VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome.

VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.

Genetic landscape of human mitochondrial genome using whole-genome sequencing.

Increasing evidences suggest that mitochondrial dysfunction is implicated in diseases and aging, and whole-genome sequencing (WGS) is the most unbiased method in analyzing the mitochondrial genome (mtDNA). However, the genetic landscape of mtDNA in the Chinese population has not been fully examined. Here, we described the genetic landscape of mtDNA using WGS data from Chinese individuals (n = 3241). We identified 3892 mtDNA variants, of which 3349 (86%) were rare variants. Interestingly, we observed a trend toward extreme heterogeneity of mtDNA variants. Our study observed a distinct purifying selection on mtDNA, which inhibits the accumulation of harmful heteroplasmies at the individual level: (1) mitochondrial dN/dS ratios were much <1; (2) the dN/dS ratio of heteroplasmies was higher than homoplasmies; (3) heteroplasmies had more indels and predicted deleterious variants than homoplasmies. Furthermore, we found that haplogroup M (20.27%) and D (20.15%) had the highest frequencies in the Chinese population, followed by B (18.51%) and F (16.45%). The number of variants per individual differed across haplogroup groups, with a higher number of homoplasmies for the M lineage. Meanwhile, mtDNA copy number was negatively correlated with age but positively correlated with the female sex. Finally, we developed an mtDNA variation database of Chinese populations called MTCards (http://genemed.tech/mtcards/) to facilitate the query of mtDNA variants in this study. In summary, these findings contribute to different aspects of understanding mtDNA, providing a better understanding of the genetic basis of mitochondrial-related diseases.

Anisotropic fluid flows in black phosphorus nanochannels.

With the development of advanced micro/nanoscale technologies, two-dimensional materials have emerged from laboratories and have been applied in practice. To investigate the mechanisms of solid-liquid interactions in potential applications, molecular dynamics simulations are employed to study the flow behavior of n-dodecane (C12) molecules confined in black phosphorus (BP) nanochannels. Under the same external conditions, a significant difference in the velocity profiles of fluid molecules is observed when flowing along the armchair and zigzag directions of the BP walls. The average velocity of C12 molecules flowing along the zigzag direction is 9-fold higher than that along the armchair direction. The friction factor at the interface between C12 molecules and BP nanochannels and the orientations of C12 molecules near the BP walls are analyzed to explain the differences in velocity profiles under various flow directions, external driving forces, and nanochannel widths. The result shows that most C12 molecules are oriented parallel to the flow direction along the zigzag direction, leading to a relatively smaller friction factor hence a higher average velocity. In contrast, along the armchair direction, most C12 molecules are oriented perpendicular to the flow direction, leading to a relatively larger friction factor and thus a lower average velocity. This work provides important insights into understanding the anisotropic liquid flows in nanochannels.

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction.

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

Pentiptycene-based ladder polymers with configurational free volume for enhanced gas separation performance and physical aging resistance.

Polymers of intrinsic microporosity (PIMs) have shown promise in pushing the limits of gas separation membranes, recently redefining upper bounds for a variety of gas pair separations. However, many of these membranes still suffer from reductions in permeability over time, removing the primary advantage of this class of polymer. In this work, a series of pentiptycene-based PIMs incorporated into copolymers with PIM-1 are examined to identify fundamental structure-property relationships between the configuration of the pentiptycene backbone and its accompanying linear or branched substituent group. The incorporation of pentiptycene provides a route to instill a more permanent, configuration-based free volume, resistant to physical aging via traditional collapse of conformation-based free volume. PPIM-ip-C and PPIM-np-S, copolymers with C- and S-shape backbones and branched isopropoxy and linear n-propoxy substituent groups, respectively, each exhibited initial separation performance enhancements relative to PIM-1. Additionally, aging-enhanced gas permeabilities were observed, a stark departure from the typical permeability losses pure PIM-1 experiences with aging. Mixed-gas separation data showed enhanced CO2/CH4 selectivity relative to the pure-gas permeation results, with only ∼20% decreases in selectivity when moving from a CO2 partial pressure of ∼2.4 to ∼7.1 atm (atmospheric pressure) when utilizing a mixed-gas CO2/CH4 feed stream. These results highlight the potential of pentiptycene's intrinsic, configurational free volume for simultaneously delivering size-sieving above the 2008 upper bound, along with exceptional resistance to physical aging that often plagues high free volume PIMs.

Analytical model of optical force on supercavitating plasmonic nanoparticles.

