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The growing awareness of the health advantages offered by forests has underscored the significance of forest exposure as an upstream preventive measure against disease. While numerous studies have confirmed the physical and mental health benefits associated with forests, there is still a lack of quantitative understanding regarding the relationship between forest exposure and physiological health benefits (PHB). Particularly, there is insufficient knowledge about the threshold effects derived from short-term forest exposure. In this study, we propose a PHB threshold model for assessing forest exposure that introduces the concepts of efficiency threshold and benefits threshold. A pilot study was conducted in three typical natural forest sites to validate the proposed model. Electroencephalogram (EEG) was continuously measured as the physiological indicator, while meteorological, environmental, and demographic factors were simultaneously collected. The results show that: (1) the proposed PHB threshold model is applicable in a natural forest environment; (2) despite the longer time required to reach the PHB thresholds, forest exposure yielded more significant and prolonged health benefits compared to urban green spaces; (3) meteorological factors, such as temperature and relative humidity, play a crucial role in impacting the PHB threshold model; and (4) exposure to forests is better for deep thinking and relaxation than urban green spaces. These findings emphasize the potential of forests to offer a respite from the stresses of modern life and promote holistic well-being. [ABSTRACT FROM AUTHOR]

Nitrogen (N), an important element for crop growth, has a great impact on dry matter weight and yield. Currently, improving N fertilizer use rate is an urgent problem to be solved in agricultural production in the world. In this field experiment, a self-developed water-soluble polymer material (PPM) with water retention and slow-release characteristics was combined with different doses of N fertilizer (N300 (100% N), PN300 (PPM + 100% N), PN240 (PPM + 80% N), PN180 (PPM + 60% N), CK (no N and PPM)) to analyze the impacts on N uptake and use efficiency of wheat plants. The results showed that the combined application of PPM and N fertilizer significantly improved yield, plant height, biomass, and N uptake and use efficiency of drip irrigated wheat, and the PN240 group had the highest N use rate. In addition, the PN300 group had the highest yield. N use efficiency in the PN240 group was 40.23% higher than that in the N300 group. Therefore, the combined application of PPM and N fertilizer, especially PN240, can reduce the N fertilizer application rate by increasing N use efficiency. This study provides technical reference for improving the N use efficiency of drip-irrigated wheat in arid areas. [ABSTRACT FROM AUTHOR]

Because of human-induced habitat deterioration and climate change, a large number of species are threatened and even endangered. Batrachium pekinense Liang Liu, a perennial aquatic plant endemic to Beijing, was synonymized as a widely distributed species in Northern China, B. bungei Steudel, but it was later listed in the national key protected wild plants in China. Taxonomic uncertainty should be clarified especially when related species may be allocated with a limited conservation budget. In this study, we reappraised the identity of B. pekinense based on results from molecular phylogenetic analysis and 15-year field observations. Our result shows that 77% of the 65 individuals collected from various sites share identical sequences in both the nuclear ribosomal ITS and the plastid psbA-trnH markers, all samples of the two species consist of one monophyletic clade with strong support, and continuous morphological variations on the key distinguishing character, the leaf shape, are observed in the field. Integrative evidence from both our and the others' studies supports the same identity for B. pekinense and B. bungei. Hence, we suggest excluding B. pekinense from the list of national key protected wild plants of China in its next update and advocate the integrative evidence-based taxonomy and systematics for biodiversity conservation and management. [ABSTRACT FROM AUTHOR]

Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios. [ABSTRACT FROM AUTHOR]

