Targeting Neuronal GPR65 With Delayed BTB09089 Treatment Improves Neurorehabilitation Following Ischemic Stroke.

BACKGROUND: GPR65 (G protein-coupled receptor 65) can sense extracellular acidic environment to regulate pathophysiological processes. Pretreatment with the GPR65 agonist BTB09089 has been proven to produce neuroprotection in acute ischemic stroke. However, whether delayed BTB09089 treatment and neuronal GPR65 activation promote neurorestoration remains unknown. METHODS: Ischemic stroke was induced in wild-type (WT) or GPR65 knockout (GPR65-/-) mice by photothrombotic ischemia. Male mice were injected intraperitoneally with BTB09089 every other day at days 3, 7, or 14 poststroke. AAV-Syn-GPR65 (adenoassociated virus-synapsin-GPR65) was utilized to overexpress GPR65 in the peri-infarct cortical neurons of GPR65-/- and WT mice. Motor function was monitored by grid-walk and cylinder tests. The neurorestorative effects of BTB09089 were observed by immunohistochemistry, Golgi-Cox staining, and Western blotting. RESULTS: BTB09089 significantly promoted motor outcomes in WT but not in GPR65-/- mice, even when BTB09089 was delayed for 3 to 7 days. BTB09089 inhibited the activation of microglia and glial scar progression in WT but not in GPR65-/- mice. Meanwhile, BTB09089 reduced the decrease in neuronal density in WT mice, but this benefit was abolished in GPR65-/- mice and reemerged by overexpressing GPR65 in peri-infarct cortical neurons. Furthermore, BTB09089 increased the GAP43 (growth-associated protein-43) and synaptophysin puncta density, dendritic spine density, dendritic branch length, and dendritic complexity by overexpressing GPR65 in the peri-infarct cortical neurons of GPR65-/- mice, which was accompanied by increased levels of p-CREB (phosphorylated cAMP-responsive element-binding protein). In addition, the therapeutic window of BTB09089 was extended to day 14 by overexpressing GPR65 in the peri-infarct cortical neurons of WT mice. CONCLUSIONS: Our findings indicated that delayed BTB09089 treatment improved neurological functional recovery and brain tissue repair poststroke through activating neuronal GRP65. GPR65 overexpression may be a potential strategy to expand the therapeutic time window of GPR65 agonists for neurorehabilitation after ischemic stroke.

An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network.

Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective computing. For EEG-based emotion recognition systems, it is crucial to utilize state-of-the-art learning strategies to automatically learn emotion-related brain cognitive patterns from emotional EEG signals, and the learned stable cognitive patterns effectively ensure the robustness of the emotion recognition system. In this work, to realize the efficient decoding of emotional EEG, we propose a graph learning system Graph Convolutional Network framework with Brain network initial inspiration and Fused attention mechanism (BF-GCN) inspired by the brain cognitive mechanism to automatically learn graph patterns from emotional EEG and improve the performance of EEG emotion recognition. In the proposed BF-GCN, three graph branches, i.e., cognition-inspired functional graph branch, data-driven graph branch, and fused common graph branch, are first elaborately designed to automatically learn emotional cognitive graph patterns from emotional EEG signals. And then, the attention mechanism is adopted to further capture the brain activation graph patterns that are related to emotion cognition to achieve an efficient representation of emotional EEG signals. Essentially, the proposed BF-CGN model is a cognition-inspired graph learning neural network model, which utilizes the spectral graph filtering theory in the automatic learning and extracting of emotional EEG graph patterns. To evaluate the performance of the BF-GCN graph learning system, we conducted subject-dependent and subject-independent experiments on two public datasets, i.e., SEED and SEED-IV. The proposed BF-GCN graph learning system has achieved 97.44% (SEED) and 89.55% (SEED-IV) in subject-dependent experiments, and the results in subject-independent experiments have achieved 92.72% (SEED) and 82.03% (SEED-IV), respectively. The state-of-the-art performance indicates that the proposed BF-GCN graph learning system has a robust performance in EEG-based emotion recognition, which provides a promising direction for affective computing.

Association between plant-based dietary index and gallstone disease: A cross sectional study from NHANES.

