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This study aims to predict bone metastasis in lung cancer patients using radiomics and deep learning. Early prediction of bone metastasis is crucial for timely intervention and personalized treatment plans. This can improve patient outcomes and quality of life. By integrating advanced imaging techniques with artificial intelligence, this study seeks to enhance predictive accuracy and clinical decision-making. Methods: We included 189 lung cancer patients, comprising 89 with non-bone metastasis and 100 with confirmed bone metastasis. Radiomic features were extracted from CT images, and feature selection was performed using Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO). We developed and validated a radiomics model and a deep learning model using DenseNet-264. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Statistical comparisons were made using the DeLong test. Results: The radiomics model achieved an AUC of 0.815 on the training set and 0.778 on the validation set. The DenseNet-264 model demonstrated superior performance with an AUC of 0.990 on the training set and 0.971 on the validation set. The DeLong test confirmed that the AUC of the DenseNet-264 model was significantly higher than that of the radiomics model (p

Co-infection with diverse bacteria is commonly seen in patients infected with the novel coronavirus, SARS-CoV-2. This type of co-infection significantly impacts the occurrence and development of novel coronavirus infection. Bacterial co-pathogens are typically identified in the respiratory system and blood culture, which complicates the diagnosis, treatment, and prognosis of COVID-19, and even exacerbates the severity of disease symptoms and increases mortality rates. However, the status and impact of bacterial co-infections during the COVID-19 pandemic have not been properly studied. Recently, the amount of literature on the co-infection of SARS-CoV-2 and bacteria has gradually increased, enabling a comprehensive discussion on this type of co-infection. In this study, we focus on bacterial infections in the respiratory system and blood of patients with COVID-19 because these infection types significantly affect the severity and mortality of COVID-19. Furthermore, the progression of COVID-19 has markedly elevated the antimicrobial resistance among specific bacteria, such as Klebsiella pneumoniae, in clinical settings including intensive care units (ICUs). Grasping these resistance patterns is pivotal for the optimal utilization and stewardship of antibiotics, including fluoroquinolones. Our study offers insights into these aspects and serves as a fundamental basis for devising effective therapeutic strategies. We primarily sourced our articles from PubMed, ScienceDirect, Scopus, and Google Scholar. We queried these databases using specific search terms related to COVID-19 and its co-infections with bacteria or fungi, and selectively chose relevant articles for inclusion in our review.

Coupling faults that simultaneously occur during the operation of mechanical equipment are widespread. These faults encompass a diverse range of high-order coupling relationships, involving multiple base fault types. Based on the advantages of hypergraphs for higher-order relationship descriptions, two coupling fault diagnosis architectures based on the hypergraph neural network are proposed in this paper: 1. In the coupling fault diagnosis framework based on feature generation, the base faults serve as the hypergraph nodes, and each hyperedge connects the base faults. The generator, which consists of the hypergraph neural network, generates coupling faults as negative samples to enforce regularization constraints for the discriminator training. 2. In the coupling fault diagnosis framework based on feature extraction, each node represents a fault mode, and each hyperedge connects nodes with common failure modes. The multi-head attention mechanism extracts the features of base faults, and the common fault features in a hyperedge are aggregated via the hypergraph neural network. The inner product correlation is used to diagnose the fault modes. The results show that the diagnostic accuracy for coupling faults with the two frameworks reaches 88.6% and 86.76%, respectively. Both frameworks can be used for the diagnosis and analysis of high-order coupling faults.

