[Effects of water and nitrogen management on photosynthetic matter production and yield of wheat]. / æ°´æ°®è¿ç­¹å¯¹å°éº¦å ‰åˆç‰©è´¨ç”Ÿäº§å’Œäº§é‡çš„å½±å“.

The shortage of water resources and the irrational application of nitrogen fertilizer restrict the synergistic enhancement of yield and water- and fertilizer-use efficiencies of wheat in the Huang-Huai-Hai region. In this study, we conducted an experiment following two-factor split zone design with three irrigation levels and four nitrogen application rates. The relative water content of the 0-40 cm soil layer was supplemented to 65% (W1), 75% (W2), and 85% (W3) of field water capacity at the jointing and anthesis stages of wheat. The rates of nitrogen application were 0 (N0), 150 (N1), 180 (N2), and 210 (N3) kg·hm-2. We analyzed the effects of these different managements on post-anthesis photosynthetic matter production, yield, and water- and nitrogen-use efficiencies. The results showed that yield first increased with increases in the levels of irrigation and nitrogen application, peaking under the W2N2 treatment (9103.53 kg·hm-2). However, further increases in water and nitrogen input did not have further enhancement of wheat yield. Under the same nitrogen application condition, compared with W1 treatment, the canopy light interception rate, chlorophyll relative content and actual photochemical efficiency after anthesis increased by 4.5%-6.0%, 19.7%-28.2%, and 7.5%-9.8% in response to the W2 treatment, respectively, without any difference between the W2 and W3 irrigation levels. At the same irrigation level, post-anthesis dry matter accumulation in repose to the N2 treatment increased by 80.1%-88.9% and 16.7%-22.2% compared with N0 and N1 treatments, respectively, without significant difference between the N2 and N3 treatments. Both the irrigation water-use efficiency (IWUE) and the nitrogen partial factor productivity declined with increases in the levels of irrigation and nitrogen application. Under the W1, W2, and W3 treatments, the values obtained for IWUE were 16.23, 11.01, and 7.91 kg·hm-2·m-3, respectively, whereas in response to the N1, N2, and N3 treatments, N partial factor productivity was 50.8%, 48.4%, and 42.5%, respectively. In all, based on soil moisture measurements and assessments of wheat yield and water- and nitrogen-use efficiencies, the optimal water and nitrogen management strategy for enhancing wheat yield in the Huang-Huai-Hai region is supplementation of water content of 0-40 cm soil layer at the jointing and anthesis stages to 75% field capacity combined with the application of 180 kg·hm-2 nitrogen (W2N2). This approach could achieve high yield and efficiency and promote conservation of water and fertilizer.

TVDO: Tchebycheff Value-Decomposition Optimization for Multiagent Reinforcement Learning.

In cooperative multiagent reinforcement learning (MARL), centralized training with decentralized execution (CTDE) has recently attracted more attention due to the physical demand. However, the most dilemma therein is the inconsistency between jointly-trained policies and individually executed actions. In this article, we propose a factorized Tchebycheff value-decomposition optimization (TVDO) method to overcome the trouble of inconsistency. In particular, a nonlinear Tchebycheff aggregation function is formulated to realize the global optimum by tightly constraining the upper bound of individual action-value bias, which is inspired by the Tchebycheff method of multiobjective optimization (MOO). We theoretically prove that, under no extra limitations, the factorized value decomposition with Tchebycheff aggregation satisfies the sufficiency and necessity of individual-global-max (IGM), which guarantees the consistency between the global and individual optimal action-value function. Empirically, in the climb and penalty game, we verify that TVDO precisely expresses the global-to-individual value decomposition with a guarantee of policy consistency. Meanwhile, we evaluate TVDO in the StarCraft multiagent challenge (SMAC) benchmark, and extensive experiments demonstrate that TVDO achieves a significant performance superiority over some SOTA MARL baselines.

Cross-Attention Regression Flow for Defect Detection.

