build the elastic constant ultrasonic measurement system. Then, the elastic constants and Thomsen anisotropic parameters of SLM samples were measured, and the key process parameters’ influence was discussed. The standard static tensile test validated the result of the ultrasonic test, and the maximum relative error was 6.94%. This study will provide a novel nondestructive measurement method and system of anisotropic elastic constants to ensure internal quality and improve the additive manufacturing process.
Polypyrrole-coated copper@graphene core-shell nanoparticles for supercapacitor application
Authors: Ho Hsiao-Yun , Chu Hsuan-I , Huang Yi-June , Tsai Dung-Sheng , Lee Chuan-Pei
Published: December 2022

The performance of supercapacitors strongly depends on the electrochemical characterizations of electrode materials. Herein, a composite material consisted of polypyrrole (PPy) and multilayer graphene-wrapped copper nanoparticles (PPy/MLG-Cu NPs) is fabricated on a flexible carbon cloth (CC) substrate via two-step synthesis process for supercapacitor application. Where, MLG-Cu NPs are prepared on CC by one-step chemical vapor deposition synthesis approach; thereafter, the PPy is further deposited on the MLG-Cu NPs/CC via electropolymerization. The related material characterizations of PPy/MLG-Cu NPs are well investigated by scanning electron microscopic, high resolution transmission electron microscopy, Raman spectrometer and x-ray photoelectron spectroscopy; the electrochemical behaviors of the pertinent electrodes are studied by cyclic voltammogram, galvanostatic charge/discharge and electrochemical impedance spectroscopy measurements. The flexible electrode with PPy/MLG-Cu NPs composites exhibits the best specific capacitance of 845.38 F g−1 at 1 A g−1, which is much higher than those of electrodes with PPy (214.30 F g−1), MLG-Cu NPs (6.34 F g−1), multilayer graphene hollow balls (MLGHBs; 52.72 F g−1), and PPy/MLGHBs (237.84 F g−1). Finally, a supercapacitor system consisted of four PPy/MLG-Cu NPs/CC electrodes can efficiently power various light-emitting diodes (i.e. red, yellow, green and blue lighs), demonstrating the practical application of PPy/MLG-Cu NPs/CC electrode.
Scalable multi-physics for fusion reactors with AURORA
Authors: Brooks H , Davis A
Published: December 2022

To support the design of fusion reactors for energy production, there is an urgent need to develop multi-physics software tools capable of modelling critical tokamak components as a single cohesive whole. Although some loosely-coupled physics tools do exist, these lack fidelity and will fundamentally fail to capture any closed-loop feedback effects between separate physics modules. To address this need, we introduce a new open-source code: AURORA: A Unified Resource for OpenMC (fusion) Reactor Applications. Anticipating the need for high-fidelity simulation of complex physics and geometry mandates the need to target high performance computing from the outset. AURORA has been built upon two demonstrably scalable open-source codes: MOOSE for the provision of finite element analysis and OpenMC for Monte Carlo neutron transport. In this application, the heat deposited by neutrons is calculated by OpenMC and tallied upon an unstructured mesh, providing a source term for transient heat conduction and thermal expansion. MOOSE calculates the corresponding change in temperature and density on the same mesh, whereafter local temperature and density regions are defined via binning in these variables. Finally, these regions are updated within OpenMC as new materials having modified nuclear cross sections. The procedure is subsequently iterated until a desired stopping condition is reached. We present some qualitative results as a proof-of-concept demonstration of AURORA. We further demonstrate that there is no measurable degradation in shared-memory performance arising from wrapping OpenMC in a MOOSE application. While AURORA should provide utility as a standalone tool for coupled thermo-mechanical and neutronics analyses, we view it as a first step towards our ultimate goal of having a single suite capable of capturing non-trivial couplings between the many disciplines—encompassing in addition fluid dynamics, electromagnetism, materials science and chemistry—involved in the simulation of a tokamak.
Study on single-pulse UIS capability of SiC MOSFET with different P-base morphology
Authors: Wang Antao , Bai Yun , Li Chengzhan , Wu Zhikang , Tang Yidan , Tian Xiaoli , Yang Chengyue , Hao Jilong , Liu Xinyu
Published: December 2022

