Search

Intelligent early warning of rockburst hazards is critical for ensuring safe and efficient coal mining operations. The utilization of monitoring techniques, such as microseismic (MS), acoustic emission (AE), and electromagnetic radiation (EMR), has become standard practice for monitoring dynamic hazards in mining environments. However, the inherent complexity and unpredictability of the signals generated by these monitoring systems present significant challenges. While the application of deep-learning methods has gained traction in the field of coal-rock dynamic disaster management, their reliance on vast amounts of data and susceptibility to subjective labeling and poor generalization have hindered the achievement of timely, efficient, accurate, and comprehensive warning of rockburst hazards. In response to these challenges, this study applied an unsupervised learning method based on long short-term memory and an autoencoder to identify precursors of rockburst hazards and predict signals. The robustness and universality of the model were evaluated using MS, AE, and EMR data from the mine site. Then, the entropy method was used to comprehensively process the MS, AE, and EMR signals and conduct risk assessment. Finally, impressive results were achieved: the accuracy of precursor recognition reached 99.18% and the fitting rate of signal prediction reached 93%. Through on-site verification, the efficacy of this approach is evidenced by its synchronization with field records, enabling proactive responses to potential rockburst risks. This method is expected to enhance intelligent warning systems and ensure the safety of coal mine activities. [ABSTRACT FROM AUTHOR]

The migration and transformation of bromine in the pyrolysis process of copper-containing glass fiber resin (CGFR) under microwave heating have been studied, and the pyrolysis products analyzed. With the increase of microwave power, the solid-phase yield and the bromine content in the solid-phase product showed a trend of first decreasing and then increasing. Under the optimized conditions (700°C, 1000 W, 60 min), the solid yield was 74.41%, the bromine content in the solid product was 78 ppm, and the removal rate of bromine was 14.19% higher than that of conventional pyrolysis. Bromine mainly exists in organic form and is encapsulated in CGFR polymers. Microwave pyrolysis can heat the CGFR as a whole, promote the release of bromine in these organic inclusions, and ultimately improve the removal rate of bromine. However, the higher microwave power will cause the CGFR to violently pyrolyze, and the organic matter will carbonize, which will hinder the release of bromine. In this study, the migration and transformation mechanism of bromine in microwave pyrolysis of CGFR has been proposed, which proves that microwave pyrolysis plays a positive role in the recovery process of CGFR, improved its recovery and utilization efficiency. [ABSTRACT FROM AUTHOR]

As an immune checkpoint molecule, CD200 serves a foundational role in regulating immune homeostasis and promoting self-tolerance. While CD200 expression occurs in various immune cell subsets and normal tissues, its aberrant expression patterns in hematologic malignancies and solid tumors have been linked to immune evasion and cancer progression under pathological conditions, particularly through interactions with its cognate receptor, CD200R. Through this CD200/CD200R signaling pathway, CD200 exerts its immunosuppressive effects by inhibiting natural killer (NK) cell activation, cytotoxic T cell functions, and M1-polarized macrophage activity, while also facilitating expansion of myeloid-derived suppressor cells (MDSCs) and Tregs. Moreover, CD200/CD200R expression has been linked to epithelial-to-mesenchymal transition and distant metastasis, further illustrating its role in cancer progression. Conversely, CD200 has also been shown to exert anti-tumor effects in certain cancer types, such as breast carcinoma and melanoma, indicating that CD200 may exert bidirectional effects on cancer progression depending on the specific tumor microenvironment (TME). Regardless, modulating the CD200/CD200R axis has garnered clinical interest as a potential immunotherapeutic strategy for cancer therapy, as demonstrated by early-phase clinical trials. However, further research is necessary to fully understand the complex interactions of CD200 in the tumor microenvironment and to optimize its therapeutic potential in cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Visualizing polymer chain growth is always a hot topic for tailoring structure‐function properties in polymer chemistry. However, current characterization methods are limited in their ability to differentiate the degree of polymerization in real‐time without isolating the samples from the reaction vessel, let alone to detect insoluble polymers. Herein, a reliable relationship is established between polymer chain growth and fluorescence properties through polymerization induced emission. (TPE‐C2)2‐Te is used to realize in situ oxidative polymerization, leading to the aggregation of fluorophores. The relationship between polymerization degree of growing polytelluoxane (PTeO) and fluorescence intensity is constructed, enabling real‐time monitoring of the polymerization reaction. More importantly, this novel method can be further applied to the observation of the polymerization process for growing insoluble polymer via surface polymerization. Therefore, the development of visualization technology will open a new avenue for visualizing polymer chain growth in real‐time, regardless of polymer solubility. [ABSTRACT FROM AUTHOR]