Optical manipulation of nanoparticles (NPs) in liquid has garnered increasing interest for various applications, ranging from biological systems to nanofabrication. A plane wave as an optical source has recently been shown to be capable of pushing or pulling an NP when the NP is encapsulated by a nanobubble (NB) in water. However, the lack of an accurate model to describe the optical force on NP-in-NB systems hinders a comprehensive understanding of NP motion mechanisms. In this study, we present an analytical model using vector spherical harmonics to accurately capture the optical force and the resultant trajectory of an NP in an NB. We test the developed model using a solid Au NP as an example. By visualizing the vector field line of the optical force, we reveal the possible moving paths of the NP in the NB. This study can provide valuable insights for designing experiments to manipulate supercaviting NPs using plane waves.

Effect of side-chain π-π stacking on the thermal conductivity switching in azobenzene polymers: a molecular dynamics simulation study.

The light switchable thermal conductivity displayed by some polymers makes them promising for applications like data storage, temperature regulation and light switchable devices. In this study, the mechanism of thermal conductivity switching in poly[6-(4-phenyldiazenyl phenoxy)hexyl metharylate] is studied using molecular dynamics (MD) simulations. The π-π stacking and amorphous polymer structures are specifically prepared through different simulation procedures, and the thermal conductivity of these structures is calculated. It is found that due to the π-π stacking structure, the thermal conductivity along the side-chain direction can change by 30-50% (from 0.34 to 0.51 W m-1 K-1). Through heat flux decomposition, it is found that the thermal conductivity change is dominated by the contribution from bonding interactions. This is because π-π stacking, which enforces the trans conformation, extends the side-chains of azobenzene polymers, making thermal transport in the side-chain direction more efficient. Along the polymer main-chain direction, the thermal conductivity is not affected by the π-π stacking of the side chains. This mechanism may be generalized to other types of polymers with azobenzene side-chains, which will develop a class of photo-responsive polymers.

Molecular understanding of the effect of hydrogen on graphene growth by plasma-enhanced chemical vapor deposition.

Plasma-enhanced chemical vapor deposition (PECVD) provides a low-temperature, highly-efficient, and catalyst-free route to fabricate graphene materials by virtue of the unique properties of plasma. In this paper, we conduct reactive molecular dynamics simulations to theoretically study the detailed growth process of graphene by PECVD at the atomic scale. Hydrocarbon radicals with different carbon/hydrogen (C/H) ratios are employed as dissociated precursors in the plasma environment during the growth process. The simulation results show that hydrogen content in the precursors significantly affects the growth behavior and properties of graphene (e.g., the quality of obtained graphene, which is indicated by the number of hexagonal carbon rings formed in the graphene sheets). Moreover, increasing the content of hydrogen in the precursors is shown to reduce the growth rate of carbon clusters, and prevent the formation of curved carbon structures during the growth process. The findings provide a detailed understanding of the fundamental mechanisms regarding the effects of hydrogen on the growth of graphene in a PECVD process.

Gene4Denovo: an integrated database and analytic platform for de novo mutations in humans.

De novo mutations (DNMs) significantly contribute to sporadic diseases, particularly in neuropsychiatric disorders. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) provide effective methods for detecting DNMs and prioritizing candidate genes. However, it remains a challenge for scientists, clinicians, and biologists to conveniently access and analyse data regarding DNMs and candidate genes from scattered publications. To fill the unmet need, we integrated 580 799 DNMs, including 30 060 coding DNMs detected by WES/WGS from 23 951 individuals across 24 phenotypes and prioritized a list of candidate genes with different degrees of statistical evidence, including 346 genes with false discovery rates <0.05. We then developed a database called Gene4Denovo (http://www.genemed.tech/gene4denovo/), which allowed these genetic data to be conveniently catalogued, searched, browsed, and analysed. In addition, Gene4Denovo integrated data from >60 genomic sources to provide comprehensive variant-level and gene-level annotation and information regarding the DNMs and candidate genes. Furthermore, Gene4Denovo provides end-users with limited bioinformatics skills to analyse their own genetic data, perform comprehensive annotation, and prioritize candidate genes using custom parameters. In conclusion, Gene4Denovo conveniently allows for the accelerated interpretation of DNM pathogenicity and the clinical implication of DNMs in humans.

Optically Driven Gold Nanoparticles Seed Surface Bubble Nucleation in Plasmonic Suspension.

