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Aiming at the issue of low accuracy in crop mapping and growth monitoring caused by imprecise calibration of radar time-series data, this paper proposed a Synthetic Aperture Radar (SAR) spatio-temporal error compensation method. By constructing a compensation reference image using the buildings with stable backscattering coefficients in the study area, the error correction of time-series SAR data was realized based on the compensation model. The compensated SAR data were used to establish the Sugarcane Growth Index (SGI) according to the growth curve of sugarcane. The sugarcane area was extracted after combining the compensated SGI and optical image, and the accuracy of F1 on sugarcane distribution extraction was 92.12%. There was an improvement of 4.11% compared with no compensation method. On this basis, the newly planted and ratoon sugarcane were further classified by the expectation maximization algorithm based on the compensated SAR image at the seedling stage. The newly planted sugarcane and the ratoon sugarcane could be accurately distinguished with area extraction accuracy of 82.06% and 80.59%, respectively. Moreover, the compensated SAR data of time series during the tillering to maturity stages were used to estimate the sugarcane height, yielding an R2 value of 0.717 using a quadratic polynomial model. The Spatio-temporal error compensation method for distributed targets proposed in this study can reduce the loss of SAR data across various times and regions, and achieve accurate results of sugarcane distribution extraction and plant height estimation.

Aimed at the problems of difficult drilling construction of blast penetration, undeveloped fractures produced by blast penetration, and the fractures is easy to recompact in the soft coal seam, the blasting operations carried out in the floor was proposed to achieve the purpose of increasing the penetration of soft coal seams and thus improve the effectiveness of gas extraction. In order to monitor the cross-interface stress wave propagation pattern and the damage state of the coal seam, a physical model was constructed in the laboratory and a similar simulation test of coal seam floor blasting was carried out. Concurrently, the numerical simulation research method was employed to supplement the evolution of damage and fractures within the coal and rock during the blasting process of the coal seam floor. The results shown that, the blast cracks expanded along the blast hole to the surrounding rock mass in soft coal seam floor, causing more serious damage at the location of blast hole, the coal-rock interface of the floor, and within the coal seam, producing cross-interfacial damage cracks. When the explosive stress wave propagated from the soft coal seam base rock layer to the soft coal seam, the interface between the rock and the soft coal generated transmitted compressive stress wave and reflected tensile stress wave. Transmission waves acted on the soft coal to increase the fractures in the coal seam. Meanwhile, the reflected waves reacted on the rock, forming cross-cracks in the coal-rock interface, making the fractures of the rock floor and the soft coal seam penetrated, which was conducive to the vertical transport flow of gas in the soft coal seam and the extraction of pressure-relief gas. Field application at Pan Yidong coal mine shown that, the pure volume of gas extraction and its concentration increased rapidly after blasting the coal seam floor to improve the penetration. The pure volume of gas extraction increased from 0.06 m3/min to 1.46 m3/min, an increase of 23.33 times. Gas concentration surged from 10.46% before blasting to approximately 45.5%, an increase of 3.34 times, and remained at a high level for a long time. The research results can provide theoretical basis and technical support for efficient gas extraction from deep soft coal seams.

ABSTRACTChina’s Earth Observation(EO) System has undergone significant development since the 1970s, as China has dedicated substantial efforts to advancing remote sensing technology. With fifty years of development, China has successfully narrowed the remote sensing technology gap with foreign countries through collaborative endeavors of the government and enterprises. At present, China has constructed a comprehensive EO system that has been proven indispensable for driving economic growth and facilitating sustainable development. This paper provides an overview of the development, missions, andapplications of China’s EO system, while also exploring future directions and technical trends of China’s EO system.

Abstract Background To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. Methods A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. Results Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. Conclusion The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.

Abstract Aim To investigate whether resilience mediates the connection between caregiver burden and hope among caregivers of patients with inflammatory bowel disease (IBD). Design A cross‐sectional study. Methods Two hundred patients with IBD were conveniently sampled from two tertiary hospitals in Jiangsu Province, China. The main instruments involved the Zarit Burden Interview, the 10‐item Connor‐Davidson resilience scale, and the Herth Hope Index. We conducted descriptive analysis and Pearson correlations using SPSS 25.0. The PROCESS v3.3 macro analysed the mediating effect. We report the results in line with the STROBE checklist. Results Caregiver burden was significantly negatively connected with hope and resilience, whereas resilience positively correlated with hope. The mediation role of resilience was significant in the relationship between caregiver burden and hope, with mediating effects accounting for 47.86% of the overall effect. Conclusions Resilience partially mediates the association between caregiver burden and hope among patients with IBD. This finding highlights the protective role of resilience in undermining caregiver burden and strengthening hope. Implications for Practice In clinical practice, healthcare providers should perform routine psychological assessments for caregivers of patients with IBD. Furthermore, resilience training should be incorporated into interventions to alleviate caregiver burden and enhance hope. Patient or Public Contribution None.

IntroductionThe objective of this study was to evaluate the effects of dietary supplementation of postbiotics on growth performance, mortality rate, immunity, small intestinal health, tibia characteristics, and hematological parameters of broiler chicks. he postbiotics were derived from Bacillus subtilis ACCC 11025.MethodsA total of 480 day-old Arbor acre broiler chicks (52.83 ± 1.38 g) were used in a 42-day study and were randomly allocated into four groups. Each group comprised 6 replicate cages, each containing 20 birds. Dietary treatments were based on a basal diet, supplemented with postbiotics at concentrations of 0.000%, 0.015%, 0.030%, or 0.045%.Results and discussionThe results demonstrated an improvement in growth performance, antibody titers against avian influenza virus and Newcastle disease virus, serum albumin levels, and serum total protein levels, as well as a reduction in mortality rate among broiler chicks with increasing levels of postbiotic supplementation. The most significant effect were observed in the group receiving 0.015% postbiotics. Furthermore, a dose-dependent enhancement in tibia weight and tibia weight to length ratio, coupled with a reduction in the robusticity index, was noted. The most favorable outcomes for tibia health were observed in the group receiving 0.030% postbiotics. This improvement in tibia health corresponded to a linear increase in serum calcium and inorganic phosphorus contents. In summary, supplementing broiler chicks with 0.015% postbiotics effectively enhances immunity, leading to improved growth performance and reduced mortality rates. Additionally, a postbiotic dose of 0.030% is suitable for optimizing tibia health.

Copper peptides (GHK-Cu) are a powerful hair growth promoter with minimal side effects when compared with minoxidil and finasteride; however, challenges in delivering GHK-Cu topically limits their non-invasive applications. Using theoretical calculations and pseudo-ternary phase diagrams, we designed and constructed a thermodynamically stable ionic liquid (IL)-based microemulsion (IL-M), which integrates the high drug solubility of ILs and high skin permeability of microemulsions, thus improving the local delivery of copper peptides by approximately three-fold while retaining their biological function. Experiments in mice validated the effectiveness of our proposed IL-M system. Furthermore, the exact effects of the IL-M system on the expression of growth factors, such as vascular endothelial growth factor, were revealed, and it was found that microemulsion increased the activation of the Wnt/β-catenin signaling pathway, which includes factors involved in hair growth regulation. Overall, the safe and non-invasive IL microemulsion system developed in this study has great potential for the clinical treatment of hair loss.

Wide field of view (WFV) push-broom optical satellites can acquire ground images over hundreds to thousands of kilometers in a single pass, thanks to their large field of view (FOV) cameras consisting of tens or even hundreds of thousands of detectors, which also makes their relative radiometric correction (RRC) difficult. Existing side-slither-based RRC approaches overlook the distinct structural design of WFV push-broom optical satellite cameras, thus failing to rectify nonlinear distortions in side-slither images stemming from the nonflat arrangement of camera detectors. Furthermore, these methods necessitate the corresponding pixels (pixels of the same ground object) covering the entire FOV in side-slither images. However, the brief duration of side-slither imaging makes it unfeasible to cover the entire FOV with calibration data. To address these issues, we propose a novel RRC approach for WFV push-broom optical satellites, achieved based on a thorough analysis of the unique structural traits of WFV push-broom optical cameras, enabling precise standardization of the nonlinear distortions in side-slither data. Additionally, a local-to-global side-slither calibration strategy is proposed to obviate the requirement for corresponding pixels to cover the entire FOV in calibration data. Experiments using the Haiyang-1D Coastal Zone Imager satellite indicate that our method effectively rectified nonlinear distortions in side-slither data, and evident RRC accuracy improvement with that of the existing methods can be obtained.

Luojia 3-01 is the world's first intelligent remote sensing satellite, equipped with various imaging modes including video, frame-pushing, and scan-pushing. It boasts submeter level multimode optical imaging capabilities, on-orbit intelligent processing, and real-time intra-satellite and satellite-to-ground data transmission. Throughout the satellite's design and development, we have established the real-time intelligent information service architecture for Luojia 3-01, marking a new paradigm in on-orbit processing and real-time services for intelligent remote sensing satellites. We have proposed the on-orbit real-time processing architecture of Luojia 3-01, addressing the challenges of limited computational and storage resources, particularly in on-orbit processing of vast volumes of remote sensing data, including core algorithmic bottlenecks in “correction-extraction-compression.” A novel intelligent remote sensing satellite system has been developed, featuring multimode imaging, an open platform, intelligent processing, and satellite-to-ground interconnectivity, which significantly reduces the response time of remote sensing services to less than 8 min, thereby shortening the cycle from data acquisition to intelligent information service. This innovation spearheads a technological leap in remote sensing satellite services from data to information, from post-event to real-time, and from professional to widespread public applications, laying a solid foundation for the popularization and commercialization of China's intelligent remote sensing satellites.

The health of the Dayang River ecosystem, critical freshwater resource for Dandong City, is intertwined with local water management and coastal development. In June 2023, we conducted a biodiversity survey in the Dayang River basin using a combined morphological and environmental DNA (eDNA) approach. We assessed both species and functional diversity of summer macroinvertebrates. A total of 81 macroinvertebrates species were identified, with some overlap in species detection between the methods. The trunk stream exhibited lower species diversity with uneven distribution across sites, whereas the tributaries displayed a more stable species composition. Functional diversity analysis mirrored this pattern, revealing lower functional diversity in the trunk stream, indicative of intense competition and niche occupation. Conversely, the tributaries displayed more uniform and complete functional diversity. This study highlights the differential influence of hydrological environments on macroinvertebrate communities during the summer season, with the trunk stream favoring certain species with specific habitat preferences and adaptations. Furthermore, it underscores the value of combining traditional morphological surveys and eDNA techniques for comprehensive biodiversity assessments.

AbstractSurveys were conducted on sociodemographic characteristics, family, home- and school-related factors, and sugar-sweetened beverage (SSB) intake to identify factors associated with SSB consumption in children. In total, 2700 valid questionnaires were returned. Mean age was 10.4 ± 2.23 years and 49.3% were male. Multivariate logistic analysis showed that the frequency of SSB dinking by fathers and peers, the frequency of SSB purchasing at home and school, and the duration of daily TV and video watching were significantly independent factors affecting SSB intake, suggesting that interventions targeting children’s SSB intake should incorporate government policies, educational programs and parental engagement.

This study aimed to explore whether Lactococcus G423 could improve growth performance and lipid metabolism of broilers by the modulation of gut microbiota and metabolites. A total of 640 1-day-old AA broilers were randomly divided into 4 groups [Control (CON), Lac_L, Lac_H, and ABX]. Average daily gain (ADG), average daily feed intake (ADFI), feed conversion ratio (FCR), breast muscle, thigh muscle, and abdominal fat pad were removed and weighed at 42 days of age. Serum was obtained by centrifuging blood sample from jugular vein (10 mL) for determining high-density lipoprotein (HDL), total cholesterol (TC), low-density lipoprotein (LDL), and triglyceride (TG) using ELISA. The ileal contents were harvested and immediately frozen in liquid nitrogen for 16S rRNA and LC–MS analyses. Then, the results of 16S rRNA analysis were confirmed by quantitative polymerase chain reaction (qPCR). Compared with the CON group, FCR significantly decreased in the Lac_H group (p

Abstract Background Dysfunctional attitudes, which are characterized by distorted self-cognitions, were considered to be linked to personality traits. It was found that certain personality traits may predict dysfunctional attitudes in patients with major depressive disorder (MDD). Nonetheless, the relationship between personality traits and dysfunctional attitudes remains under-researched. Aims The aim of this study is to examine the relationship between specific domains of Sixteen Personality Factor (16PF) and dysfunctional attitudes in Chinese participants with or without MDD. In addition, the present study explores the associations between 16PF and eight subtypes of dysfunctional attitudes, based on the proposed eight-factor structure of the Chinese version of the Dysfunctional Attitude Scale-Form A (C-DAS-A). Methods One hundred and sixty-eight participants with MDD and 130 healthy participants were included in the study (Trial Registration Number: ChiCTR1800014591). Personality was assessed using the 16PF Questionnaire. Dysfunctional attitudes were measured through the C-DAS-A. Results The 16PF dimensions associated with dysfunctional attitudes and the eight subtypes were mainly concentrated in the four anxiety facets including factors C, L, O, and Q4, in both MDD and HC groups. There were significant differences in the 16 PF dimensions that would explain dysfunctional attitudes between the two groups, which were as follows: factors C, G, and O in the MDD group, and factors L and Q4 in the HC group. Conclusions Personality traits, especially the anxiety-related personality traits, were distinctly associated with the development of dysfunctional attitudes in people with or without MDD.

Objective The Xinmin contact-metasomatism iron deposit is one of the most representative achievements of resource surveys in recent years in the Taershan-Erfengshan area, southern Shanxi Province.Determining the metal source and Fe mineralization process of this deposit is of great significance for regional exploration in this area. Methods In this study, Fe isotopic compositions of the altered diorite, hosting limestone(dolomite) and different types of magnetite samples have been obtained. Results The results show that the altered diorite have δ56Fe values(0.23‰) significantly heavier than the average value of the upper crust (0.07‰), which may be related to fluid migration during skarn/albitization, as the light iron isotope is preferably leached from the intrusions. The Ordovician limestone/dolomite samples have δ56Fe values (0 to 0.19‰) obviously different from those of magnetite (-0.56‰ to 0.07‰), indicating that those sedimentary strata cannot be the major ore-metal source. Considering that magnetite samples have obviously light Fe isotopic ratios which also exhibit an obviously increasing trend from centre to margin of a specific ore body, we propose that the ore-forming fluid may have once interacted with the intrusions during fluid migration, followed by precipitation in the limestone stratum. This interpretation is also supported by the remarkable negative correlation between δ56Fe and Fe content. Conclusion Our study is consistent with previous studies on Fe isotopes of skarn iron deposits, which provides important evidence for the origin of contact metasomatism. Furthermore, this study may also indicate that altered diorite with heavy δ56Fe ratios in the Taershan-Erfengshan area may be potential targets for iron prospect.

The gut microbiota is a complex and dynamic ecosystem known as the ‘second brain’. Composing the microbiota-gut-brain axis, the gut microbiota and its metabolites regulate the central nervous system through neural, endocrine and immune pathways to ensure the normal functioning of the organism, tuning individuals’ health and disease status. Short-chain fatty acids (SCFAs), the main bioactive metabolites of the gut microbiota, are involved in several neuropsychiatric disorders, including depression. SCFAs have essential effects on each component of the microbiota-gut-brain axis in depression. In the present review, the roles of major SCFAs (acetate, propionate and butyrate) in the pathophysiology of depression are summarised with respect to chronic cerebral hypoperfusion, neuroinflammation, host epigenome and neuroendocrine alterations. Concluding remarks on the biological mechanisms related to gut microbiota will hopefully address the clinical value of microbiota-related treatments for depression.

This paper examines the dynamic behavior of a particular category of discrete predator-prey system that feature both fear effect and refuge, using both analytical and numerical methods. The critical coefficients and properties of bifurcating periodic solutions for Flip and Hopf bifurcations are computed using the center manifold theorem and bifurcation theory. Additionally, numerical simulations are employed to illustrate the bifurcation phenomenon and chaos characteristics. The results demonstrate that period-doubling and Hopf bifurcations are two typical routes to generate chaos, as evidenced by the calculation of the maximum Lyapunov exponents near the critical bifurcation points. Finally, a feedback control method is suggested, utilizing feedback of system states and perturbation of feedback parameters, to efficiently manage the bifurcations and chaotic attractors of the discrete predator-prey model.

Background: Acral melanoma (AM), an aggressive subtype of melanoma with poor prognosis, has been increasingly studied. The present study aims to discuss the current status, hotspots and future directions of AM studies through visualized analysis with bibliometrics and knowledge graph. Method: Publications related to acral melanoma from January 1999 to May 2023 were searched and retrieved from the Web of Science. Data extraction and visualization of the top 10 publications by year of publication, journal, country and core author were performed using R Studio (Version 4.3.0) and Scimago Graphica (Version 1.0.34). Co-reference graphs regarding country/region, organization, author, and keywords, as well as reference collaborative network, co-occurrence network, and references were plotted using VOSviewer (Version 1.6.19) and CiteSpace (Version 6.2.R3). Results: A total of 1387 articles related to AM published in English from 1999 to 2023 were included in the present study. A total of 7499 authors were from 2092 organizations in 50 countries. The articles were published in 356 journals, involving 4131 keywords and 28,200 references. The 1387 articles related to AM had been cited a total of 10,014 times by the time of this study. The result showed that Journal of the American Academy of Dermatology had the largest number of citations and citation rate, with a total of 60 publications having been cited 2191 times. Having the top three productivity institutions in the world, the US is the most productive country in this field, with a total of 361 publications. The authors with the highest number of publications were Guo Jun (n = 43) and Si Lu (n = 38) from Peking University. The keyword burstiness test found that “ipilimumab”, “open label”, “efficacy” and “nivolumab” appeared most frequently in recent years. The co-cited reference timeline graph showed that the clustering of “advanced melanoma” and “melanocytic lesion” has been a hotspot since 2016. Conclusions: The number of AM-related studies has been increasing. The clinical characteristics and immunotherapy of AM are still key research directions, with the US playing a leading role in this field. This bibliometric analysis found up to 1387 publications, which not only comprehensively and quantitatively reflected the research trends and hotspots, but also provided a theoretical basis for future studies of AM. Researchers can benefit from choosing the right journals and finding potential collaborators or partner institutions.

Abstract Background Previous studies suggested that childhood maltreatment is associated with poor health outcomes. While not everyone who experiences abuse as a child goes on to experience poor mental health, some traumatized people are grown to be more resilient than others. Few studies have examined the association between childhood maltreatment and adult resilience. This study aimed to determine different relationships between specific types and features of childhood maltreatment with adult resilience among Chinese with Major Depressive Disorder (MDD) and healthy controls (HCs). Methods A total of 101 patients with MDD and 116 participants in the healthy control (HC) group from Zhumadian Psychiatric Hospital and its nearby communities were included in this analysis. Childhood maltreatment was assessed retrospectively using Childhood Trauma Questionnaire (CTQ). Adults’ resilience was assessed by the Connor-Davidson Resilience Scale (CD-RISC). Generalized linear models were applied between childhood maltreatment (specific types and features) and resilience adjusting for covariates. Results The total score of CD-RISC and factor scores of strength, optimism, and tenacity in the HC group were higher than those in the MDD group. CTQ total score had a negative association with optimism score among participants in MDD (β=-0.087, P

The evaluation of coronavirus disease 2019 (COVID-19) vaccine immunogenicity remains essential as the severe acute respiratory syncytial virus 2 (SARS-CoV-2) pandemic continues to evolve and as additional variants emerge. Neutralizing antibodies are a known correlate of protection for SARS-CoV-2 vaccines. A pseudovirus neutralization (PNT) assay was developed and validated at Novavax Clinical Immunology Laboratories to allow for the detection of neutralizing antibodies in vaccine clinical trial sera. The PNT assay was precise, accurate, linear, and specific in measuring SARS-CoV-2 neutralization titers in human serum for ancestral strain and the Omicron subvariants BA.5 and XBB.1.5, with an overall geometric coefficient of variation of ≤43.4%, a percent relative bias within the expected range of −60% to 150%, and a linearity value of R2 > 0.98 for all three strains. This pseudovirus assay will be useful for the analysis of vaccine clinical trial samples to assess vaccine immunogenicity. Future work will focus on modifying the assay for emerging variants, including XBB.1.16, EG.5.1, BA.2.86, and any other variants that emerge in the ongoing pandemic.

In this paper, we delve into the dynamics of two and three coupled SRRs models, exploring their nonlinear properties such as stability, periodicity, or chaos. Additionally, we examine the energy function Hamilton within the context of these models. Numerical examples are provided to illustrate the obtained results and demonstrate the applicability of our findings.

Ethanamizuril (EZL) is a new anticoccidial drug developed by our Shanghai Veterinary Research Institute. Since EZL is almost insoluble in water, we conducted a study to improve the solubility of EZL by forming inclusion complexes with β-cyclodextrin (β-CD) and hydroxypropyl-β-cyclodextrin (HP-β-CD). In this study, we performed molecular docking and then systematically compared the interactions of EZL with β-CD and HP-β-CD in both aqueous solution and the solid state, aiming to elucidate the solubilization effect and mechanism of cyclodextrins (CDs). The interactions were also examined in the solid state using DSC, PXRD, and FT-IR. The interactions of EZL with CDs in an aqueous solution were investigated using PSA, UV-vis spectroscopy, MS, 1H NMR, and 2D ROESY. The results of phase solubility experiments revealed that both β-CD and HP-β-CD formed inclusion complexes with EZL in a 1:1 molar ratio. Among them, HP-β-CD exhibited higher Kf (stability constant) and CE (complexation efficiency) values as well as a stronger solubilization effect. Furthermore, the two cyclodextrins were found to interact with EZL in a similar manner. The results of our FT-IR and 2D ROESY experiments are in agreement with the theoretical results derived from molecular simulations. These results indicated that intermolecular hydrogen bonds existing between the C=O group on the triazine ring of EZL and the O-H group of CDs, as well as the hydrophobic interactions between the hydrogen on the benzene ring of EZL and the hydrogen of CDs, played crucial roles in the formation of EZL/CD inclusion complexes. The results of this study can lay the foundation for the future development of high-concentration drinking water delivery formulations for EZL.

ABSTRACTCloud coverage has become a significant factor affecting the availability of remote-sensing images in many applications. To mitigate the adverse impact of cloud coverage and recover ground information obscured by clouds, this paper presents a curvature-driven cloud removal method. Considering that each image can be regarded as a curved surface and the curvature can reflect the texture information well due to its dependence on the surface’s undulation degree, the presented method transforms image from natural domain to curvature domain for information reconstruction to maintain details of reference image. In order to improve the overall consistency and continuity of cloud removal results, the optimal boundary for cloud coverage area replacement is determined first to make the boundary pass through pixels with minimum curvature difference. Then, the curvature of missing area is reconstructed based on the curvature of reference image, and the reconstructed curvature is inversely transformed to natural domain to obtain a cloud-free image. In addition, considering the possible significant radiometric differences between different images, the initial cloud-free result will be further refined based on specific checkpoints to improve the local accuracy. To evaluate the performance of the proposed method, both simulated experiments and real data experiments are carried out. Experimental results show that the proposed method can achieve satisfactory results in terms of radiometric accuracy and consistency.

Objective: Kudzu root, fern root, lily, and yam bean are characteristic plant resources in Wuling Mountain Area, the gelatinization and instant properties of these four starches was analyzed in this study. Methods: SEM, RVA, and DSC were used to investigate the gelatinization properties of the starches, and the transparency and apparent fluidity of starch pastes was used to analysis the instant properties. Results: The granule of kudzu root, fern root, and yam bean starches showed similar morphology, most of which are spherical, but lily starch exhibited spindle-shaped morphology. The RVA results showed that the peak viscosity of lily starch was the highest [(7.00±0.03) Pa·s] among these four starches. The setback value of kudzu root starch and lily starch was more than fern root starch and yam bean starch, which indicating that kudzu root starch and lily starch was easy to retrograde. The DSC results showed that the initial gelatinization temperature of fern root starch was 59.4 ℃, which was the lowest among these starches, indicating that it was easy to gelatinize. Lily starch has the highest gelatinization enthalpy, which is 15.64 J/g, indicating that more heat was required for gelatinization. After gelatinization, the kudzu root starch paste has the best transparency with a light transmittance of 29.11%, while lily starch paste has a light transmittance of only 12.25%. Kudzu root starch paste and lily starch paste was easy to form gels, but the fluidity was poor. Conclusion: Lily starch and kudzu root starch have similar instant properties and have potential for the development of instant foods, fern root starch and cold potato starch showed a lower retrogradation value, and their paste showed better fluidity, which can be used to inhibit the retrogradation of food.

ABSTRACTBlock Adjustment (BA) is a critical procedure in the geometric processing of satellite images, responsible for compensating and correcting the geometric positioning errors of the images. The accuracy of the photogrammetric products, including Digital Orthophoto Map (DOM), Digital Elevation Model (DEM), Digital Line Graphic (DLG), and Digital Raster Graphic (DRG), directly depends on the accuracy of BA results. In recent years, the rapid development of related technologies such as Artificial Intelligence (AI), Computer Vision (CV), Unmanned Aerial Vehicles (UAVs) and big data has greatly facilitated and transformed the classical BA in photogrammetry. This paper first reviews the current status of BA and then looks into the future. First, this paper provides a brief review of the key technologies involved in BA, including image matching, the establishment of adjustment model, the determination of the parameters and the detection of gross error. Then, taking the intercross and fusion of current technologies such as AI, cloud computing and big data with photogrammetry into account, this paper explores the future trends of photogrammetry. Finally, four typical cases of large-scale adjustment are introduced, including large-scale BA without Ground Control Points (GCPs) for optical stereo satellite images, large-scale BA with laser altimetry data for optical stereo satellite images, large-scale BA for UAV oblique photogrammetry, and large-scale BA for indoor photogrammetry in caves with a large number of close-range images.

ABSTRACTBlock Adjustment (BA) is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery. For block adjustment with few ground-control points or without ground control, the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products. The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods, high positioning precision, and low acquisition cost, providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA. This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery (OSSI) and spaceborne laser altimeter data. Firstly, we elaborate the principle and necessity of this work through a literature review of existing methods. Then, the framework of our geo-positioning models. Secondly, four key technologies of the proposed model are expounded in order, including the acquisition and management of global Laser Control Points, the association of LCPs and OSSI, the block adjustment model combining LCPs with OSSI, and the accuracy estimation and quality control of the combined BA. Next, the combined BA experiment using Ziyuan-3 (ZY-3) OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province, China. Experimental results prove that our method can automatically select LCPs with high accuracy. The elevation deviation of the combined BA eventually achieved the Mean Error (ME) of 0.06 m and the Root Mean Square Error (RMSE) of 1.18 m, much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment. A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints.

Stereo vision has attracted much interest in computer vision and remote sensing. Large-scale terrain extraction from optical satellites has achieved various applications due to the continuous development of optical satellite technology in the last forty years. This study analyzes the literature sources published in the last decade on stereo vision, optical flow, and remote sensing and finds that applications of the optical flow method in very high resolution (VHR) optical satellite stereo matching are inadequate. Therefore, it proposes a comprehensive optical flow-based terrain extraction framework from optical satellite stereo images. Experiments are conducted using stereo images from four different optical satellites, and the results are compared to the pertinent algorithms of ENVI, ERDAS, and ASP software. The qualitative and quantitative analysis demonstrates that the proposed method has significantly superior matching effect and robustness with a higher accuracy level compared with other approaches, especially for mountainous areas. In addition, the root mean square errors (RMSEs) of elevation on four datasets are 1.228 m, 2.159 m, 3.150 m, and 5.763 m, indicating the feasibility of the proposed method for terrain extraction from optical satellite stereo images.

The Eurasian otter Lutra lutra is a generalist carnivore that is widely distributed in many aquatic ecosystems. Based on its inherent attributes of opportunistic foraging behaviour and broad dietary range, it is naturally considered a potential sampler of the diversity of aquatic vertebrates. To test the ability and efficiency of otters as a diversity sampler, we used DNA metabarcoding to investigate the composition in vertebrates of the diet of otters that inhabit a forest stream area in northeast China. Twenty vertebrate prey taxa were detected in 98 otter spraints. Otter diet mainly comprised aquatic fishes (59.4%) and amphibians (39.0%). We also used traditional approaches to investigate fish communities at 60 sampling sites in the same area to determine the relationship between fish population composition in the environment and otter diet. The comparison revealed that 28 species of fish were distributed in this area, of which five are simultaneously detected in otter spraints. This indicates that molecular analysis of the diet of otters is not an ideal approach for investigating fish diversity, at least when using the 12SV5 primer pair. Based on a review of the available molecular research on otter diet, we conclude that the low species resolution may be due to the presence of many closely-related prey species in native habitats and lack of suitable barcodes. Considering the remarkable power of diet metabarcoding analysis in capturing elusive and rare species, it represents an approach that can compensate for the defects associated with fishing methods and we suggest that it can be used as an auxiliary means of measuring traditional fish diversity.

This article proposes a new satellite-based framework for global-scale remote sensing that is integrated with on-orbit cloud computing and artificial intelligence (AI) services. These spaced-based services cover the entire earth surfaces using massive low earth orbit (LEO) satellite constellation. Global-scale sensing of earth resources must be supported by massive number of LEO satellites equipped with cloud/AI computing services in real time. New satellite computer architectural features are presented along with some satellite constellation deployment topologies. We design satellite-based computers to support on-orbit remote sensing and AI scene analysis. This demands real-time performance without transmitting the sensed data back to earth for delayed processing. Notable space data services include on-orbit data sensing of large areas, machine learning from earth resources data, earth scene/event analysis, geomorphology observation, smart city management, disaster relief, global healthcare Internet of Things, environmental ecology protection, etc. We attempt to achieve high-efficiency earth resources utilization along with green energy, low cost, and robustness in real-life services.

Abstract The study of the vibration phenomenon of pebbles under turbulence is still a gap despite recent technological advancements in measurement capabilities. In this study, the vibration process of a fully exposed, isolated smart pebble on a rough bed was measured using a miniature inertial accelerometer and combined with simultaneous local measurements of the near-bed velocities. The paper conducts a series of experimental studies with different flow conditions. The test data match well with the manually observed phenomena, indicating the authenticity of the sediment vibration data collected by the measurement system. The test results show that the pebble motion (before entrainment) subjected to turbulence is a nonlinear vibration process, and its vibration types include in-situ vibration and ex-situ strong vibration. The probability distribution for the amplitude of vibration acceleration is well parameterized by the normal distribution. The vibration intensity tends to increase before approaching the threshold, but it weakens when approaching the point. The sediment vibration frequency is within 20–25 Hz, similar to the flow pulsation frequency and belongs to low-frequency vibration. The data indicate that the near-bed flow velocity is most directly related to the particle vibration events.

Neutralizing antibody responses from COVID-19 vaccines are pivotal in conferring protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Effective COVID-19 vaccines and assays measuring neutralizing antibodies against emerging variants (i.e., XBB.1.5, XBB.1.16, and XBB.2.3) are needed. The use of biosafety level (BSL)-3 laboratories for live virus assays results in higher costs and a longer turnaround time; therefore, a BSL-2–based pseudovirus neutralization assay (PNT) was developed. The pseudoviruses were produced by cotransfecting cells with plasmids encoding a lentiviral backbone-expressing luciferase reporter; non-surface proteins for lentiviral production; and ancestral or Omicron (BA.1 and BA.5) SARS-CoV-2 spike (S) proteins. The PNT was developed and optimized in dose and kinetics experiments. The representative serum samples (COVID-19–convalescent or NVX-CoV2373–vaccinated participants enrolled in the 2019nCoV-101 trial) demonstrated a wide dynamic range. The neutralization data showed robust correlation with validated anti-recombinant spike IgG levels and angiotensin-converting enzyme 2 inhibition titers (ancestral). This assay is suitable for measurement of the neutralization ability in clinical samples from individuals infected with SARS-CoV-2 or immunized with a COVID-19 vaccine. The results suggest that this PNT provides a lower cost, high-throughput, rapid turnaround alternative to BSL-3–based microneutralization assays and enables the discovery and development of effective vaccines against emerging variants.

As the number and variety of remote sensing satellites continue to grow, user demands are becoming increasingly complex and diverse. Concurrently, there is an escalating requirement for timeliness in satellite observations, thereby augmenting the complexity of task processing and resource allocation. In response to these challenges, this paper proposes an innovative method for dynamic task planning in multi-source remote sensing satellite cooperative observations tailored to complex scenarios. In the task processing phase, this study develops a preprocessing model suitable for various types of targets, enabling the decomposition of complex scenes into multiple point targets for independent satellite observation, thereby reducing the complexity of the problem. In the resource allocation phase, a dynamic task planning algorithm for multi-satellite cooperative observation is designed to achieve dynamic and optimized scheduling of the processed point targets, catering to the needs of multi-source remote sensing satellites. Empirical validation demonstrated that this method effectively implements dynamic adjustment plans for point targets, comprehensively optimizing the number of observation targets, computation time, task priority, and satellite resource utilization, significantly enhancing the dynamic observation efficiency of remote sensing satellites.

In this paper, the damage monitoring investigation based on the remote bonding fiber Bragg grating sensing is performed on the aerospace aluminum alloy thin-walled structure with prefabricated damage. Firstly, an ultrasonic excitation-fiber Bragg gratings (UE-FBGs) sensing experimental platform is established for the simulation of defects monitoring, in which the sensors are placed at a certain distance from the bonding area. Secondly, different arrangements of exciters and receivers are utilized for the original signals and the damage signals. Subsequently, the raw signals are processed by filter and feature extraction in order to denoise the signals and acquire the parameters sensitive to the damage. Finally, an improved Reconstruction for Image Defects (RAPID) algorithm is used to locate and reconstruct the pre-existing damage. The results show that the proposed system improves the sensitivity of the FBG receiver signal and the accuracy of the damage imaging.

Representation learning is one of the core problems in machine learning research. The transition of input representations for machine learning algorithms from handcraft features, which dominated in the past, to the potential representations learned through deep neural networks nowadays has led to tremendous improvements in algorithm performance. However, the current representations are usually highly entangled, i.e., all information components of the input data are encoded into the same feature space, thus affecting each other and making it difficult to distinguish. Disentangled representation learning aims to learn a low-dimensional interpretable abstract representation that can identify and isolate different potential variables hidden in the high-dimensional observations. Disentangled representation learning can capture information about a single change factor and control it by the corresponding potential subspace, providing a robust representation for complex changes in the data. In this paper, we first introduce and analyze the current status of research on disentangled representation and its causal mechanisms and summarize three crucial properties of disentangled representation. Then, disentangled representation learning algorithms are classified into four categories and outlined in terms of both mathematical description and applicability. Subsequently, the loss functions and objective evaluation metrics commonly used in existing work on disentangled representation are classified. Finally, the paper summarizes representative applications of disentangled representation learning in the field of remote sensing and discusses its future development.

Abstract Background Avian coccidiosis is an important parasitic disease that has serious adverse effects on the global poultry industry. The extensive use of anticoccidial drugs has resulted in an increase in drug resistance. Ethanamizuril (EZL) is a novel triazine with high anticoccidial activity. Methods We compared oocyst production and sporulation between EZL-sensitive (S) and EZL-resistant Eimeria tenella strains (R10 and R200) and used label-free quantitative proteomics to identify differentially expressed proteins (DEPs) between these strains. Results We generated two EZL-resistant E. tenella strains: strain R10, which was induced using a constant dose of 10 mg EZL/kg poultry feed, and strain R200, which was generated by gradually increasing the EZL dosage to 200 mg EZL/kg poultry feed. With an increase in resistance, the total oocyst output decreased, but the percentage of sporulation did not change significantly. We identified a total of 7511 peptides and 1282 proteins, and found 152 DEPs in the R10 strain versus the S strain, 426 DEPs in the R200 strain versus the S strain and 494 DEPs in the R200 strain versus the R10 strain. When compared with the S strain, 86 DEPs were found to have consistent trends in both resistant strains. The DEPs were primarily involved in ATP and GTP binding, invasion, and membrane components. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of the DEPs suggested that they are involved in transcription and translation processes. Protein–protein interaction network analysis of the 86 DEPs showed that 10 proteins were hubs in the functional interaction network (≥ 8 edges) and five of them were ribosomal proteins. Conclusions The results of the present study indicate that the resistance mechanisms of E. tenella against EZL might be related to the transcriptional and translational processes, especially in the factors that inhibit the growth of parasites. The DEPs found in this study provide new insights into the resistance mechanisms of E. tenella against EZL. Further research on these potential targets holds promise for new chemotherapeutic approaches for controlling E. tenella infections. Graphical Abstract

The GaoFen7 (GF7) optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic mapping. To improve the accuracy of attitude determination, an attitude determination system comprised of four star sensors is loaded. According to the measurement accuracy and steady performance, the star sensors 1a and 1b is usually used together for satellite attitude calculation, which is called the conventional mode of attitude determination. Then, the combination of star sensors 2a and 2b is called the unconventional mode of attitude determination. Affected by variations in the incident angle of sunlight and solar radiation, thermal deformation occurs in the body and installation structure of the star sensor, which causes Attitude Low-Frequency Error (ALFE) and seriously influences the consistency of attitude determination results of different combination modes for multiple star sensors system. This study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination modes. Based on the statistical characteristics of the angles of the three axes, the installation parameters of the four star sensors are first calibrated. After analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days, the segmented ALFE compensation model between the unconventional and conventional modes is proposed based on the Fourier series model and input parameter of latitude. Based on the on-orbit installation parameters and the ALFE model, the precise attitude determination results of the unconventional mode are calculated. Experimental results show that the attitude determination consistency after compensation is better than 2”. Moreover, the reliable application time range of the compensation model is 30 days to satisfy the requirements for high-precision attitude determination of GF7 satellite.

ObjectiveThe study aimed to analyze the current status and reasons for the neglect of the psychological need of patients with common skin diseases.MethodsThis cross-sectional study was conducted in China using an online self-assessment questionnaire distributed via social media. Demographic, clinical and psychological data were collected, and the main outcomes, i.e., depression (evaluated using the 9-item Patient Health Questionnaire, PHQ-9) and anxiety (evaluated using Generalized Anxiety Disorder-7, GAD-7). Multivariate regression analysis was used for the prediction of variates of mental health service seeking behaviors.ResultsA total of 1,010 patients participated in the survey, and 273 (27.0%) patients met the “with need” criteria, i.e., having the need for mental health intervention but not being treated. In the multivariate regression model, income (OR = 0.80, 95%CI: 0.65–0.99), duration of disease (OR = 0.68, 95%CI: 0.49–0.95) and suicide ideation (OR = 2.10, 95%CI: 1.14–3.87) were significant factors. For patients who did not receive mental health care, the lack of knowledge about the availability of mental health services, lack of knowledge of where to seek help, concerns about the side effects of treatment, failure to seek treatment for severe skin diseases, and absence of current psychological distress were associated with their need for psychological intervention.ConclusionThis study examined the current status of the need for psychological intervention and the reasons why the need was unmet in patients with skin diseases. Due to the confusion and a lack of knowledge about their mental health issues, mental health services are often underutilized.

Diabetic kidney disease (DKD) is a prevalent and severe complications of diabetes and serves as the primary cause of end-stage kidney disease (ESKD) globally. Increasing evidence indicates that renal inflammation is critical in the pathogenesis of DKD. The nucleotide - binding oligomerization domain (NOD) - like receptor family pyrin domain containing 3 (NLRP3) inflammasome is the most extensively researched inflammasome complex and is considered a crucial regulator in the pathogenesis of DKD. The activation of NLRP3 inflammasome is regulated by various signaling pathways, including NF- κB, thioredoxin—interacting protein (TXNIP), and non-coding RNAs (ncRNA), among others. Natural products are chemicals extracted from living organisms in nature, and they typically possess pharmacological and biological activities. They are invaluable sources for drug design and development. Research has demonstrated that many natural products can alleviate DKD by targeting the NLRP3 inflammasome. In this review, we highlight the role of the NLRP3 inflammasome in DKD, and the pathways by which natural products fight against DKD via inhibiting the NLRP3 inflammasome activation, so as to provide novel insights for the treatment of DKD.

Abstract Acoustoelastic model based on classic acoustoelastic theory of solids fails to describe rock acoustoelasticity, where wave velocities in rocks has complex non-linear relations with the applied hydrostatic pressure. In order to extend acoustoelastic model into better prediction for rock acoustoelasticity, a new acoustoelastic model is presented based on general acoustoelasticity of elastic solids having complicated constitutive law of rocks, which combines third-order elastic constants of compact rocks with the exponential terms for the influence of pore/cracks in rocks. The data for P- and S-wave velocities of diabase and greywacke samples subjected to the pressure up to 400 MPa are taken as examples to validate the presented model. The results show that the presented model could not only predict rock acoustoelasticity but also be linked with frequently-used empirical model and meso-mechanical model. The intrinsic connection among mechanical parameters, acoustical parameters, and meso-mechanical parameters could be established clearly, which will benefit the determination of high-resolution constitutive law and the compliant porosity by the measured ultrasonic-wave velocities of rock samples. Article Highlights General acoustoelasticity of elastic solids having complicated constitutive law yields a new acoustoelastic model of rock acoustoelasticity. New model predicts better non-linear characteristics of rock acoustoelasticity. New model benefits for high-resolution evaluation of constitutive law and meso-mechanical parameters for rocks.

Introduction. DN is a common complication of diabetes. However, diabetes combined with renal injury may involve DN or NDKD, with different treatment schemes. The purpose of our study was to determine the independent risk factors of DN and establish a risk score model to help differentiate DN and NDKD, providing a reference for clinical treatment. Methods. A total of 678 T2D patients who had undergone renal biopsy in four affiliated hospitals of Peking University were consecutively enrolled. Patients were assigned to the DN group and NDKD group according to histopathological results. Seventy percent of patients from PKUFH were randomly assigned to the training group, and the remaining 30% were assigned to the internal validation group. Patients from the other three centers were assigned to the external validation group. We used univariate and multivariate logistic regression analyses to identify independent risk factors of DN in the training group and conducted multivariate logistic regression analysis with these independent risk factors in the training group to find regression coefficients “β” to establish a risk score model. Finally, we conducted internal and external validation of the model with ROC curves. Results. Diabetic retinopathy, diabetes duration≥5 years, eGFR

Droplet generators convert mechanical movements of droplets into small-scale electricity. Here, Tang et al. report a humidity-driven power generator by utilizing daily humidity fluctuation in atmosphere enabling continuous generation of electricity upon moisture absorption and desorption cycles.

Abstract We propose a novel filter circuit that incorporates a double ring resonator with a balanced Mach–Zehnder interferometer (MZI). The circuit has a response equivalent to a conventional ring loaded MZI filter, but with added flexibility in terms of configurability. The second-order filter can also be cascaded to realize higher-order filters. The circuit incorporates a two-stage input and output coupler to further reduce the effect of dispersion. A combination of local and global optimization strategies to program the filter, using tailored objective functions, have been tested in simulation and experiments. To our best knowledge, this is the first time a global optimization strategy is directly used in ARMA filter synthesis and simulation without any additional requirement. We further extend the optimization strategy into experiments and demonstrated its use in practical case for programmable filter circuits.

Correcting the relative geometric errors is an essential step in the generation of digital ortho maps and digital surface models from GaoFen-7 (GF-7) regional images, in which the block adjustment (BA) technology is always adopted. However, the traditional BA methods are typically ineffective and not universal due to their dependence on sufficient control data. Although recently developed BA methods using a large number of observation images can substantially eliminate the additional control data, they are not suitable for the BA with few images in the absence of sufficient control data, which is a situation that is often encountered in practical applications. In such situation, the inevitable elevation error caused by the initial positioning error of images will transfer and accumulate in the horizontal direction of the block, resulting in the inconsistent geometric accuracies among images. Aiming at this problem, this article proposed a practical method of combining a stable free BA and a posteriori compensation. Through the stepwise correction and integration of geometric errors, the elevation error and its cumulative effect in free BA could be eliminated at a small control cost, and a robust result with consistent accuracy could be achieved, even with only a few observation images. This method was validated through experiments performed on GF-7 stereo images, and the satisfactory results indicated that it effectively restrained error accumulation and improved the geo-positioning consistency of regional GF-7 images.

This article proposes an efficient and high-fidelity panchromatic (PAN) spectral decomposition method based on smoothing filtering for multispectral (MS) and hyperspectral (HS) image pansharpening. The proposed method assumes that the high-frequency spatial details at the same scale are the same for different spectral images captured by the satellite at the same time. When the PAN image is prefiltered and down-sampled to the MS scale, it will have the same high-frequency spatial detail as the MS image, with only low-frequency spectral differences. Prefiltering for antialiasing when downsampling. Then, the spectral decomposition coefficients from down-sampled PAN image to MS image can be calculated on MS scale. The low-frequency spectral information of an image at different scales is the same, the spectral decomposition coefficients on the MS scale can be up-sampled to the PAN scale, and the original PAN image can be spectrally decomposed to obtain the sharpened image. The proposed method only decomposes the low-frequency spectrum, and the high-frequency spatial details are the same as the original PAN image, the spatial detail is well preserved. This article verifies the effect of the proposed method on MS and HS image sharpening through experiments, and the results show that the proposed method is better than the comparison method. This article also controls other variables to compare with the HPM method. The results show that the hybrid quality with no reference and spatial distortion index ($D_{s}$) of the proposed method are better than the HPM method.

