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Quantum generative models have shown promise in fields such as quantum chemistry, materials science, and optimization. However, their practical utility is hindered by a significant challenge: the lack of interpretability. In this work, we introduce model inversion to enhance both the interpretability and controllability of quantum generative models. Model inversion allows for tracing generated quantum states back to their latent variables, revealing the relationship between input parameters and generated outputs. We apply this method to models generating ground states for Hamiltonians, such as the transverse-field Ising model (TFIM) and generalized cluster Hamiltonians, achieving interpretability control without retraining the model. Experimental results demonstrate that our approach can accurately guide the generated quantum states across different quantum phases. This framework bridges the gap between theoretical models and practical applications by providing transparency and fine-tuning capabilities, particularly in high-stakes environments like drug discovery and material design. [ABSTRACT FROM AUTHOR]

We introduce the concept-driven quantum neural network (CD-QNN), an innovative architecture designed to enhance the interpretability of quantum neural networks (QNNs). CD-QNN merges the representational capabilities of QNNs with the transparency of self-explanatory models by mapping input data into a human-understandable concept space and making decisions based on these concepts. The algorithmic design of CD-QNN is comprehensively analyzed, detailing the roles of the concept generator, feature extractor, and feature integrator in improving and balancing model expressivity and interpretability. Experimental results demonstrate that CD-QNN maintains high predictive accuracy while offering clear and meaningful explanations of its decision-making process. This paradigm shift in QNN design underscores the growing importance of interpretability in quantum artificial intelligence, positioning CD-QNN and its derivative technologies as pivotal in advancing reliable and interpretable quantum intelligent systems for future research and applications. [ABSTRACT FROM AUTHOR]

Quantum neural networks (QNNs) are gaining attention for their potential, but their lack of interpretability remains a barrier to wider adoption. In this paper, we adapt and extend explainability techniques commonly used in classical neural networks to the quantum domain, making QNNs more transparent and interpretable. By applying both feature-based and example-based methods, we provide a comprehensive analysis of how QNNs generate predictions. Our results demonstrate that these adapted techniques offer valuable insights into the internal mechanisms of QNNs, paving the way for more reliable and trustworthy quantum machine learning models. This work contributes to improving the explainability of QNNs, enhancing their applicability in complex, real-world scenarios. [ABSTRACT FROM AUTHOR]

The rapid development of remote sensing technology has provided new sources of data for marine rescue and has made it possible to find and track survivors. Due to the requirement of tracking multiple survivors at the same time, multi-object tracking (MOT) has become the key subtask of marine rescue. However, there exists a significant gap between fine-grained objects in realistic marine rescue remote sensing data and the fine-grained object tracking capability of existing MOT technologies, which mainly focuses on coarse-grained object scenarios and fails to track fine-grained instances. Such a gap limits the practical application of MOT in realistic marine rescue remote sensing data, especially when rescue forces are limited. Given the promising fine-grained classification performance of recent text-guided methods, we delve into leveraging labels and attributes to narrow the gap between MOT and fine-grained maritime rescue. We propose a text-guided multi-class multi-object tracking (TG-MCMOT) method. To handle the problem raised by fine-grained classes, we design a multi-modal encoder by aligning external textual information with visual inputs. We use decoding information at different levels, simultaneously predicting the category, location, and identity embedding features of objects. Meanwhile, to improve the performance of small object detection, we also develop a data augmentation pipeline to generate pseudo-near-infrared images based on RGB images. Extensive experiments demonstrate that our TG-MCMOT not only performs well on typical metrics in the maritime rescue task (SeaDronesSee dataset), but it also effectively tracks open-set categories on the BURST dataset. Specifically, on the SeaDronesSee dataset, the Higher Order Tracking Accuracy (HOTA) reached a score of 58.8, and on the BURST test dataset, the HOTA score for the unknown class improved by 16.07 points. [ABSTRACT FROM AUTHOR]

Unlike residential settlements, Chinese traditional defensive settlements (CTDS) are fortress-shaped settlements built by forefathers to resist invasion and defend their homeland. What remains of these settlements today are not only unique defensive entities but also defensive spatial layouts and architectural techniques that reflect military defense strategies and spatial patterns of ancient Chinese society, resulting in a distinct cultural heritage. Using CiteSpace as a research tool and CNKI as a core database, this paper conducts a visual analysis and review of the research related to CTDS, aiming to accurately and intuitively elaborate on the literature distribution characteristics, collaborative characteristics, key issues, research trends, and research hotspots. We found that research on CTDS focuses on three key issues: the composition system and defense mode of northern defensive settlements, the architectural style and cultural characteristics of southern defensive dwellings, and the settlements' heritage conservation and development planning; and research trends can be divided into three phases. We also propose five future research recommendations. This study helps to fill the gap in the bibliometric review of Chinese traditional defensive settlements, serves as a reference for future priority research directions, and provides a manual for heritage conservation and sustainable development of this special type of settlement. [ABSTRACT FROM AUTHOR]

Background: Hesperetin, a flavonoid derived from citrus fruits, exhibits potent antioxidant and anti-inflammatory activities and has been implicated in cartilage protection. However, its effectiveness against T-2 toxin-induced knee cartilage damage remains unclear. Methods: In this study, high-throughput sequencing analysis was employed to identify the key signaling pathways involved in T-2 toxin-induced articular cartilage damage in rats. Animal models were divided into the following groups: control, low-dose T-2 toxin, high-dose T-2 toxin, T-2 toxin + hesperetin, hesperetin, and vehicle. Pathological staining and immunohistochemistry were used to assess pathological changes, as well as the expression levels of the cartilage matrix-related proteins MMP13 and collagen II, along with the activation of the p38 MAPK signaling pathway. Additionally, primary rat chondrocytes were cultured to establish an in vitro model for investigating the underlying mechanism. Results: High-throughput sequencing analysis revealed the involvement of the MAPK signaling pathway in T-2 toxin-induced articular cartilage damage in rats. Hesperetin intervention in T-2 toxin-exposed rats attenuated pathological cartilage damage. Immunohistochemistry results demonstrated a significant reduction in collagen II protein expression in the high-dose T-2 toxin group (p < 0.01), accompanied by a significant increase in MMP13 protein expression (p < 0.01). In both the articular cartilage and the epiphyseal plate, the T-2 toxin + hesperetin group exhibited significantly higher collagen II protein expression than the high-dose T-2 toxin group (p < 0.05), along with significantly lower MMP13 protein expression (p < 0.05). Hesperetin inhibited the over-activation of the p38/MEF2C signaling axis induced by T-2 toxin in primary rat chondrocytes. Compared to the T-2 toxin group, the T-2 toxin + hesperetin group showed significantly reduced phosphorylation levels of p38 and protein expression levels of MEF2C (p < 0.001 or p < 0.05). Moreover, the T-2 toxin + hesperetin group exhibited a significant decrease in MMP13 protein expression (p < 0.05) and a significant increase in collagen II protein expression (p < 0.01) compared to the T-2 toxin group. Conclusions: T-2 toxin activates the p38 MAPK signaling pathway, causing knee cartilage damage in rats. Treatment with hesperetin inhibits the p38/MEF2C signaling axis, regulates collagen II and MMP13 protein expression, and reduces cartilage injury significantly. [ABSTRACT FROM AUTHOR]

Collaboration in Multi-Agent Systems (MASs) is crucial but challenging in robotics, especially in heterogeneous MASs where robots have different capabilities. Nowadays, the key issue in research on collaboration in MASs is to fully utilize the capabilities of heterogeneous agents. To address this issue, we propose Auction-Based Behavior Tree Evolution (ABTE), a novel two-layer framework designed to learn BTs for heterogeneous MASs. In the first layer, we call it the command layer, and robots receive their tasks through the auction algorithm, enhanced by our innovative three-way handshaking communication protocol embedded in BT implementation, ensuring more efficient task allocation. The second layer of ABTE defines the specific execution behaviors of agents and is, therefore, named the execution layer. The behaviors in this layer are automatically generated by Grammatical Evolution (GE), which has been proven to be a general and effective method for generating swarm BTs. Our experiments are conducted within a Disaster Rescue Scenario, which requires intricate collaboration among multiple robots with diverse capabilities. The results indicate that ABTE outperforms the baseline algorithm, GEESE, in terms of resource utilization. Moreover, it demonstrates robust effectiveness in covering high-priority tasks, thereby validating the efficacy of employing an auction algorithm for generating BTs tailored for heterogeneous MAS. [ABSTRACT FROM AUTHOR]