BACKGROUND: The relationship between plant-based diets and gallstone disease has been debated. This study aimed to shed light on the association between plant-based dietary index and the risk of developing gallstone disease. METHODS: Eligible participants were selected from National Health and Nutrition Examination Survey (NHANES) 2017-2020. Three plant-based diet indexes (PDI, healthy PDI, unhealthy PDI) were calculated using data from two NHANES 24-h dietary recall interviews. Restricted Cubic Spline and multivariate logistic regression were used to analyze the associations. Subgroup analysis was adopted to make the results more robust. RESULTS: A total of 5673 eligible participants were analyzed. After adjusting for various confounding variables, uPDI was positively associated with gallstone disease (OR = 1.53, 95%CI: 1.02-2.29). No association was found between PDI/hPDI and gallstone disease (p > 0.05). The results of subgroup analysis did not show any positive association between uPDI and gallstones in specific groups. CONCLUSION: Our study shows that the elevated uPDI are linked to a higher risk of gallstone disease.

Maize residue retention shapes soil microbial communities and co-occurrence networks upon freeze-thawing cycles.

Maize residue retention is an effective agricultural practice for improving soil fertility in black soil region, where suffered from long freezing-thawing periods and intense freeze-thawing (FT) cycles. However, very few studies have examined the influence of maize residue retention on soil microbial communities under FT cycles. We investigated the response of soil microbial communities and co-occurrence networks to maize residue retention at different FT intensities over 12 cycles using a microcosm experiment conditioned in a temperature incubator. Our results indicated that maize residue retention induced dramatic shifts in soil archaeal, bacterial and fungal communities towards copiotroph-dominated communities. Maize residue retention consistently reduced soil fungal richness across all cycles, but this effect was weaker for archaea and bacteria. Normalized stochastic ratio analysis revealed that maize residue retention significantly enhanced the deterministic process of archaeal, bacterial and fungal communities. Although FT intensity significantly impacted soil respiration, it did not induce profound changes in soil microbial diversity and community composition. Co-occurrence network analysis revealed that maize residue retention simplified prokaryotic network, while did not impact fungal network complexity. The network robustness index suggested that maize residue retention enhanced the fungal network stability, but reduced prokaryotic network stability. Moreover, the fungal network in severe FT treatment harbored the most abundant keystone taxa, mainly being cold-adapted fungi. By identifying modules in networks, we observed that prokaryotic Module #1 and fungal Module #3 were enhanced by maize residue retention and contributed greatly to soil quality. Together, our results showed that maize residue retention exerted stronger influence on soil microbial communities and co-occurrence network patterns than FT intensity and highlighted the potential of microbial interactions in improving soil functionality.

Sleep stages classification by fusing the time-related synchronization analysis and brain activations.

Sleep staging plays an important role in the diagnosis and treatment of clinical sleep disorders. The sleep staging standard defines every 30 seconds as a sleep period, which may mean that there exist similar brain activity patterns during the same sleep period. Thus, in this work, we propose a novel time-related synchronization analysis framework named time-related multimodal sleep scoring model (TRMSC) to explore the potential time-related patterns of sleeping. In the proposed TRMSC, the time-related synchronization analysis is first conducted on the single channel electrophysiological signal, i.e., Electroencephalogram (EEG) and Electrooculogram (EOG), to explore the time-related patterns, and the spectral activation features are also extracted by spectrum analysis to obtain the multimodal features. With the extracted multimodal features, the feature fusion and selection strategy is utilized to obtain the optimal feature set and achieve robust sleep staging. To verify the effectiveness of the proposed TRMSC, sleep staging experiments were conducted on the Sleep-EDF dataset, and the experimental results indicate that the proposed TRMSC has achieved better performance than other existing strategies, which proves that the time-related synchronization features can make up for the shortcomings of traditional spectrum-based strategies and achieve a higher classification accuracy. The proposed TRMSC model may be helpful for portable sleep analyzers and provide a new analytical method for clinical sleeping research.

Biogenesis of Rab14-positive endosome buds at Golgi-endosome contacts by the RhoBTB3-SHIP164-Vps26B complex.

Early endosomes (EEs) are crucial in cargo sorting within vesicular trafficking. While cargoes destined for degradation are retained in EEs and eventually transported to lysosomes, recycled cargoes for the plasma membrane (PM) or the Golgi undergo segregation into specialized membrane structures known as EE buds during cargo sorting. Despite this significance, the molecular basis of the membrane expansion during EE bud formation has been poorly understood. In this study, we identify a protein complex comprising SHIP164, an ATPase RhoBTB3, and a retromer subunit Vps26B, which promotes the formation of EE buds at Golgi-EE contacts. Our findings reveal that Vps26B acts as a novel Rab14 effector, and Rab14 activity regulates the association of SHIP164 with EEs. Depletion of SHIP164 leads to enlarged Rab14+ EEs without buds, a phenotype rescued by wild-type SHIP164 but not the lipid transfer-defective mutants. Suppression of RhoBTB3 or Vps26B mirrors the effects of SHIP164 depletion. Together, we propose a lipid transport-dependent pathway mediated by the RhoBTB3-SHIP164-Vps26B complex at Golgi-EE contacts, which is essential for EE budding.

