Piecewise Trend Approximation: A Ratio-Based Time Series Representation

A time series representation, piecewise trend approximation (PTA), is proposed to improve efficiency of time series data mining in high dimensional large databases. PTA represents time series in concise form while retaining main trends in original time series; the dimensionality of original data is therefore reduced, and the key features are maintained. Different from the representations that based on original data space, PTA transforms original data space into the feature space of ratio between any two consecutive data points in original time series, of which sign and magnitude indicate changing direction and degree of local trend, respectively. Based on the ratio-based feature space, segmentation is performed such that each two conjoint segments have different trends, and then the piecewise segments are approximated by the ratios between the first and last points within the segments. To validate the proposed PTA, it is compared with classical time series representations PAA and APCA on two classical datasets by applying the commonly used K-NN classification algorithm. For ControlChart dataset, PTA outperforms them by 3.55% and 2.33% higher classification accuracy and 8.94% and 7.07% higher for Mixed-BagShapes dataset, respectively. It is indicated that the proposed PTA is effective for high dimensional time series data mining

An Efficient Universal Noise Removal Algorithm Combining Spatial Gradient and Impulse Statistic

We propose a novel universal noise removal algorithm by combining spatial gradient and a new impulse statistic into the trilateral filter. By introducing a reference image, an impulse statistic is proposed, which is called directional absolute relative differences (DARD) statistic. Operation was carried out in two stages: getting reference image and image denoising. For denoising, we introduce the spatial gradient into the Gaussian filtering framework for Gaussian noise removal and integrate our DARD statistic for impulse noise removal, and finally we combine them together to create a new trilateral filter for mixed noise removal. Simulation results show that our noise detector has a high classification rate, especially for salt-and-pepper noise. And the proposed approach achieves great results both in terms of quantitative measures of signal restoration and qualitative judgments of image quality. In addition, the computational complexity of the proposed method is less than that of many other mixed noise filters

LDP-IDS: Local Differential Privacy for Infinite Data Streams

Streaming data collection is essential to real-time data analytics in various IoTs and mobile device-based systems, which, however, may expose end users' privacy. Local differential privacy (LDP) is a promising solution to privacy-preserving data collection and analysis. However, existing few LDP studies over streams are either applicable to finite streams only or suffering from insufficient protection. This paper investigates this problem by proposing LDP-IDS, a novel $w$-event LDP paradigm to provide practical privacy guarantee for infinite streams at users end, and adapting the popular budget division framework in centralized differential privacy (CDP). By constructing a unified error analysi for LDP, we first develop two adatpive budget division-based LDP methods for LDP-IDS that can enhance data utility via leveraging the non-deterministic sparsity in streams. Beyond that, we further propose a novel population division framework that can not only avoid the high sensitivity of LDP noise to budget division but also require significantly less communication. Based on the framework, we also present two adaptive population division methods for LDP-IDS with theoretical analysis. We conduct extensive experiments on synthetic and real-world datasets to evaluate the effectiveness and efficiency pf our proposed frameworks and methods. Experimental results demonstrate that, despite the effectiveness of the adaptive budget division methods, the proposed population division framework and methods can further achieve much higher effectiveness and efficiency.Comment: accepted to SIGMOD'2

Analysis of single-cell RNAseq identifies transitional states of T cells associated with hepatocellular carcinoma

BACKGROUND: Exhausted T cells and regulatory T cells (Tregs) comprise diverse subsets of tumor immunosuppressive microenvironment that play key roles in tumor progress. Understanding subset diversity in T cells is a critical question for developing cancer immunotherapy. METHODS: A total of 235 specimens from surgical resections of hepatocellular carcinoma (HCC) patients were examined for infiltration of exhausted T cell (Tex) in tumor and adjacent tissue. We conducted deep single-cell targeted immune profiling on CD3 RESULTS: We observed transitional differentiation of exhausted CD8 CONCLUSIONS: T cell exhaustion is a progressive process, and the gene-expression profiling displayed T cell exhaustion and anergy are different. Accordingly, it is possible that functional exhaustion is caused by the combination effects of passive defects and overactivation in stress response. The results help to understand the dynamic framework of T cells function in cancer which is important for designing rational cancer immunotherapies

Inhibition of P-Glycoprotein by HIV Protease Inhibitors Increases Intracellular Accumulation of Berberine in Murine and Human Macrophages