Mechanical equipment is composed of several parts, and the interaction between parts exists throughout the whole life cycle, leading to the widespread phenomenon of fault coupling. The diagnosis of independent faults cannot meet the requirements of the health management of mechanical equipment under actual working conditions. In this paper, the dynamic vertex interpretable graph neural network (DIGNN) is proposed to solve the problem of coupling fault diagnosis, in which dynamic vertices are defined in the data topology. First, in the date preprocessing phase, wavelet transform is utilized to make input features interpretable and reduce the uncertainty of model training. In the fault topology, edge connections are made between nodes according to the fault coupling information, and edge connections are established between dynamic nodes and all other nodes. Second the data topology with dynamic vertices is used in the training phase and in the testing phase, the time series data are only fed into dynamic vertices for classification and analysis, which makes it possible to realize coupling fault diagnosis in an industrial production environment. The features extracted in different layers of DIGNN interpret how the model works. The method proposed in this paper can realize the accurate diagnosis of independent faults in the dataset with an accuracy of 100%, and can effectively judge the coupling mode of coupling faults with a comprehensive accuracy of 88.3%.

Background and objective: Due to their aggressive nature and poor prognosis, malignant femoral bone tumors present considerable hurdles. Early treatment commencement is essential for enhancing vital and practical outcomes. In this investigation, deep learning algorithms will be used to analyze magnetic resonance imaging (MRI) data to identify bone tumors that are malignant. Methodology: The study cohort included 44 patients, with ages ranging from 17 to 78 (22 women and 22 males). To categorize T1 and T2 weighted MRI data, this paper presents an improved DenseNet network model for the classification of bone tumor MRI, which is named GHA-DenseNet. Based on the original DenseNet model, the attention module is added to solve the problem that the deep convolutional model can reduce the loss of key features when capturing the location and content information of femoral bone tumor tissue due to the limitation of local receptive field. In addition, the sparse connection mode is used to prune the connection mode of the original model, so as to remove unnecessary and retain more useful fast connection mode, and alleviate the overfitting problem caused by small dataset size and image characteristics. In a clinical model designed to anticipate tumor malignancy, the utilization of T1 and T2 classifier output values, in combination with patient-specific clinical information, was a crucial component. Results: The T1 classifier's accuracy during the training phase was 92.88% whereas the T2 classifier's accuracy was 87.03%. Both classifiers demonstrated accuracy of 95.24% throughout the validation phase. During training and validation, the clinical model's accuracy was 82.17% and 81.51%, respectively. The clinical model's receiver operating characteristic (ROC) curve demonstrated its capacity to separate classes. Conclusions: The proposed method does not require manual segmentation of MRI scans because it makes use of pretrained deep learning classifiers. These algorithms have the ability to predict tumor malignancy and shorten the diagnostic and therapeutic turnaround times. Although the procedure only needs a little amount of radiologists' involvement, more testing on a larger patient cohort is required to confirm its efficacy.

Introduction: C-phycocyanin (C-PC), a photosynthetic protein obtained from Spirulina, is regarded a highly promising commercially available biochemical. Numerous in vitro and in vivo studies have provided evidence of C-PC’s ability to mitigate the inflammatory response, alleviate oxidative stress, and facilitate wound healing. However, despite the existing knowledge regarding C-PC’s protective mechanism against cellular apoptosis induced by ultraviolet B (UVB) radiation, further in vivo experiments are needed to explore its anti-photoaging mechanism.Methods: In this study, a UVB-induced skin photoaging model was established using BALB/c-nu mice, and the potential protective effects of topically administered c-PC were investigated by various molecular biology tools. In addition, a novel delivery system, C-PC nanodispersion, was developed to facilitate the transdermal delivery of C-PC.Results: C- PC demonstrated significant anti-photoaging activities in the UVB-induced skin. The application of C-PC to the dorsal skin of the mice resulted in improved macroscopic characteristics, such as reduced sagging and coarse wrinkling, under UVB irradiation Histological analyses showed that C-PC treatment significantly decreased the symptoms of epidermal thickening, prevented dermal collagen fiber loosening, increased the hydroxyproline (Hyp) content and activities of antioxidant enzymes (such as superoxide dismutase, catalase, and glutathione peroxidase) in mouse skin, decreased malondialdehyde levels and expressions of inflammatory factors (interleukin-1α [IL-1α], IL-1β, IL-6, and tumor necrosis factor-α), reduced matrix metalloproteinase [MMP-3 and MMP-9] expressions, and inhibited the phosphorylation of c-Jun N-terminal kinase, extracellular signal-regulated kinase, and p38 proteins in the mitogen-activated protein kinase family.Discussion: By analyzing the results of the study, a new drug delivery system, C-PC nano-dispersion, was proposed, and the anti-photoaging effect of C-PC and its mechanism were investigated.

