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Angle domain common image gathers (ADCIGs) play a crucial role in seismic exploration, offering prestack underground illumination information that aids in validating migration velocity and conducting prestack amplitude versus angle (AVA) analysis for reservoir characterization. This paper introduces an innovative approach for compensating amplitude errors caused by irregular seismic acquisition geometries in ADCIGs. By incorporating an angle domain illumination compensation factor, the proposed method effectively modifies these errors, preserving the amplitude of seismic reflectivity in the prestack angle domain. The effectiveness of the proposed approach is validated through comprehensive tests conducted on synthetic and field data examples. The results demonstrate the capability of the method to enhance the quality of ADCIGs derived from 3D reverse time migration (RTM), yielding accurate and reliable amplitude preservation. While the illumination compensation factor assumes a vertically linear velocity model, the method holds promise for extension to more complex media and diverse migration techniques. This suggests its applicability and adaptability beyond the specific assumptions considered in this study. In conclusion, this paper presents an innovative angle domain illumination compensation factor that significantly improves the quality of ADCIGs by addressing amplitude errors arising from irregular seismic acquisition geometries. The experimental validation using synthetic and field data confirms the effectiveness of the proposed method within the context of 3D RTM. Furthermore, the technique holds potential for broader application in more complex subsurface scenarios and various migration methodologies. [ABSTRACT FROM AUTHOR]

Body image and eating disorders pose significant challenges to the overall health of athletes. However, divergent findings exist regarding the potential association between athletes' body image and eating disorders. This systematic review aims to examine the relationship between these two variables and identify the modifiers of the association, such as gender, age, race, and exercise type. A search was conducted in five databases (Web of Science, PubMed, APA PsycINFO, ProQuest, and EBSCO), aiming to identify studies on athletes and involved body image and eating disorders in their conclusions. Ultimately, thirty-one studies were included for systematic evaluation. The results of the studies indicate that the relationship between athletes' body image and eating disorders is complex and inconclusive. In some types of sports, eating disorders can occur even when athletes are satisfied with their body image. Furthermore, female athletes, particularly young female athletes, and athletes involved in sports associated with leanness are more prone to eating disorders and body dissatisfaction. Due to limited resources in this type of research, there is a lack of comprehensive inclusivity across sports disciplines, genders, races, and levels of sports proficiency, which warrants further research. [ABSTRACT FROM AUTHOR]

Research on Satyrium nepalense var. ciliatum (Lindl.) Hook. f. has primarily focused on populations in Northwestern Yunnan, with limited studies on pollination syndromes and insect behavior. In addition, it is geographically limited in its breeding system studies. Here, pollination syndromes, florivory, and breeding systems of S. nepalense var. ciliatum from Liangwang Mountain (Central Yunnan, China) were investigated through field work, microscope, scanning electron microscope (SEM), and parafin section. It was revealed that the pollination syndrome was possessing out-crossing, such as bright color, a developed rostellum, nectar glands in the spur, and food hairs at the lip base. The color and nectar attracted flower visitors, and florivory was observed. Some flower visitors pollinated their companion species. Ants were identified as floral visitors for the first time in Satyrium, although substantial pollination was not observed. Ants might be potential pollinators. S. nepalense var. ciliatum possessed a mixed breeding system, including selfing, out-crossing, and apomixis, with apomixis being predominant in nature. It is suggested that the pollination syndrome, florivory, and pollination competition would contribute to its mixed breeding systems, particularly leading to the occurrence of apomixis. [ABSTRACT FROM AUTHOR]

By constructing a rural digital design ecosystem, this paper develops ecological villages through design empowerment, enhances the carbon sequestration benefits of plants in rural areas, and strengthens rural vitality. Combined with the carbon sequestration benefits of street trees in Bulao Village in Nanjing, the feasibility of the digital design ecosystem in rural planning was verified, and the ways and methods of rural environmental renewal were explored. Through the existing literature, the possibility of constructing a digital design ecosystem was deduced, the theoretical framework was derived, field research was carried out in the village of Bulao, the carbon sequestration benefit of street trees was quantified by the i-Tree model, and a structure chart of street trees, including breast diameter, tree height, type, etc., was formed. There were 35 species of street trees in Bulao Village, belonging to 33 genera in 22 families, including 19 species of trees, a total of 312 trees, and 16 species of shrubs. The street trees' total carbon sink benefit was equivalent to RMB 30,327.47, a single street tree's average carbon sequestration benefit was RMB 96.86, and the average CO2 absorption was 164.64 kg. The average CO2 absorption and single benefit of elm trees were the highest, reaching 465.48 kg·plant−1 and 186.81 RMB·plant−1, respectively. The CO2 absorption (185.13 kg) and the average benefit per plant (RMB 109.48) of the camphor tree were lower than those of the elm. However, because their number far exceeded that of elms, their total carbon sequestration benefit contribution was the highest, reaching RMB 25,837.28, accounting for 85.19% of the total benefit. In addition, the contribution rates of elm and willow's total annual carbon sequestration benefits were also relatively high, reaching RMB 747.24 and RMB 710.04, respectively, accounting for 2.46% and 2.34% of the total benefits. This paper uses the digital design ecosystem's theoretical framework to quantify street trees' carbon sequestration benefits through field research. It optimizes and improves the plant allocation of parking lots in Bulao Village from the ecology and carbon sink perspectives. Practice shows that inheriting the connotation values of rural culture, improving the quality of the rural environment, and increasing residents' and tourists' sense of belonging and identity to the countryside are conducive to jointly promoting sustainable rural development against the background of "dual carbon". Combining art design with quantitative scientific methods of ecological environment indicators provides a reference for future rural development. [ABSTRACT FROM AUTHOR]

Polysaccharides are the main nutritional constituents in the Armillaria mellea fruiting bodies. The structure and immunoactivity of Armillaria mellea polysaccharide are valuable to be determined for development. In the present study, two polysaccharides, including Armillaria mellea neutral polysaccharide (AMPN) and Armillaria mellea acidic polysaccharide (AMPA), were prepared and determined. AMPN and AMPA were separated and refined by the ion exchange column and gel chromatographycolumn. Analysis of AMPN and AMPA revealed molecular weights of 4.432 × 103 Da and 7.323 × 103 Da. The monosaccharide composition analysis revealed that AMPN was mainly composed of 68.3%glucose, while AMPA consisted primarily of glucose, mannose, and galactose, accompanied by 8.9% galacturonic acid and 3% fucose. Then, infrared spectra analysis, Congo red staining, methylation, and NMR spectroscopy analysis were conducted as a means to clarify the structure of AMPN and AMPA. The NMR spectra demonstrated that the two polysaccharides included both α and β-configuration glycosidic bonds. The Congo red experiment suggests that AMPN and AMPA all had obvious triple helix structures.The effects of AMPN and AMPA on immune repair ability were compared by immune deficiency mice. The activity experiment showed that AMPN and acidic polysaccharides extracted from the Armillaria fruiting body have biological immune activity. Among them, AMPA showed higher immune activity. These findings suggest that Armillaria mellea fruiting bodies may be used as a source of dietary supplements and functional products. [ABSTRACT FROM AUTHOR]

