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Abstract In this study, we conducted an assembly and analysis of the organelle genomes of Aconitum carmichaelii. Our investigation encompassed the examination of organelle genome structures, gene transfer events, and the environmental selection pressures affecting A. carmichaelii. The results revealed distinct evolutionary patterns in the organelle genomes of A. carmichaelii. Especially, the plastome exhibited a more conserved structure but a higher nucleotide substitution rate (NSR), while the mitogenome displayed a more complex structure with a slower NSR. Through homology analysis, we identified several instances of unidirectional protein-coding genes (PCGs) transferring from the plastome to the mitogenome. However, we did not observe any events which genes moved from the mitogenome to the plastome. Additionally, we observed multiple transposable element (TE) fragments in the organelle genomes, with both organelles showing different preferences for the type of nuclear TE insertion. Divergence time estimation suggested that rapid differentiation occurred in Aconitum species approximately 7.96 million years ago (Mya). This divergence might be associated with the reduction in CO2 levels and the significant uplift of the Qinghai-Tibet Plateau (QTP) during the late Miocene. Selection pressure analysis indicated that the dN/dS values of both organelles were less than 1, suggested that organelle PCGs were subject to purification selection. However, we did not detect any positively selected genes (PSGs) in Subg. Aconitum and Subg. Lycoctonum. This observation further supports the idea that stronger negative selection pressure on organelle genes in Aconitum results in a more conserved amino acid sequence. In conclusion, this study contributes to a deeper understanding of organelle evolution in Aconitum species and provides a foundation for future research on the genetic mechanisms underlying the structure and function of the Aconitum plastome and mitogenome.

Liquor has a complex system with high dimensional components. The trace components in liquor are varied and have low content and complex coordination relationships. This study aimed to solve the problem of reliance on smell and taste. Based on the characteristics of near-infrared spectrum response to hydrogen-containing groups, qualitative analysis was carried out in combination with machine learning technology. Firstly, an iterative adaptive weighted penalized least squares algorithm with spectral peak discrimination was used for baseline correction to effectively retain useful information in the feature absorption peaks. Then, the convolution smoothing algorithm was used to filter the noise, and the spectral curve smoothness was adjusted using the convolution window width. The near-infrared spectrum has a high dimension. Monte Carlo random sampling combined with an improved competitive adaptive reweighting method was used to evaluate the importance of spectral sampling points. According to the importance coefficient, the dimension of the spectral data set was optimized by using an exponential attenuation function through an iterative operation, and the data set with the smallest root-mean-square error was taken as the characteristic spectrum. The nonlinear separability of characteristic spectra was further improved by kernel principal component analysis. Finally, a liquor quality recognition model based on principal component analysis was established by using the hierarchical multiclass support vector machine method. Our key findings revealed that the prediction accuracy of the model reached 96.87% when the number of principal components was 5–12, with more than 95% of the characteristic information retained. These results demonstrated that this rapid nondestructive testing method resolved the challenge posed by relying on subjective sensory evaluation for liquor analysis. The findings provide a reliable analytical approach for studying substances with high-dimensional component characteristics.

Abstract Objective Cervical cancer, one of the common types of malignant tumors progressed in women, is on the rise in developing countries. Numerous previous studies have demonstrated that hsa‐mir‐133a‐2 miRNA is abnormally expressed in cervical cancer cells. However, its fundamental mechanism in cervical cancer needs to be further clarified. Our study set out to investigate the effect of hsa‐mir‐133a‐2 on the phenotypes of cervical cancer cells as well as any potential molecular processes involved in the proliferation and invasion of cervical cancer cells. Methods The Cancer Genome Atlas‐cervical squamous cell carcinoma and endocervical adenocarcinoma(TCGA‐CESC) was adopted in order to verify the expression of hsa‐mir‐133a‐2 in cervical cancer tissues and to identify its potential targets. The interaction between Laminin subunit beta‐3(LAMB3) and hsa‐mir‐133a‐2 was verified by TargetScan database as well as Luciferase reporter assay. The Cell Counting Kit‐8 (CCK8) and transwell methods were utilized to assess the influence of hsa‐mir‐133a‐2 on the proliferation and invasion characteristics of cervical cancer cells. We studied the role that hsa‐mir‐133a‐2 plays in cervical cancer progression through Kyoto Encyclopedia of Genes and Genomes(KEGG) analysis as well as Western Blot (WB) experiment. Results Down‐regulation of hsa‐mir‐133a‐2 was detected in cervical cancer tissues. It directly targeted LAMB3 and negatively regulated LAMB3 expression. The overexpression of hsa‐mir‐133a‐2 has a significant inhibiting effect on cervical cancer cell proliferation and invasion. The overexpression of hsa‐mir‐133a‐2 significantly inhibits the proliferation and invasion of cervical cancer cells. Moreover, the LAMB3 was able to up‐regulate the phosphorylation levels of AKT and phosphatidylinositol 3‐kinase (PI3K) protein in cervical cancer cells. hsa‐mir‐133a‐2 could also modulate the PI3K/AKT signaling pathway by targeting LAMB3. Conclusion hsa‐mir‐133a‐2 inhibits cervical cancer cell proliferation and invasion by indirectly regulating the PI3K/AKT signaling pathway, providing us with a new clinical treatment strategy for cervical cancer.

