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ABSTRACT The endophyte is closely related to medicinal plant growth and development, stress resistance, and active ingredients’ accumulation. However, a seasonal succession of endophytes and the association with active ingredients is still unclear. In this study, we used high-throughput sequencing methods to compare the endophyte diversity of Rheum palmatum under different seasons and analyze the association between endophytes and five active ingredients. The results show that the diversity of endophytic fungi increased and then decreased, while bacterial diversity increased with the change of season. Community composition showed that the dominant genera of endophytic fungi were different under the different seasons, while the dominant genera of endophytic bacteria were Delftia. Analysis of co-occurrence network maps showed that the connectivity and complexity of endophytic fungi and bacterial networks decreased with the change of season. Spearman analysis indicated that the active ingredients of R. palmatum were significantly positive correlation with genera of endophytic fungi (Chalara). FUNGuild and PICRUSt predictive analysis indicated that the function of endophytic fungi and bacteria, respectively, were symbiotroph and metabolism, and relative abundances were different under the different seasons. Our results help elucidate the mechanism of medicinal plant–endophyte interaction.IMPORTANCEThrough the investigation of the seasonal succession of endophytes and the association with active ingredients in Rheum palmatum, we found that the diversity and composition of endophytes in R. palmatum exhibited seasonal dynamics, and the active ingredients of R. palmatum showed a significantly positive correlation with the genus of endophytic fungi (Chalara). Our results may lay a foundation for understanding the interaction mechanism of endophyte and medicinal plant, and can also provide a theoretical basis for sustainable production of medicinal plants.

ABSTRACT Females and males of dioecious plants have evolved sex-specific characteristics in terms of their morphological and physiological properties. However, little is known about the difference in rhizosphere microbes in dioecious plants. In this study, we used amplicon sequencing to analyze the differences in rhizosphere microbial diversity and community composition of males and females of dioecious Hippophae tibetana at different habitats, and their key factors in driving the differences were investigated. The results showed that there were differences in the diversity, community composition, and connectivity and complexity of the co-occurrence network of rhizosphere microbes between females and males of the dioecious H. tibetana at different habitats. Zoopagomycota is a unique phylum of rhizosphere fungi in the males of the dioecious H. tibetana, while Dependentiae is a unique phylum of rhizosphere bacteria in the females of the dioecious H. tibetana. Linear discriminant analysis effect size (LEfSe) analysis indicated significant enrichment of species at different levels, suggesting that these species could be potential biomarkers for females and males of H. tibetana. Spearman’s analysis showed that the dominant genera of rhizosphere fungi were significantly positively correlated with soil physicochemical properties (total nitrogen and phosphorus; organic matter; available phosphorus, potassium and nitrogen; salt content; water content). PICRUSt and FUNGuild predictive analysis indicated that the function of rhizosphere fungi was different between females and males of the dioecious H. tibetana at different habitats, while metabolites were the dominant functions of rhizosphere bacteria in all samples. These results highlighted the sexual discrimination of rhizosphere microbes on the dioecious plants and provided important knowledge for females and males of the dioecious plant–microbe interaction.IMPORTANCEThis study explores the differences in rhizosphere microbes of dioecious Hippophae tibetana at different habitats and their key factors in driving the differences. Through employing amplicon sequencing techniques, we found that rhizosphere microbial communities and diversity were different between females and males of the dioecious H. tibetana at different habitats, and there notably existed unique phylum and potential biomarkers of rhizosphere microbes between females and males of the dioecious H. tibetana. Rhizosphere fungi were significantly positively correlated with soil physicochemical properties. This study reveals the differences in rhizosphere microbes of dioecious H. tibetana at different habitats and driving factors; it also contributes to our understanding of the dioecious plant–microbe interaction.

Abstract Plant growth promoting microbe assisted phytoremediation is considered a more effective approach to rehabilitation than the single use of plants, but underlying mechanism is still unclear. In this study, we combined transcriptomic and physiological methods to explore the mechanism of plant growth promoting microbe Trichoderma citrinoviride HT-1 assisted phytoremediation of Cd contaminated water by Phragmites australis. The results show that the strain HT-1 significantly promoted P. australis growth, increased the photosynthetic rate, enhanced antioxidant enzyme activities. The chlorophyll content and the activity of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APX) were increased by 83.78%, 23.17%, 47.60%, 97.14% and 12.23% on average, and decreased the content of malondialdehyde (MDA) by 31.10%. At the same time, strain HT-1 improved the absorption and transport of Cd in P. australis, and the removal rate of Cd was increased by 7.56% on average. Transcriptome analysis showed that strain HT-1 induced significant up-regulated the expression of genes related to oxidative phosphorylation and ribosome pathways, and these upregulated genes promoted P. australis remediation efficiency and resistance to Cd stress. Our results provide a mechanistic understanding of plant growth promoting microbe assisted phytoremediation under Cd stress.