Background HIV protease inhibitor (PI)-induced inflammatory response in macrophages is a major risk factor for cardiovascular diseases. We have previously reported that berberine (BBR), a traditional herbal medicine, prevents HIV PI-induced inflammatory response through inhibiting endoplasmic reticulum (ER) stress in macrophages. We also found that HIV PIs significantly increased the intracellular concentrations of BBR in macrophages. However, the underlying mechanisms of HIV PI-induced BBR accumulation are unknown. This study examined the role of P-glycoprotein (P-gp) in HIV PI-mediated accumulation of BBR in macrophages. Methodology and Principal Findings Cultured mouse RAW264.7 macrophages, human THP-1-derived macrophages, Wild type MDCK (MDCK/WT) and human P-gp transfected (MDCK/P-gp) cells were used in this study. The intracellular concentration of BBR was determined by HPLC. The activity of P-gp was assessed by measuring digoxin and rhodamine 123 (Rh123) efflux. The interaction between P-gp and BBR or HIV PIs was predicated by Glide docking using Schrodinger program. The results indicate that P-gp contributed to the efflux of BBR in macrophages. HIV PIs significantly increased BBR concentrations in macrophages; however, BBR did not alter cellular HIV PI concentrations. Although HIV PIs did not affect P-gp expression, P-gp transport activities were significantly inhibited in HIV PI-treated macrophages. Furthermore, the molecular docking study suggests that both HIV PIs and BBR fit the binding pocket of P-gp, and HIV PIs may compete with BBR to bind P-gp. Conclusion and Significance HIV PIs increase the concentration of BBR by modulating the transport activity of P-gp in macrophages. Understanding the cellular mechanisms of potential drug-drug interactions is critical prior to applying successful combinational therapy in the clinic

Cooperativity among Short Amyloid Stretches in Long Amyloidogenic Sequences

Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ∼30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the β-strand-turn-β-strand motif in A-βpeptide amyloid and β-solenoid structure of HET-s(218–289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues

Petrophysical, Geochemical, and Hydrological Evidence for Extensive Fracture-Mediated Fluid and Heat Transport in the Alpine Fault's Hanging-Wall Damage Zone

International audienceFault rock assemblages reflect interaction between deformation, stress, temperature, fluid, and chemical regimes on distinct spatial and temporal scales at various positions in the crust. Here we interpret measurements made in the hanging‐wall of the Alpine Fault during the second stage of the Deep Fault Drilling Project (DFDP‐2). We present observational evidence for extensive fracturing and high hanging‐wall hydraulic conductivity (∼10−9 to 10−7 m/s, corresponding to permeability of ∼10−16 to 10−14 m2) extending several hundred meters from the fault's principal slip zone. Mud losses, gas chemistry anomalies, and petrophysical data indicate that a subset of fractures intersected by the borehole are capable of transmitting fluid volumes of several cubic meters on time scales of hours. DFDP‐2 observations and other data suggest that this hydrogeologically active portion of the fault zone in the hanging‐wall is several kilometers wide in the uppermost crust. This finding is consistent with numerical models of earthquake rupture and off‐fault damage. We conclude that the mechanically and hydrogeologically active part of the Alpine Fault is a more dynamic and extensive feature than commonly described in models based on exhumed faults. We propose that the hydrogeologically active damage zone of the Alpine Fault and other large active faults in areas of high topographic relief can be subdivided into an inner zone in which damage is controlled principally by earthquake rupture processes and an outer zone in which damage reflects coseismic shaking, strain accumulation and release on interseismic timescales, and inherited fracturing related to exhumation

A Mutual Broadcast Authentication Protocol for Wireless Sensor Networks Based on Fourier Series

This thesis presents a mural broadcast authentication protocol (MBAP) for wireless sensor networks based on Fourier series according to the issues of the main broadcast authentication protocol µ TESLA being limited in authentication delay, more initial parameters, limited time, large key chain, and network congestion. Firstly, achieving the forward authentication work for common sensor nodes to base station is based on the characteristic of continuous-integrability function f ( x ) in [ - π , π ] which could be expanded into Fourier series, including entity authentication and source attestation. Secondly, assume that f ( x ) is the quadratic form function, and achieve the reverse authentication work for base station to common sensor nodes by detecting the security of f ( x ) . The analysis results of safety performance in MBAP show that the captured nodes in WSN will not affect the security of broadcast authentication protocol and have low computation and communication cost, the base station can make broadcast randomly, and common sensor nodes can authenticate messages instantly, which solves the problem of network congestion well. The most important thing of MBAP is the mutual broadcast authentication method which ensures the security of the network greatly

Discovering generalized communities in weighted networks

Recent years have witnessed the rapid development of community detection and a large collection of algorithms has been proposed. As weights may carry crucial information, community detection in weighted networks has also attracted the researchers' interest. In this paper, an algorithm is proposed to discover generalized communities of more structural patterns in weighted networks, including overlapping communities, disassortative structure, etc. It considers the network weights in the modeling and computation to study the inference of the latent continuous structures in weighted networks. The algorithm is tested both on the benchmark graphs and the real-world network. Results show good performances and favorable properties of the algorithm