Panax ginseng C.A. Meyer (P. ginseng) is one of the more common traditional Chinese medicines (TCMs). It contains numerous chemical components and exhibits a range of pharmacological effects. An enormous burden is placed on people's health and life by Alzheimer's disease (AD), a neurodegenerative condition. Recent research has shown that P. ginseng's chemical constituents, particularly ginsenosides, have a significant beneficial impact on the prevention and management of neurological disorders. To understand the current status of research on P. ginseng to improve AD, this paper discusses the composition of P. ginseng, its mechanism of action, and its clinical application. The pathogenesis of AD includes amyloid beta protein (Aβ) generation and aggregation, tau protein hyperphosphorylation, oxidant stress, neuroinflammation, mitochondrial damage, and neurotransmitter and gut microbiota disorders. This review presents the key molecular mechanisms and signaling pathways of the active ingredients in P. ginseng involved in improving AD from the perspective of AD pathogenesis. A P. ginseng-related signaling pathway network was constructed to provide effective targets for the treatment of AD. In addition, the application of spatial metabolomics techniques in studying P. ginseng and AD is discussed. In summary, this paper discusses research perspectives for the study of P. ginseng in the treatment of AD, including a systematic and in-depth review of the mechanisms of action of the active substances in P. ginseng, and evaluates the feasibility of applying spatial metabolomics in the study of AD pathogenesis and pharmacological treatment. [ABSTRACT FROM AUTHOR]

Armillaria mellea (Vahl) P. Kumm is commonly used for food and pharmaceutical supplements due to its immune regulatory function, and polysaccharides are one of its main components. The aim of this research is to study the immunological activity of the purified acidic polysaccharide fraction, namely, AMPA, isolated from Armillaria mellea crude polysaccharide (AMP). In this study, a combination of the immune activity of mouse macrophages in vitro and serum metabonomics in vivo was used to comprehensively explore the cell viability and metabolic changes in immune-deficient mice in the AMPA intervention, with the aim of elucidating the potential mechanisms of AMPA in the treatment of immunodeficiency. The in vitro experiments revealed that, compared with LPS-induced RAW264.7, the AMPA treatment elevated the levels of the cellular immune factors IL-2, IL-6, IgM, IgA, TNF-α, and IFN-γ; promoted the expression of immune proteins; and activated the TLR4/MyD88/NF-κB signaling pathway to produce immunological responses. The protein expression was also demonstrated in the spleen of the cyclophosphamide immunosuppressive model in vivo. The UHPLC-MS-based metabolomic analysis revealed that AMPA significantly modulated six endogenous metabolites in mice, with the associated metabolic pathways of AMPA for treating immunodeficiency selected as potential therapeutic biomarkers. The results demonstrate that phosphorylated acetyl CoA, glycolysis, and the TCA cycle were mainly activated to enhance immune factor expression and provide immune protection to the body. These experimental results are important for the development and application of AMPA as a valuable health food or drug that enhances immunity. [ABSTRACT FROM AUTHOR]

Q-compensated reverse time migration (Q-RTM) is a crucial technique in seismic imaging. However, stability is a prominent concern due to the exponential increase in high-frequency ambient noise during seismic wavefield propagation. The two primary strategies for mitigating instability in Q-RTM are regularization and low-pass filtering. Q-RTM instability can be addressed through regularization. However, determining the appropriate regularization parameters is often an experimental process, leading to challenges in accurately recovering the wavefield. Another approach to control instability is low-pass filtering. Nevertheless, selecting the cutoff frequency for different Q values is a complex task. In situations with low signal-to-noise ratios (SNRs) in seismic data, using low-pass filtering can make Q-RTM highly unstable. The need for a small cutoff frequency for stability can result in a significant loss of high-frequency signals. In this study, we propose a multi-task learning (MTL) framework that leverages data-driven concepts to address the issue of amplitude attenuation in seismic records, particularly when dealing with instability during the Q-RTM (reverse time migration with Q-attenuation) process. Our innovative framework is executed using a convolutional neural network. This network has the capability to both predict and compensate for the missing high-frequency components caused by Q-effects while simultaneously reconstructing the low-frequency information present in seismograms. This approach helps mitigate overwhelming instability phenomena and enhances the overall generalization capacity of the model. Numerical examples demonstrate that our Q-RTM results closely align with the reference images, indicating the effectiveness of our proposed MTL frequency-extension method. This method effectively compensates for the attenuation of high-frequency signals and mitigates the instability issues associated with the traditional Q-RTM process. [ABSTRACT FROM AUTHOR]

Featured Application: This study offers a fresh concept for the use of PV technology. The concept behind this research can serve as a model for the creation and application of other new energy sources. The "dual carbon" strategy has drawn attention to distributed PV systems for their flexibility and variability, but the rising need for direct-current (DC) loads on the load side has created additional difficulties for microgrid system upgrades. In this article, a PV-based microgrid design approach for residential buildings is suggested, working on the assumption that distributed PV systems are given top priority to handle domestic DC needs. The residential DC microgrid system's overall design concept is first put out, and the circuit system is then concentrated to supply the main idea for the ensuing verification of the system's viability. Secondly, the actual power generation in the selected area was clarified by testing, and then the electricity consumption of DC loads accounted for about 20.03% of the total power consumption according to the survey of 100 users. In addition, the circuit system is subjected to spectral model measurements and physical measurements to verify the operational performance of the circuit system; the feasibility of the PV microgrid system is further verified using dual testing of the PV system and the circuit system. The test results show that the proposed DC microgrid system can accurately provide the required voltage for small household DC appliances, such as 24 V, 14 V, 5 V, etc. Finally, the system economics were analyzed, and the equipment payback years were estimated. The supply and demand of PV power generation and DC appliances can be balanced via the construction of a microgrid. This study offers a fresh concept for the use of PV technology. The concept behind this research can serve as a model for the creation and application of other new energy sources. [ABSTRACT FROM AUTHOR]

The effective control and reduction of building energy consumption are major global focuses. The building sector is responsible for over 40% of all direct and indirect CO2 emissions. Nearly zero-energy buildings have been the subject of aims and regulations from several developed nations. An office building located in the severe cold region of China was chosen for this case study. The building was equipped with multiple NZEB technologies. Building indoor environment parameters and energy efficiency indexes were used as performance targets, and a performance-based design approach was used to optimize building design parameters. Thermal performance of the building envelope, airtightness, energy demand, and indoor thermal environment were tested according to different evaluation criteria. The total energy demand was as low as 53.93 KWh/(m2·a), and this can be attributed to the exceptional insulation of the building. In this test, the indoor thermal environment comfort was satisfactory. This study can be used as a reference for the design and evaluation of low-carbon buildings and low-energy buildings. [ABSTRACT FROM AUTHOR]

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China's national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants' growth and extreme climate, together with the reaction of plants' growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants' growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic feature network (DSMSFNet) for HSI classification is proposed. This model is composed of a decompressed spectral-spatial feature extraction module (DSFEM) and a multiscale semantic feature extraction module (MSFEM). The former is devised to extract more discriminative and representative global decompressed spectral-spatial features in a lightweight extraction manner, while the latter is constructed to expand the range of available receptive fields and generate clean multiscale semantic features at a granular level to further enhance the classification performance. Compared with progressive classification approaches, abundant experimental results on three benchmark datasets prove the superiority of our developed DSMSFNet model. [ABSTRACT FROM AUTHOR]

