Segmentation of CT Lung Images Using FCM with Active Contour and CNN Classifier

Objective: Lung cancer is one of the unsafe diseases for human which reduces the patient life time. Generally, most of the lung cancers are identified after it has been spread into the lung parts and moreover it is difficult to find the lung cancer at the early stage. It requires radiologist and special doctors to find the tumoral tissue of the lung cancer. For this reason, the recommended work helps to segment the tumoral tissue of CT lung image in an effective way. Methods:  The research work uses hybrid segmentation technique to separate the lung cancer cells to diagnose the lung tumour. It is a technique which combines active contour along with Fuzzy c means to diagnose the tumoral tissue. Further the segmented portion was trained by Convolutional Neural Network (CNN) in order to classify the segmented region as normal or abnormal. Results: The evaluation of the proposed method was done by analyzing the results of test image with the ground truth image. Finally, the results of the implemented technique provided good accuracy, Peak signal to noise ratio (PSNR), Mean Square Error (MSE) value. In future the other techniques can be utilized to improve the details before segmentation. The proposed work provides 96.67 % accuracy. Conclusion: Hybrid segmentation technique involves several steps like preprocessing, binarization, thresholding, segmentation and feature extraction using GLCM.     

痛情绪相关神经递质及神经环路研究进展＊

痛情绪是指因疼痛引发的情绪和情感体验，是疾病过程中最常见的一种情绪。痛情绪相关神经机制非常复杂，但主要与单胺类神经递质、神经肽和某些神经环路有关，笔者将结合目前研究现状分别从以上两方面展开，就痛情绪相关单胺类神经递质和神经肽在受体分类、脑区通路、共疾病以及各神经递质之间的联系和痛情绪相关神经环路中各个蛋白的作用机制等方面进行探讨。     

基于粒子群优化的BP神经网络对血脂异常气郁痰阻型中医证候的疗效预测＊

目的 选用两种药物分析对血脂异常、中医证候的疗效，以中医证候积分为基础建立血脂异常气郁痰阻型的疗效表征模型，进而提出基于粒子群优化的BP神经网络预测两种方法的疗效。方法 收集50名符合血脂异常气郁痰阻型患者的基本资料，将患者随机分为解郁祛痰化浊方组（JQHP）、血脂康胶囊组（XZKC），每组25例，观察疗程12周治疗前后的血脂水平，治疗前及治疗期间每4周记录一次中医证候积分，共4次。定性疗效分析参考血脂防治指南和中医疗效积分，采用t检验、非参数检验及X²分析的方法；神经网络模型采用粒子群优化的BP神经网络，定量构建血脂异常气郁痰阻证的预测模型，设置为三层网络，输入层节点数为15，隐含层节点数为6，输出层节点数为1。针对JQHP组和XZKC组的数据，分别选择80%的数据进行训练，20%的数据进行验证。结果 两组治疗前、治疗4周、治疗8周、治疗12周的中医证候积分随治疗周期的增加而下降（P < 0.05）。JQHP组除了“形体肥胖”外，其余14项症状均有显著的改善（P < 0.05），XZKC组治疗前后比较除“形体肥胖”、“头晕”、“胸闷”外，其余12项均改善（P < 0.05）。以中医证候积分建立的基于粒子群优化BP神经网络的预测精度高、稳定性佳，可以定量预测两种药物对未知患者的疗效。结论 JQHP和XZKC可以改善血脂异常患者的血脂水平，改善气郁痰阻型患者的症状，此为定性分析。利用中医证候积分建立的基于粒子群优化BP神经网络两种治疗方法的预测模型，可定量预测未知患者的疗效。以此思路将有助于找到一种特定治疗方法的适应人群，从而提高该治疗方法的针对性，实现中医药的智能化、精准化、定量化。     

Frame Invariance and Scalability of Neural Operators for Partial Differential Equations

Partial differential equations (PDEs) play a dominant role in the mathematical modeling of many complex dynamical processes. Solving these PDEs often requires prohibitively high computational costs, especially when multiple evaluations must be made for different parameters or conditions. After training, neural operators can provide PDEs solutions significantly faster than traditional PDE solvers. In this work, invariance properties and computational complexity of two neural operators are examined for transport PDE of a scalar quantity. Neural operator based on graph kernel network (GKN) operates on graph-structured data to incorporate nonlocal dependencies. Here we propose a modified formulation of GKN to achieve frame invariance. Vector cloud neural network (VCNN) is an alternate neural operator with embedded frame invariance which operates on point cloud data. GKN-based neural operator demonstrates slightly better predictive performance compared to VCNN. However, GKN requires an excessively high computational cost that increases quadratically with the increasing number of discretized objects as compared to a linear increase for VCNN.     

Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer

Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases.     

Biohorology and biomarkers of aging: Current state-of-the-art,challenges and opportunities

The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations. More than a dozen aging clocks use molecular features to predict an organism's age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms. We also discuss promising future directions of research in biohorology — the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field.     

Differences in EEG patterns between tonic and high frequency spinal cord stimulation in chronic pain patients

ObjectiveTo investigate the differences in neural patterns between spinal cord stimulation (SCS) waveforms (60-Hz tonic vs 10-KHz high frequency stimulation, HFS) and their correlation to stimulation-induced pain relief.MethodsWe recorded 10-channel electroencephalogram (EEG) in response to stimulation ON and OFF in 9 chronic pain patients (4 women, 5 men) during SCS surgery and examined the intraoperative spatio-spectral EEG features.ResultsWe discovered stronger relative alpha power in the somatosensory region and higher trend in alpha/theta peak power ratio in frontal cortex with HFS. We also observed a shift in peak frequency from theta to alpha rhythms in HFS as compared to baseline and tonic stimulation, where slower theta activity was maintained. Further, a positive correlation was found between changes in Oswestry disability index (ODI) scores (from preoperative to postoperative) and HFS-induced alpha/theta peak power ratio in frontal and somatosensory regions.ConclusionsAltogether, our findings suggest that dynamic spectral interactions in theta-alpha band and their spatial distributions might be the first intraoperative neural signatures of pain relief induced by HFS in chronic pain.SignificanceExamining electrophysiological changes intraoperatively has a potential to elucidate response to SCS therapy prior to device selection, reducing the healthcare expenditures associated with failed implants.     

被动吸烟与神经管缺陷关系的病例对照研究

目的 探讨妇女怀孕前后被动吸烟与神经管缺陷（NTDs）的相关性。方法 采用1[DK]∶1配对病例对照研究，选取2010-2014年山西省“削峰工程”监测医院登记的生育NTDs患儿的妇女411例作为病例组，同时选择怀孕前后居住在同一个县（或区）、民族相同、末次月经与病例妇女相差3个月以内的生育健康儿童的妇女411例作为对照组进行问卷调查。用单因素和多因素条件logistic回归模型探究妇女怀孕前后被动吸烟与NTDs的关系。结果 妇女怀孕前后被动吸烟增加生育NTDs患儿的风险（aOR=1.209，95%CI:1.043～1.401，P=0.012）。且随着被动吸烟的频次增加，生育NTDs患儿的风险增加，与未被动吸烟的妇女相比，每周被动吸烟4～6次的妇女和每周被动吸烟大于6次的妇女生育NTDs患儿的OR值分别为2.113和2.903（趋势〖XC小五号.EPS;P〗=13.381，P＜0.001）。妇女被动吸烟的主要场所是家中（71.6%），且妇女被动吸烟程度与丈夫吸烟情况相关（r=0.405）。结论 妇女怀孕前后被动吸烟会增加生育NTDs患儿的风险，且随着被动吸烟频次的增加，生育NTDs患儿的风险增加。     

Culture: The Driving Force of Human Cognition

It is often, though sometimes only implicitly, assumed that biological/genetic evolution sets neural substrates, that neural substrates fix cognitive abilities, and that cognitive abilities determine the spectrum of cultural practices exhibited by a biological species. We label this view as the “bottom‐up‐only” view. In this paper we will show that such a “chain of dependence” is much looser than usually assumed, especially as far as recent periods (the last 800,000 years vs. the last 7 million years or more) are considered. We will provide evidence and arguments supporting the idea that cultural innovation may have direct and ascertainable effects both on the cognitive capabilities of populations of hominins (via what we call “cultural exaptation”) and on the neural substrates of the individuals in those populations (via what we call “cultural neural reuse”). Together, cultural exaptation and cultural neural reuse may give raise to a plausible general mechanism for cognitive evolution in which culture is the driving force, thus offering a “top‐down‐also” view of human evolution.