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Abstract Background Breast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors. Results Our study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues. Conclusions Consequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.

Abstract In the context of globalization, the integration of cultural elements into scientific research, particularly through the incorporation of traditional Chinese cultural motifs in scientific illustrations, represents a novel frontier in enhancing the universality and appeal of scientific discoveries. This paper explores the innovative practice of embedding traditional Chinese cultural elements into scientific paper illustrations, highlighting its significant role in augmenting the global appeal of research findings, promoting diversity and innovation in scientific inquiry, and facilitating cross-cultural understanding. Through a series of case studies, including symbolic representations of ancient myths and the use of traditional themes to elucidate complex scientific phenomena, we demonstrate how this cultural integration not only makes scientific content more accessible and engaging but also fosters a deeper appreciation of Chinese heritage among international audiences. This approach not only bridges the gap between science and culture but also contributes to a more inclusive and interconnected global scientific community, underscoring the importance of cultural diversity in enriching scientific exploration and communication.

The existing body of research underscores the critical impact of intratumoral microbiomes on the progression of pancreatic ductal adenocarcinoma (PDAC), particularly in reshaping the tumor microenvironment and influencing gemcitabine resistance. However, peritumoral tissues’ microbiome, distinct from PDAC tumors, remain understudied, and Western-centric analyses overlooking potential variations in dietary-influenced microbiomes. Our study addresses this gap by 16 S rRNA sequencing of PDAC tumors and matched peritumoral tissues from Chinese Mainland patients. Our research has uncovered that the microbiome composition within tumors and paired peritumoral tissues exhibits a high degree of similarity, albeit with certain discrepancies. Notably, Exiguobacterium is found to be more abundant within the tumor tissues. Further investigations have revealed that a lower Exiguobacterium/Bacillus ratio in both the tumor and peritumoral tissues of PDAC patients is indicative of a more favorable prognosis. Further exploration utilizing an orthotopic tumor model demonstrates that the probiotic Bacillus Coagulans impedes PDAC progression, accompanied by an increased infiltration of inflammatory neutrophils in tumors. Additionally, in the subgroup with a low Exiguobacterium/Bacillus ratio, whole-exome sequencing reveals elevated missense mutations in ABL2 and MSH2. The elevated expression of ABL2 and MSH2 has been correlated with poorer prognostic outcomes in PDAC patients. Together, these insights shed light on risk factors influencing PDAC progression and unveil potential therapeutic targets, alongside probiotic intervention strategies.

With the integration of multiple energy sources into the power grid makes power quality disturbances (PQDs) more complex. Dealing with power quality problems requires automatic classification of PQDs. This paper proposes a novel semi-supervised Graph Convolutional Network (GCN) framework based on Temporal Ensembling for PQDs classification. Considering both short-term and long-term features of PQDs, a Visibility Graph (VG) based graph theory model was adopted to process PQDs to highlight features. In the proposed semi-supervised framework, Graph Convolutional Network was designed to extract features from massive PQDs and classify PQDs automatically. Due to the fact that GCN belongs to supervised learning, it is necessary to label the data in advance. However, labeling is costly and easily lead to human mistake. Therefore, this article introduces the Temporal Ensembling algorithm which provides pseudo labels to reduce the amount of labeled data and has tolerance to incorrect labels. Simulation results prove that the proposed method is capable of noise resistance, tolerates incorrect labels, and has high classification performance in both single and composite PQDs.

Solar irradiance prediction is a crucial component in the application of photovoltaic power generation, playing a vital role in optimizing energy production, managing energy storage, and maintaining grid stability. This paper proposes an irradiance prediction method based on a functionally structured inverted transformer network, which maintains the channel independence of each feature in the model input and extracts the correlations between different features through an Attention mechanism, enabling the model to effectively capture the relevant information between various features. After the channel mixing of different features is completed through the Attention mechanism, a linear network is used to predict the irradiance sequence. A data processing method tailored to the prediction model used in this paper is designed, which employs a comprehensive data preprocessing approach combining mutual information, multiple imputation, and median filtering to optimize the raw dataset, enhancing the overall stability and accuracy of the prediction project. Additionally, a Dingo optimization algorithm suitable for the self-tuning of deep learning model hyperparameters is designed, improving the model’s generalization capability and reducing deployment costs. The artificial intelligence (AI) model proposed in this paper demonstrates superior prediction performance compared to existing common prediction models in irradiance data forecasting and can facilitate further applications of photovoltaic power generation in power systems.

This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem in a fully decentralized manner. Unlike traditional centralized approaches, which require extensive data sharing and centralized control, the DSRG method ensures that each area within the power system can make independent decisions based on local information while still achieving global optimization. Numerical simulations are conducted using MATLAB (Version 24.1.0.2603908) to evaluate the performance of the DSRG method on a 3-area, 9-bus test system. The results demonstrate that the DSRG method converges significantly faster than other decentralized OPF methods, reducing the overall computation time while maintaining cost efficiency and system stability. These findings highlight the DSRG method’s potential to significantly enhance the efficiency and scalability of decentralized OPF in modern power systems.

In clinical practice, pancreatic neuroendocrine neoplasms (pNENs) with a diameter smaller than 2 cm are commonly referred to as small pNENs. Due to their generally favorable biological characteristics, the diagnosis and treatment of small pNENs differ from other pNENs and are somewhat controversial. In response to this, the Chinese Pancreatic Surgery Association, Chinese Society of Surgery, Chinese Medical Association have developed a consensus on the diagnosis and treatment of small pNENs, which is based on evidence-based medicine and expert opinions. This consensus covers various topics, including concepts, disease assessment, treatment selection, follow-up, and other relevant aspects.

