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The aquaculture industry in China is not complete without the freshwater industry. The scientific assessment of the regional competitiveness of the freshwater aquaculture industry has significant theoretical and practical implications for fostering the industry's sustainable development. Based on the diamond model, this study developed a competitiveness evaluation index system for Chinese freshwater aquaculture industry which included six primary indicators and sixteen secondary indicators. The primary indicators included resource endowment, production capacity, science and technology promotion capacity, related and supporting industries, market demand and guiding development policy. The study analyzed freshwater aquaculture industrial competitiveness during 2012–2020 and classified the Chinese 18 major freshwater aquaculture provinces into five competitive levels. The results showed that the development of related and supporting industries has the greatest contribution to the competitiveness, followed by resource endowment and market demand capacity. The analysis and tier classification showed that the competitiveness of Jiangsu Province and Hubei Province has always been at the forefront, and the province of Guangdong, Fujian, Hunan, Jiangxi, Zhejiang, Anhui, Liaoning, and Guangxi were above the middle level. Moreover, the results indicated that the competitiveness of most provinces showed an upward trend.

China is a major player in the marine fish trade. The price prediction of marine fish is of great significance to socio-economic development and the fisheries industry. However, due to the complexity and uncertainty of the marine fish market, traditional forecasting methods often struggle to accurately predict price fluctuations. Therefore, this study adopts an intelligent combination model to enhance the accuracy of food product price prediction. Firstly, three decomposition methods, namely empirical wavelet transform, singular spectrum analysis, and variational mode decomposition, are applied to decompose complex original price series. Secondly, a combination of bidirectional long short-term memory artificial neural network, extreme learning machine, and exponential smoothing prediction methods are applied to the decomposed results for cross-prediction. Subsequently, the predicted results are input into the PSO–CS intelligence algorithm for weight allocation and to generate combined prediction results. Empirical analysis is conducted using data illustrating the daily sea purchase price of larimichthys crocea in Ningde City, Fujian Province, China. The combination prediction accuracy with PSO–CS weight allocation is found to be higher than that of single model predictions, yielding superior results. With the implementation of weight allocation intelligent combinatorial modelling, the prediction of marine fish prices demonstrates higher accuracy and stability, enabling better adaptation to market changes and price fluctuations.

The location-routing-inventory problem (LRIP) is essential to cold chain logistics (CCL). To solve the problem, this paper proposes an integrated LRIP model considering the carbon transaction mechanism, which simultaneously optimizes the costs associated with location, transportation, inventory, and carbon transaction. The non-dominated sorting genetic algorithm II (NSGA-II) was improved to solve the model. Simulation shows that the improved NSGA-II can effectively reduce carbon emissions of enterprises in the context of carbon transaction. The research offers a solution for reducing total costs in the light of the environmental factors in CCL.

Chemoresistance is a major obstacle in the clinical management of metastatic, castration-resistant prostate cancer (PCa). It is imperative to develop novel strategies to overcome chemoresistance and improve clinical outcomes in patients who have failed chemotherapy. Using a two-tier phenotypic screening platform, we identified bromocriptine mesylate as a potent and selective inhibitor of chemoresistant PCa cells. Bromocriptine effectively induced cell cycle arrest and activated apoptosis in chemoresistant PCa cells but not in chemoresponsive PCa cells. RNA-seq analyses revealed that bromocriptine affected a subset of genes implicated in the regulation of the cell cycle, DNA repair, and cell death. Interestingly, approximately one-third (50/157) of the differentially expressed genes affected by bromocriptine overlapped with known p53-p21- retinoblastoma protein (RB) target genes. At the protein level, bromocriptine increased the expression of dopamine D2 receptor (DRD2) and affected several classical and non-classical dopamine receptor signal pathways in chemoresistant PCa cells, including adenosine monophosphate-activated protein kinase (AMPK), p38 mitogen-activated protein kinase (p38 MAPK), nuclear factor kappa B (NF-κB), enhancer of zeste homolog 2 (EZH2), and survivin. As a monotherapy, bromocriptine treatment at 15 mg/kg, three times per week, via the intraperitoneal route significantly inhibited the skeletal growth of chemoresistant C4-2B-TaxR xenografts in athymic nude mice. In summary, these results provided the first preclinical evidence that bromocriptine is a selective and effective inhibitor of chemoresistant PCa. Due to its favorable clinical safety profiles, bromocriptine could be rapidly tested in PCa patients and repurposed as a novel subtype-specific treatment to overcome chemoresistance.

