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Abstract Efficient harnessing of solar energy presents a significant challenge in environmental cleanup efforts. This study develops a highly effective carbon quantum dots-modified hollow core-shell TiO2-LaFeO3 heterojunction photocatalyst (CDs-TLFO). Structural analysis confirmed that nanosheets are loaded with CQDs, forming a hollow core-shell structure with intimate interconnection. Photocatalytic experiments reveal that CDs-TLFO degrads tetracycline hydrochloride (TC) 2.02 times faster than TLFO alone, and significantly outperformes h-TiO2 and LaFeO3 (11.28 and 2.78 times, respectively). This enhancement is attributed to CQDs acting as electron acceptors with upconversion properties, enhancing the separation of e–-h+ pairs and boosting visible light absorption. Integration of CQDs onto the TLFO surface creates numerous active sites and enhances visible light absorption. SEM and TEM tests confirm that the catalyst has a hollow core-shell structure. ESR tests and radical trapping experiments indicate that the high degradation efficiency of the catalyst mainly owns to the synergistic effect of hydroxyl radicals (·OH) and superoxide radicals (·O2 −). The reusability and stability of the catalysts are investigated, potential TC degradation pathways are proposed as well as the photocatalytic reaction mechanism is revealed. This research introduces promising avenues for environmental cleanup and offers a straightforward, energy-efficient, and environmentally friendly method for producing CDs-TLFO heterojunction materials with superior photocatalytic capabilities.

Chronic bioenergetic imbalances and inflammation caused by hyperglycemia are obstacles that delay diabetic wound healing. However, it is difficult to directly deliver energy and metabolites to regulate intracellular energy metabolism using biomaterials. Herein, we propose a light-driven bioenergetic and oxygen-releasing hydrogel (PTKM@HG) that integrates the thylakoid membrane-encapsulated polyphenol nanoparticles (PTKM NPs) to regulate the energy metabolism and inflammatory response in diabetic wounds. Upon red light irradiation, the PTKM NPs exhibited oxygen generation and H2O2 deletion capacity through a photosynthetic effect to restore hypoxia-induced mitochondrial dysfunction. Meanwhile, the PTKM NPs could produce exogenous ATP and NADPH to enhance mitochondrial function and facilitate cellular anabolism by regulating the leucine-activated mTOR signaling pathway. Furthermore, the PTKM NPs inherited antioxidative and anti-inflammatory ability from polyphenol. Finally, the red light irradiated PTKM@HG hydrogel augmented the survival and migration of cells keratinocytes, and then accelerated angiogenesis and re-epithelialization of diabetic wounds. In short, this study provides possibilities for effectively treating diseases by delivering key metabolites and energy based on such a light-driven bioenergetic hydrogel.

The light-converting agricultural film is a new high-end functional film that can convert ultraviolet light and yellow-green light, which is harmful or useless to plant photosynthesis, into red-orange light or blue-violet light required for photosynthesis. The potential advantages of light-converting agricultural film in efficiently utilizing solar energy and improving crop yield have attracted more and more attention from researchers and agricultural enterprises.The light-converting function is realized by adding a light-converting agent to the agricultural film. Therefore, the preparation of light-converting agents with excellent performance is the core of the development and utilization of light-converting agricultural films. The paper firstly summarizes the key research and development in the field of agricultural light-converting films in china. Then this paper summarizes the classification of light-converting agents, research progress, and preparation methods. Finally, this paper predicts the future development trend of light-converting agricultural films, in order to provide a reference for the research and development of stable and efficient light-converting agricultural films.

The impact velocities of hammer crusher directly affect the crushing effect of rock. In order to study influence of impact velocities of hammer head on the crushing effect and utilization efficiency of fracture energy in green sandstone, macroscopic and microscopic mechanical response relationship of green sandstone was established by combining with physical experiments and microscopic parameter calibration. The characteristics of stress chain, characteristics of damage and degradation, acoustic emission and changes of crack in green sandstone under different impact velocities of hammer crusher were analyzed using particle flow code, and the utilization efficiency of fracture energy was studied. The results show that: ① During the crushing process of green sandstone, the internal part of destructive micro-unit is mainly composed of tension chain, and the edge of failure is mainly composed of pressure chain. In the undamaged micro-unit, the pressure chain and tension chain cross each other to form network. The tension chain cause extension of crack, and the final failure is the result of the combined action of pressure chain and tension chain. ② The failure pattern of green sandstone is divided into the stage of local failure, extended failure, and intensified failure. The number of destructive micro-unit is divided into the stage of rapid increase, gradual slowing, and stabilization. The larger the impact velocities, the greater the stable increase in the number of destructive micro-unit, and the number of destructive micro-unit approximately follows linear distribution with the impact velocities. ③ With the increase of impact velocities, the cumulative number of crack in green sandstone is divided into the stage of rapid increase and stabilization. In the stage of crack initiation, shear crack are the main type, and the initial input energy is mainly used for the generation of shear crack, while the proportion of tensile crack gradually increases, and eventually tensile crack are the main type. ④ During the failure process of green sandstone, the fracture energy can be divided into the stage of rapid increase, gradual slowing, and stabilization. The utilization efficiency of fracture energy can be divided into the stage of rapid increase, slow increase, and slow decrease, with the maximum value of 10.851%, and the quantitative relationship between utilization efficiency of fracture energy and impact velocities is established. The result of the study not only provide preliminary exploration of the crushing effect and utilization efficiency of fracture energy in green sandstone from microscopic perspective, but also provide reference for the reasonable selection of process parameters in the crushing process of hammer crusher.

Abstract Background LACS (long-chain acyl-CoA synthetase) genes are widespread in organisms and have multiple functions in plants, especially in lipid metabolism. However, the origin and evolutionary dynamics of the LACS gene family remain largely unknown. Results Here, we identified 1785 LACS genes in the genomes of 166 diverse plant species and identified the clades (I, II, III, IV, V, VI) of six clades for the LACS gene family of green plants through phylogenetic analysis. Based on the evolutionary history of plant lineages, we found differences in the origins of different clades, with Clade IV originating from chlorophytes and representing the origin of LACS genes in green plants. The structural characteristics of different clades indicate that clade IV is relatively independent, while the relationships between clades (I, II, III) and clades (V, VI) are closer. Dispersed duplication (DSD) and transposed duplication (TRD) are the main forces driving the evolution of plant LACS genes. Network clustering analysis further grouped all LACS genes into six main clusters, with genes within each cluster showing significant co-linearity. Ka/Ks results suggest that LACS family genes underwent purifying selection during evolution. We analyzed the phylogenetic relationships and characteristics of six clades of the LACS gene family to explain the origin, evolutionary history, and phylogenetic relationships of different clades and proposed a hypothetical evolutionary model for the LACS family of genes in plants. Conclusions Our research provides genome-wide insights into the evolutionary history of the LACS gene family in green plants. These insights lay an important foundation for comprehensive functional characterization in future research.