Summary: Background: Antipsychotics and mood stabilisers are gathering attention for the disturbance of metabolism. This network meta-analysis aims to evaluate and rank the metabolic effects of the commonly used antipsychotics and mood stabilisers in treating bipolar disorder (BD). Methods: Registries including PubMed, Embase, Cochrane Library, Web of Science, Ovid, and Google Scholar were searched before February 15th, 2024, for randomised controlled trials (RCTs) applying antipsychotics or mood stabilisers for BD treatment. The observed outcomes were twelve metabolic indicators. The data were extracted by two reviewers independently, and confirmed by another four reviewers and a corresponding author. The above six reviewers all participated in data analyses. Data extraction was based on PRISMA guidelines, and quality assessment was conducted according to the Cochrane Handbook. Use a random effects model for data pooling. The PROSPERO registration number is CRD42023466669. Findings: Together, 5421 records were identified, and 41 publications with 11,678 complete-trial participants were confirmed eligible. After eliminating possible sensitivity, risperidone ranked 1st in elevating fasting serum glucose (SUCRA = 90.7%) and serum insulin (SUCRA = 96.6%). Lurasidone was most likely to elevate HbA1c (SUCRA = 82.1%). Olanzapine ranked 1st in elevating serum TC (SUCRA = 93.3%), TG (SUCRA = 89.6%), and LDL (SUCRA = 94.7%). Lamotrigine ranked 1st in reducing HDL (SUCRA = 82.6%). Amisulpride ranked 1st in elevating body weight (SUCRA = 100.0%). For subgroup analyses, quetiapine is more likely to affect indicators of glucose metabolism among male adult patients with bipolar mania, while long-term lurasidone tended to affect glucose metabolism among female patients with bipolar depression. Among patients under 18, divalproex tended to affect glucose metabolism, with lithium affecting lipid metabolism. In addition, most observed antipsychotics performed higher response and remission rates than placebo, and displayed a similar dropout rate with placebo, while no between-group significance of rate was observed among mood stabilisers. Interpretation: Our findings suggest that overall, antipsychotics are effective in treating BD, while they are also more likely to disturb metabolism than mood stabilisers. Attention should be paid to individual applicability in clinical practice. The results put forward evidence-based information and clinical inspiration for drug compatibility and further research of the BD mechanism. Funding: The National Key Research and Development Program of China (2023YFC2506200), and the Research Project of Jinan Microecological Biomedicine Shandong Laboratory (No. JNL-2023001B).

This study aims to investigate the effect of rock types, i.e., granite, conglomerate rock, limestone, and crushing mechanism, i.e., Vertical Shaft Impact (VSI) crusher, Horizontal Shaft Impact (HSI) crusher, on the morphology of manufactured sands. By combining the 3D scanning and spherical harmonic analysis, the morphological parameters of manufactured sands such as elongation ratio (EL), flatness ratio (Fl), aspect ratio (AR), edge angle (EA), sphericity (SH), and fractal dimension were quantified. The results indicate limestone is more readily broken into blade-shaped particles without being able to blunt sharp edges through constant abrasion. The proportion of bladed particles in manufactured sand prepared from limestone is 1.91 times higher than the proportion of bladed particles in conglomerates. Additionally, the HSI crusher has a more significant effect on rocks with smaller strength. For the same parent rock, manufactured sand from a VSI crusher contains 30% more spherical and oblate particles than from an HSI crusher.

Rechargeable aluminum batteries hold great promise for high energy density and low-cost energy storage applications but are stalled by severe electrochemical side reactions (e.g., dendrite, passivation, and corrosion) at aluminum (Al) metal anode. Here, we design an aluminum ion battery with an Al-free configuration to circumvent the problems caused by the above side reactions. The feasibility of AlxMnO2·nH2O cathode in aluminum ion batteries is revealed in conjunction with TiO2 anodes by using the optimal 5 M Al(OTF)3 electrolyte. The as-assembled aluminum ion battery enables high initial discharge capacity of 370.4 mAh g–1 at 30 mA g–1, favorable stability with low irreversible capacity loss, and enhanced safety. Further, the mechanism is intensively elucidated by multiple characterization results, indicative of the Al3+ ions (de)intercalation redox chemistry. Revealed by empirical analyses, the capacity contribution of high-voltage plateau, corresponding to the disproportionation reaction of Mn3+ in an AlxMnO2·nH2O battery system, tends to increase with the increasing electrolyte concentration. Our findings may provide fresh impetus to the rational design of aluminum ion batteries with excellent electrochemical properties.

Abstract Background Growing evidences suggest that circular RNAs (circRNAs) are important factors in cancer progression. Nevertheless, the role of circRNAs in the progression of pancreatic ductal adenocarcinoma (PDAC) remains unclear. Methods CircPTPRA was identified based on our previous circRNA array data analysis. Wound healing, transwell, and EdU assays were performed to investigate the effect of circPTPRA on the migration, invasion, and proliferation of PDAC cells in vitro. RNA pull‐down, fluorescence in situ hybridization (FISH), RNA immunoprecipitation (RIP), and dual‐luciferase reporter assays were conducted to verify the binding of circPTPRA with miR‐140‐5p. Subcutaneous xenograft model was constructed for in vivo experiment. Results CircPTPRA was significantly upregulated in PDAC tissues and cells compared to normal controls. Moreover, circPTPRA overexpression was positively correlated with lymph node invasion and worse prognosis in PDAC patients. In addition, overexpression of circPTPRA promoted PDAC migration, invasion, proliferation, and epithelial‐mesenchymal transition (EMT) in vitro and in vivo. Mechanistically, circPTPRA upregulates LaminB1 (LMNB1) expression by sponging miR‐140‐5p and ultimately promotes the progression of PDAC. Conclusions This study revealed that circPTPRA plays an important role in the progression of PDAC by sponging miR‐140‐5p. It can be explored as a potential prognostic marker and therapeutic target for PDAC.

Abstract Background Multiple molecular subtypes with distinct clinical outcomes in pancreatic adenocarcinoma (PAAD) have been identified in recent years. Cuproptosis is a new form of cell death that likely involved in tumor progression. However, the cuproptosis-related molecular subtypes as well as its mediated tumor microenvironment (TME) cell infiltration characteristics largely remain unclear. Methods Expression profiles of 10 cuproptosis-related genes (CRGs) and their association with patient survival, TME, cancer stemness and drug resistance were studied in 33 cancer types using the TCGA pan-cancer data. Using 437 PAAD samples from five cohorts (TCGA-PAAD cohort and four GEO cohorts), we explored the molecular subtypes mediated by CRGs, along with the associated TME cell infiltration. Unsupervised methods were utilized to perform cuproptosis subtype clustering. The cuproptosis score was constructed using the COX regression model with least absolute shrinkage and selection operator regression (LASSO) algorithm to quantify the cuproptosis characteristics of a single tumor. Results The expression of 10 CRGs varies in different cancer types with striking inter- and intra- cancer heterogeneity. We integrated the genomic profiling of the CRGs and identified three distinct cuproptosis subtypes, and found that multi-layer CRG alterations were correlated with patient prognosis and TME cell infiltration characteristics. In addition, a cuproptosis score signature was constructed to predict prognosis, and its clinical impacts were characterized in the TCGA-PAAD cohort. The cuproptosis signature was significantly associated with prognosis, tumor subtypes, CD8 T-cell infiltration, response to immune checkpoint inhibitors (ICIs) and chemotherapeutic drug sensitivity. Furthermore, the expression patterns of CRGs in pancreatic cancer cells and normal controls were validated, which was almost consistent with the results from the public database. The expression level and prognostic predictive capability of DLAT were verified in 97 PAAD patients from our patient cohort. Conclusions These findings may help understand the roles of CRGs in PAAD and the molecular characterization of cuproptosis subtypes. In addition, the cuproptosis score could serve as a promising biomarker for predicting prognosis and response to immunotherapy in PAAD patients.

