A new method for modeling preoperative diagnosis of ovarian tumors

In this paper, we present a sequential nonuniform procedure, an inference method which combines feature selection based on the Kullback information gain and a step-wise classification procedure to produce a reliable, interpretable, and robust model. We applied the model to an ovarian tumor data set to distinguish between malignant and benign tumors. The performance of the model was assessed using receiver operating characteristic (ROC) analysis and gave an overall accuracy over 85%, and area under the curve (AUC) of 0.887 which compares well with existing methods. The method presented here is significant because of its ability to handle missing values, and it only uses a small number of variables which are graded according to their discriminative relevance. This, together with the fact that the resulting model is interpretable and has good performance, is likely to lead to widespread clinical acceptance of the method. The method is also generic and can be readily adapted for other classifications problems in biomedicine.     

Challenges and opportunities for next-generation intracortically based neural prostheses

Neural prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Intracortical electrode arrays measure action potentials and local field potentials from individual neurons, or small populations of neurons, in the motor cortices and can provide considerable information for controlling prostheses. Despite several compelling proof-of-concept laboratory animal experiments and an initial human clinical trial, at least three key challenges remain which, if left unaddressed, may hamper the translation of these systems into widespread clinical use. We review these challenges: achieving able-bodied levels of performance across tasks and across environments, achieving robustness across multiple decades, and restoring able-bodied quality proprioception and somatosensation. We also describe some emerging opportunities for meeting these challenges. If these challenges can be largely or fully met, intracortically based neural prostheses may achieve true clinical viability and help increasing numbers of disabled patients.     

Detection of vesicoureteral reflux using microwave radiometry-system characterization with tissue phantoms

Microwave (MW) radiometry is proposed for passive monitoring of kidney temperature to detect vesicoureteral reflux (VUR) of urine that is externally heated by a MW hyperthermia device and thereafter reflows from the bladder to kidneys during reflux. Here, we characterize in tissue-mimicking phantoms the performance of a 1.375 GHz radiometry system connected to an electromagnetically (EM) shielded microstrip log spiral antenna optimized for VUR detection. Phantom EM properties are characterized using a coaxial dielectric probe and network analyzer (NA). Power reflection and receive patterns of the antenna are measured in layered tissue phantom. Receiver spectral measurements are used to assess EM shielding provided by a metal cup surrounding the antenna. Radiometer and fiberoptic temperature data are recorded for varying volumes (10-30 mL) and temperaturesg (40-46°C) of the urine phantom at 35 mm depth surrounded by 36.5°C muscle phantom. Directional receive pattern with about 5% power spectral density at 35 mm target depth and better than -10 dB return loss from tissue load are measured for the antenna. Antenna measurements demonstrate no deterioration in power reception and effective EM shielding in the presence of the metal cup. Radiometry power measurements are in excellent agreement with the temperature of the kidney phantom. Laboratory testing of the radiometry system in temperature-controlled phantoms supports the feasibility of passive kidney thermometry for VUR detection.     

硫化铜矿抑制剂的结构—活性关系

抑制硫化铜矿然后浮选 MoS_2是 Cu-Mo 分离最常应用的工艺。虽然过去已经研究了不少有机抑制剂,但目前使用的铜抑制剂多数还是无机化合物(如 NaHS,诺克斯抑制剂等)。有机抑制剂超过无机化合物的一个独特的优点,是适应多种需要而进行有机分子设计时有较大的多功能性。本工作着手系统研究大量有机络合剂的结构—活性关系,与常用的也常常是无效、危险的无机抑制剂进行比较。用得到的实际溶液氧化还原电位与抑制剂活性间的关系这一看法,讨论了本研究获得的结果。研究确定了为使一种药剂达到工业应用的一系列必要条件,诸如在有机分子中存在一个-SH 活性基及-OH,-COOH 亲水基等。也讨论了关于非黄药捕收剂体系捕收剂在硫化矿表面覆盖层的解吸性(或破坏)的一些初步结果。     

Classification of rheumatoid joint inflammation based on laser imaging

We describe a classification system for a novel imaging method for arthritic finger joints. The basis of this system is a laser imaging technique which is sensitive to the optical characteristics of finger joint tissue. From the laser images acquired at baseline and follow-up, finger joints can automatically be classified according to whether the inflammatory status has improved or worsened. To perform the classification task, various linear and kernel-based systems were implemented and their performances were compared. Based on the results presented in this paper, we conclude that the laser-based imaging permits a reliable classification of pathological finger joints, making it a sensitive method for detecting arthritic changes.     