Spermidine-Functionalized Injectable Hydrogel Reduces Inflammation and Enhances Healing of Acute and Diabetic Wounds In Situ.

The inflammatory response is a key factor affecting tissue regeneration. Inspired by the immunomodulatory role of spermidine, an injectable double network hydrogel functionalized with spermidine (DN-SPD) is developed, where the first and second networks are formed by dynamic imine bonds and non-dynamic photo-crosslinked bonds respectively. The single network hydrogel before photo-crosslinking exhibits excellent injectability and thus can be printed and photo-crosslinked in situ to form double network hydrogels. DN-SPD hydrogel has demonstrated desirable mechanical properties and tissue adhesion. More importantly, an "operando" comparison of hydrogels loaded with spermidine or diethylenetriamine (DETA), a sham molecule resembling spermidine, has shown similar physical properties, but quite different biological functions. Specifically, the outcomes of 3 sets of in vivo animal experiments demonstrate that DN-SPD hydrogel can not only reduce inflammation caused by implanted exogenous biomaterials and reactive oxygen species but also promote the polarization of macrophages toward regenerative M2 phenotype, in comparison with DN-DETA hydrogel. Moreover, the immunoregulation by spermidine can also translate into faster and more natural healing of both acute wounds and diabetic wounds. Hence, the local administration of spermidine affords a simple but elegant approach to attenuate foreign body reactions induced by exogenous biomaterials to treat chronic refractory wounds.

Arene-, Chlorido-, and Imido-Uranium Bis- and Tris(boryloxide) Complexes.

We introduce the boryloxide ligand {(HCNDipp)2BO}- (NBODipp, Dipp = 2,6-di-isopropylphenyl) to actinide chemistry. Protonolysis of [U{N(SiMe3)2}3] with 3 equiv of NBODippH produced the uranium(III) tris(boryloxide) complex [U(NBODipp)3] (1). In contrast, treatment of UCl4 with 3 equiv of NBODippK in THF at room temperature or reflux conditions produced only [U(NBODipp)2(Cl)2(THF)2] (2) with 1 equiv of NBODippK remaining unreacted. However, refluxing the mixture of 2 and unreacted NBODippK in toluene instead of THF afforded the target complex [U(NBODipp)3(Cl)(THF)] (3). Two-electron oxidation of 1 with AdN3 (Ad = 1-adamantyl) afforded the uranium(V)-imido complex [U(NBODipp)3(NAd)] (4). The solid-state structure of 1 reveals a uranium-arene bonding motif, and structural, spectroscopic, and DFT calculations all suggest modest uranium-arene δ-back-bonding with approximately equal donation into the arene π4 and π5 δ-symmetry π* molecular orbitals. Complex 4 exhibits a short uranium(V)-imido distance, and computational modeling enabled its electronic structure to be compared to related uranium-imido and uranium-oxo complexes, revealing a substantial 5f-orbital crystal field splitting and extensive mixing of 5f |ml,ms⟩ states and mj projections. Complexes 1-4 have been variously characterized by single-crystal X-ray diffraction, 1H NMR, IR, UV/vis/NIR, and EPR spectroscopies, SQUID magnetometry, elemental analysis, and CONDON, F-shell, DFT, NLMO, and QTAIM crystal field and quantum chemical calculations.

Research landscape of energy transition and green finance: A bibliometric analysis.

This study utilizes bibliometric analysis to examine historical and present research patterns in the area of energy transition and green finance and to forecast potential future domains. Using the bibliometric method, 328 scholarly articles from the Web of Science database were evaluated. This paper identifies influential publications, maps the research landscape, and forecasts emerging tendencies through co-citation and co-word analyses. Co-citation analysis found three main clusters, while co-word analysis revealed four main clusters. Despite the growing significance of research on energy transition and green finance research, further in-depth investigation is necessary to offer a thorough depiction of the research domain. This research represents a pioneering endeavour in the utilization of bibliometric analysis to investigate the interrelationship between two items. It offers valuable insights into the rapidly expanding field of energy transition and green finance, effectively highlighting its contours and indicating potential future developments.

Core bacteria and fungi in response to residue retention and their contribution to soil multifunctionality in maize agroecosystem.

Core microbiome has been proven to play crucial roles in soil function. However, we still lack knowledge on how core microbiome responds to crop residue retention, and whether they contribute to this process. Consequently, we examined the effect of residue retention on soil core and non-core microbial communities in maize seedling, mature stage and freezing period based on a multi-site field experiment in Sanjiang Plain, Northeast China. Totally, 247 bacterial amplicon sequence variants (ASVs) and 109 fungal ASVs were identified as core microbiota. Both core and non-core bacterial/fungal community composition were significantly influenced by residue retention across all study sites. Especially, the core fungal community shifted towards a saprotroph-dominated community. Normalized stochastic ratio pattern revealed that that deterministic process dominated both core and non-core microbial community assembly processes. Residue retention enhanced the deterministic process of core microbial community assembly, while exhibited opposite effect on non-core microbial community. This study also revealed that soil fungi were more sensitive to residue retention than bacteria, with a larger proportion of core fungi were enriched or depleted by residue retention. In addition, residue retention complicated core bacterial co-occurrence network, while simplified fungal network. Our results pointed out both no reduction in microbial diversity or collapse in microbial network structure after repeated freezing-thawing cycles. The potential function of core microbiome was evaluated through random forest analysis and structural equation model, the results indicated core microbiome contributed more to multifunctionality than non-core microbiome. Overall, this study strengthened our understanding of soil core microbiome in response to residue retention, and highlighted their importance in maintaining soil multifunctionality.

Large-scale interplant exchange of macromolecules between soybean and dodder under nutrient stresses.

Parasitic plants and their hosts communicate through haustorial connections. Nutrient deficiency is a common stress for plants, yet little is known about whether and how host plants and parasites communicate during adaptation to such nutrient stresses. In this study, we used transcriptomics and proteomics to analyze how soybean (Glycine max) and its parasitizing dodder (Cuscuta australis) respond to nitrate and phosphate deficiency (-N and -P). After -N and -P treatment, the soybean and dodder plants exhibited substantial changes of transcriptome and proteome, although soybean plants showed very few transcriptional responses to -P and dodder did not show any transcriptional changes to either -N or -P. Importantly, large-scale interplant transport of mRNAs and proteins was detected. Although the mobile mRNAs only comprised at most 0.2% of the transcriptomes, the foreign mobile proteins could reach 6.8% of the total proteins, suggesting that proteins may be the major forms of interplant communications. Furthermore, the interplant mobility of macromolecules was specifically affected by the nutrient regimes and the transport of these macromolecules was very likely independently regulated. This study provides new insight into the communication between host plants and parasites under stress conditions.

Nitrogen deposition mediates more stochastic processes in structuring plant community than soil microbial community in the Eurasian steppe.

Anthropogenic environmental changes may affect community assembly through mediating both deterministic (e.g., competitive exclusion and environmental filtering) and stochastic processes (e.g., birth/death and dispersal/colonization). It is traditionally thought that environmental changes have a larger mediation effect on stochastic processes in structuring soil microbial community than aboveground plant community; however, this hypothesis remains largely untested. Here we report an unexpected pattern that nitrogen (N) deposition has a larger mediation effect on stochastic processes in structuring plant community than soil microbial community (those <2 mm in diameter, including archaea, bacteria, fungi, and protists) in the Eurasian steppe. We performed a ten-year nitrogen deposition experiment in a semiarid grassland ecosystem in Inner Mongolia, manipulating nine rates (0-50 g N m-2 per year) at two frequencies (nitrogen added twice or 12 times per year) under two grassland management strategies (fencing or mowing). We separated the compositional variation of plant and soil microbial communities caused by each treatment into the deterministic and stochastic components with a recently-developed method. As nitrogen addition rate increased, the relative importance of stochastic component of plant community first increased and then decreased, while that of soil microbial community first decreased and then increased. On the whole, the relative importance of stochastic component was significantly larger in plant community (0.552±0.035; mean±standard error) than in microbial community (0.427±0.035). Consistently, the proportion of compositional variation explained by the deterministic soil and community indices was smaller for plant community (0.172-0.186) than microbial community (0.240-0.767). Meanwhile, as nitrogen addition rate increased, the linkage between plant and microbial community composition first became weaker and then became stronger. The larger stochasticity in plant community relative to microbial community assembly suggested that more stochastic strategies (e.g., seeds addition) should be adopted to maintain above- than below-ground biodiversity under the pressure of nitrogen deposition.