Abstract Background MicroRNAs (miRNAs), as an indispensable type of non-coding RNA (ncRNA), participate in diverse biological processes. However, the specific regulatory mechanism of certain miRNAs in pancreatic ductal adenocarcinoma (PDAC) remains unclear. Methods The expression of miR-194-5p in PDAC tissue microarray and cell lines were detected by RNA-scope and real-time quantitative PCR (RT-qPCR). The function of proliferation and migration carried by miR-194-5p in vitro and vivo was observed by several functional experiments. Informatics methods and RNA sequencing data were applied to explore the target of miR-194-5p and the upstream circular RNA (circRNA) of miR-194-5p. RNA-binding protein immunoprecipitation (RIP) assay and dual-luciferase reporter assay confirmed the relationships between miR-194-5p and SOCS2 or miR-194-5p and circPVRL3. The proliferation and migration abilities of SOCS2 and circPVRL3 were accessed by rescue experiments. Results In this study, we aimed to clarify the molecular mechanisms of miR-194-5p, which has critical roles during PDAC progression. We found that the expression of miR-194-5p was significantly upregulated in PDAC tissue compared to tumor-adjacent tissue and was highly related to age and nerve invasion according to RNAscope and RT‒qPCR. Overexpression of miR-194-5p accelerated the cell cycle and enhanced the proliferation and migration processes according to several functional experiments in vitro and in vivo. Specifically, circPVRL3, miR-194-5p, and SOCS2 were confirmed to work as competing endogenous RNAs (ceRNAs) according to informatics methods, RIP, and dual-luciferase reporter assays. Additionally, the rescue experiments confirmed the relationship among miR-194-5p, circPVRL3, and SOCS2 mRNA. Finally, the circPVRL3/miR-194-5p/SOCS2 axis activates the PI3K/AKT signaling pathway to regulate the proliferation and metastasis of PDAC. Conclusion Our findings indicated that an increase of miR-194-5p caused by circPVRL3 downregulation stimulates the PI3K/AKT signaling pathway to promote PDAC progression via the circPVRL3/miR-194-5p/SOCS2 axis, which suggests that the circPVRL3/miR-194-5p/SOCS2 axis may be a potential therapeutic target for PDAC patients.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type and composes about 90% of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis, which has been considered the least immunogenic cancer for decades. However, this characterization might be over-simplistic, and more sophisticated approaches are needed to develop effective treatment strategies. In this review, we aim to summarize studies involving PDAC immunity in different aspects to provide a multidimensional recognition and comprehensively understanding of the mechanisms underlying the tumor microenvironment (TME) of PDAC. A database search of peer-reviewed articles published in English between 2003 and 2022 in PubMed and the Web of Science was performed. Original articles and review articles relevant to the topic were selected. We emphasized the importance of investigating tumor-infiltrating lymphocytes (TILs) in pancreatic cancer, especially focusing on CD8+ T cells, along with indicating potential therapeutic strategies to turn the immune-cold PDACs into the immune-hot ones.

Abstract Immunotherapies are mainly aimed to promote a CD8+ T cell response rather than a CD4+ T cell response as cytotoxic T lymphocytes (CTLs) can directly kill target cells. Recently, CD4+ T cells have received more attention due to their diverse roles in tumors and chronic viral infections. In antitumor and antichronic viral responses, CD4+ T cells relay help signals through dendritic cells to indirectly regulate CD8+ T cell response, interact with B cells or macrophages to indirectly modulate humoral immunity or macrophage polarization, and inhibit tumor blood vessel formation. Additionally, CD4+ T cells can also exhibit direct cytotoxicity toward target cells. However, regulatory T cells exhibit immunosuppression and CD4+ T cells become exhausted, which promote tumor progression and chronic viral persistence. Finally, we also outline immunotherapies based on CD4+ T cells, including adoptive cell transfer, vaccines, and immune checkpoint blockade. Overall, this review summarizes diverse roles of CD4+ T cells in the antitumor or protumor and chronic viral responses, and also highlights the immunotherapies based on CD4+ T cells, giving a better understanding of their roles in tumors and chronic viral infections.

Individuals with bipolar disorder (BD) have a greater suicide risk than the general population. In this study, we employed latent profile analysis (LPA) to explore whether Chinese individuals with different phases of BD differed at the levels of suicidal ideation. We recruited 517 patients. Depressive symptoms were measured using the 24-item Hamilton Depression Rating Scale (HAMD-24), and manic symptoms were evaluated using the Young Mania Rating Scale (YMRS). The extent of suicidal thoughts was determined through the Beck Scale for Suicide Ideation (BSSI). The scores of HAMD and YMRS were used to perform LPA. LPA categorized participants into three classes: one exhibiting severe depressive and mild manic symptomatology, another showing severe depressive and severe manic symptomatology, and the third one displaying severe depressive and intermediate manic symptomatology. Suicidal ideation levels were found to be remarkably elevated across all three classes. Additionally, the three classes showed no significant differences in terms of suicidal ideation. Our research confirms the link between depressive symptoms and suicide, independent of the manic symptoms. These findings carry meaning as they provide insight into the suicide risk profiles within different phases of BD.

Abstract Transition metal fluorides (TMFs) cathode materials have shown extraordinary promises for electrochemical energy storage, but the understanding of their electrochemical reaction mechanisms is still a matter of debate due to the complicated and continuous changing in the battery internal environment. Here, we design a novel iron fluoride (FeF2) aggregate assembled with cylindrical nanoparticles as cathode material to build FeF2 lithium‐ion batteries (LIBs) and employ advanced in situ magnetometry to detect their intrinsic electronic structure during cycling in real time. The results show that FeF2 cannot be involved in complete conversion reactions when the FeF2 LIBs operate between the conventional voltage range of 1.0–4.0 V, and that the corresponding conversion ratio of FeF2 can be further estimated. Importantly, we first demonstrate that the spin‐polarized surface capacitance exists in the FeF2 cathode by monitoring the magnetic responses over various voltage ranges. The research presents an original and insightful method to examine the conversion mechanism of TMFs and significantly provides an important reference for the future artificial design of energy systems based on spin‐polarized surface capacitance.