This research investigates how fourth-instar larvae of the potato tuber moth, Phthorimaea operculella, respond to plant secondary metabolites (sucrose, glucose, nicotine, and tannic acid) both in terms of gustatory electrophysiology and feeding behavior. The objective is to establish a theoretical foundation for employing plant-derived compounds in potato tuber moth control. We employed single-sensillum recording techniques and dual-choice leaf disk assays to assess the gustatory electrophysiological responses and feeding preferences of these larvae towards the mentioned compounds. Sensory neurons responsive to sucrose, glucose, nicotine, and tannic acid were identified in the larvae's medial and lateral sensilla styloconica. Neuronal activity was influenced by stimulus type and concentration. Notably, the two types of sensilla styloconica displayed distinct response patterns for sucrose and glucose while they had similar firing patterns towards nicotine and tannic acid. Sucrose and glucose significantly promoted larval feeding, while nicotine and tannic acid had significant inhibitory effects. These findings demonstrate that the medial and lateral sensilla styloconica house sensory neurons sensitive to both feeding stimulants and inhibitors, albeit with differing response profiles and sensitivities. This study suggests that sucrose and glucose are promising candidates for feeding stimulants, while nicotine and tannic acid show potential as effective feeding inhibitors of P. operculella larvae. [ABSTRACT FROM AUTHOR]

Remote sensing, the Internet, the Internet of Things (IoT), artificial intelligence, and other technologies have become the core elements of modern agriculture and smart farming. Agricultural production and management modes guided by data and services have become a cutting-edge carrier of agricultural information monitoring, which promotes the transformation of the intelligent computing of remote sensing big data and agricultural intensive management from theory to practical applications. In this paper, the main research objective is to construct a new high-frequency agricultural production monitoring and intensive sharing service and management mode, based on the three dimensions of space, time, and attributes, that includes crop recognition, growth monitoring, yield estimation, crop disease or pest monitoring, variable-rate prescription, agricultural machinery operation, and other automatic agricultural intelligent computing applications. The platforms supported by this mode include a data management and agricultural information production subsystem, an agricultural monitoring and macro-management subsystem (province and county scales), and two mobile terminal applications (APPs). Taking Shandong as the study area of the application case, the technical framework of the system and its mobile terminals were systematically elaborated at the province and county levels, which represented macro-management and precise control of agricultural production, respectively. The automatic intelligent computing mode of satellite–air–ground spatiotemporal collaboration that we proposed fully couples data obtained from satellites, unmanned aerial vehicles (UAVs), and IoT technologies, which can provide the accurate and timely monitoring of agricultural conditions and real-time guidance for agricultural machinery scheduling throughout the entire process of agricultural cultivation, planting, management, and harvest; the area accuracy of all obtained agricultural information products is above 90%. This paper demonstrates the necessity of customizable product and service research in agricultural intelligent computing, and the proposed practical mode can provide support for governments to participate in agricultural macro-management and decision making, which is of great significance for smart farming development and food security. [ABSTRACT FROM AUTHOR]

In the field of urban design, current research has shifted towards resident preference perception and computer-aided design methods that rely on deep learning techniques. In this study, we aimed to provide a quantitative design method for urban space design that could take into account the preferences of different populations. Through empirical research, we collected real urban space and population data, which we then quantified using advanced intelligent recognition tools based on deep learning techniques. Our ensuing analysis illuminated the intricate interplay between constituent elements of urban spaces and the structural and emotional changes of residents. By taking into account the specific driving relationships between each element and residents, we proposed a new evaluation methodology for constructing an intelligent design evaluation model for urban spaces. This intelligent design evaluation model was subsequently used to evaluate the urban space both pre- and post-design. The standard deviation of the difference results demonstrated that the design option (SD value = 0.103) and the desired option for Space 1 were lower than the current option (SD value = 0.129) and the expected scheme. Our findings provide quantitative configuration strategies and program evaluation for urban space design, thus helping designers to design urban spaces that are more popular with residents. [ABSTRACT FROM AUTHOR]

