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. In this paper, we propose a novel hexagonal Convolutional Neural Network (hexCNN) for hexagonal image segmentation on spatially resolved transcriptomics. To address the problem of noise and dropout occurrences within spatial transcriptomics data, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Then, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent spots. We evaluated the performance of hexCNN by applying it to the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 86.8% and an average Rand index (ARI) of 77.1% (1.4% and 2.5% higher than those of GNNs). The results also demonstrate that hexCNN is capable of removing the noise caused by batch effect while preserving the biological signal differences.

Two-Dimensional Iron Silicide (FeSix) 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 (FeSix) alloys via dimensional reduction of their bulk counterparts. Our results demonstrate that 2D Fe4Si2-hex, Fe4Si2-orth, Fe3Si2, and FeSi2 nanosheets are lattice-dynamically and thermally stable, confirmed by the calculated phonon spectra and Born-Oppenheimer dynamic simulation up to 1000 K. 2D FeSix nanosheets are ferromagnetic metals with estimated Curie temperatures ranging from 547 to 971 K due to strong direct exchange interaction between Fe sites. In addition, the electronic properties of 2D FeSix alloys can be maintained on silicon substrates, providing an ideal platform for spintronics applications in the nanoscale.

Hydrogen-Bond Network Promotes Water Splitting on the TiO2 Surface.

Breaking the strong covalent O-H bond of an isolated H2O molecule is difficult, but it can be largely facilitated when the H2O 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 H2O/anatase-TiO2(001)-(1×4) interface, we report the hydrogen-bond network can promote the coupled proton and hole transfer for water splitting. The formation of a hydrogen-bond network is controlled by precisely tuning the coverage of water to above one monolayer. Under ultraviolet (UV) light irradiation, the hydrogen-bond network opens a cascaded channel for the transfer of a photoexcited hole, concomitant with the release of the proton to form surface hydroxyl groups. The yielded hydroxyl groups provide excess electrons to the TiO2 surface, causing the reduction of Ti4+ to Ti3+ and leading to the emergence of gap states, as monitored by in situ UV/X-ray photoelectron spectroscopy. The density functional theory calculation reveals that the water splitting becomes an exothermic process through hole oxidation with the assistance of the hydrogen-bond network. In addition to the widely concerned exotic activity from photocatalysts, our study demonstrates the internal hydrogen-bond network, which is ubiquitous at practical aqueous/catalyst interfaces, is also indispensable for water splitting.

Formation of Plasmonic Polarons in Highly Electron-Doped Anatase TiO2.

The existence of various quasiparticles of polarons because of electron-boson couplings plays important roles in determining electron transport in titanium dioxide (TiO2), 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 TiO2 using angle-resolved photoemission spectroscopy. Our results show that the energy separation of plasmon-loss satellites follows a dependence on √n, where n is the electron density, manifesting the characteristic of plasmonic polarons. The spectral functions enable to quantitatively evaluate the strengths of electron-plasmon and electron-phonon couplings, respectively, providing an effective approach for characterizing the interplays among different bosonic modes in the complicate many-body interactions.

Time- and momentum-resolved image-potential states of 2H-MoS2 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 (mPPE), we measured the energy positions, band dispersion, and carrier lifetimes of IPSs at the 2H-MoS2 surface. The energy minima of the IPSs (n = 1 and 2) were located at 0.77 and 0.21 eV below the vacuum level. In addition, the effective masses of these two IPSs are close to the rest mass of the free electron, clearly showing nearly-free-electron character. These properties suggest a good screening effect in the MoS2 parallel to the surface. The multi-photon resonances between the valence band and IPS (n = 1) are observed, showing a k‖-momentum-dependent behavior. Our time-resolved mPPE measurements show that the lifetime of photoexcited electrons in the IPS (n = 1) is about 33 fs.

H-NS uses an autoinhibitory conformational switch for environment-controlled gene silencing.

As an environment-dependent pleiotropic gene regulator in Gram-negative bacteria, the H-NS protein is crucial for adaptation and toxicity control of human pathogens such as Salmonella, Vibrio cholerae or enterohaemorrhagic Escherichia coli. Changes in temperature affect the capacity of H-NS to form multimers that condense DNA and restrict gene expression. However, the molecular mechanism through which H-NS senses temperature and other physiochemical parameters remains unclear and controversial. Combining structural, biophysical and computational analyses, we show that human body temperature promotes unfolding of the central dimerization domain, breaking up H-NS multimers. This unfolding event enables an autoinhibitory compact H-NS conformation that blocks DNA binding. Our integrative approach provides the molecular basis for H-NS-mediated environment-sensing and may open new avenues for the control of pathogenic multi-drug resistant bacteria.

