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Abstract Background Numerous observational studies have indicated that patients with Guillain-Barré syndrome (GBS) frequently had infections with various pathogens before the onset of the disease, particularly several viral infections. Some of these infections are linked to specific clinical and immunological subgroups of GBS, suggesting a potential correlation between viral infections and the development of GBS. However, observational studies have several limitations, including the presence of confounding factors. Method We explored the potential correlation between HIV, SARS-CoV-2, varicella-zoster virus, herpes simplex virus, Epstein-Barr virus, hepatitis B virus, and influenza virus with GBS using a two-sample Mendelian randomization approach. The data was derived from published summary statistics from genome-wide association studies (GWAS). After removing linkage disequilibrium, selecting strong instrumental variables and addressing confounding factors, we would conduct a two-sample Mendelian randomization analysis along with sensitivity testing and the MR-Steiger directional test. Result HIV may have a causal association with GBS (IVW: p = 0.010, OR [95% CI] 1.240 [1.052–1.463]), while no such relationship exists with COVID-19 (IVW: p = 0.275, OR [95% CI] 0.831[0.596–1.159]), varicella (IVW: p = 0.543, OR [95% CI] 0.919 [0.701–1.206]), herpes zoster (IVW: p = 0.563, OR [95% CI] 0.941 [0.766–1.156]), HSV (IVW: p = 0.280, OR [95% CI] 1.244 [0.837–1.851]), EBV (IVW: p = 0.218, OR [95% CI] 0.883 [0.724–1.076]), HBV (IVW: p = 0.179, OR [95% CI] 1.072 [0.969–1.187]), or influenza virus (IVW: p = 0.917, OR [95% CI] 0.971 [0.553–1.703]). We did not find any abnormal SNPs, pleiotropy, or heterogeneity, nor is there any reverse causation. Conclusion Our study results indicate a causal relationship between HIV and GBS, providing new research directions for the etiology of GBS.

Abstract Background The expression of 2’-5’-oligoadenylate synthetase 1 (OAS1) in lung cancer has been validated in numerous studies. However, the prognostic value of OAS1 expression in lung adenocarcinoma (LUAD) still remains unclear. This study aimed to reveal the prognostic value and associated molecular mechanisms of OAS1 expression in LUAD. Methods Gene expression data of LUAD were extracted from online databases. Gene and protein expression levels of OAS1 in LUAD and normal samples were revealed, followed by prognostic analysis of OAS1. Next, we conducted a thorough bioinformatics analysis to examine the enrichment of key functional and biological signaling pathways and their correlation with the abundance of immune cells. The independent prognoses, drug responses, and PPI networks associated with OAS1 were analyzed. OAS1 expression was evaluated in LUAD tissues and cell lines. OAS1 was knocked down by siRNA transfection, followed by CCK8, colony formation, and wound-healing assays. Results Gene and protein expression levels of OAS1 in LUAD samples were significantly higher than those in normal samples (all P

The effect of germination degree (shoot length) on the content of nutritional and bioactive components, antioxidant activity was investigated in tartary buckwheat whole grain powder (TBWGP). The findings demonstrated that as germination degree increased, the absolute contents of total starch, protein and fat fractions as well as the ratio of straight-chain starch to total starch in TBWGP decreased, and the content of dietary fiber (IDF, SDF, TDF) elevated (P

Objective(s): Cervical cancer (CC) is the most common gynecological malignant tumor and the fourth leading cause of cancer-related death in women. The progression of CC is significantly affected by autophagy. Our objective was to use bioinformatics analysis to explore the expression, prognostic significance, and immune infiltration of autophagy-related genes in CC. Materials and Methods: We identified a set of autophagy-related differentially expressed genes (ARDEGs) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ARDEGs were further validated by The Human Protein Atlas (HPA), GSE52903, and GSE39001 dataset. Hub genes were found by the STRING network and Cytoscape. We performed Gene Set Enrichment Analysis (GSEA), Gene ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and immune infiltration analysis to further understand the functions of the hub genes. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) were used to check the hub genes. Results: A total of 10 up-regulated (CXCR4, BAX, SPHK1, EIF2AK2, TBK1, TNFSF10, ITGB4, CDKN2A, IL24, and BIRC5) and 19 down-regulated (PINK1, ATG16L2, ATG4D, IKBKE, MLST8, MAPK3, ERBB2, ULK3, TP53INP2, MTMR14, BNIP3, FOS, CCL2, FAS, CAPNS1, HSPB8, PTK6, FKBP1B , and DNAJB1) ARDEGs were identified. The ARDEGs were enriched in cell growth, apoptosis, human papillomavirus infection, and cytokine-mediated. Then, we found that low expression of MAPK3 was associated with poor prognosis in CC patients and was significantly enriched in immune pathways. In addition, the expression of MAPK3 was significantly positively correlated with the infiltration levels of macrophages, B cells, mast cell activation, and cancer-associated fibroblasts. Furthermore, MAPK3 was positively correlated with LGALS9, and negatively correlated with CTLA4 and CD40. Conclusion: Our results show that MAPK3 can be used as a new prognostic biomarker to predict the prognosis of patients with CC.

Background: This study aims to analyze the safety and clinical efficacy of using double posterolateral coaxial portals for endoscopic treatment of posterior ankle impingement syndrome (PAIS), a procedure that has gained popularity in recent times. Methods: Six fresh foot samples were randomly selected to measure the distances of two posterolateral portals to the sural nerve in different positions (plantar flexion 10°, dorsiflexion 30°, and plantar flexion 30°) for safety evaluation. A prospective analysis was conducted on the clinical efficacy of the operative approach for endoscopic management of posterior ankle impingement syndrome, including evaluation of effectiveness and complications. Results: In this study, the mean distances of the first and second portals to the sural nerve were measured in different ankle positions. The distances were found to be 2.26 ± 0.22 cm and 1.59 ± 0.12 cm in the plantar flexion 10° position, 2.21 ± 0.21 cm and 1.55 ± 0.12 cm in the dorsiflexion 30° position, and 2.46 ± 0.29 cm and 1.73 ± 0.19 cm in the plantar flexion 30° position, demonstrating a significant safety margin from the nerve. A total of 38 patients underwent endoscopic treatment for posterior ankle impingement syndrome using double posterolateral coaxial portals between January 2012 and December 2017. This surgical approach provided access to the subtalar joint and posterior ankle region. The patients were followed up for an average of 38.2 months (24–72 months), with a satisfaction rate of 94.7%. There were no reported complications, and significant improvements were observed in both visual analogue scale (VAS) and The American Orthopedic Foot and Ankle Society Score (AOFAS) scores postoperatively. The VAS score decreased from 5.68 to 0.51 (P