Abstract Background Although gemcitabine has been considered as the first-line drug for advanced pancreatic cancer (PC), development of resistance to gemcitabine severely limits the effectiveness of this chemotherapy, and the underlying mechanism of gemcitabine resistance remains unclear. Various factors, such as ATP binding cassette (ABC) transporters, microRNAs and their downstream signaling pathways are included in chemoresistance to gemcitabine. This study investigated the potential mechanisms of microRNAs and ABC transporters related signaling pathways for PC resistance to gemcitabine both in vivo and in vitro. Methods Immunohistochemistry and Western blotting were applied to detect the expression of ABC transporters. Molecular docking analysis was performed to explore whether gemcitabine interacted with ABC transporters. Gain-of-function and loss-of-function analyses were performed to investigate the functions of hsa-miR-3178 in vitro and in vivo. Bioinformatics analysis, Western blotting and dual-luciferase reporter assay were used to confirm the downstream regulatory mechanisms of hsa-miR-3178. Results We found that P-gp, BCRP and MRP1 were highly expressed in gemcitabine-resistant PC tissues and cells. Molecular docking analysis revealed that gemcitabine can bind to the ABC transporters. Hsa-miR-3178 was upregulated in gemcitabine resistance PANC-1 cells as compared to its parental PANC-1 cells. Moreover, we found that hsa-miR-3178 promoted gemcitabine resistance in PC cells. These results were also verified by animal experiments. RhoB was down-regulated in gemcitabine-resistant PC cells and it was a downstream target of hsa-miR-3178. Kaplan–Meier survival curve showed that lower RhoB expression was significantly associated with poor overall survival in PC patients. Rescue assays demonstrated that RhoB could reverse hsa-miR-3178-mediated gemcitabine resistance. Interestingly, hsa-miR-3178 promoted gemcitabine resistance in PC by activating the PI3K/Akt pathway-mediated upregulation of ABC transporters. Conclusions Our results indicate that hsa-miR-3178 promotes gemcitabine resistance via RhoB/PI3K/Akt signaling pathway-mediated upregulation of ABC transporters. These findings suggest that hsa-miR-3178 could be a novel therapeutic target for overcoming gemcitabine resistance in PC.

Abstract. Circular RNAs (circRNAs) constitute a novel class of endogenous noncoding RNAs characterized by a covalently closed structure and involved in multiple biological processes. The main biological functions and properties of circRNAs can be defined by five features: a “sponging” effect on other RNA species, post-transcriptional regulation, rolling circle translation, generation of pseudogenes, and splicing interference. Although circRNAs were first detected decades ago, the role of circRNAs and the mechanisms underlying their actions remain incompletely characterized. Recently, circRNAs were reported to play indispensable roles in regulating metabolic and signal transduction events controlling the proliferation, migration, and survival of cells. Importantly, many studies demonstrated that dysregulated circRNA expression is associated with the development of multiple diseases, including cancer. In this review, we summarize current knowledge on the roles and mechanisms of circRNAs in cancer and discuss their functions as oncogenes or tumor suppressors in different tumor types.

BackgroundMany studies have reported that N6-methyladenosine (m6A) modification plays a critical role in the epigenetic regulation of organisms and especially in the pathogenesis of malignant diseases. However, m6A research has mainly focused on methyltransferase activity mediated by METTL3, and few studies have focused on METTL16. The aim of this study was to investigate the mechanism of METTL16, which mediates m6A modification, and its role in pancreatic adenocarcinoma (PDAC) cell proliferation.MethodsClinicopathologic and survival data were retrospectively collected from 175 PDAC patients from multiple clinical centers to detect the expression of METTL16. CCK-8, cell cycle, EdU and xenograft mouse model experiments were used to evaluate the proliferation effect of METTL16. Potential downstream pathways and mechanisms were explored via RNA sequencing, m6A sequencing, and bioinformatic analyses. Regulatory mechanisms were studied through methyltransferase inhibition, RIP, MeRIP‒qPCR assays.ResultsWe found that METTL16 expression was markedly downregulated in PDAC, and multivariate Cox regression analyses revealed that METTL16 was a protective factor for PDAC patients. We also demonstrated that METTL16 overexpression inhibited PDAC cell proliferation. Furthermore, we identified a METTL16-p21 signaling axis, with downregulation of METTL16 resulting in inhibition of CDKN1A (p21). Additionally, METTL16 silencing and overexpression experiments highlighted m6A modification alterations in PDAC.ConclusionsMETTL16 plays a tumor-suppressive role and suppresses PDAC cell proliferation through the p21 pathway by mediating m6A modification. METTL16 may be a novel marker of PDAC carcinogenesis and target for the treatment of PDAC.

ObjectivesIn this systemic review and network meta-analysis, we investigated pancreaticoduodenectomy (PD), pylorus-preserving pancreaticoduodenectomy (PPPD), and different modifications of duodenum-preserving pancreatic head resection (DPPHR) to evaluate the efficacy of different surgical procedures.MethodsA systemic search of six databases was conducted to identify studies comparing PD, PPPD, and DPPHR for treating pancreatic head benign and low-grade malignant lesions. Meta-analyses and network meta-analyses were performed to compare different surgical procedures.ResultsA total of 44 studies were enrolled in the final synthesis. Three categories of a total of 29 indexes were investigated. The DPPHR group had better working ability, physical status, less loss of body weight, and less postoperative discomfort than the Whipple group, while both groups had no differences in quality of life (QoL), pain scale scores, and other 11 indexes. Network meta-analysis of a single procedure found that DPPHR had a larger probability of best performance in seven of eight analyzed indexes than PD or PPPD.ConclusionDPPHR and PD/PPPD have equal effects on improving QoL and pain relief, while PD/PPPD has more severe symptoms and more complications after surgery. PD, PPPD, and DPPHR procedures exhibit different strengths in treating pancreatic head benign and low-grade malignant lesions.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42022342427.

Energy finite element analysis (EFEA) has been successfully applied to steady-state response prediction over the past three decades. Compared with other energy-based methods, such as statistical energy analysis (SEA), EFEA can consider more local structural information without increasing the computational consumption too much, which makes it attractive. Inspired by the transient local energy approach (TLEA), a general transient energy balance equation was derived by assuming that the plane wave condition is satisfied. The properties of the energy balance equation were studied, and the analytical solutions with different initial conditions were provided. Utilizing the derived transient energy balance equation, transient EFEA is proposed, which has the same advantages as EFEA. A general formula is presented for the energy transmission coefficients of any number of coupled in-plane beams. The present approach was validated using a single beam and a coupled collinear beam structure under unloading conditions. The coupled collinear beams were also investigated using constant and quasi-static input power. The validation results show that TEFEA can accurately predict the local response of the structure. All of these results were compared with those of finite element analysis (FEA), simplified TEFEA (sTEFEA), transient statistical energy analysis (TSEA), and analytical formulas.

