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Community search over large dynamic graph has become an important research problem in modern complex networks, such as the online social network, collaboration network and biological networks. Network data in the time-varied environment has motivated several recent studies to identify the evolution of the communities. However, these studies mostly match communities of different snapshot or utilize the aggregation of the disjoint structural information and ignores the cohesion continuity. To fill this research gap, in this work, we propose a novel $(\theta,k)$(θ,k)-core reliable community (CRC) and define the reliable community search problem which jointly considers member engagement, connection strength and cohesion continuity of the community in the dynamic network. We propose an online search algorithm based on eligible edge filtering and we further construct the Weighted Core Forest-Index (WCF-index) and develop efficient index-based querying algorithm with strong pruning properties. We also propose top-$l$l reliable community search problem that couples query based distance to reduce the free rider effect in local community search and support flexible multiple query vertices. Extensive experiments are conducted to show the efficiency and effectiveness of the proposed algorithms.

Treatment-induced neuroendocrine prostate cancer (t-NEPC) often arises from adenocarcinoma vialineage plasticity in response to androgen receptor signaling inhibitors, such as enzalutamide. However, the specific regulators and targets involved in the transition to NEPC are not well understood. Plexin D1 (PLXND1) is a cellular receptor of the semaphorin (SEMA) family that plays important roles in modulating the cytoskeleton and cell adhesion. Here, we found that PLXND1 was highly expressed and positively correlated with neuroendocrine markers in patients with NEPC. High PLXND1 expression was associated with poorer prognosis in prostate cancer patients. Additionally, PLXND1 was upregulated and negatively regulated by androgen receptor signaling in enzalutamide-resistant cells. Knockdown or knockout of PLXND1 inhibited neural lineage pathways, thereby suppressing NEPC cell proliferation, patient derived xenograft (PDX) tumor organoid viability, and xenograft tumor growth. Mechanistically, the heat shock protein 70 (HSP70) regulated PLXND1 protein stability through degradation, and inhibition of HSP70 decreased PLXND1 expression and NEPC organoid growth. In summary, our findings indicate that PLXND1 could serve as a promising therapeutic target and molecular marker for NEPC.

Castration-resistant prostate cancer (CRPC) is an aggressive disease with poor prognosis, and there is an urgent need for more effective therapeutic targets to address this challenge. Here, we showed that dihydroorotate dehydrogenase (DHODH), an enzyme crucial in the pyrimidine biosynthesis pathway, is a promising therapeutic target for CRPC. The transcript levels of DHODH were significantly elevated in prostate tumors and were negatively correlated with the prognosis of patients with prostate cancer. DHODH inhibition effectively suppressed CRPC progression by blocking cell cycle progression and inducing apoptosis. Notably, treatment with DHODH inhibitor BAY2402234 activated androgen biosynthesis signaling in CRPC cells. However, the combination treatment with BAY2402234 and abiraterone decreased intratumoral testosterone levels and induced apoptosis, which inhibited the growth of CWR22Rv1 xenograft tumors and patient-derived xenograft organoids. Taken together, these results establish DHODH as a key player in CRPC and as a potential therapeutic target for advanced prostate cancer.

Fastest route recommendation (FRR) is crucial for intelligent transportation systems. The existing methods treat it as a pathfinding problem on dynamic graphs, and extend A* algorithm with neuralized travel time estimators as cost functions. However, they fail to provide effective heuristic cost due to the neglect of its admissibility and the utilization of noise path information, resulting in sub-optimal results and inefficiency. Besides, path sequentiality is also ignored, affecting algorithm accuracy as well. In this paper, we propose a variational inference based fastest route recommendation method, which follows the framework of A* algorithm and provides effective costs for routing. Specifically, we first adopt a sequential estimator to accurately estimate the travel time of a specific path. More importantly, we design a variational inference based estimator, which models the distribution of travel time between two nodes and provides an effective heuristic cost with high probability of being admissible. We further take advantage of adversarial learning to enrich the fastest path information. To the best of our knowledge, we are the first to use variational estimator to consider the admissibility of heuristics in FRR. Extensive experiments are conducted on two real-world datasets. The results verify the performance advantage of our proposed method.

