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Land Use and Cover Change (LUCC) has emerged as a primary driver of terrestrial carbon storage changes. However, the contributions of LUCC to Above-Ground Carbon (AGC) storage in subtropical forests remain unclear due to the complex and diverse LUCC trajectory. Quantitative assessment of the impact of different LUCC trajectories on carbon storage is essential for regional carbon cycle mechanisms. Therefore, this study focuses on Zhejiang Province, a representative subtropical forest region in China, to accurately assess the contribution of LUCC to AGC storage changes from 1984 to 2019. We first mapped the land cover patterns using the random forest and spatiotemporal filtering algorithm and then applied these patterns to drive an optimized BIOME-BGC model to simulate the spatiotemporal distribution of AGC density. Finally, the LUCC trajectories were classified into three categories: afforestation, deforestation, and forest type transformations. Their contributions to AGC changes were isolated and analyzed through the trajectory analysis. The results demonstrated that the forest area of Zhejiang Province increased from 5.35 × 106 ha to 6.83 × 106 ha (+27.66%) and the total forest AGC storage increased from 80.52 Tg C to 124.16 Tg C (+54.19%) between 1984 and 2019. The increase in forest AGC due to LUCC amounted to 31.26 Tg C, contributing 71.63% to the total. Specifically, the afforestation, deforestation, and forest type transformations contributed 82.37%, −17.27%, and 6.53% to the change in AGC, respectively. Overall, the afforestation within the LUCC trajectories was the primary contributing factor to the growth of forest AGC in Zhejiang Province from 1984 to 2019. This study obtained accurate LUCC and AGC data, clarifying the contribution of different LUCC trajectories and providing a better understanding of the responses of the forest carbon storage to LUCC dynamics.

The assessment of ecological functions, such as those of forest structure zoning and carbon sinks, heavily relies on forest age classification. Therefore, monitoring forest age is a crucial element of forest resource surveys. With the increased availability of high-quality open-access satellite data and advancements in Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) technology, remote sensing has emerged as an essential method for acquiring accurate forest age information. In this study, Sentinel-2 remote sensing data, UAV-LiDAR data, and combined Sentinel-2 and LiDAR data are used as data sources. Three machine learning algorithms, Adaptive Boosting (AdaBoost), Random Forest (RF), and Extreme Random Tree (ERT), are used to predict forest age in a Masson pine (Pinus massoniana Lamb.) forest. The optimal model is used to predict the forest age and simulate the spatial age distribution. The machine learning models based on separate Sentinel-2 and LiDAR data accurately predict the age of the Masson pine forest. Nevertheless, the accuracy of the RF model with combined data was higher than that in other cases, with an accuracy R value of 0.81. The model displayed good stability, and the spatial uncertainty of age estimation was low. Compared with the RF model using only Sentinel-2 data (R = 0.43), the RF model with combined LiDAR and Sentinel-2 data achieved the highest accuracy, with R values 88.37% higher. In addition, the forest canopy structure parameters, such as the average height of the forest stand extracted from UAV-LiDAR data, had a significant impact on the estimation of forest age. Thus, when the combined Sentinel-2 and LiDAR data were used to establish these parameters, the highest accuracy in the estimation of Masson pine was obtained. The findings of this study provide new insights for forest age estimation based on multi-source remote sensing data.

Accurately classifying tree species in urban forests remains a major challenge due to the limitations of existing methods. While hyperspectral imaging is capable of capturing detailed spectral information, it struggles with the issue of ‘similar reflectance curves for different species’. On the other hand, the intensity frequency curve also has the problem of ‘similar intensity frequency curves for different species’. To overcome these challenges, a new method is proposed in this study. The method combines UAV LiDAR intensity frequency features (IF) and hyperspectral features (HF) to accurately identify tree species, abbreviated as HICM. The results of the study demonstrate that HICM can accurately identify 16 common urban tree species with an overall accuracy (OA) of 92.0% and kappa of 91.2%. Compared with using only IF or HF for identification, HICM improves OA by 16.9% and 12%, and kappa by 21.6% and 10.1%, respectively. This shows that HF and IF have complementarity, and the combination of these two features effectively addresses the problems of “same species different frequency” and “same species different spectrum”.

