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Glycogen and lipid accumulation and mobilization exhibited significant variations among different tissues of female Litopenaeus vannamei during ovarian maturation, yet the underlying mechanism remained incompletely elucidated. This study was conducted to elucidate the distinctive differences in glycogen and lipid accumulation and mobilization throughout the ovary maturation process (stages II–V) in L. vannamei. Our findings revealed that during ovarian maturation, the ovary consistently accumulated substantial amounts of glycogen and fatty acids, which were actively mobilized not only from the hepatopancreas and muscle but also from their ovary synthesis. Moreover, a significant downregulation trend (P < 0.05) among all tissues was observed in glycogen and lipids metabolism-related genes and ILPs/AKT/PI3K signaling pathway–related genes. Consequently, we proposed that stages II–III represent the active period of rapid glycogen and lipid accumulation and mobilization in L. vannamei compared to stages IV–V. These results provided deeper insights into the mechanisms governing glycogen and lipid accumulation and mobilization during ovarian maturation in L. vannamei. [ABSTRACT FROM AUTHOR]

Purpose: Endoscopic thyroidectomy utilizing the Gasless Unilateral Axillary Approach (GUA) offers distinct advantages including clear visibility, simple manipulation, safe oncological outcomes. This technique eliminates postoperative neck scarring, ensures concealed surgical incisions, and minimizes postoperative swallowing discomfort. Methods: We retrospectively reviewed 150 surgical videos to document key anatomical features and their variations during this procedure. Results: The GUA endoscopic thyroidectomy, which approaches from the contralateral side, presents significant difficulties in identifying anatomical structures, especially anatomical abnormalities in the contralateral neck, while constructing feasible operative fields. This article offers an in-depth discussion of the anatomical challenges, pitfalls, and viable strategies associated with this surgery, particularly for less experienced surgeons. Conclusions: Given the intricate interplay of muscular, vascular, and neural anatomical structures, novices in surgery must be well-acquainted with the underlying anatomy to minimize potential complications. [ABSTRACT FROM AUTHOR]

The objective of this work is to showcase the ortho-positronium lifetime as a probe for soft-tissue characterization. We employed positron annihilation lifetime spectroscopy to experimentally measure the three components of the positron annihilation lifetime—para-positronium (p-Ps), positron, and ortho-positronium (o-Ps)—for three types of porcine, non-fixated soft tissues ex vivo: adipose, hepatic, and muscle. Then, we benchmarked our measurements with X-ray phase-contrast imaging, which is the current state-of-the-art for soft-tissue analysis. We found that the o-Ps lifetime in adipose tissues (2.54 ± 0.12 ns) was approximately 20% longer than in hepatic (2.04 ± 0.09 ns) and muscle (2.03 ± 0.12 ns) tissues. In addition, the separation between the measurements for adipose tissue and the other tissues was better from o-Ps lifetime measurement than from X-ray phase-contrast imaging. This experimental study proved that the o-Ps lifetime is a viable non-invasive probe for characterizing and classifying the different soft tissues. Specifically, o-Ps lifetime as a soft-tissue characterization probe had a strong sensitivity to the lipid content that can be potentially implemented in commercial positron emission tomography scanners that feature list-mode data acquisition. [ABSTRACT FROM AUTHOR]

Mold oscillation is the key technology of steel continuous casting. The non-sinusoidal oscillator driven by the hydraulic servo system has the disadvantages of complex structure, high investment and maintenance cost, oil leakage, zero drift. Thus, the non-sinusoidal oscillator driven by mechanism was presented. However, the amplitude could not be changed on line for the existing mechanical oscillators. And the online oscillation process parameters optimizing and the billet surface quality improving become difficult. To realize the oscillation parameters adjusted online, a novel swing crank type non-sinusoidal oscillator driven by a servomotor was proposed in this paper. Firstly, the working principle of the oscillator was illustrated and the three-dimensional model of the oscillator was established. Secondly, the non-sinusoidal oscillation waveform function was given. To realize non-sinusoidal oscillation waveform, the angular speed of servomotor was determined. Thirdly, the oscillation process parameters calculation methods were presented, the multi process parameter curve was given and the synchronous control model of non-sinusoidal oscillation was established. And the oscillation process parameters were calculated. Finally, the oscillation mark depth and slag consumption were calculated. From the calculation results, it can be seen that the non-sinusoidal oscillation process parameters could meet the steel continuous casting process requirements well. For non-sinusoidal oscillation, the oscillation mark depth reduced by 9.76% and slag consumption increased by 22.78% compared with that of sinusoidal oscillation, which will help to improve the lubrication condition between the mold and the billet and enhance billet surface quality. [ABSTRACT FROM AUTHOR]