Diacylglycerol acyltransferase (DGAT) is a vital and sole rate-limiting enzyme involved in triacylglycerol synthesis. Identifying DGAT genes in Lindera glauca is essential for studying lipid metabolism pathways and developing novel oil crops with enhanced value. In the study reported in this paper, 15 LgDGAT family genes were first obtained from the L. glauca genome via bioinformatics analysis. We comprehensively analyzed their chromosome distribution, gene structure, subcellular localization, promoter prediction, phylogenetic relationships, tissue-specific expression, and expression patterns during different stages of fruit development. Our findings revealed that LgDGATs can be classified into DGAT1, DGAT2, DGAT3, and WSD (wax ester synthase/acyl-CoA: diacylglycerol acyltransferase) subfamilies distributed across chromosome 3, 5, 6, 8 and 11. LgDGATs' promoter region showed abundant elements linked to the light response and plant hormone response. Forms of LgDGAT1, LgDGAT2, and LgDGAT3 were primarily expressed in the early and late phases of fruit development, indicating their potential function in the growth and development of L. glauca, particularly in oil accumulation. Conversely, LgWSDs exhibited predominant expression in stems and leaves. This paper elucidates the gene structure and expression patterns of LgDGATs, providing a theoretical foundation for understanding the functionality of DGAT genes in Lindera species. [ABSTRACT FROM AUTHOR]

Simple Summary: Volunteer wheat is commonly found in the main wheat-producing areas of China and impacts cultivated wheat production. This study's aim was evaluating the effects of environmental conditions on the adaptability and germination of volunteer wheat grains. Our results showed that volunteer wheat was more adaptable to low temperatures of 5 °C and was poorly adaptable to high temperatures of above 30 °C. Volunteer wheat was more adaptable to higher salinity and lower osmotic potential than cultivated wheat. The secondary germination ability of volunteer wheat was also higher than that of cultivated wheat after water immersion. The deep tillage of cultivated land could effectively prevent the spread of volunteer wheat. Our study provides a basis for future studies concerning the control of volunteer wheat. Volunteer wheat commonly occurs and spreads rapidly in the main wheat-producing areas of China, seriously impacting cultivated wheat production. Limited information is currently available regarding the adaptability and germination traits of volunteer wheat. Therefore, this study's aim was to evaluate the effects of environmental conditions on the germination and emergence of volunteer wheat seeds through laboratory experiments. The results showed that the germination percentages and viability of volunteer wheat were significantly higher than those of cultivated wheat at a low temperature of 5 °C, and they were lower than those of cultivated wheat at high temperatures of above 30 °C. Compared to cultivated wheat, volunteer wheat was able to tolerate higher salinity and lower osmotic potential, especially long-dormancy volunteer wheat. The secondary germination ability of volunteer wheat was higher than that of cultivated wheat after water immersion. Furthermore, volunteer wheat could not emerge normally when the seeding depth was greater than 8 cm, and the emergence ability of the volunteer wheat was weaker than that of the cultivated wheats when the seeding depth was 4–8 cm, which indicates that the deep tillage of cultivated land could effectively prevent the spread of volunteer wheat. This study revealed differences in the germination characteristics of volunteer wheat and cultivated wheat under the influence of different environmental factors, which provides a basis for future studies concerning the control of volunteer wheat. [ABSTRACT FROM AUTHOR]

In this work, ultrasonic vibration treatment (UVT) was introduced to improve the interfacial microstructure and bonding strength of A356/AZ91D bimetal processed via lost foam compound casting (LFCC). The interfacial microstructure and mechanical properties of the Al/Mg bimetal processed via LFCC with different UVT durations were investigated. Results revealed the UVT did not change the composition of phases at the interface. The Al/Mg bimetallic interface consisted of an intermetallic compound area (β-Al3Mg2 + γ-Al12Mg17 + Mg2Si) and eutectic area (δ-Mg + γ-Al12Mg17 + Mg2Si). When the duration of the UVT was increased, the gathered Mg2Si particles at the intermetallic compound area were refined to sizes of no more than 5 μm and became more homogeneously dispersed in the intermetallic compound area and diffused in the eutectic area, which could be attributed to the removal of oxide film and the acoustic cavitation and streaming flow effects induced by the UVT. The microhardness of the Al/Mg bimetallic interface was not obviously changed by the increase in UVT duration. The shear strength of the Al/Mg bimetal was increased with UVT and reached maximum with a UVT duration of 5 s, with a value of 56.7 MPa, which was increased by 70.3%, compared with Al/Mg bimetal without UVT. This could be attributed to the removal of the oxide film at the Al/Mg bimetallic interface, which improved the metallurgical bonding of the Al/Mg interface. Additionally, the refined and homogeneously dispersed Mg2Si particles played an important role in suppressing the propagation of cracks and enhancing the shear strength of the Al/Mg bimetal. [ABSTRACT FROM AUTHOR]

LiDAR-based simultaneous localization and mapping (SLAM) approaches have obtained considerable success in autonomous robotic systems. This is in part owing to the high accuracy of robust SLAM algorithms and the emergence of new and lower-cost LiDAR products. This study benchmarks the current state-of-the-art LiDAR SLAM algorithms with a multi-modal LiDAR sensor setup, showcasing diverse scanning modalities (spinning and solid state) and sensing technologies, and LiDAR cameras, mounted on a mobile sensing and computing platform. We extend our previous multi-modal multi-LiDAR dataset with additional sequences and new sources of ground truth data. Specifically, we propose a new multi-modal multi-LiDAR SLAM-assisted and ICP-based sensor fusion method for generating ground truth maps. With these maps, we then match real-time point cloud data using a normal distributions transform (NDT) method to obtain the ground truth with a full six-degrees-of-freedom (DOF) pose estimation. These novel ground truth data leverage high-resolution spinning and solid-state LiDARs. We also include new open road sequences with GNSS-RTK data and additional indoor sequences with motion capture (MOCAP) ground truth, complementing the previous forest sequences with MOCAP data. We perform an analysis of the positioning accuracy achieved, comprising ten unique configurations generated by pairing five distinct LiDAR sensors with five SLAM algorithms, to critically compare and assess their respective performance characteristics. We also report the resource utilization in four different computational platforms and a total of five settings (Intel and Jetson ARM CPUs). Our experimental results show that the current state-of-the-art LiDAR SLAM algorithms perform very differently for different types of sensors. More results, code, and the dataset can be found at GitHub. [ABSTRACT FROM AUTHOR]

Global warming, the urban heat island effect (UHI), and the risks of fossil fuel depletion necessitate a re-evaluation of traditional settlements that have been adapted to local climatic conditions, topography, and available resources, including materials and construction methods, through passive strategies to achieve thermal comfort. Although vernacular settlements have received considerable attention, few have examined and evaluated their streetscapes. This study investigates the impact of topographical features and architectural forms on insolation and ventilation conditions in traditional settlements in China's southern subtropical climate. The aim is to explore traditional planning configurations of streetscapes at different altitudes to identify architectural forms and planning strategies that effectively improve outdoor users' thermal comfort conditions. For this purpose, case studies are conducted on three traditional settlements in Lingnan; the Lingnan region has a typical subtropical climate in southern China. The chosen cases represent the main features of different topographical conditions, architectural forms, and climate zones in the Lingnan. We systematically simulated the insolation and ventilation in these settlements' streetscapes on a monthly and quarterly basis and analyzed their sunlight hours, incident solar radiation, shading percentages, sky view factors (SVF), and wind speed. The findings show the following: (1) Specific terrains can affect streetscapes' shading percentages and wind speed. The mountain settlement (With an average elevation of 600 m) is located on a southeast-facing slope (10° < slope < 20°). It receives an additional 10% of incident solar radiation compared to gentle terrain. (2) Compared to settlements located in coastal hills and mountainous, plain settlements have better shading and ventilation conditions in streetscapes. In terms of insolation, plain settlements have denser building configurations and narrower, elongated street corridors with a height-to-width ratio (H/W) = 1.9~5.5 (the height–width ratio value as street's H/W (H = height, W = width); note that it is unitless), which can generate greater lower SVF (44.5%), and shading percentages (63.6%). Regarding ventilation, it is easier to create a "cool lane" (i) when the main street, oriented towards the dominant wind direction in summer, forms an angle <30° with it, (ii) when the primary street follows a NE–SW longitudinal orientation, while SE–NW horizontal streets intersect and weave through it, and (iii) with a H/W = 3~4 resulting in wind speeds of 2.9~4.0 m/s. (3) All the streetscapes have overshadowing occurring in winter; similarly, varying sizes of calm wind zones are created in summer. To alleviate these issues, widening the streetscapes along the buildings can permit solar penetration and natural ventilation. (4) In summer, installing shading devices along the horizontal plane of covered street corridors with a H/W = 1~4 and N–S longitudinal orientation can provide an additional shading of 3.6–22%. [ABSTRACT FROM AUTHOR]