At present, the single-pulse unclamped inductive switching (UIS) characteristics of SiC MOSFET have been fully studied. The current mainstream research suggests that the failure mechanism of MOSFET under a single UIS shock is divided into parasitic BJT conduction and metallization failure. However, there are still some shortcomings in the current research. First, it is difficult to distinguish these two failure mechanisms. Second, it is difficult to propose feasible optimization schemes. Therefore, it is still necessary to further explore the mechanism of single-pulse UIS. In this study, Sentaurus TCAD software was used to simulate different P-base doped MOSFETs, and the effect of doping concentration and morphology of the P-base region on the single-pulse UIS failure mechanism was revealed. By designing the P-base region, the BJT conduction failure and metallization failure can be distinguished. In addition, the study also proved the relationship between breakdown voltage and the maximum temperature (T
max) in the UIS process, and on this basis, an optimization scheme for the single UIS characteristics of MOSFET was proposed.
Conditions for the open pores formation in medical textile materials for the treatment of wounds using iron oxide nanopowders
Authors: Riabchykov Mykola , Tkachuk Oksana , Nazarchuk Liudmyla , Alexandrov Alexandr
Published: December 2022

One of the tasks of dressings in the process of wound treatment is to remove exudate. In the optimal case, this intensity is determined individually for each wound. Active medical textiles take advantage of the capillary effect that occurs in thin channels. For these purposes, medical materials are created from porous structures. To create back pressure in foamed medical materials, the pores must be in the form of channels, which is not realized with conventional technologies. The purpose of this study is to analyze the performance of magnetic nanopowders on the pore structure in the production of porous materials. Research methods include methods of chemical synthesis, methods of microscopic analysis, methods of modeling, statistical analysis. The main result is the study of the formation of necessary forms in foamed materials during the use of magnetic nanopowders as additives. This effect makes it possible to increase the efficiency of foam structures when used as medical materials.
Flame-retardant polymethacrylimide foams modified by grafting with amino-terminated phosphorous polyborosiloxane
Authors: Zhu ZhengLiang , Chen WeiWang , Dong YuTong , Zhou XiaoMeng
Published: December 2022

To improve the flame retardancy of Polymethacrylimide (PMI) foam, in this study, a series of flame-retardant PMI foams were prepared by grafting with flame retardant amino-terminated phosphorous polyborosiloxane (N-PBSi), using tert-butanol (TBA) as the foaming agent. The structure, mechanical properties, thermal behavior, and flame retardancy of the resultant PMI foams were characterized. These as-prepared foams tended to be more compact in structure as N-PBSi content increased. The tensile, compressive, and bending strength of these PMI foams therefore was greatly enhanced, which were about 2 times, 9 times, and 3 times that of pure PMI foam, respectively. Besides, the introduction of N-PBSi also attributed to PMI foams better performance in char forming, especially in the air, which is beneficial for obtaining better retardancy against flame. Their advantages in flame retardancy and smoke inhabitation were confirmed by limiting oxygen index (LOI) and cone calorimeter tests (CCT). The total heat release (THR) and the peak of smoke generation rate (pSPR) of PMI/N-PBSi-20 were reduced by 23.1% and 69.9%, respectively. The N-PBSi incorporated were thought to not only generate phosphorus-containing fragments to capture free radicals in gas phase, but also rearrange in the solid phase to form a denser carbon layer to provide a better barrier between external heat and internal flammable pyrolysis gases. Given these, it can be envisaged that the flame-retardant PMI foams modified by N-PBSi may be more attractive in a wider range of applications.
Material transformers: deep learning language models for generative materials design
Authors: Fu Nihang , Wei Lai , Song Yuqi , Li Qinyang , Xin Rui , Omee Sadman Sadeed , Dong Rongzhi , Siriwardane Edirisuriya M Dilanga , Hu Jianjun
Published: December 2022

