Rubisco activity and gene expression of tropical tree species under light stress

Tropical rain forests contain an ecologically and physiologically diverse range of vegetation and habitats. Sun-acclimated plants can be divided into two groups, shade-tolerant and shade-intolerant, according to the plant’s physiological and genetic responses. Some tropical species have potential capacity for light damage in a shaded environment as well as shade-tolerance to compensate for the impaired light harvesting complex. In particular, ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco) is regulated by the Calvin cycle, which participated in protein synthesis. Rubisco plays a role in CO2 fixation, which helps supply the energy to regulate Rubisco for ribulose 1,5-bisphosphate (RuBP) reduction. Light intensity is associated with the photosynthetic rate and genetic response to moderate growth environments.Keywords: Gene expression, growth, light intensity, Rubisco activityAfrican Journal of Biotechnology Vol. 12(20), pp. 2764-276

Contribution of the family environment to depression in Korean adults with epilepsy

AbstractPurposeDepression is common in adults with epilepsy and an important factor that affects quality of life in these individuals. However, there are few studies on the interactions between epilepsy and family factors in adults and we here investigate this association.MethodsThis cross-sectional, multicenter study collected data on 391 adults with epilepsy and their caregivers recruited from 27 hospitals throughout Korea. The Beck Depression Inventory (BDI), Stigma Scale, and Caregiver Burden Inventory (CBI) were used to evaluate the study population. Multivariate analysis was conducted using hierarchical linear regression. The Sobel test and structural equation modeling were used to examine interrelationships among the potential factors.ResultsThe mean patient BDI score was 16.3 (SD=11.1). Depressive symptoms (BDI≥10) were in 68.3% and 57.0% in patients and their caregivers, respectively. Hierarchical linear regression analysis only identified caregiver BDI (β=0.219; p<0.001) as an independent factor related to patient BDI. The mediational model suggested that caregiver BDI mediated the effects of other family factors on patient BDI: caregiver's educational level (p=0.002), caregiver's CBI score (p<0.001), caregiver's Stigma Scale score (p<0.001), and family APGAR score (p<0.001). In addition, structural equation modeling showed that the relation between caregiver and patient BDI was unidirectional.ConclusionCaregiver depression is the most important contributor to depression in adults with epilepsy. The other family factors such as caregiver's perception of burden and the level of family function are indirectly correlated with patient depression via the mediating effects of caregiver depression

Retrieval of total precipitable water from Himawari-8 AHI data: A comparison of random forest, extreme gradient boosting, and deep neural network

Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches-random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)-are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 ??m, respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed. ?? 2019 by the authors