With the development of aquaculture, wastewater treatment and diseases have been paid more and more attention. The question of how to improve the immunity of aquatic species, as well as treat aquaculture wastewater, has become an urgent problem. In this study, duckweed with a high protein content (37.4%) (Lemna turionifera 5511) has been employed as a feedstock for aquatic wastewater treatment and the production of antimicrobial peptides. Penaeidins 3a (Pen3a), from Litopenaeus vannamei, were expressed under the control of CaMV-35S promoter in duckweed. Bacteriostatic testing using the Pen3a duckweed extract showed its antibacterial activity against Escherichia coli and Staphylococcus aureus. Transcriptome analysis of wild type (WT) duckweed and Pen3a duckweed showed different results, and the protein metabolic process was the most up-regulated by differential expression genes (DEGs). In Pen3a transgenic duckweed, the expression of sphingolipid metabolism and phagocytosis process-related genes have been significantly up-regulated. Quantitative proteomics suggested a remarkable difference in protein enrichment in the metabolic pathway. Pen3a duckweed decreased the bacterial number, and effectively inhibited the growth of Nitrospirae. Additionally, Pen3a duckweed displayed better growth in the lake. The study showed the nutritional and antibacterial value of duckweed as an animal feed ingredient. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a worldwide chronic disease that can cause severe inflammation to damage the surrounding tissue and cartilage. There are many different factors that can lead to osteoarthritis, but abnormally progressed programmed cell death is one of the most important risk factors that can induce osteoarthritis. Prior studies have demonstrated that programmed cell death, including apoptosis, pyroptosis, necroptosis, ferroptosis, autophagy, and cuproptosis, has a great connection with osteoarthritis. In this paper, we review the role of different types of programmed cell death in the generation and development of OA and how the different signal pathways modulate the different cell death to regulate the development of OA. Additionally, this review provides new insights into the radical treatment of osteoarthritis rather than conservative treatment, such as anti-inflammation drugs or surgical operation. [ABSTRACT FROM AUTHOR]

Nitric oxide (NO) is a gaseous free radical that has been become a potential tool to maintain the quality of postharvest horticultural produce. It plays important roles in delaying ripening, alleviating chilling injury, preventing browning, and enhancing disease resistance. The regulatory function of NO is achieved through the post-transcriptional modification of proteins, such as tyrosine nitration, S-nitrosylation, and nitroalkylation. Secondly, NO can also induce the expression of stress-related genes by synergistically interacting with other signaling substances, such as Ca2+, ethylene (ETH), salicylic acid (SA), and jasmonic acid (JA). Here, research progress on the role of NO and its donors in regulating the quality of postharvest fruits and vegetables under storage is reviewed. The function of NO crosstalk with other phytohormones is summarized. Future research directions for NO commercial application and the endogenous NO regulatory mechanism are also discussed. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have demonstrated impressive performance and have been broadly applied in hyperspectral image (HSI) classification. However, two challenging problems still exist: the first challenge is that redundant information is averse to feature learning, which damages the classification performance; the second challenge is that most of the existing classification methods only focus on single-scale feature extraction, resulting in underutilization of information. To resolve the two preceding issues, this article proposes a multiscale cross interaction attention network (MCIANet) for HSI classification. First, an interaction attention module (IAM) is designed to highlight the distinguishability of HSI and dispel redundant information. Then, a multiscale cross feature extraction module (MCFEM) is constructed to detect spectral–spatial features at different scales, convolutional layers, and branches, which can increase the diversity of spectral–spatial features. Finally, we introduce global average pooling to compress multiscale spectral–spatial features and utilize two fully connection layers, two dropout layers to obtain the output classification results. Massive experiments on three benchmark datasets demonstrate the superiority of our presented method compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objectives: In the "dual evaluation" of land space, the evaluation of the importance of ecosystem service functions and residential areas is important, playing a significant role in plants acting as carbon sinks and thereby achieving the transformation of low-carbon settlements. Methods: The paper compares and analyzes five models for quantifying carbon sink benefits and focuses on the national tree benefit calculator (NTBC) model, which is suitable for the carbon sequestration benefits of plants in residential areas, to (i) estimate and compare the economic benefits brought by trees and shrubs in residential areas, (ii) analyze the reasons for the differences between the current data and data for the next 20 years, and (iii) comprehensively evaluate the technical points related to the plant landscape in residential areas to assess whether they comply with the "Green Settlement Standard." The index system was scored according to the standard. Result: The current data collected for existing trees and shrubs include the following: When the trees are in good condition, the order of the trees according to their economic benefits in the current year is Zelkova serrata > Cedrus deodara > Sapindus saponaria > Sophora japonica > Cinnamomum camphora > Prunus cerasifera > Magnolia grandiflora > Ulmus pumila > Acer L. > Lagerstroemia indica L. > Sapium sebiferum > Sabina > Punica granatum L. > Acer palmatum > Sapium sebiferum > Celtis sinensis Pers > Bambusa multiplex > Cycas > Melia azedarach L. > Pinus parviflora, and that of the trees in the next 20 years is Zelkova serrata > Cinnamomum camphora > Sophora japonica > Sapindus saponaria > Ulmus pumila > Cedrus deodara > Prunus cerasifera > Magnolia grandiflora > Acer L. > Sapium sebiferum > Cycas > Punica granatum L. > Lagerstroemia indica L. > Acer palmatum Thunb > Sabina > Bambusa multiplex > Broussonetia papyrifera > Celtis sinensis Pers > Melia azedarach L. > Pinus parviflora. The order of shrubs according to their economic gain in the current year is Photinia beauverdiana > Pittosporum tobira > Ligustrum lucidum > Viburnum odoratissimum > Buxus cephalantha, and that of the shrubs in the next 20 years is Ligustrum lucidum > Photinia beauverdiana > Pittosporum tobira > Buxus cephalantha > Viburnum odoratissimum. Conclusion: Using plants, the construction ideas, community structure and landscape maintenance of the carbon sink estimation system of residential areas should be updated according to three aspects to promote the quantification of the carbon sink benefits of green areas in urban settlements and the development of low-carbon settlements in China. [ABSTRACT FROM AUTHOR]