To tackle the problems of over-reliance on traditional experience, poor troubleshooting robustness, and slow response by maintenance personnel to changes in faults in the current aircraft health management field, this paper proposes the use of a knowledge graph. The knowledge graph represents troubleshooting in a new way. The aim of the knowledge graph is to improve the correlation between fault data by representing experience. The data source for this study consists of the flight control system manual and typical fault cases of a specific aircraft type. A knowledge graph construction approach is proposed to construct a fault knowledge graph for aircraft health management. Firstly, the data are classified using the ERNIE model-based method. Then, a joint entity relationship extraction model based on ERNIE-BiLSTM-CRF-TreeBiLSTM is introduced to improve entity relationship extraction accuracy and reduce the semantic complexity of the text from a linguistic perspective. Additionally, a knowledge graph platform for aircraft health management is developed. The platform includes modules for text classification, knowledge extraction, knowledge auditing, a Q&A system, and graph visualization. These modules improve the management of aircraft health data and provide a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis.

The graphical abstract of the potential mechanism of m6A modulates long non-coding RNA in the developing mouse heart.

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases. Therefore, it is important to develop an efficient heart segmentation algorithm.Methods: A total of 275 nuclear magnetic resonance imaging (MRI) heart scans were collected, analyzed, and preprocessed from Huaqiao University Affiliated Strait Hospital, and the data were used in our improved deep learning model, which was designed based on the U-net network. The training set included 80% of the images, and the remaining 20% was the test set. Based on five time phases from end-diastole (ED) to end-systole (ES), the segmentation findings showed that it is possible to achieve improved segmentation accuracy and computational complexity by segmenting the left ventricle (LV), right ventricle (RV), and myocardium (myo).Results: We improved the Dice index of the LV to 0.965 and 0.921, and the Hausdorff index decreased to 5.4 and 6.9 in the ED and ES phases, respectively; RV Dice increased to 0.938 and 0.860, and the Hausdorff index decreased to 11.7 and 12.6 in the ED and ES, respectively; myo Dice increased to 0.889 and 0.901, and the Hausdorff index decreased to 8.3 and 9.2 in the ED and ES, respectively.Conclusion: The model obtained in the final experiment provided more accurate segmentation of the left and right ventricles, as well as the myocardium, from cardiac MRI. The data from this model facilitate the prediction of cardiovascular disease in real-time, thereby providing potential clinical utility.

Extreme weather events are happening more frequently as a result of global climate change. Dust storms broke out in the spring of 2017 in China and drastically impacted the local air quality. In this study, a variety of data, including aerosol vertical profiles, surface particle concentration, meteorological parameters, and MODIS–derived aerosol optical depth, as well as backward trajectory analysis, were employed to analyze two dust events from April to May in Beijing. The dust plumes were mainly concentrated below 0.8 km, with peak PM10 values of 1000 μg·m−3 and 300 μg·m−3 in the two cases. The aerosols showed different vertical distribution characteristics. The pure dust in case 1 from 4 to 5 May 2017 had a longer duration (2 days) and presented a larger aerosol extinction coefficient (2.27 km−1 at 355 nm and 1.25 km−1 at 532 nm) than that of the mixed dust in case 2 on 17 April 2017 (2.01 km−1 at 355 nm and 1.33 km−1 at 532 nm). The particle depolarization ratio (PDR) remained constant (0.24 ± 0.03 in case 1) from the surface to 0.8 km in height. In contrast, the PDR profile in the mixed dust (case 2) layer was split into two regions—large values exceeding 0.15 above 0.6 km and small values of 0.11 ± 0.03 below 0.6 km. The influence of meteorological information on aerosol distribution was also investigated, and wind was predominant through the observing period. The pure dust in case 1 was mainly from Mongolia, with strong northwest winds, while the near-surface mixed pollution was caused by the combination of long-transported sand and local emission. Furthermore, lidar-derived profiles of dust mass concentrations in the two cases were presented. This study reveals the vertical characteristics of dust aerosols in the production and dissipation of localized dust events and confirms the efficacy of thorough observations with multiple approaches from the ground to space to monitor dust events in real time.

