Search

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.

Semi-supervised node classification is a crucial challenge in relational data mining and has attracted increasing interest in research on graph neural networks (GNNs). However, previous approaches merely utilize labeled nodes to supervise the overall optimization, but fail to sufficiently explore the information of their underlying label distribution. Even worse, they often overlook the robustness of models, which may cause instability of network outputs to random perturbations. To address the aforementioned shortcomings, we develop a novel framework termed Hybrid Curriculum Pseudo-Labeling (HCPL) for efficient semi-supervised node classification. Technically, HCPL iteratively annotates unlabeled nodes by training a GNN model on the labeled samples and any previously pseudo-labeled samples, and repeatedly conducts this process. To improve the model robustness, we introduce a hybrid pseudo-labeling strategy that incorporates both prediction confidence and uncertainty under random perturbations, therefore mitigating the influence of erroneous pseudo-labels. Finally, we leverage the idea of curriculum learning to start from annotating easy samples, and gradually explore hard samples as the iteration grows. Extensive experiments on a number of benchmarks demonstrate that our HCPL beats various state-of-the-art baselines in diverse settings. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip and collect. Among various multi-behavior recommendation methods, non-sampling methods have shown superiority over negative sampling methods. However, two observations are usually ignored in existing state-of-the-art non-sampling methods based on binary regression: (1) users have different preference strengths for different items, so they cannot be measured simply by binary implicit data; (2) the dependency across multiple behaviors varies for different users and items. To tackle the above issue, we propose a novel non-sampling learning framework named Criterion-guided Heterogeneous Collaborative Filtering (CHCF). CHCF introduces both upper and lower thresholds to indicate selection criteria, whichwill guide user preference learning. Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior. We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by the CHCF learning framework in a non-sampling form effectively. Extensive experiments on three real-world datasets show the effectiveness of CHCF in heterogeneous scenarios. [ABSTRACT FROM AUTHOR]

Nearest neighbor search aims at obtaining the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods. In this survey, we detailedly investigate current deep hashing algorithms including deep supervised hashing and deep unsupervised hashing. Specifically, we categorize deep supervised hashing methods into pairwise methods, rankingbased methods, pointwise methods as well as quantization according to how measuring the similarities of the learned hash codes. Moreover, deep unsupervised hashing is categorized into similarity reconstruction-based methods, pseudo-label-based methods, and prediction-free self-supervised learning-based methods based on their semantic learning manners. We also introduce three related important topics including semi-supervised deep hashing, domain adaption deep hashing, and multi-modal deep hashing. Meanwhile, we present some commonly used public datasets and the scheme to measure the performance of deep hashing algorithms. Finally, we discuss some potential research directions in conclusion. [ABSTRACT FROM AUTHOR]

Under the fierce market competition and the demand of low-carbon economy, the freshness of fresh products directly determines the degree of customer satisfaction. Cold chain logistics companies must pay attention to the freshness and carbon emissions of fresh products to obtain better service development. In the cold chain logistics path optimization problem, considering the cost, product freshness and carbon emission environmental factors at the same time, based on the cost-benefit idea, a comprehensive cold chain vehicle routing problem optimization model is proposed to minimize the unit cost of product freshness and the carbon trading mechanism for calculating the cost of carbon emissions. The improved adaptive chaotic ant colony algorithm is used to perform calculation experiments on the model, and the feasibility and effectiveness of the model are analyzed through classic examples and actual cases, which proves the feasibility of the model and the effectiveness of the algorithm. This model enriches the logistics and distribution of cold chain optimization studies, and the research results supplement the impact of carbon prices on carbon emissions, emissions and customer satisfaction. Finally, there are some practical enlightenments for the provision of business management and government services. [ABSTRACT FROM AUTHOR]

