COOLAIR and PRC2 function in parallel to silence FLC during vernalization.

Noncoding transcription induces chromatin changes that can mediate environmental responsiveness, but the causes and consequences of these mechanisms are still unclear. Here, we investigate how antisense transcription (termed COOLAIR) interfaces with Polycomb Repressive Complex 2 (PRC2) silencing during winter-induced epigenetic regulation of Arabidopsis FLOWERING LOCUS C (FLC). We use genetic and chromatin analyses on lines ineffective or hyperactive for the antisense pathway in combination with computational modeling to define the mechanisms underlying FLC repression. Our results show that FLC is silenced through pathways that function with different dynamics: a COOLAIR transcription-mediated pathway capable of fast response and in parallel a slow PRC2 switching mechanism that maintains each allele in an epigenetically silenced state. Components of both the COOLAIR and PRC2 pathways are regulated by a common transcriptional regulator (NTL8), which accumulates by reduced dilution due to slow growth at low temperature. The parallel activities of the regulatory steps, and their control by temperature-dependent growth dynamics, create a flexible system for registering widely fluctuating natural temperature conditions that change year on year, and yet ensure robust epigenetic silencing of FLC.

Dynamic Phytomeric Growth Contributes to Local Adaptation in Barley.

Vascular plants have segmented body axes with iterative nodes and internodes. Appropriate node initiation and internode elongation are fundamental to plant fitness and crop yield; however, how these events are spatiotemporally coordinated remains elusive. We show that in barley (Hordeum vulgare L.), selections during domestication have extended the apical meristematic phase to promote node initiation, but constrained subsequent internode elongation. In both vegetative and reproductive phases, internode elongation displays a dynamic proximal-distal gradient, and among subpopulations of domesticated barleys worldwide, node initiation and proximal internode elongation are associated with latitudinal and longitudinal gradients, respectively. Genetic and functional analyses suggest that, in addition to their converging roles in node initiation, flowering-time genes have been repurposed to specify the timing and duration of internode elongation. Our study provides an integrated view of barley node initiation and internode elongation and suggests that plant architecture should be recognized as a collection of dynamic phytomeric units in the context of crop adaptive evolution.

Lithium Metal-Compatible Antifluorite Electrolytes for Solid-State Batteries.

Lithium (Li) metal solid-state batteries feature high energy density and improved safety and thus are recognized as promising alternatives to traditional Li-ion batteries. In practice, using Li metal anodes remains challenging because of the lack of a superionic solid electrolyte that has good stability against reduction decomposition at the anode side. Here, we propose a new electrolyte design with an antistructure (compared to conventional inorganic structures) to achieve intrinsic thermodynamic stability with a Li metal anode. Li-rich antifluorite solid electrolytes are designed and synthesized, which give a high ionic conductivity of 2.1 × 10-4 S cm-1 at room temperature with three-dimensional fast Li-ion transport pathways and demonstrate high stability in Li-Li symmetric batteries. Reversible full cells with a Li metal anode and LiCoO2 cathode are also presented, showing the potential of Li-rich antifluorites as Li metal-compatible solid electrolytes for high-energy-density solid-state batteries.

Agroecological genetics of biomass allocation in wheat uncovers genotype interactions with canopy shade and plant size.

How plants distribute biomass among organs influences resource acquisition, reproduction and plant-plant interactions, and is essential in understanding plant ecology, evolution, and yield production in agriculture. However, the genetic mechanisms regulating allocation responses to the environment are largely unknown. We studied recombinant lines of wheat (Triticum spp.) grown as single plants under sunlight and simulated canopy shade to investigate genotype-by-environment interactions in biomass allocation to the leaves, stems, spikes, and grains. Size-corrected mass fractions and allometric slopes were employed to dissect allocation responses to light limitation and plant size. Size adjustments revealed light-responsive alleles associated with adaptation to the crop environment. Combined with an allometric approach, we demonstrated that polymorphism in the DELLA protein is associated with the response to shade and size. While a gibberellin-sensitive allelic effect on stem allocation was amplified when plants were shaded, size-dependent effects of this allele drive allocation to reproduction, suggesting that the ontogenetic trajectory of the plant affects the consequences of shade responses for allocation. Our approach provides a basis for exploring the genetic determinants underlying investment strategies in the face of different resource constraints and will be useful in predicting social behaviours of individuals in a crop community.