Simple Summary: Since the ban of antibiotic growth promoters (AGP) in animal feed, establishing an enteric inflammation model can help to accurately evaluate the effects of feed additives (e.g., prebiotics, probiotics, and organic acids) to maintain intestinal health and improve growth performance in broilers. The goal of this study was to assess the feasibility of using DSS (dextran sodium sulfate) to establish an enteritis broiler model. The results showed that DSS effectively induced enteric inflammation in broilers, as evidenced by poor growth performance, an increased inflammatory score, gross lesions, and severe histopathological damage in the jejunum. This study aimed to evaluate the effectiveness of oral gavage of dextran sodium sulfate (DSS) to establish an enteric inflammation model in broilers. Forty 1-day-old male, yellow-feathered broilers were randomly divided into 2 groups with 5 replicates of 4 birds each for a 42-day trial. The experiment design used 2 groups: (1) the control group (CT), normal broilers fed a basal diet, and (2) the DSS group, DSS-treated broilers fed a basal diet. The DSS group received 1 mL of 2.5% DSS solution once a day by oral gavage from 21 to 29 days of age. The results showed that compared with those in CT, DSS treatment significantly increased histological scores for enteritis and mucosal damage at 29 and 42 days of age (p < 0.01) and the disease activity index (DAI) from 23 to 29 days of age (p < 0.01). DSS-treated broilers showed poor growth performance at 42 days of age, including decreased body weight and average daily gain and an increased feed conversion ratio (p < 0.01). DSS also caused gross lesions and histopathological damage in the jejunum of broilers, such as obvious hemorrhagic spots, loss of villus architecture, epithelial cell disruption, inflammatory cell infiltration, and decreased villus height. These results suggest that oral gavage of DSS is an effective method for inducing mild and non-necrotic enteric inflammation in broilers. [ABSTRACT FROM AUTHOR]

Osteosarcoma is the most common malignant bone tumor, often occurring in children and adolescents. The etiology of most patients is unclear, and the current conventional treatment methods are chemotherapy, radiotherapy, and surgical resection. However, the sensitivity of osteosarcoma to radiotherapy and chemotherapy is low, and the prognosis is poor. The development of new and useful treatment strategies for improving patient survival is an urgent need. It has been found that endoplasmic reticulum (ER) stress (ERS) affects tumor angiogenesis, invasion, etc. By summarizing the literature related to osteosarcoma and ERS, we found that the unfolded protein response (UPR) pathway activated by ERS has a regulatory role in osteosarcoma proliferation, apoptosis, and chemoresistance. In osteosarcoma, the UPR pathway plays an important role by crosstalk with autophagy, oxidative stress, and other pathways. Overall, this article focuses on the relationship between ERS and osteosarcoma and reviews the potential of drugs or gene targets associated with ERS for the treatment of osteosarcoma. [ABSTRACT FROM AUTHOR]

Robot-assisted orthopedic surgery has great application prospects, and the accuracy of the robot is the key to its overall performance. The aim of this study was to develop a new orthopedic surgical robot to assist in spinal surgeries and to compare its feasibility and accuracy with the existing orthopedic robot. A new type of high-precision orthopedic surgical robot (Tuoshou) was developed. A multicenter, randomized controlled trial was carried out to compare the Tuoshou with the TiRobot (TINAVI Medical Technologies Co., Ltd., Beijing) to evaluate the accuracy and safety of their navigation and positioning. A total of 112 patients were randomized, and 108 patients completed the study. The position deviation of the Kirschner wire placement in the Tuoshou group was smaller than that in the TiRobot group (p = 0.014). The Tuoshou group was better than the TiRobot group in terms of the pedicle screw insertion accuracy (p = 0.016) and entry point deviation (p < 0.001). No differences were observed in endpoint deviation (p = 0.170), axial deviation (p = 0.170), sagittal deviation (p = 0.324), and spatial deviation (p = 0.299). There was no difference in security indicators. The new orthopedic surgical robot was highly accurate and optimized for clinical practice, making it suitable for clinical application. [ABSTRACT FROM AUTHOR]

The foot is an important part of humanoid robot locomotion that can help with shock absorption while making contact with the ground. The mechanism of the foot directly affects walking stability. A novel foot mechanism inspired by the toes of felids is proposed. The foot has four bionic modules with soft pads and sharp claws installed at the four corners of a flat foot. This foot can reduce the impact experienced during foot landing and increase the time that the foot is in contact with the ground, which can improve the adaptability of the robot to different ground surface conditions with different levels of stiffness. The main structure of the bionic module is a four-bar linkage consisting of a slide way and a spring. Furthermore, the length of the four-bar linkage and the posture of the claw during insertion into soft ground are optimized to improve the stability and buffering performance. The validity of the proposed foot mechanism has been proved in simulations. [ABSTRACT FROM AUTHOR]

Elastic full waveform inversion (EFWI) is essential for obtaining high-resolution multi-parameter models. However, the conventional EFWI may suffer from severe cycle skipping without the low-frequency components in elastic seismic data. To solve this problem, we propose a multistage phase correction-based elastic full waveform inversion method in the frequency-wavenumber domain, which we call PC-EFWI for short. Specifically, the seismic data are first split using 2-D sliding windows; for each window, the seismic data are then transformed into the frequency-wavenumber domain for PC-EFWI misfit. In addition, we introduced a phase correction factor in the PC-EFWI misfit. In this way, it is possible to reduce phase differences between measured and synthetic data to mitigate cycle skipping by adjusting the phase correction factor in different scales. Numerical examples with the 2-D Marmousi model demonstrate that the frequency-wavenumber domain PC-EFWI with multistage strategy is an excellent way to reduce the risk of EFWI cycle skipping and build satisfactory start models for the conventional EFWI. [ABSTRACT FROM AUTHOR]

Recently, hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have shown impressive performance. However, HSI classification still faces two challenging problems: the first challenge is that most existing classification approaches only focus on exploiting the fixed-scale convolutional kernels to extract spectral–spatial features, which leads to underutilization of information; the second challenge is that HSI contains a large amount of redundant information and noise, to a certain extent, which influences the classification performance of CNN. In order to tackle the above problems, this article proposes a multibranch crossover feature attention network (MCFANet) for HSI classification. The MCFANet involves two primary submodules: a cross feature extraction module (CFEM) and rearranged attention module (RAM). The former is devised to capture joint spectral–spatial features at different convolutional layers, scales and branches, which can increase the diversity and complementarity of spectral–spatial features, while the latter is constructed to spontaneously concentrate on recalibrating spatial-wise and spectral-wise feature responses, meanwhile exploit the shifted cascade operation to rearrange the obtained attention-enhanced features to dispel redundant information and noise, and thus, boost the classification performance. Compared with the state-of-the-art classification methods, massive experiments on four benchmark datasets demonstrate the meliority of our presented method. [ABSTRACT FROM AUTHOR]