Defect detection from images is a crucial and challenging topic of industry scenarios due to the scarcity and unpredictability of anomalous samples. However, existing defect detection methods exhibit low detection performance when it comes to small-size defects. In this work, we propose a Cross-Attention Regression Flow (CARF) framework to model a compact distribution of normal visual patterns for separating outliers. To retain rich scale information of defects, we build an interactive cross-attention pattern flow module to jointly transform and align distributions of multi-layer features, which is beneficial for detecting small-size defects that may be annihilated in high-level features. To handle the complexity of multi-layer feature distributions, we introduce a layer-conditional autoregression module to improve the fitting capacity of data likelihoods on multi-layer features. By transforming the multi-layer feature distributions into a latent space, we can better characterize normal visual patterns. Extensive experiments on four public datasets and our collected industrial dataset demonstrate that the proposed CARF outperforms state-of-the-art methods, particularly in detecting small-size defects.

Vitrification cryo-foil method for shoot tip cryopreservation and virus eradication in apple.

Establishment of a new method for improved shoot tip cryopreservation is crucial to facilitate the long-term preservation of plant germplasm as well as the use of cryotherapy for pathogen eradication. The present study reported a vitrification (V) cryo-foil method for shoot tip cryopreservation and virus eradication in apple. Shoot tip regrowth levels after cryopreservation were comparable among V cryo-foil (53 %), V cryo-plate (46 %) and conventional droplet vitrification (Dr-vi, 48 %). The V cryo-foil is more efficient to perform than Dr-vi as more shoot tips can be cryopreserved by one person. In the histological study applying an image-overlaying strategy, shoot tips cryopreserved by V cryo-foil showed a higher survival chance in the youngest leaf primordia than in the apical dome. When V cryo-foil was tested for virus eradication, fifty-five percent (55 %) of cryo-derived shoots were free of the apple stem pitting virus (ASPV), while none and less than 10 % were free of the apple stem grooving virus (ASGV) and the apple chlorotic leaf spot virus (ACLSV), respectively. Thus, these two viruses were efficiently preserved by V cryo-foil cryopreservation. Noticeably, although the shoot regrowth level was reduced to 27 %, a higher frequency (81 %) of ASPV eradication was achieved when a reduced duration of cryoprotectant exposure was applied in V cryo-foil, supporting the use of insufficient cryoprotection for improved virus eradication.

Heterojunction photoelectric device with fast response speed and low power consumption composed of WSSe and AlN.

Through the accurate calculation of density functional theory, reveal the excellent photoelectric properties of the AlN/WSSe and WSSe/AlN heterojunction. Especially, the hole mobility of the AlN/WSSe heterojunction is as high as 3919 cm2Vs-1in armchair direction, and the hole mobility of the WSSe/AlN heterojunction is as high as 4422 cm2Vs-1in the zigzag direction. Interestingly, when two H atoms are adsorbed in the WSSe surface, the Gibbs free energy change are -0.093 eV and -0.984 eV, which tends to zero, which can promote the spontaneous reaction of electrocatalytic water decomposition to produce H2. In addition, the AlN/WSSe heterojunction exhibits significant photoelectric effect photocurrent (1.15 a02/photon) in the armchair direction and the heterojunctions have lower threshold voltage (1.5 V), that indicate the AlN/WSSe and WSSe/AlN heterojunction have great application prospect in manufacturing high-performance optoelectronic devices with fast response and low power consumption.

Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm.

Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task due to the mixture of trend, seasonal and residual components in time series and the nonlinear relationships between Chla and the hydro-environmental factors. Here we developed a hybrid approach for long-term trend forecast of Chla in lakes, taking the Lake Taihu as an instantiation case, by the integration of Seasonal and Trend decomposition using Loess (STL), wavelet coherence, and Convolutional Neural Network with Bidirectional Long Short-Term Memory (CNN-BiLSTM). The results showed that long-term trends of Chla and the hydro-environmental factors could be effectively separated from the seasonal and residual terms by STL method, thereby enhancing the characterization of long-term variation. The resonance pattern and time lag between Chla and the hydro-environmental factors in the time-frequency domain were accurately identified by wavelet coherence. Chla responded quickly to variations in TP, but showed a time lag response to variations in WT in Lake Taihu. The forecasting method using multivariate and CNN-BiLSTM largely outperformed the other methods for Lake Taihu with regards to R2, RMSE, IOA and peak capture capability, owning to the combination of CNN for extracting local features and the integration of bidirectional propagation mechanism for the acquisition of higher-level features. The proposed hybrid deep learning approach offers an effective solution for the long-term trend forecast of algal blooms in eutrophic lakes and is capable of addressing the complex attributes of hydro-environmental data.