With the rapid development of the fresh cold chain logistics distribution and the prevalence of low carbon concept, this paper proposed an optimization model of low carbon fresh cold chain logistics distribution route considering customer satisfaction, and combined with time, space, weight, distribution rules and other constraints to optimize the distribution model. At the same time, transportation cost, penalty cost, overloading cost, carbon tax cost and customer satisfaction were considered as the components of the objective function, and the thought of cost efficiency was taken into account, so as to establish a distribution model based on the ratio of minimum total cost to maximum satisfaction as the objective function. Then, the improved A* algorithm and ant colony algorithm were used to construct the model solution. Through the simulation analysis results of different calculation examples, the effectiveness, efficiency and correctness of the design of the single target low-carbon fresh agricultural products cold chainmodel by using the improved ant colony algorithm were verified. [ABSTRACT FROM AUTHOR]

With the improvement of enterprise services, location-routing-inventory problem with time window constraint (LRIPTW) has become an essential problem in cold chain logistics network (CCLN). This paper aims to optimize the location cost, inventory cost, transportation cost, and penalty cost in CCLN simultaneously. Firstly, an optimization model was established for the LRIP with soft time window constraint (STW). Then, the multi-objective ant colony optimization (MACO) was improved to solve the model. Simulation results show that the improved MACO can solve the LRIPSTW effectively and efficiently. The research findings provide a reference for enterprises to reduce total cost and improve service quality. [ABSTRACT FROM AUTHOR]

Due to emissions from power station using fossil fuels, the decrease of existing pollution as well as of operational costs should be taken into consideration, when resolving environmental economic dispatch problems. In this research, we will evidence that nonlinear constraints of generating units, forbidden regions, and ramp-rate of generating units will reduce operational costs and environmental pollution, to achieve environmental economic dispatch effectiveness, by employing an improved multi-objective optimization algorithm based on human social behavior. With reward and penalty learning factors leading to excellent particles matting and optimization capability to achieve optimal solution, data transactions among particles have been conducted in the suggested approach. To get a more effective comparable result from the recommended algorithm, we conducted simulation experiments on IEEE 10-bus power systems in different load levels. Then we compared the outcomes with those other algorithms that were validated. The results show that the proposed algorithm can achieve diverse Pareto optimal solutions, fast convergence and high robustness, and unlikely to be trapped in local minima. It is revealed that the proposed technique is superior in terms of accuracy and speed in solving power system complex problems over the other methods. [ABSTRACT FROM AUTHOR]

The translation initiation factor eIF4E mediates a rate-limiting process that drives selective translation of many oncongenic proteins such as cyclin D1, survivin and VEGF, thereby contributing to tumour growth, metastasis and therapy resistance. As an essential regulatory hub in cancer signalling network, many oncogenic signalling pathways appear to converge on eIF4E. Therefore, targeting eIF4E-mediated cap-dependent translation is considered a promising anticancer strategy. This paper reviews the strategies that can be used to target eIF4E, highlighting agents that target eIF4E activity at each distinct level. [ABSTRACT FROM AUTHOR]

Background: 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. Methodology/Principal Findings: 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 hypoxiainducible 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. Conclusions/Significance: 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. [ABSTRACT FROM AUTHOR]