Quadrupole mass spectrometers (QMS) are widely used for clinical diagnosis and chemical analysis. To obtain the best experimental results, mass spectrometers must be calibrated to an ideal setting before use. However, tuning the current QMS is challenging. Traditional tuning techniques possess low automation levels and rely primarily on skilled engineers. Therefore, in this study, we propose an innovative auto-tuning algorithm for QMS based on the improved particle swarm optimization (PSO) algorithm to automatically find the optimal solution of QMS parameters and make the QMS reach the optimal state. The improved PSO algorithm is combined with simulated annealing, multiple inertia weights, dynamic boundaries, and other methods to prevent the traditional PSO algorithm from the issue of a local optimal solution and premature convergence. According to the characteristics of the mass spectrum peaks, a termination function is proposed to simplify the termination conditions of the PSO algorithm and further improve the automation level of the mass spectrometer. The results of auto-calibration testing of resolution and mass axis show that both resolution and mass axis calibration could effectively meet the requirements of mass spectrometry experiments. By the experiment of auto-optimization testing of lens and ion source parameters, these parameters were all in the vicinity of the optimal solution, which achieved the expected performance. Through numerous experiments, the reproducibility of the algorithm was established as meeting the auto-tuning function of the QMS. The proposed method can automatically tune the mass spectrometer from its non-optimal condition to the optimal one, which can effectively reduce the tuning difficulty of QMS. [ABSTRACT FROM AUTHOR]

This study investigated the removal of cadmium (Cd) and lead (Pb) from the soil through phytoremediation using ryegrass combined with chelating agents. Soil leaching experiments were employed to determine the extraction efficiencies of chelating agents, including ethylenediaminetetraacetic acid (EDTA), citric acid (CA), sodium glutamate tetra acetate (GLDA), oxalic acid (OA), and diethylenetriaminepentaacetic acid (DTPA) on Cd and Pb. Soil pot experiments were conducted to determine the effects of five different chelating agents—GLDA, EDTA, DTPA, CA, and OA—on the growth of ryegrass and the enrichment of Cd and Pb. The main findings were as follows: (1) the extraction efficiencies for Cd and Pb in soil were found to be GLDA > EDTA > DTPA > CA > OA and EDTA > DTPA > GLDA > CA > OA, respectively. (2) The aminopolycarboxylic acid class of chelating agents significantly reduced Cd and Pb contents in the weak acid extractable and reducible states in the studied soil, yet were less effective in the extraction of their residue state. Using chelating agents increased the proportion of residual heavy metals while reducing those in the weak acid extractable and reducible states in the soil, thereby mitigating the harmful effects of these heavy metals on the soil ecology. [ABSTRACT FROM AUTHOR]

Current optical fire smoke detectors use scattering light intensity as an indicator of smoke concentration and trigger fire alarms when the intensity exceeds a threshold value. However, such detectors are prone to false alarms caused by non-fire aerosols since both fire smokes and non-fire aerosols scatter light. Thus, in order to reduce false alarms caused by non-fire aerosols such as dust and water vapor, fire detectors must be capable of distinguishing fire smoke from non-fire aerosols. Since the light scattering signals depend on the particle size information of aerosols, it is essential to study and characterize the particle size distribution of fire smoke and non-fire aerosols for differentiating them. In this paper, a comprehensive aerosol experimental platform is built to measure the particle size distribution of various typical fire smokes and non-fire aerosols. Through the conducted experiments, we note that there are significant differences in the particle size distributions of typical fire smokes and non-fire aerosols, with a boundary of about 1μm. The experimental results provide fundamental data support of the particle size distribution for developing a better fire detector that accurately identifies smoke. [ABSTRACT FROM AUTHOR]

Chlorophyll a concentration and suspended matter concentration, as typical water quality parameters related to spectral characteristics, are essential for characterizing the degree of eutrophication in water bodies. They have become crucial indicators for water quality assessment of inland water bodies. The support vector regression model (SVR) is suitable for small samples, has excellent generalization ability, and has high prediction accuracy. Still, it has the problem of difficult selection of model parameters and quickly falling into local extremes. To solve this problem, a hybrid Differential Evolution-Grey Wolf Optimizer (DE-GWO) algorithm is introduced into the parameter selection process of the support vector regression model, and an improved SVR algorithm (DE-GWO-SVR) is proposed for the remote sensing inversion of chlorophyll a concentration and suspended sediment concentration in water bodies. In this paper, the spectral reflectance of the water surface and the chlorophyll a and broken matter concentration values were obtained by field measurements in the Tangdao Bay waters of Qingdao, Shandong Province. The inverse model between the concentration values of the two water quality parameters and the corresponding sensitive factors was established by first determining the sensitive factors based on the response of the spectral reflectance to the two water quality parameters and introducing the DE-GWO optimization algorithm into the parameter selection process of the SVR model. Finally, the accuracy of the model was verified using Sentinel II satellite remote sensing spectral data, and then the inverse accuracy of the two water quality parameters was obtained. The mean relative error (MRE) of the chlorophyll a prediction model built by the DE-GWO algorithm optimizing the SVR is 25.1%, and the mean relative error (MRE) of the suspended matter prediction model is 32.5%. The inversion results were all better than the other models (linear regression, SVR, and GWO-SVR model). When the best model, built from the measured water surface spectral data, was applied to the Sentinel II satellite data, the improved SVR model outperformed the other models in terms of mean relative error. The experimental results confirm that the DE-GWO-SVR algorithm is an effective method for remote sensing inversion of chlorophyll a and suspended matter concentrations in water bodies, which can provide a reference for remote sensing inversion of chlorophyll a and suspended matter concentrations in Chinese offshore waters and subsequent scientific management of waters. [ABSTRACT FROM AUTHOR]