BackgroundAlcohol dependence (AD) is a complex addictive disorder with a high relapse rate. Previous studies have shown that both repetitive transcranial magnetic stimulation (rTMS) and cognitive behavioral therapy (CBT) may be effective for AD, and we aim to explore more effective treatment options to reduce relapse rates for AD.Materials and methodsA total of 263 AD patients were recruited. They were divided into six groups according to the location and the type of rTMS: left dorsolateral prefrontal cortex (DLPFC), right DLPFC, sham stimulation, and whether they received CBT treatment: with a fixed schedule (C1) and without a fixed plan (C0). There were included in sham rTMS + C0 group (n = 50), sham rTMS + C1 group (n = 37), right rTMS + C0 group (n = 45), right rTMS + C1 group (n = 42), left rTMS + C0 group (n = 49), left rTMS + C1 group (n = 40). We used obsessive compulsive drinking scale (OCDS), visual analogue scale (VAS), alcohol dependence scale (ADS), montreal cognitive assessment (MoCA), generalized anxiety disorder-7 (GAD-7), patient health questionnaire-9 items (PHQ-9), and Pittsburgh sleep quality index (PSQI) to assess alcohol cravings, alcohol dependence, cognition, anxiety, depression, and sleep quality. They were followed up and evaluated for relapse.ResultsThe sham rTMS + C0 group relapse rate was significantly higher than the right rTMS + C1 group (P = 0.006), the left rTMS + C0 group (P = 0.031), the left rTMS + C1 group (P = 0.043). The right rTMS + C0 group showed significantly higher relapse rate compared to the right rTMS + C1 group (P = 0.046). There was no significant difference in relapse rates between other groups. The repeated-measures ANOVA showed an interaction effect between group and time was significant in the rate of patient health questionnaire-9 items (PHQ-9) scale reduction (P = 0.020). Logistic analysis indicated that smoking and alcohol consumption were independent determinants of relapse (P < 0.05). At 24 weeks of follow-up, Kaplan–Meier survival analysis reveal that there is statistically significant relapse rate between six groups (P = 0.025), left rTMS + C1 group has the best treatment effect for alcohol dependent patients. Cox regression analysis confirmed that current smoking, total cholesterol, and total bilirubin (TBIL) level were risk factors of relapse (P < 0.05).ConclusionThis study is the first to suggest that the combination of rTMS and CBT may be a potentially effective treatment for reducing relapse.

Traffic information is collected from sensors in the urban road network, and traffic information can be said to be a mapping of people’s activities, which are difficult to model as a linear function, so this makes traffic information difficult to be predicted. In other words, traffic information is difficult to build effective models to predict traffic information because of its non-linear characteristics that are difficult to capture. As researchers go deeper, researchers have been able to extract good spatio-temporal features for modern urban road networks. However, it is worth mentioning that most researchers have neglected the importance of models for global potential features under the topology map of urban road networks, yet this global potential feature is very important for traffic prediction. In this paper, we propose a new spatio-temporal graph convolutional network model A Pagerank Self-attention Network (hereafter we abbreviate as PSN) in order to solve this problem based on a full consideration of the urban road network topology features, in which we employ a global spatio-temporal self-attention module to capture the global spatio-temporal features well. and the graph wandering module is used to propagate the spatio-temporal feature information effectively and widely. It is worth mentioning that experiments on two well-known datasets show that our proposed method achieves better prediction results compared to existing baseline methods.

With the global climate change, the frequency of extreme weather is getting higher and higher, and the threat to the safe operation of the power system is gradually increasing, which is likely to cause large-scale power outages and then result in N-k contingencies. Meanwhile, the Modular Multi-level Converter (MMC) based Multi-Terminal High Voltage Direct Current (MTDC) power system has been confirmed that can provide the possibility for the network interconnection between regional power systems and various renewable energy resources to boost supply reliability and economy. To enhance the resilience of the power grid, a generation rescheduling scheme by optimal emergency control that considers the risk-based dynamic security constraint and reactive power constraints is proposed. Based on the transient stability criterion of the rotor angle speed of the center of inertia (COI), the active power is adjusted to improve the transient stability of the system. The usefulness of the proposed control strategy is highlighted on a 10-machine-39-bus hybrid power system built on MATMTDC, a MATLAB-based open-source software. The obtained results demonstrate that the state optimization control strategy is capable of enhancing the resilience of hybrid power systems and improving the transient stability when suffering N-k contingencies.

With global warming and the depletion of fossil energy, the development of photovoltaic (PV) energy has attracted attention in many countries. However, when large-scale PV power stations are connected to the power grid through inverters, the inertia and damping capacity of the power system are greatly reduced, leading to frequent power oscillations in the power system. To solve this problem, this study proposes a control strategy for PV grid-connected inverters based on the model predictive control (MPC) algorithm. Based on the MPC algorithm and the establishment of a discrete-time predictive model, the PV grid-connected inverter dynamically adjusts its output current to suppress power oscillations. The simulation results demonstrated the effectiveness of the proposed control strategy in damping power oscillations in the power grid and enhancing the stability of the power system.

In recent years, with the continued development and popularity of sustainable energy sources and the increasing utilization of solar energy, accurate solar radiation prediction has become important. In this paper, we propose a new model based on deep learning, Feature-enhanced Gated Recurrent Unit, hereafter referred to as FEGRU, for solar radiation prediction. This model takes the source data with one-dimensional convolution and self-attention to feature attention and processes the data features, and then GRU performs feature extraction on solar irradiance data. Finally, the data dimensionality is transformed by a fully connected layer. The main advantage of FEGRU is that it does not require auxiliary data, but only time series data of solar irradiance can be used for good solar irradiance prediction. Our experiments with solar irradiance samples in Lyon, France, show that our model has better prediction results than the baseline model.

Abstract. Acute pancreatitis (AP) is a common acute abdominal condition of the digestive system. In recent years, treatment concepts, methods, and strategies for the diagnosis of AP have advanced, and this has played an important role in promoting the standardization of AP diagnosis and treatment and improving the treatment quality of AP patients. On the basis of previous guidelines and expert consensus, this guideline adopts an evidence-based, problem-based expression; synthesizes important clinical research data at home and abroad in the most recent 5 years; and forms 29 recommendations through multidisciplinary expert discussion, including diagnosis, treatment, and follow-up. It is expected to provide evidence support for the treatment of AP in the clinical setting in China.