This work proposes a sub-optimal method based on a two-layer structured meta-deep reinforcement learning (MDRL) approach to address the hardware impairment (HWI) optimization issue in large intelligent surface (LIS) systems. This method, designed for distributed LIS systems with reflection matrices, effectively enhances the system capacity and performance despite HWIs. Building upon existing techniques of dividing large-area LIS systems into multiple small-area subsystems, the simulated results demonstrate that sub-optimal LIS performance can be achieved with fewer samples in diverse dynamic wireless environments. This innovative approach enhances the adaptability of distributed LIS systems and offers an effective HWI management strategy, paving the way for future LIS system optimization.

To investigate the impact of the effect of high temperature stimulation on Monopterus albus larvae after a certain period of time, five experimental groups were established at different temperatures. Then, the M. albus under high temperature stress was fed at 30°C for 70 days. After that, the growth index of the M. albus was counted and analyzed. In terms of growth index, high temperature stress had significant effects on FCR, FBW, WGR, and SGR of M. albus (p < 0.05). The SR increased after being stimulated by temperature (p < 0.1). The study revealed that liver cells of M. albus were harmed by elevated temperatures of 36°C and 38°C. In the experimental group, the activities of digestive enzymes changed in the same trend, reaching the highest point in the 32°C group and then decreasing, and the AMS activity in the 38°C group was significantly different from that in the 30°C group (p < 0.05). The activities of antioxidase in liver reached the highest at 34°C, which was significantly different from those at 30°C (p < 0.05). In addition, the expression levels of TLR1, C3, TNF-α, and other genes increased in the experimental group, reaching the highest point at 34°C, and the expression level of the IL-1β gene reached the highest point at 32°C, which was significantly different from that at 30°C (p < 0.05). However, the expression level of the IRAK3 gene decreased in the experimental group and reached its lowest point at 34°C (p < 0.05). The expression level of the HSP90α gene increased with the highest temperature stimulus and reached its highest point at 38°C (p < 0.05). In the α diversity index of intestinal microorganisms in the experimental group, the observed species, Shannon, and Chao1 indexes in the 34°C group were the highest (p < 0.05), and β diversity analysis revealed that the intestinal microbial community in the experimental group was separated after high temperature stimulation. At the phylum level, the three dominant flora are Proteus, Firmicutes, and Bacteroides. Bacteroides and Macrococcus abundance increased at the genus level, but Vibrio and Aeromonas abundance decreased. To sum up, appropriate high-temperature stress can enhance the immunity and adaptability of M. albus. These results show that the high temperature stimulation of 32°C–34°C is beneficial to the industrial culture of M. albus.

Accurate prediction of cattle weight is essential for enhancing the efficiency and sustainability of livestock management practices. However, conventional methods often involve labor-intensive procedures and lack instant and non-invasive solutions. This study proposed an intelligent weight prediction approach for cows based on semantic segmentation and Back Propagation (BP) neural network. The proposed semantic segmentation method leveraged a hybrid model which combined ResNet-101-D with the Squeeze-and-Excitation (SE) attention mechanism to obtain precise morphological features from cow images. The body size parameters and physical measurements were then used for training the regression-based machine learning models to estimate the weight of individual cattle. The comparative analysis methods revealed that the BP neural network achieved the best results with an MAE of 13.11 pounds and an RMSE of 22.73 pounds. By eliminating the need for physical contact, this approach not only improves animal welfare but also mitigates potential risks. The work addresses the specific needs of welfare farming and aims to promote animal welfare and advance the field of precision agriculture.

Abstract Light Field (LF) imaging empowers many attractive applications by simultaneously recording spatial and angular information of light rays. In order to meet the challenges of LF storage and transmission, many view reconstruction-based LF compression methods are put forward. However, occlusion issue and under-exploitation of LF rich structure information limit the view reconstruction qualities, which further influence LF compression efficiency. In order to alleviate these problems, in this paper, we propose a geometry-aware view reconstruction network for LF compression. In our method, only sparsely-sampled LF views are encoded, which are further used as priors to reconstruct the un-sampled LF views at the decoder side. The proposed reconstruction process contains two stages including geometry-aware reconstruction and texture refinement. The geometry-aware reconstruction stage utilizes a multi-stream framework, which can fully explore LF spatial-angular, location and geometry information. The texture refinement stage can adequately fuse such rich LF information to further improve LF reconstruction quality. Comprehensive experimental results validate the superiority of the proposed method. The rate-distortion performance and the perceptual quality of reconstructed views further demonstrate that the proposed method can save more bitrate while increasing LF reconstruction quality.