Atopic dermatitis (AD) is a persistent and recurrent inflammatory skin condition with a genetic basis. However, the fundamental reasons and mechanisms behind this phenomenon remain incompletely understood. While tea extracts are known to reduce histamine-induced skin allergies and inflammation, the specific mechanisms by which various types of Chinese tea provide their protective effects are still not fully elucidated. In this study, a model of skin itching induced by histamine is used to explore the functions and mechanisms of three types of tea extract (Keemun black tea (HC), Hangzhou green tea (LC), and Fujian white tea (BC)) in alleviating histamine-induced dermatitis. The components of three tea extracts are identified by UPLC-Q-TOF-MS, and we found that their main components are alkaloids, fatty acyls, flavonoids, organic acids, and phenols. The inhibitory effects of three types of tea extract on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) in skin injury are investigated by MIC and flow cytometry. The three types of tea extract have an inhibitory effect on the growth of bacterial flora, with HC showing the best inhibitory activity. The effect of the three types of tea extract on histamine-induced dermatitis is also evaluated. Furthermore, itchy skin experiments, HE staining, toluidine blue staining, and immunohistochemical staining of mouse skin tissues were performed to determine the variations of scratching, epidermal thickness, mast cell number, IL-1β, and NGF content after the administration of the tea extracts. The three types of tea extracts all alleviate and inhibit skin itching, epidermal hyperplasia, and allergic dermatitis. BC effectively alleviates epidermal hyperplasia caused by skin allergies, and LC significantly downregulates NGF. HC reduces histamine-induced mast cell infiltration and downregulates IL-1β to alleviate skin itching. Consequently, tea emerges a potent natural product that can inhibit the growth of skin wound bacterial flora and exhibit skin repair effects on histamine-induced allergic dermatitis.

Background: Fluctuating neuronal network states influence brain responses to transcranial magnetic stimulation (TMS). Our previous studies revealed that transient spontaneous bihemispheric brain states in the EEG, driven by oscillatory power, information flow and regional domination, modify cortical EEG responses to TMS. However, the impact of ongoing fluctuations of large-scale brain network states on TMS-EEG responses has not been explored. Objectives: To determine the effects of large-scale brain network states on TMS-EEG responses. Methods: Resting-state EEG and structural MRI from 24 healthy subjects were recorded to infer large-scale brain states. TMS-EEG was acquired with TMS at state-related targets, identified by the spatial distribution of state activation power from resting-state EEG. TMS-induced oscillations were measured by event-related spectral perturbations (ERSPs), and classified with respect to the brain states preceding the TMS pulses. State-locked ERSPs with TMS at specific state-related targets and during state activation were compared with state-unlocked ERSPs. Results: Intra-individual comparison of ERSPs by threshold free cluster enhancement (TFCE) revealed that posterior and visual state-locked TMS, respectively, increased beta and alpha responses to TMS of parietal and occipital cortex compared to state-unlocked TMS. Also, the peak frequencies of ERSPs were increased with state-locked TMS. In addition, inter-individual correlation analyses revealed that posterior and visual state-locked TMS-induced oscillation power (ERSP clusters identified by TFCE) positively correlated with state-dependent oscillation power preceding TMS. Conclusions: Spontaneous transient large-scale brain network states modify TMS-induced natural oscillations in specific brain regions. This significantly extends our knowledge on the critical importance of instantaneous state on explaining the brain's varying responsiveness to external perturbation.

