Degradation of carbamazepine by the UVA-LED365/ClO2/NaClO process: Kinetics, mechanisms and DBPs yield.

A mixed oxidant of chlorine dioxide (ClO2) and NaClO was often used in water treatment. A novel UVA-LED (365 nm)-activated mixed ClO2/NaClO process was proposed for the degradation of micropollutants in this study. Carbamazepine (CBZ) was selected as the target pollutant. Compared with the UVA365/ClO2 process, the UVA365/ClO2/NaClO process can improve the degradation of CBZ, with the rate constant increasing from 2.11×10-4 sec-1 to 2.74×10-4 sec-1. In addition, the consumption of oxidants in the UVA365/ClO2/NaClO process (73.67%) can also be lower than that of UVA365/NaClO (86.42%). When the NaClO ratio increased, both the degradation efficiency of CBZ and the consumption of oxidants can increase in the UVA365/ClO2/NaClO process. The solution pH can affect the contribution of NaClO in the total oxidant ratio. When the pH range of 6.0-8.0, the combination process can generate more active species to promote the degradation of CBZ. The change of active species with oxidant molar ratio was investigated in the UVA365/ClO2/NaClO process. When ClO2 acted as the main oxidant, HO• and Cl• were the main active species, while when NaClO was the main oxidant, ClO• played a role in the system. Both chloride ion (Cl-), bicarbonate ion (HCO3-), and nitrate ion (NO3-) can promote the reaction system. As the concentration of NaClO in the reaction solution increased, the generation of chlorates will decrease. The UVA365/ClO2/NaClO process can effectively control the formation of volatile disinfection by-products (DBPs), and with the increase of ClO2 dosage, the formation of DBPs can also decrease.

Automated Prediction of Infant Cognitive Development Risk by Video: A Pilot Study.

OBJECTIVE: Cognition is an essential human function, and its development in infancy is crucial. Traditionally, pediatricians used clinical observation or medical imaging to assess infants' current cognitive development (CD) status. The object of pediatricians' greater concern is however their future outcomes, because high-risk infants can be identified early in life for intervention. However, this opportunity has not yet been realized. Fortunately, some recent studies have shown that the general movement (GM) performance of infants around 3-4 months after birth might reflect their future CD status, which gives us an opportunity to achieve this goal by cameras and artificial intelligence. METHODS: First, infants' GM videos were recorded by cameras, from which a series of features reflecting their bilateral movement symmetry (BMS) were extracted. Then, after at least eight months of natural growth, the infants' CD status was evaluated by the Bayley Infant Development Scale, and they were divided into high-risk and low-risk groups. Finally, the BMS features extracted from the early recorded GM videos were fed into the classifiers, using late infant CD risk assessment as the prediction target. RESULTS: The area under the curve, recall and precision values reached 0.830, 0.832, and 0.823 for two-group classification, respectively. CONCLUSION: This pilot study demonstrates that it is possible to automatically predict the CD of infants around the age of one year based on their GMs recorded early in life. SIGNIFICANCE: This study not only helps clinicians better understand infant CD mechanisms, but also provides an economical, portable and non-invasive way to screen infants at high-risk early to facilitate their recovery.

Gait Analysis by Causal Decomposition.

Recent studies have investigated bilateral gaits based on the causality analysis of kinetic (or kinematic) signals recorded using both feet. However, these approaches have not considered the influence of their simultaneous causation, which might lead to inaccurate causality inference. Furthermore, the causal interaction of these signals has not been investigated within their frequency domain. Therefore, in this study we attempted to employ a causal-decomposition approach to analyze bilateral gait. The vertical ground reaction force (VGRF) signals of Parkinson's disease (PD) patients and healthy control (HC) individuals were taken as an example to illustrate this method. To achieve this, we used ensemble empirical mode decomposition to decompose the left and right VGRF signals into intrinsic mode functions (IMFs) from the high to low frequency bands. The causal interaction strength (CIS) between each pair of IMFs was then assessed through the use of their instantaneous phase dependency. The results show that the CISes between pairwise IMFs decomposed in the high frequency band of VGRF signals can not only markedly distinguish PD patients from HC individuals, but also found a significant correlation with disease progression, while other pairwise IMFs were not able to produce this. In sum, we found for the first time that the frequency specific causality of bilateral gait may reflect the health status and disease progression of individuals. This finding may help to understand the underlying mechanisms of walking and walking-related diseases, and offer broad applications in the fields of medicine and engineering.