Building change detection (BuCD) can offer fundamental data for applications such as urban planning and identifying illegally-built new buildings. With the development of deep neural network-based approaches, BuCD using high-spatial-resolution remote sensing images (RSIs) has significantly advanced. These deep neural network-based methods, nevertheless, typically demand a considerable number of computational resources. Additionally, the accuracy of these algorithms can be improved. Hence, LightCDNet, a lightweight Siamese neural network for BuCD, is introduced in this paper. Specifically, LightCDNet comprises three components: a Siamese encoder, a multi-temporal feature fusion module (MultiTFFM), and a decoder. In the Siamese encoder, MobileNetV2 is chosen as the feature extractor to decrease computational costs. Afterward, the multi-temporal features from dual branches are independently concatenated based on the layer level. Subsequently, multiscale features computed from higher levels are up-sampled and fused with the lower-level ones. In the decoder, deconvolutional layers are adopted to gradually recover the changed buildings. The proposed network LightCDNet was assessed using two public datasets: namely, the LEVIR BuCD dataset (LEVIRCD) and the WHU BuCD dataset (WHUCD). The F1 scores on the LEVIRCD and WHUCD datasets of LightCDNet were 89.6% and 91.5%, respectively. The results of the comparative experiments demonstrate that LightCDNet outperforms several state-of-the-art methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Advancements in the fields of ionic liquids (ILs) broaden its applications not only in traditional use but also in different pharmaceutical and biomedical fields. Ionic liquids "Solutions for Your Success" have received a lot of interest from scientists due to a myriad of applications in the pharmaceutical industry for drug delivery systems as well as targeting different diseases. Solubility is a critical physicochemical property that determines the drug's fate at the target site. Many promising drug candidates fail in various phases of drug research due to poor solubility. In this context, ionic liquids are regarded as effective drug delivery systems for poorly soluble medicines. ILs are also able to combine different anions/cations with other cations/anions to produce salts that satisfy the concept behind the ILs. The important characteristics of ionic liquids are the modularity of their physicochemical properties depending on the application. The review highlights the recent advancement and further applications of ionic liquids to deliver drugs in the pharmaceutical and biomedical fields. [ABSTRACT FROM AUTHOR]

Microbial fuel cells (MFCs) have shown great advantages in electricity production, heavy metal removal, and energy recovery. However, the impact and mechanism of conflicting effects of numerous electron acceptors on heavy metal removal remain unknown. The effects of different initial heavy metal concentrations, cathodic dissolved oxygen, and electrode materials on the electricity generation and heavy metal removal efficiencies of Cu(II) and Cr(VI) were investigated in this study. When the initial concentration of Cr(VI) increased from 10 mg/L to 150 mg/L, the maximum voltage, coulomb efficiency, and maximum power density declined from 99 to 44 mV, 28.63% to 18.97%, and 14.29 to 0.62 mW/m2, and the removal efficiencies of Cu(II) and Cr(VI) decreased dramatically from 98.34% and 99.92% to 67.09% and 37.06%, respectively. Under anaerobic cathodic conditions, the removal efficiency and removal rate of Cu(II) and Cr(VI) were lower than those under aerobic conditions. When the cathode electrode was titanium sheet and graphite plate, the coulomb efficiency and maximum power density increased to 38.18%, 50.71%, 33.95 mW/m2, and 62.23 mW/m2. The removal efficiency and removal rates of Cu(II) and Cr(VI) were significantly increased to 98.09%, 86.13%, and 0.47, 0.50 mg/(L h) with a graphite plate, respectively. The pH of the cathode varied considerably greater as the MFC current increased. Cu(II) and Cr(VI) were removed and reduced to elemental Cu, Cu2O, and its oxides as well as Cr(OH)3 and Cr2O3 precipitates on the cathode electrode by cathodic bioelectrochemical reduction. [ABSTRACT FROM AUTHOR]