Photothermal surface bubbles play important roles in applications like microfluidics and biosensing, but their formation on transparent substrates immersed in a plasmonic nanoparticle (NP) suspension has an unknown origin. Here, we reveal NPs deposited on the transparent substrate by optical forces are responsible for the nucleation of such photothermal surface bubbles. We show the surface bubble formation is always preceded by the optically driven NPs moving toward and deposited to the surface. Interestingly, such optically driven motion can happen both along and against the photon stream. The laser power density thresholds to form a surface bubble drastically differ depending on if the surface is forward- or backward-facing the light propagation direction. We attributed this to different optical power densities needed to enable optical pulling and pushing of NPs in the suspension, as optical pulling requires higher light intensity to excite supercavitation around NPs to enable proper optical configuration.

Phonon Dephasing Dynamics in MoS2.

A variety of quantum degrees of freedom, e.g., spins, valleys, and localized emitters, in atomically thin van der Waals materials have been proposed for quantum information applications, and they inevitably couple to phonons. Here, we directly measure the intrinsic optical phonon decoherence in monolayer and bulk MoS2 by observing the temporal evolution of the spectral interference of Stokes photons generated by pairs of laser pulses. We find that a prominent optical phonon mode E2g exhibits a room-temperature dephasing time of ∼7 ps in both the monolayer and bulk. This dephasing time extends to ∼20 ps in the bulk crystal at ∼15 K, which is longer than previously thought possible. First-principles calculations suggest that optical phonons decay via two types of three-phonon processes, in which a pair of acoustic phonons with opposite momentum are generated.

PI1M: A Benchmark Database for Polymer Informatics.

Open-source data on large scale are the cornerstones for data-driven research, but they are not readily available for polymers. In this work, we build a benchmark database, called PI1M (referring to ∼1 million polymers for polymer informatics), to provide data resources that can be used for machine learning research in polymer informatics. A generative model is trained on ∼12 000 polymers manually collected from the largest existing polymer database PolyInfo, and then the model is used to generate ∼1 million polymers. A new representation for polymers, polymer embedding (PE), is introduced, which is then used to perform several polymer informatics regression tasks for density, glass transition temperature, melting temperature, and dielectric constants. By comparing the PE trained by the PolyInfo data and that by the PI1M data, we conclude that the PI1M database covers similar chemical space as PolyInfo, but significantly populate regions where PolyInfo data are sparse. We believe that PI1M will serve as a good benchmark database for future research in polymer informatics.

Increasing ammonia recovery from high-level ammonium wastewater via adding sodium sulfate to prevent nitrogen generation in the cathode.

The high-level ammonium-nitrogen (NH4+-N) is a contaminant for aqueous environment but a potential hydrogen fuel. This study investigated an approach of increasing ammonia recovery via adding sodium sulfate of 0-1.5 M to prevent from nitrogen generation. The results of experiment tests, electrochemical analysis and MD simulation demonstrated that the added Na2SO4 assisted ammonium transport inhibited nitrogen gas generation in a certain concentration range. In electric double layer (EDL), with Na2SO4 concentration increasing, both the migration velocities of NH4+ and Na+ are accelerated for Na2SO4 of 0-0.25 M, whereas they are decelerated for concentrate Na2SO4 that 0.5 M). A thick layer formed by Na+ that imposed a fierce competitive adsorption blocked the migration of NH4+ and the transportation of electrons. The decrease of electrons and the accumulation of water molecules caused the potential drop in the EDL. 0.25 M Na2SO4 was the optimal concentration from the aspect of ion transports. The results obtained in this study can allow the manipulation of EDI capacity optimization.

Identification of diagnostic biomarkers in patients with gestational diabetes mellitus based on transcriptome gene expression and methylation correlation analysis.