Few-shot learning (FSL) is designed to equip models with the capability to quickly adapt to new, unseen domains in open-world scenarios. However, there is a notable discrepancy between the multitude of new concepts encountered in the open world and the limited scale of existing FSL studies, which focus predominantly on a small number of novel classes. This limitation hinders the practical implementation of FSL in real-world situations. To address this issue, we introduce a novel problem called Few-Shot Learning with Many Novel Classes (FSL-MNC), which expands the number of novel classes more than 500 times compared to traditional FSL settings. This new challenge presents two main difficulties: increased computational load during meta-training and reduced classification accuracy due to the larger number of classes during meta-testing. To tackle these problems, we introduce the Simple Hierarchy Pipeline (SHA-Pipeline). In response to the inefficiency of traditional Episode Meta-Learning (EML) protocols, we redesign a more efficient meta-training strategy to manage the increased number of novel classes. Moreover, to distinguish distinct semantic features across a broad array of novel classes, we effectively reconstruct and utilize class hierarchy information during meta-testing. Our experiments demonstrate that the SHA-Pipeline substantially outperforms both the ProtoNet baseline and current leading alternatives across various numbers of novel classes. [ABSTRACT FROM AUTHOR]

The Uhelchulu quartz diorite-granodiorite intrusions in Xiwuqi, Inner Mongolia, are exposed along the northwestern margin of the Xilinhot microcontinental block, located within the central and eastern parts of the southeastern Hegenshan suture zone. LA-ICP-MS zircon U-Pb dating yielded crystallization ages of (396 ± 8) Ma for the quartz diorite and (385 ± 5) Ma for the granodiorite, indicating an Early-Middle Devonian magmatic event. The quartz diorite exhibits I-type granite features, characterized by elevated Al2O3 (14.33–15.43 wt%), MgO (3.73–5.62 wt%), and Na (Na2O/K2O = 1.04–1.44), coupled with low P2O5 (0.15–0.20 wt%) and TiO2 (0.73–0.99 wt%). Trace element patterns show relative enrichments in Rb, Th, U, and Pb, while Nb, Ta, Sr and Ti are relatively depleted. Total REE contents are relatively low (123–178 ppm), with significant LREE enrichment (ΣLREE/ΣHREE = 4.75–5.20), and a non-obvious Eu anomaly (δEu = 0.75–0.84). In contrast, the granodiorite displays S-type granite characteristics, with high SiO2 (70.48–73.01 wt%), K (K2O/Na2O = 1.35–1.83), Al2O3 (A/CNK = 1.16–1.31), and a high differentiation index (DI = 76–82). Notably, MgO (1.44–2.24 wt%) contents are low, and significant depletions of Ba, Sr, Ti, and Eu are observed, while Rb, Pb, Th, U, Zr, and Hf are significantly enriched. Total REE contents are relatively low (178–314 ppm), exhibiting significant LREE enrichment (LREE/HREE = 6.17–8.36) and a pronounced negative Eu anomaly (δEu = 0.34–0.49). The overall characteristics point towards an active continental margin arc background for the Uhelchulu intrusions. Previous studies have suggested that the Hegenshan ocean continuously subducted northward from the Early Carboniferous to the Late Permian, but there is a lack of evidence for its geological evolution during the pre-Early Carboniferous. Therefore, this paper provides a certain basis for studying the geological evolution during the pre-Early Carboniferous in the Hegenshan ocean. We preliminarily believed that the Hegenshan ocean underwent a southward subduction towards the Xilinhot microcontinental block in the Xiwuqi area, at least from the Early Devonian to the Middle Devonian and the Hegenshan ocean may might have undergone a shift in subduction mechanism during the Late Devonian or Early Carboniferous. [ABSTRACT FROM AUTHOR]

The treatment of antibiotic wastewater, which is known for its micro-toxicity, inhibition, and poor biochemistry, poses significant challenges, including complex processes, high energy demands, and secondary pollution. Bio-Fenton, a novel Fenton technology, enables the in situ production of H2O2 at near-neutral pH, having low energy requirements and sustainable properties, and reduces the hazards of H2O2 transportation and storage. We preliminary self-designed a heterogeneous Bio-Fenton reactor. An aerobic SBBR system with pure algae, pure bacteria, and bacteria–algae symbiosis was first constructed to investigate the optimal process conditions through the effects of carbon source concentration, light duration, bamboo charcoal filling rate, and dissolved oxygen (DO) content on the H2O2 production and COD removal. Second, the reactor was constructed by adding iron-carrying catalysts to remove ROX and SDZ wastewater. The results demonstrated that the optimal operating parameters of aerobic SBBR were an influent carbon source concentration of 500 mg/L, a water temperature of 20 ± 2 °C, pH = 7.5, a dissolved oxygen content of 5 mg/L, a light–dark ratio of 12 h:12 h, a light intensity of 2500 Lux, an HRT of 10 h, and a bamboo charcoal filling rate of 33%. Given these conditions, the bacterial–algal system was comprehensively found to be the most suitable biosystem for this experiment. Ultimately, the dynamically coupled Bio-Fenton process succeeded in the preliminary removal of 41.32% and 42.22% of the ROX and SDZ from wastewater, respectively. [ABSTRACT FROM AUTHOR]

As a toxic Volatile Organic Pollutant (TVOC), formaldehyde has a toxic effect on microorganisms, consequently inhibiting the biochemical process of formaldehyde wastewater treatment. Therefore, the selective degradation of formaldehyde is of great significance in achieving high-efficiency and low-cost formaldehyde wastewater treatment. This study constructed a heterogeneous Fe-ZSM-5/H2O2 Fenton system f or the selective degradation of target compounds. By immobilizing Fe3+ onto the surface of a ZSM-5 molecular sieve, Fe-ZSM-5 was prepared successfully. XRD, BET and FT-IR spectral studies showed that Fe-ZSM-5 was mainly composed of micropores. The influences of different variables on formaldehyde-selective heterogeneous Fenton degradation performance were studied. The 93.7% formaldehyde degradation and 98.2% selectivity of formaldehyde compared with glucose were demonstrated in the optimized Fenton system after 360 min. Notably, the resultant selective Fenton oxidation system had a wide range of pH suitability, from 3.0 to 10.0. Also, the Fe-ZSM-5 was used in five consecutive cycles without a significant drop in formaldehyde degradation efficiency. The use of reactive oxygen species scavengers indicated that the hydroxyl radical was the primary active species responsible for degrading formaldehyde. Furthermore, great degradation performance was acquired with high concentrations of formaldehyde for this system, and the degradation efficiency was more than 95.0%. [ABSTRACT FROM AUTHOR]

Aliphatic glucosinolates are an abundant group of plant secondary metabolites in Brassica vegetables, with some of their degradation products demonstrating significant anti-cancer effects. The transcription factors MYB28 and MYB29 play key roles in the transcriptional regulation of aliphatic glucosinolates biosynthesis, but little is known about whether BoMYB28 and BoMYB29 are also modulated by upstream regulators or how, nor their gene regulatory networks. In this study, we first explored the hierarchical transcriptional regulatory networks of MYB28 and MYB29 in a model plant, then systemically screened the regulators of the three BoMYB28 homologs in cabbage using a yeast one-hybrid. Furthermore, we selected a novel RNA binding protein, BoRHON1, to functionally validate its roles in modulating aliphatic glucosinolates biosynthesis. Importantly, BoRHON1 induced the accumulation of all detectable aliphatic and indolic glucosinolates, and the net photosynthetic rates of BoRHON1 overexpression lines were significantly increased. Interestingly, the growth and biomass of these overexpression lines of BoRHON1 remained the same as those of the control plants. BoRHON1 was shown to be a novel, potent, positive regulator of glucosinolates biosynthesis, as well as a novel regulator of normal plant growth and development, while significantly increasing plants' defense costs. [ABSTRACT FROM AUTHOR]

