Mapping Local and Global Liquid Phase Behavior in Living Cells Using Photo-Oligomerizable Seeds.

Liquid-liquid phase separation plays a key role in the assembly of diverse intracellular structures. However, the biophysical principles by which phase separation can be precisely localized within subregions of the cell are still largely unclear, particularly for low-abundance proteins. Here, we introduce an oligomerizing biomimetic system, "Corelets," and utilize its rapid and quantitative light-controlled tunability to map full intracellular phase diagrams, which dictate the concentrations at which phase separation occurs and the transition mechanism, in a protein sequence dependent manner. Surprisingly, both experiments and simulations show that while intracellular concentrations may be insufficient for global phase separation, sequestering protein ligands to slowly diffusing nucleation centers can move the cell into a different region of the phase diagram, resulting in localized phase separation. This diffusive capture mechanism liberates the cell from the constraints of global protein abundance and is likely exploited to pattern condensates associated with diverse biological processes. VIDEO ABSTRACT.

Molecular organization of the early stages of nucleosome phase separation visualized by cryo-electron tomography.

It has been proposed that the intrinsic property of nucleosome arrays to undergo liquid-liquid phase separation (LLPS) in vitro is responsible for chromatin domain organization in vivo. However, understanding nucleosomal LLPS has been hindered by the challenge to characterize the structure of the resulting heterogeneous condensates. We used cryo-electron tomography and deep-learning-based 3D reconstruction/segmentation to determine the molecular organization of condensates at various stages of LLPS. We show that nucleosomal LLPS involves a two-step process: a spinodal decomposition process yielding irregular condensates, followed by their unfavorable conversion into more compact, spherical nuclei that grow into larger spherical aggregates through accretion of spinodal materials or by fusion with other spherical condensates. Histone H1 catalyzes more than 10-fold the spinodal-to-spherical conversion. We propose that this transition involves exposure of nucleosome hydrophobic surfaces causing modified inter-nucleosome interactions. These results suggest a physical mechanism by which chromatin may transition from interphase to metaphase structures.

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as-to our knowledge-no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

Exchangeable manganese regulates carbon storage in the humus layer of the boreal forest.

The huge carbon stock in humus layers of the boreal forest plays a critical role in the global carbon cycle. However, there remains uncertainty about the factors that regulate below-ground carbon sequestration in this region. Notably, based on evidence from two independent but complementary methods, we identified that exchangeable manganese is a critical factor regulating carbon accumulation in boreal forests across both regional scales and the entire boreal latitudinal range. Moreover, in a novel fertilization experiment, manganese addition reduced soil carbon stocks, but only after 4 y of additions. Our results highlight an underappreciated mechanism influencing the humus carbon pool of boreal forests.

General reversal of N-decomposition relationship during long-term decomposition in boreal and temperate forests.

Decomposition of dead organic matter is fundamental to carbon (C) and nutrient cycling in terrestrial ecosystems, influencing C fluxes from the biosphere to the atmosphere. Theory predicts and evidence strongly supports that the availability of nitrogen (N) limits litter decomposition. Positive relationships between substrate N concentrations and decomposition have been embedded into ecosystem models. This decomposition paradigm, however, relies on data mostly from short-term studies analyzing controls on early-stage decomposition. We present evidence from three independent long-term decomposition investigations demonstrating that the positive N-decomposition relationship is reversed and becomes negative during later stages of decomposition. First, in a 10-y decomposition experiment across 62 woody species in a temperate forest, leaf litter with higher N concentrations exhibited faster initial decomposition rates but ended up a larger recalcitrant fraction decomposing at a near-zero rate. Second, in a 5-y N-enrichment experiment of two tree species, leaves with experimentally enriched N concentrations had faster decomposition initial rates but ultimately accumulated large slowly decomposing fractions. Measures of amino sugars on harvested litter in two experiments indicated that greater accumulation of microbial residues in N-rich substrates likely contributed to larger slowly decomposing fractions. Finally, a database of 437 measurements from 120 species in 45 boreal and temperate forest sites confirmed that higher N concentrations were associated with a larger slowly decomposing fraction. These results challenge the current treatment of interactions between N and decomposition in many ecosystems and Earth system models and suggest that even the best-supported short-term controls of biogeochemical processes might not predict long-term controls.

Resolving the overlooked photochemical nitrophenol transformation mechanism induced by nonradical species under visible light.

Nitrophenols present on the surface of particulates are ubiquitous in the atmosphere. However, its atmospheric photochemical transformation pathway remains unknown, for which the crucial effect of visible light is largely overlooked, resulting in an incomplete understanding of the effects of nitrophenols in the atmospheric environment. This study delves into the photolysis mechanism of 4-nitrophenol (4NP), one of the most abundant atmospheric nitrophenol compounds, on the surface of photoactive particulates under visible light irradiation. Unexpectedly, the nonradical species (singlet oxygen, 1O2) was identified as a dominant factor in driving the visible photolysis of 4NP. The pathways of HONO and p-benzoquinone (C6H4O2) generation were clarified by acquiring direct evidence of C-N and O-H bond breakage in the nitro (-NO2) and hydroxyl (-OH) groups of 4NP. The further decomposition of HONO results in the generation of NO and hydroxyl radicals, which could directly contribute to atmospheric oxidizing capacity and complicate the PM2.5 composition. Significantly, the behavior of 1O2-induced visible photolysis of 4NP was universal on the surface of common particulates in the atmosphere, such as A1 dust and Fe2O3. This work advances the understanding of the photochemical transformation mechanism of particulate-phase atmospheric nitrophenols, which is indispensable in elucidating the role of nitrophenols in atmospheric chemistry.