In bridge health monitoring, in order to closely monitor the structural state changes of the bridge under heavy traffic load and other harsh environments, the monitoring system is required to give the change process of structural modal parameters. Due to the symmetric variables of bridge monitoring during operation, the evaluation needs to be completed by the recursive identification of modal parameters based on environmental excitation, especially the recursive recognition of the random subspace method with high recognition accuracy. We have studied the recursive identification methods of covariance-driven and data-driven random subspace categories respectively, established the corresponding recursive format, and used the model structure of the ASCE structural health monitoring benchmark problem as a numerical example to verify the reliability of the proposed method. First, based on the similar interference environment of the observation data at the same time, a reference point covariance-driven random subspace recursive algorithm (IV-RSSI/Cov) based on the auxiliary variable projection approximation tracking (IV-PAST) algorithm is established. The recursive format of the system matrix and modal parameters is obtained. Based on Givens rotation, the rank-2 update form of the row space projection matrix is established, and the recursive format of the data-driven recursive random subspace method (RSSI/Data) under the PAST algorithm is obtained. Then, based on the benchmark problem of ASCE-SHM, the response of the model structure under environmental excitation is numerically simulated, the frequency, damping ratio and vibration mode of the structure are recursively tracked, and their reliability and shortcomings are studied. After improving the recursive method, the frequency tracking accuracy has been improved, with a maximum accuracy of 99.8%.

Rice is an important cereal crop, which provides staple food for more than half of the world's population. To meet the demand of the ever-growing population in the next few decades, an extra increase in rice yield is an urgent need. Given that various agronomic traits contribute to the yield of rice, deciphering the key regulators involved in multiple agronomic trait formation is particularly important. As a superfamily of transcription factors, zinc finger proteins participate in regulating multiple genes in almost every stage of rice growth and development. Therefore, understanding zinc finger proteins underlying regulatory network would provide insights into the regulation of agronomic traits in rice. To this end, we intend to summarize the current advances in zinc finger proteins, with emphasis on C2H2 and CCCH proteins, and then discuss their potential in improving rice yield.

Abstract Background Photosynthetic pigments participating in the absorption, transformation and transfer of light energy play a very important role in plant growth. While, the spatial distribution of foliar pigments is an important indicator of environmental stress, such as pests, diseases and heavy metal stress. Results In this paper, in situ quantitative visualization of chlorophyll and carotenoid was realized by combining the Raman spectroscopy with calibration model transfer, and a laboratory Raman spectral model was successfully extended to a portable field spectral measurement. Firstly, a nondestructive and fast model for determination of chlorophyll and carotenoid in tea leaf was established based on confocal micro-Raman spectrometer in the laboratory. Then the spectral model was extended to a real-time foliar map scanning spectra of a field portable Raman spectrometer through calibration model transfer, and the spectral variation between the confocal micro-Raman spectrometer in the laboratory and the portable Raman spectrometer were effectively corrected by the direct standardization (DS) algorithm. The portable map scanning Raman spectra of the tea leaves after the model transfer were got into the established quantitative determination model to predict the concentration of photosynthetic pigments at each pixel of the tea leaves. The predicted photosynthetic pigments concentration of each pixel was imaged to illustrate the distribution map of foliar pigments. Statistical analysis showed that the predicted pigment contents were highly correlated with the real contents. Conclusions It can be concluded that the Raman spectroscopy was applicable for in situ, non-destructive and rapid quantitative detecting and imaging of photosynthetic pigment concentration in tea leaves, and the spectral detection model established based on the laboratory Raman spectrometer can be applied to a portable field spectrometer for quantitatively imaging of the foliar pigments.

