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A new efficient method for the separation of Ba from REEs for the accurate determination of REE content in Ba-rich samples by ICP-MS.

This paper aims to interpret the coupling modal properties of cyclic blades under impact–friction interactions and their evolution mechanism versus operating points. Therefore, a coupling analytical model of cyclic blades is developed based on a Lagrange method and the assumed mode method (AMM), after considering centrifugal stiffening, spin softening, stagger angle, and twist angle. Then a mixed modal analysis method (MMAM) for this analytical model is extended by combining the extended periodic motion concept (EPMC) with AMM. Wherein a classic alternating frequency/time method (AFT) and the continuation method are employed to overcome the numerical divergence problem. Then damped nonlinear normal modes (dNNMs), including eigenfrequencies, modal damping ratios, and mode shapes, of the coupling system with shroud joints are finally computed and discussed under different excitation levels and contact conditions through a modal synthesis algorithm. After that, the influence laws of centrifugal stiffening and spin softening on the dNNMs are explored to reveal its evolution mechanism versus operation speeds. Finally, the Campbell diagrams of dNNMs are successfully obtained to discuss the effects of the impact–friction coupling on critical speeds (CSs) of the shrouded blades system.

Through field surveys, sampling, and laboratory experiments, the content, enrichment, and migration characteristics of heavy metals in the rock-soil-tea system in the Guizhou metamorphic rock area were analyzed herein. The results show that the As and Hg contents in metamorphic sandstone in the study area are higher than those in slate, while the Mn, Zn, Pb, and Ni contents in slate are higher. The content of heavy metals in the sandstone of the Jialu formation of the Xiajiang group in Neoproterozoic is the highest, and that of the slate of the Fanzhao formation is the lowest. The content of As in the rock samples in the study area is higher than that in the Epicontinental crust, while Cd is found to be deficient. The heavy metals at some soil points exceed the standard, but this does not affect the growth and development of tea. The content of heavy metals in most tea is lower than the safety limit standards, and the THQ value is less than 1, indicating that the heavy metals in tea in Guizhou metamorphic rock areas pose no concern for human health. In the metamorphic rock distribution areas of Guizhou, tea does not enrich As, Cr, Hg, and Pb in soil, but enriches Mn, Cu, Ni, and Zn to varying degrees, with the enrichment of Mn being the strongest.

This study aimed to understand the suicidal ideation and suicidal attempts among cancer patients during the COVID-19 pandemic. The data were collected from patients diagnosed with cancer while attending the largest cancer center in the south of China. A structured questionnaire was used to investigate patients' demographic data, suicidal behavior, and factors related to COVID-19. Mental health conditions were measured by the Generalized Anxiety Disorder-7, the Patient Health Questionnaire-9, and the Brief Symptom Inventory. Comorbidities and medical conditions of cancer patients were extracted from the electronic healthcare records. Among the 5670 cancer patients, 755 (13.3%) reported suicidal ideation, and 266 (4.7%) reported suicidal attempts during the COVID-19 pandemic. The age group with the highest risk of suicidal ideation was 20-24 years (23.9%). Lifetime history of suffering from mental disorders, longer time since cancer diagnosis, regional and distant tumor stage, depression, anxiety, hostility, having a higher frequency of worrying about cancer management due to COVID-19, higher frequency feeling of overwhelming psychological pressure due to COVID-19, having a higher level of barriers to manage cancer due to COVID-19, and higher barriers to continue treatment of cancer due to inconveniences caused by COVID-19, were all significantly associated with increased risk of suicidal ideation. We also identified the risk factors of suicide attempts. This is the first study investigating the prevalence and risk factors associated with suicidal ideation and suicidal attempts in Chinese cancer patients during the COVID-19 pandemic. Our findings suggest that it is essential to monitor the mental health conditions of this vulnerable population, especially for cancer patients who have comorbidity with a history of mental disorders. Also, government policymakers should take action to protect cancer patients to avoid any interruption of their continued treatment. Further efforts are urgently required to develop specific psychological interventions to reduce the risk factors among cancer patients during the COVID-19 pandemic.

