Effects of Different Inducers on Higher Alcohol Dehydrogenase from Galactomyces geotrichum

The inducers of the chemical or physical factors could affect the gene expression and transcription of strain directly or indirectly. In order to explore the inductive effect of higher alcohol on Galactomyces geotrichum, the G. geotrichum S12, derived from the soil, was induced by five higher alcohols, including n-propanol, n-butanol, isobutanol, n-hexanol and isoamyl alcohol. The effects of induction dose and induction time for degradation activity of different higher alcohols by strain S12 and its transforming enzyme were studied. The results showed that the enzyme activity formed by strain S12 was higher when the inducers were chosen as n-hexanol and isobutanol. The optimum induction time was 6 h when n-propanol, n-butanol, isobutanol and n-hexanol were used as inducers. While the optimum induction time of strain S12 and enzyme induced by isoamyl alcohol was 12 h. When n-propanol and n-hexanol were used as inducers, the optimal concentration was 1.5 g/L. While the optimal concentrations of strain S12 and enzyme induced by n-butanol, isobutanol and isoamyl alcohol were 1.0, 0.5 and 2.5 g/L, respectively. The results of native polyacryplamide gel electrophoresis (Native-PAGE) indicated that dehydrogenase formation, molecular weight about 223 kDa, was induced by five higher alcohols. In particular, the strain, induced by hexanol, had much higher capability in degrading five higher alcohols at the same time. The products of the above five higher alcohols after catalyzing by G. geotrichum S12 induced by hexanol were their corresponding acids and esters

Identification of novel immune-related molecular subtypes and a prognosis model to predict thyroid cancer prognosis and drug resistance

Background: Thyroid cancer is a common malignant tumor of the endocrine system that has shown increased incidence in recent decades. We explored the relationship between tumor-infiltrating immune cell classification and the prognosis of thyroid carcinoma.Methods: RNA-seq, SNV, copy number variance (CNV), and methylation data for thyroid cancer were downloaded from the TCGA dataset. ssGSEA was used to calculate pathway scores. Clustering was conducted using ConsensusClusterPlus. Immune infiltration was assessed using ESTIMATE and CIBERSORT. CNV and methylation were determined using GISTIC2 and the KNN algorithm. Immunotherapy was predicted based on TIDE analysis. Results: Three molecular subtypes (Immune-enrich(E), Stromal-enrich(E), and Immune-deprived(D)) were identified based on 15 pathways and the corresponding genes. Samples in Immune-E showed higher immune infiltration, while those in Immune-D showed increased tumor mutation burden (TMB) and mutations in tumor driver genes. Finally, Immune-E showed higher CDH1 methylation, higher progression-free survival (PFS), higher suitability for immunotherapy, and higher sensitivity to small-molecule chemotherapeutic drugs. Additionally, an immune score (IMScore) based on four genes was constructed, in which the low group showed better survival outcome, which was validated in 30 cancers. Compared to the TIDE score, the IMScore showed better predictive ability.Conclusion: This study constructed a prognostic evaluation model and molecular subtype system of immune-related genes to predict the thyroid cancer prognosis of patients. Moreover, the interaction network between immune genes may play a role by affecting the biological function of immune cells in the tumor microenvironment

Dynamic Modeling, Simulation, and Optimization of Vehicle Electronic Stability Program Algorithm Based on Back Propagation Neural Network and PID Algorithm

The vehicle lateral stability control algorithm is an essential component of the electronic stability program (ESP), and its control effect directly affects the vehicle’s driving safety. However, there are still numerous shortcomings and challenges that need to be addressed, including enhancing the efficiency of processing intricate pavement condition data, improving the accuracy of parameter adjustment, and identifying subtle and elusive patterns amidst noisy and ambiguous data. The introduction of machine learning algorithms can address the aforementioned issues, making it imperative to apply machine learning to the research of lateral stability control algorithms. This paper presented a vehicle lateral electronic stability control algorithm based on the back propagation (BP) neural network and PID control algorithm. Firstly, the dynamics of the whole vehicle have been analytically modeled. Then, a 2 DOF prediction model and a 14 DOF simulation model were built in MATLAB Simulink to simulate the data of the electronic control units (ECU) in ESP and estimate the dynamic performance of the real vehicle. In addition, the self-correction of the PID algorithm was verified by a Simulink/CarSim combined simulation. The improvement of the BP neural network to the traditional PID algorithm was also analyzed in Simulink. These simulation results show the self-correction of the PID algorithm on the lateral stability control of the vehicle under different road conditions and at different vehicle speeds. The BP neural network smoothed the vehicle trajectory controlled by traditional PID and improved the self-correction ability of the control system by iterative training. Furthermore, it shows that the algorithm can automatically tune the control parameters and optimize the control process of the lateral electronic stability control algorithm, thus improving vehicle stability and adapting it to many different vehicle models and road conditions. Therefore, the algorithm has a high practical value and provides a feasible idea for developing a more intelligent and general vehicle lateral electronic stability system