Abstract For the airports worldwide, it is important to establish a "passenger integrity system" based on the basic information of passengers and their related credit system. Correspondingly, this paper develops a new risk assessment model for the passenger graded security check by introducing several new technologies to obtain the passengers’ real-time status information as well as historical data. We first propose to deploy a variety of 5G-IoT devices to monitor the passengers in real time, including high-definition cameras, millimeter-wave security detectors, etc. We then rely on machine learning to analyze the passenger risk level and integrate improved analytic hierarchy process (AHP) with group decision theory, namely GD-AHP. According to the risk level, the passengers can be classified into known, ordinary and dangerous targets. The differentiated handling of different targets could significantly save the time of security check and improve the passenger experience.

Electronic packaging solder joints are the key parts of mechanical fixation and electrical interconnection between electronic chips and printed circuit boards, which are prone to failure and lead to electronic device failures under the action of vibration environment stress. In regard to the failure characterization and degradation modeling of electronic packaging solder joints under vibration load, this paper adopts environmental stress tests, builds a vibration failure test platform, designs a vibration load excitation spectrum, and obtains solder joint degradation data under vibration stress; it uses the square root amplitude, form factors, and kurtosis factors to characterize the solder joint degradation process, which effectively identify the solder joint degradation node, and improve the data monotonicity of the solder joint degradation process; the Wiener process is used to build a solder joint multi-stage degradation model. Based on the EM algorithm, the hyperparameters of the degradation model have been optimized, and the universal test of the Wiener degradation model with multiple samples is carried out in accordance with the LB index. The analysis shows that the effectiveness of different samples is as high as 87.5%, which verifies the universality of the Wiener degradation model; Based on the crack morphology subject to the solder joint test and the features of the solder joint in the multi-stages, the paper analyzes the crack propagation behavior of the solder joint, and clarifies the failure mechanism of the solder joint.

Interconnect structure, as the weak point of reliability, is often the critical part in electronic devices during their service. In order to monitor the health state and degradation of interconnect structure, the charging time is used as the health indicator for interconnect structure of QFP package in this paper. Firstly, a monitoring circuit, based on the proposed electrical model of interconnect structure, is designed to measure the charging time in real time. Secondly, the equivalent circuit of the monitoring circuit is derived and the effect of the parameters of the monitoring circuit on the charging time is analyzed. Then, experiments under vibration and steady temperature are conducted to analyze the failure of interconnect structure and derive the damage model. Finally, the effect of charging time as the health indicator to monitor the damage of interconnection structures is analyzed and the uncertainty is discussed. Results show that the charging time is directly related to the interconnect damage and can well characterize the interconnect failure, which can be used as a health indicator to assess the damage and predict the failure of interconnect structure.

Transition metal organic framework materials and their selenides are considered to be one of the most promising cathode materials for nickel-zinc (denoted as Ni-Zn) batteries due to their low cost, environmental friendliness, and controllable microstructure. Yet, their low capacity and poor cycling performance severely restricts their further development. Herein, we developed a simple one-pot hydrothermal process to directly synthesize NiSe2 (denotes as NiSe2-X based on the molar amount of SeO2 added) stacked layered sheets. Benefiting from the peculiar architectures, the fabricated NiSe2−1//Zn battery based on NiSe2 and the Zn plate exhibits a high specific capacity of 231.6 mAh g−1 at 1 A g−1, and excellent rate performance (162.8 mAh g−1 at 10 A g−1). In addition, the NiSe2//Zn battery also presents a satisfactory cycle life at the high current density of 8 A g−1 (almost no decay compared to the initial specific capacity after 1000 cycles). Additionally, the battery device also exhibits a satisfactory energy density of 343.2 Wh kg−1 and a peak power density of 11.7 kW kg−1. This work provides a simple attempt to design a high-performance layered cathode material for aqueous Ni-Zn batteries.