In order to broaden the study of energetic cations, a cation 3,5-diamino-4H-pyrazol-4-one oxime (DAPO) with good thermal stability was proposed, and its three salts were synthesized by a simple and efficient method. The structures of the three salts were verified by infrared spectroscopy, mass spectrometry, elemental analysis, and single crystal X-ray diffraction. The thermal stabilities of the three salts were verified by differential scanning calorimetry and thermos-gravimetric analysis. DAPO-based energetic salts are analysed using a variety of theoretical techniques, such as 2D fingerprint, Hirshfeld surface, and non-covalent interaction. Among them, the energy properties of perchlorate (DAPOP) and picrate (DAPOT) were determined by EXPLO5 program combined with the measured density and enthalpy of formation. These compounds have high density, acceptable detonation performance, good thermal stability, and satisfactory sensitivity. The intermolecular interactions of the four compounds were studied by Hirshfeld surface and non-covalent interactions, indicating that hydrogen bonds and π–π stacking interactions are the reasons for the extracellular properties of perchlorate (DAPOP) and picrate (DAPOT), indicating that DAPO is an optional nitrogen-rich cation for the design and synthesis of novel energetic materials with excellent properties. [ABSTRACT FROM AUTHOR]

In order to improve the mechanical strength and imprinting efficiency, a novel bovine serum albumin (BSA) molecularly imprinted poly(ionic liquid)/calcium alginate composite cryogel membrane (MICM) was prepared. The results of the tensile test indicated that the MICM had excellent mechanical strength which could reach up to 90.00 KPa, 30.30 times higher than the poly (ionic liquid) membrane without calcium alginate; the elongation of it could reach up to 93.70%, 8.28 times higher than the poly (ionic liquid) membrane without calcium alginate. The MICM had a very high welling ratio of 1026.56% and macropore porosity of 62.29%, which can provide effective mass transport of proteins. More remarkably, it had a very high adsorption capacity of 485.87 mg g−1 at 20 °C and 0.66 mg mL−1 of the initial concentration of BSA. Moreover, MICM also had good selective and competitive recognition toward BSA, exhibiting potential utility in protein separation. This work can provide a potential method to prepare the protein-imprinted cryogel membrane with both high mechanical strength and imprinting efficiency. [ABSTRACT FROM AUTHOR]

Using a controlled optical bias and balanced geometry, we propose a new scheme for broadband terahertz detection by laser-gas interaction without high-voltage manipulation. Compared to the conventional optical bias scheme, the common noise is reduced and the dynamic range as well as the signal-to-noise ratio are doubled. It provides a simple alternative for coherent broadband terahertz detection. The influence of optical bias on terahertz waveform is also investigated, and the evolution of the terahertz-induced second harmonic with probe delay is further revealed. This new detection scheme for broadband terahertz will boost the application of terahertz time-domain spectroscopy for its miniaturization and integrability. [ABSTRACT FROM AUTHOR]