Convolutional neural network based methods have been proposed to address blind deblurring. Aiming at the difficulty of reconstructing high-frequency features of images with existing network models, this paper proposes a two-stage convolution-based encoder-decoder for fusing high-frequency a prior. In the first stage, both blurred and high-frequency images are input, and the image features extracted by the network and the high-frequency features are fused. In the second stage, the fused features are further refined and recovered as potentially clear images, and the reconstruction ability of the model for high-frequency features is enhanced. In addition, this paper proposes a deep feature reorganization module that integrates multi-layer semantic information in the encoder-decoder and targets the encoder semantics to further enhance the feature characterization capability of the model. Comprehensive experimental results show that our method achieves 0.9085 structural similarity index (SSIM) and 30.66db peak signal-to-noise ratio (PSNR) on the GoPro dataset. Meanwhile, our method achieves 0.8514 SSIM and 27.39db PSNR on the Lai dataset. [ABSTRACT FROM AUTHOR]

Airway hillocks are stratified epithelial structures of unknown function1. Hillocks persist for months and have a unique population of basal stem cells that express genes associated with barrier function and cell adhesion. Hillock basal stem cells continually replenish overlying squamous barrier cells. They exhibit dramatically higher turnover than the abundant, largely quiescent classic pseudostratified airway epithelium. Hillocks resist a remarkably broad spectrum of injuries, including toxins, infection, acid and physical injury because hillock squamous cells shield underlying hillock basal stem cells from injury. Hillock basal stem cells are capable of massive clonal expansion that is sufficient to resurface denuded airway, and eventually regenerate normal airway epithelium with each of its six component cell types. Hillock basal stem cells preferentially stratify and keratinize in the setting of retinoic acid signalling inhibition, a known cause of squamous metaplasia2,3. Here we show that mouse hillock expansion is the cause of vitamin A deficiency-induced squamous metaplasia. Finally, we identify human hillocks whose basal stem cells generate functional squamous barrier structures in culture. The existence of hillocks reframes our understanding of airway epithelial regeneration. Furthermore, we show that hillocks are one origin of ‘squamous metaplasia’, which is long thought to be a precursor of lung cancer.In the lungs, recently identified epithelial structures known as hillocks can act as injury-resistant reservoirs of stem cells. [ABSTRACT FROM AUTHOR]

A new strain of Bacillus velezensis NDB was isolated from Xiangshan Harbor and antibacterial test revealed antibacterial activity of this strain against 12 major pathogenic bacteria. The whole genome of the bacterium was sequenced and found to consist of a 4,214,838 bp circular chromosome and a 7410 bp circular plasmid. Furthermore, it was predicted by AntiSMASH and BAGEL4 to have 12 clusters of secondary metabolism genes for the synthesis of the inhibitors, fengycin, bacillomycin, macrolactin H, bacillaene, and difficidin, and there were also five clusters encoding potentially novel antimicrobial substances, as well as three bacteriocin biosynthesis gene clusters of amylocyclicin, ComX1, and LCI. qRT-PCR revealed significant up-regulation of antimicrobial secondary metabolite synthesis genes after 24 h of antagonism with pathogenic bacteria. Furthermore, MALDI-TOF mass spectrometry revealed that it can secrete surfactin non-ribosomal peptide synthase and polyketide synthase to exert antibacterial effects. GC-MS was used to analyze methanol extract of B. velezensis NDB, a total of 68 compounds were identified and these metabolites include 16 amino acids, 17 acids, 3 amines, 11 sugars, 11 alcohols, 1 ester, and 9 other compounds which can inhibit pathogenic bacteria by initiating the antibiotic secretion pathway. A comparative genomic analysis of gene families showed that the specificity of B. velezensis NDB was mainly reflected in environmental adaptability. Overall, this research on B. velezensis NDB provides the basis for elucidating its biocontrol effect and promotes its future application as a probiotic. [ABSTRACT FROM AUTHOR]

Thyroid cancer is the endocrine tumor with the highest incidence at present. It originates from the thyroid follicular epithelium or follicular paraepithelial cells. There is an increasing incidence of thyroid cancer all over the world. We found that SRPX2 expression level was higher in papillary thyroid tumors than in normal thyroid tissues, and SRPX2 expression was closely related to tumor grade and clinical prognosis. Previous reports showed that SRPX2 could function by activating PI3K/AKT signaling pathway. In addition, in vitro experiments showed that SRPX2 promoted the proliferation and migration of papillary thyroid cancer (PTC). In conclusion, SRPX2 could promote the malignant development of PTC. This may be a potential treatment target for PTC. [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]