There are extensive studies focusing on the application scenario that all the bipartite cohesive subgraphs need to be discovered in a bipartite graph. However, we observe that, for some applications, one is interested in finding bipartite cohesive subgraphs containing a specific vertex. In this paper, we study a new query-dependent bipartite cohesive subgraph search problem based on $k$k-wing model, named as personalized $k$k-wing search problem. We study the $k$k-wing equivalence relationship to summarize the edges of a bipartite graph $G$G into groups. Therefore, all the edges of $G$G are segregated into different groups, i.e., $k$k-wing equivalence class, forming an efficient and wing number conserving index called EquiWing-Graph. Further, we propose a more compact index, EquiWing-Tree, which is achieved by using our proposed $k$k-butterfly loose approach and discovered hierarchy properties. These indices are used to expedite the personalized $k$k-wing search with a non-repetitive access to $G$G, which leads to linear algorithms for searching the personalized $k$k-wing. Moreover, we conduct a thorough study on the maintenance of the proposed indices for evolving bipartite graphs. We discover novel properties that help us localize the scope of the maintenance at a low cost. By exploiting the discoveries, we propose novel algorithms for maintaining the two indices, which substantially reduces the cost of maintenance. We perform extensive experimental studies in real-world graphs to validate the efficiency and effectiveness of EquiWing-Graph and EquiWing-Tree compared to the baseline.

Castration-resistant prostate cancer (CRPC) is the main driving force of mortality in prostate cancer patients. Among the parameters contributing to the progression of CRPC and treatment failure, elevation of the steroidogenic enzyme AKR1C3 and androgen receptor variant 7 (AR-V7) are frequently reported. The AKR1C3/AR-V7 complex has been recognized as a major driver for drug resistance in advanced prostate cancer. Herein we report that the level of AKR1C3 is reciprocally regulated by the full-length androgen receptor (AR-FL) through binding to the distal enhancer region of the AKR1C3 gene. A novel function of PTUPB in AKR1C3 inhibition was discovered and PTUPB showed more effectiveness than indomethacin and celecoxib in suppressing AKR1C3 activity and CRPC cell growth. PTUPB synergizes with enzalutamide treatment in tumor suppression and gene signature regulation. Combination treatments with PTUPB and enzalutamide provide benefits by blocking AR/AR-V7 signaling, which inhibits the growth of castration relapsed VCaP xenograft tumors and patient-derived xenograft organoids. Targeting of the ARK1C3/AR/AR-V7 axis with PTUPB and enzalutamide may overcome drug resistance to AR signaling inhibitors in advanced prostate cancer.

Overexpressed P-glycoprotein (P-gp), as the key factor in multidrug resistance (MDR), can greatly reduce intracellular drug levels as a result of chemotherapeutic drug efflux out of cancer cells. Although various P-gp antagonists have been developed to circumvent the MDR effect, the common strategies of physical entrapment or chemical conjugation of anticancer drugs may inevitably induce unstable circulation in vivo or poor response in cancer cells, greatly limiting the therapeutic efficacy. Herein a double-drug-based supramolecular complex (DDSC) was first constructed based on the host–guest interactions between two active drugs, curcumin (Cur)-bridged bis(β-cyclodextrin) and ferrocene-linked camptothecin (CPT). Supramolecular dual drug nanomicelles (SDDNMs) formed by the DDSC self-assembly are proposed to work against the MDR effect, in which Cur as a P-gp antagonist in combination with CPT realized stable transport in vivo and efficient stimuli-responsiveness in cells. In vitro and in vivo studies further confirmed that SDDNMs effectively circumvented the drug efflux induced by the MDR effect and remarkably enhanced the synergistic therapeutic effects.