Understanding and projecting drought, especially in the face of climate change, is crucial for assessing its impending risks. However, the causes of drought are multifaceted. As the environmental research paradigm pivots towards machine learning (ML) for predictions, our investigation contrasted multiple ML techniques to simulate the Standardized Precipitation Evapotranspiration Index (SPEI) from 2009 to 2022, utilizing various potential evapotranspiration (PET) methods. Our primary focus was Australia, the world's driest inhabited continent. Given the challenges with ML model interpretation, SHAP (SHapley Additive exPlanations) values were employed to decipher SPEI variations and to gauge the relative importance of precipitation (Prec) and PET in six key Australian cities. Our findings revealed that while different PET methods resulted in distinct mean values, their trends remained consistent. Post the Millennium Drought, Australia experienced several drought events. SPEI discrepancies based on PET methods were minimal in humid regions like Brisbane and Darwin. However, for arid cities, the Priestley–Taylor equation-driven SPEI differed notably from other methods. Ridge regression was the most adept at mirroring SPEI changes among the assessed ML models. Furthermore, the SHAP explainer discerned that PET-related climate variables had a greater impact on SPEI in drier cities, whereas in humid cities, Prec was more influential. Notably, the research emphasised CO2′s role in influencing drought dynamics in humid cities. These insights are invaluable for enhancing drought mitigation strategies and refining predictive models. Such revelations are crucial for stakeholders aiming to improve drought prediction and management, especially in drought-prone regions like Australia. [ABSTRACT FROM AUTHOR]

Real-time monitoring of human joint motion holds paramount importance in assessing joint health status, preventing and treating joint diseases, and evaluating physical flexibility and coordination. However, traditional strain sensors face limitations in meeting the substantial strain requirements associated with human joint motion. Recently, there has been considerable attention directed towards flexible strain sensors prepared using pliable substrates combined with silk and cotton fabrics. Nonetheless, these sensors exhibit insufficient linearity across the entire measurement range, thereby compromising the predictability of real joint motion based on the output signal. This paper introduced a flexible strain sensor designed to address this issue by offering an enhanced range and high linearity. Specifically, the core wire of the strain sensor was produced by coating a polybutylene terephthalate thread with conductive carbon ink integrated with carbon nanotubes, encapsulated in a thin layer of polydimethylsiloxane in an "S" configuration. The proposed strain sensor maintained excellent linearity within its strain range of 60%, along with advantages such as rapid response speed and robust durability. On-trial tests further affirmed the sensor's capability to effectively monitor the motion of human joints. [ABSTRACT FROM AUTHOR]

This study investigated the effects of subchronic arsenic exposure on behavior, neurological function, and hippocampal damage in rats. Thirty-two male Wistar rats were divided into four groups and exposed to different concentrations of arsenic in their drinking water for 12 weeks, while weekly water intake and body weight were recorded. Various neurobehavioral tests were conducted, evaluating overall activity levels, exploratory behavior, short-term memory, spatial learning and memory, anxiety-like behavior, and depressive-like states. Arsenic levels in urine, serum, and brain tissue were measured, and histopathological analysis assessed hippocampal damage using hematoxylin and eosin staining. The results demonstrated that arsenic exposure did not significantly affect overall activity or exploratory behavior. However, it impaired short-term memory and spatial learning and memory functions. Arsenic-exposed rats exhibited increased anxiety-like behavior and a depressive-like state. Arsenic levels increased dose-dependently in urine, serum, and brain tissue. The histopathological examinations revealed significant hippocampal damage, including neuronal shrinkage, cell proliferation, irregular structure, disordered arrangement, and vacuolation. These findings emphasize the importance of understanding the impact of arsenic exposure on behavior and brain health, highlighting its potential neurological consequences. [ABSTRACT FROM AUTHOR]

Arsenic is a natural toxin which is widely distributed in the environment, incurring diverse toxicities and health problems. Previous studies have shown that long non-coding RNAs (LncRNAs) are also reported to contribute to As-induced adverse effects. LncRNAs are involved in the development of nerve injury, generally acting as sponges for microRNAs (miRNAs). This study aimed to investigate the competitive endogenous RNA (ceRNA) regulatory networks associated with arsenic-induced nerve damage. A total of 40 male Wistar rats were exposed to different doses of arsenic for 12 weeks, and samples were collected for pathological observation and high-throughput sequencing. The ceRNA network was constructed using Cytoscape, and key genes were identified through the PPI network and CytoHubba methods. A real-time quantitative PCR assay was performed to validate gene expression levels. The results showed that subchronic exposure to arsenic in drinking water resulted in pathological and ultrastructural damage to the hippocampal tissue, including changes in neuron morphology, mitochondria, and synapses. Exposure to arsenic results in the dysregulation of LncRNA and mRNA expression in the hippocampal tissues of rats. These molecules participated in multiple ceRNA axes and formed a network of ceRNAs associated with nerve injury. This study also verified key molecules within the ceRNA network and provided preliminary evidence implicating the ENRNOT-00000022622-miR-206-3p-Bdnf axis in the mechanism of neural damage induced by arsenic in rats. These findings provide novel insights into the underlying mechanism of nervous system damage induced by arsenic exposure. [ABSTRACT FROM AUTHOR]

Hemerocallis is a characteristic vegetable with outstanding edible and medicinal value. Flavonoids are important bioactive components of Hemerocallis. To improve the extraction efficiency and detection accuracy of flavonoids from Hemerocallis, we established a high-performance liquid chromatography (HPLC) detection system, which can simultaneously detect multiple flavonoids. In addition to the previously developed organic solvent extraction method, an ultrasonic-assisted extraction technique that uses fewer samples was established to extract flavonoids from Hemerocallis. The extraction conditions of the ultrasonic-assisted extraction were optimized via a single-factor experiment and a response surface experiment. The HPLC system detected and determined the contents of rutin, isoquercetin, myricetin, quercetin, apigenin, and diosmetin from 70 Hemerocallis germplasm resources. In addition, we evaluated the antioxidant activity of flavonoids in Hemerocallis using DPPH free radical scavenging capacity with ascorbic acid (Vc) as a positive control. The results showed that the optimum conditions for the ultrasonic extraction process were as follows: sample weight of 0.25 g, ethanol volume fraction of 72%, ethanol volume of 2.5 mL, and ultrasonic extraction time of 17 min. Under these conditions, flavonoid extraction had a strong scavenging effect on DPPH. With the increase in the sample solutions' concentrations, its antioxidant capacity was gradually enhanced, and the DPPH scavenging rate reached 70.2%. The optimized ultrasonic-assisted extraction technology can increase the total content of six flavonoids in day lily bud by 59.01%, especially the content of rutin (increased by 64.41%) in Hemerocallis flower buds. Among 70 Hemerocallis plant resources, we selected materials H0087 and H0059 with high and stable flavonoid content, with the total content of six substances being 4390.54 ug/g and 3777.13 ug/g. Thus, this study provides a reference for extracting and determining flavonoid contents in Hemerocallis materials. It also provides a theoretical basis for high-quality individual plant breeding. [ABSTRACT FROM AUTHOR]

Microbial fermentation for the production of tetramethylpyrazine (TTMP) is considered to be the most promising method, and the development of a cheap fermentation substrate is of great importance for large-scale TTMP production. In this study, inexpensive by-products from the food industry, i.e., molasses and soybean meal (instead of glucose and tryptone), were used as substrates for TTMP fermentation. The pretreatment of soybean meal was explored in order to achieve a better fermentation effect. The contents of each component in the fermentation medium were optimized by central composite design (CCD). The optimum contents were as follows: 72.5 g/L of molasses, 37.4 g/L of diammonium hydrogen phosphate (DAP), 53.4 g/L of soybean meal, and 5 g/L of yeast powder. The software predicted a maximum TTMP yield of 1469.03 mg/L, and the actual TTMP yield was 1328.95 mg/L for the validation experiment in the optimum medium. Under the optimum conditions (72.5 g/L of molasses, 37.4 g/L of DAP, 53.4 g/L of soybean meal, and 5 g/L of yeast powder), the actual maximum TTMP yield (1328.95 mg/L) in this study was much higher than the TTMP yield (895.13 mg/L) under the conditions (150 g/L of molasses, 30 g/L of DAP, 30 g/L of tryptone, and 10 g/L of yeast powder) of our previous study published in Molecules. In this study, the TTMP yield improved by 48.46%, with decreased molasses (more than half), decreased yeast powder (half) and by-product soybean meal instead of tryptone compared to our previous study. In summary, the cheaper fermentation medium had a higher TTMP yield in this study, which improves the application potential of Bacillus sp. TTMP20. [ABSTRACT FROM AUTHOR]