Continuous top-k trajectory similarity Search (CkSearch) is now commonly required in real-time large-scale trajectory analysis, enabling the distributed stream processing engines to discover various dynamic patterns. As a fundamental operator, CkSearch empowers various applications, e.g., contact tracing during an outbreak and smart transportation. Although extensive efforts have been made to improve the efficiency of non-continuous top-k search, they do not consider dynamic capability of indexing (R1) and incremental capability of computing (R2). Therefore, in this paper, we propose a generic CkSearch-oriented framework for distributed real-time trajectory stream processing on Apache Flink, termed as Garden. To answer R1, we design a sophisticated distributed dynamic spatial index called Y-index, which consists of a real-time load scheduler and a two-layer indexing structure. To answer R2, we introduce a state reusing mechanism and index-based pruning methods that significantly reduce the computational cost. Empirical studies on real-world data validate the usefulness of our proposal and prove the huge advantage of our approach over state-of-the-art solutions in the literature. [ABSTRACT FROM AUTHOR]

Few-shot relation classification is to recognize the semantic relation between an entity pair with very few samples. Prototypical network has proven to be a simple yet effective few-shot learning method for relation extraction. However, under the condition of data scarcity, the relation prototypes we achieve are usually biased compared to the real ones computed from all samples within a relation class. To alleviate this issue, we propose hybrid enhancement-based prototypical networks. In particular, our model contains three main enhancement modules: 1) a query-guided prototype enhancement module using rich interactive information between the support instances and the query instance as guidance to obtain more accurate prototype representations; 2) a query enhancement module to diminish the distribution gap between the query set and the support set; 3) a support enhancement module adopting a pseudo-label strategy to expand the scale of available data. On basis of these modules, we further design a novel prototype attention fusion mechanism to fuse information and compute discriminative relation prototypes for classification. In this way, we hope to obtain unbiased representations closer to our expected prototypes by improving the available data scale and data utilization efficiency. Extensive experimental results on the widely-used FewRel dataset demonstrate the superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Recommender systems play an important role in providing users required information in a timely and effective manner. However, the cold-start problem of limited historical records of a new user in target domain makes it difficult to model user's comprehensive preferences. This severely affects the accuracy of recommendation. Although some meta-learning based approaches have alleviated the cold-start problem by learning well-generalized initial parameters for each user, they neglect user's information in source domains, and seem to be weak in providing each user suitable initial parameters separately. To tackle these challenges, we propose a novel cluster-based domain adaptive meta-learning model for cross-domain recommendation (CDAML). Specially, we utilize the adversarial cross-domain methods to introduce domain adaptation into the meta-learning framework, which can transfer domain-independent user preferences (i.e. intrinsic preferences) from source domains for improved recommendation in target domain via adversarial learning. Besides, we further design a soft-clustering based method to guide the globally shared parameter initialization in a finer granularity of cluster level, which not only contribute to avoid local optima, but also better transfer the shared knowledge among users with similar cross-domain preferences. Finally, comprehensive experiments are conducted on three real-world datasets to demonstrate the superior performance of CDAML compared with state-of-the-art recommendation methods. [ABSTRACT FROM AUTHOR]

Traffic forecasting is crucial for location-based services. Recent studies tend to utilize dynamic graph neural networks to capture spatial-temporal correlations. However, urban traffic faces spatial heterogeneity of different lane structure at intersections, leading to different traffic patterns of lanes in different directions. It also confronts temporal heterogeneity of varying traffic trends in different time periods. Unfortunately, the influence of such spatial-temporal heterogeneity on traffic evolution are not fully considered in existing methods. To this end, this paper proposes a novel dynamic-graph-based model called HA-STGN, which integrates these heterogeneous features into spatial-learning components to model traffic networks in a finer granularity. Specifically, we design a dynamic graph model, which performs on a direction-aware road network to extract the structural information of intersections. Then, a time-sensitive attention mechanism is proposed to perceive the effect of time by introducing explicit temporal features. Moreover, an adaptive fusion module is provided to balance the spatial-temporal information adaptively. Finally, extensive experiments are conducted on two real-world datasets to verify the effectiveness of our model. The results show that our proposed HA-STGN can effectively capture spatial-temporal dependencies and outperform all the baseline methods. [ABSTRACT FROM AUTHOR]