An attribution graph-based interpretable method for CNNs.

Convolutional Neural Networks (CNNs) have demonstrated outstanding performance in various domains, such as face recognition, object detection, and image segmentation. However, the lack of transparency and limited interpretability inherent in CNNs pose challenges in fields such as medical diagnosis, autonomous driving, finance, and military applications. Several studies have explored the interpretability of CNNs and proposed various post-hoc interpretable methods. The majority of these methods are feature-based, focusing on the influence of input variables on outputs. Few methods undertake the analysis of parameters in CNNs and their overall structure. To explore the structure of CNNs and intuitively comprehend the role of their internal parameters, we propose an Attribution Graph-based Interpretable method for CNNs (AGIC) which models the overall structure of CNNs as graphs and provides interpretability from global and local perspectives. The runtime parameters of CNNs and feature maps of each image sample are applied to construct attribution graphs (At-GCs), where the convolutional kernels are represented as nodes and the SHAP values between kernel outputs are assigned as edges. These At-GCs are then employed to pretrain a newly designed heterogeneous graph encoder based on Deep Graph Infomax (DGI). To comprehensively delve into the overall structure of CNNs, the pretrained encoder is used for two types of interpretable tasks: (1) a classifier is attached to the pretrained encoder for the classification of At-GCs, revealing the dependency of At-GC's topological characteristics on the image sample categories, and (2) a scoring aggregation (SA) network is constructed to assess the importance of each node in At-GCs, thus reflecting the relative importance of kernels in CNNs. The experimental results indicate that the topological characteristics of At-GC exhibit a dependency on the sample category used in its construction, which reveals that kernels in CNNs show distinct combined activation patterns for processing different image categories, meanwhile, the kernels that receive high scores from SA network are crucial for feature extraction, whereas low-scoring kernels can be pruned without affecting model performance, thereby enhancing the interpretability of CNNs.

The O-Defective g-ZnO Sensor for VOC Gases: The Adsorption-Desorption, Electronic, and Sensitivity Properties.

Adsorption-desorption performance, electronic properties, and sensitivity of O-defective g-ZnO (ODZO) gas sensors for volatile organic compounds (VOCs) are calculated using density functional theory and nonequilibrium Green's formalism. The VOCs are CH2O, CH4, C2H4O, CH4O, and C2H6. The intrinsic g-ZnO (IZO) and ODZO exhibit strong adsorption capabilities for C2H4O and CH4O. The IZO (0.118 e) and ODZO (0.059 e), which act as electron donors, exhibit the highest charge transfer to CH2O, indicating a strong interaction. The VOCs adsorption on the IZO and ODZO systems maintain nonmagnetic semiconductor characteristics. Additionally, the introduction of an O-defect causes the adsorption energy and charge transfer amount of ODZO to show an overall decrease, indicating better desorption ability. Notably, the sensitivity results show that the ODZO gas sensors exhibit high sensitivity to CH2O (39.3%), C2H4O (29.0%), and CH4O (19.6%) at a voltage of 2.6 V, consistent with the adsorption-desorption performance and electronic properties.

Stacking engineering induced Z-scheme MoSSe/WSSe heterostructure for photocatalytic water splitting.

Stacking engineering is a popular method to tune the performance of two-dimensional materials for advanced applications. In this work, Jansu MoSSe and WSSe monolayers are constructed as a van der Waals (vdWs) heterostructure by different stacking configurations. Using first-principle calculations, all the relaxed stacking configurations of the MoSSe/WSSe heterostructure present semiconductor properties while the direct type-II band structure can be obtained. Importantly, the Z-scheme charge transfer mode also can be addressed by band alignment, which shows the MoSSe/WSSe heterostructure is an efficient potential photocatalyst for water splitting. In addition, the built-in electric field of the MoSSe/WSSe vdWs heterostructure can be enhanced by the S-Se interface due to further asymmetric structures, which also results in considerable charge transfer comparing with the MoSSe/WSSe vdWs heterostructure built by the S-S interface. Furthermore, the excellent optical performances of the MoSSe/WSSe heterostructure with different stacking configurations are obtained. Our results provide a theoretical guidance for the design and control of the two-dimensional heterostructure as photocatalysts through structural stacking.

