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Abstract Background Platycodon grandiflorum (P. grandiflorum) is a commonly used medicinal plant in China. Transcriptome sequencing studies of different tissues of P. grandiflorum have been widely conducted. However, studies on transcriptome sequencing and expression patterns of key genes in the saponin synthesis pathway of Tongcheng P. grandiflorum, a high-quality medicinal resource of different years, are relatively limited. Results This study involved transcriptome sequencing and bioinformatics analysis of the roots from annual, biennial, and triennial P. grandiflorum in the Tongcheng area. After data filtering and assembly, we obtained 111.44 Gb of clean base data, including 742,880616 clean reads. We identified 5,156 differential expression unigenes between at least two sample groups, with differences noted among annual, biennial, and triennial P. grandiflorum plants. GO enrichment analysis annotated 3509, 1819, and 1393 DEGs in comparison TC1vsTC2, TC1vsTC3, and TC2vsTC3, respectively. Furthermore, KEGG enrichment analysis identified 16 genes encoding key enzymes in the terpene skeleton biosynthesis, sesquiterpene and triterpene biosynthesis pathways, including SE, AACT, FPPS, DXR, HMGR, HMGS, and DXS. The results of qRT-PCR experiments showed that most of the genes were most highly expressed in annual P. grandiflorum. Conclusions The present study provided transcriptomic data from the roots of Tongcheng P. grandiflorum of different years, which provides critical bioinformatics data on the growth and development of P. grandiflorum, laying a foundation for further research on saponins and identifying key enzymes involved in this process across different growth stages.

Abstract Catalytic transformation of renewable biomass into value-added chemicals is an appealing strategy to upgrade biomass resources. Due to the presence of abundant oxygen-containing groups such as hydroxyl and aldehyde, biomass and its derived platform molecules have been served as ideal starting feedstock to synthesize valuable N-containing chemicals through reductive amination. In this mini review, we overviewed the recent advances in the reductive amination of several key bio-platform molecules including hydroxyl carboxylic acids, furfural, 5-hydroxylmethyl furfural and levulinic acid, with a focus on the production of amino acids, furan amines and pyrrolidones using thermocatalysis, electrocatalysis or photocatalysis. Moreover, the functions of active sites and the reaction mechanisms in different catalytic systems are discussed to get insights into the key factors in the reductive amination of biomass resources.

Historical unsustainable mining activities have led to significant ecological degradation, emphasizing the importance and urgency of ecological restoration and its effectiveness assessment. This study evaluates the economic, ecological, and social benefits of ecological restoration in the Longshan mining area from the perspective of ecological product value, utilizing an input–output research framework based on ecosystem service valuation. This comprehensive assessment includes consideration of restoration cost inputs, aiming to delve into the effectiveness of ecological restoration projects in mining areas. The findings reveal: (1) The indirect value of the ecological products generated by the ecological restoration is approximately 3.13 times greater than their direct value, highlighting the significantly positive impacts of the project. This underscores the necessity for future assessments of mining area ecological restoration projects to not only focus on direct economic returns but also to thoroughly examine their long-term positive impacts on social structures, environmental quality, and residents’ quality of life. (2) When considered solely from an economic benefit perspective, the input–output ratio of the project is 0.34, insufficient to cover the total cost of the project. However, after integrating economic, ecological, and social benefits, the input–output ratio increases to 1.40. This indicates the importance of exploring the mechanisms through which the indirect values of ecological and social benefits contribute directly to the project’s economic sustainability.

Abstract Background Class III peroxidase (POD) enzymes play vital roles in plant development, hormone signaling, and stress responses. Despite extensive research on POD families in various plant species, the knowledge regarding the POD family in Chinese pear (Pyrus bretschenedri) is notably limited. Results We systematically characterized 113 POD family genes, designated as PbPOD1 to PbPOD113 based on their chromosomal locations. Phylogenetic analysis categorized these genes into seven distinct subfamilies (I to VII). The segmental duplication events were identified as a prevalent mechanism driving the expansion of the POD gene family. Microsynteny analysis, involving comparisons with Pyrus bretschenedri, Fragaria vesca, Prunus avium, Prunus mume and Prunus persica, highlighted the conservation of duplicated POD regions and their persistence through purifying selection during the evolutionary process. The expression patterns of PbPOD genes were performed across various plant organs and diverse fruit development stages using transcriptomic data. Furthermore, we identified stress-related cis-acting elements within the promoters of PbPOD genes, underscoring their involvement in hormonal and environmental stress responses. Notably, qRT-PCR analyses revealed distinctive expression patterns of PbPOD genes in response to melatonin (MEL), salicylic acid (SA), abscisic acid (ABA), and methyl jasmonate (MeJA), reflecting their responsiveness to abiotic stress and their role in fruit growth and development. Conclusions In this study, we investigated the potential functions and evolutionary dynamics of PbPOD genes in Pyrus bretschenedri, positioning them as promising candidates for further research and valuable indicators for enhancing fruit quality through molecular breeding strategies.

In recent years, there has been significant research interest in the field of immunotherapy for non-small cell lung cancer (NSCLC) within the academic community. Given the observed variations in individual responses, despite similarities in histopathologic type, immunohistochemical index, TNM stage, or mutation status, the identification of a reliable biomarker for early prediction of therapeutic responses is of utmost importance. Conventional medical imaging techniques primarily focus on macroscopic tumor monitoring, which may no longer adequately fulfill the requirements of clinical diagnosis and treatment. CT (computerized tomography) or PEF/CT-based radiomics has the potential to investigate the molecular-level biological attributes of tumors, such as PD-1/PD-L1 expression and tumor mutation burden, which offers a novel approach to assess the effectiveness of immunotherapy and forecast patient prognosis. The utilization of cutting-edge radiological imaging techniques, including radiomics, PET/CT, machine learning, and artificial intelligence, demonstrates significant potential in predicting diagnosis, treatment response, immunosuppressive characteristics, and immune-related adverse events. The current review highlights that CT scan-based radiomics is a reliable and feasible way to predict the benefits of immunotherapy in patients with advanced NSCLC.