Session-based recommendation aims to predict the next item to be interacted by a specific type of behavior (e.g., click or purchase) within a session. However, the main challenge comes from the lack of interactions in the target behavior. Despite state-of-the-art approaches aim to alleviate this issue by incorporating auxiliary behaviors through multi-behavior modeling, they are still weak in supporting cold-start recommendation due to the limited generalization ability. Having witnessed the effectiveness of meta-optimized models for few-shot learning, in this paper, we propose a memory-augmented meta-learning framework for session-based target behavior recommendation. It adopts meta-learning to learn well-generalized global sharing initialization parameters for all sessions, and derives personalized local parameters for each session through fine-tuning. Particularly, we first extract multi-behavior characteristics to derive dynamic user intentions within a session. Then we apply soft-clustering in meta-learning based on well-designed memory structures, so that multi-behavior sessions with similar intention could share related knowledge. The experimental results on two datasets show that our MMFSR model effectively outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Introduction and hypothesis: Human menopause transition and post-menopausal syndrome, driven by reduced ovarian activity and estrogen levels, are associated with an increased risk for symptoms including but not limited to sexual dysfunction, metabolic disease, and osteoporosis. Current treatments are limited in efficacy and may have adverse consequences, so investigation for additional treatment options is necessary. Previous studies have demonstrated that percutaneous tibial nerve stimulation (PTNS) and electro-acupuncture near the tibial nerve are minimally invasive treatments that increase vaginal blood perfusion or serum estrogen in the rat model. We hypothesized that PTNS would protect against harmful reproductive and systemic changes associated with menopause. Methods: We examined the effects of twice-weekly PTNS (0.2 ms pulse width, 20 Hz, 2× motor threshold) under ketamine-xylazine anesthesia in ovariectomized (OVX) female Sprague-Dawley rats on menopause-associated physiological parameters including serum estradiol, body weight, blood glucose, bone health, and vaginal blood perfusion. Rats were split into three groups (n = 10 per group): (1) intact control (no stimulation), (2) OVX control (no stimulation), and (3) OVX stimulation (treatment group). Results: PTNS did not affect serum estradiol levels, body weight, or blood glucose. PTNS transiently increased vaginal blood perfusion during stimulation for up to 5 weeks after OVX and increased areal bone mineral density and yield load of the right femur (side of stimulation) compared to the unstimulated OVX control. Conclusions: PTNS may ameliorate some symptoms associated with menopause. Additional studies to elucidate the full potential of PTNS on menopause-associated symptoms under different experimental conditions are warranted. [ABSTRACT FROM AUTHOR]

The unprecedented growth of publications in many research domains brings the great convenience for tracing and analyzing the evolution and development of research topics. Despite the significant contributions made by existing studies, they usually extract topics from the titles of papers, instead of obtaining topics from the authoritative sessions provided by venues (e.g., AAAI, NeurIPS, and SIGMOD). To make up for the shortcoming of existing work, we develop a novel framework namely RTTP(Research Topic Trend Prediction). Specifically, the framework contains the following two components: (1) a topic alignment strategy called TAS is designed to obtain the detailed contents of research topics in each year, (2) an enhanced prediction network called EPN is designed to capture the research trend of known years for prediction. In addition, we construct two real-world datasets of specific research domains in computer science, i.e., database and data mining, computer architecture and parallel programming. The experimental results demonstrate that the problem is well solved and our solution outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Purpose: Although papillary thyroid cancer (PTC) has a low mortality rate, the rate of recurrence remains relatively high. This study aims to develop a molecular signature to predict the recurrence of PTC. Methods: A total of 333 PTC patients' data from The Cancer Genome Atlas (TCGA) were included. We calculated tumor mutation burden (TMB) and analyzed the mutation status of BRAF and TERT promoter. Results: Tumor recurrence occurred in 17 of 263 cases in TMB-L patients versus 14 of 70 cases in TMB-H patients (hazard ratio [HR], 3.55; 95% confidence interval [CI], 1.75–7.21; P < 0.001). The HR for recurrence in TMB-H patients remained significant after adjustment for classical clinicopathologic factors (patient age, gender, extrathyroidal extension and lymph node metastasis). These clinical factors had no effect on recurrence rate in TMB-L patients, but had a strong adverse effect on the prognosis of TMB-H patients. Compared with TMB-L patients lacking mutation, the HR (95% CI) of recurrence for TMB-H patients with coexisting BRAF V600E and/or TERT C228/250 T mutations was 6.68 (2.41–18.57), which remained significant after adjustment for clinicopathological factors. The mutation status of BRAF V600E and TERT C228/250 T had little effect on PTC recurrence in TMB-L patients. Either of the mutation was associated with high recurrence rate in TMB-H patients. Conclusions: The presence of BRAF V600E and/or TERT promoter mutations denotes a high risk of recurrence in TMB-H patients. This represents a powerful molecular prognostic genotype that can help predict patients with the highest risk of recurrence. [ABSTRACT FROM AUTHOR]

Travel time estimation (TTE) is an important research topic in many geographic applications for smart city research. However, existing approaches either ignore the impact of transportation modes, or assume the mode information is known for each training trajectory and the query input. In this paper, we propose a multi-task learning model for travel time estimation called MTLM, which recommends the appropriate transportation mode for users, and then estimates the related travel time of the path. It integrates transportation-mode recommendation task and travel time estimation task to capture the mutual influence between them for more accurate TTE results. Furthermore, it captures spatio-temporal dependencies and transportation mode effect by learning effective representations for TTE. It combines the transportation-mode recommendation loss and TTE loss for training. Extensive experiments on real datasets demonstrate the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