Structural and optoelectronic properties of sprayed Sb:SnO_2 thin films:Effects of substrate temperature and nozzle-to-substrate distance

The influence of substrate temperature and nozzle-to-substrate distance（NSD） on the structural,morphological, optical and electrical properties of Sb:SnO₂ thin films prepared by chemical spray pyrolysis has been analyzed.The structural,morphological,optical and electrical properties were characterized by using XRD,SEM, UV-visible spectrophotometry and Hall effect measurement techniques.It was seen that the films are polycrystalline, having a tetragonal crystal structure with strong orientation along the（200） reflection.The pyramidal crystallites formed due to coalescence were observed from SEM images.The values of highest conductivity,optical transmittance and figure of merit of about 1449（Ω·cm）^-1,70%and 5.2×10^-3□/Ω,respectively,were observed for a typical film deposited using optimal conditions（substrate temperature = 500℃and NSD = 30 cm）.     

A comparison framework for breathing motion estimation methods from 4-D imaging

Motion estimation is an important issue in radiation therapy of moving organs. In particular, motion estimates from 4-D imaging can be used to compute the distribution of an absorbed dose during the therapeutic irradiation. We propose a strategy and criteria incorporating spatiotemporal information to evaluate the accuracy of model-based methods capturing breathing motion from 4-D CT images. This evaluation relies on the identification and tracking of landmarks on the 4-D CT images by medical experts. Three different experts selected more than 500 landmarks within 4-D CT images of lungs for three patients. Landmark tracking was performed at four instants of the expiration phase. Two metrics are proposed to evaluate the tracking performance of motion-estimation models. The first metric cumulates over the four instants the errors on landmark location. The second metric integrates the error over a time interval according to an a priori breathing model for the landmark spatiotemporal trajectory. This latter metric better takes into account the dynamics of the motion. A second aim of this paper is to estimate the impact of considering several phases of the respiratory cycle as compared to using only the extreme phases (end-inspiration and end-expiration). The accuracy of three motion estimation models (two image registration-based methods and a biomechanical method) is compared through the proposed metrics and statistical tools. This paper points out the interest of taking into account more frames for reliably tracking the respiratory motion.     

Wavelet-based reconstruction for limited-angle X-ray tomography

The aim of X-ray tomography is to reconstruct an unknown physical body from a collection of projection images. When the projection images are only available from a limited angle of view, the reconstruction problem is a severely ill-posed inverse problem. Statistical inversion allows stable solution of the limited-angle tomography problem by complementing the measurement data by a priori information. In this work, the unknown attenuation distribution inside the body is represented as a wavelet expansion, and a Besov space prior distribution together with positivity constraint is used. The wavelet expansion is thresholded before reconstruction to reduce the dimension of the computational problem. Feasibility of the method is demonstrated by numerical examples using in vitro data from mammography and dental radiology.     

Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change

This paper is motivated by the analysis of serial structural magnetic resonance imaging (MRI) data of the brain to map patterns of local tissue volume loss or gain over time, using registration-based deformation tensor morphometry. Specifically, we address the important confound of local tissue contrast changes which can be induced by neurodegenerative or neurodevelopmental processes. These not only modify apparent tissue volume, but also modify tissue integrity and its resulting MRI contrast parameters. In order to address this confound we derive an approach to the voxel-wise optimization of regional mutual information (RMI) and use this to drive a viscous fluid deformation model between images in a symmetric registration process. A quantitative evaluation of the method when compared to earlier approaches is included using both synthetic data and clinical imaging data. Results show a significant reduction in errors when tissue contrast changes locally between acquisitions. Finally, examples of applying the technique to map different patterns of atrophy rate in different neurodegenerative conditions is included.     

Development and evaluation of the piecewise Prony method for evoked potential analysis

A new method is presented to decompose nonstationary signals into a summation of oscillatory components with time varying frequency, amplitude, and phase characteristics. This method, referred to as piecewise Prony method (PPM), is an improvement over the classical Prony method, which can only deal with signals containing components with fixed frequency, amplitude and phase, and monotonically increasing or decreasing rate of change. PPM allows the study of the temporal profile of post-stimulus signal changes in single-trial evoked potentials (EPs), which can lead to new insights in EP generation. We have evaluated this method on simulated data to test its limitations and capabilities, and also on single-trial EPs. The simulation experiments showed that the PPM can detect amplitude changes as small as 10%, rate changes as small as 10%, and 0.15 Hz of frequency changes. The capabilities of the PPM were demonstrated using single electroencephalogram/EP trials of flash visual EPs recorded from one normal subject. The trial-by-trial results confirmed that the stimulation drastically attenuates the alpha activity shortly after stimulus presentation, with the alpha activity returning about 0.5 s later. The PPM results also provided evidence that delta activity undergoes phase alignment following stimulus presentation.