Understanding the characteristics of the traditional village distribution contributes to the formulation of relevant protection and development strategies. We adopted a series of spatial analysis methods to investigate the characteristics of the traditional village distribution in China by using the watershed as the research unit. Moreover, we conducted quantitative and qualitative analyses of the relevant influencing factors affecting the distribution pattern using Geodetector and mathematical statistics. The findings indicate that traditional villages are distributed unevenly across watershed units. High–High clusters tend to occur at the boundaries of first-level watersheds. Traditional villages have a clear agglomeration trend in space, with a concentrated and contiguous distribution pattern based on the "core density area–ring-core expansion group–belt area". The key factors affecting the traditional village distribution are annual precipitation, annual average temperature, and river density. The traditional village number has a clear inverted U-shaped relationship with the annual average temperature, river density, and road density. The study reveals the complex and various characteristics of the traditional village distribution and its influence mechanism and offers scientific advice for traditional villages' future protection and development. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) is an important characterized parameter of lakes. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication. Sentinel Multispectral Imager (MSI) images from May to September between 2020 and 2021 were used along with in-situ measurements to estimate Chl-a in Lake Chagan, which is located in Jilin Province, Northeast China. In this study, the extreme gradient boosting (XGBoost) and Random Forest (RF) models, which had similar performances, were generated by six single bands and six band combinations. The RF model was then selected based on the assessments (R2 = 0.79, RMSE = 2.51 μg L−1, MAPE = 9.86%), since its learning of the input features in the model conformed to the bio-optical properties of Case 2 waters. The study considered Chl-a concentrations in Lake Chagan as a seasonal pattern according to the K-Nearest-Neighbors (KNN) classification. The RF model also showed relatively stable performance for three seasons (spring, summer and autumn) and it was applied to map Chl-a in the whole lake. The research presents a more reliable machine learning (ML) model with higher precision than previous empirical models, as shown by the effects of the input features linked with the biological mechanisms of Chl-a. Its robustness was revealed by the temporal and spatial distributions of Chl-a concentrations, which were consistent with in-situ measurements in the map. This research was capable of revealing the current ecological situation in Lake Chagan and can serve as a reference in remote sensing of inland lakes. [ABSTRACT FROM AUTHOR]

LC-MS/MS uses information on the mass peaks and peak areas of samples to conduct quantitative analysis. However, in the detection of clinical samples, the spectrograms of the compounds are interfered with for different reasons, which makes the identification of chromatographic peaks more difficult. Therefore, to improve the chromatographic interference problem, this paper first proposes a multi-window-based signal-to-noise ratio estimation algorithm, which contains the steps of raw data denoising, peak identification, peak area calculation and curve fitting to obtain accurate quantitative analysis results of the samples. Through the chromatographic peak identification of an extracted ion chromatogram of VD2 in an 80 ng/mL standard and the spectral peak identification of data from an open-source database, the identification results show that the algorithm has a better peak detection performance. The accuracy of the quantitative analysis was verified using the LC-HTQ-2020 triple quadrupole mass spectrometer produced by our group for the application of steroid detection in human serum. The results show that the algorithm proposed in this paper can accurately identify the peak information of LC-MS/MS chromatographic peaks, which can effectively improve the accuracy and reproducibility of steroid detection results and meet the requirements of clinical testing applications such as human steroid hormone detection. [ABSTRACT FROM AUTHOR]

Background: The existing studies on primary bladder lymphoma (PBL) are retrospective analyses based on individual cases or small series studies, and the research on PBL is not unified and in-depth enough at present because of the scarcity of PBL and the lack of relevant literature. This study is designed to develop and validate nomograms for overall survival (OS) and cancer-specific survival (CSS) prediction in patients with PBL. Methods: According to the Surveillance, Epidemiology, and End Results (SEER) database, 405 patients diagnosed with PBL from 1975 to 2016 were collected and randomly assigned to training (n = 283) and validation (n = 122) cohort. After the multivariable Cox regression, the OS and CSS nomograms were developed. The discrimination, calibration and clinical usefulness of the nomograms were assessed and validated, respectively, by the training and validation cohort. Furthermore, all of the patients were reclassified into high- and low-risk groups and their survival were compared through Kaplan-Meier method and log-rank test. Results: Age, subtype, Ann Arbor stage, radiation and chemotherapy were identified as independent prognostic factors for OS and age, sex, and subtype for CSS, then corresponding nomograms predicting the 3- and 5-year survival were constructed. The presented nomograms demonstrated good discrimination and calibration, which the C-index in the training and validation cohort were 0.744 (95% confidence interval [CI], 0.705–0.783) and 0.675 (95% CI, 0.603–0.747) for OS nomogram and 0.692 (95% CI, 0.632–0.752) and 0.646 (95% CI, 0.549–0.743) for CSS nomogram, respectively. Furthermore, the nomograms can be used to effectively distinguish Patients with PBL at high risk of death. The clinical usefulness of the nomograms was visually displayed by decision curve analysis. Conclusion: We updated the baseline characteristics of patients with PBL and constructed OS and CSS nomograms to predict their 3- and 5-year survival. Using these nomograms, it would be convenient to individually predict the prognosis of patients with PBL and provide guidance for clinical treatment. [ABSTRACT FROM AUTHOR]