Targeting androgen signaling with the second-generation anti-androgen drugs, such as enzalutamide (Enza), abiraterone (Abi), apalutamide (Apal), and darolutamide (Daro), is the mainstay for the treatment of castration-resistant prostate cancer (CRPC). While these treatments are effective initially, resistance occurs frequently. Continued expression of androgen receptor (AR) and its variants such as AR-V7 despite AR-targeted therapy contributes to treatment resistance and cancer progression in advanced CRPC patients. This highlights the need for new strategies blocking continued AR signaling. Here, we identify a novel AR/AR-V7 degrader (ARVib) and found that ARVib effectively degrades AR/AR-V7 protein and attenuates AR/AR-V7 downstream target gene expression in prostate cancer cells. Mechanistically, ARVib degrades AR/AR-V7 protein through the ubiquitin-proteasome pathway mediated by HSP70/STUB1 machinery modulation. ARVib suppresses HSP70 expression and promotes STUB1 nuclear translocation, where STUB1 binds to AR/AR-V7 and promotes its ubiquitination and degradation. ARVib significantly inhibits resistant prostate tumor growth and improves enzalutamide treatment in vitro and in vivo. These data suggest that ARVib has potential for development as an AR/AR-V7 degrader to treat resistant CRPC.

Supramolecular drug self-delivery systems (SDSDSs) involving active drugs as building blocks linked by supramolecular interactions have been well defined as an advanced chemotherapy strategy. However, the lack of detecting release of drugs from SDSDSs at specific tumor sites inevitably leads to unsatisfactory therapeutic effects, owing to the lack of information regarding the administration of these drugs. In this work, predesigned platinum-containing supramolecular drug self-delivery nanomicelles (SDSDNMs) were employed to synchronously realize drug monitoring by computed tomography imaging, immediately reflecting the evolution of drug release and real-time treatment at the tumor site. The appropriate administration dosage (1.2 mg mL–1,100 μL) and the injection interval (once every 3 days) needed to guide the antitumor activity of SDSDNMs were then defined, thereby attaining the aim of efficient synergistic combination chemotherapy. In vivotumor inhibition and histological analyses showed that SDSDNMs exhibited a strong tumor inhibition effect and good safety with respect to normal organs. Such a supramolecular drug self-delivery strategy with monitored functions may offer new potential opportunities for application in the field of synergistic combination chemotherapy.

Microseismic source location is crucial for the early warning of rockburst risks. However, the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy. Intelligent techniques, such as the full convolutional neural network (FCNN), can capture spatial information but struggle with complex microseismic sequence. Combining the FCNN with the long short-term memory (LSTM) network enables better time-series signal classification by integrating multi-scale information and is therefore suitable for waveform location. The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction. In this study, we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus. Initially, the method of short-time-average/long-time-average (STA/LTA) arrival time picking was employed to augment spatiotemporal information. Subsequently, oversampling the on-site data was performed to address the issue of data imbalance, and finally, the performance of LSTM-FCNN was tested. Meanwhile, we compared the LSTM-FCNN model with previous deep-learning models. Our results demonstrated remarkable location capabilities with a mean absolute error (MAE) of only 7.16 m. The model can realize swift training and high accuracy, thereby significantly improving risk warning of rockbursts.

