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Accurate and cost-effective prediction of aboveground biomass (AGB), belowground biomass (BGB), and the total (ABGB) at stand-level within tropical forests is crucial for effective forest ecological management and the provision of forest ecosystem services. Although there has been research on simultaneously fitting biomass equations for tree components, rather few studies focus on simultaneously predicting AGB and BGB at stand-level while maintaining additivity. We developed innovative Deep Learning Additive Models (DLAMs) for the simultaneous predictions of stand-level AGB, BGB, and ABGB integrating forest stand, ecological, and environmental factors as predictive covariates and compared them with conventional weighted nonlinear seemingly unrelated regression (WNSUR) and multivariate adaptive regression splines (MARS). Data for this study were collected from 121 plots distributed in two tropical forest types (dipterocarp and evergreen broadleaf) across five ecological regions of Vietnam, capturing three response variables (AGB, BGB, and ABGB), and 12 predictors. Factor analysis for mixed data was employed to identify the optimal covariates. Cross-validation results demonstrated that DLAMs substantially enhanced the reliability of simultaneous predictions of forest biomass components compared to the conventional WNSUR and MARS methods. The optimal DLAMs included seven predictive covariates (stand basal area (G), stand volume (V), mean annual temperature (T), elevation (EL), forest type (FT), average height (Hg), and soil group (SG)). They had mean absolute percent errors (MAPEs) of 6.3 %, 4.3 %, and 5.3 % for the simultaneous prediction of AGB, BGB, and ABGB, respectively. The MAPEs for the DLAMs approach were substantially lower than those for the WNSUR alternative by 2.9 %, 14.0 %, and 2.4 %, and lower than those for the MARS method by 4.3 %, 11.6 %, and 4.1 % for predicting AGB, BGB, and ABGB simultaneously, respectively. Conducting experiments in designing multi-input multi-output deep neural networks was essential for significantly improving the reliability of the simultaneous predictions from the DLAMs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Climate change models predict increasing precipitation variability in the mid-latitude regions of Earth, generating a need to reduce the negative impacts of these changes on crop production. Despite considerable research on how cover crops support agriculture in a changing climate, understanding is limited of how climate change influences the growth of cover crops. We investigated the early development of two common cover crop species-crimson clover ( Trifolium incarnatum ) and rye ( Secale cereale )-and hypothesized that growing them in the mixture would ameliorate stress from drought or waterlogging. This hypothesis was tested in a 25-day greenhouse experiment, where the two factors (species number and water stress) were fully crossed in randomized blocks, and plant responses were quantified through survival, growth rate, biomass production and root morphology. Water stress negatively influenced the early growth of these two species in contrasting ways: crimson clover was susceptible to drought while rye performed poorly under waterlogging. Per-plant biomass in rye was always greater in mixture than in monoculture, while per-plant biomass of crimson clover was greater in mixture under drought. Both species grew longer roots in mixture than in monoculture under drought, and total biomass of mixtures did not differ significantly from the more-productive monoculture (rye) in any water condition. In the face of increasingly variable precipitation, growing crimson clover and rye together has potential to ameliorate water stress, a possibility that should be further investigated in field experiments., Competing Interests: The authors have no conflicts of interest to declare. , (© The Author(s) 2024. Published by Oxford University Press on behalf of the Annals of Botany Company.)

Background: The protective or pathogenic role of T lymphocytes during the acute phase of dengue virus (DENV) infection has not been fully understood despite its importance in immunity and vaccine development. Objectives: This study aimed to clarify the kinetics of T lymphocyte subsets during the clinical course of acute dengue patients. Study design: In this hospital-based cohort study, 59 eligible Vietnamese dengue patients were recruited and admitted. They were investigated and monitored for T cell subsets and a panel of clinical and laboratory parameters every day until discharged and at post-discharge from the hospital. Results: We described for the first time the kinetics of T cell response during the clinical course of DENV infection. Severe cases showed significantly lower levels of effector CD8 + T cells compared to mild cases at day -1 ( p = 0.017) and day 0 ( p = 0.033) of defervescence. After defervescence, these cell counts in severe cases increased rapidly to equalize with the levels of mild cases. Our results also showed a decline in total CD4 + T, Th1, Th1/17 cells during febrile phase of dengue patients compared to normal controls or convalescent phase. On the other hand, Th2 cells increased during DENV infection until convalescent phase. Cytokines such as interferon-γ, IL-12p70, IL-5, IL-23, IL-17A showed tendency to decrease on day 0 and 1 compared with convalescence and only IL-5 showed significance indicating the production during acute phase was not systemic. Conclusion: With a rigorous study design, we uncovered the kinetics of T cells in natural DENV infection. Decreased number of effector CD8 + T cells in the early phase of infection and subsequent increment after defervescence day probably associated with the T cell migration in DENV infection., (Copyright © 2020 Manh, Weiss, Thuong, Mizukami, Dumre, Luong, Thanh, Thang, Huu, Phuc, Nhung, Mai, Truong, Ngu, Quoc, Ha, Ton, An, Halhouli, Quynh, Kamel, Karbwang, Huong, Huy and Hirayama.)

Deep learning-based marker detection for autonomous drone landing is widely studied, due to its superior detection performance. However, no study was reported to address non-uniform motion-blurred input images, and most of the previous handcrafted and deep learning-based methods failed to operate with these challenging inputs. To solve this problem, we propose a deep learning-based marker detection method for autonomous drone landing, by (1) introducing a two-phase framework of deblurring and object detection, by adopting a slimmed version of deblur generative adversarial network (DeblurGAN) model and a You only look once version 2 (YOLOv2) detector, respectively, and (2) considering the balance between the processing time and accuracy of the system. To this end, we propose a channel-pruning framework for slimming the DeblurGAN model called SlimDeblurGAN, without significant accuracy degradation. The experimental results on the two datasets showed that our proposed method exhibited higher performance and greater robustness than the previous methods, in both deburring and marker detection.

Detection and classification of road markings are a prerequisite for operating autonomous vehicles. Although most studies have focused on the detection of road lane markings, the detection and classification of other road markings, such as arrows and bike markings, have not received much attention. Therefore, we propose a detection and classification method for various types of arrow markings and bike markings on the road in various complex environments using a one-stage deep convolutional neural network (CNN), called RetinaNet. We tested the proposed method in complex road scenarios with three open datasets captured by visible light camera sensors, namely the Malaga urban dataset, the Cambridge dataset, and the Daimler dataset on both a desktop computer and an NVIDIA Jetson TX2 embedded system. Experimental results obtained using the three open databases showed that the proposed RetinaNet-based method outperformed other methods for detection and classification of road markings in terms of both accuracy and processing time., Competing Interests: The authors declare no conflict of interest.

Autonomous landing of an unmanned aerial vehicle or a drone is a challenging problem for the robotics research community. Previous researchers have attempted to solve this problem by combining multiple sensors such as global positioning system (GPS) receivers, inertial measurement unit, and multiple camera systems. Although these approaches successfully estimate an unmanned aerial vehicle location during landing, many calibration processes are required to achieve good detection accuracy. In addition, cases where drones operate in heterogeneous areas with no GPS signal should be considered. To overcome these problems, we determined how to safely land a drone in a GPS-denied environment using our remote-marker-based tracking algorithm based on a single visible-light-camera sensor. Instead of using hand-crafted features, our algorithm includes a convolutional neural network named lightDenseYOLO to extract trained features from an input image to predict a marker's location by visible light camera sensor on drone. Experimental results show that our method significantly outperforms state-of-the-art object trackers both using and not using convolutional neural network in terms of both accuracy and processing time.

Despite the public health significance and potential applications to medical research, the evolutionary history of the Asian Lance-headed pitvipers (genus Protobothrops) remains inadequately studied. Previous research generally focused on a few selected species with no comprehensive molecular study of Protobothrops. Here, we conduct the first study to infer the phylogenetic relationships of all currently recognized species in this genus based on four mitochondrial DNA fragments and four nuclear genes. Bayesian Inference and Maximum Likelihood analyses show that within Protobothrops there are four strongly supported clades forming distinct subgroups. The first subgroup, which is sister to the other three, consists of three species, P. himalayanus, P. kaulbacki, and P. sieversorum. The second subgroup contains only P. mangshanensis. The final two subgroups, which are sister groups, include the other four and six Protobothrops species. Although our findings provide additional information on the phylogenetic relationships of the genus Protobothrops, the placement of P. dabieshanensis and P. elegans remains problematic. In addition, our molecular results indicate that P. trungkhanhensis, currently considered endemic to Vietnam, should be added to the species known from China. Our ancestral area estimation indicated that Protobothrops likely originated in southwestern China. This study improves our understanding of the evolutionary relationships among species of Asian Lance-headed pitvipers, providing a greater framework for future studies.

Medical pathogens induce infections, illnesses and sometimes serious medical conditions in the infected hosts. Diagnosis of these pathogens is important for proper treatment and investigation of pathogenesis processes. Molecular techniques have been developed for facilitating accurate, sensitive and low-cost diagnosis of these pathogens. Based on these techniques, diagnostic devices have been developed for a number of pathogens. More devices are needed for comprehensive coverage of medical pathogens. To facilitate the development of these devices, a database with integrated information about diagnostic methods, targets, and primers/probes for the known bacterial, fungal and viral pathogens is needed. We developed the microbial pathogen diagnostic methods database MicrobPad MD (http://bidd.nus.edu.sg/group/MicrobPad/MicrobPad.asp or http://pha-bidd.nus.edu.sg/group/MicrobPad/MicrobPad.asp) to provide comprehensive information about the molecular diagnostic techniques, targets, primers/probes, detection procedures and conditions, and tested diagnostic accuracies and limit of diagnosis for 314 bacterial, fungal and viral species from 61 genera. While available, additional information such as pathogen strains and hosts, tissue distribution or habitats, cultivation methods, biochemical characteristics, virulence factors, morphology, diseases, symptoms, treatment and prevention methods are provided. Our Database covers 242 gene targets, 700 primers/probes, 340 virulence factors, and 261 diseases. Cross-links to the NCBI genome and SwissProt/UniProt databases are provided., (Copyright © 2012 Elsevier B.V. All rights reserved.)

With an understudied amphibian fauna, the highest deforestation rate on the planet and high harvesting pressures, Southeast Asian amphibians are facing a conservation crisis. Owing to the overriding threat of habitat loss, the most critical conservation action required is the identification and strict protection of habitat assessed as having high amphibian species diversity and/or representing distinctive regional amphibian faunas. Long-term population monitoring, enhanced survey efforts, collection of basic biological and ecological information, continued taxonomic research and evaluation of the impact of commercial trade for food, medicine and pets are also needed. Strong involvement of regional stakeholders, students and professionals is essential to accomplish these actions.