Although sulfonation plays crucial roles in various biological processes and is frequently utilized in medicinal chemistry to improve water solubility and chemical diversity of drug leads, it is rare and underexplored in ribosomally synthesized and post-translationally modified peptides (RiPPs). Biosynthesis of RiPPs typically entails modification of hydrophilic residues, which substantially increases their chemical stability and bioactivity, albeit at the expense of reducing water solubility. To explore sulfonated RiPPs that may have improved solubility, we conducted co-occurrence analysis of RiPP class-defining enzymes and sulfotransferase (ST), and discovered two distinctive biosynthetic gene clusters (BGCs) encoding both lanthipeptide synthetase (LanM) and ST. Upon expressing these BGCs, we characterized the structures of novel sulfonated lanthipeptides and determined the catalytic details of LanM and ST. We demonstrate that SslST-catalyzed sulfonation is leader-independent but relies on the presence of A ring formed by LanM. Both LanM and ST are promiscuous towards residues in the A ring, but ST displays strict regioselectivity toward Tyr5. The recognition of cyclic peptide by ST was further discussed. Bioactivity evaluation underscores the significance of the ST-catalyzed sulfonation. This study sets up the starting point to engineering the novel lanthipeptide STs as biocatalysts for hydrophobic lanthipeptides improvement.

Background: Nonsmall cell lung cancer is a leading cause of cancer-related death worldwide. The long noncoding RNA MIR4435-2HG has been shown to play a carcinogenic role in various cancers. The purpose of this study was to explore the role and regulatory mechanism of MIR4435-2HG in non-small cell lung cancer. Methods: Quantitative real-time polymerase chain reaction was used to detect MIR4435-2HG and SRY-box transcription factor 2 in nonsmall cell lung cancer cells. Gain- or loss-of-function assays of MIR4435-2HG and SRY-box transcription factor 2 were subsequently conducted. Cell proliferation, apoptosis, migration, glycolysis, and invasion were tested. A nude mouse tumor model was constructed to determine the role of MIR4435-2HG and SRY-box transcription factor 2 in the growth of tumor cells in vivo. Furthermore, the interactions between MIR4435-2HG, miR-371a-5p and SRY-box transcription factor 2 were analyzed via a dual-luciferase reporter gene assay. Results: Quantitative real-time polymerase chain reaction revealed that MIR4435-2HG and SRY-box transcription factor 2 were upregulated in nonsmall cell lung cancer cells. Forced MIR4435-2HG overexpression led to increased cell proliferation, migration, invasion, and glycolysis and repressed cell apoptosis. Overexpressing MIR4435-2HG promoted SRY-box transcription factor 2 expression and PI3K/Akt/mTOR pathway activation. Downregulating MIR4435-2HG had antitumor effects both in vitro and in vivo. SRY-box transcription factor 2 overexpression mostly reversed the suppressive effects of MIR4435-2HG downregulation. Mechanistic studies revealed that MIR4435-2HG, a competitive endogenous RNA, directly targeted and inhibited miR-371a-5p. Rescue assays revealed that miR-371a-5p overexpression or SRY-box transcription factor 2 downregulation significantly inhibited MIR4435-2HG-mediated oncogenic effects. Conclusion: MIR4435-2HG promotes nonsmall cell lung cancer cell malignant behaviors and glycolysis by regulating the miR-371a-5p/SOX2 axis.

As one of the key components of the immune system, mast cells are well known for their role in allergic reactions. However, they are also involved in inflammatory and fibrotic processes. Mast cells participate in all the stages of acute inflammatory responses, playing an immunomodulatory role in both innate and adaptive immunity. Mast cell-derived histamine, TNF-α, and IL-6 contribute to the inflammatory processes, while IL-10 mediates the suppression of inflammation. Crosstalk between mast cells and other immune cells is also involved in the development of inflammation. The cell–cell adhesion of mast cells and fibroblasts is crucial for fibrosis. Mast cell mediators, including cytokines and proteases, play contradictory roles in the fibrotic process. Here, we review the double-edged role of mast cells in inflammation and fibrosis.

Semantic segmentation of high-resolution multispectral remote sensing image has been intensely studied. However, the shadow occlusions, or the similar color and textures, between the categories influence the segmentation accuracy. Concomitantly, the size of targets in the remote sensing images is diverse and the network cannot balance their segmentation. This paper introduces a network, Transformer-based Multi-modal Fusion Network (TMFNet), which fuses the multi-modal features and incorporates height features from the digital surface model (DSM) to supplement the extra different features between each category. Particularly, we introduce two parallel encoders to extract the features from different modalities, a Multi-Modal fusion model based on the Transformer (MMformer) to complete the multi-modal fusion, and a Border Region Attention based multi-level Fusion Module (BRAFM) to integrate the cross-level features and enhance the small target segmentation by utilizing the details around the border. The experiment results on the ISPRS Vaihingen and Potsdam benchmark datasets indicate that the proposed TMFNet outperforms the SOTA methods on the segmentation performance.

Lung cancer ranks as the 2nd most common cancer globally. It’s the most prevalent cancer in men and the 2nd most common in women. The prominent events in EGFR-mutated non-small-cell lung cancer (NSCLC) include the emergence of the L858R mutation within EGFR exon 21. Despite the promising efficacy of EGFR inhibitors in managing lung cancer, the development of acquired resistance poses a significant hurdle. In the current investigation, we focused on the screening of two phytochemicals, namely Dehydrocostus lactone and Mokkolactone, derived from the Saussurea lappa plant, as potential inhibitors targeting EGFR L858R mutant lung cancer. The chloroform and ethanol extract of the plant demonstrated anti-proliferative activity through the Resazurin chemosensitivity assay, exhibiting an IC50 value of 37.90 ± 0.29 µg/ml with selectivity index 2.4. Through a GC–MS study, we identified 11 phytochemicals for further insilico analysis. These compounds underwent ADMET assessment followed by drug likeliness analysis before being subjected to molecular docking against EGFR L858R, identified through protein–protein interaction network analysis. All phytochemicals exhibited binding energy scores ranging from −6.9 to −8.1 kcal/mol. Dehydrocostus lactone and Mokkolactone were specifically identified for their binding profile. Findings from 100 ns molecular dynamics simulations demonstrated their enhanced stability compared to the reference ligand DJK. This was evident in the root mean square deviation (RMSD) values, ranging from 0.23 ± 0.01 nm to 0.30 ± 0.05 nm, the radius of gyration values, from 1.71 ± 0.01 nm to 1.72 ± 0.01 nm, and the solvent accessible surface area values, from 155.39 ± 2.40 nm2 to 159.32 ± 2.14 nm2. Additionally, favourable characteristics were observed in terms of hydrogen bonding, principal component analysis, and free energy landscape analysis. Examination of their electronic structure via density functional theory revealed efficient properties, with the highest occupied molecular orbital-least unoccupied molecular orbital energy gap values ranging from −3.984 eV to −6.547 eV. Further, in vivo analysis is required to gain a more comprehensive understanding and efficacy of these identified phytochemicals against lung cancer.

