Enhancing Magnetic Ordering in Two-Dimensional Metal-Organic Frameworks via Frontier Molecular Orbital Engineering.

Two-dimensional (2D) metal-organic frameworks (MOFs) have promise for use in lightweight permanent magnets in contrast to inorganic solid- or molecule-based magnets, but the realization of 2D MOF magnets with a high ordering temperature is limited by the typically weak spin exchange interactions. Here, we have proposed a frontier molecular orbital engineering strategy for modulating magnetism in 2D MOFs. It shows that the magnetic ground state can be mediated by two intra-atomic spin exchange pathways in organic ligands, akin to the Bloch and Heisenberg models, depending on the shape of the frontier orbitals of the organic ligands.

Current status and obstacles of narrowing yield gaps of four major crops.

Background: Maize, wheat, rice and soybean production are intimately linked to food security. Identifying the key factors affecting crop yields and determining the countries where increased irrigation and nitrogen application most effectively enhance yields are essential steps towards achieving sustainable development goals and ensuring food security. Identifying these areas is crucially dependent on yield gaps.

Interfacial Charge-Transfer Excitonic Insulator in a Two-Dimensional Organic-Inorganic Superlattice.

Excitonic insulators are long-sought-after quantum materials predicted to spontaneously open a gap by the Bose condensation of bound electron-hole pairs, namely, excitons, in their ground state. Since the theoretical conjecture, extensive efforts have been devoted to pursuing excitonic insulator platforms for exploring macroscopic quantum phenomena in real materials. Reliable evidence of excitonic character has been obtained in layered chalcogenides as promising candidates.

County-level intensity of carbon emissions from crop farming in China during 2000-2019.

Agriculture is an important contributor to global carbon emissions. With the implementation of the Sustainable Development Goals of the United Nations and China's carbon neutral strategy, accurate estimation of carbon emissions from crop farming is essential to reduce agricultural carbon emissions and promote sustainable food production systems in China. However, previous long-term time series estimates in China have mainly focused on the national and provincial levels, which are insufficient to characterize regional heterogeneity.

Unveiling diverse coordination-defined electronic structures of reconstructed anatase TiO(001)-(1 × 4) surface.

Transition metal oxides (TMOs) exhibit fascinating physicochemical properties, which originate from the diverse coordination structures between the transition metal and oxygen atoms. Accurate determination of such structure-property relationships of TMOs requires to correlate structural and electronic properties by capturing the global parameters with high resolution in energy, real, and momentum spaces, but it is still challenging. Herein, we report the determination of characteristic electronic structures from diverse coordination environments on the prototypical anatase-TiO(001) with (1 × 4) reconstruction, using high-resolution angle-resolved photoemission spectroscopy and scanning tunneling microscopy/atomic force microscopy, in combination with density functional theory calculation.

Prediction of protein-ligand binding affinity with deep learning.

The prediction of binding affinities between target proteins and small molecule drugs is essential for speeding up the drug research and design process. To attain precise and effective affinity prediction, computer-aided methods are employed in the drug discovery pipeline. In the last decade, a variety of computational methods has been developed, with deep learning being the most commonly used approach.

Hexagonal image segmentation on spatially resolved transcriptomics.

Spatial transcriptomics is a rapidly evolving field that enables researchers to capture comprehensive molecular profiles while preserving information about the physical locations. One major challenge in this research area involves the identification of spatial domains, which are distinct regions characterized by unique gene expression patterns. However, current unsupervised methods have struggled to perform well in this regard due to the presence of high levels of noise and dropout events in spatial transcriptomic profiles.

Reassessment of growth-climate relations indicates the potential for decline across Eurasian boreal larch forests.

Larch, a widely distributed tree in boreal Eurasia, is experiencing rapid warming across much of its distribution. A comprehensive assessment of growth on warming is needed to comprehend the potential impact of climate change. Most studies, relying on rigid calendar-based temperature series, have detected monotonic responses at the margins of boreal Eurasia, but not across the region.

Two-Dimensional Iron Silicide (FeSi) Alloys with Above-Room-Temperature Ferromagnetism.

Two-dimensional (2D) materials with intrinsic room-temperature ferromagnetism have gathered tremendous interest as promising candidates for next-generation spintronics. Here, on the basis of first-principles calculations, we report a family of stable 2D iron silicide (FeSi) alloys via dimensional reduction of their bulk counterparts. Our results demonstrate that 2D FeSi-hex, FeSi-orth, FeSi, and FeSi nanosheets are lattice-dynamically and thermally stable, confirmed by the calculated phonon spectra and Born-Oppenheimer dynamic simulation up to 1000 K.

ContactLib-ATT: A Structure-Based Search Engine for Homologous Proteins.

General-purpose protein structure embedding can be used for many important protein biology tasks, such as protein design, drug design and binding affinity prediction. Recent researches have shown that attention-based encoder layers are more suitable to learn high-level features. Based on this key observation, we propose a two-level general-purpose protein structure embedding neural network, called ContactLib-ATT.

Hydrogen-Bond Network Promotes Water Splitting on the TiO Surface.