In order to solve the problem that the target is easily lost in the process of multisource target data fusion tracking, a multisource target data fusion tracking method based on data mining is proposed. Multisource target data fusion tracking belongs to location level fusion. Firstly, a hybrid heterogeneous network fusion model is established, and then, data features are extracted, and a fusion source big data acquisition algorithm is designed based on compressed sensing to complete data preprocessing to reduce the amount of data acquisition. Based on data mining association multisource fusion target, get the relationship between each measurement and target, and build multisource target data fusion tracking model to ensure the stable state of fusion results. It shows that the proposed method can save the tracking time and improve the tracking accuracy compared with the methods based on NNDA and PDA, which is more conducive to the real-time tracking of multisource targets.

Many ectomycorrhizal (ECM) fungi produce commercially valuable edible sporocarps. However, the effects of nitrogen (N) application on ECM fungal sporocarp formation remain poorly understood. In this study, we investigated the effect of application of various N concentrations (0, 5, 25, 50, 100, and 200 mg/L) on the growth of Laccaria japonica mycelia in vitro for 1 month. The results showed that L. japonica mycelial biomass was highest in the 50 mg/L treatment and was significantly inhibited at N concentrations higher than 200 mg/L. Next, we investigated the effects of N application on mycorrhizal colonization and sporocarp formation in L. japonica colonizing Pinus densiflora seedlings in pots. The seedlings were watered with nutrient solutions containing 0, 5, 25, 50, or 100 mg N/L. The biomass, photosynthetic rate, and mycorrhizal colonization rates of the seedlings were measured at 45 days (first appearance of primordia), 65 days (sporocarp appearance on the substrate surface), and 4 months after seedlings were transplanted. The numbers of primordia and sporocarps were recorded during the experimental period. Total carbon (C) and N content were determined in seedlings at 4 months after transplantation, and in L. japonica sporocarps. Both mycelial growth and sporocarp production reached their maximum at an N application concentration of 50 mg/L, suggesting that the most suitable N concentration for ECM fungal sporocarp formation can easily be estimated in vitro during mycelial growth. This finding may help determine the most suitable N conditions for increasing edible ECM fungus sporocarp production in natural forests.

Bolt connection is one of the main fixing methods of cylindrical shell structures. A typical bolted connection model is considered as a tuned system. However, in the actual working conditions, due to the manufacturing error, installation error and uneven materials of bolts, there are always random errors between different bolts. To investigate the influence of non-uniform parameters of bolt joint, including the stiffness and the distribution position, on frequency complexity characteristics of cylindrical shell through a statistical method is the main aim of this paper. The bolted joints considered here were simplified as a series of springs with random features. The vibration equation of the bolted joined cylindrical shell was derived based on Sanders’ thin shell theory. The Monte Carlo simulation and statistical theory were applied to the statistical analysis of mode characteristics of the system. First, the frequency and mode shape of the tuned system were investigated and compared with FEM. Then, the effect of the random distribution and the random constraint stiffness of the bolts on the frequency and mode shape were studied. And the statistical analysis on the natural frequencies was evaluated for different mistuned levels. And some special cases were presented to help understand the effect of random mistuning. This research introduces random theory into the modeling of bolted joints and proposes a reference result to interpret the complexity of the modal characteristics of cylindrical shells with non-uniform parameters of bolt joints.

Precise detection of recurrence in patients with treated nasopharyngeal carcinoma (NPC) facilitates timely intervention and prolongs survival. However, there is no compelling tool realizing real-time precise recurrence detection as scale hitherto. Here we present a deep learning-based sequential scan model called RAIN, harnessing 10,212 time-series follow-up head and neck magnetic resonance (MR) scans of 1,808 patients with treated NPC in a multicenter observational study (Blinded ID). The RAIN yields larger area under the receiver operating curve (AUC) values than single scan model (internal: 0.916 vs 0.855, p = 0.004; external: 0.900 vs 0.709, p