The Poyang Lake wetland is home to many unique and threatened species. However, it has been severely degraded in recent decades due to the joint effects of human influence and climate change. Here we establish a wetland health index (WHI) for Poyang Lake, which considers five types of attributes (biological, water quality, sediment, land use and remote sensing, and socio-economic attributes) of the wetland to evaluate wetland conditions. Forty-nine variables across five categories were assembled as candidate metrics for the WHI through field surveys conducted in 2019 at 30 sample sites. Principal component analyses were performed to identify the most important variables in each of the five categories as the primary metrics of each index category (e.g., biological index). Eighteen variables were finally selected from the five categories to construct the WHI. The WHI scores varied from 0.34 to 0.80 at the 30 sample sites, with a mean of 0.55. The Poyang Lake wetland is generally in fair condition according to our WHI scores. Sample sites where connected rivers flow into the lake were assessed to be in a poor condition, highlighting the importance of reducing pollution input from rivers for wetland conservation. Scores of individual indices of the five categories were not highly correlated (0.29 ≤ pairwise Spearman's r ≤ 0.69), suggesting that information provided by each index is different and might be complementary. The composite WHI as well as the individual category indices can provide comprehensive information on wetland conditions that would facilitate the development of more targeted and effective strategies for wetland management. [ABSTRACT FROM AUTHOR]

Entodon challengeri (Paris) Cardot has important environmental monitoring and medical value. It is critical we inspect the influence of climate warming on its spatiotemporal distribution pattern. Based on actual geographical distribution records and environmental datasets, a MaxEnt model coupled with ArcGIS was executed to display the potential suitable habitats of E. challengeri in China under future climate warming scenarios. We showed the following. (i) The simulation accuracy of the MaxEnt model was excellent, with an AUC value of 0.918. (ii) Annual precipitation and precipitation during the wettest month were the critical factors that restricted the distribution range of E. challengeri. (iii) Current suitable habitats were concentrated in the northern temperate zone in eastern China. (iv) Under climate warming scenarios, on the spatial scale, the distributional pattern presented a shrinkage in the south and expansion in the north, which was more obvious in the RCP8.5 than in the RCP2.6 scenario. On the time scale, shrinkage of the potential distributional range was greater in the 2070s than in the 2050s. (v) The distributional centroids shifted to the northeast. In general, future climate warming will have a great negative effect on the suitability of habitats of E. challengeri. [ABSTRACT FROM AUTHOR]

Volcanic lava cave habitats are extreme environments. We carried out field investigations for five years and reported the results of bryophyte diversity in eight volcanic lava caves of Jingpo Lake World Geopark, where the largest underground lava caves in China are preserved. The results are as follows: (1) A total of 230 quadrats were set up, and 2041 bryophyte specimens were collected. The specimens belong to 272 species of 107 genera in 47 families, including 26 liverworts (13 genera, 11 families) and 246 mosses (94 genera, 36 families). (2) The α diversity of bryophytes in Underground Lava Fall Cave was the highest, while that in Foggy Cave was the lowest. (3) The dominant families included Mniaceae, etc, accounting for 55.9% of the total species. The dominant genera included Plagiomnium, etc, accounting for 24.3% of the total species. The dominant species included Sanionia uncinata (Hedw.) Loeske etc. (4) There are no shared species among all eight lava caves, and each cave has a unique species composition. (5) Compared with that in other habitats in our previous studies, the similarity of bryophyte species between lava caves and underground forests of craters was high (113 species, 40.07%), while it was low between lava caves and lava platforms (9 species, 4.65%). Our study revealed that the lava caves have a high potential for bryophyte diversity, and such ancient ecological disaster sites are now rare refuges for bryophytes. Mosses are more adapted to cave habitats than liverworts. Bryophytes in this special eco-environment need to be considered and protected in order to preserve high-quality gene resources for humans, which is of great significance for the maintenance and development of biodiversity. [ABSTRACT FROM AUTHOR]

Wild relatives of wheat are essential gene pools for broadening the genetic basis of wheat. Chromosome rearrangements and genomic variation in alien chromosomes are widespread. Knowledge of the genetic variation between alien homologous chromosomes is valuable for discovering and utilizing alien genes. In this study, we found that 5113 and II-30-5, two wheat–A. cristatum 6P addition lines, exhibited considerable differences in heading date, grain number per spike, and grain weight. Genome resequencing and transcriptome analysis revealed significant differences in the 6P chromosomes of the two addition lines, including 143,511 single-nucleotide polymorphisms, 62,103 insertion/deletion polymorphisms, and 757 differentially expressed genes. Intriguingly, genomic variations were mainly distributed in the middle of the chromosome arms and the proximal centromere region. GO and KEGG analyses of the variant genes and differentially expressed genes showed the enrichment of genes involved in the circadian rhythm, carbon metabolism, carbon fixation, and lipid metabolism, suggesting that the differential genes on the 6P chromosome are closely related to the phenotypic differences. For example, the photosynthesis-related genes PsbA, PsbT, and YCF48 were upregulated in II-30-5 compared with 5113. ACS and FabG are related to carbon fixation and fatty acid biosynthesis, respectively, and both carried modification variations and were upregulated in 5113 relative to II-30-5. Therefore, this study provides important guidance for cloning desirable genes from alien homologous chromosomes and for their effective utilization in wheat improvement. [ABSTRACT FROM AUTHOR]

The analysis of spatiotemporal changes of landscape patterns is of great significance for forest protection. However, the selection of landscape metrics is often subjective, and existing composite landscape metrics rarely consider the effects of spatial correlation. A more objective approach to formulating composite landscape metrics involves proper weighting that incorporates spatial structure information into integrating individual conventional metrics selected for building a composite metric. This paper proposes an integrated spatial landscape index (ISLI) based on variogram modeling and entropy weighting. It was tested through a case study, which sought to analyze spatiotemporal changes in the landscape pattern in the Changbai Mountains over 30 years based on six global land-cover products with a fine classification system at 30 m resolution (GLC_FCS30). The test results confirm: (1) spatial structure information is useful for weighting conventional landscape pattern metrics when constructing ISLI as validated by correlation analysis between the incorporated conventional metrics and their variogram ranges. In terms of the range parameters of different land cover types, broadleaf forest and needleleaf forest have much larger range values than those of other land cover types; (2) DIVISION and PLAND, two of the conventional landscape metrics considered for constructing ISLI, were assigned the greatest weights in computing ISLI for this study; and (3) ISLI values can be used to determine the dominant landscape types. For the study area, ISLI values of broadleaf forests remained the largest until 2020, indicating that forest landscape characteristics were the most prominent during that period. After 2020, the dominance of needleleaf forest gradually increased, with its ISLI value reaching a maximum of 0.91 in 2025. Therefore, the proposed ISLI not only functions as an extension and complement to conventional landscape metrics but also provides more comprehensive information concerning landscape pattern dynamics. [ABSTRACT FROM AUTHOR]