Abstract Many learning‐based approaches to image deblurring have received increasing attention in recent years. However, the models trained on existing synthetic datasets do not generalize well to real‐world blur, resulting in undesirable artifacts and residual blur. This work attempts to address this problem from two aspects: training data synthesis and network architecture. To narrow the domain gap between synthetic and real domains, a realistic blur synthesis pipeline to generate high‐quality blurred data is proposed. Since the blur is non‐uniform and has different scales and degrees, a parallel feature complementary module to fully exploit the local and non‐local information, which improves the feature representation and helps the network to perceive the non‐uniform blur, is developed. In addition, a spatial Fourier reconstruction block to facilitate correct detail recovery in the spatial and Fourier domains is introduced. Based on these two designs, an effective encoder–decoder network for deblurring is designed. Extensive experiments demonstrate the validity and superiority of the proposed blur synthesis method and deblurring network. In particular, the proposed deblurring network can achieve superior or comparable performance to Restormer, while saving 70% of network parameters and 53% of floating point operations (FLOPs).

PurposeInsulin-like growth factor-1 (IGF-1) has a variety of neurotrophic effects, including neurogenesis, remyelination and synaptogenesis, and is an effective regulator of neuronal plasticity. Although multiple studies have investigated IGF-1 in depression-related disorders, few studies have focused on patients with a first episode of clearly diagnosed depression who had never used antidepressants before. Therefore, this study investigated first-episode and drug-naïve patients with depression to supplement the current evidence around IGF-1 levels in depressive disorders.Patients and methodsThis study consisted of two parts. In the first part, 60 patients with first-episode and drug-naïve depression and 60 controls matched for age, sex, and BMI were recruited from the outpatient department of the Fourth Hospital of Wuhu City, and the community. The case-control method was used to compare differences in serum IGF-1 levels between the two groups. In the second part, 13 case-control studies were screened through the database for meta-analysis to verify the reliability of the results.ResultsResults of the case-control study demonstrated that serum IGF-1 levels are significantly higher in patients with first-episode and drug-naïve depression compared to healthy controls (p

Abstract Background To explore the early curative effect of unilateral biportal endoscopy (UBE) in the treatment of multi-level lumbar spinal stenosis with the help of multiple small incisions. Methods A retrospective analysis was performed on 26 patients with multi-level lumbar spinal stenosis treated by UBE in our hospital from August 1, 2021, to March 1, 2022. We collect patients’ basic medical records and independently design surgical incisions. The visual analog score (VAS) and Oswestry Disability Index (ODI) were compared before surgery, 7 days after surgery and 6 months after surgery. Spinal canal diameters on CT were compared before surgery and 7 days after surgery. The modified MacNab standard was used to evaluate the efficacy satisfaction at 6 months after operation. Results In this study, 26 patients were operated according to the predetermined surgical plan. The operative time was 145 ± 40.11 min, the intraoperative blood loss was 156.25 ± 44.32 ml, and the postoperative hospital stay was 4.79 ± 1.31 days. The VAS scores of postoperative lumbago and leg pain were lower than those before surgery (P

Abstract Putrescine plays a role in superficial scald development during the cold storage of pear fruit. However, the molecular mechanism behind this phenomenon has not been un-fully clarified until recently. In this study, a conjoint analysis of metabolites and gene expression profiles in the putrescine-metabolic pathway of P. bretschneideri Rehd. fruit followed by experimental validation revealed that PbrADC1, forming a homodimer in the chloroplast, was involved in putrescine biosynthesis and thus fruit chilling resistance. Additionally, the substrate-binding residue Cys546 in PbrADC1, whose activity was modified by H2O2, played a crucial role in arginine decarboxylation into agmatine. Through a combined analysis of the distribution of cis-acting elements in the PbrADC1 promoter as well as the expression profiles of related transcription factors (TFs), several TFs were identified as upstream regulators of PbrADC1 gene. Further investigation revealed that the nuclear PbrWRKY62 could directly bind to the W-box elements in the PbrADC1 promoter, activate its expression, enhance putrescine accumulation, and thus increase fruit chilling tolerance. In conclusion, our results suggest that the PbrWRKY62-PbrADC1 module is involved in the development of superficial scald in P. bretschneideri Rehd. fruit via regulating putrescine biosynthesis. Consequently, these findings could serve as valuable genetic resources for breeding scald-resistant pear fruit. Graphical Abstract

Abstract Background Hypertensive disorders of pregnancy (HDP) is the most common cause of indicated preterm delivery, but the impact of prenatal steroid exposure on the outcomes of preterm infants born to HDP mothers, who may be at risk for intrauterine hypoxia-ischemia, remains uncertain. The study objective is to evaluate the mortality and morbidities in HDP for very preterm infants (VPIs) exposed to different course of ANS. Methods This is a prospective cohort study comprising infants with

Due to the advantage of high spectral resolution, hyperspectral imaging techniques have been extensively used in a variety of fields. Hyperspectral images (HSIs) classification is one of the fundamental tasks and attracts significant research interest. HSIs classification is pivotal as it facilitates precise identification of objects, providing invaluable insights for Earth observation tasks, such as resource management and land cover analysis. In existing studies, convolutional operations have been broadly applied for HSIs classification, especially 3-D convolution, which has shown its effectiveness in extracting spectral–spatial features from the raw HSIs. However, HSIs exhibit the characteristic of high dimensionality and pose challenges in extracting more discriminative features. In order to enhance the capability of capturing discriminative spectral–spatial features, in this article, a novel and effective 3-D sharpened cosine similarity (SCS) operation is proposed, serving as a replacement for conventional 3-D convolutional operation in HSIs classification and enhancing the classification accuracy. The 3-D SCS operation calculates and sharpens the cosine similarity between kernels and HSI input data. Based on the 3-D SCS operation, a 3-D SCS neural network is developed for HSIs classification tasks. To evaluate the effectiveness of 3-D SCS operation, experiments are conducted on three real-world HSIs datasets, including the University of Pavia, the University of Trento, and the University of Houston. Quantitative and qualitative experimental results illustrate that the SCS operation can effectively extract discriminative spectral-spatial features, achieving superior performance over the CNNs under the same model configuration.

ObjectiveTo optimize experimental conditions of the enrichment and concentration of viruses in purified water with mixed cellulose ester (MCE) membrane adsorption-ultrafiltration method. MethodsBacteriophage MS2 was used as the target virus. First, water sample cation concentration, MCE membrane pore size, type of eluent, and elution method were included in the orthogonal experiment on the MCE adsorption of bacteriophage MS2. Then, the optimized conditions were further screened by applying centrifugal force of 3 000 and 4 000 grams during the ultrafiltration process. Reverse transcription-fluorescence quantitative polymerase chain reaction (RT-qPCR) was used in the detection and the recovery rates of bacteriophage MS2 under various conditions were calculated and analyzed statistically. ResultsThe optimal enrichment conditions for MCE-ultrafiltration method were as follows: water sample cation concentration of 0.025 mol/L, MCE membrane pore size of 0.22 μm), using 1% Tris-glycine-beef extract (TGBE) solution in elution, adopting vibration method in elution, use of ultrafiltration tubes retaining organics with the molecular weight of 30 kD in the ultrafiltration, and centrifugal force of 3 000 grams. A recovery rate of 81.20% (95% confidence interval: 71.86% – 90.54%) for bacteriophage MS2 viruses in purified water samples was achieved under the above conditions in the experiment. ConclusionThe optimized membrane adsorption MCE-ultrafiltration method could enrich bacteriophage MS2 viruses in purified water samples at a high recovery rate and be used in the enrichment and concentration of viruses in purified water.