Abstract The disintegration of red-bed soft rock exhibits a strong correlation with various geological disasters. However, the investigation into the evolutionary mechanisms underlying disintegration breakage has not yet received extensive exploration. In order to comprehensively examine the disintegration characteristics of red-bed soft rock, the slake durability tests were conducted to red-bed soft rocks of varying burial depths. Subsequently, an investigation was carried out to examine the disintegration characteristics and the evolution of disintegration parameters, including the coefficient of uniformity (Cu), coefficient of curvature (Cc), disintegration rate (DRE), disintegration ratio (Dr), and fractal dimension (D), throughout the disintegration process. Finally, employing the energy dissipation theory, an energy dissipation model was developed to predicate the disintegration process of samples at various burial depths. The findings demonstrate a decrease in the abundance of large particles and a concurrent increase in the abundance of small particles as the number of drying-wetting cycles increases. Furthermore, as the number of drying-wetting cycles increases, a significant alteration is observed in the content of particles larger than 10 mm, whereas the content of particles smaller than 10 mm undergoes only minor changes. The disintegration parameters, including the curvature coefficient, non-uniformity coefficient, disintegration rate, and fractal dimension, exhibit a positive correlation with the number of drying-wetting cycles. Conversely, the disintegration index demonstrates a decreasing trend with the increasing number of cycles. Nevertheless, as the burial depth increases, a notable trend emerges in the disintegration process, characterized by a gradual increase in the content of large particles alongside a progressive decrease in the content of small particles. Concurrently, the curvature coefficient, non-uniformity coefficient, disintegration rate, and fractal dimension exhibit a gradual decline, while the durability index experiences a gradual increase. In addition, based on the principle of energy dissipation, it is revealed that the surface energy increment of red-bed soft rock increases with the increase of the number of drying-wetting cycles, but decreases with the increase of burial depth. Ultimately, by leveraging the outcomes of energy dissipation analyses, a theoretical model is constructed to elucidate the correlation between surface energy and both the number of drying-wetting cycles and burial depth. This model serves as a theoretical reference for predicting the disintegration behavior of samples, offering valuable insights for future research endeavors.

Hyperspectral target detection has a wide range of applications in marine target monitoring. Traditional methods for target detection take less consideration of the inherent structural information of hyperspectral images and make insufficient use of spatial information. These algorithms may experience degradation in efficacy during complex scenarios. To address these issues, this study introduces a hyperspectral target detection approach based on tensor adaptive reconstruction cascade spatial-spectral fusion, named as TRSSF. First, the position of the pixel that best matches the prior spectrum is obtained. Second, tensor decomposition and reconstruction of the original hyperspectral data are performed. Linear total variation smoothing is used to acquire the principal components in the spatial dimensionality unfolding of data, and correlation regularization robust principal component analysis is employed to derive the spectral dimensionality unfolding’s principal components of data. Finally, the spatial-spectral fusion method is proposed for detecting hyperspectral targets on the reconstructed data. The use of multi-morphological feature fusion can fully utilize the spatial features to complement the spectral detection results and improve the integrity of target detection. The experiments conducted on the publicly available dataset and collected datasets demonstrated the effective detection achieved by the proposed method.

The series-parallel hybrid system has attracted much attention from scholars for its effective integration of the power advantages and operating characteristics of different power sources, which is influenced by international emission regulations, energy-saving and emission reduction policies. As such, a series-parallel hybrid powertrain is introduced to the amphibious vehicle, and an innovative powertrain topology architecture is proposed. Meanwhile, the operation mode and energy efficiency characteristics are investigated during the working process. Firstly, the energy flow simulation model of a series-parallel gas-electric hybrid propulsion system is constructed using a modular modeling approach. Secondly, four operating modes, namely mechanical propulsion, electric propulsion, hybrid propulsion and charging mode, were formulated due to the fact that the propulsion system has multiple forms of power sources in the form of natural gas engine and reversible motor. Meanwhile, the energy flow states were investigated under different operating modes. Meanwhile, a comprehensive investigation of the energy efficiency associated with propulsion, storage and start-up energy was conducted for each specific mode. The results of the research indicated that the energy efficiency of the electric propulsion mode can reach up to 35.15 %, which is the gain from the wide operating range of the motor's high efficiency. The hybrid propulsion mode can obtain the highest energy efficiency of 35.88 %, which fully demonstrates the advantages of coordinating and complementing the two power sources, the natural gas engine and the reversible electric motor. This investigation also provides theoretical and empirical support for optimizing energy matching and formulating energy management strategies.

Background: Cervical cancer is among the most prevalent malignancies worldwide. This study explores the relationships between angiogenesis-related genes (ARGs) and immune infiltration, and assesses their implications for the prognosis and treatment of cervical cancer. Additionally, it develops a diagnostic model based on angiogenesis-related differentially expressed genes (ARDEGs). Methods: We systematically evaluated 15 ARDEGs using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA). Immune cell infiltration was assessed using a single-sample gene-set enrichment analysis (ssGSEA) algorithm. We then constructed a diagnostic model for ARDEGs using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and evaluated the diagnostic value of this model and the hub genes in predicting clinical outcomes and immunotherapy responses in cervical cancer. Results: A set of ARDEGs was identified from the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and UCSC Xena database. We performed KEGG, GO, and GSEA analyses on these genes, revealing significant involvement in cell proliferation, differentiation, and apoptosis. The ARDEGs diagnostic model, constructed using LASSO regression analysis, showed high predictive accuracy in cervical cancer patients. We developed a reliable nomogram and decision curve analysis to evaluate the clinical utility of the ARDEG diagnostic model. The 15 ARDEGs in the model were associated with clinicopathological features, prognosis, and immune cell infiltration. Notably, ITGA5 expression and the abundance of immune cell infiltration (specifically mast cell activation) were highly correlated. Conclusion: This study identifies the prognostic characteristics of ARGs in cervical cancer patients, elucidating aspects of the tumor microenvironment. It enhances the predictive accuracy of immunotherapy outcomes and establishes new strategies for immunotherapeutic interventions.

