Mechanisms of Nausea and Vomiting: Current Knowledge and Recent Advances in Intracellular Emetic Signaling Systems

Nausea and vomiting are common gastrointestinal complaints that can be triggered by diverse emetic stimuli through central and/or peripheral nervous systems. Both nausea and vomiting are considered as defense mechanisms when threatening toxins/drugs/bacteria/viruses/fungi enter the body either via the enteral (e.g., the gastrointestinal tract) or parenteral routes, including the blood, skin, and respiratory systems. While vomiting is the act of forceful removal of gastrointestinal contents, nausea is believed to be a subjective sensation that is more difficult to study in nonhuman species. In this review, the authors discuss the anatomical structures, neurotransmitters/mediators, and corresponding receptors, as well as intracellular emetic signaling pathways involved in the processes of nausea and vomiting in diverse animal models as well as humans. While blockade of emetic receptors in the prevention of vomiting is fairly well understood, the potential of new classes of antiemetics altering postreceptor signal transduction mechanisms is currently evolving, which is also reviewed. Finally, future directions within the field will be discussed in terms of important questions that remain to be resolved and advances in technology that may help provide potential answers.     

Evaluation of Surface Residual Stresses in Friction Stir Welds Due to Laser and Shot Peening

The effects of laser, and shot peening on the residual stresses in friction stir welds (FSW) has been investigated. The surface residual stresses were measured at five different locations across the weld in order to produce an adequate residual stress profile. The residual stresses before and after sectioning the coupon from the welded plate were also measured, and the effect of coupon size on the residual stress relaxation was determined and characterized. Measurements indicate that residual stresses were not uniform along the welded plate, and large variation in stress magnitude could be exhibited at various locations along the FSW plate. Sectioning resulted in significant residual stress relaxation in the longitudinal direction attributed to the large change in dimensions in this direction. Overall, laser and shot peening resulted in a significant reduction in tensile residual stresses at the surface of the specimens.     

An improved cutting force and surface topography prediction model in end milling

During the milling operation, the cutting forces will induce vibration on the cutting tool, the workpiece, and the fixtures, which will affect the surface integrity of the final part and consequently the product's quality. In this paper, a generic and improved model is introduced to simultaneously predict the conventional cutting forces along with 3D surface topography during side milling operation. The model incorporates the effects of tool runout, tool deflection, system dynamics, flank face wear, and the tool tilting on the surface roughness. An improved technique to calculate the instantaneous chip thickness is also presented. The model predictions on cutting forces and surface roughness and topography agreed well with experimental results.     

Asymmetric Sharpless epoxidation of 13S‐hydroxy‐ 9Z, 11E‐octadecadienoic acid (13S‐HODE)

The asymmetric Sharpless epoxidation of methyl 13S‐hydroxy‐9Z, 11E‐octadeca‐dienoate (13S‐HODE, 1 ) with tert‐butyl hydroperoxide (TBHP) catalysed by titanium tetraisopropoxide {Ti(ⁱOPr)₄} in the presence of L(+)‐diisopropyl tartrate (L‐DIPT) gave methyl 13S‐hydroxy‐11S, 12S‐epoxy‐9Z‐octadecenoate 2 (erythro isomer) in 84% diastereomeric excess (de). The epoxidation of 1 with TBHP catalysed by Ti(ⁱOPr)₄ in the presence of D(‐)‐DIPT yielded methyl 13S‐hydroxy‐11RR12R‐epoxy‐9Z‐octadecenoate (threo isomer) 3 in 76% de.     

Efficient mining of discriminative co-clusters from gene expression data

Discriminative models are used to analyze the differences between two classes and to identify class-specific patterns. Most of the existing discriminative models depend on using the entire feature space to compute the discriminative patterns for each class. Co-clustering has been proposed to capture the patterns that are correlated in a subset of features, but it cannot handle discriminative patterns in labeled datasets. In certain biological applications such as gene expression analysis, it is critical to consider the discriminative patterns that are correlated only in a subset of the feature space. The objective of this paper is twofold: first, it presents an algorithm to efficiently find arbitrarily positioned co-clusters from complex data. Second, it extends this co-clustering algorithm to discover discriminative co-clusters by incorporating the class information into the co-cluster search process. In addition, we also characterize the discriminative co-clusters and propose three novel measures that can be used to evaluate the performance of any discriminative subspace pattern-mining algorithm. We evaluated the proposed algorithms on several synthetic and real gene expression datasets, and our experimental results showed that the proposed algorithms outperformed several existing algorithms available in the literature.     

Capture device identification from digital images using Kullback-Leibler divergence

Multimedia Tools and Applications - It is proposed a forensic method for the capture device identification from digital images, which requires two elements: i) a digital image subject to...     

Infrared thermography for inspecting the adhesion integrity of plastic welded joints

This work aim at developing a non-destructive tool for the evaluation of bonded plastic joints. The paper examines infrared thermographic transmission and reflection mode imaging and validates the feasibility of the thermal NDT approach for this application. Results demonstrate good estimation performance for adhesion integrity, uniformity and bond strength using a transmission mode application of infrared thermography. In addition, results from a pulsed infrared thermographic application using a modified dynamic infrared tomography scheme show good performance for estimating adhesion layer thickness mapping and detecting delaminations.     

A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: study case on Malay word recognition

An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications.     

On semantic annotation of decision models

The growth of service sector in recent years has led to renewed research interests in the design and management of service systems. Decision support systems (DSS) play an important role in supporting this endeavor, through management of organizational resources such as models and data, thus forming the “back stage” of service systems. In this article, we identify the requirements for semantically annotating decision models and propose a model representation scheme, termed Semantically Annotated Structure Modeling Markup Language (SA-SMML) that extends Structure Modeling Markup Language (SMML) by incorporating mechanisms for linking semantic models such as ontologies that represent problem domain knowledge concepts. This model representation format is also amenable to a scalable Service-Oriented Architecture (SOA) for managing models in distributed environments. The proposed model representation technique leverages recent advances in the areas of semantic web, and semantic web services. Along with design considerations, we demonstrate the utility of this representation format with an illustrative usage scenarios with a particular emphasis on model discovery and composition in a distributed environment.