Showing papers by "Worcester Polytechnic Institute published in 2017"

A single-cell survey of the small intestinal epithelium

Abstract: Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens.

Anomaly Detection with Robust Deep Autoencoders

Abstract: Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, $X = L_{D} + S$, where $L_{D}$ can be effectively reconstructed by a deep autoencoder and $S$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems.

Soft Robotics: Review of Fluid‐Driven Intrinsically Soft Devices; Manufacturing, Sensing, Control, and Applications in Human‐Robot Interaction

Abstract: The emerging field of soft robotics makes use of many classes of materials including metals, low glass transition temperature (Tg) plastics, and high Tg elastomers. Dependent on the specific design, all of these materials may result in extrinsically soft robots. Organic elastomers, however, have elastic moduli ranging from tens of megapascals down to kilopascals; robots composed of such materials are intrinsically soft − they are always compliant independent of their shape. This class of soft machines has been used to reduce control complexity and manufacturing cost of robots, while enabling sophisticated and novel functionalities often in direct contact with humans. This review focuses on a particular type of intrinsically soft, elastomeric robot − those powered via fluidic pressurization.

A single-cell survey of the small intestinal epithelium

Abstract: Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens.

Effects of heat treatments on microstructure and properties of Ti-6Al-4V ELI alloy fabricated by electron beam melting (EBM)

Abstract: Electron beam melting (EBM) is a metal powder bed fusion additive manufacturing (AM) technology that is used to fabricate three-dimensional near-net-shaped parts directly from computer models. Ti-6Al-4V is the most widely used and studied alloy for this technology and is the focus of this work in its ELI (Extra Low Interstitial) variation. Microstructure evolution and its influence on the mechanical properties of the alloy in the as-fabricated condition have been documented by various researchers. In the present work, different heat treatments were performed based on three approaches in order to study the effects of heat treatments on the unique microstructure formed during the EBM fabrication process. In the first approach, the effect of various cooling rates after the solutionizing process was studied. In the second approach, a correlation between the variation of α lath thickness during aging and the subsequent effect on mechanical properties was established. Lastly, several combined solutionizing and aging experiments were conducted; the results will be systematically discussed in the context of structural performance and design.

CacheZoom: How SGX Amplifies the Power of Cache Attacks

Abstract: In modern computing environments, hardware resources are commonly shared, and parallel computation is widely used. Parallel tasks can cause privacy and security problems if proper isolation is not enforced. Intel proposed SGX to create a trusted execution environment within the processor. SGX relies on the hardware, and claims runtime protection even if the OS and other software components are malicious. However, SGX disregards side-channel attacks. We introduce a powerful cache side-channel attack that provides system adversaries a high resolution channel. Our attack tool named CacheZoom is able to virtually track all memory accesses of SGX enclaves with high spatial and temporal precision. As proof of concept, we demonstrate AES key recovery attacks on commonly used implementations including those that were believed to be resistant in previous scenarios. Our results show that SGX cannot protect critical data sensitive computations, and efficient AES key recovery is possible in a practical environment. In contrast to previous works which require hundreds of measurements, this is the first cache side-channel attack on a real system that can recover AES keys with a minimal number of measurements. We can successfully recover AES keys from T-Table based implementations with as few as ten measurements.

The emerging role of zinc transporters in cellular homeostasis and cancer

Abstract: Zinc is an essential micronutrient that plays a role in the structural or enzymatic functions of many cellular proteins. Cellular zinc homeostasis involves the opposing action of two families of metal transporters: the ZnT (SLC30) family that functions to reduce cytoplasmic zinc concentrations and the ZIP (SLC39) family that functions to increase cytoplasmic zinc concentrations. Fluctuations in intracellular zinc levels mediated by these transporter families affect signaling pathways involved in normal cell development, growth, differentiation and death. Consequently, changes in zinc transporter localization and function resulting in zinc dyshomeostasis have pathophysiological effects. Zinc dyshomeostasis has been implicated in the progression of cancer. Here we review recent progress toward understanding the structural basis for zinc transport by ZnT and ZIP family proteins, as well as highlight the roles of zinc as a signaling molecule in physiological conditions and in various cancers. As zinc is emerging as an important signaling molecule in the development and progression of cancer, the ZnT and ZIP transporters that regulate cellular zinc homeostasis are promising candidates for targeted cancer therapy.