Modulating the Coordination Chemistry of Cobalt Catalytic Sites by Ruthenium Species to Accelerate the Polysulfide Conversion Kinetics in Lithium-Sulfur Batteries.

The performance of lithium-sulfur batteries is compromised by the loss of sulfur as dissolved polysulfides in the electrolyte and consequently the polysulfide redox shutting effect. Accelerating the conversion kinetics of polysulfide intermediates into sulfur or lithium sulfide through electrocatalysis has emerged as a root-cause solution. Co-N-C composite electrocatalyst is commonly used for this purpose. It is illustrated here that how the effectiveness can be improved by modulating the coordination chemistry of Co-N-C catalytic sites through introducing Ru species (RuCo-NC). The well-dispersed Ru in the Co-NC carbon matrix altered the total charge distribution over the Co-N-C catalytic sites and led to the formation of electron-rich Co-N, which is highly active for the polysulfide conversion reactions. Using Ru to modulate the electronic structure in the Co-N-C configuration and the additional catalytic sites over the Ru-Nx species can manifest optimal adsorption behavior of polysulfides. Consequently, the sulfur cathode with RuCo-NC can reduce the capacity fade rate from 0.11 % per cycle without catalyst (initial capacity of 701 mAh g-1) to 0.054 % per cycle (initial capacity of 1074 mAh g-1) over 400 cycles at 0.2 C rate. The results of this study provide the evidence for a feasible catalyst modification strategy for the polysulfide electrocatalysis.

Exploring the Underlying Correlation between the Structure and Ionic Conductivity in Halide Spinel Solid-State Electrolytes with Neutron Diffraction.

The development of cutting-edge solid-state electrolytes (SSEs) entails a deep understanding of the underlying correlation between the structure and ionic conductivity. Generally, the structure of SSEs encompasses several interconnected crystal parameters, and their collective influence on Li+ transport can be challenging to discern. Here, we systematically investigate the structure-function relationship of halide spinel LixMgCl2+x (2 ≥ x ≥ 1) SSEs. A nonmonotonic trend in the ionic conductivity of LixMgCl2+x SSEs has been observed, with the maximum value of 8.69 × 10-6 S cm-1 achieved at x = 1.4. The Rietveld refinement analysis, based on neutron diffraction data, has revealed that the crystal parameters including cell parameters, Li+ vacancies, Debye-Waller factor, and Li-Cl bond length assume diverse roles in influencing ionic conductivity of LixMgCl2+x at different stages within the range of x values. Besides, mechanistic analysis demonstrates Li+ transport along three-dimensional pathways, which primarily governs the contribution to ionic conductivity of LixMgCl2+x SSEs. This study has shed light on the collective influence of crystal parameters on Li+ transport behaviors, providing valuable insights into the intricate relationship between the structure and ionic conductivity of SSEs.

Formaldehyde Exposure Racial Disparities in Southeast Texas.

