Structural Isomeric Effect on Spin Transport in Molecular Semiconductors

Molecular semiconductor (MSC) is a promising candidate for spintronic applications benefiting from its long spin lifetime caused by light elemental-composition essence and thus weak spin-orbit coupling (SOC). According to current knowledge, the SOC effect, normally dominated by the elemental composition, is the main spin-relaxation causation in MSCs, and thus the molecular structure-induced SOC change is one of the most concerned issues. In theoretical study, molecular isomerism, a most prototype phenomenon, has long been considered to possess little difference on spin transport previously, since elemental compositions of isomers are totally the same. However, here in this study, quite different spin-transport performances are demonstrated in ITIC and its structural isomers BDTIC experimentally, for the first time, though the charge transport and molecular stacking of the two films are very similar. By further experiments of electron-paramagnetic resonance and density-functional-theory calculations, it is revealed that noncovalent-conformational locks (NCLs) formed in BDTIC can lead to enhancement of SOC and thus decrease the spin lifetime. Hence, this study suggests the influences from the structural-isomeric effect must be considered for developing highly efficient spin-transport MSCs, which also provides a reliable theoretical basis for solving the great challenge of quantificational measurement of NCLs in films in the future.

Machine Learning in Bioelectrocatalysis

At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

Microphysiological Constructs and Systems: Biofabrication Tactics, Biomimetic Evaluation Approaches, and Biomedical Applications

In recent decades, microphysiological constructs and systems (MPCs and MPSs) have undergone significant development, ranging from self-organized organoids to high-throughput organ-on-a-chip platforms. Advances in biomaterials, bioinks, 3D bioprinting, micro/nanofabrication, and sensor technologies have contributed to diverse and innovative biofabrication tactics. MPCs and MPSs, particularly tissue chips relevant to absorption, distribution, metabolism, excretion, and toxicity, have demonstrated potential as precise, efficient, and economical alternatives to animal models for drug discovery and personalized medicine. However, current approaches mainly focus on the in vitro recapitulation of the human anatomical structure and physiological-biochemical indices at a single or a few simple levels. This review highlights the recent remarkable progress in MPC and MPS models and their applications. The challenges that must be addressed to assess the reliability, quantify the techniques, and utilize the fidelity of the models are also discussed.

Recent Advances in Chemical Synthesis of Structural Domains of Lipopolysaccharides from the Commensal Gut-Associated Microbiota

Lipopolysaccharides from the commensal gut-associated microbiota are interesting biomolecules for the treatment of various inflammatory diseases. Different from pathogenic lipopolysaccharides, commensal lipopolysaccharides have distinct chemical structures and mediate beneficial homeostasis with the immune system of the host. However, the accessibility issues of homogenous and pure commensal lipopolysaccharides hampered the in-depth studies of their functions. In this concept article, we highlight the recent synthesis of lipopolysaccharides from gut-associated lymphoid-tissue-resident Alcaligenes faecalis and Bacteroides vulgatus, which hopes to inspire the more efforts devoting to these fantastic biomolecules.

Simple One-Step Molten Salt Method for Synthesizing Highly Efficient MXene-Supported Pt Nanoalloy Electrocatalysts

MXene-supported noble metal alloy catalysts exhibit remarkable electrocatalytic activity in various applications. However, there is no facile one-step method for synthesizing these catalysts, because the synthesis of MXenes requires a strongly oxidizing environment and the preparation of platinum nanoalloys requires a strongly reducing environment and high temperatures. Hence, achieving coupling in one step is extremely challenging. In this paper, a straightforward one-step molten salt method for preparing MXene-supported platinum nanoalloy catalysts is proposed. The molten salt acts as the reaction medium to dissolve the transition metals and platinum ions at high temperatures. Transition metal ions oxidize the A-site element from its MAX precursor at high temperatures, and the resulting transition metals further reduce platinum ions to form alloys. By coupling Al oxidation and platinum ion reduction using a molten salt solvent, this method directly converts Ti3 AlC2 to a Pt-M@Ti3 C2 Tx catalyst (where M denotes the transition metal). It further offers the possibility of extending the Pt-M phase to binary, ternary, or quaternary platinum-containing nanoalloys and converting the Al-containing MAX phase to Ti2 AlC and Ti3 AlCN. Due to the strong interfacial interaction, the as-prepared Pt-Co@Ti3 C2 Tx is superior to commercial Pt/C (20 wt.%) in the hydrogen evolution reaction.