Pre-trained transformer language models (LMs) on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequence generation. However, no such models have been applied to learn the composition patterns for the generative design of material compositions. Here we train a series of seven modern transformer models (GPT, GPT-2, GPT-Neo, GPT-J, BLMM, BART, and RoBERTa) for materials design using the expanded formulas of the ICSD, OQMD, and Materials Projects databases. Six different datasets with/out non-charge-neutral or EB samples are used to benchmark the generative design performances and uncover the biases of modern transformer models for the generative design of materials compositions. Our experiments show that the materials transformers based on causal LMs can generate chemically valid material compositions with as high as 97.61% to be charge neutral and 91.22% to be electronegativity balanced, which has more than six times higher enrichment compared to the baseline pseudo-random sampling algorithm. Our LMs also demonstrate high generation novelty and their potential in new materials discovery is proved by their capability to recover the leave-out materials. We also find that the properties of the generated compositions can be tailored by training the models with selected training sets such as high-bandgap samples. Our experiments also show that different models each have their own preference in terms of the properties of the generated samples and their running time complexity varies a lot. We have applied our materials transformers to discover a set of new materials as validated using density functional theory calculations. All our trained materials transformer models and code can be accessed freely at http://www.github.com/usccolumbia/MTransformer.
Concentrated electrolytes for rechargeable lithium metal batteries
Authors: Tian Chunxi , Qin Kun , Suo Liumin
Published: December 2022

Traditional lithium-ion batteries with graphite anodes have gradually been limited by the glass ceiling of energy density. As a result, lithium metal batteries (LMBs), regarded as the ideal alternative, have attracted considerable attention. However, lithium is highly reactive and susceptible to most electrolytes, resulting in poor cycle performance. In addition, lithium grows Li dendrites during charging, adversely affecting the safety of LMBs. Therefore, LMBs are more sensitive to the chemical composition of electrolytes and their relative ratios (concentrations). Recently, concentrated electrolytes have been widely demonstrated to be friendly to lithium metal anodes (LMAs). This review focuses on the progress of concentrated electrolytes in LMBs, including the solvation structure varying with concentration, unique functions in stabilizing the LMA, and their interfacial chemistry with LMA.
SWR1 chromatin remodeling complex prevents mitotic slippage during spindle position checkpoint arrest
Authors: Caydasi Ayse Koca , Khmelinskii Anton , Darieva Zoulfia , Kurtulmus Bahtiyar , Knop Michael , Pereira Gislene
Published: December 2022

Caydasi et al. identified the SWR1 chromatin-remodeling complex (SWR1-C) among novel factors that are essential for the integrity of the spindle position checkpoint (SPOC). They show that cells lacking SWR1-C activate the SPOC upon spindle misorientation but undergo mitotic slippage due to the inability of holding a prolonged SPOC arrest.
Respiratory tract infection: an unfamiliar risk factor in high-altitude pulmonary edema
Authors: Choudhary Raushni , Kumari Swati , Ali Manzoor , Thinlas Tashi , Rabyang Stanzen , Mishra Aastha
Published: December 2022

The dramatic changes in physiology at high altitude (HA) as a result of the characteristic hypobaric hypoxia condition can modify innate and adaptive defense mechanisms of the body. As a consequence, few sojourners visiting HA with mild or asymptomatic infection may have an enhanced susceptibility to high-altitude pulmonary edema (HAPE), an acute but severe altitude sickness. It develops upon rapid ascent to altitudes above 2500 m, in otherwise healthy individuals. Though HAPE has been studied extensively, an elaborate exploration of the HA disease burden and the potential risk factors associated with its manifestation are poorly described. The present review discusses respiratory tract infection (RTI) as an unfamiliar but important risk factor in enhancing HAPE susceptibility in sojourners for two primary reasons. First, the symptoms of RTI s resemble those of HAPE. Secondly, the imbalanced pathways contributing to vascular dysfunction in HAPE also participate in the pathogenesis of the infectious processes. These pathways have a crucial role in shaping host response against viral and bacterial infections and may further worsen the clinical outcomes at HA. Respiratory tract pathogenic agents, if screened in HAPE patients, can help in ascertaining their role in disease risk and also point toward their association with the disease severity. The microbial screenings and identifications of pathogens with diseases are the foundation for describing potential molecular mechanisms underlying host response to the microbial challenge. The prior knowledge of such infections may predict the manifestation of disease etiology and provide better therapeutic options.
VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants
Authors: Ge Fang , Li Chen , Iqbal Shahid , Muhammad Arif , Li Fuyi , Thafar Maha A , Yan Zihao , Worachartcheewan Apilak , Xu Xiaofeng , Song Jiangning , Yu Dong-Jun
Published: December 2022

Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a ‘one-stop’ framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants’ pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.
dSCOPE: a software to detect sequences critical for liquid–liquid phase separation
Authors: Yu Kai , Liu Zekun , Cheng Haoyang , Li Shihua , Zhang Qingfeng , Liu Jia , Ju Huai-Qiang , Zuo Zhixiang , Zhao Qi , Kang Shiyang , Liu Ze-Xian
Published: December 2022

