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Abstract Background Liver cancer has a high global incidence, particularly in East Asia. Early detection difficulties lead to poor prognosis. Single-cell sequencing precisely identifies gene expression differences in specific cell types, making it valuable in tumor microenvironment research and immune drug development. However, the characteristics of tumor cells themselves are equally important for patient prognosis and treatment. Methods We downloaded single-cell sequencing data from GSE189903, grouped cells by cluster markers, and classified epithelial cells into adjacent non-tumor, normal, and tumor cells. Differential gene and survival analyses identified significant differential genes. Using TCGA-LIHC data, we divided 370 patients into test and training sets. We constructed and validated a LASSO model based on these genes in both sets and two external datasets. Functional, immune infiltration, and mutation analyses were performed on high and low-risk groups. We also used RNA-seq and IC50 data of 15 liver cancer cell lines from GDSC, scoring them with our prognostic model to identify potential drugs for high-risk patients. Results Dimensionality reduction and clustering of 34 single-cell samples identified five subgroups, with epithelial cells further classified. Differential gene analysis identified 124 significant genes. An 11-gene prognostic model was constructed, effectively stratifying patient prognosis (p

Conflicting information in online social networks, often stemming from opposing views, can lead to rumors and the fragmentation of society.A reputation-based dynamic information management mechanism was proposed, adopting a dynamic reward and punishment strategy to decrease unnecessary information conflict so as to activate self-purification of network.By utilizing evolutionary game theory, a cumulative-prospect-based evolutionary game model of social network information conflict was constructed to validate the efficacy of the proposed mechanism.Numerical simulations were conducted to analyze the evolutionary trends of 18 different theoretical application scenarios under 9 constraints.The results demonstrate that, under constraint 5, both sides of the game tend to exhibit relatively rational behavior towards conflicts.Furthermore, dynamic evolution analysis and sensitivity analysis of the model parameters were performed while satisfying constraint 5, providing practical strategic suggestions for the dynamic information management mechanism.This model represents a novel attempt to capture the interaction of multiple factors, such as interest-driven behavior and conformity psychology, in the evolution of public opinion.Moreover, the model successfully simulates well-known patterns of rumor dissemination, confirming its validity.The proposed model offers valuable insights for the development and design of malicious information control systems in social networks and provides new ideas for the research and implementation of public opinion governance.

Threat analysis relies on knowledge bases that contain a large number of security entities.The scope and impact of security threats and risks are evaluated by modeling threat sources, attack capabilities, attack motivations, and threat paths, taking into consideration the vulnerability of assets in the system and the security measures implemented.However, the lack of entity relations between these knowledge bases hinders the security event tracking and attack path generation.To complement entity relations between CAPEC and ATT＆amp;CK techniques and enrich threat paths, an entity correlation prediction method called WGS was proposed, in which entity descriptions were analyzed based on word embedding and a graph convolution network.A Word2Vec model was trained in the proposed method for security domain to extract domain-specific semantic features and a GCN model to capture the co-occurrence between words and sentences in entity descriptions.The relationship between entities was predicted by a Siamese network that combines these two features.The inclusion of external semantic information helped address the few-shot learning problem caused by limited entity relations in the existing knowledge base.Additionally, dynamic negative sampling and regularization was applied in model training.Experiments conducted on CAPEC and ATT＆amp;CK database provided by MITRE demonstrate that WGS effectively separates related entity pairs from irrelevant ones in the sample space and accurately predicts new entity relations.The proposed method achieves higher prediction accuracy in few-shot learning and requires shorter training time and less computing resources compared to the Bert-based text similarity prediction models.It proves that word embedding and graph convolutional network based entity relation prediction method can extract new entity correlation relationships between attack patterns and techniques.This helps to abstract attack techniques and tactics from low-level vulnerabilities and weaknesses in security threat analysis.