Raspberry Ketone Attenuates Hepatic Fibrogenesis and Inflammation via Regulating the Crosstalk of FXR and PGC-1α Signaling.

Hepatic fibrosis is a compensatory response to chronic liver injury and inflammation, and dietary intervention is recommended as one of the fundamental prevention strategies. Raspberry ketone (RK) is an aromatic compound first isolated from raspberry and widely used to prepare food flavors. The current study investigated the hepatoprotection and potential mechanism of RK against hepatic fibrosis. In vitro, hepatic stellate cell (HSC) activation was stimulated with TGF-ß and cultured with RK, farnesoid X receptor (FXR), or peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) agonist or inhibitor, respectively. In vivo, C57BL/6 mice were injected intraperitoneally with thioacetamide (TAA) at 100/200 mg/kg from the first to the fifth week. Mice were intragastrically administrated with RK or Cur once a day from the second to the fifth week. In activated HSCs, RK inhibited extracellular matrix (ECM) accumulation, inflammation, and epithelial-mesenchymal transition (EMT) process. RK both activated FXR/PGC-1α and regulated their crosstalk, which were verified by their inhibitors and agonists. Deficiency of FXR or PGC-1α also attenuated the effect of RK on the reverse of activated HSCs. RK also decreased serum ALT/AST levels, liver histopathological change, ECM accumulation, inflammation, and EMT in mice caused by TAA. Double activation of FXR/PGC-1α might be the key targets for RK against hepatic fibrosis. Above all, these discoveries supported the potential of RK as a novel candidate for the dietary intervention of hepatic fibrosis.

Diallyl disulfide, the bioactive component of Allium species, ameliorates pulmonary fibrosis by mediating the crosstalk of farnesoid X receptor and yes-associated protein 1 signaling pathway.

Environmental pollution, virus infection, allergens, and other factors may cause respiratory disease, which could be improved by dietary therapy. Allium species are common daily food seasoning and have high nutritional and medical value. Diallyl disulfide (DADS) is the major volatile oil compound of Allium species. The present study aims to explore the preventive effect and potential mechanism of DADS on pulmonary fibrosis. C57BL/6J mice were intratracheally injected with bleomycin (BLM) to establish pulmonary fibrosis and then administrated with DADS. Primary lung fibroblasts or A549 were stimulated with BLM, followed by DADS, farnesoid X receptor (FXR) agonist (GW4064), yes-associated protein 1 (YAP1) inhibitor (verteporfin), or silencing of FXR and YAP1. In BLM-stimulated mice, DADS significantly ameliorated histopathological changes and interleukin-1ß levels in bronchoalveolar lavage fluid. DADS decreased fibrosis markers, HIF-1α, inflammatory cytokines, and epithelial-mesenchymal transition in pulmonary mice and activated fibroblasts. DADS significantly enhanced FXR expression and inhibited YAP1 activation, which functions as GW4064 and verteporfin. A deficiency of FXR or YAP1 could result in the increase of these two protein expressions, respectively. DADS ameliorated extracellular matrix deposition, hypoxia, epithelial-mesenchymal transition, and inflammation in FXR or YAP1 knockdown A549. Taken together, targeting the crosstalk of FXR and YAP1 might be the potential mechanism for DADS against pulmonary fibrosis. DADS can serve as a potential candidate or dietary nutraceutical supplement for the treatment of pulmonary fibrosis.

Wasserstein Discriminant Dictionary Learning for Graph Representation.