Membrane-based cells are the fundamental structural and functional units of organisms, while evidences demonstrate that liquid–liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation (PS), but these studies lacked an effective tool to identify the sequence segments that critical for LLPS. In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the PS-driving regions. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted PS-driving regions enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted PS-driving regions enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS-related research.
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings
Authors: Chen Yuanyuan , Zhang Hao , Sun Xiao
Published: December 2022

The advent of single-cell RNA-sequencing (scRNA-seq) provides an unprecedented opportunity to explore gene expression profiles at the single-cell level. However, gene expression values vary over time and under different conditions even within the same cell. There is an urgent need for more stable and reliable feature variables at the single-cell level to depict cell heterogeneity. Thus, we construct a new feature matrix called the delta rank matrix (DRM) from scRNA-seq data by integrating an a priori gene interaction network, which transforms the unreliable gene expression value into a stable gene interaction/edge value on a single-cell basis. This is the first time that a gene-level feature has been transformed into an interaction/edge-level for scRNA-seq data analysis based on relative expression orderings. Experiments on various scRNA-seq datasets have demonstrated that DRM performs better than the original gene expression matrix in cell clustering, cell identification and pseudo-trajectory reconstruction. More importantly, the DRM really achieves the fusion of gene expressions and gene interactions and provides a method of measuring gene interactions at the single-cell level. Thus, the DRM can be used to find changes in gene interactions among different cell types, which may open up a new way to analyze scRNA-seq data from an interaction perspective. In addition, DRM provides a new method to construct a cell-specific network for each single cell instead of a group of cells as in traditional network construction methods. DRM’s exceptional performance is due to its extraction of rich gene-association information on biological systems and stable characterization of cells.
Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers
Authors: Du Wenjie , Yang Xiaoting , Wu Di , Ma FenFen , Zhang Baicheng , Bao Chaochao , Huo Yaoyuan , Jiang Jun , Chen Xin , Wang Yang
Published: December 2022

The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain types, particularly conformational and chiral ones that share the same bonding connectivity but differ in spatial arrangement. Here, we designed a hybrid molecular graph network, Chemical Feature Fusion Network (CFFN), to address the issue by integrating planar and stereo information of molecules in an interweaved fashion. The three-dimensional (3D, i.e., stereo) modality guarantees precision and completeness by providing unabridged information, while the two-dimensional (2D, i.e., planar) modality brings in chemical intuitions as prior knowledge for guidance. The zipper-like arrangement of 2D and 3D information processing promotes cooperativity between them, and their synergy is the key to our model’s success. Experiments on various molecules or conformational datasets including a special newly created chiral molecule dataset comprised of various configurations and conformations demonstrate the superior performance of CFFN. The advantage of CFFN is even more significant in datasets made of small samples. Ablation experiments confirm that fusing 2D and 3D molecular graphs as unambiguous molecular descriptors can not only effectively distinguish molecules and their conformations, but also achieve more accurate and robust prediction of quantum chemical properties.
Chemotherapy induced oxidative stress in the ovary: drug-dependent mechanisms and potential interventions
Authors: Trujillo Madison , Odle Angela K , Aykin-Burns Nukhet , Allen Antiño R
Published: December 2022

Cancer incidence and relative survival are expected to increase over the next few decades. With the majority of patients receiving combinatorial chemotherapy, an increasing proportion of patients experience long-term side effects from treatment—including reproductive disorders and infertility. A limited number of studies have examined mechanisms of single-agent chemotherapy-induced gonadotoxicity, with chemotherapy-induced oxidative stress being implicated in the loss of reproductive functions. Current methods of female fertility preservation are costly, invasive, only moderately successful, and seldom presented to cancer patients. The potential of antioxidants to alleviate chemotherapy has been overlooked at a time when it is becoming increasingly important to develop strategies to protect reproductive functions during chemotherapy. This review will summarize the importance of reactive oxygen species homeostasis in reproduction, chemotherapy-induced mitochondrial dysfunction in oocytes, chemotherapy-induced oxidative stress, and several promising natural adjuvants.
Methods of assessing value for money of UK-based early childhood public health interventions: a systematic literature review
Authors: Murphy Peter , Hinde Sebastian , Fulbright Helen , Padgett Louise , Richardson Gerry
Published: December 2022

Introduction

Economic evaluation has an important role to play in the demonstration of value for money of early childhood public health interventions; however, concerns have been raised regarding their consistent application and relevance to commissioners. This systematic review of the literature therefore aims to collate the breadth of the existing economic evaluation evidence of these interventions and to identify the approaches adopted in the assessment of value.