The terrestrial end-Permian mass extinction in the paleotropics postdates the marine extinction.

The end-Permian mass extinction was the most severe ecological event during the Phanerozoic and has long been presumed contemporaneous across terrestrial and marine realms with global environmental deterioration triggered by the Siberian Traps Large Igneous Province. We present high-precision zircon U-Pb geochronology by the chemical abrasion-isotope dilution-thermal ionization mass spectrometry technique on tuffs from terrestrial to transitional coastal settings in Southwest China, which reveals a protracted collapse of the Cathaysian rainforest beginning after the onset of the end-Permian marine extinction. Integrated with high-resolution geochronology from coeval successions, our results suggest that the terrestrial extinction occurred diachronously with latitude, beginning at high latitudes during the late Changhsingian and progressing to the tropics by the early Induan, spanning a duration of nearly 1 million years. This latitudinal age gradient may have been related to variations in surface warming with more degraded environmental conditions at higher latitudes contributing to higher extinction rates.

Predictors of anxiety among pregnant women with gestational diabetes mellitus and their partners: The mediating role of marital satisfaction.

AIMS: This study aims to examine the prevalence of anxiety symptoms and identify predictors of anxiety among pregnant women with gestational diabetes mellitus and their partners and explore the mediating role of marital satisfaction between maternal and paternal anxiety. DESIGN: A cross-sectional study was conducted in Guangzhou, China, from July 2021 to May 2022. METHODS: A total of 306 dyads of pregnant women with gestational diabetes mellitus and their partners completed the State-Trait Anxiety Inventory, Locke-Wallace Marital Adjustment Test and the socio-demographic and clinical data sheet. RESULTS: The prevalence of anxiety symptoms was 32.4% and 36.6% in pregnant women with gestational diabetes mellitus and their partners, respectively. The predictors of maternal anxiety were paternal anxiety, maternal marital satisfaction, maternal monthly salary, fasting glucose value and 1-h glucose value. By contrast, the predictors of paternal anxiety were maternal anxiety, paternal marital satisfaction and paternal monthly salary. Moreover, the relationship between maternal and paternal anxiety was mediated by marital satisfaction. CONCLUSIONS: The anxiety symptoms of pregnant women with gestational diabetes mellitus and their partners influence each other, and this relationship was mediated by marital satisfaction. Every couple should be screened for anxiety symptoms and treated as a team rather than focusing solely on the pregnant woman.

U-Net-Based Assistive Identification of Bladder Cancer: A Promising Approach for Improved Diagnosis.

INTRODUCTION: Bladder cancer (BC) is a major health concern that poses a significant threat to the population, with an increasing incidence rate and a high risk of recurrence and progression. The primary clinical method for diagnosing BC is cystoscopy, but due to the limitations of traditional white light cystoscopy and inadequate clinical experience among junior physicians, its detection rate for bladder tumor, especially small and flat lesions, is relatively low. However, recent years have seen remarkable advancements in the application of artificial intelligence (AI) technology in the field of medicine. This has led to the development of numerous AI algorithms that have been successfully integrated into medical practices, providing valuable assistance to clinicians. The purpose of this study is to develop a cystoscopy algorithm that is real time, cost effective, high performing, and accurate, with the aim of enhancing the detection rate of bladder tumors during cystoscopy. MATERIALS AND METHODS: For this study, a dataset of 3,500 cystoscopic images obtained from 100 patients diagnosed with BC was collected, and a deep learning model was developed utilizing the U-Net algorithm within a convolutional neural network for training purposes. RESULTS: This study randomly divided 3,500 images from 100 BC patients into training and validation groups, and each patient's pathology result was confirmed. In the validation group, the accuracy of tumor recognition by the U-Net algorithm reached 98% compared to primary urologists, with greater accuracy and faster detection speed. CONCLUSION: This study highlights the potential of U-Net-based deep learning techniques in the detection of bladder tumors. The establishment and optimization of the U-Net model is a significant breakthrough and it provides a valuable reference for future research in the field of medical image processing.

EEG based emotion recognition by hierarchical bayesian spectral regression framework.