In response to uncertainty in remotely sensed land cover products, there is continuing research on accuracy assessment and analysis. Given reference sample data, accuracy indicators are commonly estimated based on error matrices, from which areal extents of different cover types are also estimated. There are merits to explore the ways utilities of land cover products may be further enhanced beyond map face values and conventional area estimation. This paper presents an integrative method (CCAErrMat) for uncertainty characterization and utility enhancement. This works through reference-map cover type co-occurrence analyses based on error matrices localized in canonical correspondence analysis (CCA) ordination space rather than in geographic space to overcome the sparsity of reference sample data. The aforementioned co-occurrence analyses facilitate quantification of accuracy indicators, identification of correctly classified and perfectly misclassified pixels, and prediction of reference class probabilities, all at individual pixels. Moreover, these predicted reference class probabilities are used as auxiliary variables to formulate model-assisted area estimation, further enhancing map utilities. Extensions to CCAErrMat are also investigated as a way to bypass the pre-computing of map class occurrence pattern indices as candidate explanatory variables for CCAErrMat, leading to two variant methods: CCACCAErrMat and CNNCCAErrMat. A case study based in Wuhan municipality, central China was undertaken to compare the proposed method against alternative methods, including CCA-separate and CNN-separate. The advantages of CCAErrMat and CCACCAErrMat were confirmed. The proposed method is recommendable for characterizing uncertainty and enhancing utilities in land cover maps by analyzing locally constrained error matrices. The method is also cost-effective in terms of reference sample data, as requirements for them are similar to those for conventional accuracy assessments. [ABSTRACT FROM AUTHOR]

Paris polyphylla Smith is a perennial medicinal herb with records from around 2000 years ago. Polyphyllins are the main bioactive compounds of this herb, which are found to have remarkable effects on bacteriostatic, antiphlogistic, sedative, and antitumor. However, the market demand for P. polyphylla is sharply increasing, and the wild resources are threatened by plundering exploitation. Integrating molecular data of P. polyphylla can benefit the sustainable resource exploitation. Here, we constructed PPDP (Paris polyphylla Data Portal) to provide a data platform for polyphyllin biosynthesis and germplasm resource research. PPDP integrates related molecular data resources, functional genomics analysis, and morphological identification. The database provides abundant data (transcriptome, CDS, lncRNA, alternative splicing, gene family, SSR, and chloroplast genome) and practical analytical tools (network construction, heatmap of expression profiles, enrichment, and pathway search) with a user-friendly interface. So far, PPDP is the first biomolecular database for the genus Paris plants. In the future, we will gradually add genomic data and other necessary molecular biological information to improve the database. [ABSTRACT FROM AUTHOR]

Background: The co-inhibitory molecule B7-H4 is located in the genomic regions associated with type 1 diabetes (T1D) susceptibility. However, the correlation of B7-H4 with glycometabolism and dyslipidemia has never been studied. Objective: To explore the influence of B7-H4 polymorphism on the prevalence of diabetes mellitus (DM) and dyslipidemia in psoriasis. Methods: In this single-center cross-sectional study, we recruited 265 psoriatic patients receiving methotrexate (MTX) treatment. Thirteen single-nucleotide polymorphisms (SNPs) in B7-H4 were genotyped. Serum levels of total cholesterol (TC), triglycerides (TG), lipoprotein (a) (LP(a)), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB) were measured at baseline and week 12. Results: The GG genotype carriers of rs12025144 in B7-H4 had a higher prevalence of DM (57.14% vs. 17.71% vs. 18.67%, p = 0.0018), and had a poorer response to MTX in diabetic patients (p < 0.05), compared with AA or AG genotype carriers. The AG genotype of rs2066398 was associated with higher levels of pro-atherogenic lipids. MTX significantly downregulated the level of anti-atherogenic lipid ApoA1 in AA genotype carriers of rs2066398. Conclusions: The genotypes rs12025144 and rs2066398 in B7-H4 were correlated with a higher prevalence of DM and dyslipidemia in psoriasis, respectively. [ABSTRACT FROM AUTHOR]

Improving the water vapor barrier of cellulose-based packaging in tropical conditions is very challenging for moisture-sensitive products. In this work, we developed a crosslinked polyvinyl alcohol/nanoclay (PVA/NC) hydrogel for paper surface coating. Layered NC and crosslinking can effectively improve the stability of PVA and block the flow paths of water molecules at elevated temperature and humidity. The result indicates that the crosslinked PVA/NC hydrogel coating (4 g/m2) can reduce the water vapor transmission rate of copy paper from 1861 g/m2/day to 195 g/m2/day at 38 °C (90% RH). The coated paper has an initial contact angle of 108° and can maintain hydrophobicity (>90°) under direct contact with water for three minutes. A Kit No. as high as 12 and a Cobb No. of 10 were also achieved by the coating. [ABSTRACT FROM AUTHOR]

Lateral organ boundaries domain (LBD) proteins are plant-specific transcription factors that play important roles in organ development and stress response. However, the function of LBD genes has not been reported in Euphorbiaceae. In this paper, we used Jatropha curcas as the main study object and added rubber tree (Hevea brasiliensis), cassava (Manihot esculenta Crantz) and castor (Ricinus communis L.) to take a phylogenetic analysis of LBD genes. Of LBD, 33, 58, 54 and 30 members were identified in J. curcas, rubber tree, cassava and castor, respectively. The phylogenetic analysis showed that LBD members of Euphorbiaceae could be classified into two major classes and seven subclasses (Ia-Ie,IIa-IIb), and LBD genes of Euphorbiaceae tended to cluster in the same branch. Further analysis showed that the LBD genes of Euphorbiaceae in the same clade usually had similar protein motifs and gene structures, and tissue expression patterns showed that they also have similar expression profiles. JcLBDs in class Ia and Ie are mainly expressed in male and female flowers, and there are multiple duplication genes with similar expression profiles in these clades. It was speculated that they are likely to play important regulatory roles in flower development. Our study provided a solid foundation for further investigation of the role of LBD genes in the sexual differentiaion of J. curcas. [ABSTRACT FROM AUTHOR]

Fairy rings are a unique ecological phenomenon caused by the growth of the fungal mycelium in the soil. Fairy rings formed by Leucocalocybe mongolica (LM) are generally distributed in the Mongolian Plateau, where they promote plant growth without fertilization and alleviate fertilizer use. We previously investigated the soil factors regulating growth promotion in a fairy ring ecosystem; however, the aspects of the plant (Leymus chinensis, LC) that promote growth have not been explored. Therefore, the present study investigated the endophyte diversity and metabolome of LC in an LM fairy ring ecosystem. We analyzed the leaf and root samples of LC from the DARK (FR) and OUT (CK) zones. The fairy rings significantly improved the fungal diversity of roots and leaves and the bacterial diversity of leaves in the FR zone. Ralstonia was the dominant bacteria detected in the LC leaves. In addition, Marasmius, another fairy ring fungal genus, was also detected with a high abundance in the roots of the FR zone. Furthermore, widely targeted metabolome analysis combined with KEGG annotation identified 1011 novel metabolites from the leaves and roots of LC and seven pathways significantly regulated by the fairy ring in the FR zone. The fairy ring ecosystem significantly downregulated the flavonoid metabolism in the leaves and roots of LC. The correlation analysis found Ralstonia is a potential regulatory factor of flavonoid biosynthesis in LC. In addition, salicylic acid and jasmonic acid were found upregulated in the leaves, probably related to Marasmius enrichment. Thus, the study details plant factors associated with enhanced growth in an LM fairy ring ecosystem. These findings lay a theoretical foundation for developing the fairy ring ecosystem in an agricultural system. [ABSTRACT FROM AUTHOR]

Paris polyphylla is an important medicinal plant that can biosynthesize polyphyllins with multiple effective therapies, ranging from anti-inflammation to antitumor; however, the genetic diversity of Paris polyphylla is still unclear. To explore the genetic characteristics of cultivation populations in primary planting areas, we developed 10 expressed sequence tag simple sequence repeat (EST-SSR) markers related to polyphyllin backbone biosynthesis and utilized them in 136 individuals from 10 cultivated populations of P. polyphylla var. yunnanensis. The genetic diversity index showed that ten loci had relatively high genetic polymorphism levels. Shannon information of loci suggested that more information occurred within population and less information occurred among population. In addition, the overall populations exhibited a low degree of differentiation among populations, but maintained a high degree of genetic diversity among individuals, resulting in high gene flow and general hybridization. The genetic structure analysis revealed that 10 populations possibly derived from two ancestral groups and all individuals were found with different levels of admixture. The two groups were different from the cultivation groups at population level, suggesting the cross-pollination among cultivars. These findings will provide insights into the genetic diversity of the germplasm resources and facilitate marker-assisted breeding for this medicinal herb. [ABSTRACT FROM AUTHOR]