Abstract Background Prolyl endopeptidase from Aspergillus niger (AN-PEP) is a prominent serine proteinase with various potential applications in the food and pharmaceutical industries. However, the availability of efficient and low-cost AN-PEP remains a challenge owing to its low yield and high fermentation cost. Results Here, AN-PEP was recombinantly expressed in Trichoderma reesei (rAN-PEP) under the control of the cbh1 promoter and its secretion signal. After 4 days of shaking flask cultivation with the model cellulose Avicel PH101 as the sole carbon source, the extracellular prolyl endopeptidase activity reached up to 16.148 U/mL, which is the highest titer reported to date and the secretion of the enzyme is faster in T. reesei than in other eukaryotic expression systems including A. niger and Komagataella phaffii. Most importantly, when cultivated on the low-cost agricultural residue corn cob, the recombinant strain was found to secret a remarkable amount of rAN-PEP (37.125 U/mL) that is twice the activity under the pure cellulose condition. Furthermore, treatment with rAN-PEP during beer brewing lowered the content of gluten below the ELISA kit detection limit (

Abstract Background Ergothioneine (ERG) is a potent histidine-derived antioxidant that confers health-promoting effects. Only certain bacteria and fungi can biosynthesize ERG, but the ERG productivity in natural producers is low. ERG overproduction through genetic engineering represents an efficient and cost-effective manufacturing strategy. Results Here, we showed that Trichoderma reesei can synthesize ERG during conidiogenesis and hyphal growth. Co-expression of two ERG biosynthesis genes (tregt1 and tregt2) from T. reesei enabled E. coli to generate 70.59 mg/L ERG at the shaking flask level after 48 h of whole-cell biocatalysis, whereas minor amounts of ERG were synthesized by the recombinant E. coli strain bearing only the tregt1 gene. By fed-batch fermentation, the extracellular ERG production reached 4.34 g/L after 143 h of cultivation in a 2-L jar fermenter, which is the highest level of ERG production reported thus far. Similarly, ERG synthesis also occurred in the E. coli strain engineered with the two well-characterized genes from N. crassa and the ERG productivity was up to 4.22 g/L after 143 h of cultivation under the above-mentioned conditions. Conclusions Our results showed that the overproduction of ERG in E. coli could be achieved through two-enzymatic steps, demonstrating high efficiency of the fungal ERG biosynthetic pathway. Meanwhile, this work offers a more promising approach for the industrial production of ERG.

As nodes in Social Internet of Things (SIoT) become more intelligent, malicious information occurs more frequently and spreads more widely. This problem can severely affect the trustworthiness of services and applications in SIoT. Methods to effectively control malicious information spreading in SIoT are essential and necessary. Reputation mechanism provides a powerful tool to tackle this challenge. In this paper, we propose a reputation-based mechanism to activate the self-purification capacity of the SIoT network by balancing information conflicts triggered by reporters and supporters. In order to find the best rewarding and punishment strategy, a bilateral cumulative-prospect-based evolutionary game model of SIoT network information conflict is constructed. Using local stability analysis and numerical simulation, the evolutionary trends of the proposed game model under different theoretical application scenarios are analyzed. The findings indicate that the basic income and deposit of both sides, the popularity of information as well as the importance of the conformity effect all have a significant impact on the system's steady state and evolutionary path. The specific conditions that both participating sides of the game tend to treat conflicts relatively rationally are analyzed. Dynamic evolution analysis and sensitivity analysis of selected parameters show that basic income is positively related to smart object's feedback strategies, while deposit is negatively related to that. While weight of conformity effect or the information popularity goes up, the rising of feedback probability is observed. Based on the above results, suggestions on dynamic reward and punishment strategies are given. The proposed model is a helpful attempt to model the evolution of information spreading in SIoT networks, with the ability to simulate several well-known regularities of message dissemination. Proposed model and suggested quantitative strategies can be helpful to build feasible malicious information control facilities in SIoT networks.

More and more electronic devices are integrated into the modern vehicles with the development of intelligent vehicles.There are various design flaws and vulnerabilities hidden in a large number of hardware, firmware and software.Therefore, the vulnerabilities of intelligent vehicles have become the most important factor affecting the vehicle safety.The safety of vehicles is seriously affected by the disclosure of a large number of vulnerabilities, and the wide application of smart cars is also restricted.Vulnerability management is an effective method to reduce the risk of vulnerabilities and improve vehicle security.And vulnerability scoring is one the important step in vulnerability management procedure.However, current method have no capability assessing automotive vulnerabilities reasonably.In order to handle this problem, a vulnerability scoring model for intelligent vehicles was proposed, which was based on CVSS.The attack vector and attack complexity were optimized, and property security, privacy security, functional safety and life safety were added to characterize the possible impact of the vulnerabilities according to the characteristics of intelligent vehicles.With the machine learning method, the parameters in CVSS scoring formula were optimized to describe the characteristics of intelligent vehicle vulnerabilities and adapt to the adjusted and new added weights.It is found in case study and statistics that the diversity and distribution of the model are better than CVSS, which means the model can better score different vulnerabilities.And then AHP is used to evaluate the vulnerability of the whole vehicle based on the vulnerability score of the model, a score is given representing the risk level of whole vehicle.The proposed model can be used to evaluate the severity of information security vulnerabilities in intelligent vehicles and assess the security risks of the entire vehicle or part of the system reasonably, which can provide an evidence for fixing the vulnerabilities or reinforcing the entire vehicle.