The advent of the Internet of Vehicles (IoV) has sparked strong scholarly interest in the determination of dependable offloading destinations for tasks. The lack of global information, however, prevents the existing research from being applied in the context of information asymmetry. This paper proposes an original framework for intermediary vehicle-assisted task offloading (IVATO) in scenarios of information asymmetry. Through IVATO, we introduce an intermediary vehicle election mechanism fully based on trust and information mastery. Specifically, a new method is developed to evaluate trust based on resilience. In addition, we conceive a degree of information mastery to measure the amount of information in the vehicle. Based on the intermediary vehicle selected, an objective function for designing the offloading strategy is formulated to maximize both reliability and effectiveness. Proximal Policy Optimization (PPO) based deep reinforcement learning algorithm is adopted to tackle the optimization problem. Simulation results show that the proposed trust evaluation method is more rational than the existing methodologies in the long term. The proposed offloading mechanism shows a 49% increase in utility and a 45% increase in reliability over other schemes.

Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model.

Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health indicator and proposes a data-driven method for capacity prediction. Specifically, this method mainly utilizes Convolutional Neural Network (CNN) for automatic feature extraction from raw data and combines it with the Bidirectional Long Short-Term Memory (BiLSTM) algorithm to realize the capacity prediction of LIBs. In addition, the sparrow search algorithm (SSA) is used to optimize the hyper-parameters of the neural network to further improve the prediction performance of original network structures. Ultimately, experiments with a public dataset of batteries are carried out to verify and evaluate the effectiveness of capacity prediction under two temperature conditions. The results show that the SSA-CNN-BiLSTM framework for capacity prediction of LIBs has higher accuracy compared with other original network structures during the multi-battery cycle experiments.

The Meconopsis species are widely distributed in the Qinghai-Tibet Plateau, Himalayas, and Hengduan Mountains in China, and have high medicinal and ornamental value. The high diversity of plant morphology in this genus poses significant challenges for species identification, given their propensity for highland dwelling, which makes it a question worth exploring how they cope with the harsh surroundings. In this study, we recently generated chloroplast (cp) genomes of two Meconopsis species, Meconopsis paniculata (M. paniculata) and M. pinnatifolia, and compared them with those of ten Meconopsis cp genomes to comprehend cp genomic features, their phylogenetic relationships, and what part they might play in plateau adaptation. These cp genomes shared a great deal of similarities in terms of genome size, structure, gene content, GC content, and codon usage patterns. The cp genomes were between 151,864 bp and 154,997 bp in length, and contain 133 predictive genes. Through sequence divergence analysis, we identified three highly variable regions (trnD-psbD, ccsA-ndhD, and ycf1 genes), which could be used as potential markers or DNA barcodes for phylogenetic analysis. Between 22 and 38 SSRs and some long repeat sequences were identified from 12 Meconopsis species. Our phylogenetic analysis confirmed that 12 species of Meconopsis clustered into a monophyletic clade in Papaveraceae, which corroborated their intrageneric relationships. The results indicated that M. pinnatifolia and M. paniculata are sister species in the phylogenetic tree. In addition, the atpA and ycf2 genes were positively selected in high-altitude species. The functions of these two genes might be involved in adaptation to the extreme environment in the cold and low CO2 concentration conditions at the plateau.