Biodiversity mitigates drought effects in the decomposer system across biomes.

Multiple facets of global change affect the earth system interactively, with complex consequences for ecosystem functioning and stability. Simultaneous climate and biodiversity change are of particular concern, because biodiversity may contribute to ecosystem resistance and resilience and may mitigate climate change impacts. Yet, the extent and generality of how climate and biodiversity change interact remain insufficiently understood, especially for the decomposition of organic matter, a major determinant of the biosphere-atmosphere carbon feedbacks. With an inter-biome field experiment using large rainfall exclusion facilities, we tested how drought, a common prediction of climate change models for many parts of the world, and biodiversity in the decomposer system drive decomposition in forest ecosystems interactively. Decomposing leaf litter lost less carbon (C) and especially nitrogen (N) in five different forest biomes following partial rainfall exclusion compared to conditions without rainfall exclusion. An increasing complexity of the decomposer community alleviated drought effects, with full compensation when large-bodied invertebrates were present. Leaf litter mixing increased diversity effects, with increasing litter species richness, which contributed to counteracting drought effects on C and N loss, although to a much smaller degree than decomposer community complexity. Our results show at a relevant spatial scale covering distinct climate zones that both, the diversity of decomposer communities and plant litter in forest floors have a strong potential to mitigate drought effects on C and N dynamics during decomposition. Preserving biodiversity at multiple trophic levels contributes to ecosystem resistance and appears critical to maintain ecosystem processes under ongoing climate change.

KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.

Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.

SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.

Tensor-based insights into systems immunity and infectious disease.

Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.

The role of dynamics in heterogeneous catalysis: Surface diffusivity and N2 decomposition on Fe(111).

Dynamics has long been recognized to play an important role in heterogeneous catalytic processes. However, until recently, it has been impossible to study their dynamical behavior at industry-relevant temperatures. Using a combination of machine learning potentials and advanced simulation techniques, we investigate the cleavage of the N[Formula: see text] triple bond on the Fe(111) surface. We find that at low temperatures our results agree with the well-established picture. However, if we increase the temperature to reach operando conditions, the surface undergoes a global dynamical change and the step structure of the Fe(111) surface is destabilized. The catalytic sites, traditionally associated with this surface, appear and disappear continuously. Our simulations illuminate the danger of extrapolating low-temperature results to operando conditions and indicate that the catalytic activity can only be inferred from calculations that take dynamics fully into account. More than that, they show that it is the transition to this highly fluctuating interfacial environment that drives the catalytic process.

A quantum information processing machine for computing by observables.

A quantum machine that accepts an input and processes it in parallel is described. The logic variables of the machine are not wavefunctions (qubits) but observables (i.e., operators) and its operation is described in the Heisenberg picture. The active core is a solid-state assembly of small nanosized colloidal quantum dots (QDs) or dimers of dots. The size dispersion of the QDs that causes fluctuations in their discrete electronic energies is a limiting factor. The input to the machine is provided by a train of very brief laser pulses, at least four in number. The coherent band width of each ultrashort pulse needs to span at least several and preferably all the single electron excited states of the dots. The spectrum of the QD assembly is measured as a function of the time delays between the input laser pulses. The dependence of the spectrum on the time delays can be Fourier transformed to a frequency spectrum. This spectrum of a finite range in time is made up of discrete pixels. These are the visible, raw, basic logic variables. The spectrum is analyzed to determine a possibly smaller number of principal components. A Lie-algebraic point of view is used to explore the use of the machine to emulate the dynamics of other quantum systems. An explicit example demonstrates the considerable quantum advantage of our scheme.

Simultaneous clustering and estimation of networks in multiple graphical models.

Gaussian graphical models are widely used to study the dependence structure among variables. When samples are obtained from multiple conditions or populations, joint analysis of multiple graphical models are desired due to their capacity to borrow strength across populations. Nonetheless, existing methods often overlook the varying levels of similarity between populations, leading to unsatisfactory results. Moreover, in many applications, learning the population-level clustering structure itself is of particular interest. In this article, we develop a novel method, called Simultaneous Clustering and Estimation of Networks via Tensor decomposition (SCENT), that simultaneously clusters and estimates graphical models from multiple populations. Precision matrices from different populations are uniquely organized as a three-way tensor array, and a low-rank sparse model is proposed for joint population clustering and network estimation. We develop a penalized likelihood method and an augmented Lagrangian algorithm for model fitting. We also establish the clustering accuracy and norm consistency of the estimated precision matrices. We demonstrate the efficacy of the proposed method with comprehensive simulation studies. The application to the Genotype-Tissue Expression multi-tissue gene expression data provides important insights into tissue clustering and gene coexpression patterns in multiple brain tissues.

