Using multiple indexes for efficient subsequence matching in time-series databases

A time-series database is a set of data sequences, each of which is a list of changing values of an object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence in a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We claim that index interpolation is a fairly effective tool to solve this problem. Index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their distinct sizes. For index interpolation, we need to decide the sizes of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes from the perspective of physical database design. Given a set of pairs 〈length, frequency〉 of query sequences to be performed in a target application and a set of window sizes for building multiple indexes, we devise a formula that estimates the overall cost of all the subsequence matchings performed in a target application. By using this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally prove the optimality as well as the effectiveness of the algorithm. Finally, we show the superiority of our approach by performing extensive experiments with a real-life stock data set and a large volume of synthetic data sets.     

CAR T Cell Therapy in Hematological Malignancies: Implications of the Tumor Microenvironment and Biomarkers on Efficacy and Toxicity

Chimeric antigen receptor (CAR) T cell therapy has ushered in a new era in cancer treatment. Remarkable outcomes have been demonstrated in patients with previously untreatable relapsed/refractory hematological malignancies. However, optimizing efficacy and reducing the risk of toxicities have posed major challenges, limiting the success of this therapy. The tumor microenvironment (TME) plays an important role in CAR T cell therapy’s effectiveness and the risk of toxicities. Increasing research studies have also identified various biomarkers that can predict its effectiveness and risk of toxicities. In this review, we discuss the various aspects of the TME and biomarkers that have been implicated thus far and discuss the role of creating scoring systems that can aid in further refining clinical applications of CAR T cell therapy and establishing a safe and efficacious personalised medicine for individuals.     

Modeling and performance analysis of a novel M-shaped piezoelectric energy harvester employing magnets

Journal of Mechanical Science and Technology - Piezoelectric energy harvesters convert the vibration energy of a mechanical system into the electrical energy. Among them, cantilever type is the...     

Dual-wavelength cascaded Raman fibre laser

Dual-wavelength lasing at 1480 and 1500 nm has been demonstrated from a cascaded Raman fibre laser with a WDM coupler and two pairs of Bragg gratings. Intensity-adjustable, wavelength-tunable laser operation was achieved by tensile stress wavelength tuning of the gratings     

Single-Step Fast Tissue Clearing of Thick Mouse Brain Tissue for Multi-Dimensional High-Resolution Imaging

Recent advances in optical clearing techniques have dramatically improved deep tissue imaging by reducing the obscuring effects of light scattering and absorption. However, these optical clearing methods require specialized equipment or a lengthy undertaking with complex protocols that can lead to sample volume changes and distortion. In addition, the imaging of cleared tissues has limitations, such as fluorescence bleaching, harmful and foul-smelling solutions, and the difficulty of handling samples in high-viscosity refractive index (RI) matching solutions. To address the various limitations of thick tissue imaging, we developed an Aqueous high refractive Index matching and tissue Clearing solution for Imaging (termed AICI) with a one-step tissue clearing protocol that was easily made at a reasonable price in our own laboratory without any equipment. AICI can rapidly clear a 1 mm thick brain slice within 90 min with simultaneous RI matching, low viscosity, and a high refractive index (RI = 1.466), allowing the imaging of the sample without additional processing. We compared AICI with commercially available RI matching solutions, including optical clear agents (OCAs), for tissue clearing. The viscosity of AICI is closer to that of water compared with other RI matching solutions, and there was a less than 2.3% expansion in the tissue linear morphology during 24 h exposure to AICI. Moreover, AICI remained fluid over 30 days of air exposure, and the EGFP fluorescence signal was only reduced to ~65% after 10 days. AICI showed a limited clearing of brain tissue >3 mm thick. However, fine neuronal structures, such as dendritic spines and axonal boutons, could still be imaged in thick brain slices treated with AICI. Therefore, AICI is useful not only for the three-dimensional (3D) high-resolution identification of neuronal structures, but also for the examination of multiple structural imaging by neuronal distribution, projection, and gene expression in deep brain tissue. AICI is applicable beyond the imaging of fluorescent antibodies and dyes, and can clear a variety of tissue types, making it broadly useful to researchers for optical imaging applications.     

Obtaining depth map from segment-based stereo matching using graph cuts

In the paper, the algorithm of segment-based stereo matching using graph cuts is developed for extracting depth information from the stereo image pairs. The first step of the algorithm employs the mean-shift algorithm to segment the reference image, which ensures our method to correctly estimate in large untextured regions and precisely localize depth boundaries, followed by the use of Adaptive Support Weighted Self-Adaptation dissimilarity algorithm (ASW-SelfAd) for the estimation of initial disparity. This is followed by application of Singular Value Decomposition (SVD) in solving the robust disparity plane fitting. In order to ensure reliable pixel sets for the segment, we filter out outliers which contain occlusion region through three main rules, namely; cross-checking, judging reliable area and disparity distance measurement. Lastly, we apply improved clustering algorithm to merge the neighboring segments. The geometrical relationship of adjacent planes such as parallelism and intersection is employed for determination of whether two planes shall be merged. A new energy function is subsequently formulated with the use of graph cuts for the refinement of the disparity map. Finally, the depth information is extracted from the final disparity map. Experimental results on the Middlebury dataset demonstrate that our approach is effective in improving the state of the art.     

Red bean extract reduces inflammation and increases survival in a murine sepsis model

This study used in vitro and in vivo experimental models to investigate the anti-inflammatory potential of the butanol fraction of red bean ethanol extract (BF-RBEE), which contains the biologically active molecule catechin-7-β-d-glucopyranoside. Treatment with BF-RBEE inhibited nitric oxide production in lipopolysaccharide (LPS)-stimulated macrophages through suppression of extracellular signalregulated kinase and IκBα activation. In an in vivo murine sepsis model, oral administration of BF-RBEE improved mouse survival. Specifically, the survival rate of mice injected with LPS was 0 and 40% in ICR and BALB/c mice, respectively, whereas the survival of mice co-treated with BF-RBEE was 100% in both mouse types. This increase in survival with the BF-RBEE administration was correlated with decreased tumor-necrosis factor-α and interferon-γ and increased interleukin-10. Oral administration of BF-RBEE also inhibited total and ovalbumin-specific IgE production in experimental mice. These results suggest the possible usefulness of red beans in the treatment of inflammatory diseases.