ObjectiveAdipose tissue is recognized as a crucial regulator of atrial fibrillation (AF). However, the effect of epicardial adipose tissue (EAT) on the pathophysiology of AF might be different from that of other adipose tissues. The purpose of this study was to explore the distribution features of different adipose tissues in AF patients and their relationships with left atrial (LA) remodeling and function.MethodsA total of 205 participants (including 112 AF and 93 non-AF patients) were recruited. Color doppler ultrasound was used to measure the thickness of subcutaneous, extraperitoneal, and intra-abdominal adipose tissue. Cardiac CT scan was performed to measure the mean thickness of EAT surrounding the whole heart (total-EAT) and specific regions, including left atrium (LA-EAT), left ventricle, right ventricle, interventricular groove, and atrioventricular groove. LA anatomical remodeling and function were measured by echocardiography, while electrical remodeling was evaluated by P-wave duration and dispersion using Electrocardiography (obtained after cardioversion or ablation in AF patients). Relationship between the thickness of different adipose tissues and LA remodeling and function was analyzed.ResultsThe thickness of subcutaneous, extraperitoneal, and intra-abdominal adipose tissue was similar between AF and non-AF patients, and had no or only weak association with LA remodeling and dysfunction. However, compared to non-AF participants, total-EAT thickness significantly increased in both paroxysmal and persistent AF patients (non-AF vs. paroxysmal AF vs. persistent AF: 6.31 ± 0.63 mm vs. 6.76 ± 0.79 mm vs. 7.01 ± 1.18 mm, P < 0.001), which was positively correlated with the LA size and P-wave duration and dispersion, and negatively correlated with LA ejection fraction and peak strain rate. More interestingly, EAT thickness in AF patients did not increase uniformly in different regions of the heart. Compared to EAT surrounding the other regions, LA-EAT was found to accumulate more greatly, and had a closer relationship to LA remodeling and dysfunction. Multivariate logistic regression analysis also showed that LA-EAT was significantly correlated with the presence of AF (OR = 4.781; 95% CI 2.589–8.831, P < 0.001).ConclusionRather than other adipose tissues, accumulation and redistribution of EAT, especially surrounding the LA, is associated with LA remodeling and dysfunction in AF patients.

Farnesyl pyrophosphate synthase (FPPS) plays an important role in the synthesis of plant secondary metabolites, but its function and molecular regulation mechanism remain unclear in Pogostemon cablin. In this study, the full-length cDNA of the FPP synthase gene from P. cablin (PcFPPS) was cloned and characterized. The expressions of PcFPPS are different among different tissues (highly in P. cablin flowers). Subcellular localization analysis in protoplasts indicated that PcFPPS was located in the cytoplasm. PcFPPS functionally complemented the lethal FPPS deletion mutation in yeast CC25. Transient overexpression of PcFPPS in P. cablin leaves accelerated terpene biosynthesis, with an ~47% increase in patchouli alcohol. Heterologous overexpression of PcFPPS in tobacco plants was achieved, and it was found that the FPP enzyme activity was significantly up-regulated in transgenic tobacco by ELISA analysis. In addition, more terpenoid metabolites, including stigmasterol, phytol, and neophytadiene were detected compared with control by GC-MS analysis. Furthermore, with dual-LUC assay and yeast one-hybrid screening, we found 220 bp promoter of PcFPPS can be bound by the nuclear-localized transcription factor PcWRKY44. Overexpression of PcWRKY44 in P. cablin upregulated the expression levels of PcFPPS and patchoulol synthase gene (PcPTS), and then promote the biosynthesis of patchouli alcohol. Taken together, these results strongly suggest the PcFPPS and its binding transcription factor PcWRKY44 play an essential role in regulating the biosynthesis of patchouli alcohol.