This study aimed to compare the surgical safety and outcomes of the transoral endoscopic thyroidectomy vestibular approach (TOETVA) and gasless endoscopic thyroidectomy transaxillary approach (GETTA). This retrospective study assessed 150 patients managed with the TOETVA at the Yantai Yuhuangding hospital and 150 patients managed via the GETTA at the Zhenjiang Provincial People's Hospital. The procedures were compared in terms of workspace creation time, operating time, complications, post-operative complaints, cosmetic satisfaction, and the efficacy of central neck lymph-node dissection. There was no significant between-group difference in terms of post-operative complications. The average workspace creation and operating times were significantly shorter for GETTA than for TOETVA (P values for both < 0.001). The average number of lymph nodes dissected from the central compartment of the neck was higher in the TOETVA group than in the GETTA group (7.2 ± 4.6 vs. 3.9 ± 3.0, P < 0.001). The mean swallowing impairment index-6 scores at 1 month were significantly lower in the GETTA group than in the TOETVA group (1.5 ± 1.2 vs 2.6 ± 1.4, P < 0.001). Over 97% of all patients (both groups) were either satisfied or very satisfied with the cervical cosmetic outcomes at 3 months post-surgery (P = 0.099). TOETVA and GETTA are both safe procedures with good cervical cosmetic outcomes for well-selected patients. Although TOETVA is more efficacious in terms of central lymph nodes dissection, GETTA has a greater time–cost advantage. [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]

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]

A Hyperledger Fabric is a popular permissioned blockchain platform and has great commercial application prospects. However, the limited transaction throughput of Hyperledger Fabric hampers its performance, especially when transactions with concurrency conflicts are initiated. In this paper, we focus on transactions with concurrency conflicts and propose solutions to optimize the performance of Hyperledger Fabric. Firstly, we propose a novel method LMLS to improve the Write-Write Conflict. This method introduces a lock mechanism in the transaction flow to enable some conflicting transactions to be marked at the beginning of the transaction process. And indexes are added to conflicting transactions to optimize the storage of the ledger. Secondly, we propose a cache-based method to improve the Read-Write Conflict. The cache is used to speed up reading data, and a cache log is added to Hyperledger Fabric to ensure the data consistency. Extensive experiments demonstrate that the proposed novel methods can significantly increase transaction throughput in the case of concurrency conflicts, and maintain high efficiency in transactions without concurrency conflicts. [ABSTRACT FROM AUTHOR]

Point of interest (POI) recommendation is of great value for both service providers and users. However, it is hard due to data scarcity. To this end, in this paper, we propose a transfer learning based deep neural model, which fuses valueable cross-domain knowledge to achieve more accurate POI recommendation. We first learn the user's spatial and non-spatial preferences based on their historical POI interactions. The model further captures user interactions in other domains and introduces useful preferences into POI recommendations, which can address data sparsity problems. Compared to the matrix factorization based cross-domain techniques, our method utilizes deep transfer learning, which can learn complex user-item interaction relationships and accurately capture user general preferences to transfer. Finally, we evaluate the proposed model using three real-world datasets. The experimental results show that our model significantly outperforms the state-of-the-art approaches for POI recommendation. [ABSTRACT FROM AUTHOR]

Multi-objective spatial keyword query aims to find a set of objects that are reasonably distributed in spatial, with all query objectives to be satisfied. However, existing approaches mainly take the coverage of query keywords into account, while leaving the semantics of the textual data to be largely ignored. This limits us to return those rational results that are synonyms but morphologically different. To address this problem, this paper studies the problem of multi-objective spatial keyword query with semantics, and targets to return the object set that is optimum regarding to both spatial proximity and semantic relevance. Specifically, we take advantage of the probabilistic topic model and locality sensitive hashing (LSH), so that all query objectives can be satisfied in terms of their semantics. Afterwards, a novel indexing structure called LIR-tree is designed to integrate the spatial and semantic information of all objects in a balanced way. On top of the LIR-tree, we further propose a distance-owner based query processing algorithm, which provides tight bounds to achieve superb pruning effect in the searching phase. To speed up the processing, a distance owners based replacement strategy can be used to conduct approximate querying more efficiently. Empirical study based on a real dataset demonstrates the good effectiveness and efficiency of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Urodynamic studies, used to understand bladder function, diagnose bladder disease, and develop treatments for dysfunctions, are ideally performed with awake subjects. However, in small and medium-sized animal models, anesthesia is often required for these procedures and can be a research confounder. This study compared the effects of select survival agents (dexmedetomidine, alfaxalone, and propofol) on urodynamic (Δpressure, bladder capacity, bladder compliance, non-voiding contractions, bladder pressure slopes) and anesthetic (change in heart rate [∆HR], average heart rate [HR], reflexes, induction/recovery times) parameters in repeated cystometrograms across five adult male cats. The urodynamic parameters under isoflurane and α-chloralose were also examined in terminal procedures for four cats. Δpressure was greatest with propofol, bladder capacity was highest with α-chloralose, non-voiding contractions were greatest with α-chloralose. Propofol and dexmedetomidine had the highest bladder pressure slopes during the initial and final portions of the cystometrograms respectively. Cats progressed to a deeper plane of anesthesia (lower HR, smaller ΔHR, decreased reflexes) under dexmedetomidine, compared to propofol and alfaxalone. Time to induction was shortest with propofol, and time to recovery was shortest with dexmedetomidine. These agent-specific differences in urodynamic and anesthetic parameters in cats will facilitate appropriate study-specific anesthetic choices. [ABSTRACT FROM AUTHOR]

