Virtual Exam for Parkinson's Disease Enables Frequent and Reliable Measurements of Motor Function

Sensor-based remote monitoring could help us better track Parkinson's disease (PD) progression, and measure patients' response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. 388 study participants with early-stage PD (Personalized Parkinson Project, 64% men, average age 63 years) wore a smartwatch for a median of 390 days, allowing for continuous passive monitoring. Participants performed unsupervised motor tasks both in the clinic (once) and remotely (twice weekly for one year). Dropout rate was 2% at the end of follow-up. Median wear-time was 21.1 hours/day, and 59% of per-protocol remote assessments were completed. In-clinic performance of the virtual exam verified that most participants correctly followed watch-based instructions. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (R=0.70), bradykinesia (R=-0.62), and gait (R=-0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC: 0.75 - 0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen's d: 0.19 - 0.54). Of note, in-clinic assessments often did not reflect the patients' typical status at home. This demonstrates the feasibility of using smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements can create a more complete picture of patient functioning by providing a real-life distribution of disease severity, as it fluctuates over time. Sensitivity to medication-induced change, together with the improvement in test-retest reliability from temporal aggregation implies that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic intervention or disease progression.

ecc_finder: A Robust and Accurate Tool for Detecting Extrachromosomal Circular DNA From Sequencing Data

Extrachromosomal circular DNA (eccDNA) has been observed in different species for decades, and more and more evidence shows that this specific type of DNA molecules may play an important role in rapid adaptation. Therefore, characterizing the full landscape of eccDNA has become critical, and there are several protocols for enriching eccDNAs and performing short-read or long-read sequencing. However, there is currently no available bioinformatic tool to identify eccDNAs from Nanopore reads. More importantly, the current tools based on Illumina short reads lack an efficient standardized pipeline notably to identify eccDNA originating from repeated loci and cannot be applied to very large genomes. Here, we introduce a comprehensive tool to solve both of these two issues.1 Applying ecc_finder to eccDNA-seq data (either mobilome-seq, Circle-Seq and CIDER-seq) from Arabidopsis, human, and wheat (with genome sizes ranging from 120Mb to 17 Gb), we document the improvement of computational time, sensitivity, and accuracy and demonstrate ecc_finder wide applicability and functionality.

Optimization of Vehicle Transportation Route Based on IoT

With the rapid development of logistics industry, optimization of road transport has become a constraint that must be overcome in the development of related industries. In the IoT era, classic car routing solutions could not meet many different needs. The relevant research findings are endless but not suitable to reduce costs in logistics and distribution processes and meet the needs of customers. This paper researches on vehicle path optimization using IoT technology and intelligent algorithms. Firstly, the traditional GA is optimized, and its coding mode, fitness function, selection, crossover, and mutation operators are studied. The crossover probability was set to 0.6, and the mutation probability was set to 0.1; then, according to the improved GA, a vehicle route optimization model was created. Finally, simulations were conducted to optimize vehicle routes for some distribution centers and 15 customer sites, and the model’s validity was tested. Experimental data show that the improved genetic algorithm begins to converge in 100 generations with a running time of 37.265 s. We calculate the time sensitivity of the customer. An algorithmic model is then used to determine distribution plans based on product demand and time sensitivity. In addition, we compare distribution costs and customer satisfaction of algorithmic and randomized plans. The distribution cost and customer satisfaction of the algorithmic and random patterns were 498.09 yuan and 573.13 yuan and 140.45 and 131.35, respectively. This shows that the vehicle routing optimization model using IoT technology and an improved GA can reduce distribution costs and increase customer satisfaction.