Coherently combining distributed apertures adjusts the transmitted/received signals of multiple distributed small apertures, allowing coordinated distributed systems to obtain high power aperture products at much lower cost than large aperture. This is a promising and viable technology as an alternative to using large apertures. This study describes the concept and principles of coherently combining distributed apertures. Depending on whether external signal inputs at the combination destination are necessary, the implementation architecture of coherent combination is classified into two categories: closed- and open-loop. The development of coherently combining distributed apertures and their application in fields such as missile defense, deep space telemetry control, radar detection over ultralong range, and radio astronomy are then comprehensively presented. Furthermore, key techniques for aligning the time and phase of the transmitted/received signals for each aperture are elaborated, which are also necessary for coherently combining distributed apertures, including high-precision distributed time-frequency transfer and synchronization, and coherently combining parameters estimation, measurement and calibration, and prediction. Finally, summary is presented, and the scope of future works in this field is explored.

Traditional villages along the Jinzhong section of the Wanli Tea Road possess unique geographical locations and cultural heritage, showcasing a rich variety of cultural landscapes. Among these, village temple buildings play a crucial role as the core of spiritual and belief life in traditional villages. In the context of rapid urbanization and modernization, a new perspective is needed to thoroughly explore the deeper connotations of cultural landscape characteristics in traditional villages along the Wanli Tea Road, particularly by examining the composition and inherent features of their landscape genes. This study employs landscape gene theory, using the village temple system as a lens to identify and analyze the landscape genes of traditional villages along the Jinzhong section of the Wanli Tea Road. Based on the results, a genealogy and a map of landscape genes are constructed to clearly and effectively present the typical characteristics and expressions of these genes in a logical and visual manner. The findings indicate the following: (1) The village temple system in traditional villages along the Jinzhong section of the Wanli Tea Road is closely related to the formation and expression of their landscape genes and can play a critical role in the protection and transmission of cultural landscapes. (2) As important nodes along the historical trade route, the landscape gene characteristics of these villages reflect their unique natural environments and historical development trajectories, illustrating the deep intertwining of commercial activities and local cultural landscapes. (3) This study establishes a replicable research framework that is not only applicable to local studies but also provides valuable references for broader cultural landscape research.

Suillus is one of the most important genera of ectomycorrhizal fungi. As a model for studying host specificity, its molecular fragments and nuclear genome have been analyzed. However, its mitochondrial genome has not yet been reported. In this study, we assembled five mitogenomes of Suillus and analyzed and compared their basic characteristics. Owing to the large number of introns as well as intergenic regions, the mitogenomic lengths of species of Suillus were greater than those of other species of Boletales. We identified two main patterns of gene order arrangement in the members of the order Boletales. The Ka/Ks values of 15 protein-coding genes were Suillus was monophyletic. Phylogenetic results based on the internal transcribed spacer region and mitogenome were used to confirm the distribution of Suillus placidus in China. The results showed that the mitogenome was superior in distinguishing species compared with a single molecular fragment. This is the first study to investigate the mitogenome of Suillus, enriching the mitogenome information and providing basic data for the phylogeny, resource conservation, and genetic diversity of this genus.

With the increase of extreme weather events such as cold waves, power transmission line icing has become more and more severe, affecting the safe and stable operation of power systems. Thus, icing prediction has become crucial for power grids. In this study, we collect multi-source data including the historical observations of meteorological elements and transmission line icing in Sichuan during 2017–2019, and develop an artificial intelligence (artificial intelligence)-based integrated model to achieve icing thickness prediction according to meteorological elements. Using the Weather Research and Forecasting model and the three-dimensional variational data assimilation method, we analyze the weather conditions in Sichuan of China during the 2020 winter, and obtain the high-precision meteorological element fields that are related to icing prediction. The forecasted meteorological elements are then combined with the AI-based integrated model to predict icing conditions, assisting in the warning of transmission line icing. The results indicate that the AI-based integrated model displays superior performance on the accurate prediction of icing thickness in the test set, with only two samples having prediction errors of more than 3 mm. Data assimilation can effectively improve the forecast accuracy of meteorological elements near icing observation stations and thus enhance the accuracy of icing thickness prediction. In particular, icing thickness prediction is remarkably improved at Gaoqiao, Laolinkou and Erlangshan stations.

Previous studies have successfully applied a lightweight recurrent neural network (RNN) called Echo State Network (ESN) for EEG-based emotion recognition. These studies use intrinsic plasticity (IP) and synaptic plasticity (SP) to tune the hidden reservoir layer of ESN, yet they require extra training procedures and are often computationally complex. Recent neuroscientific research reveals that the brain is modular, consisting of internally dense and externally sparse subnetworks. Furthermore, it has been proved that this modular topology facilitates information processing efficiency in both biological and artificial neural networks (ANNs). Motivated by these findings, we propose Modular Echo State Network (M-ESN), where the hidden layer of ESN is directly initialized to a more efficient modular structure. In this paper, we first describe our novel implementation method, which enables us to find the optimal module numbers, local and global connectivity. Then, the M-ESN is benchmarked on the DEAP dataset. Lastly, we explain why network modularity improves model performance. We demonstrate that modular organization leads to a more diverse distribution of node degrees, which increases network heterogeneity and subsequently improves classification accuracy. On the emotion arousal, valence, and stress/calm classification tasks, our M-ESN outperforms regular ESN by 5.44, 5.90, and 5.42%, respectively, while this difference when comparing with adaptation rules tuned ESNs are 0.77, 5.49, and 0.95%. Notably, our results are obtained using M-ESN with a much smaller reservoir size and simpler training process.

