钒胁迫对芥菜生理特性的影响

通过盆栽试验研究了V（Ⅴ）在不同胁迫浓度和胁迫时间内对芥菜叶片中叶绿素含量、丙二醛（MDA）含量、过氧化氢酶（CAT）活性及过氧化物酶（POD）活性的影响。结果表明：随着V（Ⅴ）浓度的增加和胁迫时间的延长,叶绿素含量呈先上升后下降趋势;MDA含量随着V（Ⅴ）胁迫浓度的增加而增加,各浓度条件下MDA含量随时间的延长呈先升后降趋势;叶片中POD、CAT活性呈现出一定的规律性变化,均随V（Ⅴ）胁迫浓度的增加先升高后降低,但均高于对照,在50 mg/kg处理时都达到最大值。可见,芥菜对土壤低浓度V（Ⅴ）（≤100 mg/kg）的胁迫有较好的抗性和耐性,当V（Ⅴ）浓度增加时芥菜受的毒害逐渐增大,说明荠菜对重金属离子胁迫产生的适应性是有一定条件的。     

驾驶疲劳声音对策有效性的驾驶模拟

采用驾驶模拟实验,验证了声音刺激作为驾驶疲劳对策的有效性。通过声强、声频、持续时间和持续间隔4个因素合成不同类型的声音刺激源。选择容易出现驾驶疲劳的单调性场景和中午时分进行实验,采用眼动和驾驶员的面部特征作为驾驶疲劳的评价标准,当疲劳现象出现后启动声音刺激,分析不同类型的声音刺激前、后的脑电、心电、眼动等生理指标和驾驶操作水平的变化规律。结果表明:驾驶员在疲劳状态下,对单一声音刺激存在警觉反应,但刺激的效果不能持久,在声音刺激之后仅仅能够维持几分钟。同时发现声音类型对刺激的效果有显著影响。因此,需要进一步分析声音的各个因素对驾驶疲劳指标的影响程度,构建持续警觉的组合声音作为有效的驾驶疲劳对策。     

水分胁迫下高羊茅幼苗的生理适应性

采用不同浓度PEG6000模拟水分胁迫处理6种高羊茅幼苗,测定了胁迫72 h后叶片叶绿素含量、相对电导率、丙二醛含量、超氧化物歧化酶和过氧化物酶活性等抗旱指标,以综合评价6种高羊茅的抗旱能力。综合分析可知,6种高羊茅的苗期抗旱能力最强的是阳光宝贝。     

基于生理振动分析的手机用户身份识别

为保护手机用户的个人信息,对用户进行身份识别认证,提出一种生物识别方法。利用智能手机内置的传感器,采集人体的生理振动信息;通过对采集数据进行去重力等预处理,提取均值、方差、能量、峰度、偏度等特征指标,利用z‐score方法进行标准化处理;进行分类器设计,得出分类器的查准率、误检率等评价指标。分析60组不同用户的生理振动数据,分析结果表明,该分类器的查准率最高可达98?33％。     

Surface enhanced Raman spectroscopy and its application to molecular and cellular analysis

In this paper, we review the state-of-the-art in surface-enhanced Raman scattering (SERS) based optical detection techniques with an application focus on cancer diagnostics. As we describe herein, SERS has several analytical, biological and engineering advantages over other methods including extremely high sensitivity, inherent molecular specificity of unlabeled targets, and narrow spectral bands. We review advances in both in vitro and in vivo applications of SERS and examine how technical issues with the technology are being addressed. A special technology focus is given to emerging optofluidic devices which aim to merge microfluidic and optical detection technologies into simple packages. We conclude with a brief discussion of some of the emerging challenges in the field and some of the approaches that are likely to enhance their application. Y. S. Huh and A. J. Chung contributed equally.     

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.     

基于STM32和小波自适应滤波算法的生理参数监测系统的研究

针对心血管疾病的高死亡率以及人口老龄化的现象,本篇文章开发了基于STM32单片机和小波自适应阈值滤波算法的可穿戴式健康监测系统。系统可分为系统微处理器、数字系统模块、人机交互模块、信号采集模块和无线通信模块等几个部分,针对人体的心率、血氧、体温等重要生理参数进行处理分析,进而对人体实时监护。系统处理器选取STM32F103C8T6作为控制芯片,显示模块选用了OLED。生理参数采集系统选用了MAX30102传感器和Pulse sense传感器分别对人体腕部和指尖心率进行采集。生理参数采集完毕后,通过进一步的A/D转化,基于提出的一种改进小波自适应阈值滤波算法降噪滤波,从而将人体的生理特征参数记录下来。再将采集的生理数据通过蓝牙传输至手机端,其中的ZigBee模块主要是把获得的数据再次输送到远程控制端内,让患者能够远程得到更好的医疗监控。本系统通过软件与硬件相结合的方式。最后通过对比论证其中心率（BPM）结果误差为±2BPM,血氧含量监测结果误差在±2%以内。     

RFID Positioning and Physiological Signals for Remote Medical Care

The safety of patients and the quality of medical care provided to them are vital for their wellbeing. This study establishes a set of RFID (Radio Frequency Identification)-based systems of patient care based on physiological signals in the pursuit of a remote medical care system. The RFID-based positioning system allows medical staff to continuously observe the patient's health and location. The staff can thus respond to medical emergencies in time and appropriately care for the patient. When the COVID-19 pandemic broke out, the proposed system was used to provide timely information on the location and body temperature of patients who had been screened for the disease. The results of experiments and comparative analyses show that the proposed system is superior to competing systems in use. The use of remote monitoring technology makes user interface easier to provide high-quality medical services to remote areas with sparse populations, and enables better care of the elderly and patients with mobility issues. It can be found from the experiments of this research that the accuracy of the position sensor and the ability of package delivery are the best among the other related studies. The presentation of the graphical interface is also the most cordial among human-computer interaction and the operation is simple and clear.     

Sensitive Detection of Carcinoembryonic Antigen Using Stability‐Limited Few‐Layer Black Phosphorus as an Electron Donor and a Reservoir

The instability of few‐layer black phosphorus (FL‐BP) hampers its further applications. Here, it can be demonstrated that the instability of FL‐BP can also be the advantage for application in biosensor. First, gold nanoparticle/FL‐BP (BP‐Au) hybrid is facilely synthesized by mixing Au precursor with FL‐BP. BP‐Au shows outstanding catalytic activity (K = 1120 s^?1 g^?1) and low activation energy (17.53 kJ mol^?1) for reducing 4‐nitrophenol, which is attributed to the electron‐reservoir and electron‐donor properties of FL‐BP, and synergistic interaction of Au nanoparticles and FL‐BP. Oxidation of FL‐BP after catalytic reaction is further confirmed by transmission electron microscope, X‐ray photoelectron spectroscopy, and zeta potentials. Second, the catalytic activity of BP‐Au can be reversibly switched from “inactive” to “active” upon treatment with antibody and antigen in solution, thus providing a versatile platform for label‐free colorimetric detection of biomarkers. The sensor shows a wide detection range (1 pg mL^?1 to –10 µg mL^?1), high sensitivity (0.20 pg mL^?1), and selectivity for detecting carcinoembryonic antigen (CEA). Finally, the biosensor has been used to detect CEA in colon and breast cancer clinical samples with satisfactory results. Therefore, the instability of BP can also be the advantage for application in detecting cancer biomarker in clinic.