Cross-language opinion lexicon extraction using mutual-reinforcement label propagation.

There is a growing interest in automatically building opinion lexicon from sources such as product reviews. Most of these methods depend on abundant external resources such as WordNet, which limits the applicability of these methods. Unsupervised or semi-supervised learning provides an optional solution to multilingual opinion lexicon extraction. However, the datasets are imbalanced in different languages. For some languages, the high-quality corpora are scarce or hard to obtain, which limits the research progress. To solve the above problems, we explore a mutual-reinforcement label propagation framework. First, for each language, a label propagation algorithm is applied to a word relation graph, and then a bilingual dictionary is used as a bridge to transfer information between two languages. A key advantage of this model is its ability to make two languages learn from each other and boost each other. The experimental results show that the proposed approach outperforms baseline significantly

Large Kernel Separable Mixed ConvNet for Remote Sensing Scene Classification

Among tasks related to intelligent interpretation of remote sensing data, scene classification mainly focuses on the holistic information of the entire scene. Compared with pixel-level or object-based tasks, it involves a richer semantic context, making it more challenging. With the rapid advancement of deep learning, convolutional neural networks (CNNs) have found widespread applications across various domains, and some work has introduced them into scene classification tasks. However, traditional convolution operations involve sliding small convolutional kernels across an image, primarily focusing on local details within a small receptive field. To achieve better modeling of the entire image, the smaller receptive field limits the ability of convolution operation to capture features over a broader range. To this end, we introduce large kernel CNNs into the scene classification task to expand the receptive field of the mode, which allows us to capture comprehensive nonlocal information while still acquiring rich local details. However, in addition to encoding spatial association, the effective information within the feature maps is also strongly channel related. Therefore, to fully model this channel dependency, a novel channel separation and mixing module has been designed to realize feature correlation in the channel dimension. The combination of them forms a large kernel separable mixed ConvNet, enabling the model to capture effective dependencies of feature maps in both spatial and channel dimensions, thus achieving enhanced feature expression. Extensive experiments conducted on three datasets have also validated the effectiveness of the proposed method

Organic extracts in PM2.5 are the major triggers to induce ferroptosis in SH-SY5Y cells

As a major air pollutant, PM2.5 can induce apoptosis of nerve cells, causing impairment of the learning and memory capabilities of humans and animals. Ferroptosis is a newly discovered way of programmed cell death. It is unclear whether the neurotoxicity induced by PM2.5 is related to the ferroptosis of nerve cells. In this study, we observed the changes in ferroptosis hallmarks of SH-SY5Y cells after exposure to various doses (40, 80, and 160 μg/mL PM2.5) for 24 h, exposure to 40 μg/mL PM2.5 for various times (24, 48, and 72 h), as well as exposure to various components (Po, organic extracts; Pw, water-soluble extracts; Pc, carbon core component). The results showed that PM2.5 reduced the cell viability, the content of GSH, and the activity of GSH-PX and SOD in SH-SY5Y cells with exposure dose and duration increasing. On the other hand, PM2.5 increased the content of iron, MDA, and the level of lipid ROS in SH-SY5Y cells with exposure dose and duration increasing. Additionally, PM2.5 reduced the expression levels of HO-1, NRF2, SLC7A11, and GPX4. The ferroptosis inhibitors Fer-1 and DFO significantly increase the cells viabilities and significantly reversed the changes of other above ferroptosis hallmarks. We also observed the different effects on ferroptosis hallmarks in the SH-SY5Y cells exposed to PM2.5 (160 μg/mL) and its various components (organic extracts, water-soluble extracts, and carbon core) for 24 h. We found that only the organic extracts shared similar results with PM2.5 (160 μg/mL). This study demonstrated that PM2.5 induced ferroptosis of SH-SY5Y cells, and organic extracts might be the primary component that caused ferroptosis

