Cryo-EM Structures of a Group II Intron Reverse Splicing into DNA.

Group II introns are a class of retroelements that invade DNA through a copy-and-paste mechanism known as retrotransposition. Their coordinated activities occur within a complex that includes a maturase protein, which promotes splicing through an unknown mechanism. The mechanism of splice site exchange within the RNA active site during catalysis also remains unclear. We determined two cryo-EM structures at 3.6-Å resolution of a group II intron reverse splicing into DNA. These structures reveal that the branch-site domain VI helix swings 90°, enabling substrate exchange during DNA integration. The maturase assists catalysis through a transient RNA-protein contact with domain VI that positions the branch-site adenosine for lariat formation during forward splicing. These findings provide the first direct evidence of the role the maturase plays during group II intron catalysis. The domain VI dynamics closely parallel spliceosomal branch-site helix movement and provide strong evidence for a retroelement origin of the spliceosome.

Moiré synaptic transistor with room-temperature neuromorphic functionality.

Moiré quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional heterostructures1-4. When combined with the exceptionally high electrostatic control in atomically thin materials5-8, moiré heterostructures have the potential to enable next-generation electronic devices with unprecedented functionality. However, despite extensive exploration, moiré electronic phenomena have thus far been limited to impractically low cryogenic temperatures9-14, thus precluding real-world applications of moiré quantum materials. Here we report the experimental realization and room-temperature operation of a low-power (20 pW) moiré synaptic transistor based on an asymmetric bilayer graphene/hexagonal boron nitride moiré heterostructure. The asymmetric moiré potential gives rise to robust electronic ratchet states, which enable hysteretic, non-volatile injection of charge carriers that control the conductance of the device. The asymmetric gating in dual-gated moiré heterostructures realizes diverse biorealistic neuromorphic functionalities, such as reconfigurable synaptic responses, spatiotemporal-based tempotrons and Bienenstock-Cooper-Munro input-specific adaptation. In this manner, the moiré synaptic transistor enables efficient compute-in-memory designs and edge hardware accelerators for artificial intelligence and machine learning.

Constructing protein polyhedra via orthogonal chemical interactions.

Many proteins exist naturally as symmetrical homooligomers or homopolymers1. The emergent structural and functional properties of such protein assemblies have inspired extensive efforts in biomolecular design2-5. As synthesized by ribosomes, proteins are inherently asymmetric. Thus, they must acquire multiple surface patches that selectively associate to generate the different symmetry elements needed to form higher-order architectures1,6-a daunting task for protein design. Here we address this problem using an inorganic chemical approach, whereby multiple modes of protein-protein interactions and symmetry are simultaneously achieved by selective, 'one-pot' coordination of soft and hard metal ions. We show that a monomeric protein (protomer) appropriately modified with biologically inspired hydroxamate groups and zinc-binding motifs assembles through concurrent Fe3+ and Zn2+ coordination into discrete dodecameric and hexameric cages. Our cages closely resemble natural polyhedral protein architectures7,8 and are, to our knowledge, unique among designed systems9-13 in that they possess tightly packed shells devoid of large apertures. At the same time, they can assemble and disassemble in response to diverse stimuli, owing to their heterobimetallic construction on minimal interprotein-bonding footprints. With stoichiometries ranging from [2 Fe:9 Zn:6 protomers] to [8 Fe:21 Zn:12 protomers], these protein cages represent some of the compositionally most complex protein assemblies-or inorganic coordination complexes-obtained by design.

Double-Shelled Open Hollow Metal-Organic Frameworks for Efficient Aqueous Zn-Ion Batteries.