Spectral regression (SR), a graph-based learning regression model, can be used to extract features from graphs to realize efficient dimensionality reduction. However, due to the SR method remains a regularized least squares problem and being defined in L2-norm space, the effect of artifacts in EEG signals cannot be efficiently resisted. In this work, to further improve the robustness of the graph-based regression models, we propose to utilize the prior distribution estimation in the Bayesian framework and develop a robust hierarchical Bayesian spectral regression framework (named HB-SR), which is designed with the hierarchical Bayesian ensemble strategies. In the proposed HB-SR, the impact of noises can be effectively reduced by the adaptive adjustment approach in model parameters with the data-driven manner. Specifically, in the current work, three different distributions have been elaborately designed to enhance the universality of the proposed HB-SR, i.e., Gaussian distribution, Laplace distribution, and Student-t distribution. To objectively evaluate the performance of the HB-SR framework, we conducted both simulation studies and emotion recognition experiments based on emotional EEG signals. Experimental results have consistently indicated that compared with other existing spectral regression methods, the proposed HB-SR can effectively suppress the influence of noises and achieve robust EEG emotion recognition.

A hybrid graph network model for ASD diagnosis based on resting-state EEG signals.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder and early diagnosis is crucial for effective treatment. Stable and effective biomarkers are essential for understanding the underlying causes of the disorder and improving diagnostic accuracy. Electroencephalography (EEG) signals have proven to be reliable biomarkers for diagnosing ASD. Extracting stable connectivity patterns from EEG signals helps ensure robustness in ASD diagnostic systems. In this study, we propose a hybrid graph convolutional network framework called Rest-HGCN, which utilizes resting-state EEG signals to capture differential patterns of brain connectivity between normal children and ASD patients using graph learning strategies. The Rest-HGCN combines brain network analysis techniques and data-driven strategies to extract discriminative graph features from resting-state EEG signals. By automatically extracting differential graph patterns from these signals, the Rest-HGCN achieves reliable ASD diagnosis. To evaluate the performance of Rest-HGCN, we conducted ASD diagnosis experiments using k-fold cross-validation on the public ABC-CT resting EEG dataset. The proposed Rest-HGCN model achieved accuracies of 87.12 % and 85.32 % in single-subject and cross-experiment analyses, respectively. The results suggest that Rest-HGCN can effectively capture discriminant graph patterns from resting EEG signals and achieve robust ASD diagnosis. This may provide an effective and convenient tool for clinical ASD diagnosis.

Botrytis cinerea hypovirulent strain △BcSpd1 induced Panax ginseng defense.

Background: Gray mold, caused by Botrytis cinerea, is one of the major fungal diseases in agriculture. Biological methods are preferred over chemical fungicides to control gray mold since they are less toxic to the environment and could induce the resistance to pathogens in plants. In this work, we try to understand if ginseng defense to B. cinerea could be induced by fungal hypovirulent strain △BcSpd1. BcSpd1 encodes Zn(II)2Cys6 transcription factor which regulates fungal pathogenicity and we recently reported △BcSpd1 mutants reduced fungal virulence. Methods: We performed transcriptomic analysis of the host to investigate the induced defense response of ginseng treated by B. cinerea △BcSpd1. The metabolites in ginseng flavonoids pathway were determined by UPLC-ESI-MS/MS and the antifungal activates were then performed. Results: We found that △BcSpd1 enhanced the ginseng defense response when applied to healthy ginseng leaves and further changed the metabolism of flavonoids. Compared with untreated plants, the application of △BcSpd1 on ginseng leaves significantly increased the accumulation of p-coumaric acid and myricetin, which could inhibit the fungal growth. Conclusion: B. cinerea △BcSpd1 could effectively induce the medicinal plant defense and is referred to as the biological control agent in ginseng disease management.

Capture of small clusters by ligand-solvent interaction.

Clusters are considered to become increasingly significant for elaborating the nanocrystal's formation mechanism. However, capturing the clusters with high chemical potential is challenging because of the lack of effective strategies. In this work, the key role of ligand-solvent interaction has been revealed for the stabilization of clusters in silver telluride synthesis. The Flory interaction coefficient that comprehensively regards the temperature and dispersion, polarity, and hydrogen bonding of the solvent has been used to evaluate the ligand-solvent interaction and thus assist in the design of synthetic systems. Small silver telluride clusters have been successfully captured, and the composition of the smallest cluster is determined as Ag7Te8(SCy)2 (SCy represents the ligand). This work provides new insights into the design of cluster/nanocrystal synthesis systems and paves the way to revealing the mechanism of precursor-cluster-nanocrystal conversion.