Mining discriminative graph topological information plays an important role in promoting graph representation ability. However, it suffers from two main issues: (1) the difficulty/complexity of computing global inter-class/intra-class scatters, commonly related to mean and covariance of graph samples, for discriminant learning; (2) the huge complexity and variety of graph topological structure that is rather challenging to robustly characterize. In this paper, we propose the Wasserstein Discriminant Dictionary Learning (WDDL) framework to achieve discriminant learning on graphs with robust graph topology modeling, and hence facilitate graph-based pattern analysis tasks. Considering the difficulty of calculating global inter-class/intra-class scatters, a reference set of graphs (aka graph dictionary) is first constructed by generating representative graph samples (aka graph keys) with expressive topological structure. Then, a Wasserstein Graph Representation (WGR) process is proposed to project input graphs into a succinct dictionary space through the graph dictionary lookup. To further achieve discriminant graph learning, a Wasserstein discriminant loss (WD-loss) is defined on the graph dictionary, in which the graph keys are optimizable, to make the intra-class keys more compact and inter-class keys more dispersed. Hence, the calculation of global Wasserstein metric (W-metric) centers can be bypassed. For sophisticated topology mining in the WGR process, a joint-Wasserstein graph embedding module is constructed to model both between-node and between-edge relationships across inputs and graph keys by encapsulating both the Wasserstein metric (between cross-graph nodes) and proposed novel Kron-Gromov-Wasserstein (KGW) metric (between cross-graph adjacencies). Specifically, the KGW-metric comprehensively characterizes the cross-graph connection patterns with the Kronecker operation, then adaptively captures those salient patterns through connection pooling. To evaluate the proposed framework, we study two graph-based pattern analysis problems, i.e. graph classification and cross-modal retrieval, with the graph dictionary flexibly adjusted to cater to these two tasks. Extensive experiments are conducted to comprehensively compare with existing advanced methods, as well as dissect the critical component of our proposed architecture. The experimental results validate the effectiveness of the WDDL framework.

Intra-Inter Graph Representation Learning for Protein-Protein Binding Sites Prediction.

Graph neural networks have drawn increasing attention and achieved remarkable progress recently due to their potential applications for a large amount of irregular data. It is a natural way to represent protein as a graph. In this work, we focus on protein-protein binding sites prediction between the ligand and receptor proteins. Previous work just simply adopts graph convolution to learn residue representations of ligand and receptor proteins, then concatenates them and feeds the concatenated representation into a fully connected layer to make predictions, losing much of the information contained in complexes and failing to obtain an optimal prediction. In this paper, we present Intra-Inter Graph Representation Learning for protein-protein binding sites prediction (IIGRL). Specifically, for intra-graph learning, we maximize the mutual information between local node representation and global graph summary to encourage node representation to embody the global information of protein graph. Then we explore fusing two separate ligand and receptor graphs as a whole graph and learning affinities between their residues/nodes to propagate information to each other, which could effectively capture inter-protein information and further enhance the discrimination of residue pairs. Extensive experiments on multiple benchmarks demonstrate that the proposed IIGRL model outperforms state-of-the-art methods.

Late coronary artery injury following chemoradiotherapy for thymic carcinoma: a case report.

INTRODUCTION: Surgery remains the primary treatment modality for thymic carcinoma, with adjuvant radiotherapy being recommended to effectively mitigate local recurrence and metastasis rates subsequent to incomplete or complete resection. Chemoradiotherapy has the potential to induce coronary artery occlusion, thereby potentially impacting patients' long-term survival rates. The existing literature currently lacks comprehensive research on the lesion characteristics of coronary artery injury resulting from chemoradiotherapy. CASE PRESENTATION: The male patient, aged 55, was admitted to the hospital due to recurrent chest tightness and pain persisting for one week. Notably, the patient had previously undergone curative resection surgery for thymic carcinoma seven years ago. After the surgical procedure, the patient underwent a course of adjuvant chemotherapy comprising docetaxel and platinum. 11 months later, imaging examination diagnosed tumor recurrence, and concurrent chemoradiotherapy was administered at a total dose of 62 Gy/31F for planning gross target volume (PGTV) and 54 Gy/31F for planning target volume (PTV) with 2 cycles of paclitaxel and cisplatin. Re-admission of the patient occurred after a 7-year interval subsequent to the completion of concurrent chemoradiotherapy, leading to a subsequent diagnosis of acute non-ST segment elevation myocardial infarction. Following administration of antiplatelet, anticoagulant, and anti-myocardial ischemia therapy, coronary angiography revealed the presence of a bifurcation lesion at the distal end of the left main trunk. Intravascular ultrasound (IVUS) examination demonstrated significant negative remodeling of both the main trunk and its branches at the bifurcation site, characterized by minimal atherosclerotic plaque components. CONCLUSIONS: Chemoradiotherapy may induce damage to endothelial cells, resulting in an inflammatory response. Negative remodeling of blood vessels is likely to occur, primarily characterized by vasoconstriction but with less atherosclerotic plaque burden. Routine stent implantation in negatively remodeled areas may lead to vascular rupture, necessitating intravascular imaging examination.