Short-term power load forecasting is the basis for ensuring the safe and stable operation of the power system. However, because power load forecasting is affected by weather, economy, geography, and other factors, it has strong instability and nonlinearity, making it difficult to improve the accuracy of short-term power load forecasting. To solve the above problems, a load forecasting method combining empirical modal decomposition (EMD) and long short-term memory neural network (LSTM) has been proposed. The original signal is first decomposed into a series of eigenmode functions and a residual quantity using the EMD algorithm. Subsequently, all the components are fed into the LSTM network. To further improve the load prediction accuracy, a self-attention mechanism is introduced for large component signals to further explore the internal correlation of the data, and the Sparrow Optimisation Algorithm (SSA) is used to optimize the LSTM hyperparameters. Combining EMD, LSTM, self-attention mechanism (SAM), and SSA, the EMD-SSA- SAM -LSTM method for short-term power load forecasting is further proposed. The results show that the coefficient of determination (R2) of the method is 0.98, the mean absolute error (MAE) is 0.013, the root mean square error (RMSE) is 0.018, and the mean absolute percentage error (MAPE) is 2.57%, which verifies that the proposed model can improve the accuracy of load forecasting, and has a certain application prospect.

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

Abstract Background LATS1/2 are frequently mutated and down-regulated in endometrial cancer (EC), but the contributions of LATS1/2 in EC progression remains unclear. Impaired antigen presentation due to mutations or downregulation of the major histocompatibility complex class I (MHC-I) has been implicated in tumor immune evasion. Herein, we elucidate the oncogenic role that dysregulation of LATS1/2 in EC leads to immune evasion through the down-regulation of MHC-I. Methods The mutation and expression as well as the clinical significance of LATS1/2 in EC was assessed in the TCGA cohort and our sample cohort. CRISPR-Cas9 was used to construct knockout cell lines of LATS1/2 in EC. Differentially expressed genes were analyzed by RNA-seq. The interaction between LATS1/2 and STAT1 was verified using co-immunoprecipitation and GST pull-down assays. Mass spectrometry, in vitro kinase assays, ChIP-qPCR, flow cytometry, immunohistochemistry, immunofluorescence and confocal microscopy were performed to investigate the regulation of LATS1/2 on MHC-I through interaction with and phosphorylate STAT1. The killing effect of activated PBMCs on EC cells were used to monitor anti-tumor activity. Results Here, we demonstrate that LATS1/2 are frequently mutated and down-regulated in EC. Moreover, LATS1/2 loss was found to be associated with a significant down-regulation of MHC-I, independently of the Hippo-YAP pathway. Instead, LATS1/2 were found to directly interact with and phosphorylate STAT1 at Ser727, a crucial transcription factor for MHC-I upregulation in response to interferon-gamma (IFN-γ) signaling, to promote STAT1 accumulating and moving into the nucleus to enhance the transcriptional activation of IRF1/NLRC5 on MHC-I. Additionally, the loss of LATS1/2 was observed to confer increased resistance of EC cells to immune cell-mediated killing and this resistance could be reversed by over-expression of MHC-I. Conclusion Our findings indicate that dysregulation of LATS1/2 in EC leads to immune evasion through the down-regulation of MHC-I, leading to the suppression of infiltrating activated CD8 + T cells and highlight the importance of LATS1/2 in IFN-γ signaling-mediated tumor immune response, suggesting that LATS1/2 is a promising target for immune checkpoint blockade therapy in EC.

In order to study the difference effects of wheat bran dietary fiber (WBDF) and raw wheat bran (RWB) on the quality of steamed bread, WBDF was prepared by complex enzyme hydrolysis (moderate temperature α-amylase, cellulase, and alkaline protease), the sensory quality, textural property, in vitro starch digestibility and microstructure of steamed bread with different WBDF and RWB addition level were determined by millet replacement method, colorimeter, texture analyzer, in vitro simulated total starch hydrolysis method and SEM. The different effects between WBDF and RWB on the quality and property of steamed bread were compared under the same total dietary fiber content in the mixed flour. The results showed that both WBDF and RWB could affect the quality of steamed bread, but they could also inhibit the in vitro digestion of starch in steamed bread. When the total dietary fiber content was same, the steamed bread in WBDF group had larger specific volume, deeper color, rounder and plumper appearance, less separation of skin and pulp, higher bulkiness, lower internal structure density, better texture (low hardness, high springiness, low chewiness, and high resilience), more continuous and smooth microstructure, besides the damage of gluten network was lower than that of the steamed bread of RWB group. RWB had a slightly more ability to inhibit starch digestion than WBDF. The results suggested that compared with raw wheat bran, the relatively pure wheat bran dietary fiber was a more potential form of dietary fiber supplement under the condition of achieving the same dietary fiber supplement requirements, which was more conducive to developing good quality high-fiber wheat products.

Abstract Mitochondria are highly dynamic organelles capable of altering their sizes and shapes to maintain metabolic balance through coordinated fission and fusion processes. In various cancer types, mitochondrial hyperfragmentation has been frequently observed, contributing to the progression of cancer toward metastasis. Inverted formin 2 (INF2), which resides in the endoplasmic reticulum (ER), has been found to accelerate actin polymerization and drive mitochondrial fission. In this study, we demonstrate that INF2 expression is significantly upregulated in endometrial cancer (EC) and is associated with a poor prognosis in EC patients. INF2 promotes anchorage-dependent and independent EC cell growth in part by facilitating mitochondrial fission. Furthermore, in conditions of energy stress, AMP-activated protein kinase (AMPK) phosphorylates INF2 at Ser1077, leading to increased localization of INF2 to the ER and enhanced recruitment of the dynamin-related protein 1 (DRP1) to mitochondria. This AMPK-mediated phosphorylation of INF2 at Ser1077 facilitates mitochondrial division and promotes EC cell growth. Pathological examination using immunohistochemical analyses revealed a positive correlation between AMPK activity and phosphorylated INF2 (Ser1077) in EC specimens. Collectively, our findings uncover novel molecular mechanisms involving the AMPK-INF2 axis, which regulates mitochondrial dynamics and malignant cell growth in EC.