Supplementary Table S1 shows the list of primary antibodies used for immunohistochemical staining and immunoblotting assays. Supplementary Table S2 shows the clinical characteristics of 626"driver-gene-negative" LUAD patients from four cohorts. Supplementary Table S3 shows the clinical characteristics of 52 "driver-gene-negative" LUAD patients whose samples were used for microarray analysis. Supplementary Table S4 shows the microarray results of the genes in the Wnt-signaling pathway. Supplementary Table S5 shows the univariate association of 41 candidate genes with overall survival in the training SYSUFH cohort. Supplementary Table S6 shows the C-index of the three enrolled factors in the nomogram in the SYSUFH training cohort. Supplementary Table S7 indicates the summary of relevant findings of ITH analysis of twenty-one patients with "driver-gene-negative" LUAD. Supplementary Figure S1 shows the workflow to determine "driver-gene-negative" status in LUAD patients. Supplementary Figure S2 shows the pathological diagnosis of enrolled LUAD patients. Supplementary Figure S3 shows the study flowchart. Supplementary Figure S4 shows the signaling GO analysis and cellular component classification analysis for the genome-wide microarray. Supplementary Figure S5 shows the β-Catenin expression characteristics in patients with "driver-gene-negative" LUAD from the SYSUFH training cohort. Supplementary Figure S6 shows the different expression profiles of β-catenin in EGFRmt, KRASmt, and ALKft LUAD. Supplementary Figure S7 shows the protein expression characteristics of 41 candidate genes in 8 "driver-gene-negative" LUAD patients. Supplementary Figure S8 shows these representative immunohistochemical staining for 41 candidate molecules in 626 "driver-gene-negative" LUAD patient tissue samples. Supplementary Figure S9 shows kaplan-Meier estimates of progression-free survival (PFS) based on the CSDW signature in the four cohorts. Supplementary Figure S10 shows that the CSDW signature predicts clinical outcomes in all 626 patients with "driver-gene-negative" LAUD. Supplementary Figure S11 shows that the optimum cutoff value of the four prognostic markers was determined using the X-tile program in the training set and Kaplan-Meier analysis of all 626 LUAD samples. Supplementary Figure S12 shows the correlation between the four genes in the CSDW signature and tumor size, lymphatic metastasis and distant organ metastasis. Supplementary Figure S13 shows that kaplan-Meier analysis was used to compare the performance of the CSDW signature in all 626 LUAD patients with different clinicopathological risk factors. Supplementary Figure S14 shows that the nomogram to predict the 3-year and 5-year overall survival (OS) of "driver-gene-negative" LUAD patients. Supplementary Figure S15 shows that time-dependent ROC curves to evaluate the sensitivity and specificity of the nomogram in predicting overall survival in "driver-gene-negative" LUAD patients. Supplementary Figure S16 shows that intra- and inter-sample variability of β-catenin, SOX9, DVL3 and Wnt2b protein levels detected by IHC.

Purpose:Examining the role of developmental signaling pathways in “driver gene–negative” lung adenocarcinoma (patients with lung adenocarcinoma negative for EGFR, KRAS, BRAF, HER2, MET, ALK, RET, and ROS1 were identified as “driver gene–negative”) may shed light on the clinical research and treatment for this lung adenocarcinoma subgroup. We aimed to investigate whether developmental signaling pathways activation can stratify the risk of “driver gene–negative” lung adenocarcinoma.Experimental Design:In the discovery phase, we profiled the mRNA expression of each candidate gene using genome-wide microarrays in 52 paired lung adenocarcinoma and adjacent normal tissues. In the training phase, tissue microarrays and LASSO Cox regression analysis were applied to further screen candidate molecules in 189 patients, and we developed a predictive signature. In the validation phase, one internal cohort and two external cohorts were used to validate our novel prognostic signature.Results:Kyoto Encyclopedia of Genes and Genomes pathway analysis based on whole-genome microarrays indicated that the Wnt/β-catenin pathway was activated in “driver gene–negative” lung adenocarcinoma. Furthermore, the Wnt/β-catenin pathway–based gene expression profiles revealed 39 transcripts differentially expressed. Finally, a Wnt/β-catenin pathway–based CSDW signature comprising 4 genes (CTNNB1 or β-catenin, SOX9, DVL3, and Wnt2b) was developed to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter overall survival [HR, 10.42; 6.46–16.79; P < 0.001) than patients with low-risk scores.Conclusions:The CSDW signature is a reliable prognostic tool and may represent genes that are potential drug targets for “driver gene–negative” lung adenocarcinoma.

This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas (‘non-Zhejiang’). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R2 reaching 0.9979, while that of the second derivative was the lowest with an R2 of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.