Copper-Catalyzed [3 + 2] Annulation of O-Acyl Oximes with 4-Sulfonamidophenols for the Synthesis of 5-Sulfonamidoindoles and 2-Amido-5-sulfonamidobenzofuran-3(2H)-ones.

A copper-catalyzed [3 + 2] annulation of O-acyl oximes with 4-sulfonamidophenols is developed. The advantage of this method lies in the concurrent double activation of two substrates to form nucleophilic enamines and electrophilic quinone monoimines. The substituent on the α-carbon of O-acyl oxime determines two different reaction pathways, thereby leading to the selective generation of 5-sulfonamidoindoles and 2-amido-5-sulfonamidobenzofuran-3(2H)-ones.

Design, synthesis, antifungal evaluation and mechanism study of novel norbornene derivatives as potential laccase inhibitors.

BACKGROUND: To discover novel fungicide candidates, five series of novel norbornene hydrazide, bishydrazide, oxadiazole, carboxamide and acylthiourea derivatives (2a-2t, 3a-3f, 4a-4f, 5a-5f and 7a-7f) were designed, synthesized and assayed for their antifungal activity toward seven representative plant fungal pathogens. RESULTS: In the in vitro antifungal assay, some title norbornene derivatives presented good antifungal activity against Botryosphaeria dothidea, Sclerotinia sclerotiorum and Fusarium graminearum. Especially, compound 2b exhibited the best inhibitory activity toward B. dothidea with the median effective concentration (EC50) of 0.17 mg L-1, substantially stronger than those of the reference fungicides boscalid and carbendazim. The in vivo antifungal assay on apples revealed that 2b had significant curative and protective effects, both of which were superior to boscalid. In the preliminary antifungal mechanism study, 2b was able to injure the surface morphology of hyphae, destroy the cell membrane integrity and increase the intracellular reactive oxygen species (ROS) level of B. dothidea. In addition, 2b could considerably inhibit the laccase activity with the median inhibitory concentration (IC50) of 1.02 µM, much stronger than that of positive control cysteine (IC50 = 35.50 µM). The binding affinity and interaction mode of 2b with laccase were also confirmed by molecular docking. CONCLUSION: This study presented a promising lead compound for the study of novel laccase inhibitors as fungicidal agrochemicals, which demonstrate significant anti-B. dothidea activity and laccase inhibitory activity. © 2024 Society of Chemical Industry.

Increased LACTB2 Expression Regulates Oxidative Phosphorylation and mTORC1 Signaling of Colorectal Cancer.

This study aimed to investigate the mechanisms of LACTB2 in colorectal cancer (CRC). Microarrays and sequencing data of CRC were acquired from UCSC Xena, GTEx, Gene Expression Omnibus, and TCGA. Pooled analysis of the mRNA expression of LACTB2 in CRC was performed using Stata software. The protein expression of LACTB2 in CRC tissues was evaluated by immunohistochemistry. The relationship between immune cell infiltration and LACTB2 expression was investigated using CIBERSORT. The potential signaling pathways and biological mechanisms of LACTB2 were explored using GSEA, KEGG, and GO. Subsequently, further screening of small molecular compounds with potential therapeutic effects on CRC was conducted through the HERB database, followed by molecular docking studies of these compounds with the LACTB2 protein. The integration and analysis of expression data obtained from 2294 CRC samples and 1286 noncancerous colorectal samples showed that LACTB2 was highly expressed in CRC. Immunohistochemistry performed on in-house tissue samples confirmed that LACTB2 protein expression was upregulated in CRC. CIBERSORT revealed lower B cell infiltration levels in the high LACTB2 expression group than in the low expression group. GO, KEGG, and GSEA analyses showed that LACTB2 expression and genes positively correlating with it were mainly related to DNA synthesis and repair, mitochondrial translational elongation and translational termination, phosphorylation, and mTORC1 signaling. Finally, molecular docking simulations confirmed the ability of quercitin to target and bind to LACTB2. This is the first study to demonstrate that LACTB2 is upregulated in CRC. LACTB2 promotes colorectal tumorigenesis and tumor progression.