Accurate state-of-charge (SOC) estimation is the core index of battery management system (BMS). When the battery equivalent circuit model (ECM) identifies the parameters under complex operating conditions, there is more jitter or even divergence, which will affect the estimation accuracy of battery SOC. To solve this problem, this paper proposes a new algorithm, namely the cross time scale fusion (CTSF) algorithm. Firstly, the cross-time scales Δt1 and Δt2 are determined, the number of cross-time cycles is calculated according to the total amount of complex operating condition data N. Then the ECM parameters are identified in Δt1 by using forgetting factor recursive least square (FFRLS), and the battery SOC is estimated in Δt2 based on the identified parameters, finally the battery parameters are identified and the SOC is estimated by cycling in the cross-time. The experimental results show that, no matter at the same temperature in different conditions or at different temperatures in the same condition, The proposed algorithm not only effectively solves the ECM parameter identification jitter problem, but also improves the accuracy of SOC estimation, the Mean Absolute Error (MAE) minimum of SOC result is 1.42% for different operating conditions at the same temperature and 0.25% for different temperatures at the same operating conditions, respectively.

Summary: The extracellular superoxide dismutases (ecSODs) secreted by Microplitis bicoloratus reduce the reactive oxygen species (ROS) stimulated by the Microplitis bicoloratus bracovirus. Here, we demonstrate that the bacterial transferase hexapeptide (hexapep) motif and bacterial-immunoglobulin-like (BIg-like) domain of ecSODs bind to the cell membrane and transiently open hemichannels, facilitating ROS reductions. RNAi-mediated ecSOD silencing in vivo elevated ROS in host hemocytes, impairing parasitoid larva development. In vitro, the ecSOD-monopolymer needed to be membrane bound to open hemichannels. Furthermore, the hexapep motif in the beta-sandwich of ecSOD49 and ecSOD58, and BIg-like domain in the signal peptides of ecSOD67 were required for cell membrane binding. Hexapep motif and BIg-like domain deletions induced ecSODs loss of adhesion and ROS reduction failure. The hexapep motif and BIg-like domain mediated ecSOD binding via upregulating innexins and stabilizing the opened hemichannels. Our findings reveal a mechanism through which ecSOD reduces ROS, which may aid in developing anti-redox therapy.

Societal Impact Statement Society anticipates a world in which more food and fiber must be produced at warmer temperatures, which, on the contrary, have greater constraints on the use of water and fertilizers. Tree legumes are often the climax vegetation on the semi‐arid and arid lands, covering ~25% of the planet, but the knowledge of their genomes is limited. A draft genome sequence for Prosopis alba, a salt and heat tolerant tree that is able to fix nitrogen under harsh conditions, yields new clues about its adaptations. Its rich genetic and ecological diversity makes Prosopis well‐suited to the investigation of gene functions important to its own greater utilization and/or the improvement of climate resilience of other crops. Summary In arid lands that comprise 41% of the Earth's surface and are growing, tree legumes are often the climax vegetation. Now found in much of arid America, Prosopis alba is a salt‐tolerant nitrogen‐fixing tree native to Argentina. We present a Prosopis alba genome assembly that is 707 Mb in size, comprising of 6087 contigs of up to 2,077,851 bp in length and of ~359.3 Mb (50.8%) being repetitive elements dominated (20.3%) by long terminal repeats (LTR) retrotransposons. Among a total of 57,572 coding sequences (CDS), 42,475 are putative protein coding genes with median length of 2748 bp. The Prosopis alba genome shares the legume‐common tetraploidy (LCT) but has not reduplicated, evolving 3.5% and 23.1% faster than Phaseolus vulgaris and Glycine max, respectively, since the LCT. The 50 most expanded gene families include many that are involved in ion homeostasis, perhaps related to drought and/or salt adaptation, together with photosynthetic genes carbonic anhydrase (CA), malate dehydrogenase (MDH) and malic enzyme and gene families involved in circadian clock systems, synthesis of brassinosteroids, auxin and gibberellin. Some expanded gene families include members showing molecular signatures of positive selection, as do numerous multi‐copy orthologous groups with features associated with pathogen resistance and single‐copy orthogroups related to drought and salt stress response, root and root hair development, nodulation, heavy metal detoxification and stay‐green habit. Coupling genomics‐based clues about possible causes of its striking physiological adaptations with rich diversity in ecological context offers means to further investigate functional roles of specific Prosopis genes/alleles.

Abstract Background The cytochrome P450 (CYP) superfamily is the largest enzyme metabolism family in plants identified to date, and it is involved in many biological processes, including secondary metabolite biosynthesis, hormone metabolism and stress resistance. However, the P450 gene superfamily has not been well studied in pear (Pyrus spp.). Results Here, the comprehensive identification and a comparative analysis of P450 superfamily members were conducted in cultivated and wild pear genomes. In total, 338, 299 and 419 P450 genes were identified in Chinese white pear, European pear and the wild pear, respectively. Based on the phylogenetic analyses, pear P450 genes were divided into ten clans, comprising 48 families. The motif and gene structure analyses further supported this classification. The expansion of the pear P450 gene family was attributed to whole-genome and single-gene duplication events. Several P450 gene clusters were detected, which have resulted from tandem and proximal duplications. Purifying selection was the major force imposed on the long-term evolution of P450 genes. Gene dosage balance, subfunctionalization and neofunctionalization jointly drove the retention and functional diversification of P450 gene pairs. Based on the association analysis between transcriptome expression profiles and flavonoid content during fruit development, three candidate genes were identified as being closely associated with the flavonoid biosynthesis, and the expression of one gene was further verified using qRT-PCR and its function was validated through transient transformation in pear fruit. Conclusions The study results provide insights into the evolution and biological functions of P450 genes in pear.

Abstract The unique dumbbell-shape of grass guard cells (GCs) is controlled by their cell walls which enable their rapid responses to the environment. The molecular mechanisms regulating the synthesis and assembly of GC walls are as yet unknown. Here we have identified BZU3, a maize gene encoding UDP-glucose 4-epimerase that regulates the supply of UDP-glucose during GC wall synthesis. The BZU3 mutation leads to significant decreases in cellular UDP-glucose levels. Immunofluorescence intensities reporting levels of cellulose and mixed-linkage glucans are reduced in the GCs, resulting in impaired local wall thickening. BZU3 also catalyzes the epimerization of UDP-N-acetylgalactosamine to UDP-N-acetylglucosamine, and the BZU3 mutation affects N-glycosylation of proteins that may be involved in cell wall synthesis and signaling. Our results suggest that the spatiotemporal modulation of BZU3 plays a dual role in controlling cell wall synthesis and glycosylation via controlling UDP-glucose/N-acetylglucosamine homeostasis during stomatal morphogenesis. These findings provide insights into the mechanisms controlling formation of the unique morphology of grass stomata.

Parasitoid wasps control pests via a precise attack leading to the death of the pest. However, parasitoid larvae exhibit self-protection strategies against bracovirus-induced reactive oxygen species impairment. This has a detrimental effect on pest control. Here, we report a strategy for simulating Microplitis bicoloratus bracovirus using Mix-T dsRNA targeting 14 genes associated with transcription, translation, cell–cell communication, and humoral signaling pathways in the host, and from wasp extracellular superoxide dismutases. We implemented either one-time feeding to the younger instar larvae or spraying once on the corn leaves, to effectively control the invading pest Spodoptera frugiperda. This highlights the conserved principle of “biological pest control,” as elucidated by the triple interaction of parasitoid-bracovirus-host in a cooperation strategy of bracovirus against its pest host.