Ectomycorrhizal (ECM) fungi improve the host plant's tolerance to abiotic and biotic stresses. Cenococcum geophilum (Cg) is among the most common ECM fungi worldwide and often grows in saline environments. However, the physiological and molecular mechanisms of salt tolerance in this fungus are largely unknown. In the present study, 12 isolates collected from different ecogeographic regions were used to investigate the mechanism of salt tolerance of Cg. The isolates were classified into four groups (salt-sensitive, moderately salt-tolerant, salt-tolerant, and halophilic) based on their in vitro mycelial growth under 0, 50, 125, 250, and 500 mM NaCl concentrations. Hence, the Na, Ca, P, and K concentrations of mycelia and the pH of the culture solution were determined. Compared with salt-tolerant isolates, treatment with 250 mM NaCl significantly increased the sodium concentration and decreased the potassium concentration of salt-sensitive isolates. RNA-sequencing and qRT-PCR analysis were conducted to identify differentially expressed genes (DEGs) involved in transmembrane transport and oxidoreductase activity pathways. The hydrogen peroxide concentration and activities of peroxidase and superoxide dismutase in mycelia were determined, and the accumulation and scavenging of reactive oxygen species in the salt-sensitive isolates were more active than those in the salt-tolerant isolates. The results supply functional validations to RNA-seq and qRT-PCR analysis. This study provides novel insights into the salt-stress response of Cg isolates and provides a foundation for elucidation of the salt-tolerance mechanism of ECM fungi.

Background Accurate detection is essential for brain metastasis (BM) management, but manual identification is laborious. This study developed, validated, and evaluated a BM detection (BMD) system. Methods Five hundred seventy-three consecutive patients (10 448 lesions) with newly diagnosed BMs and 377 patients without BMs were retrospectively enrolled to develop a multi-scale cascaded convolutional network using 3D-enhanced T1-weighted MR images. BMD was validated using a prospective validation set comprising an internal set (46 patients with 349 lesions; 44 patients without BMs) and three external sets (102 patients with 717 lesions; 108 patients without BMs). The lesion-based detection sensitivity and the number of false positives (FPs) per patient were analyzed. The detection sensitivity and reading time of three trainees and three experienced radiologists from three hospitals were evaluated using the validation set. Results The detection sensitivity and FPs were 95.8% and 0.39 in the test set, 96.0% and 0.27 in the internal validation set, and ranged from 88.9% to 95.5% and 0.29 to 0.66 in the external sets. The BMD system achieved higher detection sensitivity (93.2% [95% CI, 91.6–94.7%]) than all radiologists without BMD (ranging from 68.5% [95% CI, 65.7–71.3%] to 80.4% [95% CI, 78.0–82.8%], all P < .001). Radiologist detection sensitivity improved with BMD, reaching 92.7% to 95.0%. The mean reading time was reduced by 47% for trainees and 32% for experienced radiologists assisted by BMD relative to that without BMD. Conclusions BMD enables accurate BM detection. Reading with BMD improves radiologists’ detection sensitivity and reduces their reading times.

Analysis of the vibration reduction characteristics of shock absorbers is crucial for engines. In this study, the fractal theory was applied to the contact surface of an under-platform damper (UPD), and the influence of the excitation force in the same and opposite directions on the roughness of the contact surface was studied. First, based on fractal geometry theory (FGT), the roughness characterization method of a UPD contact surface was proposed. Then, the friction mechanical model of the rough contact surface was established by combining it with a 3D contact mechanical model. Furthermore, a finite element dynamic model of a blade with a UPD structure was set up. Next, the harmonic balance method was used to calculate the nonlinear response characteristics of a blade under different levels of contact surface roughness. Finally, the influence of the contact surface roughness on the vibration reduction ability of a UPD under different excitation modes was analyzed. According to the simulation results, as the contact surface became rougher, the vibration suppression ability of the UPD on the blade became stronger and stronger. With the change in the centrifugal force of the UPD and the amplitude of the same/reverse excitation force, the influencing law of the contact surface roughness on the vibration suppression ability of the UPD remained unchanged, indicating that the rougher the contact surface roughness, the better the vibration suppression effect.

Green finance (GF) is a vital strategy implemented by China to minimize carbon emissions to achieve targets of carbon peak and carbon neutrality. Thus, the objective of this study is to reduce carbon emissions by developing green finance practices in China. This study identifies, evaluates, and ranks the factors and sub-factors of green finance because it is the core issue for sustainable development. In this regard, this study utilizes the Delphi and fuzzy Analytical Hierarchy Process (FAHP) method to analyze the main factors and sub-factors of green finance. The Delphi method finalizes the 6 factors and 26 sub-factors after a thorough investigation. The FAHP method was used to assess and rank the identified factors and sub-factors of green finance. The findings show that the political (POF) is the most crucial factor of green finance in the Chinese economy. The economic (ECF) and environmental (ENF) factors are ranked second and third important factors. The further results of the FAHP reveal that ecological and political identification (POF1), political stability (POF3), and climate commitments (POF2) are the top-ranked sub-factors of green finance. The results specify that green finance development is a very crucial strategy to minimize carbon emissions.