Magnesium-based energy materials, which combine promising energy-related functional properties with low cost, environmental compatibility and high availability, have been regarded as fascinating candidates for sustainable energy conversion and storage. In this review, we provide a timely summary on the recent progress in three types of important Mg-based energy materials, based on the fundamental strategies of composition and structure engineering. With regard to Mg-based materials for batteries, we systematically review and analyze different material systems, structure regulation strategies as well as the relevant performance in Mg-ion batteries (MIBs) and Mg-air batteries (MABs), covering cathodes, electrolytes, anodes for MIBs, and anodes for MABs; as to Mg-based hydrogen storage materials, we discuss how catalyst adding, composite, alloying and nanostructuring improve the kinetic and thermodynamic properties of de/hydrogenation reactions, and in particular, the impacts of composition and structure modification on hydrogen absorption/dissociation processes and free energy modification mechanism are focused; regarding Mg-based thermoelectric materials, the relations between composition/structure and electrical/thermal transport properties of Mg3X2 (X = Sb, Bi), Mg2X (X = Si, Ge, Sn) and MgAgSb-based materials, together with the representative research progress of each material system, are summarized and discussed. Finally, by pointing out remaining challenges and providing possible solutions, this review aims to shed light on the directions and perspectives for practical applications of magnesium-based energy materials in the future.

Abstract We downloaded the mRNA expression profiles of patients with LUAD and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and used the Least Absolute Shrinkage and Selection Operator Cox regression model to construct a multigene signature in the TCGA cohort, which was validated with patient data from the GEO cohort. Results showed differences in the expression levels of 120 necroptosis-related genes between normal and tumor tissues. An eight-gene signature (CYLD, FADD, H2AX, RBCK1, PPIA, PPID, VDAC1, and VDAC2) was constructed through univariate Cox regression, and patients were divided into two risk groups. The overall survival of patients in the high-risk group was significantly lower than of the patients in the low-risk group in the TCGA and GEO cohorts, indicating that the signature has a good predictive effect. The time-ROC curves revealed that the signature had a reliable predictive role in both the TCGA and GEO cohorts. Enrichment analysis showed that differential genes in the risk subgroups were associated with tumor immunity and antitumor drug sensitivity. We then constructed an mRNA–miRNA–lncRNA regulatory network, which identified lncRNA AL590666. 2/let-7c-5p/PPIA as a regulatory axis for LUAD. Real-time quantitative PCR (RT-qPCR) was used to validate the expression of the 8-gene signature. In conclusion, necroptosis-related genes are important factors for predicting the prognosis of LUAD and potential therapeutic targets.

Abstract Background Idiopathic pulmonary fibrosis (IPF), a chronic progressive interstitial lung disease of unknown etiology, is characterized by continuous damage to alveolar epithelial cells, abnormal repair of alveolar tissue, and alveolar wall scar formation. Currently, the recommended treatment for IPF in Western medicine is relatively limited. In contrast, traditional Chinese medicine and compound prescriptions show advantages in the diagnosis and treatment of IPF, which can be attributed to their multi-channel and multi-target characteristics and minimal side-effects. The purpose of this study was to further corroborate the effectiveness and significance of the traditional Chinese medications Astragalus and Danshen in IPF treatment. Methods We performed whole-genome methylation analysis on nine rat lung tissue samples to determine the epigenetic variation between IPF and non-fibrotic lungs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and quantitative reverse transcription polymerase chain reactions. Results We identified differentially methylated regions and 105 associated key functional genes in samples related to IPF and Chinese medicine treatment. Based on the methylation levels and gene expression profiles between the Chinese medicine intervention and pulmonary fibrosis model groups, we speculated that Astragalus and Salvia miltiorrhiza (traditionally known as Danshen) act on the Isl1, forkhead box O3, and Sonic hedgehog genes via regulation at transcriptional and epigenetic levels during IPF. Conclusions These findings provide novel insights into the epigenetic regulation of IPF, indicate the effectiveness of Astragalus and Danshen in treating IPF, and suggest several promising therapeutic targets for preventing and treating IPF.

Mg3Sb2 has attracted intensive attention as a typical Zintl-type thermoelectric material. Despite the exceptional thermoelectric performance in n-type Mg3Sb2, the dimensionless figure of merit (zT) of p-type Mg3Sb2 remains lower than 1, which is mainly attributed to its inferior electrical properties. Herein, we synergistically optimize the thermoelectric properties of p-type Mg3Sb2 materials via codoping of Cd and Ag, which were synthesized by high-energy ball milling combined with hot pressing. It is found that Cd doping not only increases the carrier mobility of p-type Mg3Sb2, but also diminishes its thermal conductivity (κtot), with Mg2.85Cd0.5Sb2 achieving a low κtot value of ∼0.67 W m−1 K−1 at room temperature. Further Ag doping elevates the carrier concentration, so that the power factor is optimized over the entire temperature range. Eventually, a peak zT of ∼0.75 at 773 K and an excellent average zT of ∼0.41 over 300 − 773 K are obtained in Mg2.82Ag0.03Cd0.5Sb2, which are ∼240% and ∼490% higher than those of pristine Mg3.4Sb2, respectively. This study provides an effective pathway to synergistically improve the thermoelectric performance of p-type Mg3Sb2 by codoping Cd and Ag, which is beneficial to the future applications of Mg3Sb2-based thermoelectric materials.