EIEPCF: accurate inference of functional gene regulatory networks by eliminating indirect effects from confounding factors

Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF.

CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data

Single-cell omics technologies have made it possible to analyze the individual cells within a biological sample, providing a more detailed understanding of biological systems. Accurately determining the cell type of each cell is a crucial goal in single-cell RNA-seq (scRNA-seq) analysis. Apart from overcoming the batch effects arising from various factors, single-cell annotation methods also face the challenge of effectively processing large-scale datasets. With the availability of an increase in the scRNA-seq datasets, integrating multiple datasets and addressing batch effects originating from diverse sources are also challenges in cell-type annotation. In this work, to overcome the challenges, we developed a supervised method called CIForm based on the Transformer for cell-type annotation of large-scale scRNA-seq data. To assess the effectiveness and robustness of CIForm, we have compared it with some leading tools on benchmark datasets. Through the systematic comparisons under various cell-type annotation scenarios, we exhibit that the effectiveness of CIForm is particularly pronounced in cell-type annotation. The source code and data are available at https://github.com/zhanglab-wbgcas/CIForm.

Arabidopsis AGAMOUS-LIKE16 and SUPPRESSOR OF CONSTANS1 regulate the genome-wide expression and flowering time

Flowering transition is tightly coordinated by complex gene regulatory networks, in which AGAMOUS-LIKE 16 (AGL16) plays important roles. Here, we identified the molecular function and binding properties of AGL16 and demonstrated its partial dependency on the SUPPRESSOR OF CONSTANS 1 (SOC1) function in regulating flowering. AGL16 bound to promoters of more than 2,000 genes via CArG-box motifs with high similarity to that of SOC1 in Arabidopsis (Arabidopsis thaliana). Approximately 70 flowering genes involved in multiple pathways were potential targets of AGL16. AGL16 formed a protein complex with SOC1 and shared a common set of targets. Intriguingly, only a limited number of genes were differentially expressed in the agl16-1 loss-of-function mutant. However, in the soc1-2 knockout background, AGL16 repressed and activated the expression of 375 and 182 genes, respectively, with more than a quarter bound by AGL16. Corroborating these findings, AGL16 repressed the flowering time more strongly in soc1-2 than in the Col-0 background. These data identify a partial inter-dependency between AGL16 and SOC1 in regulating genome-wide gene expression and flowering time, while AGL16 provides a feedback regulation on SOC1 expression. Our study sheds light on the complex background dependency of AGL16 in flowering regulation, thus providing additional insights into the molecular coordination of development and environmental adaptation.

Numerical analysis of vibration modes of a qPlus sensor with a long tip

We study the oscillatory behavior of qPlus sensors with a long tilted tip by means of finite element simulations. The vibration modes of a qPlus sensor with a long tip are quite different from those of a cantilever with a short tip. Flexural vibration of the tungsten tip will occur. The tip can no longer be considered as a rigid body that moves with the prong of the tuning fork. Instead, it oscillates both horizontally and vertically. The vibration characteristics of qPlus sensors with different tip sizes were studied. An optimized tip size was derived from obtained values of tip amplitude, ratio between vertical and lateral amplitude components, output current, and quality factor. For high spatial resolution the optimal diameter was found to be 0.1 mm.

Genome-wide Association Study of Creativity Reveals Genetic Overlap With Psychiatric Disorders, Risk Tolerance, and Risky Behaviors

Creativity represents one of the most important and partially heritable human characteristics, yet little is known about its genetic basis. Epidemiological studies reveal associations between creativity and psychiatric disorders as well as multiple personality and behavioral traits. To test whether creativity and these disorders or traits share genetic basis, we performed genome-wide association studies (GWAS) followed by polygenic risk score (PRS) analyses. Two cohorts of Han Chinese subjects (4,834 individuals in total) aged 18-45 were recruited for creativity measurement using typical performance test. After exclusion of the outliers with significantly deviated creativity scores and low-quality genotyping results, 4,664 participants were proceeded for GWAS. We conducted PRS analyses using both the classical pruning and thresholding (P+T) method and the LDpred method. The extent of polygenic risk was estimated through linear regression adjusting for the top 3 genotyping principal components. R2 was used as a measurement of the explained variance. PRS analyses demonstrated significantly positive genetic overlap, respectively, between creativity with schizophrenia ((P+T) method: R2(max) ~ .196%, P = .00245; LDpred method: R2(max) ~ .226%, P = .00114), depression ((P+T) method: R2(max) ~ .178%, P = .00389; LDpred method: R2(max) ~ .093%, P = .03675), general risk tolerance ((P+T) method: R2(max) ~ .177%, P = .00399; LDpred method: R2(max) ~ .305%, P = .00016), and risky behaviors ((P+T) method: R2(max) ~ .187%, P = .00307; LDpred method: R2(max) ~ .155%, P = .00715). Our results suggest that human creativity is probably a polygenic trait affected by numerous variations with tiny effects. Genetic variations that predispose to psychiatric disorders and risky behaviors may underlie part of the genetic basis of creativity, confirming the epidemiological associations between creativity and these traits.