Assessing the Impact of the Lead/Lag Times on the Project Duration Estimates in Highway Construction

The literature mentions multiple factors that can affect the accuracy of estimating the project duration in highway construction, such as weather, location, and soil conditions. However, there are other factors that have not been explored, yet they can have significant impact on the accuracy of the project time estimate. Recently, TxDOT raised a concern regarding the importance of the proper estimating of the lead/lag times in project schedules. These lead/lag times are often determined based on the engineer’s experience. However, inaccurate estimates of the lead/lag time can result in unrealistic project durations. In order to investigate this claim, the study utilizes four time sensitivity measures (TSM), namely the Criticality Index (CI), Significance Index (SI), Cruciality Index (CRI), and the Schedule Sensitivity Index (SSI) to statistically analyze and draw conclusions regarding the impact of the lead/lag time estimates on the total duration in highway projects. An Excel-based scheduling software was developed with Monte Carlo simulation capabilities to calculate these TSM. The results from this paper show that the variability of some lead/lag times can significantly impact the accuracy of the estimated total project duration. It was concluded that the current practices used for estimating the lead/lag times are insufficient. As such, it is recommended to utilize more robust methods, such as the time sensitivity measures, to accurately estimate the lead/lad times in the projects scheduled.

Investigation on the Printed CNT-Film-Based Electrochemical Sensor for Detection of Liquid Chemicals

We studied electrochemical sensors using printed carbon nanotubes (CNT) film on a polyethylene telephtalate (PET) substrate. The mechanical stability of the printed CNT film (PCF) was confirmed by using bending and Scotch tape tests. In order to determine the optimum sensor structure, a resistance-type PCF sensor (R-type PCF sensor) and a comb-type PCF sensor (C-type PCF sensor) were fabricated and compared using a diluted NH3 droplet with various concentrations. The magnitude of response, response time, sensitivity, linearity, and limit of detection (LOD) were compared, and it was concluded that C-type PCF sensor has superior performance. In addition, the feasibility of PCF electrochemical sensor was investigated using 12 kinds of hazardous and noxious substances (HNS). The detection mechanism and selectivity of the PCF sensor are discussed.

Towards the Optimal RF Sensing Strategy for IoT Applications: An Ice Sensing Case Study

Remote ice detection has recently emerged as an application of Radio Frequency (RF) sensors. While RF sensing is a feasible approach used for detecting various stimuli, the optimal system architecture and design strategy for RF-based sensing in future Internet of Things (IoT) systems remains unclear. In this paper, we propose a systematic methodology for designing an RF-based sensing system, applicable to a plethora of IoT applications. The proposed methodology is used to design printable antennas as highly-sensitive sensors for detecting and measuring the thickness of ice, demonstrating best-in-class sensory response. Antenna design is investigated systematically for wireless interrogation in the 2.4 GHz band, to support a variety of IoT protocols. Following the proposed methodology, the antenna's realized gain was identified as the optimum parameter-under-test. The developed loop antenna sensor exhibits a high linearity, resilience to interference, and applicability to different real-world deployment environments, demonstrated through over 90% average ice thickness measurement accuracy and at least 5 dB real-time sensitivity to ice deposition.

Towards the Optimal RF Sensing Strategy for IoT Applications: An Ice Sensing Case Study

Remote ice detection has recently emerged as an application of Radio Frequency (RF) sensors. While RF sensing is a feasible approach used for detecting various stimuli, the optimal system architecture and design strategy for RF-based sensing in future Internet of Things (IoT) systems remains unclear. In this paper, we propose a systematic methodology for designing an RF-based sensing system, applicable to a plethora of IoT applications. The proposed methodology is used to design printable antennas as highly-sensitive sensors for detecting and measuring the thickness of ice, demonstrating best-in-class sensory response. Antenna design is investigated systematically for wireless interrogation in the 2.4 GHz band, to support a variety of IoT protocols. Following the proposed methodology, the antenna's realized gain was identified as the optimum parameter-under-test. The developed loop antenna sensor exhibits a high linearity, resilience to interference, and applicability to different real-world deployment environments, demonstrated through over 90% average ice thickness measurement accuracy and at least 5 dB real-time sensitivity to ice deposition.

Numerical Solution of Fractional Order Anomalous Subdiffusion Problems Using Radial Kernels and Transform

By coupling of radial kernels and localized Laplace transform, a numerical scheme for the approximation of time fractional anomalous subdiffusion problems is presented. The fractional order operators are well suited to handle by Laplace transform and radial kernels are also built for high dimensions. The numerical computations of inverse Laplace transform are carried out by contour integration technique. The computation can be done in parallel and no time sensitivity is involved in approximating the time fractional operator as contrary to finite differences. The proposed numerical scheme is stable and accurate.