This paper proposes a flexible sensor for detecting cracks on bridges. Strain and deflection sensing modules are integrated on the film that is made of composite conductive materials. By optimizing the preparation ratio and internal structure, the strain detection accuracy and sensitivity of the sensor have been improved. The bridge crack detection accuracy reached 91%, which is higher than current sensors. Experimental results show that the composite material containing 2.23 wt% carbon black (CB) mixed hybrid filler has good linearity, higher accuracy than sensors in use, excellent stretchability (>155%), high gauge factor (GF ~ 43.3), and excellent durability over 2000 stretching-releasing cycles under 10 N. The designed flexible sensor demonstrates the practicality and effectiveness of bridge crack detection and provides a feasible solution for accurate bridge health monitoring in the future.

The deinking pulp (DIP) is a main resource for paper making, and the wastewater from DIP process needs to be treated. Anaerobic biochemical technique has been widely applied in DIP wastewater treatment, due to the remarkable capability in reducing high chemical oxygen demand (COD). In this study, a mathematical simulation model was established to investigate the performance of a full-scale anaerobic biochemical system for treating DIP wastewater. The model was based on Anaerobic Digestion Model No. 1 (ADM1), which was modified according to the specific anaerobic digestion process for DIP wastewater treatment. The hydrodynamic behavior of a full-scale anaerobic biochemical system was considered in this model. The characteristics of the influent DIP wastewater were assessed, and then, the substrate COD proportion was divided successfully for the necessity of ADM1 applying. The Monte Carlo technique was implemented to distinguish the most sensitive parameters that influenced the model output indicators comprising effluent COD and biogas production. The sensitive parameters were estimated and optimized. The optimized value of k_m_pro is 12.02, K_S_pro is 0.35, k_m_ac is 4.26, K_S_ac is 0.26, k_m_h2 is 16.62, and K_S_h2 is 3.21 × 10–5. The model was calibrated with 150 days operation values measured in the field. The subsequent 100 days on-site values were used to validate the model, and the results obtained by the simulations were in good agreement. This study provides a meaningful and theoretical model guidance for full-scale wastewater anaerobic biochemical treatment simulation.

Microwave curing technology has been widely used in resin and its composite materials. In order to study its effect for curing unsaturated polyester resin (UPR) composites containing calcium carbonate (CaCO3) filler, this paper first investigated the influence of microwave power and microwave irradiation time on the curing characteristics of UPR. Then, CaCO3 particles were added to the UPR to investigate the microwave curing effect of the UPR composites containing the CaCO3. The results showed that microwave irradiation could heat the UPR sample evenly, and rapidly cause the chain growth reaction, thus greatly shortening the curing time. The curing degree and products of the samples after microwave curing were consistent with that of the thermal curing. The addition of CaCO3 particles could increase the heating rate of the UPR composites, which would accelerate the curing rate of the UPR. However, higher microwave power could lead to pore defects inside the UPR composites with higher CaCO3 content, resulting in a lower strength. Thus, the compactness of the samples should be improved by reducing the microwave power and prolonging the microwave treatment time.