Simple Summary: Castration-resistant prostate cancer (CRPC) treatments include next-generation anti-androgen therapies (NGATs), taxane therapy, and PARP inhibitors (PARPi). However, resistance often occurs across and within therapeutic classes, which can complicate sequential treatment options. We developed acquired resistant models to study therapeutic resistance. Our findings indicate that while NGAT-resistant cells are cross-resistant to other NGATs, they remain sensitive to taxanes and olaparib. Cells resistant to docetaxel display cross-resistance to cabazitaxel and olaparib but respond to NGATs. Olaparib-resistant cells are cross-resistant to other PARPi but still sensitive to NGATs and docetaxel. Our research underscores the significance of rationale drug sequencing in CRPC treatment and underlying mechanisms of resistance. Current common treatments for castration-resistant prostate cancer (CRPC) typically belong to one of three major categories: next-generation anti-androgen therapies (NGAT) including enzalutamide, abiraterone acetate, apalutamide, and darolutamide; taxane therapy represented by docetaxel; and PARP inhibitors (PARPi) like olaparib. Although these treatments have shown efficacy and have improved outcomes for many patients, some do not survive due to the emergence of therapeutic resistance. The clinical landscape is further complicated by limited knowledge about how the sequence of treatments impacts the development of therapeutic cross-resistance in CRPC. We have developed multiple CRPC models of acquired therapeutic resistance cell sublines from C4-2B cells. These include C4-2B MDVR, C4-2B AbiR, C4-2B ApaR, C4-2B DaroR, TaxR, and 2B-olapR, which are resistant to enzalutamide, abiraterone, apalutamide, darolutamide, docetaxel, and olaparib, respectively. These models are instrumental for analyzing gene expression and assessing responses to various treatments. Our findings reveal distinct cross-resistance characteristics among NGAT-resistant cell sublines. Specifically, resistance to enzalutamide induces resistance to abiraterone and vice versa, while maintaining sensitivity to taxanes and olaparib. Conversely, cells with acquired resistance to docetaxel exhibit cross-resistance to both cabazitaxel and olaparib but retain sensitivity to NGATs like enzalutamide and abiraterone. OlapR cells, significantly resistant to olaparib compared to parental cells, are still responsive to NGATs and docetaxel. Moreover, OlapR models display cross-resistance to other clinically relevant PARP inhibitors, including rucaparib, niraparib, and talazoparib. RNA-sequencing analyses have revealed a complex network of altered gene expressions that influence signaling pathways, energy metabolism, and apoptotic signaling, pivotal to cancer's evolution and progression. The data indicate that resistance mechanisms are distinct among different drug classes. Notably, NGAT-resistant sublines exhibited a significant downregulation of androgen-regulated genes, contrasting to the stable expression noted in olaparib and docetaxel-resistant sublines. These results may have clinical implications by showing that treatments of one class can be sequenced with those from another class, but caution should be taken when sequencing drugs of the same class. [ABSTRACT FROM AUTHOR]

With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction's confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network (Hybrid-CTEN), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed. [ABSTRACT FROM AUTHOR]

The production of waste printed circuit boards (WPCBs) is increasing, and its complex composition makes recycling difficult. In addition, the presence of heavy metals and brominated flame retardants makes it a hazardous waste. Therefore, its recycling is a necessary way for resource recycling and green sustainable development. The purpose of this study is to propose a green, efficient, and pollution-free recycling process as an alternative to recycle WPCBs. In this work, an alkaline metal oxide catalytic pyrolysis process was used to recover WPCBs. In the presence of alkali metal oxides (such as Ca(OH)2) and coexisting copper, Ca(OH)2 and coexisting copper are transformed into CaBr2 and Cu Br by reacting with organic bromine in WPCBs and remaining in the solid phase product. The bromine content and the proportion of inorganic bromine in the solid phase products were 87.68% and 87.56%, respectively. In addition, the content of organic bromine in the pyrolysis oil obtained by co-pyrolysis was significantly reduced. This study demonstrated the feasibility of Ca(OH)2 catalytic pyrolysis for WPCB recovery. [ABSTRACT FROM AUTHOR]

Bromine in waste printed circuit boards has caused serious harm to the environment. How to deal with it safely is a problem. In this paper, the debromination of waste printed circuit boards by microwave gasification pyrolysis was studied, and the properties of pyrolysis products and the migration of bromine were analyzed. The optimum pyrolysis conditions of microwave steam atmosphere were 600 °C, 1000 W, 50 min, 2 mL/min. Compared with conventional pyrolysis and conventional steam pyrolysis, less solid products (60.39 wt %) were obtained, and more metal copper was enriched. The content increased from 27.46 % and 28.35 % to 30.63 %, respectively, and the diffraction peak intensity of copper was also enhanced. There is no obvious change in the type of functional groups of the solid product, and its microscopic morphology is smoother. Compared with conventional pyrolysis, the phenolic compounds in pyrolysis oil decreased, and increased compared with conventional steam pyrolysis. The content of H2 in the pyrolysis gas increased significantly, and the content of CO decreased significantly. Compared with conventional pyrolysis, the bromine content in pyrolysis gas and pyrolysis oil decreased from 94.5 % and 5.5 % to 1 %, respectively, and more bromine (98 %) in the raw material entered the aqueous solution. [ABSTRACT FROM AUTHOR]

Microwave-assisted co-pyrolysis of waste printed circuit boards (WPCBs) with alkaline metal oxides was conducted to investigate the properties of pyrolysis products and the fixation of bromine. The yield of solid pyrolysis products decreased with increasing temperature, while the bromine content and percentage of inorganic bromine increased with the rise in temperature, reaching 93.34% and 94.27%, respectively, at 600°C. The addition of Ca(OH)2 facilitated the composite reaction of brominated epoxy resin, resulting in an increase in the relative percentage of phenol and other compounds. The bromine content in the pyrolysis residue was determined by oxygen bomb ion chromatography. The conversion and immobilization of organic bromine in the product were promoted by Ca(OH)2, which reacted with bromine radicals to form CaBr2, leading to a significant improvement in the immobilization efficiency of bromine in the solid pyrolysis product and a reduction in the content of bromine in the gas and liquid phases. The study proposed a mechanism of the conversion of organic bromine by Ca(OH)2. This study demonstrates the ability of Ca(OH)2 to immobilize organic bromine in the green recovery process of WPCB by pyrolysis. [ABSTRACT FROM AUTHOR]