Drug–drug self-delivery systems serving as both carriers and cargos have been explored as advanced combination chemotherapy strategies to overcome the limitations of the traditional single-drug chemotherapy. However, most known drug–drug self-delivery systems may cause a rapid increase in drug concentration when the single covalent bond is broken, thus leading to high toxicity to organs and low therapeutic efficiency against tumors. To address the above problem, in this study, a novel supramolecular drug–drug complex (SDDC) simultaneously containing both covalent and noncovalent bonds was proposed to realize the sequential release of two drugs in tumor cells for enhanced combination therapy. The SDDC could self-assemble into uniform bilayer supramolecular vesicles (SVs) with a remarkable drug loading capacity and stable drug transport. Notably, the SVs with controlled sequential release ability in tumor cells exhibited a superior synergistic effect and significantly improved therapeutic efficiency with reduced toxicity in in vivoantitumor activity and histological analyses in comparison to either individual free drugs or a mixture of two free drugs. Therefore, by combining the advantages of noncovalent interactions with the dynamic nature and stable covalent bonds, this study opens a new way for cancer therapy.

Castration-resistant prostate cancer remains as an incurable disease. Exploiting DNA damage repair defects via inhibition of poly (ADP-ribose) polymerase (PARP) is becoming an attractive therapeutic option. The TOPARP-A clinical trial demonstrated that the PARP inhibitor olaparib may be an effective strategy for treating prostate cancer. However, several unanswered questions regarding the use of olaparib remain: 1) How do we best stratify patients for olaparib treatment? 2) Where do we place olaparib in the treatment sequence paradigm? 3) Is there cross-resistance between olaparib and currently used therapies? Here, we tested putative cross-resistance between current therapies and olaparib in treatment-resistant castration-resistant prostate cancer models. Docetaxel-resistant cells exhibited robust resistance to olaparib which could be attributed to blunted PARP trapping in response to olaparib treatment. Upregulated ABCB1 mediates cross-resistance between taxanes and olaparib, which can be overcome through decreasing ABCB1 expression or inhibiting ABCB1 using elacridar or enzalutamide. We also show that combining olaparib with enzalutamide is more effective in olaparib-sensitive cells than either single agent. Our results demonstrate that cross-resistance between olaparib and other therapies could blunt response to treatment and highlight the need to develop strategies to maximize olaparib efficacy.

The problem of k-truss search has been well defined and investigated to find the highly correlated user groups in social networks. But there is no previous study to consider the constraint of users' spatial information in k-truss search, denoted as co-located community searchin this paper. The co-located community can serve many real applications. To search the maximum co-located communities efficiently, we first develop an efficient exact algorithm with several pruning techniques. After that, we further develop an approximation algorithm with adjustable accuracy guarantees and explore more effective pruning rules, which can reduce the computational cost significantly. To accelerate the real-time efficiency, we also devise a novel quadtree based index to support the efficient retrieval of users in a region and optimise the search regions with regards to the given query region. Finally, we verify the performance of our proposed algorithms and index using five real datasets.

In this paper, we report previously unknown roles for collectin-11 (CL-11, a soluble C-type lectin) in modulating the retinal pigment epithelial (RPE) cell functions of phagocytosis and cytokine production. We found that CL-11 and its carbohydrate ligand are expressed in both the murine and human neural retina; these resemble each other in terms of RPE and photoreceptor cells. Functional analysis of murine RPE cells showed that CL-11 facilitates the opsonophagocytosis of photoreceptor outer segments and apoptotic cells, and also upregulates IL-10 production. Mechanistic analysis revealed that calreticulin on the RPE cells is required for CL-11-mediated opsonophagocytosis whereas signal-regulatory protein α and mannosyl residues on the cells are involved in the CL-11-mediated upregulation of IL-10 production. This study is the first to demonstrate the role of CL-11 and the molecular mechanisms involved in modulating RPE cell phagocytosis and cytokine production. It provides a new insight into retinal health and disease and has implications for other phagocytic cells.