Colon cancer is a highly malignant cancer with poor prognosis. Astragalus membranaceus (Fisch.) Bunge (Huang Qi in Chinese, HQ), a well-known Chinese herbal medicine and a popular food additive, possesses various biological functions and has been frequently used for clinical treatment of colon cancer. However, the underlying mechanism is not fully understood. Isoflavonoids, including formononetin (FMNT) and calycosin (CS), are the main bioactive ingredients isolated from HQ. Thus, this study aimed to explore the inhibitory effects and mechanism of HQ, FMNT and CS against colon cancer by using network pharmacology coupled with experimental validation and molecular docking. The network pharmacology analysis revealed that FMNT and CS exerted their anticarcinogenic actions against colon cancer by regulating multiple signaling molecules and pathways, including MAPK and PI3K-Akt signaling pathways. The experimental validation data showed that HQ, FMNT and CS significantly suppressed the viability and proliferation, and promoted the apoptosis in colon cancer Caco2 and HT-29 cells. HQ, FMNT and CS also markedly inhibited the migration of Caco2 and HT-29 cells, accompanied by a marked increase in E-cadherin expression, and a notable decrease in N-cadherin and Vimentin expression. In addition, HQ, FMNT and CS strikingly decreased the expression of ERK1/2 phosphorylation (p-ERK1/2) without marked change in total ERK1/2 expression. They also slightly downregulated the p-Akt expression without significant alteration in total Akt expression. Pearson correlation analysis showed a significant positive correlation between the inactivation of ERK1/2 signaling pathway and the HQ, FMNT and CS-induced suppression of colon cancer. The molecular docking results indicated that FMNT and CS had a strong binding affinity for the key molecules of ERK1/2 signaling pathway. Conclusively, HQ, FMNT and CS exerted good therapeutic effects against colon cancer by mainly inhibiting the ERK1/2 signaling pathway, suggesting that HQ, FMNT and CS could be useful supplements that may enhance chemotherapeutic outcomes and benefit colon cancer patients.

Highlights Interfacial bonding strategy has been successfully applied to address the high overpotential issue of sacrificial additives, which reduced the decompositon potential of Na2C2O4 from 4.50 to 3.95 V. Ultra-low-dose technique assisted commercial sodium ion capacitor (AC//HC) could deliver a remarkable energy density of 118.2 Wh kg−1 as well as excellent cycle stability. In-depth decomposition mechanism of sacrificial compound and the relative influence after pre-metallation were revealed by advanced in situ and ex situ characterization approaches. Abstract Sacrificial pre-metallation strategy could compensate for the irreversible consumption of metal ions and reduce the potential of anode, thereby elevating the cycle performance as well as open-circuit voltage for full metal ion capacitors (MICs). However, suffered from massive-dosage abuse, exorbitant decomposition potential, and side effects of decomposition residue, the wide application of sacrificial approach was restricted. Herein, assisted with density functional theory calculations, strongly coupled interface (M–O–C, M = Li/Na/K) and electron donating group have been put forward to regulate the band gap and highest occupied molecular orbital level of metal oxalate (M2C2O4), reducing polarization phenomenon and Gibbs free energy required for decomposition, which eventually decrease the practical decomposition potential from 4.50 to 3.95 V. Remarkably, full sodium ion capacitors constituted of commercial materials (activated carbon//hard carbon) could deliver a prominent energy density of 118.2 Wh kg−1 as well as excellent cycle stability under an ultra-low dosage pre-sodiation reagent of 15–30 wt% (far less than currently 100 wt%). Noteworthily, decomposition mechanism of sacrificial compound and the relative influence on the system of MICs after pre-metallation were initially revealed by in situ differential electrochemical mass spectrometry, offering in-depth insights for comprehending the function of cathode additives. In addition, this breakthrough has been successfully utilized in high performance lithium/potassium ion capacitors with Li2C2O4/K2C2O4 as pre-metallation reagent, which will convincingly promote the commercialization of MICs.