Multi-shelled hollow metal-organic frameworks (MH-MOFs) are highly promising as electrode materials due to their impressive surface area and efficient mass transfer capabilities. However, the fabrication of MH-MOFs has remained a formidable challenge. In this study, two types of double-shelled open hollow Prussian blue analogues, one with divalent iron (DHPBA-Fe(II)) and the other with trivalent iron (DHPBA-Fe(III)), through an innovative inner-outer growth strategy are successfully developed. The growth mechanism is found to involve lattice matching growth and ligand exchange processes. Subsequently, DHPBA-Fe(II) and DHPBA-Fe(III) are employed as cathodes in aqueous Zn-ion batteries. Significantly, DHPBA-Fe(II) demonstrated exceptional performance, exhibiting a capacity of 92.5 mAh g-1 at 1 A g-1, and maintaining remarkable stability over an astounding 10 000 cycles. This research is poised to catalyze further exploration into the fabrication techniques of MH-MOFs and offer fresh insights into the intricate interplay between electronic structure and battery performance.

Fast Fusion Clustering via Double Random Projection.

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples.

Positional Isomers of Covalent Organic Frameworks for Indoor Humidity Regulation.

Humidity is one of the most important indicators affecting human health. Here, a pair of covalent organic frameworks (COFs) of positional isomers (p-COF and o-COF) for indoor humidity regulation is reported. Although p-COF and o-COF have the same sql topology and pore size, they exhibit different water adsorption behaviors due to the subtle differences in water adsorption sites. Particularly, o-COF exhibits a steep adsorption isotherm in the range of 45-65% RH with a hysteresis loop, which is perfectly suitable for indoor humidity regulation. In the laboratory experiment, when the humidity of the external environment is 20-75% RH, o-COF can control the humidity of the room in the range of 45-60% RH. o-COF has shown great potential as a dual humidification/dehumidification adsorbent for indoor humidity regulation.

Construction and Properties of High-Toughness Soft-Soft Interfaces Based on the Adhesion of Natural Polyphenols.

Artificial joint replacement is the most effective way to treat osteoarthritis. However, these artificial joints are too stiff with high interfacial contact stress and poor surface lubrication, resulting in stress shielding and severe wear and tear lead to an extremely high failure rate. At present, hydrogels are considered the most promising substitute for artificial joint prostheses owing to their good biocompatibility, adjustable mechanical properties, and excellent flexibility. Nevertheless, a traditional single-layer hydrogel has poor bearing capacity and lubrication, which are far from the properties of natural articular cartilage. The high strength and low friction properties of natural articular cartilage are based on its own multilayer fibrous structure. Therefore, by simulating the multilayer structure of natural cartilage, a bilayer bionic cartilage hydrogel was prepared; that is, the upper hydrogel realized excellent lubrication and the lower hydrogel realized high load-bearing capacity. However, the interface binding of bilayer hydrogels is a challenge at present. Therefore, the interfacial adhesion of the bilayer hydrogel is improved by adding tannic acid (TA) based on the adhesion of the natural polyphenol structure. The average interfacial toughness reaches 3650 J/m2, and the average interfacial shear force reaches 800 kPa. In the preparation of the bilayer hydrogel, taking advantage of the coordination reaction between TA and metal cations, Fe3+ is further added to endow the bilayer hydrogel with excellent mechanical properties and good sliding friction performance. Therefore, this work opens up a new way to construct cartilage-like materials with high toughness and a soft-soft interface.

Propensity score analysis with missing data using a multi-task neural network.