Background: Myeloid cell leukemia-1 (Mcl-1) is a member of the Bcl-2 family, which inhibits cell apoptosis by sequestering pro-apoptotic proteins Bim and Bid. Mcl-1 overexpression has been associated with progression in leukemia and some solid tumors including prostate cancer (PCa). However, the regulatory mechanism for Mcl-1 expression in PCa cells remains elusive. Results: Immunohistochemical analyses revealed that Mcl-1 expression was elevated in PCa specimens with high Gleason grades and further significantly increased in bone metastasis, suggesting a pivotal role of Mcl-1 in PCa metastasis. We further found that vascular endothelial growth factor (VEGF) is a novel regulator of Mcl-1 expression in PCa cells. Inhibition of endogenous Mcl-1 induced apoptosis, indicating that Mcl-1 is an important survival factor in PCa cells. Neuropilin-1 (NRP1), the "co-receptor" for VEGF165 isoform, was found to be highly expressed in PCa cells, and indispensible in the regulation of Mcl-1. Intriguingly, VEGF165 promoted physical interaction between NRP1 and hepatocyte growth factor (HGF) receptor c-MET, and facilitated c-MET phosphorylation via a NRP1-dependent mechanism. VEGF165 induction of Mcl-1 may involve rapid activation of Src kinases and signal transducers and activators of transcription 3 (Stat3). Importantly, NRP1 overexpression and c-MET activation were positively associated with progression and bone metastasis in human PCa specimens and xenograft tissues. Conclusions: This study demonstrated that Mcl-1 overexpression is associated with PCa bone metastasis. Activation of VEGF165-NRP1-c-MET signaling could confer PCa cells survival advantages by up-regulating Mcl-1, contributing to PCa progression. [ABSTRACT FROM AUTHOR]

Prostatic glandular epithelial cells express protein kinase C? (PKC?), an oncoprotein that coordinately disrupts the reactivation of the tumor suppressor Rb, derepressess transcriptional elongation of the c-myc oncogene, and propagates survival signals in LNCaP cells. Since the activation of such a program may contribute to the progression of human prostate cancer, a proteomic analysis was performed to gain a more global perspective on the signaling network that PKC? might be capable of engaging in prostate cancer cells. Using CWR22 xenografts, we identified at least 18 different structural, signaling, and stress-related proteins that associated with PKC?, including an interaction with the proapoptotic protein Bax that was novel to recurrent CWR22 tumors. An investigation into the biological significance of the PKC? association with Bax provided the first evidence of an inverse relationship between endogenous levels of PKC? and susceptibility of prostate cancer cells to the apoptotic effects of phorbol esters. Western blot and antisense experiments demonstrated that CWR-R1 cells expressed moderate levels of PKC? and relied on this protein to survive in the presence of phorbol esters, while the apoptosis normally induced by phorbol esters in PKC?-deficient LNCaP cells was dependent on the presence of Bax. Forced expression of PKC? in LNCaP cells was sufficient to confer a significant resistance to phorbol esters and this resistance was associated with an inhibition of phorbol ester-induced Bax conformational rearrangements that are important for Bax oligomerization, mitochondrial integration, and cytochrome c release. Considered in their entirety, our data suggest that an association of PKC? with Bax may neutralize apoptotic signals propagated through a mitochondrial death-signaling pathway.Oncogene (2003) 22, 6014-6024. doi:10.1038/sj.onc.1206795 [ABSTRACT FROM AUTHOR]

A biodegradable polymer network hydrogel with both hydrophobic and hydrophilic components was synthesized and characterized. The hydrophobic and hydrophilic components were a three-arm poly(ε-caprolactone) maleic acid (PGCL-Ma, as the hydrophobic constituent) and poly(ethylene glycol) diacrylate macromer (PEGDA, as a hydrophilic constituent), respectively. These two polymers were chemically photo-crosslinked to generate a three-dimensional network structure, which were characterized by FT-IR, DSC and SEM. The swelling property of the networks was studied in phosphate-buffered saline (PBS, pH 7.4). The results of this study showed that a wide-range swelling property was obtained by changing the composition ratio of PGCL-Ma to PEGDA. The in vitro release of bovine serum albumin (BSA) from these hydrogels as a function of the PEGDA to PGCL-Ma composition ratio and incubation time was examined and we found that the incorporation of PEGDA into PGCL-Ma increased the initial burst release of BSA. As the PEGDA component increased, the rate of formation of a loose three-dimensional (3D) network structure increased; consequently, the sustained rate and extent of BSA release increased. We suggest that the release of BSA was controlled by both diffusion of BSA through swelling of the hydrophilic phase during an early stage and degradation of the hydrophobic phase during a late stage; and that the relative magnitude of diffusion versus degradation controlled release depended on composition ratio and immersion time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]