Divide-and-Conquer Matrisome Protein (DC-MaP) Strategy: An MS-Friendly Approach to Proteomic Matrisome Characterization

Currently, the extracellular matrix (ECM) is considered a pivotal complex meshwork of macromolecules playing a plethora of biomolecular functions in health and disease beyond its commonly known mechanical role. Only by unraveling its composition can we leverage related tissue engineering and pharmacological efforts. Nevertheless, its unbiased proteomic identification still encounters some limitations mainly due to partial ECM enrichment by precipitation, sequential fractionation using unfriendly-mass spectrometry (MS) detergents, and resuspension with harsh reagents that need to be entirely removed prior to analysis. These methods can be technically challenging and labor-intensive, which affects the reproducibility of ECM identification and induces protein loss. Here, we present a simple new method applicable to tissue fragments of 10 mg and more. The technique has been validated on human ovarian tissue and involves a standardized procedure for sample processing with an MS-compatible detergent and combined centrifugation. This two-step protocol eliminates the need for laborious sample clarification and divides our samples into 2 fractions, soluble and insoluble, successively enriched with matrisome-associated (ECM-interacting) and core matrisome (structural ECM) proteins.     

Bibliometric analysis of the evolution of biochar research trends and scientific production

Clean Technologies and Environmental Policy - Biochar has caught great attention over the last decade, yielding a large number of publications in a broad range of disciplines. This scientometric...     

A 1 GHz CMOS analog front-end for a generalized PRML read channel

A 1 GHz CMOS analog front-end for general partial response maximum likelihood (GPRML) read channel in hard disk drive application has been implemented in 0.35 /spl mu/m CMOS. A continuous time analog filter fulfills the relaxed equalization for GPRML detection and can save up to 35% power consumption for the whole read channel. An analog DFE-based timing recovery loop is implemented to avoid the extremely long latency in the digital signal processing path (Viterbi decoder). The measured performances is 1.1 dB off simulations at 800 MHz and 1.6 dB off at 1GHz. The chip draws 240 mW from a 3.3 V supply at 800MHz clock and 380 mW from a 3.6 V supply at 1 GHz clock.     

A CMOS channel-select filter for a direct-conversion wirelessreceiver

A highly selective and linear switched-capacitor channel-select filter is fabricated in 1-μm CMOS for a direct-conversion wireless receiver operating in the 902-928 MHz ISM band. The filter selects a 230-kHz wide channel and attenuates by at least 50 dB from 320 kHz to 57 MHz. The input IP3 is +30 dBm, the input-referred noise in the passband is 70 nV/√Hz, and the circuit takes 4.6 mA from a 3.3 V supply. Direct subsampling of the 915 MHz RF input signal by the filter front-end is also demonstrated with only a small degradation in linearity. The input noise voltage is halved in a redesign while keeping the current drain unchanged     

Analysis of e-business models in real estate

We use Canvas Application Framework to analyze e-business models of Realestate.yahoo.com, Yahoo.com, Redfin.com, Realtor.com, Trulia.com, Zillow.com, and Craigslist.com. We investigate the similarities and differences among e-business models and how e-business model differentiation affects company success. We looked at which business model components, such as distribution channels, business partnerships, attracting customers, revenue streams, etc., are important. We further investigate whether the web-advertising, brokerage, virtual value-chain, or the diversified internet business model is used by each company, and which ones are more successful. We discover similarities and differences among e-business models. Differences include the use of revenue streams for generating revenue, the extent of partnerships, and the role they play in a company’s success.     

Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In₂O₃) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.     

3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video

To achieve size preserving tracking, in addition to controlling the camera’s pan and tilt motions to keep the object of interest in the camera’s field of view (FOV), the camera’s focal length is adjusted automatically to compensate for the changes in the target’s image size caused by the relative motion between the camera and the target. The estimation accuracy of these changes determines the effectiveness of the resulting zoom control. The existing method of choice for real-time target scale estimation applies structure from motion (SFM) based on the weak perspective projection model. In this paper we propose a target scale estimation algorithm with a linear solution based on the more advanced paraperspective projection model, which improves the accuracy of scale estimation by considering center offset. Another key issue in SFM based algorithms is the separation of target and background features, especially when composite camera (pan/tilt/zoom) and target motions are involved. This paper designs a fast target feature separation/grouping algorithm, the 3D affine shape method. The resulting separation automatically adapts to the target’s 3D geometry and motion and is able to accommodate a large amount of off-plane rotation, which most existing separation/grouping algorithms find difficult to achieve. Experimental results illustrate the effectiveness of the proposed scale estimation and feature separation algorithms in tracking translating and rotating objects with a PTZ camera while preserving their sizes. In comparison with the leading size preserving tracking algorithm described by Tordoff and Murray, our algorithm is able to reduce the cumulative tracking error significantly from 17.4% to 3.3%.     

Fusion of thermal infrared and visible spectra for robust moving object detection

The detection of moving objects is a crucial step for many video surveillance applications whether using a visible camera (VIS) or an infrared (IR) one. In order to profit from both types, several fusion methods were proposed in the literature: low-level fusion, medium-level fusion and high-level fusion. The first one is the most used for moving objects’ detection in IR and VIS spectra. In this paper, we present an overview of the different moving object detection methods in IR and VIS spectra and a state of the art of the low-level fusion techniques. Moreover, we propose a new method for moving object detection using low-level fusion of IR and VIS spectra. In order to evaluate quantitatively and qualitatively our proposed method, three series of experiments were carried out using two well-known datasets namely “OSU Color-Thermal Database” and “INO-Database”; the results of these evaluations show promising results and demonstrate the effectiveness of the proposed method.     

Runoff Estimation for an Ungauged Catchment Using Geomorphological Instantaneous Unit Hydrograph (GIUH) and Copulas

A methodology is proposed to apply Geomorphological Instantaneous Unit Hydrograph to ungauged basins using Monte Carlo Simulations and copulas. The effective rainfall, input of GIUH is assumed to be unknown; it is estimated with infiltration index method (?-index). Correlations are detected between this index and the characteristics of rainfall. They are modeled with copulas, and are used to derive effective rainfall hyetographs. The generated hydrographs from GIUH are analyzed and give statistically the same results: dispersion and variability for all studied characteristics (volume, peak discharge, peak time and base time). However, only these hydrographs derived from ? conditioned to maximum intensity distribution allow reconstituting the observed hydrographs. Moreover comparing the series of order statistics of interest output and observed series, leads to decide on the representative hydrograph of the catchment behavior.     

Point fingerprint: a new 3-D object representation scheme

This paper proposes a new, efficient surface representation method for surface matching. A feature carrier for a surface point, which is a set of two-dimensional (2-D) contours that are the projections of geodesic circles on the tangent plane, is generated. The carrier is named point fingerprint because its pattern is similar to human fingerprints and plays a role in discriminating surface points. Corresponding points on surfaces from different views are found by comparing their fingerprints. The point fingerprint is able to carry curvature, color, and other information which can improve matching accuracy, and the matching process is faster than 2-D image comparison. A novel candidate point selection method based on the fingerprint irregularity is introduced. Point fingerprint is successfully applied to pose estimation of real range data.