IntroductionWomen with perinatal anxiety have reduced coping capacity during labor, which affects labor progress and increases the likelihood of a cesarean section. Several non-pharmacological interventions for anxiety during childbirth are available. This study used the “lite touch” method, a non-pharmacological intervention based on physiological responses and obstetric clinical experience in women. We aimed to evaluate whether lite touch could relieve perinatal anxiety and investigate the effect of light skin stroking on the maternal hormones, catecholamine, and cortisol.MethodsThis randomized clinical trial involved women with low-risk singleton pregnancies at full term or near term. Eligible pregnant women who were latent and did not undergo epidural anesthesia were randomized into two groups. Participants in the intervention group underwent routine prenatal care, including lite touch, whereas the control group underwent routine prenatal care alone. Demographic data were collected through a questionnaire. Labor anxiety was assessed using the State Anxiety Inventory, and saliva was collected before and after the intervention. Changes in saliva cortisol and catecholamine levels were analyzed using a double-antibody sandwich enzyme-linked immunosorbent assay.ResultsIn total, 83 participants were included, with 43 and 40 in the intervention and control groups, respectively. In the intervention group, pre-intervention anxiety scores were significantly lower (p 0.05). Cortisol and catecholamine levels in saliva were significantly lower in the intervention group than in the control group after the intervention (p

ABSTRACT Our study aims to analyze the hypovirulent Klebsiella pneumoniae with the NDM-5 gene on IncN plasmids. This strain was isolated from a patient with a liver abscess, and the ST type was ST147. The characterization, antimicrobial drug resistance, and virulence of the strain with the NDM-5 gene on IncN plasmids were analyzed by whole-genome sequencing. Virulence testing was conducted using the Galleria mellonella infection model, serum killing, and biofilm formation assay. Growth measurement analysis, an in vitro competition experiment, and a plasmid stability assay were calculated using fitness costs. A conjugation assay was used to test NDM-5 transfer between bacterial strains. For statistical analysis and image visualization, GraphPad Prism 8.0 was employed. The result revealed that the hypovirulent strain with the NDM-5 gene was located on the IncN plasmid (92.284 kb), which is rare in Klebsiella pneumoniae. Whole-genome sequencing revealed the presence of 25 and 22 resistance and virulence genes, respectively. The strain KP4089 was resistant to carbapenems, β-lactam, ceftazidime-avibactam, and macrolides but remained susceptible to aminoglycosides, mucilage, and tigecycline in antimicrobial susceptibility tests. Furthermore, the strain KP4089 exhibited significantly lower serum killing and biofilm formation rates, demonstrating that the strain with the NDM-5 gene had low virulence. The strain with the NDM-5 gene on the IncN plasmid was genetically stable and had no fitness cost associated with identifying IncN plasmids. Future research should focus on clinical surveillance of hypovirulent multidrug-resistant Klebsiella pneumoniae to identify the potential strain and mechanistic studies to prevent its spread. IMPORTANCE It is crucial to strengthen the ongoing clinical surveillance of non-highly virulent, multi-resistant Klebsiella pneumoniae.

IntroductionKNOX plays a pivotal role in governing plant growth, development, and responses to diverse abiotic and biotic stresses. However, information on the relationship between the KNOX gene family and expression levels under different treatments in Dendrobium is still limited.MethodsTo address this problem, we first used bioinformatics methods and revealed the presence of 19 KNOX genes distributed among 13 chromosomes in the Dendrobium huoshanense genome. Through an analysis of phylogenetic relationships, these genes were classified into three distinct clades: class I, class II, and class M. Our investigation included promoter analysis, revealing various cis-acting elements associated with hormones, growth and development, and abiotic stress responses. Additionally, qRT-PCR experiments were conducted to assess the expression patterns of DhKNOX genes under different treatments, including ABA, MeJA, SA, and drought.ResultsThe results demonstrated differential expression of DhKNOX genes in response to these treatments, thereby highlighting their potential roles in stress adaptation.DiscussionOverall, our results contribute important insights for further investigations into the functional characterization of the Dendrobium KNOX gene family, shedding light on their roles in plant development and stress responses.

Lung cancer is the most common malignant cancer in the world, and non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancer cases. Presently, chemotherapy is the main treatment, but the therapeutic effect is not satisfactory due to drug resistance. Feiyanning decoction (FYN), a widely used Chinese Tradition Medicine (TCM), shows a good effect on the treatment of lung cancer. However, the underlying mechanism seems unclear. In this study, FYN was applied to cisplatin-resistant A549 cell line. Cell viability and apoptosis were detected by cell counting kit 8 (CCK-8) and Annexin V/PI kit. Xenograft mouse model was constructed to analyze the tumor growth. The results showed that FYN inhibited cell viability and promoted cell apoptosis in a dose-dependent manner. Downregulated of Bcl-2 and upregulated of HEATR1, TAp73, p53 and Bax were observed after treatment with FYN. Interestingly, all these effects induced by FYN were alleviated by HEATR1 overexpression in A549/DDP cells. Furthermore, FYN played a synergistic effect with cisplatin on the increase of cell apoptosis in vitro and the inhibition of tumor growth in vivo. In conclusion, FYN played a synergistic effect with cisplatin on the inhibition of cell viability in NSCLC via negatively regulating HEATR1.