Formaldehyde (HCHO) exposures during a full year were calculated for different race/ethnicity groups living in Southeast Texas using a chemical transport model tagged to track nine emission categories. Petroleum and industrial emissions were the largest anthropogenic sources of HCHO exposure in Southeast Texas, accounting for 44% of the total HCHO population exposure. Approximately 50% of the HCHO exposures associated with petroleum and industrial sources were directly emitted (primary), while the other 50% formed in the atmosphere (secondary) from precursor emissions of reactive compounds such as ethylene and propylene. Biogenic emissions also formed secondary HCHO that accounted for 11% of the total population-weighted exposure across the study domain. Off-road equipment contributed 3.7% to total population-weighted exposure in Houston, while natural gas combustion contributed 5% in Beaumont. Mobile sources accounted for 3.7% of the total HCHO population exposure, with less than 10% secondary contribution. Exposure disparity patterns changed with the location. Hispanic and Latino residents were exposed to HCHO concentrations +1.75% above average in Houston due to petroleum and industrial sources and natural gas sources. Black and African American residents in Beaumont were exposed to HCHO concentrations +7% above average due to petroleum and industrial sources, off-road equipment, and food cooking. Asian residents in Beaumont were exposed to HCHO concentrations that were +2.5% above average due to HCHO associated with petroleum and industrial sources, off-road vehicles, and food cooking. White residents were exposed to below average HCHO concentrations in all domains because their homes were located further from primary HCHO emission sources. Given the unique features of the exposure disparities in each region, tailored solutions should be developed by local stakeholders. Potential options to consider in the development of those solutions include modifying processes to reduce emissions, installing control equipment to capture emissions, or increasing the distance between industrial sources and residential neighborhoods.

Ternary Rotational Polyanion Coupling Enables Fast Li Ion Dynamics in Tetrafluoroborate Ion Doped Antiperovskite Li2OHCl Solid Electrolyte.

Li-rich antiperovskite (LiRAP) hydroxyhalides are emerging as attractive solid electrolyte (SEs) for all-solid-state Li metal batteries (ASSLMBs) due to their low melting point, low cost, and ease of scaling-up. The incorporation of rotational polyanions can reduce the activation energy and thus improve the Li ion conductivity of SEs. Herein, we propose a ternary rotational polyanion coupling strategy to fasten the Li ion conduction in tetrafluoroborate (BF4 -) ion doped LiRAP Li2OHCl. Assisted by first-principles calculation, powder X-ray diffraction, solid-state magnetic resonance and electrochemical impedance spectra, it is confirmed that Li ion transport in BF4 - ion doped Li2OHCl is strongly associated with the rotational coupling among OH-, BF4 - and Li2-O-H octahedrons, which enhances the Li ion conductivity for more than 1.8 times with the activation energy lowering 0.03 eV. This work provides a new perspective to design high-performance superionic conductors with multi-polyanions.

Implementation of theoretical non-photochemical quenching (NPQ(T)) to investigate NPQ of chickpea under drought stress with High-throughput Phenotyping.

Non-photochemical quenching (NPQ) is a protective mechanism for dissipating excess energy generated during photosynthesis in the form of heat. The accelerated relaxation of the NPQ in fluctuating light can lead to an increase in the yield and dry matter productivity of crops. Since the measurement of NPQ is time-consuming and requires specific light conditions, theoretical NPQ (NPQ(T)) was introduced for rapid estimation, which could be suitable for High-throughput Phenotyping. We investigated the potential of NPQ(T) to be used for testing plant genetic resources of chickpea under drought stress with non-invasive High-throughput Phenotyping complemented with yield traits. Besides a high correlation between the hundred-seed-weight and the Estimated Biovolume, significant differences were observed between the two types of chickpea desi and kabuli for Estimated Biovolume and NPQ(T). Desi was able to maintain the Estimated Biovolume significantly better under drought stress. One reason could be the effective dissipation of excess excitation energy in photosystem II, which can be efficiently measured as NPQ(T). Screening of plant genetic resources for photosynthetic performance could take pre-breeding to a higher level and can be implemented in a variety of studies, such as here with drought stress or under fluctuating light in a High-throughput Phenotyping manner using NPQ(T).

First-Principles Study on a Layered Antiperovskite Li7O2Br3 Solid Electrolyte.