Logistic tensor decomposition with sparse subspace learning for prediction of multiple disease types of human-virus protein-protein interactions.

Viral infection involves a large number of protein-protein interactions (PPIs) between the virus and the host, and the identification of these PPIs plays an important role in revealing viral infection and pathogenesis. Existing computational models focus on predicting whether human proteins and viral proteins interact, and rarely take into account the types of diseases associated with these interactions. Although there are computational models based on a matrix and tensor decomposition for predicting multi-type biological interaction relationships, these methods cannot effectively model high-order nonlinear relationships of biological entities and are not suitable for integrating multiple features. To this end, we propose a novel computational framework, LTDSSL, to determine human-virus PPIs under different disease types. LTDSSL utilizes logistic functions to model nonlinear associations, sets importance levels to emphasize the importance of observed interactions and utilizes sparse subspace learning of multiple features to improve model performance. Experimental results show that LTDSSL has better predictive performance for both new disease types and new triples than the state-of-the-art methods. In addition, the case study further demonstrates that LTDSSL can effectively predict human-viral PPIs under various disease types.

LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

Cooperation of structural motifs controls drug selectivity in cyclin-dependent kinases: an advanced theoretical analysis.

Understanding drug selectivity mechanism is a long-standing issue for helping design drugs with high specificity. Designing drugs targeting cyclin-dependent kinases (CDKs) with high selectivity is challenging because of their highly conserved binding pockets. To reveal the underlying general selectivity mechanism, we carried out comprehensive analyses from both the thermodynamics and kinetics points of view on a representative CDK12 inhibitor. To fully capture the binding features of the drug-target recognition process, we proposed to use kinetic residue energy analysis (KREA) in conjunction with the community network analysis (CNA) to reveal the underlying cooperation effect between individual residues/protein motifs to the binding/dissociating process of the ligand. The general mechanism of drug selectivity in CDKs can be summarized as that the difference of structural cooperation between the ligand and the protein motifs leads to the difference of the energetic contribution of the key residues to the ligand. The proposed mechanisms may be prevalent in drug selectivity issues, and the insights may help design new strategies to overcome/attenuate the drug selectivity associated problems.

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series.

High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learning to the industrial context use as inputs extracted features, which are a manually and often arduously obtained compact representation of the original signal. In this paper, we propose a fully unsupervised deep-learning framework that is able to extract a meaningful and sparse representation of raw HF signals. We embed in our architecture important properties of the fast discrete wavelet transform (FDWT) such as 1) the cascade algorithm; 2) the conjugate quadrature filter property that links together the wavelet, the scaling, and transposed filter functions; and 3) the coefficient denoising. Using deep learning, we make this architecture fully learnable: Both the wavelet bases and the wavelet coefficient denoising become learnable. To achieve this objective, we propose an activation function that performs a learnable hard thresholding of the wavelet coefficients. With our framework, the denoising FDWT becomes a fully learnable unsupervised tool that does not require any type of pre- or postprocessing or any prior knowledge on wavelet transform. We demonstrate the benefits of embedding all these properties on three machine-learning tasks performed on open-source sound datasets. We perform an ablation study of the impact of each property on the performance of the architecture, achieve results well above baseline, and outperform other state-of-the-art methods.

Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide.

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecasting methodology and discuss its application to the production of global and regional risk maps.

High-Mg calcite nanoparticles within a low-Mg calcite matrix: A widespread phenomenon in biomineralization.

During the process of biomineralization, organisms utilize various biostrategies to enhance the mechanical durability of their skeletons. In this work, we establish that the presence of high-Mg nanoparticles embedded within lower-Mg calcite matrices is a widespread strategy utilized by various organisms from different kingdoms and phyla to improve the mechanical properties of their high-Mg calcite skeletons. We show that such phase separation and the formation of high-Mg nanoparticles are most probably achieved through spinodal decomposition of an amorphous Mg-calcite precursor. Such decomposition is independent of the biological characteristics of the studied organisms belonging to different phyla and even kingdoms but rather, originates from their similar chemical composition and a specific Mg content within their skeletons, which generally ranges from 14 to 48 mol % of Mg. We show evidence of high-Mg calcite nanoparticles in the cases of six biologically different organisms all demonstrating more than 14 mol % Mg-calcite and consider it likely that this phenomenon is immeasurably more prevalent in nature. We also establish the absence of these high-Mg nanoparticles in organisms whose Mg content is lower than 14 mol %, providing further evidence that whether or not spinodal decomposition of an amorphous Mg-calcite precursor takes place is determined by the amount of Mg it contains. The valuable knowledge gained from this biostrategy significantly impacts the understanding of how biominerals, although composed of intrinsically brittle materials, can effectively resist fracture. Moreover, our theoretical calculations clearly suggest that formation of Mg-rich nanoprecipitates greatly enhances the hardness of the biomineralized tissue as well.