Drug self‐delivery systems (DSDSs) offer new ways to create novel drug delivery systems (DDSs). In typical DSDSs, therapeutic reagents are not considered passive cargos but active delivery agents of actionable targets. As an advanced drug delivery strategy, DSDSs with positive cooperativity of both free drugs and nanocarriers exhibit the clear merits of unprecedented drug‐loading capacity, minimized systemic toxicity, and flexible preparation of nanoscale deliverables for passive targeted therapy. This review highlights the recent advances and future trends in DSDSs on the basis of two differently constructed structures: covalent and noncovalent bond‐based DSDSs. Specifically, various chemical and architectural designs, fabrication strategies, and responsive and functional features are comprehensively discussed for these two types of DSDSs. In addition, additional comments on the current development status of DSDSs and the potential applications of their molecular designs are presented in the corresponding discussion. Finally, the promising potential of DSDSs in biological applications is revealed and the relationship between preliminary molecular design of DSDSs and therapeutic effects of subsequent DSDSs biological applications is clarified. This review highlights the recent advances and future trends in drug self‐delivery systems (DSDSs) on the basis of two differently constructed structures: covalent and noncovalent bond‐based DSDSs. Various chemical and architectural designs, fabrication strategies, and responsive and functional features are comprehensively discussed for these two types of DSDSs.

TWEAK participates in various cellular effects by engaging its receptor of Fn14. Increased levels of soluble TWEAK are associated with systemic autoimmunity in patients with lupus erythematosus, rheumatoid arthritis, or dermatomyositis. However, the role of TWEAK in bullous pemphigoid (BP) remains unknown. In this study, we found an elevated serum level of TWEAK and a positive correlation between serum TWEAK and anti-BP180 antibodies. Immunohistochemistry showed strong TWEAK and Fn14 expression and implied an opposite relationship between the TWEAK and BP180 expression in skin samples from BP patients. In vitro TWEAK stimuli reduced BP180 expression in HaCaT cells and inhibited the adhesion of cells to the culture dish. Consistently, the transfection of Fn14 small interfering RNA preserved BP180 and protected cells from losing adherence. Moreover, such effect of TWEAK correlated with activation of the extracellular signal-regulated kinase and NF-κB pathways and downstream ADAMs. By silencing ADAM17 with small interfering RNA, we showed that ADAM17 participated in TWEAK-induced BP180 loss. Therefore, TWEAK may contribute to the pathogenesis of BP by reducing BP180 expression and cellular adherence, involving the activation of ERK and NF-κB pathways. TWEAK may serve as a biomarker or therapeutic target of BP.

ObjectiveTo evaluate the quality of evidence, potential biases, and validity of all available studies on dietary sugar consumption and health outcomes.DesignUmbrella review of existing meta-analyses.Data sourcesPubMed, Embase, Web of Science, Cochrane Database of Systematic Reviews, and hand searching of reference lists.Inclusion criteriaSystematic reviews and meta-analyses of randomised controlled trials, cohort studies, case-control studies, or cross sectional studies that evaluated the effect of dietary sugar consumption on any health outcomes in humans free from acute or chronic diseases.ResultsThe search identified 73 meta-analyses and 83 health outcomes from 8601 unique articles, including 74 unique outcomes in meta-analyses of observational studies and nine unique outcomes in meta-analyses of randomised controlled trials. Significant harmful associations between dietary sugar consumption and 18 endocrine/metabolic outcomes, 10 cardiovascular outcomes, seven cancer outcomes, and 10 other outcomes (neuropsychiatric, dental, hepatic, osteal, and allergic) were detected. Moderate quality evidence suggested that the highest versus lowest dietary sugar consumption was associated with increased body weight (sugar sweetened beverages) (class IV evidence) and ectopic fatty accumulation (added sugars) (class IV evidence). Low quality evidence indicated that each serving/week increment of sugar sweetened beverage consumption was associated with a 4% higher risk of gout (class III evidence) and each 250 mL/day increment of sugar sweetened beverage consumption was associated with a 17% and 4% higher risk of coronary heart disease (class II evidence) and all cause mortality (class III evidence), respectively. In addition, low quality evidence suggested that every 25 g/day increment of fructose consumption was associated with a 22% higher risk of pancreatic cancer (class III evidence).ConclusionsHigh dietary sugar consumption is generally more harmful than beneficial for health, especially in cardiometabolic disease. Reducing the consumption of free sugars or added sugars to below 25 g/day (approximately 6 teaspoons/day) and limiting the consumption of sugar sweetened beverages to less than one serving/week (approximately 200-355 mL/week) are recommended to reduce the adverse effect of sugars on health.Systematic review registrationPROSPERO CRD42022300982.