Microblog information tracing refers to finding the source set of microblog topics according to the analysis of crawled microblog texts and it’s of great significance in the aspect of public opinion control and information security.A user interests-based tracing method (ITM) was proposed.The proposed method calculates the influence of the blogger based on the interest of the microblog blogger,and also calculates the influence of the commentators based on the interest of the commentators.The ranking algorithm was used to score the blogs according to publication time,notability and influence,and the source of the blogs was traced according to the blog score rank.Experimental results show that the accuracy of the proposed algorithm improved about 21% compared with the traditional tracing algorithms.

Abstract Background Changes of soil microbial communities are one of the main factors of continuous cropping problem. Andrographis paniculata has been reported to have replant problem in cultivation. However, little is known about the variations of rhizosphere soil microbial communities of A. paniculata under a continuous cropping system. Here, Illumina MiSeq was used to investigate the shifts of rhizospheric bacterial and fungal communities after continuous cropping of A. paniculata. Results The bacterial diversity increased whereas the fungal diversity decreased in rhizosphere soil after consecutive A. paniculata monoculture; and the soil microbial community structure differed between newly plant soil and continuous cropped soil. Taxonomic analyses further revealed that the bacterial phyla Proteobacteria, Acidobacteria and Bacteroidetes and the fungal phyla Zygomycota, Ascomycota and Cercozoa were the dominant phyla across all soil samples. The relative abundance of phyla Acidobacteria and Zygomycota were significantly increased after continuous cropping. Additionally, the most abundant bacterial genus Pseudolabrys significantly decreased, while the predominant fungal genus Mortierella increased considerably in abundance after continuous cropping. Conclusions Our results revealed the changes on diversity and composition of bacterial and fungal communities in rhizospheric soil under continuous cropping of A. paniculata. These data contributed to the understanding of soil micro-ecological environments in the rhizosphere of A. paniculata.

With the development of automobile intelligence, the security of the Internet of Vehicles has become a key factor that affects the development of intelligent vehicles. However, existing security risk analysis methods for the IoV either focus only on certain levels, such as the component level, or perform only a static analysis. This paper proposes a dynamic attack graph generation method for the IoV to identify and visually display the security risks caused by the associated vulnerabilities in an IoV system. First, using the actual architecture of the IoV, this paper shows how to model the security elements and their relationships in the IoV system and proposes a network security ontology model for this system. Second, it shows how to construct a reasoning rule base according to the causal relationship between the vulnerabilities using the Semantic Web Rule Language Finally, in view of the rapid change in the network topology of the IoV, a dynamic attack graph generation algorithm based on an ontology reasoning engine is proposed, which can effectively reduce the overhead caused by the changes in the attack graph. The effectiveness of the algorithm is demonstrated through an actual security event scenario and a constructed scenario. The experimental results show that the algorithm can dynamically and accurately display the network attack graph of the IoV. The proposed method is helpful in globally analyzing the threat caused by the combined exploitation of the vulnerabilities in an IoV system and risk management.

Abstract Background Pogostemon cablin (Blanco) Benth. (Patchouli) is an important aromatic and medicinal plant and widely used in traditional Chinese medicine as well as in the perfume industry. Patchoulol is the primary bioactive component in P. cablin, its biosynthesis has attracted widespread interests. Previous studies have surveyed the putative genes involved in patchoulol biosynthesis using next-generation sequencing method; however, technical limitations generated by short-read sequencing restrict the yield of full-length genes. Additionally, little is known about the expression pattern of genes especially patchoulol biosynthesis related genes in response to methyl jasmonate (MeJA). Our understanding of patchoulol biosynthetic pathway still remained largely incomplete to date. Results In this study, we analyzed the morphological character and volatile chemical compounds of P. cablin cv. ‘Zhanxiang’, and 39 volatile chemical components were detected in the patchouli leaf using GC-MS, most of which were sesquiterpenes. Furthermore, high-quality RNA isolated from leaves and stems of P. cablin were used to generate the first full-length transcriptome of P. cablin using PacBio isoform sequencing (Iso-Seq). In total, 9.7 Gb clean data and 82,335 full-length UniTransModels were captured. 102 transcripts were annotated as 16 encoding enzymes involved in patchouli alcohol biosynthesis. Accorded with the uptrend of patchoulol content, the vast majority of genes related to the patchoulol biosynthesis were up-regulated after MeJA treatment, indicating that MeJA led to an increasing synthesis of patchoulol through activating the expression level of genes involved in biosynthesis pathway of patchoulol. Moreover, expression pattern analysis also revealed that transcription factors participated in JA regulation of patchoulol biosynthesis were differentially expressed. Conclusions The current study comprehensively reported the morphological specificity, volatile chemical compositions and transcriptome characterization of the Chinese-cultivated P. cablin cv. ‘Zhanxiang’, these results contribute to our better understanding of the physiological and molecular features of patchouli, especially the molecular mechanism of biosynthesis of patchoulol. Our full-length transcriptome data also provides a valuable genetic resource for further studies in patchouli.