A unique ecological landscape distributed in the Mongolian Plateau, called fairy rings, caused by the growth of the fungus Leucocalocybe mongolica (LM) in the soil could promote plant growth without fertilization. Therefore, this landscape can alleviate fertilizer use and has excellent value for agricultural production. The previous studies only investigated several parameters of the fairy rings, such as soil microbial diversity and some soil chemical properties, thus conclusions based on the studies on fairy rings lack comprehension. Therefore, the present study systematically investigated the chemical properties, metabolome, and metabarcoding of LM-transformed soil. We analyzed fairy ring soils from DARK (FR) and OUT (CK) zone correlated growth promotion with ten soil chemical properties, including N, nitrate-N, inorganic-P, cellulose, available boron, available sulfur, Fe, Mn, Zn, and Cu, which were identified as important markers to screen fairy ring landscapes. Metabolomics showed that the accumulation of 17 carbohydrate-dominated metabolites was closely associated with plant growth promotion. Finally, metabarcoding detected fungi as the main components affecting soil conversion. Among the various fungi at the family level, Lasiosphaeriaceae, unidentified_Auriculariales_sp, and Herpotrichiellaceae were markers to screen fairy ring. Our study is novel and systematically reveals the fairy ring soil ecology and lists the key factors promoting plant growth. These findings lay a theoretical foundation for developing the fairy ring landscape in an agricultural system. [ABSTRACT FROM AUTHOR]

Phospholipase B (PLB) harbors three distinct activities with broad substrate specificities and application fields. Its hydrolyzing of sn-1 and sn-2 acyl ester bonds enables it to catalyze the production of L-alpha-glycerylphosphorylcholine (L-α-GPC) from phosphatidylcholine (PC) without speed-limiting acyl migration. This work was intended to obtain high-level active PLB and apply it to establish an efficient system for L-α-GPC synthesis. PLB from Pseudomonas fluorescens was co-expressed with five different molecular chaperones, including trigger factor (Tf), GroEL-GroES (GroELS), DnaK-DnaJ-GrpE (DnaKJE), GroELS and DnaKJE, or GroELS and Tf or fused with maltose binding protein (MBP) in Escherichia coli BL21(DE3) to improve PLB expression. PLB with DnaKJE-assisted expression exhibited the highest catalytic activity. Further optimization of the expression conditions identified an optimal induction OD600 of 0.8, IPTG concentration of 0.3 mmol/L, induction time of 9 h, and temperature of 25 °C. The PLB activity reached a maximum of 524.64 ± 3.28 U/mg under optimal conditions. Subsequently, to establish an efficient PLB-catalyzed system for L-α-GPC synthesis, a series of organic-aqueous mixed systems and surfactant-supplemented aqueous systems were designed and constructed. Furthermore, the factors of temperature, reaction pH, metal ions, and substrate concentration were further systematically identified. Finally, a high yield of 90.50 ± 2.21% was obtained in a Span 60-supplemented aqueous system at 40 °C and pH 6.0 with 0.1 mmol/L of Mg2+. The proposed cost-effective PLB production and an environmentally friendly PLB-catalyzed system offer a candidate strategy for the industrial production of L-α-GPC. [ABSTRACT FROM AUTHOR]

The water pollution caused by industry emissions makes effluent treatment a serious matter that needs to be settled. Heterogeneous Fenton oxidation has been recognized as an effective means to degrade pollutants in water. Attapulgite can be used as a catalyst carrier because of its distinctive spatial crystal structure and surface ion exchange. In this study, iron ions were transported on attapulgite particles to generate an iron-supporting attapulgite particles catalyst. BET, EDS, SEM and XRD characterized the catalysts. The particle was used as a heterogeneous catalyst to degrade rhodamine B (RhB) dye in wastewater. The effects of H2O2 concentration, initial pH value, catalyst dosage and temperature on the degradation of dyes were studied. The results showed that the decolorization efficiency was consistently maintained after consecutive use of a granular catalyst five times, and the removal rate was more than 98%. The degradation and mineralization effect of cationic dyes by granular catalyst was better than that of anionic dyes. Hydroxyl radicals play a dominant role in RhB catalytic degradation. The dynamic change and mechanism of granular catalysts in catalytic degradation of RhB were analyzed. In this study, the application range of attapulgite was widened. The prepared granular catalyst was cheap, stable and efficient, and could be used to treat refractory organic wastewater. [ABSTRACT FROM AUTHOR]

Chinese cabbage (Brassica rapa L. ssp. pekinensis) is an important leafy vegetable crop cultivated worldwide. Drought is one of the most important limiting factors for the growth, production and quality of Chinese cabbage due to its weak drought tolerance. In order to deepen the understanding of drought stress response in Chinese cabbage, metabolomics studies were conducted in drought−tolerant (DT) and drought−susceptible (DS) genotypes of Chinese cabbage under water deficit−simulated mild and severe drought stress conditions. A total of 777 metabolites were detected, wherein 90 of them were proposed as the drought−responsive metabolites in Chinese cabbage, with abscisic acid (ABA), serine, choline alfoscerate, and sphingosine as potential representative drought stress biomarkers. We also found that drought−tolerant and drought−susceptible genotypes showed differential metabolic accumulation patterns with contrasting drought response mechanisms. Notably, constitutively high levels of ABA and glutathione were detected in drought−tolerant genotype in all tested and control conditions. In addition, proline, sucrose, γ−aminobutyric acid, and glutathione were also found to be highly correlated to drought tolerance. This study is the first metabolomic study on how Chinese cabbage responds to drought stress, and could provide insights on how to develop and cultivate new drought−resistant varieties. [ABSTRACT FROM AUTHOR]

The Cation Diffusion Facilitator (CDF) family, also named Metal Tolerance Protein (MTP), is one of the gene families involved in heavy metal transport in plants. However, a comprehensive study of MTPs in Brassica napus has not been reported yet. In the present study, we identified 33 BnMTP genes from the rapeseed genome using bioinformatic analyses. Subsequently, we analyzed the phylogenetic relationship, gene structure, chromosome distribution, conserved domains, and motifs of the BnMTP gene family. The 33 BnMTPs were phylogenetically divided into three major clusters (Zn-CDFs, Fe/Zn-CDFs, and Mn-CDFs) and seven groups (group 1, 5, 6, 7, 8, 9, and 12). The structural characteristics of the BnMTP members were similar in the same group, but different among groups. Evolutionary analysis indicated that the BnMTP gene family mainly expanded through whole-genome duplication (WGD) and segmental duplication events. Moreover, the prediction of cis-acting elements and microRNA target sites suggested that BnMTPs might be involved in plant growth, development, and stress responses. In addition, we found the expression of 24 BnMTPs in rapeseed leaves or roots could respond to heavy metal ion treatments. These results provided an important basis for clarifying the biological functions of BnMTPs, especially in heavy metal detoxification, and will be helpful in the phytoremediation of heavy metal pollution in soil. [ABSTRACT FROM AUTHOR]

Nyctinastic leaf movement of Fabaceae is driven by the tiny motor organ pulvinus located at the base of the leaf or leaflet. Despite the increased understanding of the essential role of ELONGATED PETIOLULE1 (ELP1)/PETIOLE LIKE PULVINUS (PLP) orthologs in determining pulvinus identity in legumes, key regulatory components and molecular mechanisms underlying this movement remain largely unclear. Here, we used WT pulvinus and the equivalent tissue in the elp1 mutant to carry out transcriptome and proteome experiments. The omics data indicated that there are multiple cell biological processes altered at the gene expression and protein abundance level during the pulvinus development. In addition, comparative analysis of different leaf tissues provided clues to illuminate the possible common primordium between pulvinus and petiole, as well as the function of ELP1. Furthermore, the auxin pathway, cell wall composition and chloroplast distribution were altered in elp1 mutants, verifying their important roles in pulvinus development. This study provides a comprehensive insight into the motor organ of the model legume Medicago truncatula and further supplies a rich dataset to facilitate the identification of novel players involved in nyctinastic movement. [ABSTRACT FROM AUTHOR]