Polymerization inside living cells provides chemists with a multitude of possibilities to modulate cell activities. Considering the advantages of hyperbranched polymers, such as a large surface area for target sites and multilevel branched structures for resistance to the efflux effect, we reported a hyperbranched polymerization in living cells based on the oxidative polymerization of organotellurides and intracellular redox environment. The intracellular hyperbranched polymerization was triggered by reactive oxygen species (ROS) in the intracellular redox microenvironment, effectively disrupting antioxidant systems in cells by an interaction between Te (+4) and selenoproteins, thus inducing selective apoptosis of cancer cells. Importantly, the obtained hyperbranched polymer aggregated into branched nanostructures in cells, which could effectively evade drug pumps and decrease drug efflux, ensuring the polymerization for persistent treatment. Finally, in vitroand in vivostudies confirmed that our strategy presented selective anticancer efficacy and well biosafety. This approach provides a way for intracellular polymerization with desirable biological applications to regulate cell activities.

The infections and diseases caused by harmful bacteria are regarded as the worldwide health-threatening issue. The most antibiotics-based delivery systems restricted the controlled drug release at specific infection sites due to the stable chemical conjugation, thus largely compromising the treatment effectiveness. Herein, we report the construction of light controlled drug-based supramolecular polymer (LDSP), which is endowed the “switch on” function for realizing the controllable antibiotics released in specific sites. LDSPwas first formed based on the host-guest interaction between β-cyclodextrin units grafted hyaluronic acid chain (HA-CD) and azobenzene moiety linked by two enoxacin units (Azo-(Eno)2), and could further self-assemble into light controlled drug-based supramolecular polymer self-assemblies (LDSPSAs). Under the UV light irradiation, the “switch on” function of LDSPSAscould be effectively turned on at the specific infected tissues due to the smartly dissociated host-guest interaction, facilitating the controlled release of the hydrophobic Azo-(Eno)2to interact with bacteria for efficient antibacterial effect. The biological evaluation demonstrated that LDSPSAsachieved good antibacterial efficiency and well biosafety.

In the context of business process management, the resources required by business processes, such as workshop staff, manufacturing machines, etc., tend to follow certain availability patterns, due to maintenance cycles, work shifts and other factors. Such availability patterns heavily influence the efficiency and effectiveness of enterprise resource management. Most existing process scheduling and resource management approaches tend to tune the process structure to seek better resource utilisation, yet neglect the constraints on resource availability. In this article, we investigate the scheduling of business process instances in accordance with resource availability patterns, to find out how enterprise resources can be rationally and sufficiently used. Three heuristic-based planning strategies are proposed to maximise the process instance throughput together with another strategy based on a genetic algorithm. The performance of these strategies has been evaluated by conducting experiments of different settings and analysing the strategy characteristics.

With urban population rapidly growing, traffic congestion is becoming a major problem. In this paper, a framework is proposed for identifying the spatial congested partitions in a dynamic urban road network and for monitoring the temporal changes in their locations and structure. To that end, a given road network is transformed into a suitable graph representation, an initial partitioning based on a spectral clustering approach is performed, and then the partitions continue to be updated incrementally on the basis of the newly obtained traffic data at each new time point. The congested partitions are then identified on the basis of traffic measures (e.g., volume and green time utilization) available from the traffic signal control system. Experiments with the proposed method are conducted with real historical traffic data collected from the 493 signalized traffic sites in Melbourne, Victoria, Australia, with a total of 1,444 road segments and 581 intersection points. Experimental results show that large-scale urban traffic networks undergo many rapid but regular and frequent traffic patterns, which often go unnoticed by the traffic network operators. Tracking these kinds of changes in real time by means of the proposed framework can improve the reaction time of the traffic management team and result in less congestion.