Simple Summary: Wuliangshan Black-bone chickens, an indigenous chicken breed in southwestern China, have characteristics of black skin, black bones, and black meat, and are suitable for rearing at high altitudes. After six generations of selection, the bodyweights of chickens have been greatly improved: cocks and hens have reached 2.0 kg and 1.5 kg weight at 126 days of age, respectively. The breast and thigh muscles are the main meat-producing parts of chicken; assessing and documenting the morphological features of breast and thigh muscle development and meat composition in Wuliangshan Black-bone chickens is very important in terms of their genetic breeding and also consumer interest. This study was conducted to illustrate the morphological features of the breast and thigh muscles at four developmental stages (1, 42, 84, and 126 days of age) of Wuliangshan Black-bone chickens and to compare the chemical composition, fatty acid, and amino acid contents of their meat at 126 days of age (D126). In total, 80 chickens (male, n = 40 and female, n = 40) in the sixth generation from the breeding farm were used in the experiment under free-range rearing conditions. The cross-sectional areas (CSA) of muscle fibers and meat composition were compared between different sex and different muscle types. The results showed that gender did not affect the CSA of the breast muscle fibers but did affect the CSA of the thigh muscle fibers at D42, D84, and D126 (p < 0.05). Muscle types affected the CSA of muscle fibers: male chickens exhibited significantly higher values than female chickens at D42, D84, and D126 (p < 0.05). The results of moisture, crude protein, and crude fat at D126 showed that moisture contents were higher in the thigh muscles than in the breast muscles in male and female chickens (p < 0.05). Gender affected the crude protein contents and crude fat contents: the breast muscle crude protein content was significantly higher than that in the thigh muscle, both in males and females (p < 0.05), whereas the crude fat contents were significantly higher in females than in males (p < 0.05); moreover, the thigh muscle fat contents were significantly higher than those of the breast muscles both in males and females (p < 0.05). Gender and muscle types also affected the fatty acid contents: the PUFA contents of the breast and thigh muscles were significantly higher in male than in female chickens (p < 0.05). Muscle types significantly influenced the total EAA, NEAA, and flavor amino acid contents. The total EAA contents of the breast muscles were significantly higher than those of the thigh muscles in males and females (p < 0.05), whereas the total NEAA and total flavor amino acid contents of the thigh muscles were significantly higher than those of the breast muscles (p < 0.05). Our results may lead to a better understanding of the effects of gender on the breast and thigh muscle development and meat composition of Wuliangshan Black-bone chicken. [ABSTRACT FROM AUTHOR]

The excessive release of phosphorus is a prime culprit for eutrophication and algal bloom in the aquatic environment, and there is always an urgent need to develop effective methods to deal with phosphorus pollution. Ce-based oxide is a type of compelling adsorbent for phosphate removal, and a self-templating strategy is used to construct high-performance Ce-based oxides for phosphate adsorption in this study. A "sea anemone"-like CeFe cyanometallate (CM) with a 3D microstructure is fabricated to provide a precursor for synthesizing CeFe-based oxides (CeFe-CM-T) by high-temperature pyrolysis. The as-prepared CeFe-CM-T maintains the "sea anemone" morphology well and has abundant micropores/mesopores, which render its superior phosphate adsorption capacity 1~2 orders of magnitude higher than that of the commercial CeO2 and Fe3O4 materials. Moreover, CeFe-CM-T shows high selectivity for phosphate removal when it co-exists with other anions and natural organic matter and exhibits excellent recycling performance. It demonstrates that both Ce3+ and Ce4+ are reserved in the oxides, where Ce3+ serves as the main active site for phosphate capture, which forms stable Ce-PO4 compounds via a ligand-exchange mechanism. Thus, the self-templating strategy using CM as a precursor is a potential method for synthesizing porous Ce-based oxides for phosphate removal. [ABSTRACT FROM AUTHOR]

In the past few years, distant retrograde orbits (DROs) have become increasingly popular due to their conspicuous stability. Nevertheless, it is this characteristic that results in the challenge to the design of transfer orbits into/out of DROs. This paper investigates the DROs around Ganymede in order to utilize their dynamical characteristics for Jupiter system exploration. In particular, the DRO family is calculated by numerical integration and numerical continuation, higher-period orbits near the DROs are detected using bifurcation theory, and characteristics including orbital stability and invariant manifolds of these orbits are investigated through stability indices and manifold theory. The stability of DROs and the higher-period orbits are first investigated in the circular restricted three-body problem and are then verified in a third-body gravitation perturbation model. The results show that the strong stability of DROs makes it possible to observe the Galilean moons for long periods and that the higher-period orbits that bifurcate from the DROs offer additional insight into the motion of probes approaching/departing from the vicinities of the DROs. Further investigation of the invariant manifolds around higher-period orbits reveals the feasibility of utilizing the DRO family and the nearby unstable structures for multi-target exploration and low-energy transfer between the Galilean moons. [ABSTRACT FROM AUTHOR]

Convolutional neural networks are widely applied in hyperspectral image (HSI) classification and show excellent performance. However, there are two challenges: the first is that fine features are generally lost in the process of depth transfer; the second is that most existing studies usually restore to first-order features, whereas they rarely consider second-order representations. To tackle the above two problems, this article proposes a hybrid-order spectral-spatial feature network (HS2FNet) for hyperspectral image classification. This framework consists of a precedent feature extraction module (PFEM) and a feature rethinking module (FRM). The former is constructed to capture multiscale spectral-spatial features and focus on adaptively recalibrate channel-wise and spatial-wise feature responses to achieve first-order spectral-spatial feature distillation. The latter is devised to heighten the representative ability of HSI by capturing the importance of feature cross-dimension, while learning more discriminative representations by exploiting the second-order statistics of HSI, thereby improving the classification performance. Massive experiments demonstrate that the proposed network achieves plausible results compared with the state-of-the-art classification methods. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a principal cause of aches and disability worldwide. It is characterized by the inflammation of the bone leading to degeneration and loss of cartilage function. Factors, including diet, age, and obesity, impact and/or lead to osteoarthritis. In the past few years, OA has received considerable scholarly attention owing to its increasing prevalence, resulting in a cumbersome burden. At present, most of the interventions only relieve short-term symptoms, and some treatments and drugs can aggravate the disease in the long run. There is a pressing need to address the safety problems due to osteoarthritis. A disintegrin-like and metalloprotease domain with thrombospondin type 1 repeats (ADAMTS) metalloproteinase is a kind of secretory zinc endopeptidase, comprising 19 kinds of zinc endopeptidases. ADAMTS has been implicated in several human diseases, including OA. For example, aggrecanases, ADAMTS-4 and ADAMTS-5, participate in the cleavage of aggrecan in the extracellular matrix (ECM); ADAMTS-7 and ADAMTS-12 participate in the fission of Cartilage Oligomeric Matrix Protein (COMP) into COMP lyase, and ADAMTS-2, ADAMTS-3, and ADAMTS-14 promote the formation of collagen fibers. In this article, we principally review the role of ADAMTS metalloproteinases in osteoarthritis. From three different dimensions, we explain how ADAMTS participates in all the following aspects of osteoarthritis: ECM, cartilage degeneration, and synovial inflammation. Thus, ADAMTS may be a potential therapeutic target in osteoarthritis, and this article may render a theoretical basis for the study of new therapeutic methods for osteoarthritis. [ABSTRACT FROM AUTHOR]

Due to end-to-end optimization characteristics and fine generalization ability, convolutional neural networks have been widely applied to hyperspectral image (HSI) classification, playing an irreplaceable role. However, previous studies struggle with two major challenges: (1) HSI contains complex topographic features, the number of labeled samples in different categories is unbalanced, resulting in poor classification for categories with few labeled samples; (2) With the deepening of neural network models, it is difficult to extract more discriminative spectral-spatial features. To address the issues mentioned above, we propose a discriminative spectral-spatial-semantic feature network based on shuffle and frequency attention mechanisms for HSI classification. There are four main parts of our approach: spectral-spatial shuffle attention module (SSAM), context-aware high-level spectral-spatial feature extraction module (CHSFEM), spectral-spatial frequency attention module (SFAM), and cross-connected semantic feature extraction module (CSFEM). First, to fully excavate the category attribute information, SSAM based on a "Deconstruction-Reconstruction" structure is designed, solving the problem of poor classification performance caused by an unbalanced number of label samples. Considering that deep spectral-spatial features are difficult to extract, CHSFEM and SFAM are constructed. The former is based on the "Horizontal-Vertical" structure to capture context-aware high-level multiscale features. The latter introduces multiple frequency components to compress channels to obtain more multifarious features. Finally, towards suppressing noisy boundaries efficiently and capturing abundant semantic information, CSFEM is devised. Numerous experiments are implemented on four public datasets: the evaluation indexes of OA, AA and Kappa on four datasets all exceed 99%, demonstrating that our method can achieve satisfactory performance and is superior to other contrasting methods. [ABSTRACT FROM AUTHOR]