End-to-end learning for lane keeping of self-driving cars

Abstract: Lane keeping is an important feature for self-driving cars. This paper presents an end-to-end learning approach to obtain the proper steering angle to maintain the car in the lane. The convolutional neural network (CNN) model takes raw image frames as input and outputs the steering angles accordingly. The model is trained and evaluated using the comma.ai dataset, which contains the front view image frames and the steering angle data captured when driving on the road. Unlike the traditional approach that manually decomposes the autonomous driving problem into technical components such as lane detection, path planning and steering control, the end-to-end model can directly steer the vehicle from the front view camera data after training. It learns how to keep in lane from human driving data. Further discussion of this end-to-end approach and its limitation are also provided.

Microscopic origin of the Drude-Smith model

Abstract: Terahertz spectroscopy of nanomaterials is one of the most active areas of terahertz physics thanks to its ability to reveal charge-carrier confinement on length scales of tens to hundreds of nanometers. Structural confinement modifies the experimental complex conductivity, such that it is Drude-like at high frequencies but suppressed at low frequencies. This response is often described as a consequence of carrier backscattering off nanoparticle boundaries and modeled using the sometimes controversial Drude-Smith formula. In this paper, it is demonstrated that the terahertz conductivity of a structurally confined Drude gas of electrons is actually suppressed at low frequencies due to carrier confinement on the diffusion length scale and not due to backscattering. A new conductivity formula is derived based on diffusion that is found to be very similar to the Drude-Smith conductivity formula, thereby explaining many of the previous successes of the Drude-Smith model.

Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification

Abstract: The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.

Virtual Human Models for Electromagnetic Studies and Their Applications

Abstract: Numerical simulation of electromagnetic, thermal, and mechanical responses of the human body to different stimuli in magnetic resonance imaging safety, antenna research, electromagnetic tomography, and electromagnetic stimulation is currently limited by the availability of anatomically adequate and numerically efficient cross-platform computational models or “virtual humans.” The objective of this study is to provide a comprehensive review of modern human models and body region models available in the field and their important features.

Valorization of coffee industry residues by subcritical water hydrolysis: Recovery of sugars and phenolic compounds

Abstract: Two abundant coffee waste residues (powder and defatted cake) were treated using subcritical water (SubCW) for hydrolysis and extraction of reducing sugars (RS), total reducing sugars (TRS), and total phenolic compounds (TPC) under semi-continuous flow conditions The flow-through process was carried out at 150, 175, 200 and 250 °C, with a water flow of 10 mL/min and reaction pressures of either 225 or 30 MPa For treated coffee powder, the maximum observed sugar recovery was 63% for RS (150 °C and 30 MPa) and 90% for TRS (150 °C and 30 MPa) The maximum TPC recovery was 2664 mg GAE (Gallic Acid Equivalent)/g powder coffee, observed at 200 °C and 225 MPa For the defatted coffee cake, the maximum sugar yields were 879% and 1723% for RS and TRS; both observed at a treatment temperature of 175 °C The highest TPC yield was 5531 mg TPC GAE/g defatted coffee cake, also at 175 °C HPLC was used to quantify specific carbohydrates (arabinose, cellobiose, glucose, and xylose), 5-hydroxy-methyl-furfural (5-HMF) and furfural in both coffee waste hydrolyzates, providing evidence of thermal degradation of the coffee carbohydrates Scanning electron microscopy of the treated samples revealed particles deposited on the surface and other signs of physical degradation of the biomass structure Fourier Transform Infrared Spectroscopy of the residues revealed that the density of surface bound acid groups increased with increasing treatment temperature The results presented here provide a basis for the use of subcritical water to obtain reducing sugars and phenolic compounds from coffee residue

High Voltage LiNi0.5Mn0.3Co0.2O2/Graphite Cell Cycled at 4.6 V with a FEC/HFDEC-Based Electrolyte

Abstract: A high voltage LiNi0.5Mn0.3Co0.2O2/graphite cell with a fluorinated electrolyte formulation 1.0 m LiPF6 fluoroethylene carbonate/bis(2,2,2-trifluoroethyl) carbonate is reported and its electrochemical performance is evaluated at cell voltage of 4.6 V. Comparing with its nonfluorinated electrolyte counterpart, the reported fluorinated one shows much improved Coulombic efficiency and capacity retention when a higher cut-off voltage (4.6 V) is applied. Scanning electron microscopy/energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy data clearly demonstrate the superior oxidative stability of the new electrolyte. The structural stability of the bulk cathode materials cycled with different electrolytes is extensively studied by X-ray absorption near edge structure and X-ray diffraction.