Underplatform dampers (UPDs) can suppress blade vibration through interface friction dissipation. At present, most of the existing UPD analysis models ignore the uncertainties induced by multi-source factors such as manufacturing, excitation, and wear, resulting in insufficient accuracy of dynamic prediction and optimization of UPDs. Therefore, this paper will deeply consider the uncertain factors such as machining tolerances, assembly tolerances, working load environment, and contact surface wear encountered in the actual manufacturing and working process, and carry out the multi-source uncertainty dynamics research of the UPDs. To solve the "dimension disaster" caused by multi-source uncertainty analysis, an adaptive algorithm based on the non-embedded polynomial chaotic expansion (PCE) is proposed. The algorithm selects the integral accuracy of the best chaotic polynomial by generalizing error convergence criteria and then constructs a surrogate model. This algorithm is in agreement with the calculated results based on Monte Carlo simulation (MCS). Through the quantitative characterization of the above uncertain factors, the efficient prediction of the uncertainty zone of the UPDs dynamic response is realized, the influence law of multi-source uncertainty factors on the vibration attenuation characteristics of UPDs is obtained, and sensitivity analysis of all parameters is carried out, which provides data support for the robust optimization design of UPDs. The simulation results show that the adaptive algorithm based on the uncertainty of multi-source parameters can predict the dynamic response of UPDs structure with high precision and high efficiency, and the uncertainty factors have a significant influence on the nonlinear dynamic characteristics of UPDs, In this case, phenomena such as "resonance band" and "frequency shift" will appear, and the uncertainty of multiple source parameters can cause large fluctuations in the dynamic response of UPDs, especially at the formant of the amplitude response. In addition, compared with other nonlinear regions, the Sobol exponents of uncertain parameters near the formant change significantly.

Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti‐cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI‐powered cancer care., This review introduced the general principle of AI, summarize major areas of its application for cancer diagnosis, treatment, and precision medicine and discuss its future directions and remaining challenges.

There is increasing evidence indicating that melts derived from subducted oceanic crust and sediments may have played a key role in building continental crust. This mechanism predicts that juvenile arc crust should have oxygen isotope characteristics ranging from mantle-like to supracrustal, but consistent mantle-like radiogenic (Nd-Hf) isotopic signatures. Here we present in-situ zircon U-Pb dating, Hf-O isotope analyses, and whole rock major-trace element and Nd isotope analyses of a granitoid from NW India. In-situ secondary ion mass spectrometry (SIMS) zircon U-Pb dating yields a weighted mean 207Pb/206Pb age of 873±6 Ma for the granitoid. It displays mantle-like zircon eHf(eHf(873 Ma)=+9.3 to +10.9) and whole-rock Nd (eNd(873 Ma)=+3.5) values but supracrustal δ18O values, the latter mostly varying between 9‰ and 10‰. The calculated whole-rock δ18O value of 11.3‰±0.6‰ matches well with those of hydrothermally-altered pillow lavas and sheeted dykes from ophiolites. The major and trace element composition of the granitoid is similar to petrological experimental melts derived from a mixture of MORB+sediments. Thus, the granitoid most likely represents the product of partial melting of the uppermost oceanic crust (MORB+sediments). We propose that the decoupling between Hf-Nd and O isotopes as observed in this granitoid can be used as a powerful tool for the identification of slab melting contributing to juvenile continental crustal growth. Such isotopic decoupling can also account for high δ18O values observed in ancient juvenile continental crust, such as Archean tonalite-trondhjemite-granodiorite suites.