As a non-linear data structure consisting of nodes and edges, the graph data span many different domains. In the real world, applications based on such data structures are always time-sensitive, that is, the value of graph data tends to decrease with time. Furthermore, the application based on spatio-temporal graph is one of the typical representatives of time-sensitive, since the time dimension is an inherent feature of spatio-temporal data. The Distributed Stream Processing Engine (DSPE) seems an excellent choice for the above requirement, which is commonly partitioned and concurrently processed by a number of threads to maximize the throughput. However, it is not feasible to do such mission directly using the traditional DSPE. In this paper, we propose a computational model suitable for handling the spatio-temporal graph in DSPE, by reconstructing the DSPE's parallel processing slots. Specifically, our proposal includes a general processing framework to deal with the data structure of the spatio-temporal graph, a state information compensation mechanism to ensure the correctness of processing such stateful operation in DSPE, a lightweight summary information calculation method to ensure the performance of the system. Empirical studies on real-world stream applications validate the usefulness of our proposals and prove the considerable advantage of our approaches over state-of-the-art solutions in the literature. [ABSTRACT FROM AUTHOR]

Collaborative Web services QoS prediction (CQoSP) has been proved to be an effective tool to predict unknown QoS values of services. Recently a number of efforts have been made in this area, focusing on improving the accuracy of prediction. In this paper, we consider a novel kind of CQoSP, shared CQoSP, where multiple parties share their data with each other in order to provide more accurate prediction than a single party could do. To encourage data sharing, we propose a privacy-preserving framework which enables shared collaborative QoS prediction without leaking the private information of the involved party. Our framework is based on differential privacy, a rigorous and provable privacy model. We conduct extensive experiments on a real Web services QoS dataset. Experimental results show the proposed framework increases prediction accuracy while ensuring the privacy of data owners. [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]

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]

Collaborative Web services QoS prediction has proved to be an important tool to estimate accurately personalized QoS experienced by individual users, which is beneficial for a variety of operations in the service ecosystem, such as service selection, composition and recommendation. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We introduce differential privacy, a rigorous and provable privacy model, into the process of collaborative QoS prediction. We first present DPS, a method that disguises a user's observed QoS values by applying differential privacy to the user's QoS data directly. We show how to integrate DPS with two representative collaborative QoS prediction approaches. To improve the utility of the disguised QoS data, we present DPA, another QoS disguising method which first aggregates a user's QoS data before adding noise to achieve differential privacy. We evaluate the proposed methods by conducting extensive experiments on a real world Web services QoS dataset. Experimental results show our approach is feasible in practice. [ABSTRACT FROM AUTHOR]

Graph Pattern Matching (GPM) plays a significant role in many real applications, where many applications often need to find Top-K matches of a specific node, (named as the designated node vd) based on a pattern graph, rather than the entire set of matching. However, the existing GPM methods for matching the designated node vd in social graphs do not consider the social contexts like the social relationships, the social trust and the social positions which commonly exist in real applications, like the experts recommendation in social graphs, leading to deliver low quality designated nodes. In this paper, we first propose the conText-Aware Graph pattern based Top-K designed nodes finding problem (TAG-K), which involves the NP-Complete Multiple Constrained GPM problem, and thus it is NP-Complete. To address the efficiency and effectiveness issues of TAG-K in large-scale social graphs, we propose two indices, MA-Tree and SSC-Index, which can help efficiently find the Top-K matching. Furthermore, we propose an approximation algorithm, A-TAG-K. Using real social network datasets, we experimentally verify that A-TAG-K outperforms the existing methods in both efficiency and effectiveness for solving the TAG-K problem. [ABSTRACT FROM AUTHOR]