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. Furthermore, on the basis of experimental measurements, we demonstrate the approximate realization of our proposal in the Bi/Cu(111) system, in which a highly dispersive DNL is observed at the boundary of the Brillouin zone. We envision that the extrinsic symmetry engineering will greatly enhance the ability for artificially constructing the exotic topological bands in the future.

Interfacial Hydrogen-Bonding Dynamics in Surface-Facilitated Dehydrogenation of Water on TiO2(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 TiO2(110) using scanning tunneling microscopy. Our measured hydrogen-bond dynamics of H2O and D2O reveal that the vibrational and electronic excitations dominate the sequential transfer of two H (D) atoms from a H2O (D2O) molecule to adjacent surface oxygen sites, manifesting the active participation of the oxide surface in the dehydrogenation processes. Our results show that, at the stoichiometric Ti5c sites, individual H2O molecules are energetically less stable than the dissociative form, where a barrier is expected to be as small as approximately 70-120 meV on the basis of our experimental and theoretical results. Moreover, our results reveal that interfacial hydrogen bonds can effectively assist H atom transfer and exchange across the surface. The revealed quantitative hydrogen-bond dynamics provide a new atomistic mechanism for water interactions on metal oxides in general.

MRE: a web tool to suggest foreign enzymes for the biosynthesis pathway design with competing endogenous reactions in mind.

To rationally design a productive heterologous biosynthesis system, it is essential to consider the suitability of foreign reactions for the specific endogenous metabolic infrastructure of a host. We developed a novel web server, called MRE, which, for a given pair of starting and desired compounds in a given chassis organism, ranks biosynthesis routes from the perspective of the integration of new reactions into the endogenous metabolic system. For each promising heterologous biosynthesis pathway, MRE suggests actual enzymes for foreign metabolic reactions and generates information on competing endogenous reactions for the consumption of metabolites. These unique, chassis-centered features distinguish MRE from existing pathway design tools and allow synthetic biologists to evaluate the design of their biosynthesis systems from a different angle. By using biosynthesis of a range of high-value natural products as a case study, we show that MRE is an effective tool to guide the design and optimization of heterologous biosynthesis pathways. The URL of MRE is http://www.cbrc.kaust.edu.sa/mre/.

Atlantic hurricane surge response to geoengineering.

Devastating floods due to Atlantic hurricanes are relatively rare events. However, the frequency of the most intense storms is likely to increase with rises in sea surface temperatures. Geoengineering by stratospheric sulfate aerosol injection cools the tropics relative to the polar regions, including the hurricane Main Development Region in the Atlantic, suggesting that geoengineering may mitigate hurricanes. We examine this hypothesis using eight earth system model simulations of climate under the Geoengineering Model Intercomparison Project (GeoMIP) G3 and G4 schemes that use stratospheric aerosols to reduce the radiative forcing under the Representative Concentration Pathway (RCP) 4.5 scenario. Global mean temperature increases are greatly ameliorated by geoengineering, and tropical temperature increases are at most half of those temperature increases in the RCP4.5. However, sulfate injection would have to double (to nearly 10 teragrams of SO2 per year) between 2020 and 2070 to balance the RCP4.5, approximately the equivalent of a 1991 Pinatubo eruption every 2 y, with consequent implications for stratospheric ozone. We project changes in storm frequencies using a temperature-dependent generalized extreme value statistical model calibrated by historical storm surges and observed temperatures since 1923. The number of storm surge events as big as the one caused by the 2005 Katrina hurricane are reduced by about 50% compared with no geoengineering, but this reduction is only marginally statistically significant. Nevertheless, when sea level rise differences in 2070 between the RCP4.5 and geoengineering are factored into coastal flood risk, we find that expected flood levels are reduced by about 40 cm for 5-y events and about halved for 50-y surges.

Molecule-Confined Engineering toward Superconductivity and Ferromagnetism in Two-Dimensional Superlattice.