Abstract Background Hepatocellular carcinoma (HCC) is among the most common and malignant cancers with no effective therapeutic approaches. Echinacoside (ECH), a phenylethanoid glycoside isolated from Chinese herbal medicine, Cistanche salsa, can inhibit HCC progression; however, poor absorption and low bioavailability limit its biological applications. Methods To improve ECH sensitivity to HepG2 cells, we developed a mesoporous silica nanoparticle (MSN)-based drug delivery system to deliver ECH to HepG2 cells via galactose (GAL) and poly(ethylene glycol) diglycidyl ether (PEGDE) conjugation (ECH@Au@MSN-PEGDE-GAL, or ECH@AMPG). Gain- and loss-of-function assays were conducted to assess the effects of UBR5 on HCC cell apoptosis and glycolysis. Moreover, the interactions among intermediate products were also investigated to elucidate the mechanisms by which UBR5 functions. Results The present study showed that ubiquitin protein ligase E3 component N-recognin 5 (UBR5) acted as an oncogene in HCC tissues and that its expression was inhibited by ECH. AMPG showed a high drug loading property and a slow and sustained release pattern over time. Moreover, owing to the valid drug accumulation, ECH@AMPG promoted apoptosis and inhibited glycolysis of HepG2 cells in vitro. In vivo experiments demonstrated that AMPG also enhanced the antitumor effects of ECH in HepG2 cell-bearing mice. Conclusions Our results indicated the clinical significance of UBR5 as a therapeutic target. On the basis of the nontoxic and high drug-loading capabilities of AMPG, ECH@AMPG presented better effects on HCC cells compared with free ECH, indicating its potential for the chemotherapy of HCC.

Light spectrum is an important environmental factor affecting fish activities. However, the existing studies on spectrum are usually conducted under monochromatic light, which are multiple variables compared to the natural light conditions and increase uncertainty. The results may be caused by the absence of all other chromatic lights. The tiger puffer Takifugu rubripes is an important commercial marine fish in China, Japan, and Korea. In this study, the effects of red light on the eyes and brain of T. rubripes brooder, including histological changes in retina and optic tectum, the levels of oxidative stress indicators in serum, and changes in gene expression at transcriptome level were examined, after the treatment of full spectrum LED (Fs) and red light filtered LED (Rf) for 52 days. The results showed that the growth and histological structure of retina and optic tectum were not significantly altered. While, Rf significantly decreased the activity of superoxide dismutase and catalase, and increased the content of cortisol (P

Magnesium-based materials have been regarded as promising candidates for large-scale, high-efficiency thermoelectric applications, owing to their excellent dimensionless figure of merit, high abundance, and low cost. In this review, we comprehensively summarize the recent advances of Mg-based thermoelectrics, including Mg2X (X = Si, Ge, Sn), Mg3(Sb,Bi)2, and α-MgAgSb, from both material and device level. Their electrical and thermal transport properties are first elucidated based on the crystallographic characteristics, band structures, and phonon dispersions. We then review the optimization strategies towards higher thermoelectric performance, as well as the device applications of Mg-based thermoelectric materials and the related engineering issues. By highlighting the challenges and possible solutions in the end, this review intends to offer perspectives on the future research work to further enhance the performance of Mg-based materials for practical applications.

Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (ρXY) and random forest feature importance (FI) (ρXY = 0.63, FI = 0.723), followed by soil temperature (ρXY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash–Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978–1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999–2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff.

Introduction Although intrapartum caesarean delivery can resolve dystocia, it would still lead to several adverse outcomes for mothers and children. The obstetric care professionals need effective tools that can help them to identify the possibility and risk factors of intrapartum caesarean delivery, and further implement interventions to avoid unnecessary caesarean birth. This study aims to develop a prediction model for intrapartum caesarean delivery with real-life data based on the artificial neural networks approach.Methods and analysis This study is a prospective nested case–control design. Pregnant women who plan to deliver vaginally will be recruited in a tertiary hospital in Southwest China from March 2022 to March 2024. The clinical data of prelabour, intrapartum period and psychosocial information will be collected. The case group will be the women who finally have a baby with intrapartum caesarean deliveries, and the control group will be those who deliver a baby vaginally. An artificial neural networks approach with the backpropagation algorithm multilayer perceptron topology will be performed to construct the prediction model.Ethics and dissemination Ethical approval for data collection was granted by the Ethics Committee of West China Second University Hospital, Sichuan University, and the ethical number is 2021 (204). Written informed consent will be obtained from all participants and they can withdraw from the study at any time. The results of this study will be published in peer-review journal.

Single molten track (SMT) is the basic unit of the selective laser melting (SLM) process, of which the morphology features and microstructures have great significance on the molding quality. In this study, the effects of laser energy density (LED) on characteristics of SMT were studied with 316 L stainless steel processing by SLM. The results indicate that an optimized range of LED was between 200 and 500 J/mm3, which was the best for SLM process of 316 L leading to beautiful SMT appearance such as continuous uniform water-ridge-like surface, good contour straightness, smooth edges, little adhesive powder, and high densification. The microstructures of the SMT molten pool are mainly composed of equiaxed cellular and columnar dendrites. As the LED decreased, the cellular became smaller and disappeared, while the length and width of columnar crystals even gradually increased. The size of the cellular varies between 0.43 and 1.70 μm with the LEDs increase. The relationship between SMT characteristics and LED found in this work can be readily used to optimize SMT and control the microstructures in situ, and the laser energy density range that the work has identified will simplify most of the guess work in additive manufacturing.