Visible light photocatalytic Ti3+-TiO2-rGO compound with controllable photocatalytic performance was prepared by a one-step microemulsion method, via altering the amount of lattice defects in TiO2. A facile method for regulating Ti3+ doping concentration and the lattice defects was obtained based on the aggregation effect of the positively charged Ti species on GO surface in the micelles. Raman and X-ray photoelectron spectroscopy were used to confirm the existence of Ti3+ and rGO in the self-doped samples and electron spin resonance (ESR) was adapted to semi-quantitatively analyze the relationship between lattice defects and the amount of GO added. It was found that the ESR signal rose progressively in intensity with the increase in the amount of GO addition, indicating that GO could regulate the lattice defects existed in the compound. A possible regulating and enhancing mechanism of visible light photocatalytic activity for self-doped Ti3+-TiO2-rGO was also proposed. [ABSTRACT FROM AUTHOR]

With the rapid development of location-based services, a particularly important aspect of start-up marketing research is to explore and characterize points of interest (PoIs) such as restaurants and hotels on maps. However, due to the lack of direct access to PoI databases, it is necessary to rely on existing APIs to query PoIs within a region and calculate PoI statistics. Unfortunately, public APIs generally impose a limit on the maximum number of queries. Therefore, we propose effective and efficient sampling methods based on road networks to sample PoIs on maps and provide unbiased estimators for calculating PoI statistics. In general, the more intense the roads, the denser the distribution of PoIs is within a region. Experimental results show that compared with state-of-the-art methods, our sampling methods improve the efficiency of aggregate statistical estimations. [ABSTRACT FROM AUTHOR]

The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword query methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial Web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose novel indexing structures, which integrate spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare them with other baseline algorithms. [ABSTRACT FROM AUTHOR]

In the graph-based security game, the defender allocates security resources strategically to protect targets against the adversary. In this paper, firstly, we come up with a new double oracle algorithm for scheduling resources optimally on nodes in graph-based security games. The police scattered on the street can only detect those terrorists on that street, while the police at the intersection place can detect all the terriorists on all the streets crisscrossing the intersection. Secondly, in real world situation, even the police meets the criminals at the same place, criminals still could escape. To match the real world situation, we define a parameter called detection probability, representing the chance the attacker is caught when they are checked by the defenders. Thirdly, we design a double oracle algorithm to find the equilibrium. But the computational complexity of best response oracles are extremely high. We design greedy algorithms and combine them with best response oracles to improve the algorithm efficiency without loss of correctness. [ABSTRACT FROM AUTHOR]

Few reports have been published on the optimization of nanostructures while doping with the Ti (Ti3+/Ti4+) elemental. Here, Ti-doped α-Fe2O3 nanorod arrays prepared via the hydrothermal method with the addition of TiCl3 as the Ti source and urea as the morphological regulator were used as photoanodes in photoelectrochemical cells. In the process of a hydrothermal reaction, Ti elemental was incorporated into α-Fe2O3 photoanodes using TiCl3 as precursor and urea was used as the morphological regulator to assist α-Fe2O3 to form nanorod arrays. The photoelectrochemical performance of the as-prepared Ti-doped α-Fe2O3 nanorod array (TF1) photoanodes exhibited a remarkable photocurrent of 0.22 mA cm−2 (275 times higher than that of the undoped α-Fe2O3 nanorod arrays) at 1.23 V (vs. RHE) and a 150-mV cathodic shift of photocurrent onset potential. The enhanced photoelectrochemical performance was ascribed to the synergistic effect of the one-dimensional nanoarray structure and the Ti elemental doping, which increased donor density and reduced photogenerated electron-hole recombination. [ABSTRACT FROM AUTHOR]

A location-aware publish/subscribe (pub/sub) system is gaining more and more interest in both industry and academia with the rapid progress of mobile Internet and the rising popularity of smart-phones. Nowadays, with the booming of E-commerce, OTO (online-to-offline) services are gaining more and more popularity, which results in millions of products with different structured descriptions and locations. To meet this requirement, a pub/sub system should handle subscriptions with location-aware boolean expressions to present users' interests. In this paper, we propose an efficient location-aware pub/sub index for boolean expressions, called RP-trees. RP-trees integrates an R-tree index and a boolean expression index together, can efficiently and simultaneously prune boolean expressions and spatial dimensions. RP-trees is also extensible to support complex environment such as prefix-matching and subscriptions in format of CNF and DNF. Our experimental results show that RP-trees achieves good performance on a synthetic dataset and two real-world datasets (58 city and ebay). [ABSTRACT FROM AUTHOR]