Superconductivity is mutually exclusive with ferromagnetism, because the ferromagnetic exchange field is often destructive to superconducting pairing correlation. Well-designed chemical and physical methods have been devoted to realize their coexistence only by structural integrity of inherent superconducting and ferromagnetic ingredients. However, such coexistence in freestanding structure with nonsuperconducting and nonferromagnetic components still remains a great challenge up to now. Here, we demonstrate a molecule-confined engineering in two-dimensional organic-inorganic superlattice using a chemical building-block approach, successfully realizing first freestanding coexistence of superconductivity and ferromagnetism originated from electronic interactions of nonsuperconducting and nonferromagnetic building blocks. We unravel totally different electronic behavior of molecules depending on spatial confinement: flatly lying Co(Cp)2 molecules in strongly confined SnSe2 interlayers weaken the coordination field, leading to spin transition to form ferromagnetism; meanwhile, electron transfer from cyclopentadienyls to the Se-Sn-Se lattice induces superconducting state. This entirely new class of coexisting superconductivity and ferromagnetism generates a unique correlated state of Kondo effect between the molecular ferromagnetic layers and inorganic superconducting layers. We anticipate that confined molecular chemistry provides a newly powerful tool to trigger exotic chemical and physical properties in two-dimensional matrixes.

CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction.

MOTIVATION: Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. METHOD: We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence-structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. RESULTS: We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM-HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods. AVAILABILITY AND IMPLEMENTATION: Our program is freely available for download from http://sfb.kaust.edu.sa/Pages/Software.aspx CONTACT: : xin.gao@kaust.edu.sa SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Finding optimal interaction interface alignments between biological complexes.

MOTIVATION: Biological molecules perform their functions through interactions with other molecules. Structure alignment of interaction interfaces between biological complexes is an indispensable step in detecting their structural similarities, which are key S: to understanding their evolutionary histories and functions. Although various structure alignment methods have been developed to successfully access the similarities of protein structures or certain types of interaction interfaces, existing alignment tools cannot directly align arbitrary types of interfaces formed by protein, DNA or RNA molecules. Specifically, they require a ': blackbox preprocessing ': to standardize interface types and chain identifiers. Yet their performance is limited and sometimes unsatisfactory. RESULTS: Here we introduce a novel method, PROSTA-inter, that automatically determines and aligns interaction interfaces between two arbitrary types of complex structures. Our method uses sequentially remote fragments to search for the optimal superimposition. The optimal residue matching problem is then formulated as a maximum weighted bipartite matching problem to detect the optimal sequence order-independent alignment. Benchmark evaluation on all non-redundant protein -: DNA complexes in PDB shows significant performance improvement of our method over TM-align and iAlign (with the ': blackbox preprocessing ': ). Two case studies where our method discovers, for the first time, structural similarities between two pairs of functionally related protein -: DNA complexes are presented. We further demonstrate the power of our method on detecting structural similarities between a protein -: protein complex and a protein -: RNA complex, which is biologically known as a protein -: RNA mimicry case. AVAILABILITY AND IMPLEMENTATION: The PROSTA-inter web-server is publicly available at http://www.cbrc.kaust.edu.sa/prosta/.

Fingerprinting protein structures effectively and efficiently.

MOTIVATION: One common task in structural biology is to assess the similarities and differences among protein structures. A variety of structure alignment algorithms and programs has been designed and implemented for this purpose. A major drawback with existing structure alignment programs is that they require a large amount of computational time, rendering them infeasible for pairwise alignments on large collections of structures. To overcome this drawback, a fragment alphabet learned from known structures has been introduced. The method, however, considers local similarity only, and therefore occasionally assigns high scores to structures that are similar only in local fragments. METHOD: We propose a novel approach that eliminates false positives, through the comparison of both local and remote similarity, with little compromise in speed. Two kinds of contact libraries (ContactLib) are introduced to fingerprint protein structures effectively and efficiently. Each contact group of the contact library consists of one local or two remote fragments and is represented by a concise vector. These vectors are then indexed and used to calculate a new combined hit-rate score to identify similar protein structures effectively and efficiently. RESULTS: We tested our method on the high-quality protein structure subset of SCOP30 containing 3297 protein structures. For each protein structure of the subset, we retrieved its neighbor protein structures from the rest of the subset. The best area under the Receiver-Operating Characteristic curve, archived by ContactLib, is as high as 0.960. This is a significant improvement compared with 0.747, the best result achieved by FragBag. We also demonstrated that incorporating remote contact information is critical to consistently retrieve accurate neighbor protein structures for all- query protein structures. AVAILABILITY AND IMPLEMENTATION: https://cs.uwaterloo.ca/∼xfcui/contactlib/.

