COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference

The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.Comment: BuildSys 201

Sucrose Facilitates Rhizome Development of Perennial Rice (<i>Oryza longistaminata</i>)

Compared with annual crops, perennial crops with longer growing seasons and deeper root systems can fix more sunlight energy, and have advantages in reducing soil erosion and saving water, fertilizer and pesticide inputs. Rice is one of the most important food crops in the world. Perennial rice can be of great significance for protecting the ecological environment and coping with the shortage of young farmers due to urbanization. Oryza longistaminata (OL) is a rhizomatous wild rice with an AA genome and has strong biotic and abiotic resistances. The AA genome makes OL easy to cross with cultivated rice, thus making it an ideal donor material for perennial rice breeding. Sucrose plays an important role in the development and growth of plants. In this study, OL seedlings were cultured in medium with different concentrations of sucrose, and it was found that sucrose of appropriate concentrations can promote the sprout of basal axillary buds and the subsequent development of rhizomes. In order to explore the molecular mechanism, comparative transcriptome analysis was carried out with OL cultured under two concentrations of sucrose, 20 g/L and 100 g/L, respectively. The results showed that the boost of sucrose to rhizome elongation may be due to the glucose and fructose, hydrolyzed from the absorbed sucrose by vacuolar acid invertase. In addition, the consequent increased osmotic pressure of the cells would promote water absorption, which is benefit for the cell elongation, eventually causing the rhizome elongation. These results may provide a reference for elucidating the regulatory mechanism of sucrose on the rhizome development of OL

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution&rsquo;s difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution’s difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks

On improving the accuracy of Visible Light Positioning system based PAPR reduction schemes

International audienceDC-biased optical Orthogonal Frequency Division Multiplexing (DCO-OFDM)-based visible light positioning (VLP) technology along with the Received Signal Strength(RSS) positioning algorithm is widely used to achieve centimeter level positioning accuracy for incoming 5G era, especially indoor environment. However, the DCO-OFDM has the peak-to-average power ratio (PAPR) issue which imposes the nonlinear distortion and it will directly affect the positioning accuracy in the VLP system. The PAPR reduction scheme is urgently needed. Therefore , in this paper, the impact of PAPR reduction scheme on positioning accuracy is investigated. The positioning accuracy with and without the selected mapping (SLM)-based PAPR reduction method are compared. The preliminary simulation results show that the positioning accuracy has been improved by 7.07 cm after using the SLM-based PAPR reduction method. Index Terms-5G, Visible Light Positioning(VLP), Visible Light Communication(VLC), Received Signal Strength, Localiza-tion, DC-biased optical orthogonal frequency division multiplex-ing (DCO-OFDM), peak-to-average power ratio (PAPR), selected mapping (SLM

Thermoresponsive nanocomposite gel for local drug delivery to suppress the growth of glioma by inducing autophagy

<p>Although the treatments of malignant glioma include surgery, radiotherapy and chemotherapy by oral drug administration, the prognosis of patients with glioma remains very poor. We developed a polyethylene glycol-dipalmitoylphosphatidyle- thanoiamine (mPEG-DPPE) calcium phosphate nanoparticles (NPs) injectable thermoresponsive hydrogel (nanocomposite gel) that could provide a sustained and local delivery of paclitaxel (PTX) and temozolomide (TMZ). In addition, the proportion of PTX and TMZ for the optimal synergistic antiglioma effect on C6 cells was determined to be 1:100 (w/w) by the Chou and Talalay method. Our results clearly indicated that the autophagy induced by PTX:TMZ NPs plays an important role in regulating tumor cell death, while autophagy inhibition dramatically reverses the antitumor effect of PTX:TMZ NPs, suggesting that antiproliferative autophagy occurs in response to PTX:TMZ NPs treatment. The antitumor efficacy of the PTX:TMZ NP-loaded gel was evaluated in situ using C6 tumor-bearing rats, and the PTX:TMZ NP-loaded gel exhibited superior antitumor performance. The antitumor effects of the nanocomposite gel in vivo were shown to correlate with autophagic cell death in this study. The in vivo results further confirmed the advantages of such a strategy. The present study may provide evidence supporting the development of nanomedicine for potential clinical application.</p

sj-docx-1-taj-10.1177_20406223221125691 – Supplemental material for Characteristics of myeloproliferative neoplasm-associated portal hypertension and endoscopic management of variceal bleeding

Supplemental material, sj-docx-1-taj-10.1177_20406223221125691 for Characteristics of myeloproliferative neoplasm-associated portal hypertension and endoscopic management of variceal bleeding by Xiaoquan Huang, Ming Zhang, Yingjie Ai, Siyu Jiang, Mei Xiao, Lifen Wang, Yourong Jian, Yuzheng Zhuge, Chunqing Zhang and Shiyao Chen in Therapeutic Advances in Chronic Disease</p

sj-tiff-2-taj-10.1177_20406223221125691 – Supplemental material for Characteristics of myeloproliferative neoplasm-associated portal hypertension and endoscopic management of variceal bleeding

Supplemental material, sj-tiff-2-taj-10.1177_20406223221125691 for Characteristics of myeloproliferative neoplasm-associated portal hypertension and endoscopic management of variceal bleeding by Xiaoquan Huang, Ming Zhang, Yingjie Ai, Siyu Jiang, Mei Xiao, Lifen Wang, Yourong Jian, Yuzheng Zhuge, Chunqing Zhang and Shiyao Chen in Therapeutic Advances in Chronic Disease</p