In China, Camellia plants are widely used to reduce atopic dermatitis and inflammation-related diseases, but their protective mechanisms remain unclear. This study investigated the anti-allergic dermatitis, anti-oxidation and anti-inflammation effect and underlying mechanism of five Camellia species, including Camellia ptilophylla Chang, Camellia assamica Chang var. Kucha Chang, Camellia parvisepala Chang, Camellia arborescens Chang, and C. assamica M. Chang. A total of about 110 chemical compositions were detected from five Camellia teas extracts. The level of mast cell infiltration in the model mice skin was determined by HE (Hematoxylin and eosin) staining and toluidine blue staining, and the level of interleukin-1β (IL-1β) and nerve growth factor was detected by immunohistochemistry. The five Camellia tea leaf extracts have histamine-induced allergic dermatitis. Lipopolysaccharide (Lipopolysaccharide)-induced murine macrophage RAW264.7 inflammation model was found to secrete NF-κB factor, as shown by immunofluorescence, and reactive oxygen species secretion and related cytokine levels were detected. The results suggested that Camellia’s five tea extracts had the ability to resist cellular oxidative stress. In addition, the results of cell inflammatory cytokines including fibronectin (FN) and interleukin-6 (IL-6) suggested that the five tea extracts of Camellia had anti-inflammatory effects. Therefore, it is suggested that five Camellia teas may possess inhibitory properties against allergic reactions, oxidative stress, and inflammation, and may prove beneficial in the treatment of allergies.

Psoriasis is a common chronic inflammatory disease, but most of its current treatments come with a high risk of side effects. As one of the world’s top three beverages, tea has a traditional history of being used as a treatment for skin conditions due to its high safety profile, anti-inflammatory and other properties. In this study, we investigated the anti-psoriasis effects of ethanol extracts of black tea, green tea and white tea from southeastern China. The compositions of the tea extracts (TEs) were first determined by UPLC-Q-Exactive-Orbitrap MS and then genetic analysis, antibacterial, anti-inflammatory, and immunocompetence assays were performed. Imiquimod was used to establish a mouse model of psoriasis-like dermatitis and treating with the extracts to examine their efficacy. A total of 88 chemical components, mainly phenols and organic acids, were identified from the TEs. These TEs ameliorated skin damage and they all reduced the expression of cytokines IL-17 and TNF-α. By analyzing the genes, TEs may affect the inflammatory signaling pathway by regulating the metabolic changes. In addition, TEs can significantly scavenge ROS, NO, and inhibit cellular inflammation. In conclusion, this study examined the inhibitory effects of three TEs on psoriasis and their potential as nutritional supplements for the treatment of skin inflammation.

Accurate individual tree detection by unmanned aerial vehicles (UAVs) is a critical technique for smart forest management and serves as the foundation for evaluating ecological functions. Existing object detection and segmentation methods, on the other hand, have reduced accuracy when detecting and segmenting individual trees in complicated urban forest landscapes, as well as poor mask segmentation quality. This study proposes a novel Mask-CSP-attention-coupled network (MCAN) based on the Mask R-CNN algorithm. MCAN uses the Cross Stage Partial Net (CSPNet) framework with the Sigmoid Linear Unit (SiLU) activation function in the backbone network to form a new Cross Stage Partial Residual Net (CSPResNet) and employs a convolutional block attention module (CBAM) mechanism to the feature pyramid network (FPN) for feature fusion and multiscale segmentation to further improve the feature extraction ability of the model, enhance its detail information detection ability, and improve its individual tree detection accuracy. In this study, aerial photography of the study area was conducted by UAVs, and the acquired images were used to produce a dataset for training and validation. The method was compared with the Mask Region-based Convolutional Neural Network (Mask R-CNN), Faster Region-based Convolutional Neural Network (Faster R-CNN), and You Only Look Once v5 (YOLOv5) on the test set. In addition, four scenes—namely, a dense forest distribution, building forest intersection, street trees, and active plaza vegetation—were set up, and the improved segmentation network was used to perform individual tree segmentation on these scenes to test the large-scale segmentation ability of the model. MCAN’s average precision (AP) value for individual tree identification is 92.40%, which is 3.7%, 3.84%, and 12.53% better than that of Mask R-CNN, Faster R-CNN, and YOLOv5, respectively. In comparison to Mask R-CNN, the segmentation AP value is 97.70%, an increase of 8.9%. The segmentation network’s precision for the four scenes in multi-scene segmentation ranges from 95.55% to 92.33%, showing that the proposed network performs high-precision segmentation in many contexts.