Abstract. The incidence of pancreatic cancer has been rising worldwide, and its clinical diagnosis and treatment remain a great challenge. To present the update and improvements in the clinical diagnosis and treatment of pancreatic cancer in recent years, Chinese Pancreatic Association, the Chinese Society of Surgery, Chinese Medical Association revised the Guidelines for the Diagnosis and Treatment of Pancreatic Cancer in China (2014) after reviewing evidence-based and problem-oriented literature published during 2015–2021, mainly focusing on highlight issues regarding diagnosis and surgical treatment of pancreatic cancer, conversion strategies for locally advanced pancreatic cancer, treatment of pancreatic cancer with oligo metastasis, adjuvant and neoadjuvant therapy, standardized processing of surgical specimens and evaluation of surgical margin status, systemic treatment for unresectable pancreatic cancer, genetic testing, as well as postoperative follow up of patients with pancreatic cancer. Forty recommendation items were finally proposed based on the above issues, and the quality of evidence and strength of recommendations were graded using the Grades of Recommendation, Assessment, Development, and Evaluation system. This guideline aims to standardize the clinical diagnosis and therapy, especially surgical treatment of pancreatic cancer in China, and further improve the prognosis of patients with pancreatic cancer.

Hereditary haemorrhagic telangiectasia (HHT) is a rare disease that lacks effective treatment. Here, the authors report the case of a 30-year-old woman presenting with abdominal pain accompanied by severe malnutrition. After a definite diagnosis of HHT involvement in the liver, liver transplantation was the first-choice treatment according to the guidelines of HHT. However, the patient firmly refused liver transplantation. Finally, a new type of surgery, right hemihepatectomy combined with ligation of the common hepatic artery and gastroduodenal artery, was performed based on careful study of the case and with the maximum benefit of the patient in mind. Although the patient developed transient liver dysfunction after surgery, she eventually recovered well and continued to be followed up. As far as we know, this is the first report of this kind of surgery for the treatment of intrahepatic HHT.

With the rapid growth of wind power penetration into modern power grids, wind speed forecasting plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the existing wind speed forecasting methods are modeled as black boxes, which are very complicated and cannot be written down explicitly due to the complex fluctuation characteristics of wind speed series. To this end, this study proposes a novel direct method based on an explainable neural network (xNN) for deterministic and probabilistic wind speed forecasting. It can theoretically extract the nonlinear mapping features in wind speed, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Then, the uncertainties in wind speed are statistically synthesized via the kernel density estimation method. Finally, we use wind speed data from real wind farms in Belgium to verify the feasibility and effectiveness of the proposed method. The simulation results demonstrate that it is not only able to accurately extract the non-stationary feature in the wind speed series but also superior to other benchmark algorithms in prediction accuracy. Therefore, the proposed method has a high potential for practical applications in real electric power and energy systems.

Large-scale wind power integration into the power system has promoted the development of a multiterminal DC (MTDC) transmission grid with a modular multilevel converter (MMC). Basically, MTDC with MMC is a typical cyber–physical system with continuous coupling interactions between cyber assets and power systems. However, cyber events may introduce many internet-based vulnerabilities and even result in the loss of transient stability of the power system. Therefore, a voltage compensation-based two-level hierarchical adaptive control strategy is proposed in this article. At the higher level, a modified MMC output current reference calculation method is developed in the αβ framework to guarantee the transient stability of the power system, whereas a feedback adjustment method is proposed in the MMC control framework, at the bottom level, to contain the controller from deviating from its output reference while eliminating the impact of cyber communication delay on transient stability. The article shows that the proposed hierarchical control strategy can improve the transient stability of the power grid under the interference of three-phase ground faults in physical and communication delays in the cyber layer. Finally, the simulation results of the modified IEEE 9-bus test system with MMC–MTDC are used to demonstrate the effectiveness of the proposed scheme.

The increasing penetration of various distributed and renewable energy resources at the consumption premises, along with the advanced metering, control and communication technologies, promotes a transition on the structure of traditional distribution systems towards cyber-physical multi-microgrids (MMGs). The networked MMG system is an interconnected cluster of distributed generators, energy storage as well as controllable loads in a distribution system. And its operation complexity can be decomposed to decrease the burdens of communication and control with a decentralized framework. Consequently, the multi-microgrid energy management system (MMGEMS) plays a significant role in improving energy efficiency, power quality and reliability of distribution systems, especially in enhancing system resiliency during contingencies. A comprehensive overview on typical functionalities and architectures of MMGEMS is illustrated. Then, the emerging communication technologies for information monitoring and interaction among MMG clusters are surveyed. Furthermore, various energy scheduling and control strategies of MMGs for interactive energy trading, multi-energy management, and resilient operations are thoroughly analyzed and investigated. Lastly, some challenges with great importance in the future research are presented.

The energy management and energy efficiency optimization are of particularly significant for promoting the sustainable development of industrial processes. However, industrial raw data with uncertain, relevant and inaccuracy characteristics influence the reliability and accuracy of energy efficiency analysis and optimization modeling. Therefore, an energy efficiency analysis and optimization method based on a novel data reconciliation (DR) integrating Gaussian mixture model (GMM) and mutual information (MI) is put forward. First, the material flow information with multiple data characteristics corresponding operation modes is divided through the GMM. Moreover, the novel data reconciliation model integrated with critical variable and mutual information is established considering time-scale redundancy information in different modes, then the comprehensive data reconciliation result is evaluated by the hypothesis testing. Furthermore, the reconciled data is applied to analyze the exergy balance and built the energy efficiency optimization model with multi-objective for a case study of industrial evaporation process. Finally, simulation case and industrial application case are used to analyze and discuss, and the results show that the validity and applicability of the proposed approach are illustrated in energy saving potential which is about 14.81%.

There is no effective serologic parameter to distinguish different types of pancreatitis now. To distinguish between acute pancreatitis (AP) and acute exacerbations of chronic pancreatitis (CP) and to determine whether fibrosis occurs in CP, we evaluated the ability to produce white blood cells (WBCs), the neutrophil-to-retinol-binding protein (RBP) ratio (called the WNR), the product of the gamma-glutamyl transpeptidase (GGT) level, and the 5′-nucleotide-to-RBP ratio (called the GNR). We evaluated the newly proposed difference index RBP and analyzed the effectiveness of the WNR and GNR in 691 patients with pancreatic diseases. We performed univariate and multivariate analyses of serological indices and their correlations with RBP and performed receiver operating characteristic (ROC) curve analyses of the WNR and GNR. The serum RBP level decreased markedly in AP compared with that in the acute stage of CP (p < 0.05). The GGT, alkaline phosphatase (ALP), total protein (TP), albumin (ALB), prealbumin (PA), 5′-nucleotide, and uric acid (UC) serum levels were significantly higher for fibrotic CP than for the acute stage of CP without fibrosis (p < 0.05). With progressing to pancreatic fibrosis, the liver injury-related indicators, prothrombin time (PT), activated partial thromboplastin time (APTT), D-Dimer, aspartate aminotransferase (AST), and GGT, gradually increased (p < 0.05). ROC curve analysis suggests that both the WNR (area under the curve [AUC] = 0.821) and GNR (AUC = 0.778) can be used to differentiate pancreatitis types.