Colored dissolved organic matter (CDOM) is an important part of aquatic ecosystems and plays a key role in the biogeochemical cycle. In this study, CDOM absorption spectrum curves and water quality parameters from 30 sampling sites in the wetlands of Poyang Lake, Jiangxi Province, China, were collected in October 2016. The optical absorption characteristics and spatial distribution of CDOM, the correlation between the absorption coefficient of CDOM at a wavelength of 355 nm (ag(355)), and the concentration of dissolved organic carbon (DOC) were analyzed. Spectral characteristic parameters—namely, E2/E3 (the ratio of the CDOM absorption coefficient at a wavelength of 250 nm to the CDOM absorption coefficient at a wavelength of 365 nm), SUVA254 (the ratio of the CDOM absorption coefficient at a wavelength of 254 nm to the DOC concentration), and spectral slopes—were used to infer the composition and sources of CDOM in the Poyang Lake wetlands. The results showed the following: (1) the CDOM absorption spectrum of water of the Poyang Lake wetlands presented significant spatial variation, showing a trend of south > west > north > east; (2) there was a strong linear correlation between the CDOM absorption coefficient and the DOC concentration in the water of the Poyang Lake wetlands (ag(355) = 1.075DOC–0.659 (r2 = 0.723, p < 0.001, n = 30)); (3) the analysis of the spectral characteristic parameters E2/E3, SUVA254, and spectral slopes showed that the CDOM in the Poyang Lake wetlands has relatively high aromaticity and molecular weight, which were shown to be mainly affected by terrestrial inputs. The results showed that the molecular weight and aromaticity of CDOM were higher in the south of the lake than in other parts. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicle (AUV) has become a hotspot in the field of robot in recent years. As a special kind of AUV, the robotic fish can achieve better propulsion efficiency and maneuverability than traditional AUVs. Studies show that robotic fish formation can save energy and perform more complex tasks than single robotic fish, but it is difficult to maintain a stable formation because the nearby environmental condition is hard to obtain. Inspired by the lateral line system (LLS) of fish, this paper constructs a predictive model of flow velocity and a judgement model of spacing between individual platforms for robotic fish formation through monitoring sensors on robotic fish surface. The models are built by methods of polynomial fitting and neural networks based on Computational Fluid Dynamics (CFD) simulation. The results show that the flow velocity predicted by our model could reduce the error to 0.4 % , and the spacing judgement accuracy could reach at least 80%. The findings are useful for maintaining a stable formation and will provide significant guidance for the control of robotic fish formation and sensor installation position on the robotic fish surface. [ABSTRACT FROM AUTHOR]

Artificial fish-like robot is an important branch of underwater robot research. At present, most of fish-like robot research focuses on single robot mechanism behavior, some research pays attention to the influence of the hydro-environment on robot crowds but does not reach a unified conclusion on the efficiency of fish-like robots swarm. In this work, the fish-like robots swarm is studied by numerical simulation. Four different formations, including the tandem, the phalanx, the diamond, and the rectangle are conducted by changing the spacing between fishes. The results show that at close spacing, the fish in the back can obtain a large wake from the front fish, but suffers large lateral power loss from the lateral fish. On the contrary, when the spacing is large, both the wake and pressure caused by the front and side fishes become small. In terms of the average swimming efficiency of fish swarms, we find that when the fish spacing is less than 1.25 L (L is the length of the fish body), the tandem swarm is the best choice. When the spacing is 1.25 L , the tandem, diamond and rectangle swarms have similar efficiency. When the spacing is larger than 1.25 L , the rectangle swarm is more efficient than other formations. The findings will provide significant guidance for the control of fish-like robots swarm. [ABSTRACT FROM AUTHOR]

Human-induced changes in landscapes are one of the major drivers of wetland loss and degradation. The Poyang Lake wetland in China has been experiencing severe degradation due to human disturbance and landscape modification. Indicators to assess the condition of this wetland are thus needed urgently. Here, a landscape-based multi-metric index (LMI) is developed to evaluate the condition of the Poyang Lake wetland. Twenty-three candidate metrics that have been applied to wetland health assessment in published studies were tested. Metrics that show strong discriminative power to identify reference and impaired sites, having significant correlations with either benthic macroinvertebrate- or vegetation-based indices of biotic integrity (B-IBI or V-IBI), were chosen to form the LMI index. Five of these metrics (largest patch index, modified normalized differential built-up index, Shannon's diversity index, connectance index, and cultivated land stress index) were selected as our LMI metrics. A 2 km buffer zone around sample sites had the strongest explanatory power of any spatial scale on IBIs, suggesting that protecting landscapes at local scales is essential for wetland conservation. The LMI scores ranged between 1.05 and 5.00, with a mean of 3.25, suggesting that the condition of the Poyang Lake wetland is currently in the "fair" category. The areas along lakeshores were mainly in poor or very poor conditions, while the less accessible inner areas were in better conditions. This study demonstrates significant links between landscape characteristics and wetland biotic integrity, which validates the utility of satellite imagery-derived data in assessing wetland health. The LMI method developed in this study can be used by land managers to quickly assess broad regions of the Poyang Lake wetland. [ABSTRACT FROM AUTHOR]

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas. [ABSTRACT FROM AUTHOR]

Recently, deep learning to hash has extensively been applied to image retrieval, due to its low storage cost and fast query speed. However, there is a defect of insufficiency and imbalance when existing hashing methods utilize the convolutional neural network (CNN) to extract image semantic features and the extracted features do not include contextual information and lack relevance among features. Furthermore, the process of the relaxation hash code can lead to an inevitable quantization error. In order to solve these problems, this paper proposes deep hash with improved dual attention for image retrieval (DHIDA), which chiefly has the following contents: (1) this paper introduces the improved dual attention mechanism (IDA) based on the ResNet18 pre-trained module to extract the feature information of the image, which consists of the position attention module and the channel attention module; (2) when calculating the spatial attention matrix and channel attention matrix, the average value and maximum value of the column of the feature map matrix are integrated in order to promote the feature representation ability and fully leverage the features of each position; and (3) to reduce quantization error, this study designs a new piecewise function to directly guide the discrete binary code. Experiments on CIFAR-10, NUS-WIDE and ImageNet-100 show that the DHIDA algorithm achieves better performance. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification is the subject of intense research in remote sensing. The tremendous success of deep learning in computer vision has recently sparked the interest in applying deep learning in hyperspectral image classification. However, most deep learning methods for hyperspectral image classification are based on convolutional neural networks (CNN). Those methods require heavy GPU memory resources and run time. Recently, another deep learning model, the transformer, has been applied for image recognition, and the study result demonstrates the great potential of the transformer network for computer vision tasks. In this paper, we propose a model for hyperspectral image classification based on the transformer, which is widely used in natural language processing. Besides, we believe we are the first to combine the metric learning and the transformer model in hyperspectral image classification. Moreover, to improve the model classification performance when the available training samples are limited, we use the 1-D convolution and Mish activation function. The experimental results on three widely used hyperspectral image data sets demonstrate the proposed model's advantages in accuracy, GPU memory cost, and running time. [ABSTRACT FROM AUTHOR]

Due to spatial inhomogeneity of land-cover types and spectral confusions among them, land-cover maps suffer from misclassification errors. While much research has focused on improving image classification by re-processing source images with more advanced algorithms and/or using images of finer resolution, there is rarely any systematic work on re-processing existing maps to increase their accuracy. We propose refining existing maps to achieve accuracy gains by exploring and utilizing relationships between reference data, which are often already available or can be collected, and map data. For this, we make novel use of canonical correspondence analysis (CCA) to analyze reference-map class co-occurrences to facilitate probabilistic re-classification of map classes in CCA ordination space, a synthesized feature space constrained by map class occurrence patterns. Experiments using GlobeLand30 land-cover (2010) over Wuhan, China were carried out using reference sample data collected previously for accuracy assessment in the same area. Reference sample data were stratified by map classes and their spatial heterogeneity. To examine effects of model-training sample size on refinements, three subset samples (360, 720, and 1480 pixels) were selected from a pool of 3000 sample pixels (the full training sample). Logistic regression modeling was employed as a baseline method for comparisons. Performance evaluation was based on a test sample of 1020 pixels using a strict and relaxed definitions of agreement between reference classification and map classification, resulting in measures of types I and II, respectively. It was found that the CCA-based method is more accurate than logistic regression in general. With increasing sample sizes, refinements generally lead to greater accuracy gains. Heterogeneous sub-strata usually see greater accuracy gains than in homogeneous sub-strata. It was also revealed that accuracy gains in specific strata (map classes and sub-strata) are related to strata refinability. Regarding CCA-based refinements, a relatively small sample of 360 pixels achieved a 3% gain in both overall accuracy (OA) and F0.01 score (II). By using a selective strategy in which only refinable strata of cultivated land and forest are included in refinement, accuracy gains are further increased, with 5–11% gains in users' accuracies (UAs) (II) and 4–10% gains in F0.01 scores (II). In conclusion, on condition of refinability, map refinement is well worth pursuing, as it increases accuracy of existing maps, extends utility of reference data, facilitates uncertainty-informed map representation, and enhances our understanding about relationships between reference data and map data and about their synthesis. [ABSTRACT FROM AUTHOR]