A major challenge to the treatment of advanced prostate cancer (PCa) is the development of resistance to androgen-deprivation therapy (ADT) and chemotherapy. It is imperative to discover effective therapies to overcome drug resistance and improve clinical outcomes. We have developed a novel class of silicon-containing compounds and evaluated the anticancer activities and mechanism of action using cellular and animal models of drug-resistant PCa. Five organosilicon compounds were evaluated for their anticancer activities in the NCI-60 panel and established drug-resistant PCa cell lines. GH1504 exhibited potent in vitro cytotoxicity in a broad spectrum of human cancer cells, including PCa cells refractory to ADT and chemotherapy. Molecular studies identified several potential targets of GH1504, most notably androgen receptor (AR), AR variant 7 (AR-v7) and survivin. Mechanistically, GH1504 may promote the protein turnover of AR, AR-v7 and survivin, thereby inducing apoptosis in ADT-resistant and chemoresistant PCa cells. Animal studies demonstrated that GH1504 effectively inhibited the in vivo growth of ADT-resistant CWR22Rv1 and chemoresistant C4-2B-TaxR xenografts in subcutaneous and intraosseous models. These preclinical results indicated that GH1504 is a promising lead that can be further developed as a novel therapy for drug-resistant PCa.

In the operational, strategic and tactical decision-making problems of the agri-food supply chain, the perishable nature of the commodities can represent a particular complexity problem. It is, therefore, appropriate to consider decision support tools that take into account the characteristics of the products, the needs and the requirements of producers, sellers and consumers. This paper presents a green vehicle routing model for fresh agricultural product distribution and designs an adaptive hybrid nutcracker optimization algorithm (AH-NOA) based on k-means clustering to solve the problem. In the process, the AH-NOA uses the CW algorithm to increase population diversity and adds genetic operators and local search operators to enhance the global search ability for nutcracker optimization. Finally, the experimental data show that the proposed approaches effectively avoid local optima, promote population diversity and reduce total costs and carbon emission costs.

The therapies for bone metastatic prostate cancer are limited and the underlying mechanisms are unclear. Here, the authors show that bone derived TGF-β induces acetylation of KLF5 (Ac-KLF5), and Ac-KLF5 promotes prostate cancer bone metastasis and resistance to docetaxel by upregulating CXCR4.

An integrated hog futures price forecasting model based on whale optimization algorithm (WOA), LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. The simulation process begins with a grey correlation analysis of the hog futures price index system in order to identify influencing factors; after that, the WOA-LightGBM model is developed, and the WOA algorithm is used to optimize the LightGBM model parameters; and, finally, the residual sequence is decomposed and corrected by using the CEEMDAN method to build a combined WOA-LightGBM-CEEMDAN model. Furthermore, it is used for comparison experiments to check the validity of the model by using data from CSI 300 stock index futures. Based on all experimental results, the proposed combined model shows the highest prediction accuracy, surpassing the comparative model. The model proposed in this study is accurate enough to meet the forecasting accuracy requirements and provides an effective method for forecasting future prices.

With continuous improvements in people’s consumption levels, consumers’ demands for safe and fresh agricultural products increase. The increase in the number of vehicles and serious congestion on roads has led to problems, such as the weak timeliness of urban cold chain logistics, high carbon emissions, low customer value and reduced customer satisfaction. In this study, carbon emissions, customer satisfaction, customer value and cost are considered, and an optimization algorithm is established to solve the time-dependent vehicle routing problem in urban cold chain logistics. For road congestion at different time periods during the cold chain distribution process, the segment function is used to express the vehicle speed. According to the characteristics of the model, considering the constraints of the time window and vehicle capacity, an improved NSGA-II algorithm with the local optimization characteristics of the greedy algorithm (G-NSGA-II) is proposed, and the sorting fitness strategy is optimized. In addition, we carry out a series of experiments on existing vehicle routing problem examples and analyze them in a real background to evaluate and prove the effectiveness of the proposed model and algorithm. The experiment results show that the proposed approach effectively reduces the total cost, enhances customer value and promotes the long-term development of logistics companies.