Accurate and reliable farmland crop mapping is an important foundation for relevant departments to carry out agricultural management, crop planting structure adjustment and ecological assessment. The current crop identification work mainly focuses on conventional crops, and there are few studies on parcel-level mapping of horticultural crops in complex mountainous areas. Using Miaohou Town, China, as the research area, we developed a parcel-level method for the precise mapping of horticultural crops in complex mountainous areas using very-high-resolution (VHR) optical images and Sentinel-2 optical time-series images. First, based on the VHR images with a spatial resolution of 0.55 m, the complex mountainous areas were divided into subregions with their own independent characteristics according to a zoning and hierarchical strategy. The parcels in the different study areas were then divided into plain, greenhouse, slope and terrace parcels according to their corresponding parcel characteristics. The edge-based model RCF and texture-based model DABNet were subsequently used to extract the parcels according to the characteristics of different regions. Then, Sentinel-2 images were used to construct the time-series characteristics of different crops, and an LSTM algorithm was used to classify crop types. We then designed a parcel filling strategy to determine the categories of parcels based on the classification results of the time-series data, and accurate parcel-level mapping of a horticultural crop orchard in a complex mountainous area was finally achieved. Based on visual inspection, this method appears to effectively extract farmland parcels from VHR images of complex mountainous areas. The classification accuracy reached 93.01%, and the Kappa coefficient was 0.9015. This method thus serves as a methodological reference for parcel-level horticultural crop mapping and can be applied to the development of local precision agriculture. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a crucial approach for remote sensing interpretation. High-precision semantic segmentation results are obtained at the cost of manually collecting massive pixelwise annotations. Remote sensing imagery contains complex and variable ground objects and obtaining abundant manual annotations is expensive and arduous. The semi-supervised learning (SSL) strategy can enhance the generalization capability of a model with a small number of labeled samples. In this study, a novel semi-supervised adversarial semantic segmentation network is developed for remote sensing information extraction. A multiscale input convolution module (MICM) is designed to extract sufficient local features, while a Transformer module (TM) is applied for long-range dependency modeling. These modules are integrated to construct a segmentation network with a double-branch encoder. Additionally, a double-branch discriminator network with different convolution kernel sizes is proposed. The segmentation network and discriminator network are jointly trained under the semi-supervised adversarial learning (SSAL) framework to improve its segmentation accuracy in cases with small amounts of labeled data. Taking building extraction as a case study, experiments on three datasets with different resolutions are conducted to validate the proposed network. Semi-supervised semantic segmentation models, in which DeepLabv2, the pyramid scene parsing network (PSPNet), UNet and TransUNet are taken as backbone networks, are utilized for performance comparisons. The results suggest that the approach effectively improves the accuracy of semantic segmentation. The F1 and mean intersection over union (mIoU) accuracy measures are improved by 0.82–11.83% and 0.74–7.5%, respectively, over those of other methods. [ABSTRACT FROM AUTHOR]