Deep-learning-driven multi-image super-resolution (MISR) reconstruction techniques have significant application value in the field of aerospace remote sensing. In particular, Transformer-based models have shown outstanding performance in super-resolution tasks. However, current MISR models have some deficiencies in the application of multi-scale information and the modeling of the attention mechanism, leading to an insufficient utilization of complementary information in multiple images. In this context, we innovatively propose a Multi-Attention Multi-Image Super-Resolution Transformer (MAST), which involves improvements in two main aspects. Firstly, we present a Multi-Scale and Mixed Attention Block (MMAB). With its multi-scale structure, the network is able to extract image features from different scales to obtain more contextual information. Additionally, the introduction of mixed attention allows the network to fully explore high-frequency features of the images in both channel and spatial dimensions. Secondly, we propose a Collaborative Attention Fusion Block (CAFB). By incorporating channel attention into the self-attention layer of the Transformer, we aim to better establish global correlations between multiple images. To improve the network’s perception ability of local detailed features, we introduce a Residual Local Attention Block (RLAB). With the aforementioned improvements, our model can better extract and utilize non-redundant information, achieving a superior restoration effect that balances the global structure and local details of the image. The results from the comparative experiments reveal that our approach demonstrated a notable enhancement in cPSNR, with improvements of 0.91 dB and 0.81 dB observed in the NIR and RED bands of the PROBA-V dataset, respectively, in comparison to the existing state-of-the-art methods. Extensive experiments demonstrate that the method proposed in this paper can provide a valuable reference for solving multi-image super-resolution tasks for remote sensing.

Worldwide, cancer is a growing epidemic that results in large social and economic burdens. Despite advances in current diagnosis and treatment, most of the prognosis in cancer patients remains poor. It is urgent to find alternative therapies with effective cancer prevention and treatment. Chinese herbal medicines (CHMs) have been increasingly used worldwide for cancer prevention and treatment due to their privileged properties. CHMs are useful in the suppression of various types of cancers through different mechanisms of action. Non-coding RNAs (ncRNAs), including microRNAs, long non-coding RNAs and circular RNAs, are closely involved in the cancer progression and development. Regulation of ncRNAs in tumor cells may be a useful pharmacological strategy for the cancer prevention and treatment. Substantial evidence exists that various phytochemicals from CHMs exert potent anticarcinogenic effects by regulating ncRNAs-related targets and signaling pathways. Herein, the purpose of this paper is to conclude the current understanding of phytochemicals from CHMs in ncRNAs-mediated cancer suppression and the molecular mechanisms. This review will help to provide beneficial clues related to the clinical use of CHMs in the cancer prevention and treatment and further promote new drug discovery against cancer.

At present, the number of megaprojects in China is rapidly increasing, with multiple projects ranked as the world’s highest. To showcase the theoretical and practical achievements of China’s megaprojects to the world, a visual analysis was carried out using CiteSpace software based on data from 520 pieces of core literature related to megaproject governance in the CNKI database from 2001 to 2023. Our findings indicate that: the number of publications has significantly increased since 2016, with a core group of researchers significantly contributing to this field, while team collaboration needs to be strengthened; the research hotspots include social stability risks, transaction governance, and innovation in megaprojects; the research can be divided into three stages, starting with early macro-studies, gradually forming clear and hierarchical research branches, and beginning to develop in the direction of Chinese characteristics and innovative deepening in recent years; research on the megaproject governance system is becoming gradually more enriched, but there is a lack of research at the levels of top-level governance and operational governance. Finally, in order to advance the study of megaproject governance in China, future research directions are suggested.

Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors, and its results are inaccurate and unstable. This study developed a near-infrared (NIR) spectral characteristic extraction method based on a three-dimensional analysis space and establishes a high-accuracy qualitative identification model. First, the Norris derivative filtering algorithm was used in the pre-processing of the NIR spectrum to obtain a smooth main absorption peak. Then, the third-order tensor robust principal component analysis (TRPCA) algorithm was used for characteristic extraction, which effectively reduced the dimensionality of the raw NIR spectral data. Finally, on this basis, a qualitative identification model based on support vector machines (SVM) was constructed, and the classification accuracy reached 98.94%. Therefore, it is possible to develop a non-destructive, rapid qualitative detection system based on NIR spectroscopy to mine the subtle differences between classes and to use low-dimensional characteristic wavebands to detect the quality of complex multi-component mixtures. This method can be a key component of automatic quality control in the production of multi-component products.

Src family kinases (SFKs) is a non-receptor protein tyrosine kinases family. They are crucial in signal transduction and regulation of various cell biological processes, such as proliferation, differentiation and apoptosis. The role and mechanism of SFKs in tumorigenesis have been widely studied. However, more and more studies have also shown that SFKs are involved in the pathogenesis of pulmonary fibrosis (PF). Myofibroblasts activation, epithelial-mesenchymal transition and inflammation response are three pivotal pathomechanisms in the development of pulmonary fibrotic disease. In this article, we summarize the roles of SFKs in these biological processes. SFKs play a crucial role in the pathogenesis of PF, making it a promising molecular target for the treatment of these diseases. We will pay special attention to the role of SFKs in idiopathic pulmonary fibrosis (IPF), and also emphasize the important findings in other pulmonary fibrotic diseases because their pathological mechanisms are similar. We will then describe the translation results obtained with SFKs inhibitors in basic and clinical studies.