Breaking the strong covalent O-H bond of an isolated HO molecule is difficult, but it can be largely facilitated when the HO molecule is connected with others through hydrogen-bonding. How a hydrogen-bond network forms and performs becomes crucial for water splitting in natural photosynthesis and artificial photocatalysis and is awaiting a microscopic and spectroscopic understanding at the molecular level. At the prototypical photocatalytic HO/anatase-TiO(001)-(1×4) interface, we report the hydrogen-bond network can promote the coupled proton and hole transfer for water splitting.

VP-Detector: A 3D multi-scale dense convolutional neural network for macromolecule localization and classification in cryo-electron tomograms.

Background And Objective: Cryo-electron tomography (cryo-ET) with subtomogram averaging (STA) is indispensable when studying macromolecule structures and functions in their native environments. Due to the low signal-to-noise ratio, the missing wedge artifacts in tomographic reconstructions, and multiple macromolecules of varied shapes and sizes, macromolecule localization and classification remain challenging. To tackle this bottleneck problem for structural determination by STA, we design an accurate macromolecule localization and classification method named voxelwise particle detector (VP-Detector).

Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches.

The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up. Here, we introduce a deep learning method based on the Ontology-aware Neural Network approach, ONN4MST, for large-scale source tracking.

Time- and momentum-resolved image-potential states of 2H-MoS surface.

Rydberg-like image potential states (IPSs) form special series surface states on metal and semiconducting surfaces. Here, using time-resolved and momentum-resolved multi-photon photoemission (PPE), we measured the energy positions, band dispersion, and carrier lifetimes of IPSs at the 2H-MoS surface. The energy minima of the IPSs ( = 1 and 2) were located at 0.

DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network.

Analysis and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, as well as drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, while providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs.

Formation of Ultra-High Temperature Ceramic Hollow Microspheres as Promising Lightweight Thermal Insulation Materials via a Molten Salt-Assisted Template Method.

Ultra-high temperature ceramics (UHTCs) have become a vital candidate material system for thermal protection systems in aerospace applications. However, high thermal conductivity and high density are the main obstacles to the application of UHTCs. It is a promising solution to prepare porous UHTCs using UHTC hollow microspheres (HMs) as a pore-forming agent.

SVLR: Genome Structural Variant Detection Using Long-Read Sequencing Data.

Genome structural variants (SVs) have great impacts on human phenotype and diversity, and have been linked to numerous diseases. Long-read sequencing technologies arise to make it possible to find SVs of as long as 10,000 nucleotides. Thus, long read-based SV detection has been drawing attention of many recent research projects, and many tools have been developed for long reads to detect SVs recently.

Dilated-DenseNet For Macromolecule Classification In Cryo-electron Tomography.

Cryo-electron tomography (cryo-ET) combined with subtomogram averaging (STA) is a unique technique in revealing macromolecule structures in their near-native state. However, due to the macromolecular structural heterogeneity, low signal-to-noise-ratio (SNR) and anisotropic resolution in the tomogram, macromolecule classification, a critical step of STA, remains a great challenge. In this paper, we propose a novel convolution neural network, named 3D-Dilated-DenseNet, to improve the performance of macromolecule classification in STA.

Determining structural and chemical heterogeneities of surface species at the single-bond limit.

The structure determination of surface species has long been a challenge because of their rich chemical heterogeneities. Modern tip-based microscopic techniques can resolve heterogeneities from their distinct electronic, geometric, and vibrational properties at the single-molecule level but with limited interpretation from each. Here, we combined scanning tunneling microscopy (STM), noncontact atomic force microscopy (AFM), and tip-enhanced Raman scattering (TERS) to characterize an assumed inactive system, pentacene on the Ag(110) surface.

Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology.

Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.

Formation of Plasmonic Polarons in Highly Electron-Doped Anatase TiO.

The existence of various quasiparticles of polarons because of electron-boson couplings plays important roles in determining electron transport in titanium dioxide (TiO), which affects a wealth of physical properties from catalysis to interfacial superconductivity. In addition to the well-defined Fröhlich polarons whose electrons are dressed by the phonon clouds, it has been theoretically predicted that electrons can also couple to their own plasmonic oscillations, namely, the plasmonic polarons. Here we experimentally demonstrate the formation of plasmonic polarons in highly doped anatase TiO using angle-resolved photoemission spectroscopy.

Creation of the Dirac Nodal Line by Extrinsic Symmetry Engineering.

The formation of the Dirac nodal line (DNL) requires intrinsic symmetry that can protect the degeneracy of continuous Dirac points in momentum space. Here, as an alternative approach, we propose an extrinsic symmetry protected DNL. On the basis of symmetry analysis and numerical calculations, we establish a general principle to design the nonsymmorphic symmetry protected 4-fold degenerate DNL against spin-orbit coupling in the nanopatterned 2D electron gas.

Interfacial Hydrogen-Bonding Dynamics in Surface-Facilitated Dehydrogenation of Water on TiO(110).

Molecular-level understanding of the dehydrogenation of interfacial water molecules on metal oxides and their interactive nature relies on the ability to track the motion of light and small hydrogen atoms, which is known to be difficult. Here, we report precise measurements of the surface-facilitated water dehydrogenation process at terminal Ti sites of TiO(110) using scanning tunneling microscopy. Our measured hydrogen-bond dynamics of HO and DO reveal that the vibrational and electronic excitations dominate the sequential transfer of two H (D) atoms from a HO (DO) molecule to adjacent surface oxygen sites, manifesting the active participation of the oxide surface in the dehydrogenation processes.