Bone metastasis is a frequent and lethal complication of many cancer types (i.e., prostate cancer, breast cancer, and multiple myeloma), and a cure for bone metastasis remains elusive. To recapitulate the process of bone metastasis and understand how cancer cells metastasize to bone, intracardiac injection and intracaudal arterial animal models were developed. The intratibial injection animal model was established to investigate the communication between cancer cells and the bone microenvironment and to mimic the setting of prostate cancer patients with bone metastasis. Given that detailed protocols of intratibial injection and its quantitative analysis are still insufficient, in this protocol, we provide hands-on procedures for how to prepare cells, perform the tibial injection, monitor tibial tumor growth, and quantitatively evaluate the tibial tumors in pathological samples. This manuscript provides a ready-to-use experimental protocol for investigating cancer cell behaviors in bone and developing novel therapeutic strategies for bone metastatic cancer patients.

Changes in the consumption price of aquatic products will affect demand and fishermen’s income. The accurate prediction of consumer price index provides important information regarding the aquatic product market. Based on the non-linear and non-smooth characteristics of fishery product price series, this paper innovatively proposes a fishery product price forecasting model that is based on Variational Modal Decomposition and Improved bald eagle search algorithm optimized Long Short Term Memory Network (VMD-IBES-LSTM). Empirical analysis was conducted using fish price data from the Department of Marketing and Informatization of the Ministry of Agriculture and Rural Affairs of China. The proposed model in this study was subsequently compared with common forecasting models such as VMD-LSTM and SSA-LSTM. The research results show that the VMD-IBES-LSTM model that was constructed in this paper has good fitting results and high prediction accuracy, which can better explain the seasonality and trends of the change of China’s aquatic product consumer price index, provide a scientific and effective method for relevant management departments and units to predict the aquatic product consumer price, and have a certain reference value for reasonably coping with the fluctuation of China’s aquatic product market price.

In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic (PV) models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy (EBLSHADE) is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems.

Due to the diversity and the different distribution conditions of agricultural products, split delivery plays an important role in the last mile distribution of agricultural products distribution. The time-dependent split delivery green vehicle routing problem with multiple time windows (TDSDGVRPMTW) is studied by considering both economic cost and customer satisfaction. A calculation method for road travel time across time periods was designed. A satisfaction measure function based on a time window and a measure function of the economic cost was employed by considering time-varying vehicle speeds, fuel consumption, carbon emissions and customers’ time windows. The object of the TDSDGVRPMTW model is to minimize the sum of the economic cost and maximize average customer satisfaction. According to the characteristics of the model, a variable neighborhood search combined with a non-dominated sorting genetic algorithm II (VNS-NSGA-II) was designed. Finally, the experimental data show that the proposed approaches effectively reduce total distribution costs and promote energy conservation and customer satisfaction.

The high prevalence and long latency period of prostate cancer (PCa) provide a unique opportunity to control disease progression with dietary and nutraceutical approaches. We developed ProFine, a standardized composition of luteolin, quercetin, and kaempferol, and investigated its potential as a nutraceutical for PCa in preclinical models. The three ingredients of ProFine demonstrated synergistic in vitro cytotoxicity and effectively induced apoptosis in PCa cells. ProFine markedly affected the transcriptome of PCa cells, suppressed the expression of androgen receptor, and inhibited androgen-regulated genes. Oral administration of ProFine did not exhibit obvious toxicities in mice, and the three ingredients retained their individual pharmacokinetic and bioavailability profiles. Importantly, ProFine significantly retarded the growth of PCa xenografts in athymic nude mice and extended the survival of animals. This study provides preclinical evidence supporting the promise of ProFine as a safe, efficacious, and affordable intervention to control PCa progression and improve clinical outcomes.

HER family members are interdependent and functionally compensatory. Simultaneously targeting EGFR/HER2/HER3 by antibody combinations has demonstrated superior treatment efficacy over targeting one HER receptor. However, antibody combinations have their limitations, with high immunogenicity and high cost. In this study, we have developed a three-in-one nucleic acid aptamer-small interfering RNA (siRNA) chimera, which targets EGFR/HER2/HER3 in one molecule. This inhibitory molecule was constructed such that a single EGFR siRNA is positioned between the HER2 and HER3 aptamers to create a HER2 aptamer-EGFR siRNA-HER3 aptamer chimera (H2EH3). EGFR siRNA was delivered into HER2-expressing cells by HER2/HER3 aptamer-induced internalization. HER2/HER3 aptamers act as antagonist molecules for blocking HER2 and HER3 signaling pathways and also as tumor-targeting agents for siRNA delivery. H2EH3 enables down-modulation of the expression of all three receptors, thereby triggering cell apoptosis. In breast cancer xenograft models, H2EH3 is able to bind to breast tumors with high specificity and significantly inhibits tumor growth via either systemic or intratumoral administration. Owing to low immunogenicity, ease of production, and high thermostability, H2EH3 is a promising therapeutic to supplement current single HER inhibitors and may act as a treatment for HER2+ breast cancer with intrinsic or acquired resistance to current drugs.