Abstract As a nano-sized drug carrier with the advantage of modifiability and proper biocompatibility, DNA tetrahedron (DNA tetra) delivery is hopeful to enhance the inhibitory efficiency of nontargeted anticancer drugs. In this investigation, doxorubicin (Dox) was assembled to a folic acid-modified DNA tetra via click chemistry to prepare a targeted antitumor agent. Cellular uptake efficiency was measured via fluorescent imaging. Cytotoxicity, inhibition efficiency, and corresponding mechanism on colon cancer cell line HT-29 were evaluated by MTT assay, cell proliferation curve, western blot, and flow cytometry. No cytotoxicity was induced by DNA tetra, but the cellular uptake ratio increased obviously resulting from the DNA tetra-facilitated penetration through cellular membrane. Accordingly, folic acid-DNA tetra-Dox markedly increased the antitumor efficiency with increased apoptosis levels. In details, 100 μM was the effective concentration and a 6-h incubation period was needed for apoptosis induction. In conclusion, nano-sized DNA tetrahedron was a safe and effective delivery system for Dox and correspondingly enhanced the anticancer efficiency.

Raw Pinelliae Rhizoma (RPR) is a representative toxic herb that is widely used for eliminating phlegm or treating cough and vomiting. Given its irritant toxicity, its processed products, including Pinelliae Rhizoma Praeparatum (PRP) and Pinelliae Rhizoma Praeparatum cum Zingibere et Alumine (PRPZA), are more commonly applied and administered concomitantly with other chemical drugs, such as cough medications. This study aimed to investigate the effects of RPR, PRP, and PRPZA on CYP3A activity. Testosterone (Tes) and buspirone (BP) were used as specific probe substrates ex vivo and in vivo, respectively. CYP3A activity was determined by the metabolite formation ratios from the substrates. Ex vivo results show that the metabolite formation ratios from Tes significantly decreased, indicating that RPR, PRP, and PRPZA could inhibit CYP3A activity in rats. CYP3A protein and mRNA levels were determined to explore the underlying mechanism. These levels showed marked and consistent down-regulation with CYP3A activity. A significant decrease in metabolite formation ratios from BP was also found in PRPZA group in vivo, implying that PRPZA could inhibit CYP3A activity. Conclusively, co-administration of PR with other CYP3A-metabolizing drugs may cause drug–drug interactions. Clinical use of PR-related formulae should be monitored carefully to avoid adverse interactions.

Aim: The current study explored the usefulness of a mechanosensitive channel blocker, GsMTX4 in intracerebral hemorrhage (ICH)-associated deleterious effects along with the possible mechanisms. Materials and Methods: Type VII collagenase was administered in the right basal ganglia using stereotactic apparatus to induce ICH in rats. The ICHassociated injury was assessed using corner turn and forelimb placement tests (behavioral test); Evans blue extravasation test (blood-brain barrier damage), brain edema, apoptotic markers (caspase-3 and Bcl-2 levels). The levels of TNF-a (neuroinflammation), reactive oxygen species, and Brain-derived neurotrophic factor (BDNF) were also measured in ICH-subjected rats. GsMTx4, as a mechanosensitive channel blocker, and ANA-12, as a selective BDNF receptor blocker were employed as pharmacological agents. Results: Administration of GsMTx4 attenuated ICH-associated behavioral changes, preserved blood-brain barrier, prevented brain edema, and decreased apoptosis. GsMTx4 also decreased neuroinflammation and oxidative stress, while it increased the levels of BDNF. Moreover, administration of ANA-12 attenuated the neuroprotective effects of GsMTx4 suggesting that BDNF may be important in inducing neuroprotective effects of GsMTx4. Conclusion: GsMTx4 exerts neuroprotective effects in intracerebral hemorrhageassociated deleterious effects, which may be possibly due to an increase in BDNF levels along with a decrease in neuroinflammation and oxidative stress. [ABSTRACT FROM AUTHOR]