Tropical cyclone (TC) intensity forecasting poses challenges due to complex dynamical processes and data inadequacies during model initialization. This paper describes efforts to improve TC intensity prediction in the Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model by implementing a Vortex Initialization (VI) technique. The GFDL SHiELD model, relying on the Global Forecast System (GFS) analysis for initialization, faces deficiencies in initial TC structure and intensity. The VI method involves adjusting the TC vortex inherited from the GFS analysis and merging it back into the environment at the observed location, enhancing the analyzed representation of storm structure. We made modifications to the VI package implemented in the operational Hurricane Analysis and Forecast System, including handling initial condition data, reducing input domain size, and improving storm intensity enhancement. Experiments using the T-SHiELD configuration demonstrate that using VI significantly improves the representation of initial TC intensity and size, enhancing TC predictions, particularly in storm intensity and outer wind forecasts within the first 48 h.

Enhanced protein synthesis is a crucial molecular mechanism that allows cancer cells to survive, proliferate, metastasize, and develop resistance to anti-cancer treatments, and often arises as a consequence of increased signaling flux channeled to mRNA-bearing eukaryotic initiation factor 4F (eIF4F). However, the post-translational regulation of eIF4A1, an ATP-dependent RNA helicase and subunit of the eIF4F complex, is still poorly understood. Here, we demonstrate that IBTK, a substrate-binding adaptor of the Cullin 3-RING ubiquitin ligase (CRL3) complex, interacts with eIF4A1. The non-degradative ubiquitination of eIF4A1 catalyzed by the CRL3IBTK complex promotes cap-dependent translational initiation, nascent protein synthesis, oncogene expression, and cervical tumor cell growth both in vivo and in vitro. Moreover, we show that mTORC1 and S6K1, two key regulators of protein synthesis, directly phosphorylate IBTK to augment eIF4A1 ubiquitination and sustained oncogenic translation. This link between the CRL3IBTK complex and the mTORC1/S6K1 signaling pathway, which is frequently dysregulated in cancer, represents a promising target for anti-cancer therapies.

Modified Jiawei Juanbi decoction (MJD) is used for the treatment of early-stage knee osteoarthritis (KOA). Here, modified Jiawei Juanbi decoction (MJD) was employed for the treatment of early-stage knee osteoarthritis (KOA) and its mechanisms were assessed via metabonomics and network pharmacology. A total of 24 male Sprague-Dawley rats were randomly allocated into a normal control group, a model group, and an MJD group (n = 8 rats per group). Each rat group was further equally divided into two subgroups for investigation for either 14 or 28 days. A rat model of early-stage KOA was constructed and rats were treated with MJD. Effects were evaluated based on changes in knee circumference, mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL). We also analyzed histopathological changes in articular cartilage. High-resolution mass spectrometry was used to analyze the chemical profile of MJD, identifying 228 components. Using an LC-Q-TOF-MS metabonomics approach, 33 differential metabolites were identified. The relevant pathways significantly associated with MJD include arginine and proline metabolism, vitamin B6 metabolism, as well as the biosynthesis of phenylalanine, tyrosine and tryptophan. The system pharmacology paradigm revealed that MJD contains 1027 components and associates with 1637 genes, of which 862 disease genes are related to osteoarthritis. The construction of the MJD composition-target-KOA network revealed a total of 140 intersection genes. A total of 39 hub genes were identified via integration of betweenness centrality values greater than 100 using CytoHubba. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed several significantly affected signaling pathways including the HIF-1, AGE-RAGE (in diabetic complications), IL-17, rheumatoid arthritis and TNF pathways. Integrated-omics and network pharmacology approaches revealed a necessity for further detailed investigation focusing on two major targets, namely NOS2 and NOS3, along with their essential metabolite (arginine) and associated pathways (HIF-1 signaling and arginine and proline metabolism). Real-time PCR validated significantly greater downregulation of NOS2 and HIF-1ɑ in the MJD as compared to the model group. Molecular docking analysis further confirmed the binding of active MJD with key active components. Our findings elucidate the impact of MJD on relevant pathophysiological and metabolic networks relevant to KOA and assess the drug efficacy of MJD and its underlying mechanisms of action.

The Long Short-Term Memory neural network is a specialized architecture designed for handling time series data, extensively applied in the field of predicting gas concentrations. In the harsh conditions prevalent in coal mines, the time series data of gas concentrations collected by sensors are susceptible to noise interference. Directly inputting such noisy data into a neural network for training would significantly reduce predictive accuracy and lead to deviations from the actual values. The Empirical Mode Decomposition method, commonly employed in gas concentration prediction, faces challenges in practical engineering applications due to the substantial influence of newly acquired data on the initial decomposition subsequence values. Consequently, it is difficult to use this method as intended. Conversely, the Wavelet Threshold Denoising method does not encounter this issue. Furthermore, gas concentration sequences exhibit chaotic characteristics. Performing phase space reconstruction allows for the extraction of additional valuable hidden information. In light of these factors, a prediction model is proposed, integrating WTD, Phase Space Reconstruction, and LSTM neural networks. Initially, the gas concentration sequence itself is subjected to wavelet threshold denoising. Subsequently, phase space reconstruction is performed, and the resulting reconstructed phase space matrix serves as the input for the LSTM neural network. The outcomes from the final LSTM neural network reveal that the PS method indeed extracts more valuable information. The Mean Absolute Error and Root Mean Square Error are reduced by 35.1% and 25%, respectively. Additionally, when compared to the PS-LSTM model without utilizing the WTD method, the WTD-PS-LSTM predictive model showcases reductions of 77.1% and 80% in MAE and RMSE, respectively. Compared with the LSTM model, the MAE and RMSE of the WTD-PS-LSTM prediction model were reduced by 81.4% and 82.6%, respectively. This greatly improves the credibility of whether or not a response related to coal mine safety management is implemented.