Despite the recent successes in siRNA therapeutics, targeted delivery beyond the liver remains the major hurdle for the widespread application of siRNA in vivo. Current cationic liposome or polymer-based delivery agents are restricted to the liver and suffer from off-target effects, poor clearance, low serum stability, and high toxicity. In this study, we genetically engineered a non-cationic non-viral tumor-targeted universal siRNA nanocarrier (MW 26 KDa). This protein nanocarrier consists of three function domains: a dsRNA binding domain (dsRBD) (from human protein kinase R) for any siRNA binding, 18-histidine for endosome escape, and two RGD peptides at the N- and C-termini for targeting tumor and tumor neovasculature. We showed that cloned dual-RGD-dsRBD-18his (dual-RGD) protein protects siRNA against RNases, induces effective siRNA endosomal escape, specifically targets integrin αvβ3 expressing cells in vitro, and homes siRNA to tumors in vivo. The delivered siRNA leads to target gene knockdown in the cell lines and tumor xenografts with low toxicity. This multifunctional and biomimetic siRNA carrier is biodegradable, has low toxicity, is suitable for mass production by fermentation, and is serum stable, holding great potential to provide a widely applicable siRNA carrier for tumor-targeted siRNA delivery.

The time-dependent vehicle routing problem of time windows of fresh agricultural products distribution have been studied by considering both economic cost and environmental cost. A calculation method for road travel time across time periods is designed in this study. A freshness measure function of agricultural products and a measure function of carbon emission rate are employed by considering time-varying vehicle speeds, fuel consumptions, carbon emissions, perishable agricultural products, customers’ time windows, and minimum freshness. A time-dependent green vehicle routing problem with soft time windows (TDGVRPSTW) model is formulated. The object of the TDGVRPSTW model is to minimize the sum of economic cost and environmental cost. According to the characteristics of the model, a new variable neighborhood adaptive genetic algorithm is designed, which integrates the global search ability of the genetic algorithm and the local search ability of the variable neighborhood descent algorithm. Finally, the experimental data show that the proposed approaches effectively avoid traffic congestions, reduce total distribution costs, and promote energy conservation and emission reduction.

The majority of cancer-related deaths are caused by tumor recurrence, metastasis and therapeutic resistance. During the late stages of tumor progression, multiple factors are involved, including the downregulation and/or loss of function of metastasis suppressors. Epithelial protein lost in neoplasm (EPLIN), an actin-binding protein, was initially identified as a putative tumor suppressor that is frequently downregulated in epithelial tumors. Recent evidence indicates that EPLIN may negatively regulate epithelia-to-mesenchymal transition (EMT), a crucial process by which cancer cells acquire invasive capabilities and therapeutic resistance. Importantly, downregulation of EPLIN is associated with clinical metastasis in a variety of solid tumors, suggesting that EPLIN could be a suppressor of metastasis. In this review, I will discuss the regulation and function of EPLIN in human cancer cells and explore the clinical significance of EPLIN in metastatic disease.

A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

Aberrant platelet derived growth factor (PDGF) signaling has been associated with prostate cancer (PCa) progression. However, its role in the regulation of PCa cell growth and survival has not been well characterized.Using experimental models that closely mimic clinical pathophysiology of PCa progression, we demonstrated that PDGF is a survival factor in PCa cells through upregulation of myeloid cell leukemia-1 (Mcl-1). PDGF treatment induced rapid nuclear translocation of β-catenin, presumably mediated by c-Abl and p68 signaling. Intriguingly, PDGF promoted formation of a nuclear transcriptional complex consisting of β-catenin and hypoxia-inducible factor (HIF)-1α, and its binding to Mcl-1 promoter. Deletion of a putative hypoxia response element (HRE) within the Mcl-1 promoter attenuated PDGF effects on Mcl-1 expression. Blockade of PDGF receptor (PDGFR) signaling with a pharmacological inhibitor AG-17 abrogated PDGF induction of Mcl-1, and induced apoptosis in metastatic PCa cells.Our study elucidated a crucial survival mechanism in PCa cells, indicating that interruption of the PDGF-Mcl-1 survival signal may provide a novel strategy for treating PCa metastasis.