Blockchains are becoming increasingly important in today’s Internet, enabling large-scale decentralized applications with strong security and transparency properties. In a blockchain system, participants maintain and update the server-side state of an application by appending data as blocks onto an immutable, distributed ledger through a consensus protocol within a peer-to-peer network. There has been a significant increase in profit in mining blocks. For instance, Bitcoin miners currently receive over USD 200,000 per mined block. An essential determinant of these rewards is the time it takes to disseminate newly mined blocks across the network. This paper addresses the challenge of optimizing mining rewards by exploring topology design in a wide-area blockchain network utilizing a Proof-of-Work consensus protocol. We show that under low block times, the geographical location of a miner critically impacts the number of successful blocks mined by the miner. We also show that a miner may improve its success rate by increasing its connectivity to the network. However, contrary to the general wisdom that a faster network is always better for a miner, we show that increasing network connectivity (e.g., by adding more neighbors) is beneficial to a miner only up to a point after which the miner’s rewards degrade. This is because when a miner improves its connectivity, it inadvertently also aids other miners in increasing their connectivity. We also present a network-level collusion attack in which a miner can increase its block success rate by becoming part of a tightly connected cluster. Here too, we observe that the mining gains obtained increase with cluster size only up to a point, and decrease thereafter. Our findings highlight that the network topology is a key variable affecting miner performance in PoW blockchains that must not be overlooked. We demonstrate our observations via detailed simulations modeled using real-world measurement data.

Abstract The hydrogen‐bonded organic frameworks (HOFs) have rarely been considered for photocatalytic application, given their weak stability and low activity. One presumably effective strategy to improve the photocatalytic performance of the HOFs is to produce a core–shell composite by fabricating a particular nanostructure using stable HOFs. To this end, the surface‐functionalized metal–organic frameworks (MOFs) are used as the host matrix to support the in situ assembly and subsequent multisite growth of the stable HOFs. MOF@HOF eventually obtains core–shell hybrids, i.e., NH2‐UiO‐66@DAT‐HOF. This newly synthesized core–shell nanostructure exhibits excellent stability and superb photocatalytic performance. For example, in terms of tetracycline degradation, the optimal composite presents an apparent reaction rate constant of 60.7 and 7.6 times higher than its parent materials NH2‐UiO‐66 and DAT‐HOF. Such a pronounced enhancement in photocatalytic efficiency of the hybrid material is attributed to the broader visible‐light utilization range compared to its individual parent material as well as the efficient separation of charge carriers supported by the S‐scheme heterojunction. In addition, it is particularly notable that the photocatalytic efficiency of the yielded core–shell nanostructure can remain high after several‐cycle applications. This work provides a universal scheme for synthesizing the MOF@HOF core–shell hybrids.

Insulator is an important equipment of power transmission line. Insulator icing can seriously affect the stable operation of power transmission line. So insulator icing condition monitoring has great significance of the safety and stability of power system. Therefore, this paper proposes a lightweight intelligent recognition method of insulator icing thickness for front-end ice monitoring device. In this method, the residual network (ResNet) and feature pyramid network (FPN) are fused to construct a multi-scale feature extraction network framework, so that the shallow features and deep features are fused to reduce the information loss and improve the target detection accuracy. Then, the full convolution neural network (FCN) is used to classify and regress the iced insulator, so as to realize the high-precision identification of icing thickness. Finally, the proposed method is compressed by model quantization to reduce the size and parameters of the model for adapting the icing monitoring terminal with limited computing resources, and the performance of the method is verified and compared with other classical method on the edge intelligent chip.

Endothelial damage is central to the pathogenesis of many of the complications of sickle cell disease. Circulating extracellular vesicles (EVs) have been implicated in modulating endothelial behavior in a variety of different, diseases with vascular pathologies. As seen in other hemolytic diseases, the plasma of sickle cell patients contains EVs of different sizes and cellular sources. The medium-sized vesicles (microparticles) primarily derive from mature red blood cells and platelets; some of these EVs have procoagulant properties, while others stimulate inflammation or endothelial adhesiveness. Most of the small EVs (including exosomes) derive from erythrocytes and erythrocyte precursors, but some also originate from platelets, white blood cells, and endothelial cells. These small EVs may alter the behavior of target cells by delivering cargo including proteins and nucleic acids. Studies in model systems implicate small EVs in promoting vaso-occlusion and disruption of endothelial integrity. Thus, both medium and small EVs may contribute to the increased endothelial damage in sickle cell disease. Development of a detailed understanding of the composition and roles of circulating EVs represents a promising approach toward novel predictive diagnostics and therapeutic approaches in sickle cell disease.

Intercellular junctions maintain the integrity of the endothelium. We previously found that the adherens and tight junctions between endothelial cells are disrupted by plasma extracellular vesicles from patients with sickle cell disease (especially those with Acute Chest Syndrome). In the current study, we evaluated the effects of these vesicles on endothelial gap junctions. The vesicles from sickle cell patients (isolated during episodes of Acute Chest Syndrome) disrupted gap junction structures earlier and more severely than the other classes of intercellular junctions (as detected by immunofluorescence). These vesicles were much more potent than those isolated at baseline from the same subject. The treatment of endothelial cells with these vesicles led to reduced levels of connexin43 mRNA and protein. These vesicles severely reduced intercellular communication (transfer of microinjected Neurobiotin). Our data suggest a hierarchy of progressive disruption of different intercellular connections between endothelial cells by circulating extracellular vesicles that may contribute to the pathophysiology of the endothelial disturbances in sickle cell disease.