BACKGROUND: Propensity score analysis is increasingly used to control for confounding factors in observational studies. Unfortunately, unavoidable missing values make estimating propensity scores extremely challenging. We propose a new method for estimating propensity scores in data with missing values. MATERIALS AND METHODS: Both simulated and real-world datasets are used in our experiments. The simulated datasets were constructed under 2 scenarios, the presence (T = 1) and the absence (T = 0) of the true effect. The real-world dataset comes from LaLonde's employment training program. We construct missing data with varying degrees of missing rates under three missing mechanisms: MAR, MCAR, and MNAR. Then we compare MTNN with 2 other traditional methods in different scenarios. The experiments in each scenario were repeated 20,000 times. Our code is publicly available at https://github.com/ljwa2323/MTNN . RESULTS: Under the three missing mechanisms of MAR, MCAR and MNAR, the RMSE between the effect and the true effect estimated by our proposed method is the smallest in simulations and in real-world data. Furthermore, the standard deviation of the effect estimated by our method is the smallest. In situations where the missing rate is low, the estimation of our method is more accurate. CONCLUSIONS: MTNN can perform propensity score estimation and missing value filling at the same time through shared hidden layers and joint learning, which solves the dilemma of traditional methods and is very suitable for estimating true effects in samples with missing values. The method is expected to be broadly generalized and applied to real-world observational studies.

Autologous wound margin point columnar full-thickness skin grafting combined with negative pressure wound therapy improves wound healing in refractory diabetic foot ulcers.

Diabetic lower extremity ulcers (DLEUs) are a severe complication of diabetes mellitus (DM) and are difficult to heal. This study aimed to explore the efficacy of autologous point columnar full-thickness skin graft taken from the ulcer wound margin combined with negative pressure wound therapy (NPWT) in refractory DLEUs. This is a prospective cohort study. A total of 40 inpatients with refractory DLEUs were recruited in the Diabetes Foot Center of Guangxi Zhuang Autonomous Region People's Hospital from October 2019 to November 2021. According to the doctors' professional suggestions and the patients' personal wishes, these enrolled patients were divided into two groups based on different topical wound management: the graft group (n = 18) and the conventional wound therapeutic (CWT) group (n = 22). The efficacy evaluations included the time to complete re-epithelialization of the wound and healing speed within 14 days of graft treatment or after 14 days of graft treatment in the two groups. Before the treatment, the graft group had a significantly larger ulcer area than the CWT group [27.22 (15.28, 46.59) versus 10.92 (7.00, 24.93) cm2 , P < .01]. However, the time to complete wound re-epithelialization in the graft group was shorter than in the CWT group [58.22 ± 30.60 versus 86.09 ± 49.54 d, P < .05]. Meanwhile, the healing speed in graft group was markedly faster than in CWT group, whether within 14 days [0.60 (0.40, 0.92) versus 0.16 (0.07, 0.34) cm2 /d, P < .01] or after 14 days of graft treatment [0.57 (0.45, 0.91) versus 0.13 (0.08, 0.27) cm2 /d, P < .01]. However, the total treatment cost in the graft group was lower than in the CWT group [419.59 ± 137.20 versus 663.97 ± 497.02 $, P < .05]. The novel treatment modality of autologous full-thickness skin graft taken from the ulcer wound margin combined with NPWT has hereby proposed for the first time, and is a safe, effective, and reliable method with a good performance-to-cost ratio to promote wound healing and shorten the healing time for DLEUs.

Dual-Atom Catalysts Derived from a Preorganized Covalent Organic Framework for Enhanced Electrochemical Oxygen Reduction.

Dual-atom catalysts (DAC) are deemed as promising electrocatalysts due to the abundant active sites and adjustable electronic structure, but the fabrication of well-defined DAC is still full of challenges. Herein, bonded Fe dual-atom catalysts (Fe2 DAC) with Fe2 N6 C8 O2 configuration were developed through one-step carbonization of a preorganized covalent organic framework with bimetallic Fe chelation sites (Fe2 COF). The transition from Fe2 COF to Fe2 DAC involved the dissociation of the nanoparticles and the capture of atoms by carbon defects. Benefitting from the optimized d-band center and enhanced adsorption of OOH* intermediates, Fe2 DAC exhibited outstanding oxygen reduction activity with a half-wave potential of 0.898 V vs. RHE. This work will guide more fabrication of dual-atom and even cluster catalysts from preorganized COF in the future.

A Hexagonal Nut-Like Metal-Organic Framework and Its Conformal Transformation.

