A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light.

Hazardous compounds from microplastics in coastal and marine environments are adsorbed by live organisms, affecting human and marine life. It takes time, money and effort to study the distribution and type of microplastics in the environment, using appropriate expensive equipment in a laboratory. However, deep learning can assist in identifying and quantifying microplastics from an image. This paper presents a novel microplastic classification method that combines the benefits of UV light with deep learning. The Faster-RCNN model with a ResNet-50-FPN backbone was implemented to detect and identify microplastics. Microplastic images from the field taken under UV light were used to train and validate the model. This classification model achieved a high precision of 85.5-87.8%, and the mAP scores were 33.9% on an internal test set and 35.7% on an external test set. This classification approach provides a high-accuracy, low-cost, and time-effective automated identification and counting of microplastics.

Photosynthetic Responses of Freshwater Macrophytes to the Daily Light Cycle in Songkhla Lagoon.

Macrophytes play an important role in the freshwater ecosystem. However, human activities and climate change are currently affecting aquatic lakes and species in various ways. The heterogeneity of macrophyte ecophysiology might lead to different responses to changing environments. To understand the photosynthetic responses of freshwater macrophytes to changes in light, six freshwater macrophyte species from Songkhla Lagoon were investigated. The results showed that there was a diurnal response of photosynthetic activities in all species. The Fv/Fm and ∆F/Fm' of all species decreased at midday (9 a.m.-3 p.m.) then recovered at 6 p.m. close to the 6 a.m. level. As well as alpha, the positive relation between alpha and light showed the adaptation of plants. The saturating irradiance (Ik) revealed that plants from different light regimes showed different responses to light and temperature changes. To maintain a positive carbon balance and cope with light and temperature conditions, macrophytes had strategies such as modifying light harvesting capacity and light use. This study provides a better understanding of the vulnerability of each species to environmental changes and photosynthetic responses that enable species from different light regime to adapt to changing light environments.

Integrated biomarker responses of rice associated with grain yield in copper-contaminated soil.

Copper (Cu) contamination in soil is an environmental issue that affects rice growth and development. This study investigated changes in photosynthetic capacities in combination with integrated biomarker responses at different growth stages of rice (Oryza sativa L. var. Hom Bai Toey) exposed to various concentrations of Cu. A randomized complete block design with four replications was used. Exposure to high copper concentrations of 200 Cu mg kg-1 of soil and more resulted in a marked decline in the photosynthetic efficiency of Photosystem II (Phi2) but increased yield of non-photochemical quenching (PhiNPQ) and yield of non-regulatory energy dissipation (PhiNO) at tillering and flowering stages. In addition, these concentrations induced a delay in the flowering of rice, as a consequence of stress experienced in early growth stage. Significant lipid peroxidation and leaf area reduction were observed with 400 Cu mg kg-1 treatment at flowering stage. Rice grain yield decreased significantly at copper concentrations of 200 and 400 mg kg-1. Overall, excess copper inhibited photosynthetic capacity, growth, and development of rice in the early growth stage, and synergistic effects of yield components contributed to final grain yield reduction at harvesting stage. In addition, calculated integrated biomarker response (IBR) values reflect well the severity of Cu toxicity with a decreasing order from tillering stage to harvesting stage.

Effects of public health interventions on the epidemiological spread during the first wave of the COVID-19 outbreak in Thailand.

A novel infectious respiratory disease was recognized in Wuhan (Hubei Province, China) in December 2019. In February 2020, the disease was named "coronavirus disease 2019" (COVID-19). COVID-19 became a pandemic in March 2020, and, since then, different countries have implemented a broad spectrum of policies. Thailand is considered to be among the top countries in handling its first wave of the outbreak-12 January to 31 July 2020. Here, we illustrate how Thailand tackled the COVID-19 outbreak, particularly the effects of public health interventions on the epidemiological spread. This study shows how the available data from the outbreak can be analyzed and visualized to quantify the severity of the outbreak, the effectiveness of the interventions, and the level of risk of allowed activities during an easing of a "lockdown." This study shows how a well-organized governmental apparatus can overcome the havoc caused by a pandemic.