Learned Spatio-Temporal Texture Descriptors for RGB-D Human Action Recognition

Due to the recent arrival of Kinect, action recognition with depth images has attracted researchers' wide attentions and various descriptors have been proposed, where Local Binary Patterns (LBP) texture descriptors possess the properties of appearance invariance. However, the LBP and its variants are most artificially-designed, demanding engineers' strong prior knowledge and not discriminative enough for recognition tasks. To this end, this paper develops compact spatio-temporal texture descriptors, i.e. 3D-compact LBP (3D-CLBP) and local depth patterns (3D-CLDP), for color and depth videos in the light of compact binary face descriptor learning in face recognition. Extensive experiments performed on three standard datasets, 3D Online Action, MSR Action Pairs and MSR Daily Activity 3D, demonstrate that our method is superior to most comparative methods in respects of performance and can capture spatial-temporal texture cues in videos

Coverage Optimization Strategy for WSN based on Energy-aware

In order to optimize the wireless sensor network coverage, this paper designs a coverage optimization strategy for wireless sensor network (EACS) based on energy-aware. Under the assumption that the geographic positions of sensor nodes are available, the proposed strategy consists of energy-aware and network coverage adjustment. It is restricted to conditions such as path loss, residual capacity and monitored area and according to awareness ability of sensors, it would adjust the monitored area, repair network hole and kick out the redundant coverage. The purpose is to balance the energy distribution of working nodes, reduce the number of “dead” nodes and balance network energy consumption. As a result, the network lifetime is expanded. Simulation results show that: EACS effectively reduces the number of working nodes, improves network coverage, lowers network energy consumption while ensuring the wireless sensor network coverage and connectivity, so as to balance network energy consumption

Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants' behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user's activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people's circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN Data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users' inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion

tRNA-Derived Fragments as Novel Predictive Biomarkers for Trastuzumab-Resistant Breast Cancer

Background/Aims: Resistance to trastuzumab remains a common challenge to HER-2 positive breast cancer. Up until now, the underlying mechanism of trastuzumab resistance is still unclear. tRNA-derived small non-coding RNAs, a new class of small non-coding RNA (sncRNAs), have been observed to play an important role in cancer progression. However, the relationship between tRNA-derived fragments and trastuzumab resistance is still unknown. Methods: We detected the levels of tRNA-derived fragments expression in normal breast epithelial cell lines, trastuzumab-sensitive and -resistant breast cancer cell lines using high-throughput sequencing. qRT-PCR was conducted to validate the differentially expressed fragments in serums from trastuzumab-sensitive and -resistant patients. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the power of specific tRNA-derived fragments. Progression-free survival (PFS) was analyzed using Cox-regression. Results: Our sequence results showed that tRNA-derived fragments were differentially expressed in the HBL-100, SKBR3, and JIMT-1 cell lines. tRF-30-JZOYJE22RR33 and tRF-27-ZDXPHO53KSN were found significantly upregulated in trastuzumab-resistant patients compared to sensitive individuals, and the ROC analysis showed that tRF-30-JZOYJE22RR33 and tRF-27-ZDXPHO53KSN were correlated with trastuzumab resistance. In a multivariate analysis, higher levels of tRF-30-JZOYJE22RR33 and tRF-27-ZDXPHO53KSN expression were associated with significantly shorter PFS in patients with metastatic HER-2 positive breast cancer. Conclusion: Our results suggest that tRF-30-JZOYJE22RR33 and tRF-27-ZDXPHO53KSN play important roles in trastuzumab resistance. Patients with high levels of tRF-30-JZOYJE22RR33 and tRF-27-ZDXPHO53KSN expression benefitted less from trastuzumab-based therapy than those that express lower-levels of these molecules. tRF-30-JZOYJE22RR33 and tRF-27-ZDXPHO53KSN may be potential biomarkers and intervention targets in the clinical treatment of trastuzumab-resistant breast cancer

Disparities of time trends and birth cohort effects on invasive breast cancer incidence in Shanghai and Hong Kong pre- and post-menopausal women

© 2017 The Author(s). Background: Breast cancer is the leading cause of cancer morbidity among Shanghai and Hong Kong women, which contributes to 20-25% of new female cancer incidents. This study aimed to describe the temporal trend of breast cancer and interpret the potential effects on the observed secular trends. Methods: Cancer incident data were obtained from the cancer registries. Age-standardized incidence rate was computed by the direct method using the World population of 2000. Average annual percentage change (AAPC) in incidence rate was estimated by the Joinpoint regression. Age, period and cohort effects were assessed by using a log-linear model with Poisson regression. Results: During 1976-2009, an increasing trend of breast cancer incidence was observed, with an AAPC of 1.73 [95% confidence interval (CI): 1.54-1.92)] for women in Hong Kong and 2.83 (95% CI, 2.26-3.40) in Shanghai. Greater upward trends were revealed in Shanghai women aged 50 years old or above (AAPC = 3.09; 95% CI, 1.48-4.73). Using age at 50 years old as cut-point, strong birth cohort effects were shown in both pre- and post-menopausal women, though a more remarkable effect was suggested in Shanghai post-menopausal women. No evidence for a period effect was indicated. Conclusions: Incidence rate of breast cancer has been more speedy in Shanghai post-menopausal women than that of the Hong Kong women over the past 30 years. Decreased birth rate and increasing environmental exposures (e.g., light-at-night) over successive generations may have constituted major impacts on the birth cohort effects, especially for the post-menopausal breast cancer; further analytic studies are warranted.Link_to_subscribed_fulltex