Response of Liquid Water and Vapor Flow to Rainfall Events in Sandy Soil of Arid and Semi-Arid Regions

In arid and semi-arid regions, rainfall takes on a critical significance to both agricultural and engineering construction activities, and the transport process and driving mechanism of soil water under rainfall conditions are in need of further investigation. To clarify the variations in soil moisture, temperature, and liquid and vapor flux under various rainfall scenarios, the Mu Us Sandy Land was selected as the study region, and a water–vapor–heat transport model was established using the Hydrus-1D software with in situ observed soil and meteorological data. The simulated results were in good agreement with the measured data during both the calibration and validation periods, suggesting that the model was accurate and applicable to the study region. The variations in the selected dry and rainy periods proved the significant effect of rainfall events on soil matric potential, temperature, and driving forces. When rainfall occurred, the hydraulic conductivity for liquid water rose by three to five orders of magnitude, driving the liquid water flow downward. In contrast, the vapor flux played a vital role in soil water movement, accounting for about 15% of the total water flux in the shallow layer when the soil was dry, while it became non-significant during rainy periods due to the reduction in hydraulic conductivity for vapor and the temperature gradient. These results clarified the mechanisms of soil liquid water and vapor movement in arid areas, which could provide scientific support for future studies on vegetation restoration and ecosystem sustainability in ecologically fragile areas

Integrated Omics Analysis Reveals Key Pathways in Cotton Defense against Mirid Bug (<i>Adelphocoris suturalis</i> Jakovlev) Feeding

The recent dominance of Adelphocoris suturalis Jakovlev as the primary cotton field pest in Bt-cotton-cultivated areas has generated significant interest in cotton pest control research. This study addresses the limited understanding of cotton defense mechanisms triggered by A. suturalis feeding. Utilizing LC-QTOF-MS, we analyzed cotton metabolomic changes induced by A. suturalis, and identified 496 differential positive ions (374 upregulated, 122 downregulated) across 11 categories, such as terpenoids, alkaloids, phenylpropanoids, flavonoids, isoflavones, etc. Subsequent iTRAQ-LC-MS/MS analysis of the cotton proteome revealed 1569 differential proteins enriched in 35 metabolic pathways. Integrated metabolome and proteome analysis highlighted significant upregulation of 17 (89%) proteases in the α-linolenic acid (ALA) metabolism pathway, concomitant with a significant increase in 14 (88%) associated metabolites. Conversely, 19 (73%) proteases in the fructose and mannose biosynthesis pathway were downregulated, with 7 (27%) upregulated proteases corresponding to the downregulation of 8 pathway-associated metabolites. Expression analysis of key regulators in the ALA pathway, including allene oxidase synthase (AOS), phospholipase A (PLA), allene oxidative cyclase (AOC), and 12-oxophytodienoate reductase3 (OPR3), demonstrated significant responses to A. suturalis feeding. Finally, this study pioneers the exploration of molecular mechanisms in the plant–insect relationship, thereby offering insights into potential novel control strategies against this cotton pest

Sex differences in colorectal cancer: with a focus on sex hormone–gut microbiome axis

Abstract Sexual dimorphism has been observed in the incidence and prognosis of colorectal cancer (CRC), with men generally exhibiting a slightly higher incidence than women. Research suggests that this difference may be attributed to variations in sex steroid hormone levels and the gut microbiome. The gut microbiome in CRC shows variations in composition and function between the sexes, leading to the concept of ‘microgenderome’ and ‘sex hormone–gut microbiome axis.’ Conventional research indicates that estrogens, by promoting a more favorable gut microbiota, may reduce the risk of CRC. Conversely, androgens may have a direct pro-tumorigenic effect by increasing the proportion of opportunistic pathogens. The gut microbiota may also influence sex hormone levels by expressing specific enzymes or directly affecting gonadal function. However, this area remains controversial. This review aims to explore the differences in sex hormone in CRC incidence, the phenomenon of sexual dimorphism within the gut microbiome, and the intricate interplay of the sex hormone–gut microbiome axis in CRC. The objective is to gain a better understanding of these interactions and their potential clinical implications, as well as to introduce innovative approaches to CRC treatment. Graphical Abstrac