Through its impact on morphogenesis, light is the key environmental factor that alters plant structural development; however, the understanding how light controls plant growth and developmental processes is still poor and needs further research. For this purpose, a Petri dish and pot experiment was conducted to investigate the effects of different LEDs, i.e., white light (WL), red light (RL), blue light (BL), and orange light (OL) on morphology, gas-exchange parameters, and antioxidant capacity of Brassica napus. Compared with WL, RL significantly promoted plant growth and biomass, contents of photosynthetic pigments, and gas-exchange parameters in comparison to BL and OL. However, RL also helped decline malondialdehyde and proline contents and superoxide anion and peroxide production rate. In contrast, BL and OL significantly reduced plant growth and biomass, gas-exchange attributes and increased the activities of superoxide dismutase and peroxidase in Petri dish as well as in pot experiment. These results suggest that red light could improve plant growth in B. napus plants through activating gas-exchange attributes, reduce reactive oxygen species accumulation, and promote antioxidant capacity.

A large number of studies in the past months have proposed deep learning-based Artificial Intelligence (AI) tools for automated detection of COVID-19 using publicly available datasets of Chest X-rays (CXRs) or CT scans for training and evaluation. Most of these studies report high accuracy when classifying COVID-19 patients from normal or other commonly occurring pneumonia cases. However, these results are often obtained on cross-validation studies without an independent test set coming from a separate dataset and have biases such as the two classes to be predicted come from two completely different datasets. In this work, we investigate potential overfitting and biases in such studies by designing different experimental setups within the available public data constraints and highlight the challenges and limitations of developing deep learning models with such datasets. We propose a deep learning architecture for COVID-19 classification that combines two very popular classification networks, ResNet and Xception, and use it to carry out the experiments to investigate challenges and limitations. The results show that the deep learning models can overestimate their performance due to biases in the experimental design and overfitting to the training dataset. We compare the proposed architecture to state-of-the-art methods utilizing an independent test set for evaluation, where some of the identified bias and overfitting issues are reduced. Although our proposed deep learning architecture gives the best performance with our best possible setup, we highlight the challenges in comparing and interpreting various deep learning algorithms’ results. While the deep learning-based methods using chest imaging data show promise in being helpful for clinical management and triage of COVID-19 patients, our experiments suggest that a larger, more comprehensive database with less bias is necessary for developing tools applicable in real clinical settings.

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Nutmeg (Myristica fragrans Houtt) is a native plant of Banda Island known as the Spice Islands. Nutmeg fruit consists of the pericarp or rind, the seed kernel inside (nutmeg), and the nutmeg is a red lacy (aryl) covering the kernel (mace). Nutmeg pericarp contributing 80-85% of the total weight of the nutmeg fruit but its use is still not getting enough attention and a lot of it is wasted as agricultural waste which can pollute the environment. This is because the economic value is considered to be lower than the seeds and mace of nutmeg. This article aims to review the potential and oppurtunity benefits of nutmeg meat waste (pericarp) for human health and its application in functional foods. The method used in this paper is a literature review. The results show that, the pericarp has been reported to contain bioactive compounds similar to those of nutmeg and mace oil which have pharmacological values. Phytochemical compounds are beneficial to human health as anti-inflammatory, anti-diabetes, anti-microbial agents, antixidants, anti-depressants, anti-convulsants, and anti-cancer agents. Based on the composition, the pericarp is potentially used as a functional food to increase added value and reduce environmental pollution. In addition, the abundance of materials, relatively low prices, and the importance of healthy food for the health of the human provide opportunities for the development of functional foods based on bioactive compounds.

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