Abstract Background Regulator of G protein signaling 5 (RGS5), as a negative regulator of G protein-coupled receptor (GPCR) signaling, is highly expressed in arterial VSMCs and pericytes, which is involved in VSMC phenotypic heterogeneity and vascular remodeling in tumors. However, its role in normal and tumor vascular remodeling is controversial. Methods RGS5 knockout (Rgs5-KO) mice and RGS5 overexpression or knockdown in VSMCs in vivo by adeno-associated virus type 9 (AAV) carrying RGS5 cDNA or small hairpin RNA (shRNA) targeting RGS5 were used to determine the functional significance of RGS5 in vascular inflammation. RGS5 expression in the triple-negative (TNBCs) and non-triple-negative breast cancers (Non-TNBCs) was determined by immunofluorescent and immunohistochemical staining. The effect of breast cancer cell-conditioned media (BC-CM) on the pro-inflammatory phenotype of VSMCs was measured by phagocytic activity assays, adhesion assay and Western blot. Results We identified that knockout and VSMC-specific knockdown of RGS5 exacerbated accumulation and pyroptosis of pro-inflammatory VSMCs, resulting in vascular remodeling, which was negated by VSMC-specific RGS5 overexpression. In contrast, in the context of breast cancer tissues, the role of RGS5 was completely disrupted. RGS5 expression was increased in the triple-negative breast cancer (TNBC) tissues and in the tumor blood vessels, accompanied with an extensive vascular network. VSMCs treated with BC-CM displayed enhanced pro-inflammatory phenotype and higher adherent with macrophages. Furthermore, tumor-derived RGS5 could be transferred into VSMCs. Conclusions These findings suggest that tumor microenvironment shifts the function of RGS5 from anti-inflammation to pro-inflammation and induces the pro-inflammatory phenotype of VSMCs that is favorable for tumor metastasis.

Gellan gum (GG) is a natural polysaccharide with a wide range of industrial applications. This review aims to investigate the potential of GG-based films and coatings to act as environmentally friendly substitutes for traditional petrochemical plastics in food packaging. GG-based films and coatings exhibit versatile properties that can be tailored through the incorporation of various substances, such as plant extracts, microorganisms, and nanoparticles. These functional additives enhance properties like the light barrier, antioxidant activity, and antimicrobial capabilities, all of which are essential for extending the shelf-life of perishable food items. The ability to control the release of active compounds, along with the adaptability of GG-based films and coatings to different food products, highlights their effectiveness in preserving quality and inhibiting microbial growth. Furthermore, GG-based composites that incorporate natural pigments can serve as visual indicators for monitoring food freshness. Overall, GG-based composites present a promising avenue for the development of sustainable and innovative food packaging solutions.

MicroRNAs (miRNAs) are highly conserved endogenous single-stranded non-coding RNA molecules that play a crucial role in regulating gene expression to maintain normal physiological functions in fish. Nevertheless, the specific physiological role of miRNAs in lower vertebrates, particularly in comparison to mammals, remains elusive. Additionally, the mechanisms underlying the control of antiviral responses triggered by viral stimulation in fish are still not fully understood. In this study, we investigated the regulatory impact of miR-1388 on the signaling pathway mediated by IFN regulatory factor 3 (IRF3). Our findings revealed that following stimulation with the viral analog poly(I:C), the expression of miR-1388 was significantly upregulated in primary immune tissues and macrophages. Through a dual luciferase reporter assay, we corroborated a direct targeting relationship between miR-1388 and tumor necrosis factor receptor (TNFR)-associated factor 3 (TRAF3). Furthermore, our study demonstrated a distinct negative post-transcriptional correlation between miR-1388 and TRAF3. We observed a significant negative post-transcriptional regulatory association between miR-1388 and the levels of antiviral genes following poly(I:C) stimulation. Utilizing reporter plasmids, we elucidated the role of miR-1388 in the antiviral signaling pathway activated by TRAF3. By intervening with siRNA-TRAF3, we validated that miR-1388 regulates the expression of antiviral genes and the production of type I interferons (IFN-Is) through its interaction with TRAF3. Collectively, our experiments highlight the regulatory influence of miR-1388 on the IRF3-mediated signaling pathway by targeting TRAF3 post poly(I:C) stimulation. These findings provide compelling evidence for enhancing our understanding of the mechanisms through which fish miRNAs participate in immune responses.

Abstract Reducing greenhouse gas emissions has turned into a pillar of climate change mitigation. Truck platooning is proposed as a strategy to lower emissions from vehicles on roads. However, the potential interactive impacts of this technology on road infrastructure emissions remain unclear. Here, we evaluate the decarbonization effects of truck platooning on the integrated vehicle-road system at a large-scale road network level, spanning 1457 road sections across North America. We show that truck platooning decreases emissions induced by truck operations, but it degrades faster the durability of road infrastructure and leads to a 27.9% rise in road emissions due to more frequent maintenance work. Overall, truck platooning results in a 5.1% emission reduction of the integrated vehicle-road system. In contrast to the benefits of emission reduction, truck platooning leads to additional financial burdens on car users and transportation agencies, calling for the consideration of tradeoffs between emissions and costs and between agencies and users. Our research provides insights into the potential applications of truck platooning to mitigate climate change.

Abstract Accurate tissue segmentation is critical to characterize early cerebellar development in the first two postnatal years. However, challenges in tissue segmentation arising from tightly-folded cortex, low and dynamic tissue contrast, and large inter-site data heterogeneity have limited our understanding of early cerebellar development. In this paper, we propose an accurate self-supervised learning framework for infant cerebellum segmentation. We validate its accuracy using 358 subjects from three datasets. Our results suggest the first six months exhibit the most rapid and dynamic changes, with gray matter (GM) playing a dominant role in cerebellar growth over white matter (WM). We also find both GM and WM volumes are larger in males than females, and GM and WM volumes are larger in autistic males than neurotypical males. Application of our method to a larger population will fuel more cerebellar studies, ultimately advancing our comprehension of its structure and function in neurotypical and disordered development.

