Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

/pdf/integrating-single-cell-transcriptomic-data-across-different-3dtsi5w7ob.pdf

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

A review on time series data mining

Mo- and W-dichalcogenide compounds have a two-dimensional monolayer form that differs from graphene in an important respect: it can potentially have more than one crystal structure. Some of these monolayers exhibit tantalizing hints of a poorly understood structural metal-to-insulator transition with the possibility of long metastable lifetimes. If controllable, such a transition could bring an exciting new application space to monolayer materials beyond graphene. Here we discover that mechanical deformations provide a route to switching thermodynamic stability between a semiconducting and a metallic crystal structure in these monolayer materials. Based on state-of-the-art density functional and hybrid Hartree–Fock/density functional calculations including vibrational energy corrections, we discover that MoTe2 is an excellent candidate phase change material. We identify a range from 0.3 to 3% for the tensile strains required to transform MoTe2 under uniaxial conditions at room temperature. The potential for mechanical phase transitions is predicted for all six studied compounds. 2D transition metal dichalcogenide materials can potentially exist in more than one monolayer crystal structure, a feature likely to be useful for electronics that is absent in graphene. Here, the authors present calculations showing that such phase transitions may be readily accessible in MoTe2.

/pdf/structural-phase-transitions-in-two-dimensional-mo-and-w-1e4o9e5piu.pdf

Structural phase transitions in two-dimensional Mo- and W-dichalcogenide monolayers.

This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions The book is intended for a professional audience composed of researchers and practitioners in industry This book is also appropriate for advanced-level students in computer science

Data Streams: Models and Algorithms

The Kuramoto model in complex networks

We study the thermal transport through a quantum spin-1/2 chain heterostructure, which consists of a finite-size chain with two-site isotropic XY interaction and three-site XZX+YZY interaction coupled at its ends to two semi-infinite isotropic XY chains. After performing Jordan-Wigner transformation, we map the original spin Hamiltonian into a fermion Hamiltonian and express the heat current with a nonequilibrium Green’s function formalism. Then, the heat current as functions of the structure parameters are studied in detail. As a result, we observe that a finite magnetic field applied at the finite-size chain can efficiently induce the heat current asymmetry with ΔΩ (ΔΩ is the magnetic field difference between the finite-size chain and the semi-infinite chains). Accordingly, such a magnetic field can be viewed as a switch in manipulating the heat current.

Thermal transport through a one-dimensional quantum spin-1/2 chain heterostructure: The role of three-site spin interaction

Subsequence similarity matching in time series databases is an important research area for many applications. This paper presents a new approximate approach for automatic online subsequence similarity matching over massive data streams. With a simultaneous on-line segmentation and pruning algorithm over the incoming stream, the resulting piecewise linear representation of the data stream features high sensitivity and accuracy. The similarity definition is based on a permutation followed by a metric distance function, which provides the similarity search with flexibility, sensitivity and scalability. Also, the metric-based indexing methods can be applied for speed-up. To reduce the system burden, the event-driven similarity search is performed only when there is a potential event. The query sequence is the most recent subsequence of piecewise data representation of the incoming stream which is automatically generated by the system. The retrieved results can be analyzed in different ways according to the requirements of specific applications. This paper discusses an application for future data movement prediction based on statistical information. Experiments on real stock data are performed. The correctness of trend predictions is used to evaluate the performance of subsequence similarity matching.