Hollow structured metal-organic frameworks (MOFs) and their derivatives are desired in catalysis, energy storage, etc. However, fabrication of novel hollow MOFs and revelation of their formation mechanisms remain challenging. Herein, open hollow 2D MOFs in the form of hexagonal nut are prepared through self-template method, which can be readily scaled up at gram scale in a one-pot preparation. The evolution from the initial superstructure to the final stable MOFs is tracked by wide-angle X-ray scattering, transforming from solid hexagon to open hollow hexagon. More importantly, this protocol can be extended to synthesizing a series of open hollow structured MOFs with sizes ranging from ≈120 to ≈1200 nm. Further, open hollow structured cobalt/N-doped porous carbon composites are realized through conformal transformation of the as-prepared MOFs, which demonstrates promising applications in sustainable energy conversion technologies. This study sheds light on the kinetically controlled synthesis of novel 2D MOFs for their extended utilizations.

Heterogeneous logistic regression for estimation of subgroup effects on hypertension.

Personalized medicine has gained much attention in the past decades, and identifying the effects of factors is essential for personalized preventions and treatments. Hypertension is a major modifiable risk factor for cardiovascular disease and is influenced by complex factors. In order to decrease the incidence of hypertension effectively, the subjects should be divided into subgroups according to their characteristics. In this study, we proposed to use a heterogeneous logistic regression combined with a concave fusion penalty to analyze the population-based survey data, including common influencing factors of hypertension. The analytic steps include: (1) identifying the most important predictor; (2) estimating subgroup-based heterogeneous effects. In the present context of primary hypertension data, the modeling results showed that the calculated prediction accuracy under our method was greater than 99%, while zero under the classical logistic regression. The findings could provide a practical guide for further individualized measures implementation.

Viral neutralization by antibody-imposed physical disruption.

In adaptive immunity, organisms produce neutralizing antibodies (nAbs) to eliminate invading pathogens. Here, we explored whether viral neutralization could be attained through the physical disruption of a virus upon nAb binding. We report the neutralization mechanism of a potent nAb 8C11 against the hepatitis E virus (HEV), a nonenveloped positive-sense single-stranded RNA virus associated with abundant acute hepatitis. The 8C11 binding flanks the protrusion spike of the HEV viruslike particles (VLPs) and leads to tremendous physical collision between the antibody and the capsid, dissociating the VLPs into homodimer species within 2 h. Cryo-electron microscopy reconstruction of the dissociation intermediates at an earlier (15-min) stage revealed smeared protrusion spikes and a loss of icosahedral symmetry with the capsid core remaining unchanged. This structural disruption leads to the presence of only a few native HEV virions in the ultracentrifugation pellet and exposes the viral genome. Conceptually, we propose a strategy to raise collision-inducing nAbs against single spike moieties that feature in the context of the entire pathogen at positions where the neighboring space cannot afford to accommodate an antibody. This rationale may facilitate unique vaccine development and antimicrobial antibody design.

A Tantalum Disulfide Charge-Density-Wave Stochastic Artificial Neuron for Emulating Neural Statistical Properties.

Artificial neuronal devices that functionally resemble biological neurons are important toward realizing advanced brain emulation and for building bioinspired electronic systems. In this Communication, the stochastic behaviors of a neuronal oscillator based on the charge-density-wave (CDW) phase transition of a 1T-TaS2 thin film are reported, and the capability of this neuronal oscillator to generate spike trains with statistical features closely matching those of biological neurons is demonstrated. The stochastic behaviors of the neuronal device result from the melt-quench-induced reconfiguration of CDW domains during each oscillation cycle. Owing to the stochasticity, numerous key features of the Hodgkin-Huxley description of neurons can be realized in this compact two-terminal neuronal oscillator. A statistical analysis of the spike train generated by the artificial neuron indicates that it resembles the neurons in the superior olivary complex of a mammalian nervous system, in terms of its interspike interval distribution, the time-correlation of spiking behavior, and its response to acoustic stimuli.