Entity alignment is an essential process in knowledge graph (KG) fusion, which aims to link entities representing the same real-world object in different KGs, to achieve entity expansion and graph fusion. Recently, embedding-based entity pair similarity evaluation has become mainstream in entity alignment research. However, these methods heavily rely on labelled entity pairs, which are often unavailable. Some self-supervised methods exploit features of KGs regardless of noise when generating aligned entity pairs. To resolve this issue, we propose a generative adversarial entity alignment method, which is more robust to noise data. The proposed method then exploits both attribute and structure information in the KGs and applies a BERT-based contrastive loss function to embed entities in KGs. Experimental results on several benchmark datasets demonstrate the superiority of our framework compared with most existing state-of-the-art entity alignment methods. [ABSTRACT FROM AUTHOR]

Iron is an essential trace element, while excess iron can lead to different levels of physical abnormalities or diseases. This umbrella review aimed to conduct a systematic evaluation of the possible relationships between iron intake and various health outcomes. We retrieved PubMed, Embase, Web of Science, Scopus, and the Cochrane Database of Systematic Reviews from inception through May 2021. A total of 34 meta-analyses with 46 unique health outcomes were identified. Heme iron intake was positively associated with nine outcomes, including colorectal cancer, type 2 diabetes mellitus, and cardiovascular disease mortality, while dietary total iron intake could decrease the risk of colorectal adenoma, esophageal cancer, coronary heart disease, and depression. Iron supplementation was a protective factor against eight outcomes. However, it was associated with decreased length and weight gain. The quality of evidence for most outcomes was "low" or "very low" with the remaining eleven as "high" or "moderate". All outcomes were categorized as class III, IV, or NS based on evidence classification. Although high iron intake has been identified to be significantly associated with a range of outcomes, firm universal conclusions about its beneficial or negative effects cannot be drawn given the low quality of evidence for most outcomes. [ABSTRACT FROM AUTHOR]

Supramolecular self-assembly of molecules into highly ordered architectures is attractive for developing various advanced functional materials. Compared to the assemblies of one single building block, supramolecular coassembly (SCA) of multiple component systems has recently emerged as a promising approach for generating highly functional and complex structures. The assembly and integration of multiple building blocks at the molecular level are of paramount importance for constructing SCA systems with sophisticated architectures and diverse functions. This feature article highlights the recent advances and future trends in SCAs, ranging from their synthetic strategies, morphological control, to functional applications. The monomer pairs used to synthesize SCAs are rationalized into two classes including structural monomer pairs and functional monomer pairs. The assembly behaviors are then discussed according to the dimensionality of the coassembled morphologies from zero to three dimensions. Finally, the emergent functions and applications of SCAs are highlighted such as adsorption, catalysis, optoelectronics, and biomedicines. [ABSTRACT FROM AUTHOR]

Pyrolysis is a cost-effective and environmentally benign method for recycling organic waste, which can be converted into high-energy gases and oils. Pyrolysis technology was employed in this study to recycle copper-containing discarded circuit board material and recover copper, glass fibers, and gases and oils with high calorific values. Thermogravimetric analyses (TGA), Fourier transform infrared spectroscopy (FTIR), and gas chromatography–mass spectrometry (GC–MS) were used to evaluate pyrolyses of copper-containing waste circuit board materials conducted at different heating rates (5, 10, 20, and 40 °C/min), and the resulting volatiles were studied in detail. The effects of heating rate on the kinetics and activation energies for pyrolyses of copper-containing waste circuit boards were also investigated by using the Coats-Redfern (C-R) method. The TGA curves and FTIR spectra did not differ significantly for different heating rates, and the main functional groups identified with the FTIR results were O–H, C = C, aromatic benzene, substituted benzene, and C–Br. Additionally, GC–MS analyses showed that the heating rate had a great influence on the pyrolysis products formed; the phenol content decreased with increasing heating rate, and the highest content was realized at 5 ℃/min. Energy dispersive spectroscopy (EDS) analyses showed that bromine was removed from the solid phase products during pyrolysis, while copper was effectively enriched in the feedstock. This indicated that pyrolysis can be used to recover copper-containing waste circuit boards. [ABSTRACT FROM AUTHOR]

Signaling threshold regulating transmembrane adaptor 1 (SIT1) encodes a disulfide-linked homodimeric lymphocyte-specific glycoprotein involved in immune cell activation. However, the relationship between SIT1 and the prognosis of skin cutaneous melanoma (SKCM) and tumor-infiltrating lymphocytes remains elusive. Here, we first compared the differences in SIT1 expression levels between SKCM tissues and adjacent normal tissues. Next, we found that the immune cell infiltration levels and signature pattern of immune infiltration were positively associated with the SIT1 gene mRNA levels. TCGA_SKCM RNA-seq data unveiled that the SIT1 upregulated several immune-associated signaling pathways in GSEA analysis. The high expression of SIT1 was closely related to improved survival in patients with SKCM. A pathway enrichment analysis of SIT1-associated immunomodulators indicated the involvement of the NF-κB signaling pathways. Based on SIT1-associated immunomodulators, we built a 13-gene signature by LASSO Cox regression which served as an independent prognostic factor for the survival of melanoma patients. By using the signature risk score, we achieved a good prediction result for the immunotherapy response and survival of SKCM patients. Our findings provided evidence for SIT1's implication in tumor immunity and survival of SKCM patients. The nominated immune signature is a promising predictive model for prognosis and immunotherapy sensitivity in SKCM patients. [ABSTRACT FROM AUTHOR]