BACKGROUND: Gestational diabetes mellitus (GDM) has a high prevalence in the period of pregnancy. However, the lack of gold standards in current screening and diagnostic methods posed the biggest limitation. Regulation of gene expression caused by DNA methylation plays an important role in metabolic diseases. In this study, we aimed to screen GDM diagnostic markers, and establish a diagnostic model for predicting GDM. METHODS: First, we acquired data of DNA methylation and gene expression in GDM samples (N = 41) and normal samples (N = 41) from the Gene Expression Omnibus (GEO) database. After pre-processing the data, linear models were used to identify differentially expressed genes (DEGs). Then we performed pathway enrichment analysis to extract relationships among genes from pathways, construct pathway networks, and further analyzed the relationship between gene expression and methylation of promoter regions. We screened for genes which are significantly negatively correlated with methylation and established mRNA-mRNA-CpGs network. The network topology was further analyzed to screen hub genes which were recognized as robust GDM biomarkers. Finally, the samples were randomly divided into training set (N = 28) and internal verification set (N = 27), and the support vector machine (SVM) ten-fold cross-validation method was used to establish a diagnostic classifier, which verified on internal and external data sets. RESULTS: In this study, we identified 465 significant DEGs. Functional enrichment analysis revealed that these genes were associated with Type I diabetes mellitus and immunization. And we constructed an interactional network including 1091 genes by using the regulatory relationships of all 30 enriched pathways. 184 epigenetics regulated genes were screened by analyzing the relationship between gene expression and promoter regions' methylation in the network. Moreover, the accuracy rate in the training data set was increased up to 96.3, and 82.1% in the internal validation set, and 97.3% in external validation data sets after establishing diagnostic classifiers which were performed by analyzing the gene expression profiles of obtained 10 hub genes from this network, combined with SVM. CONCLUSIONS: This study provided new features for the diagnosis of GDM and may contribute to the diagnosis and personalized treatment of GDM.

Evaluating Polymer Representations via Quantifying Structure-Property Relationships.

Machine learning techniques are being applied in quantifying structure-property relationships for a wide variety of materials, where the properly represented materials play key roles. Although algorithms for representation learning are extensively studied, their applications to domain-specific areas, such as polymers, are limited largely due to the lack of benchmark databases. In this work, we investigate different types of polymer representations, including Morgan fingerprint (MF), molecular embedding (ME), and molecular graph (MG), based on the benchmark database from a subset of the well-known web-based polymer databases, PolyInfo. We evaluate the quality of different polymer representations via quantifying the relationships between the representations and polymer properties, including density, melting temperature, and glass transition temperature. Different representation learning schemes for MEs, such as supervised learning, semisupervised learning, and transfer learning, are investigated. In supervised learning, only labeled molecules in our benchmark database are used for representation learning, in semisupervised learning, both labeled and unlabeled molecules in our benchmark database are used, and in transfer learning, molecules from an external database that is different from the benchmark database are used for representation learning. It is found that ME (with the R2 of 0.724 in the density case, 0.684 in the melting temperature case, and 0.865 in the glass transition temperature case) outperforms the other representations for structure-property relationship quantification in all cases studied, and MG (with the R2 of 0.260 in the density case, -0.149 in the melting temperature case, and 0.711 in the glass transition case) is shown to be much inferior to ME and MF (with the R2 of 0.562 in the density case, 0.645 in the melting temperature case, and 0.849 in the glass transition case), likely due to the relatively small volumes of training data available. For MEs, it is found that the similarities of substructure MEs under different learning schemes (e.g., SL, SSL, and TL) are differently estimated, thus leading to different performance scores in structure-property relation quantification. Combinations of MEs show little effect on predictive performance when comparing to the single MEs in the corresponding regression tasks, proving no information gain in mixing MEs.

Chain length effect on thermal transport in amorphous polymers and a structure-thermal conductivity relation.

The physics of thermal transport in polymers is important in many applications, such as in heat exchangers and electronics packaging. Even though thermal conductivity models for amorphous polymers have been reported since the 1970s, none of the published models included the chain conformation and chain stiffness effects. In this study, we use molecular dynamics (MD) simulations to study the chain length effect on thermal conductivity of amorphous polyethylene (PE), and the number of repeating C2H4 units ranges from 5 to 200. The total thermal conductivity is decomposed into its contributions from energy convection (k-convection), and heat transfer through nonbonding (k-nonbonding) and bonding (k-bonding) interactions. Each part of the contributions is fitted empirically by using a scaling relationship: k-convection (Einstein's diffusion coefficient model), k-nonbonding ∝ n (Choy's model) and k-bonding (from this study), where Rg is the radius of gyration, n is the number density, and ξ is the persistence length. Summarizing these three components, we emphasize the chain conformation (Rg) and chain stiffness (ξ) effects on thermal conductivity, and we propose a structure-property relation model for amorphous polymers. Our empirical model is compared with Hansen's experimental data [D. Hansen, R. Kantayya and C. Ho, Polym. Eng. Sci., 1966, 6, 260-262] and with our MD results. Our empirical model relies on realistic structural properties to enable accurate predictions. We believe that our model has captured some key structure-property relations in amorphous polymers.

Effect of light atoms on thermal transport across solid-solid interfaces.