Lithium-rich antiperovskites (LiRAPs) have garnered recent attention as solid electrolytes for solid-state lithium-ion batteries (SSLIBs) with high safety and high energy density. Among them, the layered antiperovskite Li7O2Br3 exhibits superior Li+ conductivity compared to cubic antiperovskite Li3OBr. However, the pure phase of Li7O2Br3 has not been synthesized to date, impeding an in-depth investigation of its migration mechanism and electrochemical properties. Herein, we employ density functional theory (DFT) calculations to examine the physical and electrochemical properties of Li7O2Br3. Our results reveal that Li7O2Br3 is dynamically stable in its ground state, featuring electrical insulation with a wide bandgap of approximately 5.83 eV. Moreover, Li7O2Br3 exhibits improved malleability compared to Li3OBr, making it favorable for material processing. Notably, the calculated energy barrier for Li+ migration in Li7O2Br3 is 0.26 eV, lower than that in Li3OBr (0.4 eV), primarily attributed to the softened phonons of Li at the edge layers within the Li7O2Br3 lattice. We also investigated the impact of various defect types on Li+ diffusion in Li7O2Br3, with the results indicating that LiBr defects effectively facilitate Li+ mobility. Additionally, we constructed a pressure-temperature-Gibbs (PTG) free energy phase diagram for Li7O2Br3 to explore appropriate experimental synthesis conditions. These findings hold substantial promise for promoting the research and development of innovative solid electrolyte materials for advanced SSLIBs.

GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation.

Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing approaches predominantly focus on Euclidean data, such as images and videos, while the exploration of non-Euclidean graph data remains scarce. Recent graph neural network (GNN) approaches could suffer from serious performance decline due to domain shift and label scarcity in source-free adaptation scenarios. In this study, we propose a novel method named Graph Diffusion-based Alignment with Jigsaw (GALA) tailored for source-free graph domain adaptation. To achieve domain alignment, GALA employs a graph diffusion model to reconstruct source-style graphs from target data. Specifically, a score-based graph diffusion model is trained using source graphs to learn the generative source styles. Then, we introduce perturbations to target graphs via a stochastic differential equation instead of sampling from a prior, followed by the reverse process to reconstruct source-style graphs. We feed them into an off-the-shelf GNN and introduce class-specific thresholds with curriculum learning, which can generate accurate and unbiased pseudo-labels for target graphs. Moreover, we develop a simple yet effective graph mixing strategy named graph jigsaw to combine confident graphs and unconfident graphs, which can enhance generalization capabilities and robustness via consistency learning. Extensive experiments on benchmark datasets validate the effectiveness of GALA. The source code is available at https://github.com/luo-junyu/GALA.

Suppressing Surface Ligand-to-Metal Charge Transfer toward Stable High-Voltage LiCoO2.

Increasing the charging cutoff voltage is a viable approach to push the energy density limits of LiCoO2 and meet the requirements of the rapid development of 3C electronics. However, an irreversible oxygen redox is readily triggered by the high charging voltage, which severely restricts practical applications of high-voltage LiCoO2. In this study, we propose a modification strategy via suppressing surface ligand-to-metal charge transfer to inhibit the oxygen redox-induced structure instability. A d0 electronic structure Zr4+ is selected as the charge transfer insulator and successfully doped into the surface lattice of LiCoO2. Using a combination of theoretical calculations, ex situ X-ray absorption spectra, and in situ differential electrochemical mass spectrometry analysis, our results show that the modified LiCoO2 exhibits suppressed oxygen redox activity and stable redox electrochemistry. As a result, it demonstrates a robust long-cycle lattice structure with a practically eliminated voltage decay (0.17 mV/cycle) and an excellent capacity retention of 89.4% after 100 cycles at 4.6 V. More broadly, this work provides a new perspective on suppressing the oxygen redox activity through modulating surface ligand-to-metal charge transfer for achieving a stable high-voltage ion storage structure.

Benzofuran and Benzo[b]thiophene-2-carboxamide Derivatives as Modulators of Amyloid Beta (Aß42) Aggregation.