ABSTRACTBackground Streptococcus mutans (S. mutans) is a pivotal cariogenic pathogen contributing to its multiple virulence factors, one of which is synthesizing exopolysaccharides (EPS). VicK, a sensor histidine kinase, plays a major role in regulating genes associated with EPS synthesis and adhesion. Here we first identified an antisense vicK RNA (ASvicK) bound with vicK into double-stranded RNA (dsRNA).Objective This study aims to investigate the effect and mechanism of ASvicK in the EPS metabolism and cariogenesis of S. mutans.Methods The phenotypes of biofilm were detected by scanning electron microscopy (SEM), gas chromatography-mass spectrometery (GC-MS) , gel permeation chromatography (GPC) , transcriptome analysis and Western blot. Co-immunoprecipitation (Co-ip) assay and enzyme activity experiment were adopted to investigate the mechanism of ASvicK regulation. Caries animal models were developed to study the relationship between ASvicK and cariogenicity of S. mutans.Results Overexpression of ASvicK can inhibit the growth of biofilm, reduce the production of EPS and alter genes and protein related to EPS metabolism. ASvicK can adsorb RNase III to regulate vicK and affect the cariogenicity of S. mutans.Conclusions ASvicK regulates vicK at the transcriptional and post-transcriptional levels, effectively inhibits EPS synthesis and biofilm formation and reduces its cariogenicity in vivo.

In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTLZ4, DTLZ5, and DTLZ6 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and IGD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters.

IntroductionP. gingivalis (W83), as the keystone pathogen in chronic periodontitis, has been found to be tightly bound to systemic diseases. Outer membrane vesicles (OMVs) produced by P. gingivalis (W83) are thought to serve key functions in bacterial virulence and pathogenicity. This study aims to comprehend the biological functions of P. gingivalis OMVs isolated from different growth stages by comparing their physicochemical properties and pathogenicity.MethodsProtein composition was analyzed via isotope-labeled relative and absolute quantification (iTRAQ). Macrophage polarization and the expression of IL-6 and IL-1β were detected. The proliferation, migration, osteogenic differentiation, and IL-1b/NLRP3 expression of periodontal ligament stem cells (PDLSCs) were evaluated. P. gingivalis/P. gingivalis OMVs-induced periodontal models were also constructed in Sprague Dawley rats.ResultsThe protein composition of P. gingivalis OMVs isolated from different growth stages demonstrated obvious differences ranging from 25 KDa to 75 KDa. In the results of flow cytometry, we found that in vitro experiments the M1 subtype of macrophages was more abundant in the late-log OMVs and stationary OMVs groups which boosted the production of inflammatory cytokines more than pre-log OMVs. Compared to pre-log OMVs, late-log OMVs and stationary OMVs had more pronounced inhibitory effects on proliferation, migration, and early osteogenesis of PDLSCs. The NLRP3 inflammasome was activated to a larger extent in the stationary OMVs group. Micro-computed tomography (Micro CT), hematoxylin-eosin staining (HE), and tartrate acid phosphatase (TRAP) results showed that the periodontal damage in the stationary OMVs group was worse than that in the pre-log OMVs and late-log OMVs group, but almost equal to that in the positive control group (P. gingivalis).DiscussionIn general, both in vivo and in vitro experiments showed that late-log OMVs and stationary OMVs have more significant pathogenicity in periodontal disease.

Groundwater resources in Nebraska, U.S. are closely monitored by 23 Natural Resources Districts (NRDs) located across the state. Growers who use groundwater for irrigation are required to have flow meters installed at wells to monitor their water usage. However, many of these flow meters are still being read and recorded through in-person visits, which can be time-consuming and costly. Although some flow meters in Nebraska are monitored remotely by telemetry-enabled camera systems, yearly telemetry costs are high and making long-term operation financially burdensome. Using less expensive network protocol, such as Internet of Things (IoT), to transmit flow meter readings could enable new monitoring opportunities. However, there are challenges in directly transmitting flow meter images via IoT due to limited bandwidth. Therefore, in this study, we developed an algorithm using object detection deep learning techniques, i.e. You Only Look Once (YOLO) that can be programmed at an IoT node which can recognize readings from images of flow meters onsite before transmitting. The developed algorithm could significantly reduce data size and is essential for flow meter monitoring in an IoT network setting. The developed algorithm achieved 95.35% accuracy when recognizing 1,248 real-world flow meter images obtained at the courtesy of North Platte Natural Resources District (NPNRD) in western Nebraska. The framework and algorithm were also tested in a real-world scenario on a flow meter installed on a linear-move sprinkler irrigation system and showed promising results. By leveraging IoT and deep learning techniques, this research has the potential to revolutionize flow meter monitoring, reducing costs and improving efficiency in the management of groundwater resources in Nebraska, and potentially in other regions as well.

Hyperspectral images (HSIs) provide detailed spectral information of objects to be detected and play an important role in distinguishing targets with a similar appearance. However, the characteristics of high dimensionality and complexity impose significant challenges for realizing pixelwise classification. Although existing convolutional neural networks and transformer-based models have presented promising performance for HSIs classification, they mainly extract features from spectral–spatial perspective and do not fully consider the information in the frequency domain. To address this issue, in this article, we reconsider feature extraction and HSIs classification from the frequency domain. Specifically, inspired by the observation that high-frequency information contains detailed features within a local receptive field, whereas low-frequency information provides global smooth variations, a frequency domain feature extraction (FDFE) block with dual branches is developed. In the FDFE block, a multihead neighborhood attention block and a global filter block are designed to capture high- and low-frequency features, respectively. Besides, a pixel embedding module is constructed. Based on these, a novel hierarchical dual frequency transformer network is developed. Extensive experiments are performed on three open public hyperspectral datasets to evaluate the performance of our developed method. The experimental results demonstrate that our method is efficient and robust for HSIs classification, achieving overall accuracies of 94.14%, 86.92%, and 96.72% on the University of Pavia, University of Houston, and University of Trento datasets, respectively.al

The performance of deep convolutional neural networks has been significantly improved in recent years as a result of additional attention mechanisms applied to the standard networks. Numerous experiments conducted have demonstrated that spectral-spatial attention enhances the network's categorization ability. The three attention modules that currently use spatial attention, spectral attention, and channel attention are isolated from each other and their interrelationships are not fully considered. To solve this problem and establish the dependencies among different channels, spectral bands, spatial height, and width simultaneously, in this article, a new cross attention module called quadlet is proposed, which can capture information using simultaneous interaction of the channel, spectral depth and spatial location to improve the classification accuracy of hyperspectral images. By incorporating the quadlet attention module, a cross-dimensional residual network (QuadNet) is proposed for HSIs classification. A series of experiments conducted on four publicly available hyperspectral datasets showed that the proposed cross-attention residual network can effectively establish the dependencies among different dimensions of input tensor and achieve 98.22%, 99.88%, 99.10%, and 96.46% overall accuracy on IN, UP, SA, and UH datasets, respectively.