BackgroundLong-term excessive use of morphine leads to addictive diseases and affects cognitive function. Cognitive performance is associated with genetic characteristics.MiR-124 plays a critical regulatory role in neurogenesis, synaptic development, brain plasticity, and the use of addictive substances. As a scaffold protein, IQGAP1 affects learning and memory dose-dependent. However, the role of miR-124 and its target protein as potential addiction biomarkers and the impact on cognitive function have not been fully explored.MethodA total of 40 patients with morphine dependence and 40 cases of healthy people were recruited. We collected basic and clinical information about the two groups. The Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire-9(PHQ-9), Montreal Cognition Assessment Scale (MoCA), Pittsburgh Sleep Quality Index (PSQI) were used to assess the severity of depression, anxiety, depressive symptoms, cognitive dysfunction, and sleep quality.ResultsCompared to the control group, the morphine-dependent group had higher GAD-7, PHQ-9, PSQI scores, and more elevated miR-124 levels but lower MOCA scores and IQGAP1 levels. MiR-124, IQGAP1, the average intake last year were related to OASI scores.MiR-124, IQGAP1, PHQ-9 were associated with MOCA scores. In the multiple regression model, the levels of miR-124 and IQGAP1 were independent factors influencing the severity of morphine dependence. The level of miR-124 was an independent factor influencing the severity of cognitive impairment in patients with morphine dependence. In addition, the luciferase report confirmed that IQGAP1 mRNA is the direct target of miR-124.ConclusionMiR-124 and its target protein IQGAP1 are involved in the regulation of addiction and cognitive function in patients with morphine dependence.

Summary: The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

Objective:. The aim of this study is to investigate the current status of the diagnosis and treatment of patients with pancreatic neuroendocrine neoplasms (pNENs) undergoing surgery in China. Methods:. This is a multicenter cross-sectional study performed in China. Data from patients with pNENs undergoing surgery at 33 high-volume medical centers, where the number of pancreatectomies exceeds 20 cases per year, were collected and analyzed between March 1, 2016 and February 28, 2017. Results:. In total, 392 patients with pNENs were enrolled. The male to female ratio was 1.4. The majority of patients were aged between 40 and 70 years. 65.6% of the patients had non-functional tumors. Among those with functional tumors, the percentages of insulinomas, gastrinomas, glucagonomas, and vasoactive intestinal peptide-secreting tumors were 94.8%, 1.5%, 2.2%, and 1.5%, respectively. Multidisciplinary team (MDT) discussion was conducted for 39.0% of the patients. Minimally invasive surgery was performed on 31.1% of the 392 patients. The incidence of grade B/C pancreatic fistula formation was 4.4%. A total of 89.0% of the surgeries achieved R0 resection, and 41.6% of the tumors were well differentiated. Lymph node metastasis was present in 8.9% of the patients. The percentages of patients with grades G1, G2, and G3 disease were 49.2%, 45.7%, and 5.1%, respectively. Conclusion:. This multicenter cross-sectional study systematically presents the current status of the diagnosis and treatment of patients with pNENs undergoing surgery in China. MDT consultation for pNENs has not been widely implemented in China. Although the incidence of surgical complications is relatively low, minimally invasive procedures should be further promoted. This study shows us how to improve the outcomes of these patients.

Abstract. Objective:. The aim of this study is to investigate the current status of pancreatic cancer patients undoing surgical treatment in China and to find ways to improve the survival of these patients in the future. Methods:. This study is a national, multicenter, cross-sectional study in China. Information regarding pancreatic cancer patients undergoing surgical treatment from 34 high-volume tertiary IIIA level hospitals was collected and analyzed from the March 1, 2016 to the February 28, 2017. Results:. In total, 2200 pancreatic cancer patients were enrolled from 34 tertiary IIIA level hospitals in 16 provinces across China. The male-to-female ratio was 1.5. More than 80% of the patients were between 50 and 70 years old. The top 4 symptoms were epigastric discomfort, abdominal pain, jaundice, and weight loss. Carbohydrate antigen 19-9 and carcinoembryonic antigen were elevated in 70.9% and 27.1% of patients, respectively. A multidisciplinary team (MDT) discussion was carried out for 35.0% of patients before surgery. The proportion of minimally invasive pancreatic surgeries was approximately 20%. A total of 83.4% of the operations achieved R0 resection, and the incidence of grade 3/4 postoperative complications was 7.7%. Only 13.4% of the patients received postoperative adjuvant chemotherapy. The percentage of pathological stage I tumors was only 24.5%. Conclusion:. The majority of pancreatic cancer patients undergoing surgical resection in China are in an advanced stage. The MDT consultations for pancreatic cancer have not been widely carried out. R0 resection has been achieved in most cases, with relatively low incidence of serious complications, but minimally invasive pancreatic surgery should be further promoted. The application of postoperative chemotherapy remains low. This national, multicentre, cross-sectional study comprehensively presents the current status of pancreatic cancer patients undergoing surgical treatment and shows the road to improve survival of these patients in the future.

This work simulates the morphological evolution process of the solidification interface of silicon crystal. Based on the phase field model of single dendrite growth of pure material in a single-phase system, the control equation of the phase field is re-optimized, and an interface free energy anisotropy equation that can simulate the competitive growth of multiple crystal grains is established. The competitive growth of polysilicon is then simulated and analyzed. The results show that when the degree of undercooling exceeds a certain value, the non-facet crystals are transformed into facet crystals. The main branches in each direction are relatively thick when the anisotropy is small. With increasing anisotropy, the main branches in each direction show gradual thinning, and edges and corners appear on the interface. The dendrites are no longer smooth, and they transform from non-facet crystals to facet crystals. The main branches of different grains inhibit each other when multiple crystal grains compete for growth. The growth of the main branches is curved, which is different from existing branches. The experimental results can more realistically simulate the evolution process of single-crystal silicon and polycrystalline silicon crystal micromorphology.

Transmembrane protease serine 4 (TMPRSS4) is upregulated in various kinds of human cancers, including pancreatic cancer. However, its biological function in pancreatic ductal adenocarcinoma (PDAC) remains unclear. In the current study, real-time qPCR, immunohistochemical staining, Western blotting, and database (Cancer Genome Atlas and Gene Expression) analysis revealed remarkable overexpression of TMPRSS4 in PDAC tissue as compared to non-tumor tissue. The TMPRSS4 overexpression was associated with poor prognosis of PDAC patients. Moreover, multivariate analysis revealed that TMPRSS4 serves as an independent risk factor in PDAC. We performed gain-and loss-of-function analysis and found that TMPRSS4 promotes cellular proliferation and inhibits apoptosis of PDAC cells both in vitro and in vivo. Furthermore, we showed that TMPRSS4 might promote cell proliferation and inhibit apoptosis through activating ERK1/2 signaling pathway in pancreatic cancer cells. These findings were validated by using ERK1/2 phosphorylation inhibitor SCH772984 both in vitro and in vivo. Taken together, this study suggests that TMPRSS4 is a proto-oncogene, which promotes initiation and progression of PDAC by controlling cell proliferation and apoptosis. Our findings indicate that TMPRSS4 could be a promising prognostic biomarker and a therapeutic target for the treatment of pancreatic cancer.