Abstract Understanding fluid flow in fractured porous media under coupled thermal–hydrological–mechanical (THM) conditions is a fundamental aspect of geothermal energy extraction. In this study, we developed a fully coupled THM model, incorporating porosity and permeability variations, to scrutinize the process of geothermal energy extraction within fractured porous reservoirs. Moreover, we accentuated the significance of natural fracture orientation and hydraulic fracture permeability on fluid trajectories and heat extraction efficiency. Simulation results revealed that hydraulic fractures predominantly govern fluid channels and thermal exchange between injected water and the reservoir. Interconnected natural fractures bolster water migration into the reservoir, while detached fractures exert minimal influence on fluid dynamics, underscoring the crucial role of fracture connectivity in optimizing heat extraction efficiency. The sensitivity analysis indicated that larger fracture angles marginally hinder pressure and cool-water dispersion into the fractured reservoir, resulting in subtle enhancements in heat extraction rates and average production temperatures. An upsurge in hydraulic fracture permeability augments fluid velocity and thermal exchange, thereby fostering heat extraction efficiency. The THM model developed in this study offers a comprehensive insight into fluid flow within fractured porous media and its implications on geothermal energy extraction.

The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.

Abstract Introduction We studied the correlation of central macular fluid volume (CMFV) and central subfield thickness (CST) with best-corrected visual acuity (BCVA) in treatment-naïve eyes with diabetic macular edema (DME) 1 month after anti-vascular endothelial growth factor (VEGF) therapy. Methods This retrospective cohort study investigated eyes that received anti-VEGF therapy. All participants underwent comprehensive examinations and optical coherence tomography (OCT) volume scans at baseline (M0) and 1 month after the first treatment (M1). Two deep learning models were separately developed to automatically measure the CMFV and the CST. Correlations were analyzed between the CMFV and the logMAR BCVA at M0 and logMAR BCVA at M1. The area under the receiver operating characteristic curve (AUROC) of CMFV and CST for predicting eyes with BCVA $$\ge$$ ≥ 20/40 at M1 was analyzed. Results This study included 156 DME eyes from 89 patients. The median CMFV decreased from 0.272 (0.061–0.568) at M0 to 0.096 (0.018–0.307) mm3 at M1. The CST decreased from 414 (293–575) to 322 (252–430) μm. The logMAR BCVA decreased from 0.523 (0.301–0.817) to 0.398 (0.222–0.699). Multivariate analysis demonstrated that the CMFV was the only significant factor for logMAR BCVA at both M0 (β = 0.199, p = 0.047) and M1 (β = 0.279, p = 0.004). The AUROC of CMFV for predicting eyes with BCVA $$\ge$$ ≥ 20/40 at M1 was 0.72, and the AUROC of CST was 0.69. Conclusions Anti-VEGF therapy is an effective treatment for DME. Automated measured CMFV is a more accurate prognostic factor than CST for the initial anti-VEGF treatment outcome of DME.

Research is ongoing to find solutions to the problem of Consolidation and seepage in saturated clay in enclosure space. Firstly, the boundary of non-zero-constant values is established, considering the seepage boundary of the clay is affected by pumping water or lowering boundary pressure on the site. Secondly, the differential equation is established to reflect the spatial and temporal variations of excess pore water pressure dissipation in the clay in enclosure space, and the solution is derived using variable separation methods. Finally, based on results of the solution derived, contour maps of the water pressure are drawn corresponding with the different inhomogeneous boundary conditions.

Although the high-quality development of the marine economy has become a new paradigm to explore sustainable development in China, the path to its realization remains unclear. One path being explored in China is industrial agglomeration, but whether industry clustering leads to high-quality development of the marine economy has not been fully explored. Thus, the impact of industrial agglomeration on the marine economy deserves further investigation. This study first establishes a five-dimensional index system (innovation, coordination, greenness, openness, and inclusivity) to gauge the quality of marine economic development and then applies a hybrid method to evaluate each dimension. Furthermore, from an industry perspective, this study explores the influence mechanism of marine industry agglomeration on the high-quality development of China’s marine economy from several dimensions. The results show that (1) different regions have significantly different development levels, and the growth rates vary across each dimension. (2) From a broad standpoint, marine industry clustering has greatly promoted high-quality economic development in four sectors: innovation, coordination, openness, and inclusivity. However, its impact on green development has a time lag. (3) From the perspective of geographical heterogeneity, the impact of industry clustering is insignificant in the Bohai Rim region, positive in the South China Sea region, and negative in the Yellow Sea and East China Sea. Our study can enrich the research in this field and provide reference for the development of marine industry and marine economy in coastal areas.

To compare steamed bread quality of different modified bran whole wheat flour, wheat bran was modified by atmospheric steam, superheated steam, microwave, extrusion, enzymolysis hydrolysis (xylanase) and fermentation (yeast). The modified bran was backfilled into wheat flour to obtain whole wheat flour with different bran properties. The sensory quality, texture and aging properties of six kinds of whole wheat flour steamed bread with different bran properties were compared. At the same time, the quality of six kinds of modified bran backfilled whole wheat flour steamed bread was comprehensively analyzed by principal component analysis. The results showed that six kinds of modified bran could improve the quality of whole wheat steamed bread to a certain extent. The extruded and fermented modified bran deepened the apparent color of whole wheat steamed bread. The sensory comprehensive scores of modified whole wheat steamed bread were increased by 7.3~16.8 points compared with the control group, and the score of enzyme-digested whole wheat steamed bread was the highest. The elasticity and cohesiveness of modified whole wheat steamed bread core did not change significantly. The hardness of whole wheat steamed bread core was significantly reduced by fermentation and atmospheric steam bran, and the chewiness of whole wheat steamed bread core was significantly reduced by fermentation bran (P

The effects of process parameters on the microstructures and properties of the laser-assisted epitaxial growth layer are of great significance for the repair of nickel-based single-crystal blades. This work investigated the microstructures and properties of the epitaxial-growth laser-assisted nickel-based single-crystal superalloy. The effects of scanning path, laser power, scanning velocity and heat treatment on the microstructures and hardness of the laser-assisted epitaxial growth layer were explored. The microstructures of the epitaxial-growth samples are the same, starting from the bottom with a continuous columnar dendrite. The dendrites transform into isoaxial crystals when growing to a certain height. The scanning path, laser power, and scanning velocity mutually influence the sample's microstructures. The columnar crystal structure is very stable. Even the epitaxial-growth layer undergoes post-heat treatment, while the isoaxial crystal structure would be coarsened and the hardness decreases, but the subsequent aging treatment leads to an increase in hardness. These findings will greatly help improve the repair effect of single-crystal superalloys.