http://www.zgking.com:8080/home/donghui/publications/financial.pdf

Online event-driven subsequence matching over financial data streams

Purpose: The CyberKnife uses an online prediction model to improve radiation delivery when treatinglungtumors. This study evaluates the prediction model used by the CyberKnife radiation therapy system in terms of treatment margins about the gross tumor volume (GTV). Methods: From the data log files produced by the CyberKnife synchrony model, the uncertainty in radiation delivery can be calculated. Modeler points indicate the tracked position of the tumor and Predictor points predict the position about 115 ms in the future. The discrepancy between Predictor points and their corresponding Modeler points was analyzed for 100 treatment model data sets from 23 de-identified lung patients. The treatment margins were determined in each anatomic direction to cover an arbitrary volume of the GTV, derived from the Modeler points, when the radiation is targeted at the Predictor points. Each treatment model had about 30 min of motion data, of which about 10 min constituted treatment time; only these 10 min were used in the analysis. The frequencies of margin sizes were analyzed and truncated Gaussian normal functions were fit to each direction’s distribution. The standard deviation of each Gaussian distribution was then used to describe the necessary margin expansions in each signed dimension in order to achieve the desired coverage. In this study, 95% modeler point coverage was compared to 99% modeler coverage. Two other error sources were investigated: the correlation error and the targeting error. These were added to the prediction error to give an aggregate error for the CyberKnife during treatment of lungtumors. Results: Considering the magnitude of 2σ from the mean of the Gaussian in each signed dimension, the margin expansions needed for 95% modeler point coverage were 1.2 mm in the lateral (LAT) direction and 1.7 mm in the anterior–posterior (AP) direction. For the superior–inferior (SI) direction, the fit was poor; but empirically, the expansions were 3.5 mm. For 99% modeler point coverage, the AP margin was 3.6 mm and the lateral margin was 2.9 mm. The SI margins for 99% modeler point coverage were highly variable. The aggregate error at 95% was 6.9 mm in the SI direction, 4.6 mm in the AP direction, and 3.5 in the lateral direction. Conclusions: The Predictor points follow the Modeler points closely. Margins were found in each clinical direction that would provide 95% modeler point coverage for 95% of the models reviewed in this study. Similar margins were found in two clinical directions for 99% modeler point coverage in 95% of models. These results can offer guidance in the selection of CTV margins for treatment with the CyberKnife.

Correlation and prediction uncertainties in the CyberKnife Synchrony respiratory tracking system

Effective image guided radiation treatment of a moving tumour requires adequate information on respiratory motion characteristics. For margin expansion, beam tracking and respiratory gating, the tumour motion must be quantified for pretreatment planning and monitored on-line. We propose a finite state model for respiratory motion analysis that captures our natural understanding of breathing stages. In this model, a regular breathing cycle is represented by three line segments, exhale, end-of-exhale and inhale, while abnormal breathing is represented by an irregular breathing state. In addition, we describe an on-line implementation of this model in one dimension. We found this model can accurately characterize a wide variety of patient breathing patterns. This model was used to describe the respiratory motion for 23 patients with peak-to-peak motion greater than 7 mm. The average root mean square error over all patients was less than 1 mm and no patient has an error worse than 1.5 mm. Our model provides a convenient tool to quantify respiratory motion characteristics, such as patterns of frequency changes and amplitude changes, and can be applied to internal or external motion, including internal tumour position, abdominal surface, diaphragm, spirometry and other surrogates.

https://iopscience.iop.org/article/10.1088/0031-9155/49/23/012/pdf

A finite state model for respiratory motion analysis in image guided radiation therapy.

Subsequence matching in time series databases is a useful technique, with applications in pattern matching, prediction, and rule discovery. Internal structure within the time series data can be used to improve these tasks, and provide important insight into the problem domain. This paper introduces our research effort in using the internal structure of a time series directly in the matching process. This idea is applied to the problem domain of respiratory motion data in cancer radiation treatment. We propose a comprehensive solution for analysis, clustering, and online prediction of respiratory motion using subsequence similarity matching. In this system, a motion signal is captured in real time as a data stream, and is analyzed immediately for treatment and also saved in a database for future study. A piecewise linear representation of the signal is generated from a finite state model, and is used as a query for subsequence matching. To ensure that the query subsequence is representative, we introduce the concept of subsequence stability, which can be used to dynamically adjust the query subsequence length. To satisfy the special needs of similarity matching over breathing patterns, a new subsequence similarity measure is introduced. This new measure uses a weighted L1 distance function to capture the relative importance of each source stream, amplitude, frequency, and proximity in time. From the subsequence similarity measure, stream and patient similarity can be defined, which are then used for offline and online applications. The matching results are analyzed and applied for motion prediction and correlation discovery. While our system has been customized for use in radiation therapy, our approach to time series modeling is general enough for application domains with structured time series data.

Huanmei Wu

Papers

Thermal transport through a one-dimensional quantum spin-1/2 chain heterostructure: The role of three-site spin interaction

Online event-driven subsequence matching over financial data streams

Correlation and prediction uncertainties in the CyberKnife Synchrony respiratory tracking system

A finite state model for respiratory motion analysis in image guided radiation therapy.

Subsequence matching on structured time series data