Quantifying information content in remote-sensing images is fundamental for information-theoretic characterization of remote sensing information processes, with the images being usually information sources. Information-theoretic methods, being complementary to conventional statistical methods, enable images and their derivatives to be described and analyzed in terms of information as defined in information theory rather than data per se. However, accurately quantifying images' information content is nontrivial, as information redundancy due to spectral and spatial dependence needs to be properly handled. There has been little systematic research on this, hampering wide applications of information theory. This paper seeks to fill this important research niche by proposing a strategy for quantifying information content in multispectral images based on information theory, geostatistics, and image transformations, by which interband spectral dependence, intraband spatial dependence, and additive noise inherent to multispectral images are effectively dealt with. Specifically, to handle spectral dependence, independent component analysis (ICA) is performed to transform a multispectral image into one with statistically independent image bands (not spectral bands of the original image). The ICA-transformed image is further normal-transformed to facilitate computation of information content based on entropy formulas for Gaussian distributions. Normal transform facilitates straightforward incorporation of spatial dependence in entropy computation for the aforementioned double-transformed image bands with inter-pixel spatial correlation modeled via variograms. Experiments were undertaken using Landsat ETM+ and TM image subsets featuring different dominant land cover types (i.e., built-up, agricultural, and hilly). The experimental results confirm that the proposed methods provide more objective estimates of information content than otherwise when spectral dependence, spatial dependence, or non-normality is not accommodated properly. The differences in information content between image subsets obtained with ETM+ and TM were found to be about 3.6 bits/pixel, indicating the former's greater information content. The proposed methods can be adapted for information-theoretic analyses of remote sensing information processes. [ABSTRACT FROM AUTHOR]

The increasing concern about noise pollution has accelerated the development of acoustic absorption and damping devices. However, conventional subtractive manufacturing can only fabricate absorption devices with simple geometric shapes that are unable to achieve high absorption coefficients in wide frequency ranges. In this paper, novel multi-layer micro-perforated panels (MPPs) with tunable wideband absorption are designed and fabricated by 3D printing or additive manufacturing. Selective laser sintering (SLS), which is an advanced powder-based 3D printing technique, is newly introduced for MPP manufacturing with polyamide 12 as the feedstock. The acoustic performances of the MPPs are investigated by theoretical, numerical, and experimental methods. The results reveal that the absorption frequency bandwidths of the structures are wider than those of conventional single-layer MPPs, while the absorption coefficients remain comparable or even higher. The frequency ranges can be tuned by varying the air gap distances and the inter-layer distances. Furthermore, an optimization method is introduced for structural designs of MPPs with the most effective sound absorption performances in the target frequency ranges. This study reveals the potential of 3D printing to fabricate acoustic devices with effective tunable sound absorption behaviors and provides an optimization method for future structural design of the wideband sound absorption devices. [ABSTRACT FROM AUTHOR]

In order to improve the interfacial bonding between hydroxyapatite (HAP) and poly-l-lactic acid (PLLA), 2-Carboxyethylphosphonic acid (CEPA), a phosphonic acid coupling agent, was introduced to modify HAP nanoparticles. After this. the PLLA scaffold containing CEPA-modified HAP (C-HAP) was fabricated by selective laser sintering (frittage). The specific mechanism of interfacial bonding was that the PO32− of CEPA formed an electrovalent bond with the Ca2+ of HAP on one hand, and on the other hand, the –COOH of CEPA formed an ester bond with the –OH of PLLA via an esterification reaction. The results showed that C-HAP was homogeneously dispersed in the PLLA matrix and that it exhibited interconnected morphology pulled out from the PLLA matrix due to the enhanced interfacial bonding. As a result, the tensile strength and modulus of the scaffold with 20% C-HAP increased by 1.40 and 2.79 times compared to that of the scaffold with HAP, respectively. In addition, the scaffold could attract Ca2+ in simulated body fluid (SBF) solution by the phosphonic acid group to induce apatite layer formation and also release Ca2+ and PO43− by degradation to facilitate cell attachment, growth and proliferation. [ABSTRACT FROM AUTHOR]

Cross-linking is an effective way to enhance biodegradable poly(propylene carbonate) (PPC) from CO2 and propylene oxide (PO). Cross-linked PPC can be prepared by one-step terpolymerization of multifunctional third monomers with CO2 and PO. However, few such third monomers are available. Each molecule of maleic anhydride oligomer (MAO) contains more than two cyclic anhydride groups. Here, we use it to synthesize PPC with cross-linked networks by adding a small quantity of MAO (0.625–5 wt% of PO) in CO2/PO copolymerization that was catalyzed by zinc glutarate. The formation of networks in the prepared copolymers was confirmed by the presence of gel in copolymers combined Fourier transform infrared spectroscopy (FT-IR), 1H NMR, and the improved mechanical properties. The 5% weight-loss degradation temperatures and maximum weight-loss degradation temperatures greatly increase up to 289.8 °C and 308.8 °C, respectively, which are remarkably high when compared to those of PPC. The minimum permanent deformation of the copolymers closes to 0, while that of PPC is 173%. The maximum tensile strength of the copolymers is 25.5 MPa higher than that of PPC, reaching 38.4 MPa, and it still has some toughness with the elongation at break of 25%. The above phenomena indicate that MAO that was inserted in PPC chains play a cross-linking role, which results in enhanced thermal stability, dimensional stability, and mechanical strength, comprehensively. [ABSTRACT FROM AUTHOR]

The agricultural pastoral ecotone (APE) in Northwest China is an ecological transition zone in the arid area with a very fragile ecosystem. In recent years, the ecosystem has deteriorated sharply, and increasing desertification has made the regional ecosystem more vulnerable and sensitive. In this study, we analyzed (using classical statistical methods) spatial and temporal variations in soil water content (SWC) from 14 September 2016 to 22 April 2019 for high and low vegetation in two grassland sites in Yanchi County, Ningxia. The results showed that the largest average seasonal SWC occurred in autumn. The SWC of the first three layers (0 ÷ 15 cm) of the soil profile responded strongly to precipitation, whereas the SWC in deeper soil (30 ÷ 50 cm) could only be recharged markedly after continuous precipitation. Additionally, the growing process of plants proved to be a cause of variability in soil moisture profiles. Vegetation degradation sped up the course of desertification and decreased soil organic carbon content. These changes left the soil increasingly desiccated and enhanced soil variability. Meanwhile, vegetation degradation also prompted changes in soil temperature and shortened the soil's frozen time in winter. With the acceleration of global warming, if the process of vegetation degeneration continues and soil temperatures keep rising, the ecosystem is likely to undergo irreversible degradation. [ABSTRACT FROM AUTHOR]

The mechanism of Pd-catalyzed, Friedel-Crafts intermolecular acylation of arenes to ketones was comprehensively investigated by using DFT calculations. The calculated results revealed that this transformation was composed of several key steps: C–I bond oxidative addition, CO insertion, reductive elimination and C–H bond functionalization. Of these steps, the last was found to be the rate–determining step, and it occurred much more easily with strongly electrophilic aroyl triflate compared to other resultant counterparts. In addition, our calculation provides a rationale for experimental findings that simple Pd salts exhibit superior catalytic abilities compared to phosphine-ligated Pd catalysts. [ABSTRACT FROM AUTHOR]