The process view concept deploys a partial and temporal representation to adjust the visible view of a business process according to various perception constraints of users. Process view technology is of practical use for privacy protection and authorization control in process-oriented business management. Owing to complex organizational structure, it is challenging for large companies to accurately specify the diverse perception of different users over business processes. Aiming to tackle this issue, this article presents a role-based process view model to incorporate role dependencies into process view derivation. Compared to existing process view approaches, ours particularly supports runtime updates to the process view perceivable to a user with specific view merging operations, thereby enabling the dynamic tracing of process perception. A series of rules and theorems are established to guarantee the structural consistency and validity of process view transformation. A hypothetical case is conducted to illustrate the feasibility of our approach, and a prototype is developed for the proof-of-concept purpose.

Abstract: More and more XML data is generated and used for data exchange. In this paper, we address the problem of filtering XML documents with large number of XPath expressions, which may contain ‘ancestor’ and ‘parent’ axes. XPath expressions with these axes are more powerful and flexible for users to describe their interests in publish/subscribe systems. First, we analyze the characteristics of the ‘parent’ axis and propose a series of rules to eliminate it in XPath expressions. Then we propose a new index structure called NIndex, which is designed to efficiently store and index large number of XPath expressions. NIndex offers several features which make it especially attractive for the large scale selective dissemination of information, including the ability to handle complex XPath expressions with ‘ancestor’ and ‘parent’ axes, and efficient pruning. Based on NIndex, we design a new filtering algorithm with low complexity for our problem. Our experiment results show that our algorithm performs well across a range of XPath expressions and documents. [Copyright &y& Elsevier]

Androgen receptor (AR) signaling not only plays a pivotal role in the development of androgen-dependent prostate cancer but is also important in the growth and survival of castration-resistant prostate cancer (CRPC). The first line of treatment of androgen-dependent prostate cancer is the use of androgen deprivation therapy. However, most patients will eventually relapse due to development of CRPC. Thus, development of a strategy to target AR for treatment of CRPC is urgently needed. The authors have previously identified andrographolide as an inhibitor of interleukin-6, which can suppress tumor growth of prostate cancer cells by screening compounds from the Prestwick Natural compound library. In this study, they identified that andrographolide can inhibit AR expression and prostate cancer cell growth and induce apoptosis. Andrographolide is able to down-regulate AR expression at both mRNA and protein levels, prevents its nuclear translocation, and inhibits transactivation of its target genes. Andrographolide prevents the binding of Hsp90 to AR, resulting in proteasome-mediated AR degradation. Furthermore, andrographolide inhibits castration-resistant C4-2 cell growth by reducing AR expression and activity. Thus, andrographolide can be developed as a potential therapeutic agent for prostate cancer by inhibition of androgen receptor signaling.

Elevated interleukin-6 (IL-6), a major mediator of the inflammatory response, has been implicated in androgen receptor (AR) activation, cellular growth and differentiation, plays important roles in the development and progression of prostate cancer, and is a potential target in cancer therapy. Through drug screening using human prostate cancer cells expressing IL-6 autocrine loop, we found that andrographolide, a diterpenoid lactone isolated from a traditional Chinese and Indian medicinal plant Andrographis paniculata, could inhibit IL-6 expression and suppress IL-6–mediated signals. Andrographolide inhibits IL-6 expression at both mRNA and protein levels in a dose-dependent manner. Andrographolide suppresses both IL-6 autocrine loop– and paracrine loop–induced cell signaling including Stat3 and Erk phosphorylation. Furthermore, andrographolide inhibits cell viability and induces apoptotic cell death in both androgen-stimulated and castration-resistant human prostate cancer cells without causing significant toxicity to normal immortalized prostate epithelial cells. Moreover, treatment of andrographolide to mice bearing castration-resistant DU145 human prostate tumors that express constitutive IL-6 autocrine loop significantly suppresses tumor growth. Taken together, these results demonstrate that andrographolide could be developed as a therapeutic agent to treat both androgen-stimulated and castration-resistant prostate cancer possibly by suppressing IL-6 expression and IL-6–induced signaling.