Aims Frailty has a great impact on the quality of life of patients with chronic heart failure (CHF), which needs to be judged in time. To develop a diagnostic model based on nutritional indicators to judge the frailty status of patients with chronic heart failure (Frailty-CHF). Methods and results In the data collection part of this study, questionnaire method and biomedical measurement method were adopted. The trace elements in serum samples were detected by high performance liquid chromatography, chemiluminescence, and inductively coupled plasma mass spectrometry. We used Excel for data consolidation, and then imported the data into R software for modelling. Lasso method was used for variable screening, and Logistics regression fitting model was used after variables were determined. The internal validation of the model was completed by Bootstrap re-sampling. A total of 123 patients were included in this study. After variables’ screening, age, nutritional status-heart failure, New York Heart Association Functional Class (NYHA), micronutrients B12, Ca, folic acid, and Se were included in the model, the c statistic and Brier score of the original model were 0.9697 and 0.0685, respectively. After Bootstrap re-sampling adjustment, the c statistic and Brier score were 0.8503 and 0.1690. Conclusion In this study, a diagnostic model of age, nutritional status-heart failure, NYHA, the micronutrients B12, Ca, folic acid, and Se was established. It could help healthcare professionals better identify the frailty status in patients with CHF.

This paper considers the fixed-time (FXT) and preassigned-time (PAT) stochastic synchronization of complex dynamic networks (CDN) based on the quantized event-triggered strategy. First, by applying some constructing comparison systems, more relaxed criteria are proposed for FXT stability and the estimated settling time is more accurate. Second, the FXT quantized event-triggered control schemes with sign or saturation function are designed, in which Zeno behavior is excluded and chattering phenomenon is reduced under the control strategies with saturation function. Besides, the quantized event-triggered control schemes can more effectively save resources of communication and computation. Third, several novel quantized event-triggered strategies are designed to achieve PAT synchronization, where the settling time can be preassigned according to realistic situation and is irrelevant with parameters and initial values of CDN. Finally, numerical example proves the rationality of the theories for the FXT and PAT stochastic synchronization of CDN.

CO2 sequestration engineering is promising for carbon-negative biosynthesis, and the artificial communities can solve more complex problems than monocultures. However, obtaining an ideal photosynthetic community is still a great challenging. Here, we developed a highly-compatible photosynthetic community (HCPC) by integrating a sucrose-producing CO2 sequestration module and a super-coupled module. The cyanobacteria CO2 sequestration module was obtained using stepwise metabolic engineering and then coupled with the efficient sucrose utilization module, Vibrio natriegens. Integrated omics analysis indicated enhanced photosynthetic electron transport and extracellular vesicles promote intercellular communication. Additionally, the HCPC was used to channel CO2 into valuable chemicals, enabling an overall release of -22.27 to -606.59 kgCO2e/kg in the end products. This novel light-driven community could facilitate circular economic implementation in the future.

Populus wilsonii is an important species of section Leucoides, and the natural populations mainly grow in southwest China. In this study, a single genotype of wild P. wilsonii was sequenced and assembled at genome size of 477.35 Mb in 19 chromosomes with contig N50 of 16.3 Mb. A total of 38,054 genes were annotated, and 49.95% of the genome was annotated as repetitive elements. Phylogenetic analysis identified that the divergence between P. wilsonii and the ancestor of P. deltoides and P. trichocarpa was 12 (3–23) Mya. 4DTv and Ks distributions supported the occurrence of the salicoid WGD event (~65 Mya). The highly conserved collinearity supports the close evolutionary relationship among these species. Some key enzyme-encoding gene families related to the biosynthesis of lignin and flavonoids were expanded and highly expressed in the stems or leaves, which probably resist the damage of the natural environment. In addition, some key gene families related to cellulose biosynthesis were highly expressed in stems, accounting for the high cellulose content of P. wilsonii variety. Our findings provided deep insights into the genetic evolution of P. wilsonii and will contribute to further biological research and breeding as well as for other poplars in Salicaceae.

The coupling vibration of a blade-disk-shaft unit is generally studied on the assumption that all the blades are identical. However, the mistuning feature always occurs due to manufacturing toleran...

In this paper, we present a depth-perceived 3D visual saliency map and propose a no-reference stereoscopic image quality assessment (NR SIQA) algorithm using 3D visual saliency maps and convolutional neural network (CNN). Firstly, the 2D salient region of stereoscopic image is generated, and the depth saliency map is calculated, and then, they are combined to compute 3D visual saliency map by linear weighted method, which can better use depth and disparity information of 3D image. Finally, 3D visual saliency map, together with distorted stereoscopic pairs, is fed into a three-channel CNN to learn human subjective perception. We call proposed depth perception and CNN-based SIQA method DPCNN. The performances of DPCNN are evaluated over the popular LIVE 3D Phase I and LIVE 3D Phase II databases, which demonstrates to be competitive with the state-of-the-art NR SIQA algorithms.