In December 2020, the Draft Resolution on Reducing Space Threats through Norms, Rules and Principles of Responsible Behaviours proposed by the United Kingdom, United States, and other States was adopted at the UN General Assembly (hereafter referred to as the “New Initiative for Responsible Space Behaviours”). Nevertheless, it does not mean that the international community would achieve the expected breakthroughs and progress on the topic of preventing an arms race in outer space. The methodology of “identifying threats - characterising behaviors (responsible/irresponsible) - formulating rules” proposed by the Initiative seems reasonable; however, it might bring more unexpected uncertainties than it is supposed to do. This approach could also be found in the Defense Space Strategy promulgated by the United States in 2020. The pros and cons of this Initiative should be calmly and objectively analyzed to avoid a political deadlock in formulating rules of behaviors. It should insist on identifying the threats and risks faced by the international community as a whole rather than that by individual States and interest groups, and reexamine the dichotomy of “responsible/irresponsible behaviors,” focus on the identification and interpretation of rule of international law related to space security so as to reach a consensus on the insufficiency of lex lata and subsequently lay the foundation for the formulation of effective new rules for arms control in outer space.

Recently, personality trait recognition, which aims to identify people’s first impression behavior data and analyze people’s psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods.

Abstract: This paper investigates how regional energy endowment affects carbon emissions and whether energy capacity aggregation plays a mediating role. In the context of the “double carbon target”, it is important to study the relationship between energy endowment and carbon emission growth to formulate reasonable and effective energy policies in the region. Based on the panel data of 30 Chinese provinces from 2008 to 2019, this paper investigates the mechanism of the role between energy endowment, energy capacity aggregation, and carbon emissions using the spatial Durbin model. The results show that: 1) Carbon dioxide emissions have significant positive spatial autocorrelation. 2) Energy endowment and energy capacity aggregation increase provincial carbon emissions. For each unit increase in energy endowment and energy capacity aggregation level, regional carbon emissions will increase by 0.0423 and 0.0564 units. Moreover, the effect of energy endowment on carbon emissions has a spatial spillover effect. 3) The intermediary effect test finds that energy capacity aggregation plays an intermediary role in the energy endowment affecting carbon emissions. Therefore, this paper verifies that energy endowments promote regional carbon emissions and that the energy capacity aggregation effect plays a mediating role. The research results provide theoretical guidance for solving the contradictory problem of energy demand growth on carbon emission limitation and promoting regional green and low-carbon development.

The glaciers in northwest China are mainly distributed in the northern part of the Himalayas, the Kunlun Mountains, and the Tianshan Mountains. Glaciers are an important freshwater resource in the northern part of the Kunlun Mountains, and the melting of glaciers and snow provides an assured source of water for rivers on the southern edge of the Tarim Basin. Based on the first glacier inventory dataset on China (1968), the second glacier inventory dataset on China (2009), and the glacier inventory dataset on Western China in 2018, this study used DEM data, Landsat remote sensing images, and ERA5 atmospheric reanalysis data to investigate glacier change and its influencing factors with respect to the northern part of the Kunlun Mountains. The results showed that there were 9273 glaciers in the northern part of the Kunlun Mountains in 2018, with an area of about 11,762.72 km2, an ice inventory of about 1168.53 km3, and an average length per glacier of about 1.68 km. The glaciers were mainly distributed at altitudes of 5300–6100 m (7574.66 km2). From 1968 to 2018, the number of glaciers in the northern stretch of the Kunlun Mountains increased by 343, while the glacier area decreased by 2452.80 km2 (−0.14%/a). From 2009 to 2018, the glacier area at the altitude of 4900 m to 6100 m decreased in the northern section of the Kunlun Mountains, and the glacier area at the remaining altitude increased slightly (10.67 km2). From 1968 to 2018, the glacier area and glacier length in all river basins decreased. The relative rate of glacier area change in the Qarqan River basin from 2009 to 2018 was five times that of 1968–2009, and this needs significant attention. From 1968 to 2018, both temperature and precipitation increased to varying degrees, and the increase in precipitation was beneficial to the accumulation of glaciers. Therefore, the increase in temperature was the main cause of glacier change in the northern section of the Kunlun Mountains.

Objective:To evaluate the long-term curative efficacy and safety of olfactory ensheathing cell (OEC) transplantation by 10 years of follow-up investigation.Methods:A follow-up observation was done on 13 patients with allograft olfactory bulb-derived OEC transplantation from September 2005 to September 2007 at the Second Affiliated Hospital of Xi’an Jiaotong University. After cell purification, amplification, and identification, a 2 × 107/mL cell suspension was prepared for transplantation. In the posterior horn of the spinal cord 0.5 cm distal and proximal to the spinal cord injury zone, 4 needle points were selected to avoid the blood vessels. The needle depth was 3 mm, and the injection volume per point was 10 μL. Postoperatively and at 1 week, 4 weeks, 12 weeks, 24 weeks, 1 year, 3 years, 5 years, and 10 years after the surgery, the patient’s American Spinal Injury Association (ASIA) score, adverse reactions, and other minor observations were assessed.Results:All the patients did not have serious complications. No gliomas or other new organisms formed during the 10-year observation period. Eight of 13 patients had improvement in sensory function, and 5 patients showed improvement in motor function. The ASIA acupuncture, light touch, and exercise scores improved significantly 1 year after the surgery, and this improvement continued until the 10-year follow-up period. Three of 13 patients had improvement in defecation and urination, and 1 patient had improved neuralgia after spinal cord injury.Conclusion:OEC transplantation is safe and effective in treating spinal cord injury. The observation period of OEC transplantation is 1 to 3 years.

Image haze removal is critical for autonomous driving. However, it is a challenging task for the existing image dehazing algorithms to eliminate the block effect completely and handle objects similar to light (such as snowy objects and white buildings). To address this problem, we propose a novel single-image dehazing method based on superpixels and Markov random field. We obtain the transmission map in the superpixel domain to eliminate the block/halo effect and introduce Markov random field to revise the transmission map in the superpixel domain. The key idea is that the sparsely distributed, incorrectly estimated transmittances can be corrected by properly characterizing the spatial dependencies between the incorrectly estimated superpixels and the neighbouring well-estimated superpixels. The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods.

