Higher Drought Sensitivity of Autumn Photosynthetic Phenology in Planted Forests Than in Natural Forests of Tropical and Subtropical China.

Severe droughts advance autumn phenology, reducing terrestrial ecosystem productivity and carbon sequestration. Approximately 25% of China's tropical/subtropical forests are planted for climate mitigation, yet differences in drought sensitivity of autumn phenology between planted and natural forests remain unclear. In this study, we used four phenological fitting methods to extract end-of-photosynthetic-growing-season (EOPS) dates in China's tropical/subtropical forests over the period 2001-2020, and employed ridge regression to assess the difference in response of EOPS to drought (the standardized precipitation evapotranspiration index, SPEI) between natural and planted forests.

Increased Growth Temperatures Alter Arctic Plant Responses to Heat Wave and Drought.

Persistent warming and higher frequency of heat waves in the Arctic are causing alterations in Arctic vegetation and plant functionality, potentially redefining the role of the Arctic ecosystem. Vegetation influences atmospheric composition through exchanges of CO and volatile organic compounds (VOCs), both processes exhibiting a strong response to temperature variations. However, our quantitative understanding of how increased temperatures interact with extreme weather events, namely heat waves and drought, to affect Arctic plant processes remains limited.

Synthesis and sintering of AgNWs@PVP core-shell structures for improved transparent electrodes.

The majority of the current composite core-shell structure preparation and sintering processes applied to silver nanowire transparent electrodes are impractical for large-scale industrial production and primarily emphasize macroscopic performance parameters, with limited insights into the underlying sintering mechanism. This study presents a simple synthesis of AgNWs@PVP core-shell structures and investigates the effects of sintering temperature and time on their morphology. The morphology of AgNWs@PVP core-shell structures during sintering was systematically analyzed using characterization techniques, such as transmission electron microscopy and scanning electron microscopy.

Knowledge-based inductive bias and domain adaptation for cell type annotation.

Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotation methods is influenced by inductive bias, which refers to a set of assumptions that describe the behavior of model predictions. Different annotation methods possess distinct inductive biases, leading to varying degrees of generalizability and interpretability.

UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique.

Background And Objective: Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance of these models in practical application.

PACVP: Prediction of Anti-Coronavirus Peptides Using a Stacking Learning Strategy With Effective Feature Representation.

Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been developed to identify ACVPs, but the overall prediction performance is still not enough to meet the actual therapeutic application. In this study, we constructed an efficient and reliable prediction model PACVP (Prediction of Anti-CoronaVirus Peptides) for identifying ACVPs based on effective feature representation and a two-layer stacking learning framework.

Global warming is increasing the discrepancy between green (actual) and thermal (potential) seasons of temperate trees.

Over the past decades, global warming has led to a lengthening of the time window during which temperatures remain favorable for carbon assimilation and tree growth, resulting in a lengthening of the green season. The extent to which forest green seasons have tracked the lengthening of this favorable period under climate warming, however, has not been quantified to date. Here, we used remote sensing data and long-term ground observations of leaf-out and coloration for six dominant species of European trees at 1773 sites, for a total of 6060 species-site combinations, during 1980-2016 and found that actual green season extensions (GS: 3.

NeuroPred-CLQ: incorporating deep temporal convolutional networks and multi-head attention mechanism to predict neuropeptides.

Neuropeptides (NPs) are a particular class of informative substances in the immune system and physiological regulation. They play a crucial role in regulating physiological functions in various biological growth and developmental stages. In addition, NPs are crucial for developing new drugs for the treatment of neurological diseases.

Soil moisture regulates warming responses of autumn photosynthetic transition dates in subtropical forests.

Autumn phenology plays a key role in regulating the terrestrial carbon and water balance and their feedbacks to the climate. However, the mechanisms underlying autumn phenology are still poorly understood, especially in subtropical forests. In this study, we extracted the autumn photosynthetic transition dates (APTD) in subtropical China over the period 2003-2017 based on a global, fine-resolution solar-induced chlorophyll fluorescence (SIF) dataset (GOSIF) using four fitting methods, and then explored the temporal-spatial variations of APTD and its underlying mechanisms using partial correlation analysis and machine learning methods.

Influences of Shifted Vegetation Phenology on Runoff Across a Hydroclimatic Gradient.

Climate warming has changed vegetation phenology, and the phenology-associated impacts on terrestrial water fluxes remain largely unquantified. The impacts are linked to plant adjustments and responses to climate change and can be different in different hydroclimatic regions. Based on remote sensing data and observed river runoff of hydrological station from six river basins across a hydroclimatic gradient from northeast to southwest in China, the relative contributions of the vegetation (including spring and autumn phenology, growing season length (GSL), and gross primary productivity) and climatic factors affecting the river runoffs over 1982-2015 were investigated by applying gray relational analysis (GRA).

Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods.

Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red-green-blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502.