A group of N-phenylbenzofuran-2-carboxamide and N-phenylbenzo[b]thiophene-2-carboxamide derivatives were designed and synthesized as a novel class of Aß42 aggregation modulators. In the thioflavin-T based fluorescence aggregation kinetics study, compounds 4a, 4b, 5a and 5b possessing a methoxyphenol pharmacophore were able to demonstrate concentration dependent inhibition of Aß42 aggregation with maximum inhibition of 54% observed for compound 4b. In contrast, incorporation of a 4-methoxyphenyl ring in compounds 4d and 5d led to a significant increase in Aß42 fibrillogenesis demonstrating their ability to accelerate Aß42 aggregation. Compound 4d exhibited 2.7-fold increase in Aß42 fibrillogenesis when tested at the maximum concentration of 25 µM. These results were further confirmed by electron microscopy studies which demonstrates the ability of compounds 4a, 4b, 4d, 5a, 5b and 5d to modulate Aß42 fibrillogenesis. Compounds 5a and 5b provided significant neuroprotection to mouse hippocampal neuronal HT22 cells against Aß42-induced cytotoxicity. Molecular docking studies suggest that the orientation of the bicyclic aromatic rings (either benzofuran or benzo[b]thiophene) plays a major role in moderating their ability to either inhibit or accelerate Aß42 aggregation. Our findings support the application of these novel derivatives as pharmacological tools to study the mechanisms of Aß42 aggregation.

A Comprehensive Survey on Deep Graph Representation Learning.

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future.

A membrane associated tandem kinase from wild emmer wheat confers broad-spectrum resistance to powdery mildew.

Crop wild relatives offer natural variations of disease resistance for crop improvement. Here, we report the isolation of broad-spectrum powdery mildew resistance gene Pm36, originated from wild emmer wheat, that encodes a tandem kinase with a transmembrane domain (WTK7-TM) through the combination of map-based cloning, PacBio SMRT long-read genome sequencing, mutagenesis, and transformation. Mutagenesis assay reveals that the two kinase domains and the transmembrane domain of WTK7-TM are critical for the powdery mildew resistance function. Consistently, in vitro phosphorylation assay shows that two kinase domains are indispensable for the kinase activity of WTK7-TM. Haplotype analysis uncovers that Pm36 is an orphan gene only present in a few wild emmer wheat, indicating its single ancient origin and potential contribution to the current wheat gene pool. Overall, our findings not only provide a powdery mildew resistance gene with great potential in wheat breeding but also sheds light into the mechanism underlying broad-spectrum resistance.

Available and novel plant-based carbon dots derived from Vaccaria Semen carbonisata alleviates liver fibrosis.

Background: Liver fibrosis represents an intermediate stage in the progression of liver disease, and as of now, there exists no established clinical therapy for effective antifibrotic treatment. Purpose: Our aim is to explore the impact of Carbon dots derived from Vaccaria Semen Carbonisata (VSC-CDs) on carbon tetrachloride-induced liver fibrosis in mice. Methods: VSC-CDs were synthesized employing a modified pyrolysis process. Comprehensive characterization was performed utilizing various techniques, including transmission electron microscopy (TEM), multiple spectroscopies, X-ray photoelectron spectroscopy (XPS), and high-performance liquid chromatography (HPLC). A hepatic fibrosis model induced by carbon tetrachloride was utilized to evaluate the anti-hepatic fibrosis effects of VSC-CDs. Results: VSC-CDs, exhibiting a quantum yield (QY) of approximately 2.08%, were nearly spherical with diameters ranging from 1.0 to 5.5 nm. The VSC-CDs prepared in this study featured a negative charge and abundant chemical functional groups. Furthermore, these particles demonstrated outstanding dispersibility in the aqueous phase and high biocompatibility. Moreover, VSC-CDs not only enhanced liver function and alleviated liver damage in pathomorphology but also mitigated the extent of liver fibrosis. Additionally, this study marks the inaugural demonstration of the pronounced activity of VSC-CDs in inhibiting inflammatory reactions, reducing oxidative damage, and modulating the TGF-ß/Smad signaling pathway. Conclusion: VSC-CDs exerted significant potential for application in nanodrugs aimed at treating liver fibrosis.