Although microprisms have become an important medical means of strabismus treatment, related research concerning the design, fabrication, and testing of microprismatic glasses for preventing eyestrain has rarely been reported. In this study, the structure of microprismatic glasses for preventing eyestrain related to using electronic monitors, including computers and mobile phones, is introduced. A designing theory of anti-fatigue glasses with microprisms is developed. The fabrication technique and the process are described, and the performances of the fabricated microprisms are characterized. Finally, a compact testing system for the measurement of prismatic diopter is designed and constructed. This measuring system can be used not only for Fresnel microprisms, but also for other types of prisms. The measured results agree with our calculations. Although this study is focused on optimizing the objective prismatic diopter for anti-fatigue microprismatic glasses, 2.0–3.0 prismatic diopters (Δ) for each eye in the anti-fatigue glasses are suggested according to our experience on strabismus treatments. The clinical research for patients using the developed anti-fatigue glasses will be fully implemented in our further research to confirm the optimal subjective prismatic value. [ABSTRACT FROM AUTHOR]

Deep convolutional neural network (DCNN)-based methods have shown great improvements in building extraction from high spatial resolution remote sensing images. In this paper, we propose a postprocessing method based on DCNNs for building extraction. Specifically, building regions and boundaries are learned simultaneously or separately by DCNNs. The predicted building regions and boundaries are then combined by the postprocessing method to produce the final building regions. In addition, we introduce a manually labeled dataset based on high spatial resolution images for building detection, the XIHU building dataset. This dataset is then used in the experiments to evaluate our methods. Besides the WHU building dataset, East Asia (WHUEA) is also included. Results demonstrate that our method that combines the results of DeepLab and BDCN shows the best performance on the XIHU building dataset, which achieves 0.78% and 23.30% F1 scores, and 1.13% and 28.45% intersection-over-union (IoU) improvements compared with DeepLab and BDCN, respectively. Additionally, our method that combines the results of Mask R-CNN and DexiNed performs best on the WHUEA dataset. Moreover, our methods outperform the state-of-the-art multitask learning network, PMNet, on both XIHU and WHUEA datasets, which indicates that the overall performance can be improved although building regions and boundaries are learned in the training stage. [ABSTRACT FROM AUTHOR]

Nine new sesquiterpenoids (1–9) were isolated from ethyl ether extract of agarwood originated from Aquilaria sp., including three novel sesquiterpenoids (1–3) derived from zizaane, together with six zizaane-type sesquiterpenoids (4–9). All structures were unambiguously elucidated based on 1D and 2D NMR spectra as well as by HRESIMS data. The absolute configuration of sesquiterpenoids was determined by comparison of the experimental and computed ECD spectra. In vitro anti-inflammatory assessment showed that compound 9 exhibited inhibition of NO production in LPS-stimulated RAW264.7 cells with an IC50 value of 62.22 ± 1.27 μM. [ABSTRACT FROM AUTHOR]

Exosomes, powerful extracellular nanovesicles released from almost all types of living cells, are considered the communication engines (messengers) that control and reprogram physiological pathways inside target cells within a community or between different communities. The cell-like structure of these extracellular vesicles provides a protective environment for their proteins and DNA/RNA cargos, which serve as biomarkers for many malicious diseases, including infectious diseases and cancers. Cancer-derived exosomes control cancer metastasis, prognosis, and development. In addition to the unique structure of exosomes, their nanometer size and tendency of interacting with cells makes them a viable novel drug delivery solution. In recent years, numerous research efforts have been made to quantify and characterize disease-derived exosomes for diagnosis, monitoring, and therapeutic purposes. This review aims to (1) relate exosome biomarkers to their origins, (2) focus on current isolation and detection methods, (3) discuss and evaluate the proposed technologies deriving from exosome research for cancer treatment, and (4) form a conclusion about the prospects of the current exosome research. [ABSTRACT FROM AUTHOR]

Nine new sesquiterpenoids ( 1 - 9 ) were isolated from ethyl ether extract of agarwood originated from Aquilaria sp., including three novel sesquiterpenoids ( 1 - 3 ) derived from zizaane, together with six zizaane-type sesquiterpenoids ( 4 - 9 ). All structures were unambiguously elucidated based on 1D and 2D NMR spectra as well as by HRESIMS data. The absolute configuration of sesquiterpenoids was determined by comparison of the experimental and computed ECD spectra. In vitro anti-inflammatory assessment showed that compound 9 exhibited inhibition of NO production in LPS-stimulated RAW264.7 cells with an IC 50 value of 62.22 ± 1.27 μM.