Ultrasound (US) has become one of the most important techniques in green chemistry and emerging technologies. Many research investigations documented the usefulness of US in a wide range of applications in food science, nanotechnology, and complementary medicine, where effective extraction of natural products is important. However, as with all novel technologies, US has advantages and limitations that require clarification for full adaptation at an industrial scale. The present review discusses recent applications of US in herbal phytochemistry with the emphasis on US effects on chemical structures of bioactive compounds extracted from herbs and their bioactivities. The impact of different US processing conditions such as frequency, intensity, duration, temperature, and pressure on the effectiveness of the extraction process and the properties of the extracted materials are also discussed. Different frequencies and intensities of US have demonstrated its potential applications in modifying, determining, and predicting the physicochemical properties of herbs and their extracts. US has important applications in nanotechnology where it supports the fabrication of inexpensive and eco-friendly herbal nanostructures, as well as acoustic-based biosensors for chemical imaging of the herbal tissues. The application of US enhances the rates of chemical processes such as hydrolysis of herbal fibers, which reduces the time and energy consumed without affecting the quality of the final products. Overall, the use of US in herbal science has great potential to create novel chemical constructions and to be used as an innovative diagnostic system in various biomedical, food, and analytical applications.

Human integrin receptors are important for cell-cell and cell-matrix adhesion in normal epithelial cells. Emerging evidences have indicated integrin members are involved in cancer development and progression as well. However, the expression patterns and clinical significance of the whole integrin family in ovarian cancer (OC) have not yet been well understood. In the present study, we utilized the public datasets including GEPIA, GEO, ONCOMINE, cBioPortal, Kaplan-Meier Plotter, TIMER databases, to analyze the expression and prognostic value of integrin members in OC. We found ITGA3/B4/B6/B7/B8 were abnormally overexpressed in OC; ITGA6 was good prognosis predictor in OC; ITGA3/ B4/B8 were poor prognosis predictor specially in advanced OC patients; elevated ITGA3/B4 might promote metastasis and elevated ITGA3/B8 might promote platinum resistance of OC; ITGA3 and ITGB4 might synergistically or independently regulate cell adhesion and proliferation; ITGA4/AL/AM/AX/B2/B7 showed strong correlations with various tumor immune infiltrates (TILs), especially with pro-tumor immunes cell types like monocyte, M2 macrophage and exhaustion T cells infiltration; ITGAL/AM/B2/B7 and residing memory CD8+ T cells marker ITGAE were specially associated with early OC patients outcome. Our results implied that ITGA3/B4 were important prognostic markers of advanced OC, ITGAL/AM/ B2/B7 were immune associated prognosis markers of early OC, together they might render important therapeutic targets for OC.

Staphylococcus aureus is a notorious bacterial pathogen that often causes soft tissue and bloodstream infections and invariably garners resistance mechanisms against new antibiotics. Modulation of the host immune response by metabolites is a powerful tool against bacterial infections, but has not yet been used against S. aureus infections. In this study, we identified four metabolite biomarkers: L-proline, L-isoleucine, L-leucine, and L-valine (PILV), through a metabolomics study using animal models of S. aureus bloodstream infection. The exogenous administration of each metabolite or of PILV showed anti-infective effects, and a higher protection was achieved with PILV in comparison to individual metabolites. During the staphylococcal infection, the expression of most host arginase and nitric oxide synthase (NOS) isozymes was simultaneously induced in mouse liver, kidney, and blood samples. However, the induction of arginase isozymes was dramatically stronger than that of NOS isozymes. This elevated arginase activity was inhibited by the metabolite biomarkers thus killing S. aureus, and PILV exhibited the strongest inhibition of arginase activity and bacterial inhibition. The suppression of arginase activity also contributed to the metabolite-mediated phagocytic killing of S. aureus in mouse and human blood. Our findings demonstrate the metabolite-mediated arginase inhibition as a therapeutic intervention for S. aureus infection.