Abstract Background The ascomycete fungus Trichoderma reesei was widely used as a biotechnological workhorse for production of cellulases and recombinant proteins due to its large capacity of protein secretion. Transgenesis by random integration of a gene of interest (GOI) into the genome of T. reesei can generate series of strains that express different levels of the indicated transgene. The insertion site of the GOI plays an important role in the ultimate production of the targeted proteins. However, so far no systematic studies have been made to identify transgene integration loci for optimal expression of the GOI in T. reesei. Currently, only the locus of exocellobiohydrolases I encoding gene (cbh1) is widely used as a promising integration site to lead to high expression level of the GOI. No additional sites associated with efficient gene expression have been characterized. Results To search for gene integration sites that benefit for the secreted expression of GOI, the food-and-mouth disease virus 2A protein was applied for co-expression of an Aspergillus niger lipA gene and Discosoma sp. DsRed1 gene in T. reesei, by random integration of the expression cassette into the genome. We demonstrated that the fluorescent intensity of RFP (red fluorescent protein) inside of the cell was well correlated with the secreted lipase yields, based on which, we successfully developed a high-throughput screening method to screen strains with relatively higher secreted expression of the GOI (in this study, lipase). The copy number and the insertion sites of the transgene were investigated among the selected highly expressed strains. Eventually, in addition to cbh1 gene locus, two other genome insertion loci that efficiently facilitate gene expression in T. reesei were identified. Conclusions We have successfully developed a high-throughput screening method to screen strains with optimal expression of the indicated secreted proteins in T. reesei. Moreover, we identified two optimal genome loci for transgene expression, which could provide new approach to modulate gene expression levels while retaining the indicated promoter and culture conditions.

Abstract Trichoderma reesei is well known as an industrial workhorse fungus in cellulase production. The low dissolved oxygen supply in the highly viscous medium of T. reesei remains a major bottleneck that hampers growth and cellulase production in submerged fermentation. Vitreoscilla hemoglobin (VHb) has been demonstrated to improve metabolism and protein production in different heterologous hosts under hypoxic conditions, but the use of VHb in T. reesei remains uninvestigated. This study examines the effect of VHb in improving T. reesei performance in submerged fermentation. The VHb gene (vgb)-expressing cassette was successfully transformed into the TU-6 strain, integrated into the genome of T. reesei, and functionally expressed with biological activity, which was confirmed by carbon monoxide difference analysis. Compared to the parent strain, the expression of VHb increased the glucose consumption rate of the transformant. Moreover, in cellulase-inducing medium total protein secretion of the VHb expressing strain was 2.2-fold of the parental strain and the filter paper cellulase activity was increased by 58% under oxygen-limiting conditions. In summary, our results demonstrate that VHb has beneficial effects on improving total protein secretion and cellulase activity of T. reesei in submerged fermentation.

In eukaryotes, the organization and function of the microtubule cytoskeleton depend on the allocation of different roles to individual microtubules. For example, many asymmetrically dividing cells differentially specify microtubule behavior at old and new centrosomes. Here we show that yeast spindle pole bodies (SPBs, yeast centrosomes) differentially control the plus-end dynamics and cargoes of their astral microtubules, remotely from the minus-end. The old SPB recruits the kinesin motor protein Kip2, which then translocates to the plus-end of the emanating microtubules, promotes their extension and delivers dynein into the bud. Kip2 recruitment at the SPB depends on Bub2 and Bfa1, and phosphorylation of cytoplasmic Kip2 prevents random lattice binding. Releasing Kip2 of its control by SPBs equalizes its distribution, the length of microtubules and dynein distribution between the mother cell and its bud. These observations reveal that microtubule organizing centers use minus to plus-end directed remote control to individualize microtubule function.