Source of data

Recently published literature in Medline, EMBASE, EconLit, Health Management Information Consortium, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, Health Technology Assessment, NHS EED and Web of Science.

Areas of agreement

The importance of the early childhood period on future health and well-being as well as the potential to impact health inequalities making for a strong narrative case for expenditure in early childhood public health.

Areas of controversy

The most appropriate approaches to evaluating value for money of such preventative interventions relevant for UK decision-makers given the evident challenges.

Growing points

The presented review considered inconsistencies across methodological approaches used to demonstrate value for money. The results showed a mixed picture in terms of demonstrating value for money.

Areas timely for developing research

Future resource allocations decisions regarding early childhood public health interventions may benefit from consistency in the evaluative frameworks and health outcomes captured, as well as consistency in approaches to incorporating non-health costs and outcomes, incorporating equity concerns and the use of appropriate time horizons.
The Epidemiology of Long Coronavirus Disease in US Adults
Authors: Robertson McKaylee M , Qasmieh Saba A , Kulkarni Sarah G , Teasdale Chloe A , Jones Heidi E , McNairy Margaret , Borrell Luisa N , Nash Denis
Published: December 2022

Background

We estimated the prevalence of long COVID and impact on daily living among a representative sample of adults in the United States.

Methods

We conducted a population-representative survey, 30 June–2 July 2022, of a random sample of 3042 US adults aged 18 years or older and weighted to the 2020 US population. Using questions developed by the UK's Office of National Statistics, we estimated the prevalence of long COVID, by sociodemographics, adjusting for gender and age.

Results

An estimated 7.3% (95% confidence interval: 6.1–8.5%) of all respondents reported long COVID, corresponding to approximately 18 828 696 adults. One-quarter (25.3% [18.2–32.4%]) of respondents with long COVID reported their day-to-day activities were impacted “a lot” and 28.9% had severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection more than 12 months ago. The prevalence of long COVID was higher among respondents who were female (adjusted prevalence ratio [aPR]: 1.84 [1.40–2.42]), had comorbidities (aPR: 1.55 [1.19–2.00]), or were not (vs were) boosted (aPR: 1.67 [1.19–2.34]) or not vaccinated (vs boosted) (aPR: 1.41 [1.05–1.91]).

Conclusions

We observed a high burden of long COVID, substantial variability in prevalence of SARS-CoV-2, and risk factors unique from SARS-CoV-2 risk, suggesting areas for future research. Population-based surveys are an important surveillance tool and supplement to ongoing efforts to monitor long COVID.
Malware Family Prediction with an Awareness of Label Uncertainty
Authors: Paik Joon-Young , Jin Rize
Published: December 2022

Malware family prediction has been mainly formulated as a multiclass classification to predict one malware family. This approach suffers from label uncertainty, which can mislead malware analysts. To render malware prediction less susceptible to uncertainty, malware family prediction, which entails predicting one or more families, is performed in this study. In this regard, an encoder–decoder malware family prediction model, EnDePMal, with label uncertainty awareness, is proposed. EnDePMal aims to predict all malware families related to samples and preserve their priorities. It comprises a residual neural network-based encoder and a long short-term memory-based decoder with an attention mechanism. The model uses a sequence of malware family names, but not a family name, as a label. Once a visualized malware image is input into EnDePMal, its encoder extracts the important features from the image. Subsequently, its decoder generates family names, where the attention mechanism allows it to focus on relevant features by attending to the encoder’s output. Experimental results show that EnDePMal can predict 77.64% of malware family sequences that preserve their priorities. Moreover, it achieves an accuracy of 93.49% and an F1-score of 0.9282 for malware families with the highest priority, rendering it comparable to the typical multiclass classification model.
Predicting new-onset heart failure hospitalization of patients with atrial fibrillation: development and external validations of a risk score
Authors: Ishii Kai , Matsue Yuya , Miyauchi Katsumi , Miyazaki Sakiko , Hayashi Hidemori , Nishizaki Yuji , Nojiri Shuko , Saito Yuki , Nagashima Koichi , Okumura Yasuo , Daida Hiroyuki , Minamino Tohru
Published: December 2022

Aim

Atrial fibrillation (AF) is a well-known risk factor for heart failure (HF). We sought to develop and externally validate a risk model for new-onset HF admission in patients with AF and those without a history of HF.