Strigolactones (SLs) represent an important new plant hormone class marked by their multifunctional roles in plants and rhizosphere interactions, which stimulate hyphal branching in arbuscular mycorrhizal fungi (AMF) and seed germination of root parasitic plants. SLs have been broadly implicated in regulating root growth, shoot architecture, leaf senescence, nodulation, and legume–symbionts interaction, as well as a response to various external stimuli, such as abiotic and biotic stresses. These functional properties of SLs enable the genetic engineering of crop plants to improve crop yield and productivity. In this review, the conservation and divergence of SL pathways and its biological processes in multiple plant species have been extensively discussed with a particular emphasis on its interactions with other different phytohormones. These interactions may shed further light on the regulatory networks underlying plant growth, development, and stress responses, ultimately providing certain strategies for promoting crop yield and productivity with the challenges of global climate and environmental changes.

Autophagy is a highly conserved degradation mechanism in eukaryotes, executing the breakdown of unwanted cell components and subsequent recycling of cellular material for stress relief through vacuole-dependence in plants and yeast while it is lysosome-dependent in animal manner. Upon stress, different types of autophagy are stimulated to operate certain biological processes by employing specific selective autophagy receptors (SARs), which hijack the cargo proteins or organelles to the autophagy machinery for subsequent destruction in the vacuole/lysosome. Despite recent advances in autophagy, the conserved and diversified mechanism of autophagy in response to various stresses between plants and animals still remain a mystery. In this review, we intend to summarize and discuss the characterization of the SARs and their corresponding processes, expectantly advancing the scope and perspective of the evolutionary fate of autophagy between plants and animals.

Autophagy is an evolutionarily conserved process for turning over unwanted cellular components, thus promoting nutrient recycling and maintaining cellular homeostasis, which eventually enables plants to survive unfavorable growth conditions. In addition to plant growth and development, previous studies have demonstrated that autophagy is involved in the responses to various environmental challenges through interplaying with multiple phytohormones, including abscisic acid (ABA), jasmonic acid (JA), and salicylic acid (SA). In this review, we summarize the advances made in their synergistic interactions in response to multiple abiotic and biotic stresses; we also discuss the remaining issues and perspectives regarding their crosstalk.

Using two rice cultivars, the effect of severe, mild and no water stress, W3, W2 and W1, respectively, on flag leaf physiology, the ecological characteristics of canopy and organ temperatures were studied in 2008 and 2009. The grain yield was reduced under W3 due to decreased seed setting rate and 1000 grain weight but not under W2. Water stress had a significant effect on the flag leaf physiological characteristics along with the soluble sugar and amino proline content. Catalase and peroxidase activities, photosynthetic and transpiration rates, and stomatal conductance in W2 were significantly higher than in W3 and similar to those in W1. The organ and canopy temperatures were significantly higher in W3 than in either W1 or W2, and there was no significant difference between W1 and W2. This study clearly showed that water stress had a significant effect on leaf physiology, temperature of organs and canopy. Mild water stress (soil water potential maintained at -15- -20 kPa) could construct a population that is water-saving and resistant to heat stress. This facilitates access to a high yield as well.

In order to reduce the energy consumption of the cluster members in WSNs, this paper proposes a random compressive sensing data acquisition scheme for airborne clustering WSNs. In this scheme, hardware resource limited cluster members sample the input signals with random sampling sequence and then transmit the sampling signals to the cluster head or Sink to reconstruct. Aimed at improving the reconstruction performance of this scheme, this paper puts forward a new MP reconstruction method based on composite chaotic-genetic algorithm, which combines the excellent local searching characteristics of chaos theory with the powerful global search ability of genetic algorithm. The experimental result shows that this scheme is very suitable for the hardware resource limited clustering WSNs. On the one hand, the reconstruction precision of the composite chaotic-genetic MP method can reach a magnitude of 10 −15 , and the average search speed is about 37 time that of the MP reconstruction method, which can effectively improve the reconstruction performance of the cluster head or Sink; on the other hand, by diminishing the sampling frequency to 1/8 of the original sampling frequency, the random compressive sensing technique can dramatically reduce the sampling quantity and the energy consumption of the cluster members, with the reconstruction precision reaching a magnitude of 10 −7 .