Abstract Objectives The purpose of this study was to investigate the association of PNPLA3 single nucleotide polymorphisms (SNPs) (rs738409 C > G, rs3747207 G > A, rs4823173 G > A, and rs2896019 T > G) with hepatocellular carcinoma (HCC) susceptibility. Methods This case–control study included 484 HCC patients and 487 controls. Logistic regression analysis was performed to study the associations of PNPLA3 gene polymorphisms with HCC susceptibility, and odds ratios with their corresponding 95% confidence intervals were calculated to evaluate these correlations. Results In the overall analysis, we found that the G allele (OR = 1.25, 95% CI = 1.04–1.50, p = 0.018, false discovery rate (FDR)-p = 0.035) and GG genotype (OR = 1.59, 95% CI = 1.06–2.39, p = 0.024, FDR-p = 0.048) of rs2896019 were significantly associated with increased HCC susceptibility. In stratified analysis, we found that all four SNPs were related to increased HCC susceptibility in subjects aged > 55 years. In haplotype analysis, the GAAG haplotype was significantly associated with increased HCC susceptibility (OR = 1.25, 95% CI = 1.03–1.53, p = 0.023, FDR-p = 0.046). Besides, we noticed that rs738409 was significantly correlated with alpha-fetoprotein (AFP) (p = 0.007), and HCC patients with the GG genotype had a higher level of AFP. Conclusions Our study suggested that PNPLA3-rs2896019 was significantly associated with an increased susceptibility to HCC.

In order to explore the key components affecting the emulsification characteristics in hydrolyzed rice protein. rice protein was hydrolyzed by acidprotease, papain and trypsin, respectively. The relationships between the structural and functional properties of different hydrolyzed rice proteins were studied by surface hydrophobicity, Fourier transform infrared spectroscopy (FTIR), emulsifying activity index and emulsifying stability index. 10 kDa were separated from the rice protein with degree of 2% treated with trypsin (trypsin 2%) by ultrafiltration. The interfacial tension, quartz crystal microbalance with dissipation (QCM-D), mean diameter particle, confocal laser scanning microscope (CLSM) and storage stability were measured to explore the influence of molecular weight on the interfacial characterization and emulsion stability. The results demonstrated that hydrolysates treated with trypsin exhibited the highest yield. Compared with the rice protein, the surface hydrophobicity of the enzymatic hydrolysis products declined significantly except for the rice protein with degree of 6% treated with trypsin (trypsin 6%). The secondary structure content revealed that the β-sheet contents of hydrolyzed rice protein significantly decreased, showing that the structure of hydrolyzed rice protein was more unfolded. The trypsin 2% exhibited the better emulsifying capacity. The mean diameter particle of 10 kDa had lower interfacial tension and thicker interfacial film than the others. The emulsion prepared by >10 kDa exhibited better emulsion stability. The results indicated that the emulsion stabilized by high molecular peptides was more stable than the smaller ones.

ObjectiveTo date, the current diagnosis of major depressive disorder (MDD) still depends on clinical symptomatologic criteria, misdiagnosis and ineffective treatment are common. The study aimed to explore circulating biomarkers for MDD diagnosis.MethodsA high-throughput antibody array technology was utilized to detect 440 circulating cytokines in eight MDD patients and eight age–and gender-matched healthy controls. LASSO regression was conducted for MDD-related characteristic proteins selection. Enzyme-linked immunosorbent assay (ELISA) was used to validate the characteristic proteins in 40 MDD patients and 40 healthy controls. Receiver operating characteristic (ROC) curve was employed to evaluate the diagnostic values of characteristic proteins for discriminating MDD patients from healthy controls. Correlations between the levels of characteristic proteins and depression severity (HAMD-17 scores) were evaluated using linear regression.ResultsThe levels of 59 proteins were found aberrant in MDD patients compared with healthy controls. LASSO regression found six MDD-related characteristic proteins including insulin, CD40L, CD155, Lipocalin-2, HGF and LIGHT. ROC curve analysis showed that the area under curve (AUC) values of six characteristic proteins were more than 0.85 in discriminating patients with MDD from healthy controls. Furthermore, significant relationship was found between the levels of insulin, CD155, Lipocalin-2, HGF, LIGHT and HAMD-17 scores in MDD group.ConclusionThese results suggested that six characteristic proteins screened from 59 proteins differential in MDD may hold promise as diagnostic biomarkers in discriminating patients with MDD. Among six characteristic proteins, insulin, CD155, Lipocalin-2, HGF and LIGHT might be useful to estimate the severity of depressive symptoms.

Irrigation water management using automated irrigation decision support system (IDSS) as a smart irrigation scheduling tool can improve water use efficiency and crop production, especially under circumstances of limited water supply. The current study evaluated the performance of different artificial intelligence (AI) algorithms and their ensembles in forecasting Crop Evapotranspiration (ETc) and Crop Water Stress Index (CWSI) against calculated single crop coefficient FAO56 ETc and Jackson's theoretical CWSI, respectively. Soil moisture, canopy temperatures (Tc) and Normalized Difference Vegetation Index (NDVI) were all measured from irrigated and non-irrigated maize plots in West Central Nebraska during 2020 and 2021 growing seasons. There were fifteen and twelve input combinations used for ETc and CWSI predictions, respectively, having input variables such as weather and soil moisture as well as ancillary variables, including NDVI, reference evapotranspiration (ETr), and cumulative growing degree days (CGDDs). While evaluating the models, four statistical performance indicators including coefficient of determination (r2), root mean square error (RMSE), mean absoluter error (MAE), and mean absolute percentage error (MAPE) were used. Furthermore, ranking scores were performed on statistical results to find the overall best model across all the input combinations. Based on total ranking scores, CatBoost (RMSE ranging between 0.06 – 0.09 unitless) was the best model in predicting CWSI, while Stacked Regression (RMSE ranging between 0.27 – 0.72 mm d−1) was the best model for ETc estimation. Future research will consider designing and evaluating an IDSS using identified best machine learning models to establish soil water and plant stress feedback for automated irrigation scheduling.

Abstract Porous copper (Cu) current collectors are regarded as a promising host for stabilizing lithium (Li) metal anodes but suffer from uncontrollable Li metal deposition due to the intrinsic lithiophobic nature of Cu. This study proposes a vertically aligned Cu host with hyperbranched CuxO nanostructure to provide lithiophilic nucleation sites for homogeneous Li metal deposition. Specifically, the vertically aligned Cu nanostructure dramatically reduces the local current density and brings homogeneous Li‐ion flux. The lithiophilic hyperbranched CuxO nanostructure with a low nucleation barrier could induce homogeneous Li nucleation and growth. As a result, the Cu@CuxO nanostructured host exhibits a low nucleation overpotential of 44.3 mV and achieves highly electrochemical reversibility with high Coulombic efficiency of 98.33% in a half‐cell. The Cu@CuxO nanostructured electrode is capable of working under different current densities varying from 0.5 to 5 mA/cm2 in a symmetric cell. The assembled full cell coupling of the Li/Cu@CuxO composite anode with the LiFePO4 cathode manifests stable long‐term cycling life at 1 C. This study elaborates on the synergistic effect of electrode structure design and interfacial chemistry modification to regulate the Li deposition/dissolution behavior, thus exhibiting remarkable electrochemical performances for next‐generation Li‐metal batteries.