It is difficult to improve the seasonal prediction skill of winter temperature over North China, owing to the complex dynamics of East Asian winter and the relatively low prediction skill level of current climate models. Deep learning (DL) may be an informative and promising tool to enhance seasonal prediction, particularly in regions where the underlying mechanisms are not clear. Here, using a DL model based on the Convolutional Neural Network (CNN), we have found that the prediction skill for North China winter temperature (NCWT) can be extended up to five months by considering the remote impact of the Northeast Pacific sea-surface temperature (SST) on North China. Based on historical simulations of winter temperatures in North China, we selected six CMIP5 models with relatively small deviations for training the CNN, and the period chosen for training was 1852–1991. The ERA5 data during 1995–2017 were utilized to evaluate the performance of the CNN. Our CNN shows the best performance in a recent 10-year period (2008–2017), showing a significantly improved level of NCWT prediction skill with a correlation skill of 0.65 at a 5-month lead time, which is much better than the forecast skill of the state-of-the-art dynamic seasonal prediction system. Heat map analysis was used to explore the possible physical mechanisms associated with the NCWT anomaly from the perspective of the CNN; the results showed that the SST over the Northeast Pacific is highly relevant to NCWT prediction. The Northeast Pacific warming in the boreal summer is related to the development of the El Niño event in the coming winter, which may induce NCWT anomalies by atmospheric teleconnection. Climate model experiments support the role of Northeast Pacific warming in the boreal summer on NCWT. The improved capability for prediction from using the CNN may help to establish the energy policy for the coming winter and reduce the economic losses from extremely cold in North China. [ABSTRACT FROM AUTHOR]

Droughts and floods have proven to be threats to food security worldwide. This research used the standardized precipitation index (SPI) to examine the spatiotemporal characteristics of drought and wet events from 1961 to 2020 in the Yellow River basin (YRB). Grain yield data were combined to assess how drought and wet frequency have affected the agricultural system. The occurrence frequency of drought was greater than that of wetness in time, drought frequency decreased, and wetness increased. Spatially, the frequency of drought in all provinces except Shanxi was higher than that of wetness. The grain yield per unit area of the YRB was generally highest in Shandong province and lowest in Gansu province. The grain yield per unit area have shown a significant growth trend in the nine provinces of the YRB since 1961. Drought had a negative effect on the grain yield per unit area in each province, while wetness had a positive effect on the grain yield per unit area in all provinces except Shandong. In general, the influence of drought on grain yield per unit area decreased, while the influence of wetness on grain yield per unit area increased. The results indicate that human activities are effective against preventing and controlling drought and wet disasters and can provide a reference for other parts of the world. [ABSTRACT FROM AUTHOR]

A porous ball-flower-like Co3O4/Fe2O3 heterostructural photocatalyst was synthesized via a facile metal–organic-framework-templated method, and showed an excellent degradation performance in the model molecule rhodamine B under visible light irradiation. This enhanced photocatalytic activity can be attributed to abundant photo-generated holes and hydroxyl radicals, and the combined effects involving a porous structure, strong visible-light absorption, and improved interfacial charge separation. It is notable that the ecotoxicity of the treated reaction solution was also evaluated, confirming that an as-synthesized Co3O4/Fe2O3 catalyst could afford the sunlight-driven long-term recyclable degradation of dye-contaminated wastewater into non-toxic and colorless wastewater. [ABSTRACT FROM AUTHOR]

Featured Application: This work provides references for the selection and application of relevant solar utilization technologies on building roofs. Considering the vast areas of building rooftops and the fast development of solar utilization technologies, this paper aims to analyze the regional adaptability of solar roof utilization technologies for buildings in China. All provinces and cities in China are divided into 13 zones based on their economic development, thermal climate division, and availability of solar energy resources. Over 100 buildings are investigated, and the information of 28 buildings is analyzed to finally identify 18 typical building types. A new evaluation method is developed for both solar heating systems and solar PV. An adaptability index is developed considering the energy conservation, environment effect, and economy benefit of the systems. The developed method is used to evaluate the solar utilization technologies applied on the 18 buildings across 13 zones. The result show that the average adaptability index values for solar thermal technology and solar PV technology are 2.54 and 1.63, respectively. The solar heating system has a shorter payback time than the solar PV system for most regions of China and therefore is more favored. Recommendations on supporting policies and measures are given for policy makers with an aim to promote the utilization efficiency of building roofs. This paper provides references for the selection and application of relevant solar utilization technologies on building roofs. [ABSTRACT FROM AUTHOR]

In this study, the Cutaneotrichosporon dermatis strain M503 was isolated and could efficiently degrade tetracycline, doxycycline, and chlorotetracyline. The characteristics of tetracycline degradation were investigated under a broad range of cultural conditions. Response surface methodology (RSM) predicted that the highest degradation rate of tetracycline could be obtained under the following conditions: 39.69 °C, pH of 8.79, and inoculum dose of 4.0% (v/v, ~3.5 × 106 cells/mL in the medium). In accordance with the five identified degradation products of tetracycline, two putative degradation pathways, which included the shedding of methyl and amino groups, were proposed. Moreover, the well diffusion method showed that the strain of M503 decreases the antibacterial potency of tetracycline, doxycycline, and chlorotetracycline. These findings proposed a putative mechanism of tetracycline degradation by a fungus strain and contributed to the estimation of the fate of tetracycline in the aquatic environment. [ABSTRACT FROM AUTHOR]

Inserting porous media into the micro-scale combustor space could enhance heat recirculation from the flame zone, and could thus extend the flammability limits and improve flame stability. In the context of porous micro-combustors, the pore size is comparable to the combustor characteristic length. It is insufficient to treat the porous medium as a continuum with the volume-averaged model (VAM). Therefore, a pore-scale model (PSM) is developed to consider the detailed structure of the porous media to better understand the coupling among the gas mixture, the porous media and the combustor wall. The results are systematically compared to investigate the difference in combustion characteristics and flame stability limits. A quantified study is undertaken to examine heat recirculation, including preheating and heat loss, in the porous micro-combustor using the VAM and PSM, which are beneficial for understanding the modeled differences in temperature distribution. The numerical results indicate that PSM predicts a scattered flame zone in the pore areas and gives a larger flame stability range, a lower flame temperature and peak solid matrix temperature, a higher peak wall temperature and a larger Rp-hl than a VAM counterpart. A parametric study is subsequently carried out to examine the effects of solid matrix thermal conductivity (ks) on the PSM and VAM, and then the results are analyzed briefly. It is found that for the specific configurations of porous micro-combustor considered in the present study, the PSM porous micro-combustor is more suitable for simplifying to a VAM with a larger Φ and a smaller ks, and the methods can be applied to other configurations of porous micro-combustors. [ABSTRACT FROM AUTHOR]

Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make wrong predictions. Successful adversarial attacks on deep learning (DL)-based plant disease identification systems could result in a significant delay of treatments and huge economic losses. This paper is the first attempt to study adversarial attacks and detection on DL-based plant disease identification. Our results show that adversarial attacks with a small number of perturbations can dramatically degrade the performance of DNN models for plant disease identification. We also find that adversarial attacks can be effectively defended by using adversarial sample detection with an appropriate choice of features. Our work will serve as a basis for developing more robust DNN models for plant disease identification and guiding the defense against adversarial attacks. [ABSTRACT FROM AUTHOR]