Abstract Background Massive hemoptysis is a life-threatening condition that requires immediate treatment. This study aimed to retrospectively analyze the outcome of bronchial artery embolization (BAE) for massive hemoptysis, as well as potential factors that may contribute to the recurrence of hemoptysis after BAE. Methods A total of 105 patients with massive hemoptysis treated with BAE were analyzed. Results The immediate control rate of bleeding was 84.8% (67/79); however, during the 36-month follow-up, 45.3% (29 out of 64) of the patients had recurrent hemoptysis after BAE. Comorbidities, pituitary hormone treatment, the angiographic appearance of artery dilation and hypertrophy, and the materials used for BAE were significantly correlated with the success rate of the BAE, while lack of pituitary hormone treatment and existence of arterio-arterial or arteriovenous fistula were risk factors for the recurrence of hemoptysis after BAE. Only a small proportion of patients (9/105, 8.6%) had mild complications after BAE treatment. Conclusion Findings suggest that BAE continues to be an effective treatment for massive hemoptysis in emergency settings. Moreover, the treatment of underlying pulmonary diseases and comorbidities is important to increase BAE’s success rate of BAE and decrease the risk of recurrent hemoptysis after BAE.

The carrot has a high water content, and dehydration is an important means to extend its edible period and reduce storage and transportation costs. In the case of infrared (IR) drying, the porosity of the product is low and the structure is compact; the textural properties of the product are improved by using combined ultrasound and infrared (US-IR) drying; however, there is a lack of reports on the mechanism of this. Pectin has an important influence on the formation of the textural properties of fruit and vegetable tissues. In order to investigate the mechanism of the change in endogenous pectin properties in the carrot cell wall under US-IR drying on the improvement of the textural properties of the product, different fractions of pectins (water-soluble pectin, chelating pectin, alkali-soluble pectin) of the carrot were extracted, separated, and analysed. The thermal stability, component and content changes, Fourier infrared (FTIR), X-ray diffraction (XRD), esterification degree, molecular weight, monosaccharide composition, Ca ion content, and atomic force microscopy (AFM) of the pectins were determined. The results showed that the changes in the contents and properties of the carrot pectins under US-IR conditions had a positive effect on the improvement of the textural properties of the carrot tissues.

The escalation of jellyfish stings has drawn attention to severe skin reactions, underscoring the necessity for novel treatments. This investigation assesses the potential of hydroxybenzoic acid derivatives, specifically protocatechuic acid (PCA) and gentisic acid (DHB), for alleviating Nemopilema nomurai Nematocyst Venom (NnNV)-induced injuries. By employing an in vivo mouse model, the study delves into the therapeutic efficacy of these compounds. Through a combination of ELISA and Western blot analyses, histological examinations, and molecular assays, the study scrutinizes the inflammatory response, assesses skin damage and repair mechanisms, and investigates the compounds’ ability to counteract venom effects. Our findings indicate that PCA and DHB significantly mitigate inflammation by modulating critical cytokines and pathways, altering collagen ratios through topical application, and enhancing VEGF and bFGF levels. Furthermore, both compounds demonstrate potential in neutralizing NnNV toxicity by inhibiting metalloproteinases and phospholipase-A2, showcasing the viability of small-molecule compounds in managing toxin-induced injuries.

The mechanistic target of the rapamycin (mTOR) pathway, which participates in the regulation of cellular growth and metabolism, is aberrantly regulated in various cancer types. The mTOR complex 2 (mTORC2), which consists of the core components mTOR, Rictor, mSin1, and mLST8, primarily responds to growth signals. However, the coordination between mTORC2 assembly and activity remains poorly understood. Keap1, a major sensor of oxidative stress in cells, functions as a substrate adaptor for Cullin 3-RING E3 ubiquitin ligase (CRL3) to promote proteasomal degradation of NF-E2-related factor 2 (NRF2), which is a transcription factor that protects cells against oxidative and electrophilic stress. In the present study, we demonstrate that Keap1 binds to mLST8 via a conserved ETGE motif. The CRL3Keap1 ubiquitin ligase complex promotes non-degradative ubiquitination of mLST8, thus reducing mTORC2 complex integrity and mTORC2-AKT activation. However, this effect can be prevented by oxidative/electrophilic stresses and growth factor signaling-induced reactive oxygen species (ROS) burst. Cancer-derived Keap1 or mLST8 mutations disrupt the Keap1-mLST8 interaction and allow mLST8 to evade Keap1-mediated ubiquitination, thereby enhancing mTORC2-AKT activation and promoting cell malignancy and remodeling cell metabolism. Our findings provide new insights into the molecular mechanisms of Keap1/mLST8 mutation-driven tumorigenesis by promoting mTORC2-AKT activation, which is independent of the canonical NRF2 pathway.

Predicting passenger flow within a city is crucial for intelligent transportation management systems, especially in the context of urban development, post-pandemic policy changes, and infrastructure improvements. Traditional macro models have limitations in accurately capturing the complex structure of real traffic flows, and recent advancements in machine learning offer promising approaches for improving transportation simulations. This research aims to compare the effectiveness of traditional simulation models with a selective machine learning (ML) model for traffic flow prediction in Oslo, Norway. Sensitivity and scenario analyses are conducted to examine the models’ parameters and derive the city’s characteristics. Results substantiate that the traditional Spatial Interaction model (SIM), although interpretable and requiring fewer parameters, has limitations in accurately capturing real flow structures and exhibits greater variability compared to the ML model. Statistical analyses support these findings and raise questions about the validity of the ML model’s results over the SIM. The research highlights the potential of ML models to identify trends in passenger flows and simulate traffic flows in different scenarios related to city development. Overall, the research presents a decision support system for planners and policymakers to predict traffic flow accurately and efficiently. It highlights the benefits and drawbacks of both the traditional SIM and ML models, contributing to the ongoing discussion of the role of machine learning in transportation modeling.

Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial- and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems.

A novel microscopic Shape-From-Polarization (SFP) 3D shape reconstruction method is proposed, in which degree of polarization is calculated using Polarized Bidirectional Reflectance Distribution Function (PBRDF) to overcome azimuthal ambiguity for both specular and diffuse polarization microscopic scenes. Based on the calculated degree of polarization, a polarization modulation microscopy oriented variational model is established to estimate surface normal vector and rebuild 3D shape of the sample with “fuzzy guidance” based on atomic force microscopy (AFM). Experiments on different microscopic samples indicate the proposed method can restore the stereoscopic morphology distribution with precision in micrometer.