Curcumin exhibits antioxidant and antitumor properties, but its poor chemical stability limits its application. Insoluble peptide precipitates formed by proteolysis of rice glutelin are usually discarded, resulting in resource waste. The coupled treatment of heat-assisted pH shifting and compounded chitosan (CS) was used to fabricate rice peptide aggregate–chitosan complexes (RPA–CS). The structure, interfacial behavior, emulsion properties, and digestibility of curcumin-loaded RPA–CS Pickering emulsions were investigated. Increasing the CS concentration led to lower interfacial tension but larger particle size, and the three-phase contact angle of the RPA–CS complexes approached 90°. Quartz crystal microbalance with dissipation (QCM–D) indicated that RPA–CS complexes with 6 g·kg−1 of CS (RPA–CS6) had the highest K1 (0.592 × 106 Hz−1) and K4 (0.487 × 106 Hz−1), suggesting that the softest interfacial layers were formed. The solid–liquid balance of RPA–RPA–CS emulsions was lower than 0.5, declaring that they had more elastic behavior than that of RPA emulsions. RPA–RPA–CS4-and RPA–CS6 emulsions had better storage stability, lower FFA release (79.8% and 76.3%, respectively), and higher curcumin bioaccessibility (65.2% and 68.2%, respectively) than RPA emulsions. This study showed that a low-value insoluble rice peptide precipitate could be used as a valuable emulsifier in foods, which may increase the economics and sustainability of the food supply.

Abstract Background Malignant pleural mesothelioma (MPM) is one of the most aggressive tumors with few effective treatments worldwide. It has been suggested that alternative splicing at the transcriptome level plays an indispensable role in MPM. Methods We analyzed the splicing profile of 84 MPM patients from the TCGA cohort by using seven typical splicing types. We classified MPM patients based on their splicing status and conducted a comprehensive analysis of the correlation between the splicing classification and clinical characteristics, genetic variation, pathway changes, immune heterogeneity, and potential therapeutic targets. Results The expression of the alternative splicing regulator SRPK1 is significantly higher in MPM tissues than in normal tissues, and correlates with poor survival. SRPK1 deficiency promotes MPM cell apoptosis and inhibits cell migration in vitro. We divided the MPM patients into four clusters based on their splicing profile and identified two clusters associated with the shortest (cluster 3) and longest (cluster 4) survival time. We present the different gene signatures of each cluster that are related to survival and splicing. Comprehensive analysis of data from the GDSC and TCGA databases revealed that cluster 3 MPM patients could respond well to the small‐molecule inhibitor CHIR‐99021, a small‐molecule inhibitor of GSK‐3. Conclusion We performed unsupervised clustering of alternative splicing data from 84 MPM patients from the TCGA database and identified a cluster associated with the worst prognosis that was sensitive to a GSK‐3 inhibitor.

Abstract Since the first dendrimer was reported in 1978 by Fritz Vögtle, dendrimer research has grown exponentially, from synthesis to application in the past four decades. The distinct structure characteristics of dendrimers include nanoscopic size, multi-functionalized surface, high branching, cavernous interior, and so on, making dendrimers themselves ideal drug delivery vehicles. This mini review article provides a brief overview of dendrimer’s history and properties and the latest developments of dendrimers as drug delivery systems. This review focuses on the latest progress in the applications of dendrimers as drug and gene carriers, including 1) active drug release strategies to dissociate drug/gene from dendrimer in response to stimuli; 2) size-adaptive and charge reversal dendrimer delivery systems that can better take advantage of the size and surface properties of dendrimer; 3) bulk and micro/nano dendrimer gel delivery systems. The recent advances in dendrimer formulations may lead to the generation of new drug and gene products and enable the development of novel combination therapies.

With the rapid expansion of urban areas in both horizontal and vertical directions, the complicated building structural changes challenge the existing 3D change detection methods. The existing 3D change detection methods are mainly based on local differences and rely on setting thresholds and rules, and face difficulties when determining complex change types. In this paper, to solve these problems, we present a building object extraction method using change indicators and an object-based change type determination approach. The key steps are as follows: (1) point-level change indicators are generated using the local geometric differences between the point clouds from two epochs; (2) change indicators are used to guide the process of region growing and graph cuts for building object extraction; and (3) the object-based change types are determined by a random forest classifier, relying on the elaborate features of the building objects. Experiments were carried out on a simulated dataset and a real airborne laser scanning (ALS) dataset. The proposed method achieved the best performance on the simulated dataset, and the average precision and recall on the real ALS dataset reached 91.1% and 85.3% respectively, which demonstrates the effectiveness of the proposed method. This work enables the 3D updating of urban building maps and can be applied to building safety monitoring and identification of potential illegal structures.

Owing to the degradation of an echelon-use lithium-ion battery (EULIB), the Ohmic internal resistance (OIR) and actual capacity (AE) have both changed greatly, and the state of energy (SOE) can more accurately represent the state of a EULIB than the state of charge (SOC) because of the working voltage. To improve the accuracy and adaptability of SOE estimation, in the paper, we study the energy state estimation of a EULIB. First, the four-order resistor–capacitance equivalent model of a EULIB is established, and an unscented transformation is introduced to further improve the estimation accuracy of the SOE. Second, a EULIB’s SOE is estimated based on adaptive unscented Kalman filter (AUKF), and the OIR and AE of a EULIB are estimated based on the AUKF. Third, a Takagi–Sugeno fuzzy model is introduced to optimize the OIR and AE of the EULIB, and the SOE estimation method is established based on an adaptive dual unscented Kalman filter (ADUKF). Through simulation experiments, verification, and comparison, energy decayed to 80%, 60%, and 40% of the rated energy, respectively, even with a large initial error; with the initial value of the SOE starting at 100%, 60%, or 20%, the estimated SOE can track the actual value. It can be seen that the method has a strong adaptive ability, and the estimation accuracy error is less than 1.0%, indicating that the algorithm has high accuracy. The method presented in this paper provides a new perspective for SOE estimation of EULIBs.

Abstract Abscisic acid (ABA) is a phytohormone that plays important roles in the regulation of plant growth, seed germination, and stress responses. The pyrabactin resistance 1-like (PYR/PYL) protein, an ABA receptor, was involved in the initial step in ABA signal transduction. However, the evolutionary history and characteristics of PYL genes expression remain unclear in Chinese white pear (Pyrus bretschneideri) or other Rosaceae species. In this study, 67 PYL genes were identified in eight Rosaceae species, and have been classified into three subgroups based on specific motifs and phylogenetic analysis. Intriguingly, we observed that whole-genome duplication (WGD) and dispersed duplication (DSD) have a major contribution to PYL family expansion. Purifying selection was the major force in PYL genes evolution. Expression analysis finds that PYL genes may function in multiple pear tissues. qRT-PCR validation of 11 PbrPYL genes indicates their roles in seed germination and abiotic stress responses. Our study provides a basis for further elucidation of the function of PYL genes and analysis of their expansion, evolution and expression patterns, which helps to understand the molecular mechanism of pear response to seed germination and seedling abiotic stress.