Decision making is the process of making choices by identifying a decision, gathering information, and assessing alternative resolutions. Therefore, in this paper, they proposed an attention-based hierarchical denoised deep clustering (AHDDC) algorithm to solve the problem, which enables GCN to learn multiple layers of hidden information and uses the attention mechanism to strengthen the information. These accepted articles make contributions and novel aspects of different research in social media data scenarios, traffic data scenarios, data security scenarios, medical data scenarios, knowledge graph learning, and the other graph based deep learning models. [Extracted from the article]

Chemodynamic therapy (CDT) can efficiently combat tumor cells through a robust catalyst in the presence of H2O2. However, the insufficient intracellular H2O2 level and inefficiency of catalysts in tumor cells limit the production of enough toxic hydroxyl radicals (˙OH) to achieve satisfactory efficacy for CDT. Herein, a supramolecular organometallic drug complex (SOMDC) with H2O2 self-provision was proposed to intensify the intracellular autocatalysis for enhancing the CDT effect. The obtained SOMDC could self-assemble into supramolecular organometallic drug micelles (SOMDMs), which could be effectively dissociated because the endogenous H2O2 in tumor cells can rapidly destroy the host–guest interactions. The released DOX prodrug effectively upregulated the endogenous H2O2 level and amplified the Fenton-like intracellular autocatalysis to guarantee a remarkable ˙OH production for improving CDT efficiency. In vitro and in vivo evaluations showed that SOMDC exhibited excellent anticancer activity with reduced toxicity to normal tissues. Therefore, this novel strategy with H2O2 self-provision to intensify intracellular autocatalysis for enhancing the CDT effect may provide new insights for cancer therapy. [ABSTRACT FROM AUTHOR]

The limited efficacy of deep tumor treatments has been considered the "Achilles' heel" of anticancer therapy due to multiple biological barriers. Whether passive diffusion or active transport has difficulties completely overcoming these obstacles. Herein, cation‐π interactions are utilized to construct a tumor drug delivery system integrating the merits of both passive and active transport mechanisms. A cation‐π interaction bridged trimetallic supramolecular drug complex (Cπ‐TMSDC) is constructed based on a drug consisting of one cisplatin molecule linked by K+ (Pt‐COOK) and the other drug with a Ru metal complex containing curcumin (Ru‐Cur). The obtained Cπ‐TMSDC further self‐assembles into cation‐π‐based trimetallic supramolecular drug micelles (Cπ‐TMSDMs) with efficient and stable transportation in vivo due to the strong cation‐π interaction formed between K+ and the curcumin unit in the Cπ‐TMSDC. In acidic tumor microenvironment, the cation‐π interaction smartly dissociates, facilitating the quick release of Pt‐COOK outside Cπ‐TMSDMs to rapidly infiltrate the outer cellular layers by passive diffusion. Meanwhile, the dissociated Ru‐Cur from the core layer of the Cπ‐TMSDMs form secondary self‐assemblies to deeply penetrate inside the solid tumor. Therefore, this strategy results in an efficient tumor drug delivery platform with enhanced deep intratumoral penetration, improved therapeutic effects, and reduced systemic toxicity to normal organs. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the most common malignant tumors in men. Initially, it is androgen-dependent, but it eventually develops into castration-resistant prostate cancer (CRPC), which is incurable with current androgen receptor signaling target therapy and chemotherapy. Immunotherapy, specifically with immune checkpoint inhibitors, has brought hope for the treatment of this type of prostate cancer. Approaches such as vaccines, adoptive chimeric antigen receptor-T (CAR-T) cells, and immune checkpoint inhibitors have been employed to activate innate and adaptive immune responses to treat prostate cancer, but with limited success. Only Sipuleucel-T and the immune checkpoint inhibitor pembrolizumab are approved by the US FDA for the treatment of limited prostate cancer patients. Prostate cancer has a complex tumor microenvironment (TME) in which various immunosuppressive molecules and mechanisms coexist and interact. Additionally, prostate cancer is considered a "cold" tumor with low levels of tumor mutational burden, low amounts of antigen-presenting and cytotoxic T-cell activation, and high levels of immunosuppressive molecules including cytokines/chemokines. Thus, understanding the mechanisms of immunosuppressive signaling activation and immune evasion will help develop more effective treatments for prostate cancer. The purpose of this review is to summarize emerging advances in prostate cancer immunotherapy, with a particular focus on the molecular mechanisms that lead to immune evasion in prostate cancer. At the same time, we also highlight some potential therapeutic targets to provide a theoretical basis for the treatment of prostate cancer. [ABSTRACT FROM AUTHOR]

Density Peak Clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewer-parameter requirement. However, the resultant clusters obtained using DPC are influenced by the sensitive parameter $d_c$ d c , which depends on data distribution and requirements of different users. Besides, the original DPC algorithm requires visiting a large number of objects, making it slow. To this end, this paper investigates index-based solutions for DPC. Specifically, we propose two list-based index methods viz. (i) a simple List Index, and (ii) an advanced Cumulative Histogram Index. Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space. For memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost, provided that slight inaccuracies are admissible. Furthermore, owing to considerably lower memory requirements of existing tree-based index structures, we also present effective pruning techniques and efficient query algorithms to support DPC using the popular Quadtree Index and R-tree Index. Finally, we practically evaluate all the above indices and present the findings and results, obtained from a set of extensive experiments on six synthetic and real datasets. The experimental insights obtained can help to guide in selecting a befitting index. [ABSTRACT FROM AUTHOR]