Methods and results

Using two multicentre, prospective, observational AF registries, RAFFINE (2857 patients, derivation cohort) and SAKURA (2516 patients without a history of HF, validation cohort), we developed a risk model by selecting variables with regularized regression and weighing coefficients by Cox regression with the derivation cohort. External validity testing was used for the validation cohort. Overall, 148 (5.2%) and 104 (4.1%) patients in the derivation and validation cohorts, respectively, developed HF during median follow-ups of 1396 (interquartile range [IQR]: 1078–1820) and 1168 (IQR: 844–1309) days, respectively. In the derivation cohort, age, haemoglobin, serum creatinine, and log-transformed brain natriuretic peptide were identified as potential risk factors for HF development. The risk model showed good discrimination and calibration in both derivations (area under the curve [AUC]: 0.80 [95% confidence interval (CI) 0.76–0.84]; Hosmer–Lemeshow, P = 0.257) and validation cohorts (AUC: 0.78 [95%CI 0.74–0.83]; Hosmer–Lemeshow, P = 0.475).

Conclusion

The novel risk model with four readily available clinical characteristics and biomarkers performed well in predicting new-onset HF admission in patients with AF.
Mitral repair of myxomatous valves with simple annuloplasty: a follow-up up to 12 years
Authors: Folino Giulio , Salica Andrea , Scaffa Raffaele , Irace Francesco Giosuè , Weltert Luca Paolo , Bellisario Alessandro , Gerosa Gino , De Paulis Ruggero
Published: December 2022

OBJECTIVES

Diffuse myxomatous mitral valve degeneration (DMD) represents a challenge in the reparative mitral valve surgery. A subgroup of patients with symmetrical DMD can be effectively treated with a simple band-annuloplasty with good early and mid-term results. Here, we evaluate the long-term outcomes in terms of freedom from reoperation, recurrence of moderate or severe mitral regurgitation (MR) and overall survival.

METHODS

Between April 2006 and December 2020, patients with DMD causing severe MR and the echocardiographic features of symmetrical bileaflet prolapse, central regurgitant jet(s), annular dilation and no chordal ruptures were treated using a simple annuloplasty with a semi-rigid band. These patients were prospectively collected and retrospectively analysed.

RESULTS

Seventy-five patients were enrolled. The mean clinical follow-up time was 104 [standard deviation (SD): 43] months, and echocardiographic follow-up time was 95 (SD: 43) months. The mean age was 54 (SD: 15) years, and 56% were females. Long-term overall survival was 98.2% [standard error (SE): 1.8], 93.7% (SE: 4.7) and 93.7% (SE: 4.7) at 4, 8 and 12 years, respectively. The freedom from reoperation was 100% at 4 and 8 years and 94.1% (SE: 5.7) at 12 years. The freedom from recurrent moderate or severe MR was 98.3% (SE: 1.7), 98.3% (SE: 1.7) and 92.8% (SE: 5.5) at 4, 8 and 12 years, respectively.

CONCLUSIONS

Mitral repair with the simple band-annuloplasty for the treatment of MR due to symmetrical DMD seems to be stable and effective in the long term.
Acute aortic dissection: evidence, uncertainties, and future therapies
Authors: Rylski Bartosz , Schilling Oliver , Czerny Martin
Published: December 2022

Remarkable progress has become especially apparent in aortic medicine in the last few decades, leading to essential changes in how thoracic aortic dissection is understood and treated. This state-of-the-art review article addresses the mechanisms of acute aortic dissection, explaining the role of its primary entry location, proximal, and distal dissection extension in their clinical presentation and impact on the decision-making process towards the best treatment approach. The latest evidence on novel treatment methods for acute aortic syndromes is presented, and the diverse dissection classification systems that remain uncertain are discussed, which reveals the need for shared terminology and more clarity. Finally, future aspects are discussed in treating acute aortic dissection, such as the endovascular treatment of aortic dissection Type A and biomarkers for acute aortic syndromes.