We present a versatile, tuneable, and selective nanoparficle-based lectin biosensor, based on flocculation of ternary supramolecular nanoparticle networks （NPN）, formed through the sequential binding of three buildi...We present a versatile, tuneable, and selective nanoparficle-based lectin biosensor, based on flocculation of ternary supramolecular nanoparticle networks （NPN）, formed through the sequential binding of three building blocks. The three building blocks are ^-cyclodextrin-capped CdTe quantum dots, tetraethylene glycol-tethered mannose-adamantane cross-linkers （ADTEGMan）, and the tetravalent lectin Concanavalin A （ConA）. The working principle of this selective sensor lies in the dual orthogonal molecular interactions of the linker, uniting adamantane-^-cyclodextrin and mannose-lectin interaction motifs, respectively. Only when the lectin is present, sequential binding takes place, leading to in situ self-organization of the sensor through the formation of ternary supramolecular networks. Monitoring the loss of fluorescence signal of the quantum dots in solution, caused by controlled network formation and consecutive flocculation and sedimentation, leads to selective, qualitative, and quantitative lectin detection. Fluorescent sedimented networks can be observed by the naked eye or under UV illumination for a lectin concentration of up to 10 8 M. Quantitative detection is possible at 100 min with a lower detection limit of approximately 5 × 10 ^-8 M.展开更多
The present study aims to investigate the impact of texting and web surfing on the driving behavior and safety of young drivers on rural roads.For this purpose,driving data were gathered through a driving simulator ex...The present study aims to investigate the impact of texting and web surfing on the driving behavior and safety of young drivers on rural roads.For this purpose,driving data were gathered through a driving simulator experiment with 37 young drivers.Additionally,a survey was conducted to collect their demographic characteristics and driving behavior preferences.During the experiment,the drivers were distracted using contemporary smartphone internet applications i.e.,Facebook Messenger,Facebook and Google Maps.Regression analysis models were developed in order to identify and investigate the effect of distraction on accident probability,speed deviation,headway distance,as well as lateral distance deviation.Additionally,random forest(RF),a machine learning classification algorithm,was deployed for real-time distraction prediction.It was revealed that distraction due to web surfing and texting leads to a statistically significant increase in accident probability,headway distance and lateral distance deviation by 32%,27%and 6%,respectively.Moreover,the driving speed deviation was reduced by 47%during distraction.Apart from the real-time prediction,the RF revealed that headway distance,lateral distance,and traffic volume were important features.The RF outcomes revealed consistency with regression analysis and drivers during the distractive task are more defensive by driving at the edge of the road near the hard shoulder and maintaining longer headways.Overall,driving behavior and safety among young drivers were both significantly affected by the investigated internet applications.展开更多
文摘We present a versatile, tuneable, and selective nanoparficle-based lectin biosensor, based on flocculation of ternary supramolecular nanoparticle networks （NPN）, formed through the sequential binding of three building blocks. The three building blocks are ^-cyclodextrin-capped CdTe quantum dots, tetraethylene glycol-tethered mannose-adamantane cross-linkers （ADTEGMan）, and the tetravalent lectin Concanavalin A （ConA）. The working principle of this selective sensor lies in the dual orthogonal molecular interactions of the linker, uniting adamantane-^-cyclodextrin and mannose-lectin interaction motifs, respectively. Only when the lectin is present, sequential binding takes place, leading to in situ self-organization of the sensor through the formation of ternary supramolecular networks. Monitoring the loss of fluorescence signal of the quantum dots in solution, caused by controlled network formation and consecutive flocculation and sedimentation, leads to selective, qualitative, and quantitative lectin detection. Fluorescent sedimented networks can be observed by the naked eye or under UV illumination for a lectin concentration of up to 10 8 M. Quantitative detection is possible at 100 min with a lower detection limit of approximately 5 × 10 ^-8 M.
文摘The present study aims to investigate the impact of texting and web surfing on the driving behavior and safety of young drivers on rural roads.For this purpose,driving data were gathered through a driving simulator experiment with 37 young drivers.Additionally,a survey was conducted to collect their demographic characteristics and driving behavior preferences.During the experiment,the drivers were distracted using contemporary smartphone internet applications i.e.,Facebook Messenger,Facebook and Google Maps.Regression analysis models were developed in order to identify and investigate the effect of distraction on accident probability,speed deviation,headway distance,as well as lateral distance deviation.Additionally,random forest(RF),a machine learning classification algorithm,was deployed for real-time distraction prediction.It was revealed that distraction due to web surfing and texting leads to a statistically significant increase in accident probability,headway distance and lateral distance deviation by 32%,27%and 6%,respectively.Moreover,the driving speed deviation was reduced by 47%during distraction.Apart from the real-time prediction,the RF revealed that headway distance,lateral distance,and traffic volume were important features.The RF outcomes revealed consistency with regression analysis and drivers during the distractive task are more defensive by driving at the edge of the road near the hard shoulder and maintaining longer headways.Overall,driving behavior and safety among young drivers were both significantly affected by the investigated internet applications.