Well connected users are generally discovered in communities which is one of the most important tasks for network data analytics and has tremendous real applications. In recent years, community search in attributed graphs has begun to attract attention, which aims to find communities that are both structure and attribute cohesive. Meanwhile, searching a community that is structure cohesive but attribute diversified, denoted as attribute diversified community search, is still at an early stage. In this paper, we introduce our recent effort for discovering attribute diversified community. In fact, for different applications, the needs of attribute diversification for modelling the community are quite different. We introduce three attribute diversified community models in which attribute diversification takes different roles for presenting as an objective and as a constraint. We also discuss major techniques for speeding up the attribute diversified community search. We conduct extensive experiments to show the effectiveness and efficiency of our algorithms for finding attribute diversified communities in various settings. [ABSTRACT FROM AUTHOR]

Tumor hypoxia greatly limits the antitumor therapeutic efficacy of oxygen (O2)‐dependent treatments. The common strategies of doping O2 generating compounds into one "package" may lead to insufficient O2 supply in situ in cancer cells due to the relatively low loading content, limited reproducibility, and uncontrollable release kinetics. Herein, organometallic drug complexes combined with a supramolecular interaction strategy are proposed to realize a stable and efficient O2 self‐supply in situ for overcoming hypoxic tumors. A ruthenium (II)‐coordinated supramolecular metallodrug complex (RuSMDC) is first designed and synthesized via host–guest interactions between two functionalized drug molecules, in which ruthenium is used to catalyze the decomposition of hydrogen peroxide to produce O2. The obtained RuSMDC further self‐assembles into Ru‐containing supramolecular metallodrug micelles (RuSMDMs), which provides sufficient O2 for chemotherapy and photodynamic therapy (PDT) oncotherapy in an anaerobic tumor environment. In vitro and in vivo studies further confirm that RuSMDMs effectively alleviate the tumor hypoxic environment, greatly improve the therapeutic effect of chemotherapy and PDT oncotherapy, and reduce the systemic toxicity to normal organs. [ABSTRACT FROM AUTHOR]

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user's out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using "Asymmetric-SVD" and "TC-SeqRec" respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor's crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly. [ABSTRACT FROM AUTHOR]

Introduction: Glioma is the most common malignant brain tumor in adults. Radiation is a key therapy in glioma. However, the radioresistance of glioma was a big challenge. HLA complex P5 (HCP5) has been reported dysregulated in several types of malignant tumor, including glioma. The role of HCP5 in the radiosensitivity of glioma is so far unknown. The present study aimed to investigate the effect of HCP5 on radiosensitivity in gliomas. Methods: The levels of HCP5 and microRNA (miR)-128 were detected using qRT-PCR. The cell growth curve was used to show the cell proliferation and evaluate the radiosensitivity of glioma cells following exposure to X-ray. Senescence-associated β-galactosidase (SA-β-Gal) staining was used to test the cellular senescence. Luciferase reporter and RNA immunoprecipitation (RIP) assays were performed to determine the correlation between HCP5 and miR-128. Results: HCP5 level of glioma cells was significantly higher than human astrocytes, whereas miR-128 level was lower in glioma cells. Besides, the HCP5 expression was increased in glioma tissues compared to normal brain tissues (NBTs). Knockdown of HCP5 inhibited cell proliferation and increased radiosensitivity in glioma cells. MiR-128 was predicted to be a target of HCP5. It was demonstrated that HCP5 directly bound to miR-128 and regulated its expression in glioma cells. Furthermore, the effects of HCP5 knockdown on radiosensitivity of glioma cells were attenuated by the inhibitor of miR-128. Conclusion: These findings suggested that interaction between lncRNA HCP5 and microRNA-128 could regulate the radiosensitivity of glioma cells by intervening in cellular senescence. This might be used as the potential radio-sensitization targets for glioma therapy. [ABSTRACT FROM AUTHOR]

With the rapid development of transportation systems, traffic data have been largely produced in daily lives. Finding the insights of all these complex data is of great significance to vehicle dispatching and public safety. In this work, we propose a multitask deep learning model called Multitask Recurrent Graph Convolutional Network (MRGCN) for accurately predicting traffic flows in the city. Specifically, we design a multitask framework consisting of four components: a region-flow encoder for modeling region-flow dynamics, a transition-flow encoder for exploring transition-flow correlations, a context modeling component for contextualized fusion of two types of traffic flows and a task-specific decoder for predicting traffic flows. Particularly, we introduce Dual-attention Graph Convolutional Gated Recurrent Units (DGCGRU) to simultaneously capture spatial and temporal dependencies, which integrate graph convolution and recurrent model as a whole. Extensive experiments are carried out on two real-world datasets and the results demonstrate that our proposed method outperforms several existing approaches. [ABSTRACT FROM AUTHOR]