Although target detection based on electroencephalogram (EEG) signals has been extensively investigated recently, EEG-based target detection under weak hidden conditions remains a problem. In this paper, we proposed a rapid serial visual presentation (RSVP) paradigm for target detection corresponding to five levels of weak hidden conditions quantitively based on the RGB color space. Eighteen subjects participated in the experiment, and the neural signatures, including P300 amplitude and latency, were investigated. Detection performance was evaluated under five levels of weak hidden conditions using the linear discrimination analysis and support vector machine classifiers on different channel sets. The experimental results showed that, compared with the benchmark condition, (1) the P300 amplitude significantly decreased (8.92 ± 1.24 μV versus 7.84 ± 1.40 μV, p = 0.021) and latency was significantly prolonged (582.39 ± 25.02 ms versus 643.83 ± 26.16 ms, p = 0.028) only under the weakest hidden condition, and (2) the detection accuracy decreased by less than 2% (75.04 ± 3.24% versus 73.35 ± 3.15%, p = 0.029) with a more than 90% reduction in channel number (62 channels versus 6 channels), determined using the proposed channel selection method under the weakest hidden condition. Our study can provide new insights into target detection under weak hidden conditions based on EEG signals with a rapid serial visual presentation paradigm. In addition, it may expand the application of brain–computer interfaces in EEG-based target detection areas.

Directly applying brain signals to operate a mobile manned platform, such as a vehicle, may help people with neuromuscular disorders regain their driving ability. In this paper, we developed a novel electroencephalogram (EEG) signal-based driver–vehicle interface (DVI) for the continuous and asynchronous control of brain-controlled vehicles. The proposed DVI consists of the user interface, the command decoding algorithm, and the control model. The user interface is designed to present the control commands and induce the corresponding brain patterns. The command decoding algorithm is developed to decode the control command. The control model is built to convert the decoded commands to control signals. Offline experimental results show that the developed DVI can generate a motion control command with an accuracy of 83.59% and a detection time of about 2 s, while it has a recognition accuracy of 90.06% in idle states. A real-time brain-controlled simulated vehicle based on the DVI was developed and tested on a U-turn road. Experimental results show the feasibility of the DVI for continuously and asynchronously controlling a vehicle. This work not only advances the research on brain-controlled vehicles but also provides valuable insights into driver–vehicle interfaces, multimodal interaction, and intelligent vehicles.

Whole-genome duplication (WGD) has occurred repeatedly during plant evolution and diversification, providing genetic layers for evolving new functions and phenotypes. Advances in long-read sequencing technologies have enabled sequencing and assembly of over 1000 plant genomes spanning nearly 800 species, in which a large set of ancient WGDs has been uncovered. Here, we review the recently reported WGDs that occurred in major plant lineages and key evolutionary positions, and highlight their contributions to morphological innovation and adaptive evolution. Current gaps and challenges in integrating enormous volumes of sequenced plant genomes, accurately inferring WGDs, and developing web-based analysis tools are emphasized. Looking to the future, ambitious genome sequencing projects and global efforts may substantially recapitulate the plant tree of life based on broader sampling of phylogenetic diversity, reveal much of the timetable of ancient WGDs, and address the biological significance of WGDs in plant adaptation and radiation.

Macrophages play an essential role in maintaining the normal function of the innate and adaptive immune responses during host defence. Macrophages acquire diverse functional phenotypes in response to various microenvironmental stimuli, and are mainly classified into classically activated macrophages (M1) and alternatively activated macrophages (M2). Macrophage polarization participates in the inflammatory, fibrotic, and oncogenic processes of diverse respiratory diseases by changing phenotype and function. In recent decades, with the advent of broad-range profiling methods such as microarrays and next-generation sequencing, the discovery of RNA transcripts that do not encode proteins termed “noncoding RNAs (ncRNAs)” has become more easily accessible. As one major member of the regulatory ncRNA family, long noncoding RNAs (lncRNAs, transcripts >200 nucleotides) participate in multiple pathophysiological processes, including cell proliferation, differentiation, and apoptosis, and vary with different stimulants and cell types. Emerging evidence suggests that lncRNAs account for the regulation of macrophage polarization and subsequent effects on respiratory diseases. In this review, we summarize the current published literature from the PubMed database concerning lncRNAs relevant to macrophage polarization and the underlying molecular mechanisms during the occurrence and development of respiratory diseases. These differentially expressed lncRNAs are expected to be biomarkers and targets for the therapeutic regulation of macrophage polarization during disease development.

Abstract Alkali metals (i.e., Li, Na, and K) are promising anode materials for next‐generation high‐energy‐density batteries due to their superior theoretical specific capacities and low electrochemical potentials. However, the uneven current and ion distribution on the anode surface probably induces undesirable dendrite growth, which leads to significant safety hazards and severely hinders the commercialization of alkali metal anodes. A smart and versatile strategy that can accommodate alkali metals into porous metal current collectors (PMCCs) has been well established to resolve the issues as well as to promote the practical applications of alkali metal anodes. Moreover, the proposal of PMCCs can meet the requirement of the dendrite‐free battery fabrication industry, while the electrode material loading exactly needs the metal current collector component as well. Here, a systematic survey on advanced PMCCs for Li, Na, and K alkali metal anodes is presented, including their development timeline, categories, fabrication methods, and working mechanism. On this basis, some significant methodology advances to control pore structure, surface area, surface wettability, and mechanical properties are systematically summarized. Further, the existing issues and the development prospects of PMCCs to improve anode performance in alkali metal batteries are discussed.

Abstract Background Heterotrimeric G-proteins, composed of Gα, Gβ and Gγ subunits, are important signal transmitters, mediating the cellular response to multiple stimuli in animals and plants. The Gγ subunit is an essential component of the G-protein, providing appropriate functional specificity to the heterotrimer complex and has been well studied in many species. However, the evolutionary history, expression pattern and functional characteristics of Gγ subunits has not been explored in the Rosaceae, representing many important fruit crops. Results In this study, 35 Gγ subunit genes were identified from the eight species belonging to the Rosaceae family. Based on the structural gene characteristics, conserved protein motifs and phylogenetic analysis of the Gγ subunit genes, the genes were classified into three clades. Purifying selection was shown to play an important role in the evolution of Gγ subunit genes, while a recent whole-genome duplication event was the principal force determining the expansion of the Gγ subunit gene family in the subfamily Maloideae. Gγ subunit genes exhibited diverse spatiotemporal expression patterns in Chinese white pear, including fruit, root, ovary and bud, and under abiotic stress conditions, the relative expression of Gγ subunit genes were up-regulated or down-regulated. In addition, seven of the Gγ subunit proteins in pear were located on the plasma membrane, in the cytoplasm or nucleus. Conclusion Overall, this study of the Gγ subunit gene family in eight Rosaceae species provided useful information to better understand the evolution and expression of these genes and facilitated further exploration of their functions in these important crop plants.

To ensure the safety and reliability of an echelon-use lithium-ion battery (EULIB), the performance of a EULIB is accurately reflected. This paper presents a method of estimating the combined state of energy (SOE) and state of charge (SOC). First, aiming to improve the accuracy of the SOE and SOC estimation, a third-order resistor-capacitance equivalent model (TRCEM) of a EULIB is established. Second, long short-term memory (LSTM) is introduced to optimize the Ohmic internal resistance (OIR), actual energy (AE), and actual capacity (AC) parameters in real time to improve the accuracy of the model. Third, in the process of the SOE and SOC estimation, the observation noise equation and process noise equation are updated iteratively to make adaptive corrections and enhance the adaptive ability. Finally, an SOE and SOC estimation method based on LSTM optimization and an adaptive extended Kalman filter (AEKF) is established. In simulation experiments, when the capacity decays to 90%, 60% and 30% of the rated capacity, regardless of whether the initial value is consistent with the actual value, the values of the SOE and SOC estimation can track the actual value with strong adaptive ability, and the estimated error is less than 1.19%, indicating that the algorithm has a high level of accuracy. The method presented in this paper provides a new perspective for estimating the SOE and SOC of a EULIB.