Load frequency control (LFC) is playing an indispensable role to achieve the secure and economic operation of power grids. However, the existing LFC schemes either may rely on a nonlinear grid model under perfect operating condition with nominal parameters, or they may adopt a complicated control structure of high order. These LFC schemes may have poor control performance or even loss of stability in real-time implementation due to the grid uncertainties and the change of system operation scenarios. Consequently, a hybrid control method with two control loops considering various practical scenarios is originally proposed in this paper. In the inner loop, variable universe fuzzy logic control is applied to mitigate the impact of load disturbances on control performance. In the outer loop, an incremental genetic algorithm is employed to online optimize the control parameters. The performance of the proposed control method is comprehensively tested on a MATLAB/Simulink-based LFC model and a real-time digital simulator-based real-life 49-bus power system. The extensive results show that the proposed hybrid method exhibits comparatively better control performance than an adaptive fuzzy logic controller and an improved proportion integration controller.

Accompanied by the rising fashion of distributed energy resources requiring distributed optimization is becoming more prevalent among power system. However, research for distributed optimization to determine smooth operation point (SOP) has attracted less attention in hybrid multi-terminal high voltage direct current (MTDC) and thus, it is still a core issue. The proposed method mainly designed to deal with SOP, and this goal can be mainly achieved by taking minimal line losses and smoothness objectives (MLLSO), which is formulated as an MLLSO optimization model. Then, the MLLSO optimization is transformed to single-objective distributed optimization through weight normalization of both objective functions. A minimal line loss objective and smoothness objective-based optimal power flow (MLLOSOBOPF) model is built at first to effectively handle prevailing constraints for the power systems parameters. Then, the alternating direction method of multipliers (ADMM) is utilized to solve this model by breaking it into smaller pieces based on OPF, each of which is easier to handle. In addition, different sized power systems are integrated with the 14-bus and 30-bus hybrid MTDC-AC systems to increase the complexity of test systems. Finally, based on 14-bus and 30-bus hybrid MTDC-AC systems, the computational output and comparison of the proposed method with centralized optimization centralized MTDC show that the proposed method is feasible and effective for determining hybrid MTDC grid SOP.

This paper proposes a novel hybrid intelligent approach to solve the dynamic optimization problem of activation rules for automatic generation control (AGC) based on multiobjective reinforcement learning (MORL) and small population-based particle swarm optimization (SPPSO). The activation rule for AGC is to dynamically allocate the AGC regulating commands among various AGC units, and subsequently, the secondary control reserve of those units can be activated. Therefore, the activation rule for AGC is vital to ensure the overall control performance of AGC schemes. In this paper, MORL is applied to provide a customized platform for interactive self-learning to maximize the long-run discounted reward, i.e., minimize the generation cost, regulating error, and emission from a long-term viewpoint. SPPSO is utilized to effectively and efficiently obtain the optimality of activation rule with a fast convergence speed to fulfill the real-time requirement of AGC activation. Furthermore, a novel analytic hierarchy process-based coordination factor is introduced to identify the optimum multi-objective tradeoff in various power system operation scenarios. At last, the validation of the proposed hybrid method has been demonstrated via comprehensive tests using practical data from the dispatch center of China Southern Power Grids.

Background/Aims: Cancer stem cells (CSCs) are largely responsible for tumor relapse and metastatic behavior. Doublecortin-like kinase 1 (DCLK1) was recently reported to be a biomarker for gastrointestinal CSCs and involved in the epithelial-mesenchymal transition (EMT) and tumor progression. B cell-specific Moloney murine leukemia virus insertion site 1 (Bmi-1) is a crucial regulator of CSC self-renewal, malignant transformation and EMT, and a previous study from our group showed that Bmi-1 is upregulated in pancreatic cancer progression and participates in EMT. However, it remains unclear whether DCLK1 is involved in pancreatic cancer or whether DCLK1 is associated with the altered level of Bmi-1 expression. Methods: The correlation of DCLK1 expression and clinical features of pancreatic cancer was analyzed in 210 paraffin-embedded archived pancreatic cancer specimens by immunohistochemical analysis. The biological effects of DCLK1 siRNA on cells were investigated by examining cell proliferation using a cell counting kit and cell colony assays, cell migration by wound healing assay and cell invasion by Transwell invasion assay. We further investigated the effect of therapeutic siRNA targeting DCLK1 on pancreatic cancer cell growth in vivo. Moreover, the molecular mechanism by which DCLK1 upregulates Bmi-1 expression was explored using real-time PCR, western blotting and Co-immunoprecipitation assay. Results: DCLK1 is overexpressed in pancreatic cancer and is related to metastasis and prognosis. Knockdown of DCLK1 markedly suppressed cell growth in vitro and in vivo and also inhibited the migration and invasion of pancreatic cancer cells. Furthermore, we found that DCLK1 silencing could inhibit EMT in cancer cells via downregulation of Bmi-1 and the mesenchymal markers Snail and Vimentin and upregulation of the epithelial marker E-cadherin. Moreover, high DCLK1 expression in human pancreatic cancer samples was associated with a mesenchymal phenotype and increased cell proliferation. Further co-immunoprecipitation indicated that DCLK1 did not interact with Bmi-1 directly. Conclusion: Our data suggest that upregulation of DCLK1 may contribute to pancreatic cancer metastasis and poor prognosis by increasing Bmi-1 expression indirectly. The findings indicate that inhibiting DCLK1 expression might be a novel strategy for pancreatic cancer therapy.

Abstract Despite tremendous research efforts focused on diagnosis and treatment, pancreatic ductal adenocarcinoma remains the third leading cause of cancer-related death in the United States, with a 5-year overall survival rate of less than 5%. Although resistance is rather complex, emerging evidence has demonstrated that epigenetic alterations (e.g. miRNA) have important roles in PDAC progression as well as resistance to therapy. Certain miRNAs have been identified as potential prognostic biomarkers in PDAC. In this review, we summarize the recent developments in miRNA research related to PDAC therapeutic resistance mechanisms and the potential of miRNAs as prognostic biomarkers for future clinical management of PDAC.