Flavivirus induces and antagonizes antiviral RNA interference in both mammals and mosquitoes

Mosquito-borne flaviviruses infect both mammals and mosquitoes. RNA interference (RNAi) has been demonstrated as an anti-flavivirus mechanism in mosquitoes; however, whether and how flaviviruses induce and antagonize RNAi-mediated antiviral immunity in mammals remains unknown. We show that the nonstructural protein NS2A of dengue virus-2 (DENV2) act as a viral suppressor of RNAi (VSR). When NS2A-mediated RNAi suppression was disabled, the resulting mutant DENV2 induced Dicer-dependent production of abundant DENV2-derived siRNAs in differentiated mammalian cells. VSR-disabled DENV2 showed severe replication defects in mosquito and mammalian cells and in mice that were rescued by RNAi deficiency. Moreover, NS2As of multiple flaviviruses act as VSRs in vitro and during viral infection in both organisms. Overall, our findings demonstrate that antiviral RNAi can be induced by flavivirus, while flavivirus uses NS2A as a bona fide VSR to evade RNAi in mammals and mosquitoes, highlighting the importance of RNAi in flaviviral vector-host life cycles.

Optical orbital-angular-momentum-multiplexed data transmission under high scattering

Multiplexing multiple orbital angular momentum (OAM) channels enables high-capacity optical communication. However, optical scattering from ambient microparticles in the atmosphere or mode coupling in optical fibers significantly decreases the orthogonality between OAM channels for demultiplexing and eventually increases crosstalk in communication. Here, we propose a novel scattering-matrix-assisted retrieval technique (SMART) to demultiplex OAM channels from highly scattered optical fields and achieve an experimental crosstalk of -13.8 dB in the parallel sorting of 24 OAM channels after passing through a scattering medium. The SMART is implemented in a self-built data transmission system that employs a digital micromirror device to encode OAM channels and realize reference-free calibration simultaneously, thereby enabling a high tolerance to misalignment. We successfully demonstrate high-fidelity transmission of both gray and color images under scattering conditions at an error rate of <0.08%. This technique might open the door to high-performance optical communication in turbulent environments.

Genome-wide dynamic network analysis reveals a critical transition state of flower development in Arabidopsis

BACKGROUND

The flowering transition which is controlled by a complex and intricate gene regulatory network plays an important role in the reproduction for offspring of plants. It is a challenge to identify the critical transition state as well as the genes that control the transition of flower development. With the emergence of massively parallel sequencing, a great number of time-course transcriptome data greatly facilitate the exploration of the developmental phase transition in plants. Although some network-based bioinformatics analyses attempted to identify the genes that control the phase transition, they generally overlooked the dynamics of regulation and resulted in unreliable results. In addition, the results of these methods cannot be self-explained.

RESULTS

In this work, to reveal a critical transition state and identify the transition-specific genes of flower development, we implemented a genome-wide dynamic network analysis on temporal gene expression data in Arabidopsis by dynamic network biomarker (DNB) method. In the analysis, DNB model which can exploit collective fluctuations and correlations of different metabolites at a network level was used to detect the imminent critical transition state or the tipping point. The genes that control the phase transition can be identified by the difference of weighted correlations between the genes interested and the other genes in the global network. To construct the gene regulatory network controlling the flowering transition, we applied NARROMI algorithm which can reduce the noisy, redundant and indirect regulations on the expression data of the transition-specific genes. In the results, the critical transition state detected during the formation of flowers corresponded to the development of flowering on the 7th to 9th day in Arabidopsis. Among of 233 genes identified to be highly fluctuated at the transition state, a high percentage of genes with maximum expression in pollen was detected, and 24 genes were validated to participate in stress reaction process, as well as other floral-related pathways. Composed of three major subnetworks, a gene regulatory network with 150 nodes and 225 edges was found to be highly correlated with flowering transition. The gene ontology (GO) annotation of pathway enrichment analysis revealed that the identified genes are enriched in the catalytic activity, metabolic process and cellular process.

