Increased uptake of deep soil water promotes drought resistance in mixed forests.

The intensity and frequency of droughts are projected to rise in recent years and adversely affect forests. Thus, information on plant water use and acclimation during and after droughts is crucial. This study used the stable isotope and thermal dissipation probes to detect the water-use adaptation of mixed forests to drought using a precipitation gradient control experiment in the field. The results showed that Platycladus orientalis and Quercus variabilis mainly absorbed stable water from deep soil layers during the drought (32.05% and 28.2%, respectively). The synergetic nocturnal sap flow in both species replenished the water loss, but P. orientalis experienced a greater decline in transpiration acclimation to drought. The transpiration of Q. variabilis remained high since it was mainly induced by radiation. After short-term exposure to drought, P. orientalis majorly obtained shallow soil water, confirming its sensitivity to shallow water. Contrarily, Q. variabilis mainly absorbed stable water from deep soil layers regardless of the soil water content. Therefore, these findings suggest that Q. variabilis cannot physiologically adjust to extreme drought events, possibly limiting their future distributions and altering the composition of boreal forests.

Water use sources and its influencing factors of Pinus massoniana and Quercus acutissima community in hilly region of Southern China.

The process of plant water use is complex and changeable, which is affected by various factors. Exploring the sources and influencing factors of plant water use can provide reference for clarifying the mechanisms of forest water adaptation under climate change. We chosen the typical forest communities in the hilly region of Sou-thern China, Pinus massoniana and Quercus acutissima mixed forest as the research object. By analyzing water sources of plants in different seasons, the factors affecting the changes of water sources were explored in combination with soil water, precipitation, and plant roots. The results showed that water use characteristics of P. massoniana and Q. acutissima were similar and both mainly utilized 0-40 cm soil water during the dry season, with proportions of 60.0% and 66.6%. During the rainy season, as soil water content of deep layers increased, the main water sources of both gradually shifted towards deep soil. The similarity proportion indices of P. massoniana and Q. acutissima were above 60%, indicating that there was an obvious water competition between them. Root system of Q. acutissima had plasticity in water absorption, and played a dominant role in absorbing shallow water during the dry season. Water was the main driving factor for water source transformation of Q. acutissima and P. massoniana during the rainy season. Compared with P. massoniana, Q. acutissima was more sensitive to the changes of water sources. Under the background of future warming and drying, the competition between the two species for shallow water sources might be intensified. Those two species should be sparsely planted or thinned to optimize forest structure to cope with water stress.

Antioxidant Proteins' Identification Based on Support Vector Machine.

BACKGROUND: Evidence have increasingly indicated that for human disease, cell metabolism are deeply associated with proteins. Structural mutations and dysregulations of these proteins contribute to the development of the complex disease. Free radicals are unstable molecules that seek for electrons from the surrounding atoms for stability. Once a free radical binds to an atom in the body, a chain reaction occurs, which causes damage to cells and DNA. An antioxidant protein is a substance that protects cells from free radical damage. Accurate identification of antioxidant proteins is important for understanding their role in delaying aging and preventing and treating related diseases. Therefore, computational methods to identify antioxidant proteins have become an effective prior-pinpointing approach to experimental verification. METHODS: In this study, support vector machines was used to identify antioxidant proteins, using amino acid compositions and 9-gap dipeptide compositions as feature extraction, and feature reduction by Principal Component Analysis. RESULTS: The prediction accuracy Acc of this experiment reached 98.38%, the recall rate Sn of the positive sample was found to be 99.27%, the recall rate Sp of the negative sample reached 97.54%, and the MCC value was 0.9678. To evaluate our proposed method, the predictive performance of 20 antioxidant proteins from the National Center for Biotechnology Information(NCBI) was studied. As a result, 20 antioxidant proteins were correctly predicted by our method. Experimental results demonstrate that the performance of our method is better than the state-of-the-art methods for identification of antioxidant proteins. CONCLUSION: We collected experimental protein data from Uniport, including 253 antioxidant proteins and 1552 non-antioxidant proteins. The optimal feature extraction used in this paper is composed of amino acid composition and 9-gap dipeptide. The protein is identified by support vector machine, and the model evaluation index is obtained based on 5-fold cross-validation. Compared with the existing classification model, it is further explained that the SVM recognition model constructed in this paper is helpful for the recognition of antioxidized proteins.