This study aims to explore the molecular mechanism of tetrandrine (Tet) in alleviating pulmonary inflammation and fibrosis induced by silica (SiO2) from the perspective of autophagy. C57BL/6J mice were selected as experimental animals, and SiO2 was exposed by intranasal instillation. Tet was intervened by oral gavage. The mice were euthanized on the 7th and 42nd day of SiO2 exposure, and lung tissues were collected for histopathological, molecular biological, immunological, and transmission electron microscopy analysis. The results showed that SiO2 exposure could lead to significant lung inflammation and fibrosis, while Tet could significantly reduce SiO2 exposure-induced lung inflammation and fibrosis. Molecular mechanism research indicated that, compared with SiO2 expose group, Tet intervention could significantly reduce the expression levels of inflammatory cytokines and fibrosis markers (TNF-α, IL-1β, MCP-1, TGF-β1, HYP, Col-I, and Fn), and regulate the expression of key molecules ATG7, microtubule-associated protein 1 light chain 3B (LC3B), and P62 in the autophagy pathway to improve the blocking of autophagic flux, promote the recovery of autophagic lysosomal system function, and inhibit apoptosis. In summary, Tet can alleviate silica-induced lung inflammation and fibrosis, which may be achieved by regulating the expression of key molecules in the autophagy process and associated apoptotic pathway.

RHD variants in D¯ Chinese pregnant women arose difficulties in management during pregnancy. Therefore, this study aims to precisely manage D¯ pregnant women by evaluating the spectrum of RHD mutations in D¯ pregnant women and getting insight into the possible rare alleles of RHD. A total of 76 D¯ pregnant women were analyzed by performing polymerase chain reactions with sequence-specific primers (PCR-SSP), the 10 RHD exons Sanger sequencing, RHD zygosity detection, and mRNA sequencing (mRNA-seq). About 40% of alleles are variations of RHD, including RHD 1227A homozygous, RHD-CE(2-9)-D, et al. Therefore, we developed a molecular diagnostic strategy for Chinese women, and most D¯ pregnant women can be diagnosed with this simple decision tree. After RHD genotyping for D¯ pregnancy women, we eliminated at least 15% unnecessary ante- and postpartum injections of Rh immunoglobulin (RhIG). As the first pedigree study and the first functional analysis under physiological conditions, mRNA-seq revealed that c.336-1G>A mutation mainly led to the inclusion of the intron 2, which indirectly explained the D¯ phenotype in this family. We also developed a robust protocol for determining fetal RhD status from maternal plasma. All 31 fetuses were predicted as RhD positive and confirmed the RhD status after birth.

The estimation of characteristic parameters such as diameter at breast height (DBH), aboveground biomass (AGB) and stem volume (V) is an important part of urban forest resource monitoring and the most direct manifestation of the ecosystem functions of forests; therefore, the accurate estimation of urban forest characteristic parameters is valuable for evaluating urban ecological functions. In this study, the height and density characteristic variables of canopy point clouds were extracted as Scheme 1 and combined with the canopy structure variables as Scheme 2 based on unmanned aerial vehicle lidar (UAV-Lidar). We analyzed the spatial distribution characteristics of the canopies of different tree species, and multiple linear regression (MLR), support vector regression (SVR), and random forest (RF) models were used to estimate the DBH, AGB, and V of urban single trees. The estimation accuracy of different models was evaluated based on the field-measured data. The results indicated that the model accuracy of coupling canopy structure variables (R2 = 0.69–0.85, rRMSE = 9.87–24.67%) was higher than that of using only point-cloud-based height and density characteristic variables. The comparison of the results of different models shows that the RF model had the highest estimation accuracy (R2 = 0.76–0.85, rRMSE = 9.87–22.51%), which was better than that of the SVR and MLR models. In the RF model, the estimation accuracy of AGB was the highest (R2 = 0.85, rRMSE = 22.51%), followed by V, with an accuracy of R2 = 0.83, rRMSE = 18.51%, and the accuracy of DBH was the lowest (R2 = 0.76, rRMSE = 9.87%). The results of the study provide an important reference for the estimation of single-tree characteristic parameters in urban forests based on UAV-Lidar.