Accurately predicting the remaining useful cycle life of a lithium-ion battery is essential for health management of battery systems. Aiming at the time-varying and nonlinear problems of lithium-ion batteries, a remaining useful cycle life estimation method based on Takagi-Sugeno fuzzy model is proposed, which not only reduces the amount of data calculation, but also reduces massive data and has high accuracy. First, collect the rate of change of working voltage in the charging process, and analyze the relationship between the position of voltage rate curve and the number of cycles. Second, in order to reduce the amount of historical data, the interval with obvious mapping relationship is selected, and the recursive least square method is used to fit the curve off-line, which reduces the amount of data calculation and is easy to achieve in battery management system engineering. And then, the Takagi-Sugeno fuzzy model is applied to establish the remaining useful cycle life method based on Takagi-Sugeno fuzzy model. Finally, battery management system application shows that the proposed method can achieve high prediction accuracy and also provides a new perspective for remaining useful cycle life prediction.

ABSTRACT Accurate differential diagnosis is the key to choosing the correct treatment for pleural effusion. The present study aimed to assess whether interleukin 32 (IL-32) could be a new biomarker of tuberculous pleural effusion (TPE) and to explore the biological role of IL-32 in TPE. IL-32 levels were evaluated in the pleural effusions of 131 patients with undetermined pleural effusion from Wuhan and Beijing cohorts using an enzyme-linked immunosorbent assay method. Macrophages from TPE patients were transfected with IL-32-specific small interfering RNA (siRNA), and adenosine deaminase (ADA) expression was determined by real-time PCR and colorimetric methods. With a cutoff value of 247.9 ng/mL, the area under the curve of the receiver operating characteristic (ROC) curve for IL-32 was 0.933 for TPE, and the sensitivity and specificity were 88.4% and 93.4%, respectively. A multivariate logistic regression model with relatively good diagnostic performance was established. IL-32-specific siRNA downregulated ADA expression in macrophages, and IL-32γ treatment significantly induced ADA expression. Our results indicate that IL-32 in pleural effusion may be a novel biomarker for identifying patients with TPE. In addition, our multivariate model is acceptable to rule in or rule out TPE across diverse prevalence settings. Furthermore, IL-32 may modulate ADA expression in the tuberculosis microenvironment. (This study has been registered at ChiCTR under registration number ChiCTR2100051112 [https://www.chictr.org.cn/index.aspx].) IMPORTANCE Tuberculous pleural effusion (TPE) is a common form of extrapulmonary tuberculosis, with manifestations ranging from benign effusion with spontaneous absorption to effusion with pleural thickening, empyema, and even fibrosis, which can lead to a lasting impairment of lung function. Therefore, it is of great significance to find a rapid method to establish early diagnosis and apply antituberculosis therapy in the early stage. This study indicates that interleukin 32 (IL-32) in pleural effusion is a new high-potency marker to distinguish TPE from pleural effusions with other etiologies. A multivariate model combining age, adenosine deaminase (ADA), lactic dehydrogenase, and IL-32 may reliably rule in TPE in intermediate- or high-prevalence areas. Additionally, we observed that IL-32 might regulate ADA expression in macrophages in the tuberculosis microenvironment. Therefore, this study provides new insights into the role of IL-32 in the tuberculosis microenvironment.

Purpose: Muscle wasting is associated with prognosis in patients with chronic obstructive pulmonary disease (COPD). Computed tomography (CT) could serve as a method for muscle assessment due to its ability to measure both muscle quantity (e.g., cross-sectional muscle area) and muscle quality (e.g., muscle attenuation). Our study aimed to compare the differences in CT-derived pectoralis muscle cross-sectional area (PMA) and pectoralis muscle attenuation (PMT) between COPD patients and healthy controls and explore the association between PMA and PMT measurements and clinical characteristics in patients with COPD.Methods: A total of 252 participants included in our analysis consisted of 80 healthy controls and 172 patients with COPD. PMA and PMT were measured from a single axial slice of the CT scan above the aortic arch. Linear regression analysis was used to determine the correlation between PMA and PMT measurements and clinical characteristics in patients with COPD. Associations were adjusted for age, sex, BMI, FEV1%pred, smoking pack-years, current smoking status, emphysema percentage, and total airway count (TAC) of the right upper lobe apical bronchus (RB1).Results: PMA and PMT were lower in COPD patients, especially those with acute exacerbation, than in healthy controls. PMA and PMT were significantly associated with the severity of emphysema and the TAC of RB1 (p < 0.05). Patients with stable COPD, who had lower PMA and lower PMT, had significantly worse pulmonary function, poorer exercise tolerance, decreased quality of life, and worse dyspnea scores. In addition, patients with acute COPD exacerbation, who had lower PMA and lower PMT, had a higher risk of respiratory failure on admission.Conclusion: CT-derived measurements of the pectoralis muscle may be helpful in detecting declines in muscle quantity and quality and predicting disease severity in patients with COPD.

Chronic inflammatory airway diseases, characterized by airway inflammation and airway remodelling, are increasing as a cause of morbidity and mortality for all age groups and races across the world. The underlying molecular mechanisms involved in chronic inflammatory airway diseases have not been fully explored. MicroRNAs (miRNAs) and long noncoding RNAs (lncRNAs) have recently attracted much attention for their roles in the regulation of a variety of biological processes. A number of studies have confirmed that both lncRNAs and miRNAs can regulate the initiation and progression of chronic airway diseases by targeting mRNAs and regulating different cellular processes, such as proliferation, apoptosis, inflammation, migration, and epithelial–mesenchymal transition (EMT). Recently, accumulative evidence has shown that the novel regulatory mechanism underlying the interaction among lncRNAs, miRNAs and messenger RNAs (mRNAs) plays a critical role in the pathophysiological processes of chronic inflammatory airway diseases. In this review, we comprehensively summarized the regulatory roles of the lncRNA–miRNA–mRNA network in different cell types and their potential roles as biomarkers, indicators of comorbidities or therapeutic targets for chronic inflammatory airway diseases, particularly chronic obstructive pulmonary disease (COPD) and asthma.

To facilitate rehabilitation training for patients, we proposed the implementation of an anthropomorphic exoskeleton structure that incorporates a variable instantaneous center of rotation (ICR). This design considers the variability in knee ICR among individuals, resulting from the irregular form of the human knee joint, and leverages a double-degrees-of-freedom (2DOF) five-bar mechanism to adapt to these differences. The walking gait of the human lower limb and the corresponding knee ICR were measured and calculated using an optical 3D motion capture system. The optimal dimension parameters of the five-bar mechanism were then obtained through the optimization of human movement position inputs and rod length constraints to minimize the error in knee ICR, gait angle, and ankle trajectory between the human and the exoskeleton. Finally, we established an exoskeleton prototype to conduct relevant experimental tests. The experiment results showed that the average errors of knee ICR trajectory, hip angle, knee angle, and ankle trajectory were 5.52 × 10−4 m, 0.010 rad, 0.014 rad, and 1.57 × 10−3 m, respectively. The experimental results demonstrated that the exoskeleton’s movement trajectory was close to the human’s, reducing the human–mechanism interaction force and improving patient comfort during rehabilitation training.