XML has become the standard for publishing and exchanging data on the Web. However, most business data is managed and will remain to be managed by relational database management systems. As such, there is an increasing need to efficiently and accurately publish relational data as XML documents for Internet-based applications. One way to publish relational data is to provide virtual XML documents for relational data via an XML schema which is transformed from the underlying relational database schema such that users can access the relational database through the XML schema. In this paper, we discuss issues in transforming a relational database schema into the corresponding XML schema. We aim to preserve all integrity constraints defined in a relational database schema, to achieve high level of nesting and to avoid introducing data redundancy in the transformed XML schema. In the paper, we first propose a basic transformation algorithm which introduces no data redundancy, then we improve the algorithm by exploring further nesting of the transformed XML schema.

In the paper, it is demonstrated how to apply sticker in the sticker-based model for constructing solution space of DNA for the set-basis problem and how to apply DNA operations in the Adleman-Lipton model to solve that problem from solution space of sticker. Furthermore, this work shows the ability of DNA-based computing for resolving the NP-complete problems.

The notion of compensation is widely used in advanced transaction models as means of recovery from a failure. Similar concepts are adopted for providing “transaction-like” behaviour for long business processes supported by workflows technology. In general, it is not trivial to design compensating tasks for tasks in the context of a workflow. Actually, a task in a workflow process does not have to be compensatable in the sense that the forcibility of “reverse” operations of the task is not always guaranteed by the application semantics. In addition, the isolation requirement on data resources may make a task difficult to compensate. In this paper, we first look into the requirements that a compensating task has to satisfy. Then we introduce a new concept called confirmation. With the help of confirmation, we are able to modify most non-compensatable tasks so that they become compensatable. This can substantially increase the availability of shared resources and greatly improve backward recovery for workflow applications in case of failures. To effectively incorporate confirmation and compensation into a workflow management environment, a three level bottom-up workflow design method is introduced. The implementation issues of this design are also discussed. [Copyright &y& Elsevier]

This paper introduces a heterogeneous distributed database system called LSZ system, where LSZ is an abbreviation of Li Shizhen, an ancient Chinese medical scientist. LSZ system adopts cluster as distributed database node (or site). Each cluster consists of one or several microcomputers and one server. The paper describes its basic architecture and the prototype implementation, which includes query processing and optimization, transaction manager and data language translation. The system provides a uniform retrieve and update user interface through global relational data language GRDL.

In the procedure of extracting hot topics and detecting emerging topic, counting term frequency is one of the most inevitable and time-consuming steps. For the purpose of text exploration, users may change the query range frequently, and the adjustment of ranges would cause recalculation of term frequency when finding hot terms, bringing unacceptable time cost. In addition, real-time update of dimensions is also a challenge. To address these problems, we first propose a novel data structure based on prefix cube to store terms and their frequencies, so that the time for counting term frequency gets a significant reduction. Based on the data structure, we propose an efficient range query algorithm that significantly decreases the number of input word lists involved in top-k queries. Considering the underlying dimension update, we also design an efficient maintenance mechanism to cope with different dimension updates. Finally, we conduct comprehensive experiments to validate the effectiveness of the proposed structure and the efficiency of the optimized query algorithm. We also prove that using the proposed data structure, the time cost of our algorithms in hot topic extraction and emerging topic detection can be reduced by about ten times compared with the previous algorithms. [ABSTRACT FROM AUTHOR]