Developed and developing world responsibilities for historical climate change and CO2 mitigation.

At the United Nations Framework Convention on Climate Change Conference in Cancun, in November 2010, the Heads of State reached an agreement on the aim of limiting the global temperature rise to 2 °C relative to preindustrial levels. They recognized that long-term future warming is primarily constrained by cumulative anthropogenic greenhouse gas emissions, that deep cuts in global emissions are required, and that action based on equity must be taken to meet this objective. However, negotiations on emission reduction among countries are increasingly fraught with difficulty, partly because of arguments about the responsibility for the ongoing temperature rise. Simulations with two earth-system models (NCAR/CESM and BNU-ESM) demonstrate that developed countries had contributed about 60-80%, developing countries about 20-40%, to the global temperature rise, upper ocean warming, and sea-ice reduction by 2005. Enacting pledges made at Cancun with continuation to 2100 leads to a reduction in global temperature rise relative to business as usual with a 1/3-2/3 (CESM 33-67%, BNU-ESM 35-65%) contribution from developed and developing countries, respectively. To prevent a temperature rise by 2 °C or more in 2100, it is necessary to fill the gap with more ambitious mitigation efforts.

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. Here, we selected the county-level administrative district as the basic geographical unit and then generated a county-level dataset on the intensity of carbon emissions from crop farming in China during 2000-2019, using random forest regression with multi-source data. This dataset can be used to delineate spatio-temporal changes in carbon emissions from crop farming in China, providing an important basis for decision makers and researchers to design agricultural carbon reduction strategies in China.

Unveiling diverse coordination-defined electronic structures of reconstructed anatase TiO2(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-TiO2(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. We unveil that the shifted positions of O 2s and 2p levels and the gap-state Ti 3p levels can sensitively characterize the O and Ti coordination environments in the (1 × 4) reconstructed surface, which show distinguishable features from those in bulk. Our findings provide a paradigm to interrogate the intricate reconstruction-relevant properties in many other TMO surfaces.

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. We have gathered several deep learning methods and classified them into convolutional neural networks (CNNs), graph neural networks (GNNs), and Transformers for analysis and discussion. Initially, we conducted an analysis of the different deep learning methods, focusing on their feature construction and model architecture. We discussed the advantages and disadvantages of each model. Subsequently, we conducted experiments using four deep learning methods on the PDBbind v.2016 core set. We evaluated their prediction capabilities in various affinity intervals and statistically and visually analyzed the samples of correct and incorrect predictions for each model. Through visual analysis, we attempted to combine the strengths of the four models to improve the Root Mean Square Error (RMSE) of predicted affinities by 1.6% (reducing the absolute value to 1.101) and the Pearson Correlation Coefficient (R) by 2.9% (increasing the absolute value to 0.894) compared to the current state-of-the-art method. Lastly, we discussed the challenges faced by current deep learning methods in affinity prediction and proposed potential solutions to address these issues.

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. Here, we developed a method for constructing temporally flexible and physiologically relevant temperature series to reassess growth-temperature relations of larch across boreal Eurasia. Our method appears more effective in assessing the impact of warming on growth than previous methods. Our approach indicates widespread and spatially heterogeneous growth-temperature responses that are driven by local climate. Models quantifying these results project that the negative responses of growth to temperature will spread northward and upward throughout this century. If true, the risks of warming to boreal Eurasia could be more widespread than conveyed from previous works.

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. On local embedding level, a biologically more meaningful contact context is introduced. On global embedding level, attention-based encoder layers are employed for better global representation learning. Our general-purpose protein structure embedding framework is trained and tested on the SCOP40 2.07 dataset. As a result, ContactLib-ATT achieves a SCOP superfamily classification accuracy of 82.4% (i.e., 6.7% higher than state-of-the-art method). On the same dataset, ContactLib-ATT is used to simulate a structure-based search engine for remote homologous proteins, and our top-10 candidate list contains at least one remote homolog with a probability of 91.9%.