Thermal transport across solid interfaces is of great importance for applications like power electronics. In this work, we perform non-equilibrium molecular dynamics simulations to study the effect of light atoms on the thermal transport across SiC/GaN interfaces, where light atoms refer to substitutional or interstitial defect atoms lighter than those in the pristine lattice. Various light atom doping features, such as the light atom concentration, mass of the light atom, and skin depth of the doped region, have been investigated. It is found that substituting Ga atoms in the GaN lattice with lighter atoms (e.g. boron atoms) with 50% concentration near the interface can increase the thermal boundary conductance (TBC) by up to 50%. If light atoms are introduced interstitially, a similar increase in TBC is observed. Spectral analysis of interfacial heat transfer reveals that the enhanced TBC can be attributed to the stronger coupling of mid- and high-frequency phonons after introducing light atoms. We have also further included quantum correction, which reduces the amount of enhancement, but it still exists. These results may provide a route to improve TBC across solid interfaces as light atoms can be introduced during material growth.

Thermal Boundary Conductance Across Heteroepitaxial ZnO/GaN Interfaces: Assessment of the Phonon Gas Model.

We present experimental measurements of the thermal boundary conductance (TBC) from 78-500 K across isolated heteroepitaxially grown ZnO films on GaN substrates. This data provides an assessment of the underlying assumptions driving phonon gas-based models, such as the diffuse mismatch model (DMM), and atomistic Green's function (AGF) formalisms used to predict TBC. Our measurements, when compared to previous experimental data, suggest that TBC can be influenced by long wavelength, zone center modes in a material on one side of the interface as opposed to the '"vibrational mismatch"' concept assumed in the DMM; this disagreement is pronounced at high temperatures. At room temperature, we measure the ZnO/GaN TBC as 490[+150,-110] MW m-2 K-1. The disagreement among the DMM and AGF, and the experimental data at elevated temperatures, suggests a non-negligible contribution from other types of modes that are not accounted for in the fundamental assumptions of these harmonic based formalisms, which may rely on anharmonicity. Given the high quality of these ZnO/GaN interfaces, these results provide an invaluable, critical, and quantitative assessment of the accuracy of assumptions in the current state of the art computational approaches used to predict phonon TBC across interfaces.

Exergy Analysis of Directional Solvent Extraction Desalination Process.

This paper presents an exergy analysis to evaluate the performance of a continuous directional solvent extraction (DSE) desalination process using octanoic acid. The flow of exergy was calculated for each thermodynamic state and balanced for different components of the system to quantify the inefficiencies in the process. A parametric study was performed to evaluate the impact of three critical design variables on exergy consumption. The parametric study reveals that the total exergy input decreases significantly with an increase in heat exchanger effectiveness. The results also indicate that the heat exchangers account for the highest exergy destruction. The total exergy consumption, however, has a slightly declining trend as the recovery-ratio increases. There is a small variation in the total exergy consumption, within the uncertainty of the calculation, as the highest process temperature increases. When compared to conventional desalination processes, the exergy consumption of the DSE, with heat recovery of 90%, is comparable to those of multi-stage flashing (MSF), but much higher than reverse osmosis (RO). Octanoic acid, which has low product water yield, is identified as the primary factor negatively impacting the exergy consumptions. To exploit the low-grade and low-temperature heat source feature of the DSE process, directional solvents with higher yield should be identified or designed to enable its full implementation.

The effect of the block ratio on the thermal conductivity of amorphous polyethylene-polypropylene (PE-PP) diblock copolymers.

Block copolymers have a wide range of applications, such as battery electrolytes and nanoscale pattern generation. The thermal conductivity is a critical parameter in many of these applications (e.g., batteries), which is strongly related to the molecular conformation. In this work, the thermal transport in a representative diblock copolymer, polyethylene (PE)-polypropylene (PP), at different PE to PP block ratios is studied using molecular dynamics (MD) simulations. Our results show that the thermal conductivity of the PE-PP diblock copolymer can be tuned continuously by the block ratio, and it is strongly related to the molecular conformation, characterized by the radius of gyration (Rg). It is found that increasing the PP portion results in an overall decreasing trend in the thermal conductivity since the PP block has a more flexible backbone, which leads to a smaller spatial extension of the whole PE-PP copolymer molecule. Thermal conductivity decomposition shows that the bonding contribution is dominant in both the PE and PP portions of the block copolymer. The findings from this study can help understand thermal transport in general block copolymers.