Niclosamide has preclinical activity against a wide range of cancers. In prostate cancer, it inhibits androgen receptor variant 7 and synergizes with abiraterone. The approved niclosamide formulation has poor oral bioavailability. The primary objective of this phase Ib trial was to identify a maximum tolerated dose (MTD) and recommended phase 2 dose (RP2D) of a novel reformulated orally-bioavailable niclosamide/PDMX1001 in combination with abiraterone and prednisone in men with castration-resistant prostate cancer (CRPC). Eligible patients had progressing CRPC, adequate end-organ function, and no prior treatment with abiraterone or ketoconazole. Patients were treated with escalating doses of niclosamide/PDMX1001 and standard doses of abiraterone and prednisone. Peak and trough niclosamide plasma levels were measured. Common Terminology Criteria for Adverse Events (CTCAE) v4.0 and Prostate Cancer Working Group 2 criteria were used to evaluate toxicities and responses. Nine patients with metastatic CRPC were accrued, with no dose-limiting toxicities observed at all dose levels. The recommended Phase II dose of niclosamide/PDMX1001 was 1200 mg orally (PO) three times daily plus abiraterone 1000 mg PO once daily and prednisone 5 mg PO twice daily. Trough and peak niclosamide concentrations exceeded the therapeutic threshold of > 0.2 µM. The combination was well tolerated with most frequent adverse effects of diarrhea. Five out of eight evaluable patients achieved a PSA response; two achieved undetectable PSA and radiographic response. A novel niclosamide/PDMX1001 reformulation achieved targeted plasma levels when combined with abiraterone and prednisone, and was well tolerated. Further study of niclosamide/PDMX1001 with this combination is warranted. [ABSTRACT FROM AUTHOR]

Destination prediction is known as an important problem for many location based services (LBSs). Existing solutions generally apply probabilistic models or neural network models to predict destinations over a subtrajectory, and adopt the standard attention mechanism to improve the prediction accuracy. However, the standard attention mechanism uses fixed feature representations, and has a limited ability to represent distinct features of locations. Besides, existing methods rarely take the impact of spatial and temporal characteristics of the trajectory into account. Their accuracies in fine-granularity prediction are always not satisfactory due to the data sparsity problem. Thus, in this paper, a carefully designed deep learning model called LATL model is presented. It not only adopts an adaptive attention network to model the distinct features of locations, but also implements time gates and distance gates into the Long Short-Term Memory (LSTM) network to capture the spatial-temporal relation between consecutive locations. Furthermore, to better understand the mobility patterns in different spatial granularities, and explore the fusion of multi-granularity learning capability, a hierarchical model that utilizes tailored combination of different neural networks under multiple spatial granularities is further proposed. Extensive empirical studies verify that the newly proposed models perform effectively and settle the problem nicely. [ABSTRACT FROM AUTHOR]

Traffic congestions in urban road traffic networks originate from some crowded road segments with crucial locations, and diffuse towards other parts of the urban road network creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this paper, we investigate the problems of global influence ranking and local influence ranking of road segments. We propose an algorithm called RoadRank to compute the global influence scores of each road segment from their traffic measures, and rank them based on their overall influence. To identify the locally influential road segments, we also propose an extension called distributed RoadRank, based on road network partitions. We perform extensive experiments on real SCATS datasets of Melbourne. We found that the segments of Batman Avenue, Footscray Road, Punt Road, La Trobe Street, and Victoria Street, are highly influential in the early morning times, which are well known as congestion hotspots for both the network operators and the commuters. Our promising results and detailed insights demonstrate the efficacy of our method. [ABSTRACT FROM AUTHOR]

Process reuse is one of the important research areas that address efficiency issues in business process modeling. Similar to software reuse, business processes should be able to be componentized and specialized in order to enable flexible process expansion and customization. Current activity/control-flow centric workflow modeling approaches face difficulty in supporting highly flexible process reuse, limited by their procedural nature. In comparison, the emerging artifact-centric workflow modeling approach well fits into these reuse requirements. Beyond the classic class level reuse in existing object-oriented approaches, process reuse faces the challenge of handling synchronization dependencies among artifact lifecycles as parts of a business process. In this article, we propose a theoretical framework for business process specialization that comprises an artifact-centric business process model, a set of methods to design and construct a specialized business process model from a base model, and a set of behavioral consistency criteria to help check the consistency between the two process models. [ABSTRACT FROM AUTHOR]

Summary Process view technologies allow organizations to create different granularity levels of abstraction of their business processes, therefore enabling a more effective business process management, analysis, interoperation, and privacy controls. Existing research proposed view construction and abstraction techniques for block‐based (ie, BPEL) and graph‐based (ie, BPMN) process models. However, the existing techniques treat each type of the two types of models separately. Especially, this brings in challenges for achieving a consistent process view for a BPEL model that derives from a BPMN model. In this paper, we propose a unified framework, namely UniFlexView, for supporting automatic and consistent process view construction. With our framework, process modelers can use our proposed View Definition Language to specify their view construction requirements disregarding the types of process models. Our UniFlexView's system prototype has been developed as a proof of concept and demonstration of the usability and feasibility of our framework. [ABSTRACT FROM AUTHOR]

Service-based systems (SBSs) reuse existing loosely coupled Web services to provide value-added composite ones, which brings about much flexibility when the business changes frequently. The advent of automatic Web service composition technology allows system designers to quickly build SBSs without having to manually create process models. Despite the large number of strategies proposed so far, most of them compose Web services through the user-provided initial inputs and expected target outputs, which is not convenient for users to express their functional requirements. To address this issue, we allow users to employ keywords to represent key tasks of the composed Web services. To automatically compose Web services based on the given keywords, we study a new problem of keyword search in the AND/OR graph constructed through semantically matching input-output interfaces of existing related Web services. Due to the complexity of the problem, we propose a heuristic search approach, called UP-DFS. To improve the performance of UP-DFS, we further design two types of pruning strategies. The empirical study shows that our approach can efficiently generate a semantic input-output-based Web service composition that contains all the key tasks in the right order required by users while minimizing the number of services in the composition. [ABSTRACT FROM AUTHOR]

