Towards on-chip time-resolved thermal mapping with micro-/nanosensor arrays

In recent years, thin-film thermocouple (TFTC) array emerged as a versatile candidate in micro-/nanoscale local temperature sensing for its high resolution, passive working mode, and easy fabrication. However, some key issues need to be taken into consideration before real instrumentation and industrial applications of TFTC array. In this work, we will demonstrate that TFTC array can be highly scalable from micrometers to nanometers and that there are potential applications of TFTC array in integrated circuits, including time-resolvable two-dimensional thermal mapping and tracing the heat source of a device. Some potential problems and relevant solutions from a view of industrial applications will be discussed in terms of material selection, multiplexer reading, pattern designing, and cold-junction compensation. We show that the TFTC array is a powerful tool for research fields such as chip thermal management, lab-on-a-chip, and other novel electrical, optical, or thermal devices

Functional Magnetic Resonance Imaging for Imaging Neural Activity in the Human Brain: The Annual Progress

Functional magnetic resonance imaging (fMRI) is recently developed and applied to measure the hemodynamic response related to neural activity. The fMRI can not only noninvasively record brain signals without risks of ionising radiation inherent in other scanning methods, such as CT or PET scans, but also record signal from all regions of the brain, unlike EEG/MEG which are biased towards the cortical surface. This paper introduces the fundamental principles and summarizes the research progress of the last year for imaging neural activity in the human brain. Aims of functional analysis of neural activity from fMRI include biological findings, functional connectivity, vision and hearing research, emotional research, neurosurgical planning, pain management, and many others. Besides formulations and basic processing methods, models and strategies of processing technology are introduced, including general linear model, nonlinear model, generative model, spatial pattern analysis, statistical analysis, correlation analysis, and multimodal combination. This paper provides readers the most recent representative contributions in the area

Characterization of healing following atherosclerotic carotid plaque rupture in acutely symptomatic patients: an exploratory study using in vivo cardiovascular magnetic resonance.

BACKGROUND: Carotid plaque rupture, characterized by ruptured fibrous cap (FC), is associated with subsequent cerebrovascular events. However, ruptured FC may heal following stroke and convey decreased risk of future events. This study aims to characterize the healing process of ruptured FC by assessing the lumen conditions, quantified by the lumen curvature and roughness, using in vivo carotid cardiovascular magnetic resonance (CMR). METHODS: Patients suffering from transient ischemic attack underwent high resolution carotid MR imaging within 72 hours of the acute cerebrovascular ischemic event. CMR imaging was repeated at 3 and 12 months in 26 patients, in whom FC rupture/erosion was observed on baseline images and subsequent cerebrovascular events were recorded during the follow-up period. Lumen curvature and roughness were quantified from carotid CMR images and changes in these values were monitored on follow-up imaging. RESULTS: Healing of ruptured plaque was observed in patients (23 out of 26) without any ischemic symptom recurrence as shown by the lumen surface becoming smoother during the follow-up period, characterized by decreasing maximum lumen curvature (p < 0.05), increasing minimum lumen curvature (p < 0.05) and decreasing lumen roughness (p < 0.05) during the one year follow-up period. CONCLUSIONS: Carotid plaque healing can be assessed by quantification of the lumen curvature and roughness and the incidence of recurrent cerebrovascular events may be high in plaques that do not heal with time. The assessment of plaque healing may facilitate risk stratification of recent stroke patients on the basis of CMR results.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Geometric Distribution Weight Information Modeled Using Radial Basis Function with Fractional Order for Linear Discriminant Analysis Method

Fisher linear discriminant analysis (FLDA) is a classic linear feature extraction and dimensionality reduction approach for face recognition. It is known that geometric distribution weight information of image data plays an important role in machine learning approaches. However, FLDA does not employ the geometric distribution weight information of facial images in the training stage. Hence, its recognition accuracy will be affected. In order to enhance the classification power of FLDA method, this paper utilizes radial basis function (RBF) with fractional order to model the geometric distribution weight information of the training samples and proposes a novel geometric distribution weight information based Fisher discriminant criterion. Subsequently, a geometric distribution weight information based LDA (GLDA) algorithm is developed and successfully applied to face recognition. Two publicly available face databases, namely, ORL and FERET databases, are selected for evaluation. Compared with some LDA-based algorithms, experimental results exhibit that our GLDA approach gives superior performance

Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification

In this paper, we propose a logistic regression classification method based on the integration of a statistical learning model with linearized kernel pre-processing. The single Gaussian kernel and fusion of Gaussian and cosine kernels are adopted for linearized kernel pre-processing respectively. The adopted statistical learning models are the generalized linear model and the generalized additive model. Using a generalized linear model, the elastic net regularization is adopted to explore the grouping effect of the linearized kernel feature space. Using a generalized additive model, an overlap group-lasso penalty is used to fit the sparse generalized additive functions within the linearized kernel feature space. Experiment results on the Extended Yale-B face database and AR face database demonstrate the effectiveness of the proposed method. The improved solution is also efficiently obtained using our method on the classification of spectra data

Information and Modeling in Complexity 2013

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Critical Role of AKT in Myeloma-induced Osteoclast Formation and Osteolysis

Abnormal osteoclast formation and osteolysis are the hallmarks of multiple myeloma (MM) bone disease, yet the underlying molecular mechanisms are incompletely understood. Here, we show that the AKT pathway was up-regulated in primary bone marrow monocytes (BMM) from patients with MM, which resulted in sustained high expression of the receptor activator of NF-κB (RANK) in osteoclast precursors. The up-regulation of RANK expression and osteoclast formation in the MM BMM cultures was blocked by AKT inhibition. Conditioned media from MM cell cultures activated AKT and increased RANK expression and osteoclast formation in BMM cultures. Inhibiting AKT in cultured MM cells decreased their growth and ability to promote osteoclast formation. Of clinical significance, systemic administration of the AKT inhibitor LY294002 blocked the formation of tumor tissues in the bone marrow cavity and essentially abolished the MM-induced osteoclast formation and osteolysis in SCID mice. The level of activating transcription factor 4 (ATF4) protein was up-regulated in the BMM cultures from multiple myeloma patients. Adenoviral overexpression of ATF4 activated RANK expression in osteoclast precursors. These results demonstrate a new role of AKT in the MM promotion of osteoclast formation and bone osteolysis through, at least in part, the ATF4-dependent up-regulation of RANK expression in osteoclast precursors

Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions

This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views’ calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain