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Since 2014, highly pathogenic avian influenza (HPAI) H5 viruses of clade 2.3.4.4 have been dominating the outbreaks across Europe, causing massive deaths among poultry and wild birds. However, the factors shaping these broad‐scale outbreak patterns, especially those related to waterbird community composition, remain unclear. In particular, we do not know whether these risk factors differ from those of other H5 clades. Addressing this knowledge gap is important for predicting and preventing future HPAI outbreaks. Using extensive waterbird survey datasets from about 6883 sites, we here explored the effect of waterbird community composition on HPAI H5Nx (clade 2.3.4.4) spatial patterns in the 2016/2017 and 2020/2021 epidemics in Europe, and compared it with the 2005/2006 HPAI H5N1 (clade 2.2) epidemic. We showed that HPAI H5 occurrences in wild birds in the three epidemics were strongly associated with very similar waterbird community attributes, which suggested that, in nature, similar interspecific transmission processes operate between the HPAI H5 subtypes or clades. Importantly, community phylogenetic diversity consistently showed a negative association with H5 occurrence in all three epidemics, suggesting a dilution effect of phylogenetic diversity. In contrast, waterbird community variables showed much weaker associations with HPAI H5Nx occurrence in poultry. Our results demonstrate that models based on previous epidemics can predict future HPAI H5 patterns in wild birds, implying that it is important to include waterbird community factors in future HPAI studies to predict outbreaks and improve surveillance activities. [ABSTRACT FROM AUTHOR]

Background: Dissolved organic matter (DOM) plays a significant role in the biogeochemical cycle of crucial elements in aquatic ecosystem. However, it is still not clear on the spectral characteristics of water DOM in different types of alpine wetlands, which have less anthropogenic influences and intensive ultraviolet radiation. Here, we collected 107 water samples from marsh, lake, and river wetlands in the Zoige plateau, China, and analyzed the chemical characteristics, compositions, and potential sources of chromophoric DOM by combining UV–vis spectroscopy and excitation–emission matrix fluorescence spectroscopy coupled with parallel factor analysis (EEMs-PARAFAC). Results: UVC and UVA fulvic-like substances were the prevailing fluorescence components in water DOM, which accounted for 23.74–71.59% and 16.76–30.01% of the total fluorescence intensity, respectively. Compared with the lake and river wetlands, fluoresce intensities of UVC and UVA fulvic-like substances in DOM were higher in marsh wetland. Marsh wetlands possessed the highest SUVA254, E2/E3, E2/E4, and E4/E6 of DOM, suggesting higher humification degree, higher relative molecular nominal size, and higher aromaticity. And the E2/E4 ratios in most water samples were higher than 12, indicating water DOM was mainly derived from autochthonous sources in alpine wetlands. Conclusions: Wetland types strongly affected the spectral characteristics of water DOM in Zoige plateau. These findings may be beneficial for sustainable management of alpine wetlands. [ABSTRACT FROM AUTHOR]

Wetlands, as a crucial component of terrestrial ecosystems, play a significant role in global ecological services. Aboveground biomass (AGB) is a key indicator of the productivity and carbon sequestration potential of wetland ecosystems. The current research methods for remote-sensing estimation of biomass either rely on traditional vegetation indices or merely perform integer-order differential transformations on the spectra, failing to fully leverage the information complexity of hyperspectral data. To identify an effective method for estimating AGB of mixed-wetland-plant communities, we conducted field surveys of AGB from three typical wetlands within the Crested Ibis National Nature Reserve in Hanzhong, Shaanxi, and concurrently acquired canopy hyperspectral data with a portable spectrometer. The spectral features were transformed by applying fractional-order differentiation (0.0 to 2.0) to extract optimal feature combinations. AGB prediction models were built using three machine learning models, XGBoost, Random Forest (RF), and CatBoost, and the accuracy of each model was evaluated. The combination of fractional-order differentiation, vegetation indices, and feature importance effectively yielded the optimal feature combinations, and integrating vegetation indices with feature bands enhanced the predictive accuracy of the models. Among the three machine-learning models, the RF model achieved superior accuracy using the 0.8-order differential transformation of vegetation indices and feature bands (R2 = 0.673, RMSE = 23.196, RPD = 1.736). The optimal RF model was visually interpreted using Shapley Additive Explanations, which revealed that the contribution of each feature varied across individual sample predictions. Our study provides methodological and technical support for remote-sensing monitoring of wetland AGB. [ABSTRACT FROM AUTHOR]

In pursuit of sustainable solutions for water pollution mitigation, we have successfully employed electrospinning technology to fabricate a multilayered sodium alginate (SA)/polyethyleneimine (PEI)/polyvinyl alcohol (PVA) nanocomposite fiber membrane, with a focus on enhancing its adsorption capacity for Cu2+ ions in wastewater. Our research underscores the potential of this novel membrane, characterized by its small diameter, high uniformity, and expansive surface area, in effectively filtering heavy metal ions. By optimizing critical electrospinning parameters such as a voltage of 19.5 KV, a collector distance of 8 cm, a specific mass ratio of SA:PEI: PVA (1:2:6), and an injection rate of 8 μL/min, we achieved a nanofiber membrane with an average diameter of 112.5 nm, exhibiting exceptional morphological characteristics and high efficiency. Notably, the membrane exhibited an adsorption capacity of over 85% for Cu2+ during initial testing, maintaining over 80% efficiency throughout four consecutive filtration cycles. This work not only advances the field of nanocomposite membranes for water purification but also contributes significantly to the broader goal of achieving environmental sustainability by mitigating the impact of heavy metal contamination in water bodies. [ABSTRACT FROM AUTHOR]

Introduction: This study involves the collation and analysis of clinical characteristics and laboratory findings in patients with multiple myeloma (MM) combined with renal insufficiency. The objective was to assess the impact of various treatment methods on patient outcomes and the incidence of adverse events in individuals with MM and renal insufficiency. Methods: We analyzed the correlation between clinical characteristics, gene loci, fluorescence in situ hybridization, treatment methods, and prognosis in patients with MM and renal insufficiency. The differences in hematological and therapeutic efficacy indexes between two groups subjected to different treatments were evaluated. The assessment of treatment effectiveness was based on the total effective rate, calculated as the sum of stringent CR rate, complete remission rate, very good partial remission rate, and partial remission rate. Results: (1) The renal insufficiency group exhibited higher percentages of bone marrow abnormal plasma cells, lactate dehydrogenase (LDH), blood calcium, white blood cell count, percentage of neutrophils, and blood β2-microglobulin (β2-MG) levels compared to the normal renal function group. Conversely, hemoglobin levels and lymphocyte percentage were lower in the renal insufficiency group. Binary logistic regression analysis identified hemoglobin, blood calcium values, blood β2-MG, and LDH as independent risk factors for the development of renal insufficiency in patients with MM (p < 0.05). (2) Based on the Durie-Salmon staging criteria, the proportion of Stage III patients was the highest (up to 81.8%), indicating that patients with MM usually suffer from insidious disease, often with high tumor load and late-disease stage at the time of consultation. International Staging System (ISS) and Revised ISS staging also revealed a higher proportion of Stage III patients in the renal insufficiency group (p < 0.05), indicating a worse long-term prognosis in patients with MM and renal insufficiency. (3) Before treatment, there was no significant difference between the two groups in the analysis of various indices. Complications such as sepsis, herpes zoster, peripheral neuropathy, thrombosis, secondary pulmonary infection, and cardiac complications were significantly lower in the BCD group (Bortezomib + Cyclophosphamide + Dexamethasone) compared to the BD group (Bortezomib + Dexamethasone) (χ2 = 6.333, p < 0.05), suggesting fewer complications with the BCD regimen. (4) The clinical treatment effects analysis indicated that the BCD group demonstrated a more significant impact than the BD group in the treatment of MM. Conclusion: The application of the BCD regimen in the treatment of MM has shown significant efficiency, effectively alleviating clinical symptoms with fewer adverse reactions and high safety. [ABSTRACT FROM AUTHOR]

Background: Medical imaging datasets frequently encounter a data imbalance issue, where the majority of pixels correspond to healthy regions, and the minority belong to affected regions. This uneven distribution of pixels exacerbates the challenges associated with computer-aided diagnosis. The networks trained with imbalanced data tends to exhibit bias toward majority classes, often demonstrate high precision but low sensitivity. Method: We have designed a new network based on adversarial learning namely conditional contrastive generative adversarial network (CCGAN) to tackle the problem of class imbalancing in a highly imbalancing MRI dataset. The proposed model has three new components: (1) class-specific attention, (2) region rebalancing module (RRM) and supervised contrastive-based learning network (SCoLN). The class-specific attention focuses on more discriminative areas of the input representation, capturing more relevant features. The RRM promotes a more balanced distribution of features across various regions of the input representation, ensuring a more equitable segmentation process. The generator of the CCGAN learns pixel-level segmentation by receiving feedback from the SCoLN based on the true negative and true positive maps. This process ensures that final semantic segmentation not only addresses imbalanced data issues but also enhances classification accuracy. Results: The proposed model has shown state-of-art-performance on five highly imbalance medical image segmentation datasets. Therefore, the suggested model holds significant potential for application in medical diagnosis, in cases characterized by highly imbalanced data distributions. The CCGAN achieved the highest scores in terms of dice similarity coefficient (DSC) on various datasets: 0.965 ± 0.012 for BUS2017, 0.896 ± 0.091 for DDTI, 0.786 ± 0.046 for LiTS MICCAI 2017, 0.712 ± 1.5 for the ATLAS dataset, and 0.877 ± 1.2 for the BRATS 2015 dataset. DeepLab-V3 follows closely, securing the second-best position with DSC scores of 0.948 ± 0.010 for BUS2017, 0.895 ± 0.014 for DDTI, 0.763 ± 0.044 for LiTS MICCAI 2017, 0.696 ± 1.1 for the ATLAS dataset, and 0.846 ± 1.4 for the BRATS 2015 dataset. [ABSTRACT FROM AUTHOR]

Learning flow is an optimal state of engaged learning and total immersion, which is conducive to many academic outcomes. Although studies have examined how learning flow emerges, few have focused on the early foundations of its development. Drawing on Life History Theory, this study examines whether and how childhood environment is related to adolescents' learning flow. The findings of a large-scale survey conducted among 2451 Chinese secondary school students (45.1% boys, Mage = 13.85 years, 54.2% rural, 17.3% town, 28.5% city) showed that, in general, harsh or unpredictable childhood environment was negatively associated with adolescents' learning flow, in which the poorer future orientation and self-control served as multiple mediators in most cases, but not childhood harshness predicted worse self-control. Nevertheless, in an unsatisfactory current environment (versus a satisfactory current environment), the relationship between childhood unpredictability and learning flow became positive, and higher childhood harshness was related to better self-control, as well as the negative relations between harsh or unpredictable childhood environment and future orientation were alleviated. This study provides the first empirical evidence highlighting the role of adverse childhood environment in shaping learning flow and provides support for the adaptation of adolescents with childhood adversity in unsatisfactory environments. [ABSTRACT FROM AUTHOR]

The current study examined whether moral disengagement would mediate the association between adolescents' teacher–student relationships and classroom incivility and sex and age differences in this mediation model. We also examined whether the mediating effect of moral disengagement would be moderated by negative coping styles. Participants comprised 2646 Chinese students (mean age = 13.85 years, SD = 1.45). Results indicated that teacher–student relationships were significantly and negatively associated with adolescents' classroom incivility and moral disengagement mediated this association. Furthermore, this mediating process differed between males and females as well as early and middle adolescents. Specifically, males were more likely to use moral disengagement strategies to engage in classroom incivility than females. Age moderated the association between teacher–student relationships and moral disengagement as well as moral disengagement and classroom incivility, and these two associations became stronger for early adolescents than for middle adolescents. Moreover, negative coping styles moderated the association between teacher–student relationships and classroom incivility and the association between moral disengagement and classroom incivility. The direct association between teacher–student relationships and classroom incivility became nonsignificant for adolescents with high negative coping styles. The study contributes to clarifying the mediating and moderating mechanisms underlying the effect of teacher–student relationships on adolescents' classroom incivility. Practitioner points: Teacher–student relationships were negatively associated with adolescents' classroom incivility and moral disengagement significantly mediated this association.The mediating effect of moral disengagement differed between males and females as well as early and middle adolescents.Negative coping style moderated the link between teacher–student relationships and classroom incivility as well as moral disengagement and classroom incivility. [ABSTRACT FROM AUTHOR]

Summary Climate and edaphic properties drive the biogeographic distribution of dominant soil microbial phylotypes in terrestrial ecosystems. However, the impact of plant species and their root nutritional traits on microbial distribution in coastal wetlands remains unclear. Here, we investigated the nutritional traits of 100 halophyte root samples and the bacterial communities in the corresponding soil samples from coastal wetlands across eastern China. This study spans 22° of latitude, covering over 2500 km from north to south. We found that 1% of soil bacterial phylotypes accounted for nearly 30% of the soil bacterial community abundance, suggesting that a few bacterial phylotypes dominated the coastal wetlands. These dominated phylotypes could be grouped into three ecological clusters as per their preference over climatic (temperature and precipitation), edaphic (soil carbon and nitrogen), and plant factors (halophyte vegetation, root carbon, and nitrogen). We further provide novel evidence that plant root nutritional traits, especially root C and N, can strongly influence the distribution of these ecological clusters. Taken together, our study provides solid evidence of revealing the dominance of specific bacterial phylotypes and the complex interactions with their environment, highlighting the importance of plant root nutritional traits on biogeographic distribution of soil microbiome in coastal wetland ecosystems. [ABSTRACT FROM AUTHOR]

River and stream sediments act as biogeochemical reactors for greenhouse gases, particularly methane. However, understanding how riverbed sediment properties influence river carbon emissions remains relatively unclear. The Liao River in northern China is a typical watershed with heterogeneous water and sediment sources, characterized by varying sediment properties. In this study, we surveyed CH 4 and CO 2 emissions from its mainstem and tributaries during flood and dry seasons. We found consistent seasonal patterns in CH 4 and CO 2 emissions, with peaks occurring during the flood season. The average CH 4 and CO 2 fluxes were 1.64 ± 1.80 mmol m -2  d -1 and 59.66 ± 44.60 mmol m -2  d -1 , respectively. Notably, the percentage of sediment silt was significantly correlated with CH 4 concentration and flux (R 2  = 0.12-0.30, p < 0.05). Fine particles dominated the availability of sediment organic matter and redox conditions, which were related to riverine CH 4 production and emissions. Structural equation modeling revealed that both grain size and the percentage of TOC (total organic carbon) directly influenced riverine CH 4 and CO 2 emissions. The organic content and redox conditions of the riverbed sediment collectively explained 65% of riverine CH 4 emissions, while grain size composition indirectly controlled CH 4 emissions by altering sediment substrate quality and redox conditions. In contrast, river CO 2 emissions were only weakly dependent on anaerobic metabolism in riverbed sediments. These findings enhance our understanding of the sources and metabolic mechanisms of riverine CH 4 and CO 2 emissions and offer potential improvements for estimating carbon fluxes in regional or global riverine networks by considering riverbed sediment properties., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The construction of gene expression vectors is an important component of laboratory work in experimental biology. With technical advancements like Gibson Assembly, vector construction becomes relatively simple and efficient. However, when the full-length genome of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) cannot be easily amplified by a single polymerase chain reaction (PCR) from cDNA, or it is difficult to acquire a full-length gene expression vector by homologous recombination of multiple inserts in vitro, the current Gibson Assembly technique fails to achieve this goal. Consequently, we aimed to divide the PRRSV genome into several fragments and introduce appropriate restriction sites into the reverse primer for obtaining PCR-amplified fragments. After joining the previous DNA fragment into the vector by homologous recombination technology, the new vector acquired the restriction enzyme cleavage site. Thus, we can linearize the vector by using the newly added enzyme cleavage site and introduce the next DNA fragment downstream of the upstream DNA fragment. The introduced restriction enzyme cleavage site at the 3' end of the upstream DNA fragment will be eliminated, and a new cleavage site will be introduced into the 3' end of the downstream DNA fragment. In this way, we can join DNA fragments to the vector one by one. This method is applicable to successfully construct the PRRSV expression vector and is an effective method for assembling a large number of fragments into the expression vector.