Integrating Light-Harvesting Ruthenium(II)-based Units into Three-Dimensional Metal Covalent Organic Frameworks for Photocatalytic Hydrogen Evolution.

Three metal covalent organic frameworks (MCOFs), namely RuCOF-ETTA, RuCOF-TPB and RuCOF-ETTBA, were synthesized by incorporating the photosensitive RuII tris(2,2'-bipyridine) unit into the skeleton. Interestingly, each RuCOF contains three isostructural covalent organic frameworks that interlock together with the RuII centers serving as point of registry. The covalently linked network coupling with uniformly distributed RuII units allowed the RuCOFs to exhibit superior chemical stability, strong light-harvesting ability, and high photocatalytic activity toward hydrogen evolution (20 308 µmol g-1 h-1 ). This work illustrates the potential of developing versatile MCOFs-based photocatalysts from functionalized metal complex building unit and further enriches the MCOFs family.

Category encoding method to select feature genes for the classification of bulk and single-cell RNA-seq data.

Bulk and single-cell RNA-seq (scRNA-seq) data are being used as alternatives to traditional technology in biology and medicine research. These data are used, for example, for the detection of differentially expressed (DE) genes. Several statistical methods have been developed for the classification of bulk and single-cell RNA-seq data. These feature genes are vitally important for the classification of bulk and single-cell RNA-seq data. The majority of genes are not DE and they are thus irrelevant for class distinction. To improve the classification performance and save the computation time, removal of irrelevant genes is necessary. Removal will aid the detection of the important feature genes. Widely used schemes in the literature, such as the BSS/WSS (BW) method, assume that data are normally distributed and may not be suitable for bulk and single-cell RNA-seq data. In this article, a category encoding (CAEN) method is proposed to select feature genes for bulk and single-cell RNA-seq data classification. This novel method encodes categories by employing the rank of sequence samples for each gene in each class. Correlation coefficients are considered for gene and class with the rank of sample and a new rank of category. The highest gene correlation coefficients are considered feature genes, which are the most effective for classifying bulk and single-cell RNA-seq dataset. The sure screening method was also established for rank consistency properties of the proposed CAEN method. Simulation studies show that the classifier using the proposed CAEN method performs better than, or at least as well as, the existing methods in most settings. Existing real datasets were analyzed, with the results demonstrating superior performance of the proposed method over current competitors. The application has been coded into an R package named "CAEN" to facilitate wide use.

Long non-coding RNA BACE1-AS plays an oncogenic role in hepatocellular carcinoma cells through miR-214-3p/APLN axis.

BACE1 antisense RNA (BACE1-AS) is implicated in promoting cell proliferation in different types of tumors. However, the function and mechanism of BACE1-AS in hepatocellular carcinoma (HCC) are still unclear. In the present study, we found that the relative expression of BACE1-AS in HCC cell lines, HCC tissues, and serum samples of HCC patients was significantly increased, and its high expression was correlated with the poor prognosis of HCC patients. In addition, overexpression of BACE1 promoted HCC cell proliferation, cell cycle progression, migration, and invasion, but inhibited cell apoptosis, while knockdown of BACE1 exerted the opposite role. Furthermore, BACE1-AS sponged miR-214-3p and inhibited its expression, thus promoting Apelin (APLN) expression. Overexpression or knockdown of miR-214-3p could partially reverse the abnormal proliferation, cell cycle progression, migration, invasion, and apoptosis caused by overexpression or knockdown of BACE1. These findings suggest that the BACE1-AS/miR-214-3p/APLN axis is a novel signaling pathway that facilitates HCC.

A 3D Anionic Metal Covalent Organic Framework with soc Topology Built from an Octahedral TiIV Complex for Photocatalytic Reactions.