The accurate classification of tree species is essential for the sustainable management of forest resources and the effective monitoring of biodiversity. However, a literature review shows that most of the previous unmanned aerial vehicle (UAV) light detection and ranging (LiDAR)-based studies on fine tree species classification have used only limited intensity features, accurately identifying relatively few tree species. To address this gap, this study proposes developing a new intensity feature—intensity frequency—for the LiDAR-based fine classification of eight tree species. Intensity frequency is defined as the number of times a certain intensity value appears in the individual tree crown (ITC) point cloud. In this study, we use UAV laser scanning to obtain LiDAR data from urban forests. Intensity frequency features are constructed based on the extracted intensity information, and a random forest (RF) model is used to classify eight subtropical forest tree species in southeast China. Based on four-point cloud density sampling schemes of 100%, 80%, 50% and 30%, densities of 230 points/m2, 184 points/m2, 115 points/m2 and 69 points/m2 are obtained. These are used to analyze the effect of intensity frequency on tree species classification accuracy under four different point cloud densities. The results are shown as follows. (1) Intensity frequencies of trees are not significantly different for intraspecies (p > 0.05) values and are significantly different for interspecies (p < 0.01) values. (2) The intensity frequency features of LiDAR can be used to classify different tree species with an overall accuracy (OA) of 86.7%. Acer Buergerianum achieves a user accuracy (UA) of over 95% and a producer accuracy (PA) of over 90% for four density conditions. (3) The OA varies slightly under different point cloud densities, but the sum of correct classification trees (SCI) and PA decreases rapidly as the point cloud density decreases, while UA is less affected by density with some stability. (4) The priori feature selected by mean rank (MR) covers the top 10 posterior features selected by RF. These results show that the new intensity frequency feature proposed in this study can be used as a comprehensive and effective intensity feature for the fine classification of tree species.

In this paper, a method for extracting the height of urban forest trees based on a smartphone was proposed to efficiently and accurately determine tree heights. First, a smartphone was used to obtain person–tree images, LabelImg was used to label the images, and a dataset was constructed. Secondly, based on a deep learning method called You Only Look Once v5 (YOLOv5) and the small-hole imaging and scale principles, a person–tree scale height measurement model was constructed. This approach supports recognition and mark functions based on the characteristics of a person and a tree in a single image. Finally, tree height measurements were obtained. By using this method, the heights of three species in the validation set were extracted; the range of the absolute error was 0.02 m–0.98 m, and the range of the relative error was 0.20–10.33%, with the RMSE below 0.43 m, the rRMSE below 4.96%, and the R2 above 0.93. The person–tree scale height measurement model proposed in this paper greatly improves the efficiency of tree height measurement while ensuring sufficient accuracy and provides a new method for the dynamic monitoring and investigation of urban forest resources.

Three dimensional (3D) green volume is an important tree factor used in forest surveys as a prerequisite for estimating aboveground biomass (AGB). In this study, we developed a method for accurately calculating the 3D green volume of single trees from unmanned aerial vehicle laser scanner (ULS) data, using a voxel coupling convex hull by slices algorithm, and compared the results using voxel coupling convex hull by slices algorithm with traditional 3D green volume algorithms (3D convex hull, 3D concave hull (alpha shape), convex hull by slices, voxel and voxel coupling convex hull by slices algorithms) to estimate AGB. Our results showed the following: (1) The voxel coupling convex hull by slices algorithm can accurately estimate the 3D green volume of a single ginkgo tree (RMSE = 11.17 m3); (2) Point cloud density can significantly affect the extraction of 3D green volume; (3) The addition of the 3D green volume parameter can significantly improve the accuracy of the model to estimate AGB, where the highest accuracy was obtained by the voxel coupling convex hull by slices algorithm (CV-R2 = 0.85, RMSE = 11.29 kg, and nRMSE = 15.12%). These results indicate that the voxel coupling convex hull by slices algorithms can more effectively calculate the 3D green volume of a single tree from ULS data. Moreover, our study provides a comprehensive evaluation of the use of ULS 3D green volume for AGB estimation and could significantly improve the estimation accuracy of AGB.

