Determining variability of ophthalmic arterial Doppler signals using Lyapunov exponents

The new method presented in this study was directly based on the consideration that ophthalmic arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for determining variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. The computed Lyapunov exponents of the ophthalmic arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The ophthalmic arterial Doppler signals were classified with the accuracy varying from 93.75% to 97.06%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries.     

Two-stage classification of respiratory sound patterns

The classification problem of respiratory sound signals has been addressed by taking into account their cyclic nature, and a novel hierarchical decision fusion scheme based on the cooperation of classifiers has been developed. Respiratory signals from three different classes are partitioned into segments, which are later joined to form six different phases of the respiration cycle. Multilayer perceptron classifiers classify the parameterized segments from each phase and decision vectors obtained from different phases are combined using a nonlinear decision combination function to form a final decision on each subject. Furthermore a new regularization scheme is applied to the data to stabilize training and consultation.     

Sigma-pi molecular dielectric multilayers for low-voltage organic thin-film transistors

Very thin (2.3-5.5 nm) self-assembled organic dielectric multilayers have been integrated into organic thin-film transistor structures to achieve sub-1-V operating characteristics. These new dielectrics are fabricated by means of layer-by-layer solution phase deposition of molecular silicon precursors, resulting in smooth, nanostructurally well defined, strongly adherent, thermally stable, virtually pinhole-free, organosiloxane thin films having exceptionally large electrical capacitances (up to approximately 2,500 nF.cm(-2)), excellent insulating properties (leakage current densities as low as 10(-9) A.cm(-2)), and single-layer dielectric constant (k)of approximately 16. These 3D self-assembled multilayers enable organic thin-film transistor function at very low source-drain, gate, and threshold voltages (<1 V) and are compatible with a broad variety of vapor- or solution-deposited p- and n-channel organic semiconductors.     

胰腺无功能神经内分泌肿瘤的多层螺旋CT增强特征

目的 探讨胰腺无功能性神经内分泌肿瘤(NFPNET)的多层螺旋CT(MSCT)双期增强特征.方法 回顾性分析2009年1月至2015年12月由上海市徐汇区大华医院及交通大学附属瑞金医院病理证实的24例NFPNET患者的影像学资料,分别测量肿瘤病灶平扫、增强动脉期及增强门脉期CT值,计算肿瘤的动脉期及门脉期的强化幅度绝对值并与胰腺实质强化幅度绝对值进行统计学比较;并对肿瘤的强化方式、是否具有周边脏器受侵或远处转移进行统计.结果 增强动脉期NFPNET与胰腺实质强化绝对值分别为(59.08±34.14)、(53.82±15.35)HU,两者比较差异无统计学意义(P>0.05);门脉期NFPNET与胰腺实质增强绝对值分别为(48.05±22.80)、(39.37±11.57)HU,两者差异有统计学意义(P<0.05).NFPNET强化程度超过胰腺实质,动脉期有10例,门脉期有14例;3例片絮状强化,9例环形强化,7例壁结节样强化,8例有周边侵犯或远处转移.结论 胰腺动态增强扫描是诊断NFPNET的重要手段,NFPNET门脉期相比胰腺实质表现为相对富血供.     

Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder

The clinical misdiagnosis ratio of bipolar disorder (BD) patients to major depressive disorder (MDD) patients is high. Recent findings hypothesize that the ability to flexibly recruit functional neural networks is differently altered in BD and MDD patients. This study aimed to explore distinct aberrance of network flexibility during dynamic networks configuration in BD and MDD patients. Resting state functional magnetic resonance imaging of 40 BD patients, 61 MDD patients, and 61 matched healthy controls were recruited. Dynamic functional connectivity matrices for each subject were constructed with a sliding window method. Then, network switching rate of each node was calculated and compared among the three groups. BD and MDD patients shared decreased network switching rate of regions including left precuneus, bilateral parahippocampal gyrus, and bilateral dorsal medial prefrontal cortex. Apart from these regions, MDD patients presented specially decreased network switching rate in the bilateral anterior insula, left amygdala, and left striatum. Taken together, BD and MDD patients shared decreased network switching rate of key hubs in default mode network and MDD patients presented specially decreased switching rate in salience network and striatum. We found shared and distinct aberrance of network flexibility which revealed altered adaptive functions during dynamic networks configuration of BD and MDD.     