The carbon balance of terrestrial ecosystems is intertwined with climate and changes in land use. Over the past 30 years, the Loess Plateau (LP) has experienced temperature increases and an expansion of forest and grassland. The net ecosystem productivity (NEP) underlying these changes is worth investigating. Using three periods (i.e., 1990–2000, 2000–2010, and 2010–2019) of annual average NEP and climatic, topographic, and land use data, we analyzed changes in the spatial distribution of carbon sources and sinks of the LP. Using an optimal parameter-based geographical detector model to discuss the driving factors of carbon sources and sinks, we found that: (1) The area of carbon sinks has been increasing continuously, and that the distributions of both of these elements are zonal. The carbon sinks show a downward trend from south to north, which is mainly driven by climate and land use type. (2) Carbon sources are mainly concentrated in the middle temperate zone, and they are mainly linked to impervious land, unused land, and grassland. The carbon sinks are mainly concentrated in the south temperate zone and plateau climatic zone, and they are mainly linked to forest, grassland, and cultivated land. Additionally, the southern temperate zone has been the most green, due to its superior hydrothermal conditions that sustain carbon sinks. (3) It is not uncommon for some forests, grasslands, and cultivated land to transition between being carbon sources and carbon sinks, especially when affected by human intervention and inadequate management measures. (4) NEP was primarily influenced by CO2 concentration, temperature, and precipitation, and the interaction of these factors greatly influenced the dynamics of carbon sources and sinks, while terrain exerted insignificant impacts on the NEP. This study highlights the importance of the carbon balance in terrestrial ecosystems and can be used to guide the creation of vegetation-based carbon sequestration policies.

Amendments with activators or microorganisms to enhance phytoremediation in toxic-metal-polluted soils have been widely studied. In this research, the production of indoleacetic acid, siderophore, and 1-aminocyclopropane-1-carboxylate (ACC) deaminase by phosphate-solubilizing bacteria was investigated during a pure culture experiment. Pot experiments were performed using Cd-polluted soil with the following treatments: control (CK, only ultrapure water), orange-peel-based activator (OG), and a combination of phosphate-solubilizing bacteria (Acinetobacter pitti) and OG (APOG). Ryegrass plant height and fresh weight, Cd content in ryegrass, total and available Cd soil content, soil enzyme activity, and soil bacterial diversity were determined in this work. The findings showed that the height of ryegrass in OG and APOG increased by 14.78% and 21.23%. In the APOG group, a decreased ratio of Cd was 3.37 times that of CK, and the bioconcentration factor was 1.28 times that of CK. The neutral phosphatase activity of APOG was 1.33 times that of CK and catalase activity was 1.95 times that of CK. The activity of urease was increased by 35.48%. APOG increased the abundance of beneficial bacteria and Proteobacteria was the dominant bacterium, accounting for 57.38% in APOG. Redundancy analysis (RDA) showed that nutrient elements were conducive to the propagation of the dominant bacteria, the secretion of enzymes, and the extraction rate of Cd in the soil. The possible enhancement mechanism of phytoremediation of cadmium by A. pitti combined with OG was that, on the one hand, APOG increased soil nutrient elements and enzyme activities promoted the growth of ryegrass. On the other hand, APOG activated Cd and boosted the movement of Cd from soil to ryegrass. This research offers insight for the combination of phosphate-solubilizing bacteria with an orange-peel-based activator to improve phytoremediation of Cd-contaminated soils and also provides a new way for the resource utilization of fruit residue.

This study was conducted to investigate the effects and mechanism of quercetin on chicken quality in broilers. We selected 480 AA broilers (1 day old) and randomly allotted those to four treatments (negative control and 0.2, 0.4, or 0.6 g of quercetin per kg of diet) for 42 days. Compared with the control group, the supplementation with 0.4 g of quercetin significantly increased the pH45min and L* value of the thigh muscle and decreased the shearing force of the thigh muscle and breast muscle and drip loss of the thigh muscle (P < 0.05). The supplementation with 0.6 g/kg of quercetin significantly increased the pH45min and L* value of the thigh muscle, and pH45min of breast muscle and decreased the drip loss of the thigh muscle (P < 0.05). Sensory scores of meat color, tenderness, and juiciness also were improved with increasing quercetin concentration (P < 0.05). The inosinic acid (IMP) content of the breast and thigh muscles of broilers was significantly increased by supplementation with 0.6 g/kg of quercetin (P < 0.05). Supplementation with 0.2, 0.4, and 0.6 g of quercetin significantly reduced mRNA expression of L-FABP (P < 0.05, P < 0.05, and P < 0.05); supplementation with 0.4 and 0.6 g/kg of quercetin significantly increased mRNA expression of PKB and AMPKα1 (P < 0.05 and P < 0.05); supplementation with 0.6 g/kg of quercetin in the diet significantly reduced mRNA expression of SREBP1 and HMGR (P < 0.05 and P < 0.05) and significantly increased mRNA expression of CPT1 and PPARγ (P < 0.05 and P < 0.05); and supplementation with 0.2, 0.4, and 0.6 g/kg of quercetin significantly increased mRNA expression of PI3K, LPL, and Apo A1 and significantly reduced mRNA expression of ACC and FATP1 in the breast muscle of broilers (P > 0.05). PI3k, PKB, AMPK, SREBP1, and L-FABP were significantly and positively correlated with pH45min (P < 0.05); PPARγ was significantly and positively correlated with shear force (P < 0.05); CPT1 was significantly and positively correlated with the L* value (P < 0.05); and HMGR was significantly and positively correlated with drip loss (P < 0.05). In conclusion, quercetin improved the meat quality, protecting it against lipid oxidation and deposition by regulating the PI3K/PKB/AMPKα1 signaling pathway in the breast muscle of broilers.

Building change detection is crucial for urban development. Over the past few years, deep learning based change detection researches have achieved impressive progress. However, the limitation is that a large number of change labels are required. Semi-supervised change detection requires only a small number of change labels and is receiving increasing attention. It is true that labeling building footprint in a single temporal image is low-cost compared with labeling changes, which requires constantly comparing bi-temporal images. Meanwhile, the building ground truth (especially in pre-temporal phase) is more easily available. Therefore, single temporal building priori is used as supervision signals to improve semi-supervised change detection performance. In this paper, a multitask consistency network (MTCNet) with single temporal supervision is proposed, using a small number of change labels and single temporal building labels for semi-supervised building change detection. To make full advantage of the building prior information, the multitask learning strategy is adopted which performs both building segmentation and change detection tasks to obtain discriminative features. To exploit unlabeled data, a task-level consistency learning strategy is proposed to enhance the generalization ability. Experiments on two building change detection datasets validate the effectiveness of our method. It is found that using only 10% change labels and the corresponding single temporal building labels in Guangzhou dataset, MTCNet improves the F1-score by 21.04% compared to the supervised single change detection task method and improves by more than 9.95% compared to other semi-supervised change detection methods. Moreover, if extra T1 labels are provided, the F1-score can be further improved by 7.14%.