Ring-Contracted Artemisinin Derivatives as Novel CDK 4/6 Inhibitors: Synthesis and Anti-Breast Cancer Evaluation.

The endoperoxide group of artemisinins is universally accepted an essential group for their anti-cancer effects. In this study, a series of D-ring-contracted artemisinin derivatives were constructed by combining ring-contracted artemisinin core with fragments of functional heterocyclic molecules or classical CDK4/6 inhibitors to identify more efficacious breast cancer treatment agents. Twenty-six novel hybridized molecules were synthesized and characterized by HRMS, IR, 1H-NMR and 13C NMR. In antiproliferative activities and kinase inhibitory effects assays, we found that the antiproliferative effects of B01 were close to those of the positive control Palbociclib, with GI50 values of 4.87±0.23 µM and 9.97±1.44 µM towards T47D cells and MDA-MB-436 cells respectively. In addition, the results showed that B01 was the most potent compound against CDK6/cyclin D3 kinase, with an IC50 value of 0.135±0.041 µM, and its activity was approximately 1/3 of the positive control Palbociclib.

Characterization and Engineering of Two Highly Paralogous Sesquiterpene Synthases Reveal a Regioselective Reprotonation Switch.

Sesquiterpene synthases (STPSs) catalyze carbocation-driven cyclization reactions that can generate structurally diverse hydrocarbons. The deprotonation-reprotonation process is widely used in STPSs to promote structural diversity, largely attributable to the distinct regio/stereoselective reprotonations. However, the molecular basis for reprotonation regioselectivity remains largely understudied. Herein, we analyzed two highly paralogous STPSs, Artabotrys hexapetalus (-)-cyperene synthase (AhCS) and ishwarane synthase (AhIS), which catalyze reactions that are distinct from the regioselective protonation of germacrene A (GA), resulting in distinct skeletons of 5/5/6 tricyclic (-)-cyperene and 6/6/5/3 tetracyclic ishwarane, respectively. Isotopic labeling experiments demonstrated that these protonations occur at C3 and C6 of GA in AhCS and AhIS, respectively. The cryo-electron microscopy-derived AhCS complex structure provided the structural basis for identifying different key active site residues that may govern their functional disparity. The structure-guided mutagenesis of these residues resulted in successful functional interconversion between AhCS and AhIS, thus targeting the three active site residues [L311-S419-C458]/[M311-V419-A458] that may act as a C3/C6 reprotonation switch for GA. These findings facilitate the rational design or directed evolution of STPSs with structurally diverse skeletons.

A DNA/Poly-(L-lysine) Hydrogel with Long Shelf-Time for 3D Cell Culture.

Deoxyribonucleic acid (DNA)-based hydrogels are emerging as promising functional materials for biomedical applications. However, the shelf-time of DNA hydrogels in biological media is severely shortened by nucleases, which limit the application of DNA hydrogels. Herein, a DNA hydrogel with long shelf-time is reported for 3D cell culture. Poly-(L-lysine) (PLL) is introduced as both a cross-linker and a protectant. The electrostatic interaction between PLL and DNA drove the formation of hydrogel. PLL coating on DNA increased the steric hindrance between DNA and nucleases, thus weakening the digestion of nucleases toward phosphodiester bond. As a result, the shelf-time of DNA/PLL hydrogel for 3D cell culture is extended from generally 1 day to longer than 15 days, which has not been achieved previously. Notably, poly-AS1411-aptamers are integrated to DNA/PLL hydrogels for anchoring U87 cells, and the cell encapsulation efficiency of the DNA/PLL hydrogels with aptamer is 4-time higher than that of the hydrogels without aptamer. DNA/PLL hydrogel provided a favorable microenvironment to support the proliferation of cells, which formed cell spheroid in 15 days. This protective coating strategy solves the long-standing problem on the shelf-time of DNA hydrogel, and is envisioned to promote the development of DNA hydrogel in more biomedical applications.