武警内卫总队医院机动卫勤分队保障能力的多层次灰色分析

目的：健全武警内卫总队医院机动卫勤分队保障能力的评价方法．方法：依据武警内卫总队医院机动卫勤分队保障能力建设的要求,结合已确定的保障能力评估三级指标体系,运用多层次灰色分析方法,评价三所武警内卫总队医院机动卫勤分队的保障能力．结果：三所武警内卫总队医院机动卫勤分队的保障能力各不相同．结论：多层次灰色分析用熵的技术确定指标权重,避免了一定的主观性,评价方法直观可行,具有一定的实际意义．     

新型导流支架治疗复杂腹主动脉瘤的血流动力学机制

目的探索新型导流支架治疗复杂腹主动脉瘤的血流动力学机制,比较多层密网支架(streamliner multilayer flow modulator,SMFM)与新型导流支架对复杂腹主动脉瘤血流动力学的影响。方法将临床采集的复杂腹主动脉瘤增强CT图像数据导入后处理软件Mimics中进行数据处理和三维重构。分别进行SMFM及新型导流支架数值建模,再导入动脉瘤数值模型,使用ICEM软件(Ansys ICEM CFD v15.0)进行网格划分后导入有限元分析软件Fluent 16.0中进行血流动力学分析。结果两种支架均可以改变瘤腔内血液流态、稳定血流,在降低瘤腔内血流速度、瘤壁压力及剪切力方面起到一定的作用,同时维持分支动脉血流通畅;与SMFM相比,新型导流支架可以更为显著的降低瘤腔内血流速度、瘤壁压力及剪切力,同时可以加快分支动脉血流速度。结论新型导流支架可以显著降低复杂腹主动脉瘤内血流速度、瘤壁压力及剪切力,同时可以加快分支动脉血流速度从而利于维持分支动脉通畅。     

Recognition of Japanese finger spelling gestures using neural networks

Effective communication with the hearing and speech impaired often requires at least a basic working knowledge of sign language gestures, without which a memo pad and pen, or a mobile phone's notepad is indispensable. The aim of this study was to build a neural network that could be used to recognize static finger-hand gestures of the yubimoji, the Japanese sign language syllabary. To build the network, signal inputs from a data glove interface were taken for each of the static yubimoji gestures. The network was trained and tested 10 times using a multilayer perceptron model. Overall, only 18 of the 41 static gestures were successfully recognized. One of the reasons was attributed to the inability of the data glove to measure gesture directions particularly for yubimoji gestures with similar finger configurations. Future work will focus on these problems as well as the inclusion of dynamic yubimoji gestures.     

Tuning the Surface Properties of Poly(Allylamine Hydrochloride)-Based Multilayer Films

The layer-by-layer (LbL) method of polyelectrolyte multilayer (PEM) fabrication is extremely versatile. It allows using a pair of any oppositely charged polyelectrolytes. Nevertheless, it may be difficult to ascribe a particular physicochemical property of the resulting PEM to a structural or chemical feature of a single component. A solution to this problem is based on the application of a polycation and a polyanion obtained by proper modification of the same parent polymer. Polyelectrolyte multilayers (PEMs) were prepared using the LbL technique from hydrophilic and amphiphilic derivatives of poly(allylamine hydrochloride) (PAH). PAH derivatives were obtained by the substitution of amine groups in PAH with sulfonate, ammonium, and hydrophobic groups. The PEMs were stable in 1 M NaCl and showed three different modes of thickness growth: exponential, mixed exponential-linear, and linear. Their surfaces ranged from very hydrophilic to hydrophobic. Root mean square (RMS) roughness was very variable and depended on the PEM composition, sample environment (dry, wet), and the polymer constituting the topmost layer. Atomic force microscopy (AFM) imaging of the surfaces showed very different morphologies of PEMs, including very smooth, porous, and structured PEMs with micellar aggregates. Thus, by proper choice of PAH derivatives, surfaces with different physicochemical features (growth type, thickness, charge, wettability, roughness, surface morphology) were obtained.