Artifact-centric approach to business process modeling has received considerable attention for elevating data logic to the same level as the process flow logic. With the emergence of this modeling paradigm, several recent works have focused on synthesizing the indispensable lifecycles of key business entities called artifacts from the standalone activity-centric processes. However, synthesizing artifact lifecycles from the inter-organizational business processes (IOBP) is challenging, as the artifacts and states are shared among two or more collaborating processes. Thus, unlike a standalone process, the synthesis of artifact lifecycles from an IOBP require the process interactions to be captured by preserving the dependencies between the involved artifacts and states in the resulting lifecycles. Therefore, in this paper, we propose an automated approach that aims at merging the collaborating processes of an IOBP in order to support the synthesis of artifact lifecycles from an IOBP. The proposed approach is comprised of algorithms that combine the nodes of collaborating processes to generate an integrated process that can be used to synthesize the artifact lifecycles pertinent to an IOBP. We demonstrate the proposed approach using an e-business process scenario and the validity is proved using theorems and a prototype implementation. [ABSTRACT FROM AUTHOR]

Collective spatial keyword query (CSKQ) is one of the most useful spatial queries in location-based service systems. Although the availability of large-scale activity trajectories has given us useful knowledge of users' behavior, existing activity trajectory search methods are unable to support CSKQ queries reasonably. This paper studies effective and efficient CSKQ processing on activity trajectories to cover the gap. Specifically, we first formalize the problem by a trajectory based model that considers the spatial, activity and popularity issues, enabling more rational CSKQ results to be returned. To avoid high I/O cost, a novel hybrid index structure is further proposed to seamlessly integrate multi-domain information, so that inferior trajectories can be pruned during query processing. A novel candidate sub-trajectory search algorithm is also presented to reduce computation overhead by a linear scan on the trajectory. The experimental results on real check-in datasets demonstrate the efficiency and scalability of our proposed solution. [ABSTRACT FROM AUTHOR]

Semantic-aware spatial keyword search is an important technique for digital map services. However, existing indexing and search methods have limited pruning effect due to the high dimensionality in semantic space, causing query efficiency to be a serious issue. To handle this problem, this paper proposes a novel pivot-based hierarchical indexing structure S2R-tree to integrate spatial and semantic information in a seamless way. Instead of indexing objects in the original semantic space, we carefully design a space mechanism to transform the high dimensional semantic vectors to a low dimensional space, so that more effective pruning effect can be achieved. On top of the S2R-tree, an efficient query processing algorithm is further designed, which not only ensures efficient query processing by a set of theoretical bounds, but also returns accurate results despite of the indexing in the low dimensional space. Furthermore, we conduct extensive experiments to evaluate and compare our proposed and baseline methods. [ABSTRACT FROM AUTHOR]

Nowadays, Online shopping has been tremendous lifestyle choices due to the lower management cost for product/service providers and the cheaper prices for buyers/customers. Meanwhile, it raises a big challenge for both buyers and sellers to identify the right product items from the numerous choices and the right customers from a large number of different buyers. This motivates the study of recommendation system which computes recommendation scores for product items and filters out those with low scores. Recently, a promising direction involves the consideration of the social network influence in recommendation system. While significant performance improvement has been observed, it is still unclear to which extension the social network influence can help differentiate product items in terms of recommendation scores. This is an interesting problem in particular in the situation that the recommended product items have the highly similar (or identical) scores. As the first effort to this problem, this paper probes the boundary of social network influence to recommendation outputs by solving an optimization problem called evidence-driven dubious decision making. Two solutions have been proposed and the evaluation on two real world datasets has verified the effectiveness of the proposed solutions. [ABSTRACT FROM AUTHOR]

In many cases, users are not familiar with their exact information needs while searching complicated data sources. This lack of understanding may cause the users to feel dissatisfaction when the system retrieves insufficient results after they issue queries. However, using their original query results, we may recommend additional queries which are highly relevant to the original query. This paper presents XPloreRank to recommend top-l highly relevant keyword queries called "You May Also Like" (YMAL) queries to the users in XML keyword search. To generate such queries, we firstly analyze the original keyword query results content and construct a weighted co-occurring keyword graph. Then, we generate the YMAL queries by traversing the co-occurring keyword graph and rank them based on the following correlation aspects: (a) external correlation, which measures the similarity of the YMAL query to the original query and (b) internal correlation, which measures the capability of the YMAL query keywords in producing meaningful results with respect to the data source. Due to the complexity of generating YMAL queries, we propose a novel A* search-based technique to generate top-l YMAL queries efficiently. We also present a greedy-based approximation for it to improve the performance further. Extensive experiments verify the effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

The efficient identification of social groups with common interests is a key consideration for viral marketing in online social networking platforms. Most existing studies in social groups or community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on three real data sets to demonstrate the effectiveness and efficiency of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups using case studies. [ABSTRACT FROM AUTHOR]

A phrase is a natural, meaningful, and essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. However, from phrase quality and topical cohesion perspectives, the outcomes of existing approaches remain to be improved. Usually, the process of topical phrase mining is twofold: phrase mining and topic modeling. For phrase mining, existing approaches often suffer from order sensitive and inappropriate segmentation problems, which make them often extract inferior quality phrases. For topic modeling, traditional topic models do not fully consider the constraints induced by phrases, which may weaken the cohesion. Moreover, existing approaches often suffer from losing domain terminologies since they neglect the impact of domain-level topical distribution. In this paper, we propose an efficient method for high quality and cohesive topical phrase mining. A high quality phrase should satisfy frequency, phraseness, completeness, and appropriateness criteria. In our framework, we integrate quality guaranteed phrase mining method, a novel topic model incorporating the constraint of phrases, and a novel document clustering method into an iterative framework to improve both phrase quality and topical cohesion. We also describe efficient algorithmic designs to execute these methods efficiently. The empirical verification demonstrates that our method outperforms the state-of-the-art methods from the aspects of both interpretability and efficiency. [ABSTRACT FROM AUTHOR]