Vib1, a member of the Ndt80/PhoG-like transcription factor family, has been shown to be essential for cellulase production of Trichoderma reesei. Here, we combined transcriptomic and genetic analyses to gain mechanistic insights into the roles of Vib1 during cellulose degradation. Our transcriptome analysis showed that the vib1 deletion caused 586 genes with decreased expression and 431 genes with increased expression on cellulose. The downregulated genes were enriched for Gene Ontology terms associated with carbohydrate metabolism, transmembrane transport, oxidoreductase activity, and transcription factor activity. Of the 258 genes induced by cellulose, 229 showed no or decreased expression in Δvib1 on cellulose, including almost all (hemi)cellulase genes, crucial sugar transporter genes (IDs:69957, 3405), and the genes encoding main transcriptional activators Xyr1 and Ace3. Additionally, Vib1 also regulated the expression of genes involved in secondary metabolism. Further comparison of the transcriptomes of Δvib1 and Δxyr1 in cellulose revealed that the genes regulated by Vib1 had much overlap with Xyr1 targets especially for the gene set induced by cellulose, presumably whose expression requires the cooperativity between Vib1 and Xyr1. Genetic evidence indicated that Vib1 regulates cellulase gene expression partially via Xyr1. Our results will provide new clues for strain improvement.

This article explores the association between emotional intelligence, mental health, and English proficiency among college students. Survey results reveal that the gender ratio of college students is roughly equal, and the number of participants is stable each year. Humanities majors comprise 31% of the total. The authors collected data from nearly 1000 college students, which was analyzed using a matrix model. Results suggest that while there is a weak correlation between emotional intelligence scores and English grades, there is a strong correlation between mental health scores and English grades. The researchers conducted a 3-month psychological intervention experiment on 60 sophomore students. These students showed improvement in both mental health scores and English scores. Additionally, survey results indicate student satisfaction with the testing and psychological intervention. Overall, this study demonstrates that mental health and English proficiency are closely related, and psychological interventions can positively affect language skills.

How to accurately characterize similarities of entities is the basis of detecting virtual community structure of an Internet social network. This paper proposes a supernetwork based approach of quantitative similarity evaluation among entities with two indices of friend relation and interest similarity. The supernetwork theory is firstly introduced to model the complex relationship of online social network entities by integrating three basic networks: entity, action, and interest and establishing three kinds of mappings: from entity to action, from action to interest, and from entity to interest, that is, one hidden relation mined through the transfer characteristic of visible mappings. And further similarity degree between two entities is calculated by weighting the values of two indices: friend relation and interest similarity. Experiments show that this model not only can provide a more realistic relation of individual users within an Internet social network, but also, build a weighted social network, that is, a graph in which user entities are vertices and similarities are edges, on which the values record their similarity strength relative to one another.

To efficiently train the billions of parameters in a giant model, sharing the parameter-fragments within the Federated Learning (FL) framework has become a popular pattern, where each client only trains and shares a fraction of parameters, extending the training of giant models to the broader resources-constrained scenarios. Compared with the previous works where the models are fully exchanged, the fragment-sharing pattern poses some new challenges for the backdoor attacks. In this paper, we investigate the backdoor attack on giant models when they are trained in an FL system. With the help of fine-tuning technique, a backdoor attack method is presented, by which the malicious clients can hide the backdoor in a designated fragment that is going to be shared with the benign clients. Apart from the individual backdoor attack method mentioned above, we additionally show a cooperative backdoor attack method, in which the fragment of a malicious client to be shared only contains a part of the backdoor while the backdoor is injected when the benign client receives all the fragments from the malicious clients. Obviously, the later one is more stealthy and harder to be detected. Extensive experiments have been conducted on the datasets of CIFAR-10 and CIFAR-100 with the ResNet-34 as the testing model. The numerical results show that our backdoor attack methods can achieve an attack success rate close to 100% in about 20 rounds of iterations.