AIM: To compare the safety and effectiveness of phacotrabeculectomy versus sequential surgery in chronic angle-closure glaucoma (CACG) with coexisting cataract. METHODS: One hundred and sixty-two CACG patients (162 eyes) were retrospectively analyzed. Of them, 87 patients (87 eyes) in group A had underwent phacotrabeculectomy with intraocular lens (IOL) implantation, and 75 patients (75 eyes) in group B had underwent sequential surgery with IOL implanted. Best-corrected visual acuity (BCVA), intraocular pressure (IOP), complications and anterior chamber angle (ACA) were measured. RESULTS: Demographic characteristics of the two groups were similar. A mean follow-up period was 15±6mo (range 13 to 24mo), a mean IOP of 16.61±6.43 mm Hg in group A and 15.80±5.35 mm Hg in group B (P=0.84) at the last follow up. The Kaplan-Meier analysis revealed that the cumulative probability of success in both groups was similar (P=0.61). Anterior uveitis and hypotony were the most common complications in group A, whereas group B experienced shallow anterior chamber with trabeculectomy. With the exception of anterior uveitis, no complications occurred to 11 trabeculectomized eyes. All postoperative measurements of anterior chamber showed statistically significant differences in each group according to the preoperative data (P

Pain, a universal and burdensome condition, influences numerous individuals worldwide. It encompasses sensory, emotional, and cognitive facets, with recent research placing a heightened emphasis on comprehending pain's impact on emotion and cognition. Cognitive bias, which encompasses attentional bias, interpretation bias, and memory bias, signifies the presence of cognitive distortions influenced by emotional factors. It has gained significant prominence in pain-related research. Human studies have shown that individuals experiencing pain exhibit cognitive bias. Similarly, animal studies have demonstrated cognitive bias in pain-induced states across various species and disease models. In this study, we aimed to investigate the memory bias displayed by rats experiencing acute pain, using the affective bias test (ABT) as a tool and administering either hotplate or formalin to induce acute pain. Our data showed that rats demonstrated a significant preference for the control treatment-related substrate over the substrate associated with formalin treatment (p < 0.001), an indication of the prominent memory bias stimulated by acute formalin injections. However, when exposed to substrates related to hotplate treatment and control treatment, the acute pain induced by the hotplate treatment failed to generate a statistically significant choice bias in rats (p = 0.674). Our study demonstrates that the negative emotions associated with acute pain can be reflected by memory bias in ABT, at least for formalin-induced acute pain. This finding will augment our comprehension of the emotional and cognitive aspects of acute pain. [ABSTRACT FROM AUTHOR]

Signals transporting orbital angular momentum (OAM) have gained significant interest due to their unique structure and capability for increasing the channel capacity. OAM beams are orthogonal and can be multiplexed to increase link capacity in different scenarios. The collective combination of radio OAM modes and OFDM modulations in outdoor conditions has not been extensively explored. This paper depicts an analysis of multiple OAM modes transmitted as narrowband subcarriers using orthogonal frequency division multiplexing (OFDM) modulation with outdoor free space optical channel measurements based on OAM modes. OAM-OFDM simulations were conducted to investigate the performance of QPSK, 16QAM, and 64QAM baseband modulation and demodulation mechanisms, with code rates ½ and ¾ respectively. Four outdoor OAM free space optical channel characteristics parameters were applied. The throughput, bit error rate (BER), and packet error rate (PER) were evaluated. The results show that OAM +4 Mode outperforms other OAM modes in terms of BER and PER. In terms of the throughput, 64 QAM at code rate ¾ outperforms other coding schemes and code rates, for all OAM modes. [ABSTRACT FROM AUTHOR]