Real social networks contain many communities, where members within each community are densely connected with each other, while they are sparsely connected with the members outside of the community. Since each member can join multiple communities simultaneously, communities in social networks are usually overlapping with each other. How to efficiently and effectively identify overlapping communities in a large social network becomes a fundamental problem in the big data era. Most existing studies on community finding focused on non-overlapping communities based on several well-known community fitness metrics. However, recent investigations have shown that these fitness metrics may suffer free rider and separation effects where the overlapping region of two communities always belongs to the denser one, rather to both of them. In this paper, we study the overlapping community detection problem in social networks that not only takes the quality of the found overlapping communities but also incorporate both free rider and separation effects on the found communities into consideration. Specifically, in this paper, we first propose a novel community fitness metric - triangle based fitness metric, for overlapping community detection that can minimize the free rider and separation effects on found overlapping communities, and show that the problem is NP-hard. We then propose an efficient yet scalable algorithm for the problem that can deliver a feasible solution. We finally validate the effectiveness of the proposed fitness metric and evaluate the performance of the proposed algorithm, through conducting extensive experiments on real-world datasets with over 100 million vertices and edges. Experimental results demonstrate that the proposed algorithm is very promising. [ABSTRACT FROM AUTHOR]

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

The urban road networks undergo frequent traffic congestions during the peak hours and around the city center. Capturing the spatiotemporal evolution of the congestion scenario in real-time in an urban-scale can aid in developing smart traffic management systems, and guiding commuters in making informed decision about route choice. The congestion scenario is often represented by a set of distinguishable network partitions that have a homogeneous level of congestion inside them but are heterogeneous to others. Due to the dynamic nature of traffic, these partitions evolve with time in terms of their structure and location. In this paper, we propose a comprehensive framework to capture the evolution by incrementally updating the partitions in an efficient manner using a two-layer approach. The physical layer maintains a set of small-sized road network building blocks in a fine granularity, and performs low-level computations to incrementally update them, whereas the logical layer performs high-level computations in order to serve as an interface to query the physical layer about the congested partitions in a coarse granularity. We also propose an in-memory index called Bin that compactly stores the historical sets of building blocks in the main memory with no information loss, and facilitates their efficient retrieval. Our experimental results show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy. The proposed Bin consumes a minimum space with least redundancy at different time stamps. [ABSTRACT FROM AUTHOR]

Recording and querying time-stamped trajectories incurs high cost of data storage and computing. In this paper, we explore several characteristics of the trajectories in road networks, which have motivated the idea of coding trajectories by associating timestamps with relative spatial path and locations. Such a representation contains a large number of duplicate information to achieve a lower entropy compared with the existing representations, thereby drastically cutting the storage cost. We propose several techniques to compress spatial path and locations separately, which can support fast positioning and achieve better compression ratio. For locations, we propose two novel encoding schemes such that the binary code can preserve distance information, which is very helpful for LBS applications. In addition, an unresolved question in this area is whether it is possible to perform a search directly on the compressed trajectories, and if the answer is yes, then how. Here, we show that directly querying compressed trajectories based on our encoding scheme is possible and can be done efficiently. We design a set of primitive operations for this purpose, and propose index structures to reduce query response time. We demonstrate the advantage of our method and compare it against existing ones through a thorough experimental study on real trajectories in road network. [ABSTRACT FROM AUTHOR]

Smart cities have been recognized as a promising research focus around the world. To realize smart cities, computation and utilization of big data are key factors. More specifically, exploring the patterns of human mobility based on large amounts of multi-source data plays an important role in analyzing the formation of social-economic phenomena in smart cities. However, our acquired knowledge is still very limited for smart cities. In this article, we propose an integrated computing method to rescale heterogeneous traffic trajectory data, which leverages MLE and BIC. Our analysis is based on two real datasets generated by subway smart card transactions and taxi GPS trajectories from Shanghai, China, which contain more than 451 million trading records by 14 subway lines and 34 billion GPS records by 13,695 taxis. Specifically, we quantitatively explore the patterns of human mobility on weekends and weekdays. Through logarithmic binning and data fitness, we calculate the Bayesian weights to select the best fitting distributions. In addition, we leverage three metrics to analyze the patterns of human mobility in two datasets: trip displacement, trip duration, and trip interval. We obtain several important human mobility patterns and discover quite a few interesting phenomena, which lay a solid foundation for future research. [ABSTRACT FROM AUTHOR]

Acquisitional issues widely exist in GPS and sensor networks. They pertain to where, when, and how often data is physically acquired (sampled) and delivered to some query processing systems. Due to the dynamic environment that the data is acquired with the change of monitoring time, acquisitional data are typically a time-stamped stream where a time-stamped value could either contain noises or be missing. Aiming to improve the quality of data acquisition, we focus on the explanations of missing values in this paper. Several techniques have been developed to provide the explanation on relational data, however, they cannot be directly applied in acquisitional stream data due to its dynamic feature, such as the 'change' of acquisitional stream data between two adjacent monitoring time is often constrained by some rules. We show that an explanation could be incorrect or unreasonable if those constraints are not taken into account. We propose one novel chasing technique by considering both spatial and temporal correlations to explain missing values and guarantee a minimal explanation. Experimental results show that our approach can efficiently return high-quality and minimal explanations of missing values. [ABSTRACT FROM AUTHOR]