为研究减数分裂相关基因Spo11(meiotic protein covalently bound to DSB homolog)在罗氏沼虾(Macrobrachium rosenbergii)卵巢发育中的调控作用,采用RACE技术、荧光定量PCR、原位杂交和RNA干扰等方法,对罗氏沼虾Spo11基因(MrSpo11)及...为研究减数分裂相关基因Spo11(meiotic protein covalently bound to DSB homolog)在罗氏沼虾(Macrobrachium rosenbergii)卵巢发育中的调控作用,采用RACE技术、荧光定量PCR、原位杂交和RNA干扰等方法,对罗氏沼虾Spo11基因(MrSpo11)及其编码氨基酸的分子特征、MrSpo11基因的组织表达、分布和生物学功能进行了研究。结果表明:MrSpo11基因cDNA序列全长为2298 bp,其中,5′端和3′端非编码区分别为457、701 bp,开放阅读框为1140 bp,共编码379个氨基酸残基;MrSpo11基因在罗氏沼虾鳃中的相对表达量最高,在卵巢、肝胰腺和心脏中表达量较高,在脑、眼和肌肉中微量表达;MrSpo11基因在罗氏沼虾卵巢发育Ⅰ期相对表达量最高,在卵巢发育Ⅲ期次之,在Ⅱ期和Ⅳ期表达量最低;MrSpo11基因在卵黄发生前期、中期、晚期卵母细胞的细胞质,以及卵黄发生早期卵母细胞的细胞质和细胞核中均有表达;注射dsRNA后第2、4天,试验组MrSpo11基因的表达量分别比对照组下降45.8%、11.6%,注射dsRNA后第4天,试验组卵巢发育成熟度略低于对照组。研究表明,MrSpo11基因参与了罗氏沼虾卵巢发育过程,本研究结果可为进一步探究罗氏沼虾卵巢发育分子调控机制提供有益参考。展开更多
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...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 periph-eral 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 sen-sors),Apache Spark(to categorize the patient vitals and notify the medical pro-fessionals 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 pro-posed system provides improved care solutions,especially for those whose access to care services is limited.展开更多
Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection sensor.Different researchers have done diff...Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection sensor.Different researchers have done different work in the field of health monitoring with sensor networks.Different researchers used built-in apps,such as some used a small number of parameters,while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate,and outdated tools for study development.While no efficient,cheap,and updated work is proposed in the field of sensor-based health monitoring systems.Therefore,this study developed an android-based mobile system that can remotely monitor electrocardiograms(ECGs),pulse oximetry,heart rate,and body temperature.The microcontroller’s Wi-Fi device is used to manage wireless data transport.The findings of the patient are saved on the Firebase server for further usage in the mobile app.The performance of the proposed device is tested on ten numbers of different patients age-wise in terms of beats per minute(BPM),ECG,Temperature,and SpO2.This system uses temperature,pulse,ECG,blood pressure,and eye blink sensors.This device makes the usage of a tiny pulse sensor that has been designed to provide an accurate and optimal readout of the pulse rate and a temperature sensor is also included.With the help of an MCU,our system measures the pulse rate in beats per minute(bpm),blood oxygen level temperature measurements,and ECG readings and communicates this information to the Firebase server.To check the performance of the proposed system first,the BPM parameter was checked on the cardiac monitor.Then,the proposed model is tested on different patients age-wise.The simulation result shows that the BPM reading is not much different than the BPM of the cardiac monitor.According to the simulation findings,the proposed model achieved the best performance as compared to commercially available devices.展开更多
文摘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 periph-eral 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 sen-sors),Apache Spark(to categorize the patient vitals and notify the medical pro-fessionals 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 pro-posed system provides improved care solutions,especially for those whose access to care services is limited.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number 223202.
文摘Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection sensor.Different researchers have done different work in the field of health monitoring with sensor networks.Different researchers used built-in apps,such as some used a small number of parameters,while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate,and outdated tools for study development.While no efficient,cheap,and updated work is proposed in the field of sensor-based health monitoring systems.Therefore,this study developed an android-based mobile system that can remotely monitor electrocardiograms(ECGs),pulse oximetry,heart rate,and body temperature.The microcontroller’s Wi-Fi device is used to manage wireless data transport.The findings of the patient are saved on the Firebase server for further usage in the mobile app.The performance of the proposed device is tested on ten numbers of different patients age-wise in terms of beats per minute(BPM),ECG,Temperature,and SpO2.This system uses temperature,pulse,ECG,blood pressure,and eye blink sensors.This device makes the usage of a tiny pulse sensor that has been designed to provide an accurate and optimal readout of the pulse rate and a temperature sensor is also included.With the help of an MCU,our system measures the pulse rate in beats per minute(bpm),blood oxygen level temperature measurements,and ECG readings and communicates this information to the Firebase server.To check the performance of the proposed system first,the BPM parameter was checked on the cardiac monitor.Then,the proposed model is tested on different patients age-wise.The simulation result shows that the BPM reading is not much different than the BPM of the cardiac monitor.According to the simulation findings,the proposed model achieved the best performance as compared to commercially available devices.