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in the simulation of hydrological runoff. Considering the impact of snow cover on runoff in high-altitude mountainous areas, based on remote sensing data and atmospheric reanalysis data, in this paper we established a runoff simulation model with a random forest model and ANN (artificial neural network) model for the Xiying River Basin in the western Qilian region The verification of the measured data showed that the NSE (Nash–Sutcliffe efficiency), RMSE (root mean square error), and PBIAS (percent bias) values of the random forest model and ANN model were 0.701 and 0.748, 6.228 m3/s and 4.554 m3/s, and 4.903% and 8.329%, respectively. Considering the influence of ice and snow on runoff, the simulation accuracy of both the random forest model and ANN model was improved during the period of significant decreases in the annual snow and ice water equivalent in the Xiying River Basin from April to May, after the snow remote sensing data were introduced into the model. Specifically, for the random forest model, the NSE increased by 0.099, the RMSE decreased by 0.369 m3/s, and the PBIAS decreased by 1.689%. For the ANN model, the NSE increased by 0.207, the RMSE decreased by 0.700 m3/s, and the PBIAS decreased by 1.103%. In this study, based on remote sensing data and atmospheric reanalysis data, the random forest model and ANN model were used to effectively simulate hydrological runoff processes in high-altitude mountainous areas without observational data. In particular, the accuracy of the machine learning simulations of snowmelt runoff (especially during the snowmelt period) was effectively improved by introducing the snow remote sensing data, which can provide a methodological reference for the simulation and prediction of snowmelt runoff in alpine mountains.

Microplastics (MPs) are emerging pollutants that act as a carrier of toxic pollutants, release toxic substances, and aggregate in biota. The adsorption behavior of MPs has recently become a research hot spot. The objective of this study was to summarize the main mechanisms by which MPs adsorb organic pollutants, introduce some mathematical models commonly used to study the adsorption behavior of MPs, and discuss the factors affecting the adsorption capacity from three perspectives, i.e., the properties of MPs and organic pollutants, and environmental factors. Adsorption kinetics and isothermal adsorption models are commonly used to study the adsorption of organic pollutants on MPs. We observed that hydrophobic interaction is the most common mechanism by which MPs adsorb organic pollutants, and also reportedly controls the portion of organic pollutants. Additionally, electrostatic interaction and other non-covalent forces, such as hydrogen bonds, halogen bonds, and π–π interactions, are also mechanisms of organic pollutant adsorption on MPs. The particle size, specific surface area, aging degree, crystallinity, and polarity of MPs, and organic pollutant properties (hydrophobicity and dissociated forms) are key factors affecting adsorption capacity. Changes in the pH, temperature, and ionic strength also affect the adsorption capacity. Current research on the adsorption behavior of MPs has mainly been conducted in laboratories, and in-depth studies on the adsorption mechanism and influencing factors are limited. Therefore, studies on the adsorption behavior of MPs in the environment are required, and this study will contribute to a better understanding of this topic.

In response to the outbreak of COVID-19 that has been sweeping the world, scientists reconstructed the SARS-CoV-2 rapidly using a synthetic genomics platform, in order to accelerate therapeutics and vaccine development. However, given the dual-use nature of this technology, there exists a high biosecurity risk. This paper points out the potential risks of the engineering SARS-CoV-2 virus and puts forward 6 questions to this work. The authors emphasize that the two basic values of safety/security and intellectual freedom of research must be considered evenly. From the perspective of responsible development of biotechnology, this paper calls for a careful assessment to the risks of the technology, replacing risky technologies with safe ones. The risks of publication also need to be strictly assessed. The authors believe, in addition to enhancing the “self-government” and self-discipline of scientists and scientific communities, government supervision must be reinforced, laws and regulations should be improved, and global regulation framework ought to be constructed.

Structured lighting techniques have increasingly been employed in underwater imaging. Except for the limitation in underwater illumination, the desire of underwater image acquisition comes from the applicability of removal of the back scattered light, the critical problem of underwater imaging, using structured lighting techniques. This paper presents an approach for underwater image recovery using structured light and CCD camera. By integrating the scanned frame images, we generate an integration image which can be formulated as the convolution of the surface albedo and the illumination function. Thus, underwater image acquisition is addressed as an optimization problem of image recovery and resolved by deconvolution, rather than the traditional geometric manipulation of frame tiling. The significance of the proposed method is that the forward scattering effect in the recovered image is fully eliminated by the integration operation and collection of the forward scattered light enhances the total imaging energy. By means of the structured lighting technique, an algorithm of using virtual aperture to limit the field of view is proposed to eliminate the back scattered light in frame images. Concerned with applicability of using the broad lighting pattern in structured light systems, the coded structured light with binary pseudo-random sequence is introduced, by which the high-frequency details in image recovery can be preserved through deconvolution. The results of underwater experiments are given.

The “masking effect” of unresolved targets always results in missed detection and inaccurate parameter estimation. A feasible approach to separate the unresolved targets is improving the range resolution by increasing the signal bandwidth. In this paper, we propose a novel scheme to detect and measure the unresolved targets for the phased array radar with stepped-frequency waveform, which provides a large synthetic bandwidth. We establish the signal model of the stepped-frequency pulse train and review the conventional 1-D High Range Resolution Profile (HRRP). Since the 1-D HRRP of each element fails to integrate the spatial domain, we develop a 2-D HRRP where the target echo can be effectively integrated both in time and spatial domains. Specifically, the 2-D HRRP is generated by the range-angle beamforming technique. In order to address the range shift in the 2-D HRRP caused by target motion, we design a short-long stepped-frequency pulse train as the transmit waveform, which contains a short pulse subtrain with a small pulse repetition interval (PRI) and a long pulse subtrain with a large PRI. The proposed scheme includes two parts of beamforming detection and parameter estimation with range shift elimination. Based on two 2-D HRRPs generated by short and long pulse subtrains, each target can be successfully detected by beamforming detection and target's range, angle and velocity can be accurately estimated by parameter estimation with range shift elimination. Simulation results demonstrate the effectiveness of the proposed scheme.