The construction of three-dimensional (3D) covalent organic frameworks (COFs) remains challenging due to the limited types of organic building blocks. With octahedral TiIV complex as the building unit, this study reports on the first 3D anionic titanium-based COF (Ti-COF-1) with an edge-transitive (6, 4)-connected soc topology. Ti-COF-1 exhibits high crystallinity, superior stability, and large specific surface area (1000.4 m2 g-1 ). Moreover, Ti-COF-1 has a broad absorption band in the UV spectrum with an optical energy gap of 1.86 eV, and exhibits high photocatalytic activity toward Meerwein addition reactions. This research demonstrates an attractive strategy for the design of 3D functional COFs.

Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests.

Forest ecosystems are an important sink for terrestrial carbon sequestration. Hence, accurate modeling of the intra- and interannual variability of forest photosynthetic productivity remains a key objective in global biology. Applying climate-driven leaf phenology and growth in models may improve predictions of the forest gross primary productivity (GPP). We used a dynamic non-structural carbohydrates (NSC) model (FORCCHN2) that couples leaf development and phenology to investigate the relationships among photosynthesis and environmental factors. FORCCHN2 simulates spring and autumn phenological events from heat and chilling, respectively. Leaf area index data from satellites along with climate data estimated localized phenological parameters. NSC limitation, immediate temperature, accumulated heat, and growth potential comprised a daily leaf-growth model. Functionally, leaf growth was decoupled from photosynthesis. Leaf biomass determined overall photosynthetic production. We compared this model with outputs of the other six terrestrial biospheric models and with observations from the North American Carbon Program Site Interim Synthesis in 18 forest sites. This model improved the predicted performance of yearly GPP with a 57%-210% increase in correlation (median) and up to a 102% reduction in biases (median), compared to three prognostic models and three prescribed models. At the North America continental scale, the model predicted the average annual GPP of 7.38 Pg C/year from forest ecosystems during 1985-2016. The results showed an increasing trend of GPP in North America (1.0 Pg C/decade). The inclusion of climate-driven phenology and growth has a significant potential for improving dynamic vegetation models, and promotes a further understanding of the complex relationship between environment and photosynthesis.

RUNX2 interacts with BRG1 to target CD44 for promoting invasion and migration of colorectal cancer cells.

BACKGROUND: Cancer stem cells (CSCs) play an important role in tumor invasion and metastasis. CD44 is the most commonly used marker of CSCs, with the potential to act as a determinant against the invasion and migration of CSCs and as the key factor in epithelial-mesenchymal transition (EMT)-like changes that occur in colorectal cancer (CRC). Runt-related transcription factor-2 (RUNX2) is a mesenchymal stem marker for cancer that is involved in stem cell biology and tumorigenesis. However, whether RUNX2 is involved in CSC and in inducing EMT-like changes in CRC remains uncertain, warranting further investigation. METHODS: We evaluated the role of RUNX2 in the invasion and migration of CRC cells as a promoter of CD44-induced stem cell- and EMT-like modifications. For this purpose, western blotting was employed to analyze the expression of differential proteins in CRC cells. We conducted sphere formation, wound healing, and transwell assays to investigate the biological functions of RUNX2 in CRC cells. Cellular immunofluorescence and coimmunoprecipitation (co-IP) assays were performed to study the relationship between RUNX2 and BRG1. Real-time quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) were performed to analyze the expressions of RUNX2, BRG1, and CD44 in the CRC tissues. RESULTS: We found that RUNX2 could markedly induce the CRC cell sphere-forming ability and EMT. Interestingly, the RUNX2-mediated EMT in CRC cell may be associated with the activation of CD44. Furthermore, RUNX2 was found to interact with BRG1 to promote the recruitment of RUNX2 to the CD44 promoter. CONCLUSIONS: Our cumulative findings suggest that RUNX2 and BRG1 can form a compact complex to regulate the transcription and expression of CD44, which has possible involvement in the invasion and migration of CRC cells.