Background: Overweight people have been revealed to have poor cognitive flexibility. Cognitive flexibility reflects proactive and reactive control abilities. However, the impairment had not been explicitly positioned at the cognitive stage. Therefore, this study provides increased support for impairment of cognitive flexibility due to overweight. Method: The study included 34 overweight and 35 normal-weight participants. They were required to complete the food and flower target AX-continuous performance test (AX–CPT), including the resting-state fMRI and cue-triggered food craving subscales. We compared the performance difference between the two tasks. Furthermore, we investigated whether the cue-triggered food cravings and the corresponding brain regions mediated the effect of overweight on the two control mechanisms. Result: Significant differences were found only in the food target AX-CPT task, where overweight participants performed worse. Cue-triggered food cravings mediated this relationship. Additionally, we found that the brain regions associated with cue-triggered food cravings (bilateral SFG) can completely mediate the relationship between BMI and the z-value of the fat mass index and sensitivity to proactive control. Conclusion: In the food target task, overweight participants performed worse in both control mechanisms. Moreover, we also revealed the potential mechanism by which being overweight might affect the two control mechanisms through cue-triggered food cravings. [ABSTRACT FROM AUTHOR]

Widespread overeating has been found during the 2019 coronavirus (COVID-19) pandemic. The present study investigated whether pre-pandemic restrained eating (RE) predicted overeating during the pandemic, and further explored the behavioral (mortality threat, negative affect) mechanisms underlying this association. An eight-month longitudinal survey was conducted with a large sample of 616 undergraduates from Southwest university. From September 2019 to April 2020, three measurements were conducted. RE was tested before the pandemic (T1), and data of mortality threat, negative affect, and overeating were collected at the middle (T2) and end of (T3) the COVID-19 crisis in China. The correlation results showed that baseline RE was positively associated with mortality threat, negative affect, and overeating at T2 and T3. Moreover, negative affect and mortality threat were positively correlated with overeating. Results from longitudinal mediation showed that baseline RE would positively predict T3 overeating through T2 negative affect, but not T2 mortality threat. This study supports and extends the counterregulatory eating hypothesis that RE positively predicts future overeating, especially through negative emotions. These findings further reveal the core psychological mechanism underlying this positive RE-overeating relation in the context of COVID-19, indicating that the individuals with higher RE could not cope with negative affect adequately, contributing to more overeating. [ABSTRACT FROM AUTHOR]

Simple Summary: In order to find the effects of host diet and phylogeny on maternal milk microbes and the contribution of the maternal milk microbiota to the neonatal gut microbiota, nine species of mammals of three type groups (herbivore, omnivore, and carnivore) were selected in this study. Our results showed that different types of animals and phylogeny factors may have driven the microbiota of mammalian maternal milk. Source-tracking analysis showed that the contributions of bacteria from maternal milk to the microbiota of neonates of different animals were different at day 3 after neonatal birth. Maternal milk, a main source of nutrition for neonates in early life, has attracted attention. An increasing number of studies have found that maternal milk has a high microbial diversity, as well as factors that might influence this diversity. However, there is a lack of knowledge regarding the effects of host diet and phylogeny on maternal milk microbes and the contribution of the maternal milk microbiota to the neonatal gut microbiota. Here, we analyzed the maternal milk and fecal microbiota of nine species (lion, dog, panda, human, mouse, rhesus macaque, cow, goat, and rabbit) of mammals of three type groups (herbivore, omnivore, and carnivore) using 16S rRNA amplicon sequencing. Our study provided evidence of host diet and phylogeny on the maternal milk microbiota. Moreover, functional prediction revealed that the carnivores had a significantly higher percentage of base excision repair, glycerolipid metabolism, taurine and hypotaurine metabolism, inorganic ion transport and metabolism, and nucleotide metabolism; while arginine and proline metabolism showed enrichment in the herbivore group. Source-tracking analysis showed that the contributions of bacteria from maternal milk to the microbiota of neonates of different mammals were different at day 3 after neonatal birth. Overall, our findings provided a theoretical basis for the maternal milk microbiota to affect neonatal fecal microbiota at day 3 after neonatal birth. [ABSTRACT FROM AUTHOR]

Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance. However, hyperspectral image classification still suffers from various challenges, including abundant redundant information, insufficient spectral-spatial representation, irregular class distribution, and so forth. To address these issues, we propose a novel 2D-3D CNN with spectral-spatial multi-scale feature fusion for hyperspectral image classification, which consists of two feature extraction streams, a feature fusion module as well as a classification scheme. First, we employ two diverse backbone modules for feature representation, that is, the spectral feature and the spatial feature extraction streams. The former utilizes a hierarchical feature extraction module to capture multi-scale spectral features, while the latter extracts multi-stage spatial features by introducing a multi-level fusion structure. With these network units, the category attribute information of HSI can be fully excavated. Then, to output more complete and robust information for classification, a multi-scale spectral-spatial-semantic feature fusion module is presented based on a Decomposition-Reconstruction structure. Last of all, we innovate a classification scheme to lift the classification accuracy. Experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Nanoscale hydroxyapatite (nHA) was synthesized to investigate its potential as a phosphorus (P) ensembled nanofertilizer, using soybean (Glycine max L.) as a model plant. The conventional analogue phosphate (pi) was used for comparison with the synthesized nHA. Varied precipitation intensities (0%, 30%, 60%, and 100%) were simulated by adding selected volumes of the P fertilizers (nHA or pi) via foliar spray and soil amendment. The total amounts of added P were the same across all the treatments. The importance of a wash-off effect was investigated on foliar-treated seedlings by evaluating different watering heights (20, 120, and 240 cm above the seedlings). Fresh weight, pigment content, macro-, and micronutrient contents were measured in soybean tissues across all the treatments after 4 weeks of greenhouse cultivation. The synthesized nHA showed superior effects on plant nutrient content upon high precipitation intensities. For example, at 100% precipitation intensity, there was 32.6% more P and 33.2% more Ca in shoots, 40.6% more P and 45.4% more Ca in roots, and 37.9% more P and 82.3% more Ca in pods, as compared to those with pi treatment, respectively. No impact on soybean biomass was evident upon the application of nHA or pi. Further investigation into customizing nHA to enhance its affinity with crop leaves and to extend retention time on the leaf surface is warranted given that the present study did not show significant positive impacts of nHA on soybean growth under the effects of precipitation. Taken together, our findings increase understanding of the potential application of nHA as a nano-enabled fertilizer in sustainable agriculture. [ABSTRACT FROM AUTHOR]

The simple lens computational imaging method provides an alternative way to achieve high-quality photography. It simplifies the design of the optical-front-end to a single-convex-lens and delivers the correction of optical aberration to a dedicated computational restoring algorithm. Traditional single-convex-lens image restoration is based on optimization theory, which has some shortcomings in efficiency and efficacy. In this paper, we propose a novel Recursive Residual Groups network under Generative Adversarial Network framework (RRG-GAN) to generate a clear image from the aberrations-degraded blurry image. The RRG-GAN network includes dual attention module, selective kernel network module, and residual resizing module to make it more suitable for the non-uniform deblurring task. To validate the evaluation algorithm, we collect sharp/aberration-degraded datasets by CODE V simulation. To test the practical application performance, we built a display-capture lab setup and reconstruct a manual registering dataset. Relevant experimental comparisons and actual tests verify the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness. [ABSTRACT FROM AUTHOR]

The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]