CONCLUSIONS

This study provides a novel insight to identify the real causality of the phase transition with genome-wide dynamic network analysis.

Evaluating maize phenotype dynamics under drought stress using terrestrial lidar

BACKGROUND

Maize (Zea mays L.) is the third most consumed grain in the world and improving maize yield is of great importance of the world food security, especially under global climate change and more frequent severe droughts. Due to the limitation of phenotyping methods, most current studies only focused on the responses of phenotypes on certain key growth stages. Although light detection and ranging (lidar) technology showed great potential in acquiring three-dimensional (3D) vegetation information, it has been rarely used in monitoring maize phenotype dynamics at an individual plant level.

RESULTS

In this study, we used a terrestrial laser scanner to collect lidar data at six growth stages for 20 maize varieties under drought stress. Three drought-related phenotypes, i.e., plant height, plant area index (PAI) and projected leaf area (PLA), were calculated from the lidar point clouds at the individual plant level. The results showed that terrestrial lidar data can be used to estimate plant height, PAI and PLA at an accuracy of 96%, 70% and 92%, respectively. All three phenotypes showed a pattern of first increasing and then decreasing during the growth period. The high drought tolerance group tended to keep lower plant height and PAI without losing PLA during the tasseling stage. Moreover, the high drought tolerance group inclined to have lower plant area density in the upper canopy than the low drought tolerance group.

CONCLUSION

The results demonstrate the feasibility of using terrestrial lidar to monitor 3D maize phenotypes under drought stress in the field and may provide new insights on identifying the key phenotypes and growth stages influenced by drought stress.

Rhizosphere microorganisms can influence the timing of plant flowering

BACKGROUND

Plant phenology has crucial biological, physical, and chemical effects on the biosphere. Phenological drivers have largely been studied, but the role of plant microbiota, particularly rhizosphere microbiota, has not been considered.

RESULTS

We discovered that rhizosphere microbial communities could modulate the timing of flowering of Arabidopsis thaliana. Rhizosphere microorganisms that increased and prolonged N bioavailability by nitrification delayed flowering by converting tryptophan to the phytohormone indole acetic acid (IAA), thus downregulating genes that trigger flowering, and stimulating further plant growth. The addition of IAA to hydroponic cultures confirmed this metabolic network.

CONCLUSIONS

We document a novel metabolic network in which soil microbiota influenced plant flowering time, thus shedding light on the key role of soil microbiota on plant functioning. This opens up multiple opportunities for application, from helping to mitigate some of the effects of climate change and environmental stress on plants (e.g. abnormal temperature variation, drought, salinity) to manipulating plant characteristics using microbial inocula to increase crop potential.

Comparative transcriptomics identifies patterns of selection in roses

BACKGROUND

Roses are important plants for human beings with pivotal economical and biological traits like continuous flowering, flower architecture, color and scent. Due to frequent hybridization and high genome heterozygosity, classification of roses and their relatives remains a big challenge.

RESULTS

Here, to identify potential markers for phylogenetic reconstruction and to reveal the patterns of natural selection in roses, we generated sets of high quality and comprehensive reference transcriptomes for Rosa chinensis 'Old Blush' (OB) and R. wichuriana 'Basye's Thornless' (BT), two species exhibiting contrasted traits of high economical importance. The assembled reference transcriptomes showed transcripts N50 above 2000 bp. Two roses shared about 10,073 transcripts (N50 = 2282 bp), in which a set of 5959 transcripts was conserved within genera of Rosa. Further comparison with species in Rosaceae identified 4447 transcripts being common (Rosaceae-common) in Rosa, Malus, Prunus, Rubus, and Fragaria, while a pool of 164 transcripts being specific for roses (Rosa-specific). Among the Rosaceae-common transcripts, 409 transcripts showed a signature of positive selection and a clustered expression in different tissues. Interestingly, nine of these rapidly evolving genes were related to DNA damage repair and responses to environmental stimulus, a potential associated with genome confliction post hybridization. Coincident with this fast evolution pattern in rose genes, 24 F-box and four TMV resistant proteins were significantly enriched in the Rosa-specific genes.

CONCLUSIONS

We expect that these Rosaceae-common and Rosa-specific transcripts should facilitate the phylogenetic analysis of Rosaceae plants as well as investigations of Rosa-specific biology. The data reported here could provide fundamental genomic tools and knowledge critical for understanding the biology and domestication of roses and for roses breeding.