The proliferation of GPS-enabled smart mobile devices enables us to collect a large-scale trajectories of moving objects with GPS tags. While the raw trajectories that only consists of positional information have been studied extensively, many recent works have been focusing on enriching the raw trajectories with semantic knowledge. The resulting data, called activity trajectories, embed the information about behaviors of the moving objects and support a variety of applications for better quality of services. In this paper, we propose a Top-k Spatial Keyword (T kSK) query for activity trajectories, with the objective to find a set of trajectories that are not only close geographically but also meet the requirements of the query semantically. Such kind of query can deliver more informative results than existing spatial keyword queries for static objects, since activity trajectories are able to reflect the popularity of user activities and reveal preferable combinations of facilities. However, it is a challenging task to answer this query efficiently due to the inherent difficulties in indexing trajectories as well as the new complexity introduced by the textual dimension. In this work, we provide a comprehensive solution, including the novel similarity function, hybrid indexing structure, efficient search algorithm and further optimizations. Extensive empirical studies on real trajectory set have demonstrated the scalability of our proposed solution. [ABSTRACT FROM AUTHOR]

Recently, Reverse k Nearest Neighbors (R kNN) queries, returning every answer for which the query is one of its k nearest neighbors, have been extensively studied on the database research community. But the R kNN query cannot retrieve spatio-textual objects which are described by their spatial location and a set of keywords. Therefore, researchers proposed a RST kNN query to find these objects, taking both spatial and textual similarity into consideration. However, the RST kNN query cannot control the size of answer set and to be sorted according to the degree of influence on the query. In this paper, we propose a new problem Ranked Reverse Boolean Spatial Keyword Nearest Neighbors query called Ranked-RBSKNN query, which considers both spatial similarity and textual relevance, and returns t answers with most degree of influence. We propose a separate index and a hybrid index to process such queries efficiently. Experimental results on different real-world and synthetic datasets show that our approaches achieve better performance. [ABSTRACT FROM AUTHOR]

The pervasiveness of location-acquisition and mobile computing techniques has generated massive spatial trajectory data, which has brought great challenges to the management and analysis of such a big data. In this paper, we focus on the sub-trajectory dataset profiling problem, and aim to extract the representative sub-trajectories from the raw trajectory as a subset, called profile, which can best describe the whole dataset. This problem is very challenging subject to finding the most representative sub-trajectories set by trading off the size and quality of the profile. To tackle this problem, we model the features of the trajectory dataset from the aspects of density, speed and the direction flow. Meanwhile we present our two-step method to select the representative trajectories based on the feature model. First, a novel trajectory segmentation algorithm is applied on a raw trajectory to identify the representative segments concerning their feature representativeness and automatically estimate the number of segments and the segment borders. Then, a sub-trajectory profiling method is performed to yield the most representative sub-trajectories in the dataset, based on a local heuristic evolution strategy. We evaluate our method based on extensive experiments by using two real-world trajectory datasets generated by over 12,000 taxicabs in Beijing and Shanghai. The results demonstrate the efficiency and effectiveness of our methods in different applications. [ABSTRACT FROM AUTHOR]

Filamentous Bangia, which are distributed extensively throughout the world, have simple and similar morphological characteristics. Scientists can classify these organisms using molecular markers in combination with morphology. We successfully sequenced the complete nuclear ribosomal DNA, approximately 13 kb in length, from a marine Bangia population. We further analyzed the small subunit ribosomal DNA gene (nrSSU) and the internal transcribed spacer (ITS) sequence regions along with nine other marine, and two freshwater Bangia samples from China. Pairwise distances of the nrSSU and 5.8S ribosomal DNA gene sequences show the marine samples grouping together with low divergences (00.003; 0-0.006, respectively) from each other, but high divergences (0.123-0.126; 0.198, respectively) from freshwater samples. An exception is the marine sample collected from Weihai, which shows high divergence from both other marine samples (0.063-0.065; 0.129, respectively) and the freshwater samples (0.097; 0.120, respectively). A maximum likelihood phylogenetic tree based on a combined SSU-ITS dataset with maximum likelihood method shows the samples divided into three clades, with the two marine sample clades containing Bangia spp. from North America, Europe, Asia, and Australia; and one freshwater clade, containing Bangia atropurpurea from North America and China. [ABSTRACT FROM AUTHOR]

Online trip planning is a popular service that has facilitated a lot of people greatly. However, little attention has been paid to personalized trip planning which is even more useful. In this paper, we define a highly expressive personalized route planning query-the Personalized and Sequenced Route ( PSR) Query which considers both personalization and sequenced constraint, and propose a novel framework to deal with the query. The framework consists of three phases: guessing, crossover and refinement. The guessing phase strives to obtain one high quality route as the baseline to bound the search space into a circular region. The crossover phase heuristically improve the quality of multiple guessed routes via a modified genetic algorithm, which further narrows the radius of the search space. The refinement phase backwardly examines each candidate point and partial route to rule out impossible ones. The combination of these phases can efficiently and effectively narrow our search space via a few iterations. In the experiment part, we firstly show our evaluation results of each phase separately, proving the effectiveness of each phase. Then, we present the evaluation results of the combination of them, which offers insight into the merits of the proposed framework. [ABSTRACT FROM AUTHOR]