Owing to refraction, absorption, and scattering of light by suspended particles in water, raw underwater images have low contrast, blurred details, and color distortion. These characteristics can significantly interfere with visual tasks, such as segmentation and tracking. This paper proposes an underwater image enhancement solution through a deep residual framework. First, the cycle-consistent adversarial networks (CycleGAN) is employed to generate synthetic underwater images as training data for convolution neural network models. Second, the very-deep super-resolution reconstruction model (VDSR) is introduced to underwater resolution applications; with it, the Underwater Resnet model is proposed, which is a residual learning model for underwater image enhancement tasks. Furthermore, the loss function and training mode are improved. A multi-term loss function is formed with mean squared error loss and a proposed edge difference loss. An asynchronous training mode is also proposed to improve the performance of the multi-term loss function. Finally, the impact of batch normalization is discussed. According to the underwater image enhancement experiments and a comparative analysis, the color correction and detail enhancement performance of the proposed methods are superior to that of previous deep learning models and traditional methods.

Abstract It remains a daunting task to quantify the configurational entropy of a material from atom‐revolved electron microscopy images and correlate the results with the material's lattice thermal conductivity, which strides across statics, dynamics, and thermal transport of crystal lattice over orders of magnitudes in length and time. Here, a proof‐of‐principle study of atomic‐scale visualization and quantification of configurational entropy in relation to thermal conductivity in single crystalline trigonal GeSb2Te4 (aka t‐GeSb2Te4) with native atomic site disorder is reported. A concerted effort of large t‐GeSb2Te4 single crystal growth, in‐lab developed analysis procedure of atomic column intensity, the visualization and quantification of configurational entropy including corresponding modulation, and thermal transport measurements enable an entropic “bottom‐up” perspective to the lattice thermal conductivity of t‐GeSb2Te4. It is uncovered that the configurational entropy increases phonon scattering and reduces phonon mean free path as well as promotes anharmonicity, thereby giving rise to low lattice thermal conductivity and promising thermoelectric performance. The current study sheds lights on an atomic scale bottom‐up configurational entropy design in diverse regimes of structural and functional materials research and applications.

Objective We aimed to determine whether the prognostic value of the shock index (SI) and its derivatives is better than that of the Thrombolysis In Myocardial Infarction risk index (TRI) for predicting adverse outcomes in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI). Methods A total of 257 patients with STEMI undergoing primary PCI from January 2018 to June 2019 were analyzed in a retrospective cohort study. The SI, modified shock index (MSI), age SI (age × the SI), age MSI (age × the MSI), and TRI at admission were calculated. Clinical endpoints were in-hospital complications, including all-cause mortality, acute heart failure, cardiac shock, mechanical complications, re-infarction, and life-threatening arrhythmia. Results Multivariate analyses showed that a high SI, MSI, age SI, age MSI, and TRI at admission were associated with a significantly higher rate of in-hospital complications. The predictive value of the age SI and age MSI was comparable with that of the TRI (area under the receiver operating characteristic curve: z = 1.313 and z = 0.882, respectively) for predicting in-hospital complications. Conclusions The age SI and age MSI appear to be similar to the TRI for predicting in-hospital complications in patients with STEMI undergoing primary PCI.

While the era of private spaceflight is coming, it is getting urgent to have common understanding about the existing space legal systems, i.e., the Applicability of the Liability Convention (ALC) in handling with damage which might occur during space tourism. The applicability of the relevant articles about absolute liability, fault liability, and claimant State is systematically analyzed. The paper in turn analyzes the ALC in the situation of the participants suffering damage and in that of the participants causing damage, as a dichotomy methodology. The LC applies to a private spaceflight participant when they suffer damage on board during the spaceflight. As to the eligible claimant State, in the absence of the nationality State, the registry State, or the owner State of the space object may present the claim instead. If damage to the foreign participants on board happened on the surface of the earth, the nationality State of the participant could present claim to the launching State to protect its national’s interests under general international law rather than under LC. If the damage happens to the foreign participants on board happened elsewhere than on the surface of the earth, Article III of the LC could only apply in limited situations. Only when a spacecraft for private spaceflight of/or registered by one State is carried by the launching vehicle of another State, Article III applies. The best solution in such colaunching situation is to settle down the rights, obligations, and liabilities specifically in a binding international agreement between the launching States. The absolute liability should apply when a space object causes damage to a spaceship carried on by an aircraft before it separates from each other. After the separation, fault-based liability applies to the damage happening to spaceship. The registry State of a space object should be responsible for participants thereof who caused damage to the space object of a third-party State. The LC then applies when the damage is caused by the fault of the participant. If the spaceship is not registered, the launching State shall be responsible for the participants involved its “national activities,” no matter if the participant is its nationals or foreigners and the damage caused by them based on their fault, while the nationality State is not supposed to be responsible for its nationals in such case. At last, issuing a space visa to the participant of private spaceflight could be taken as a compliment measure for the State to implement its authorization obligation as to its national space activities, which could bring more legal certainties when defining the fault attribution.

Ezrin, as encoded by the EZR gene, is a member of the Ezrin/Radixin/Moesin (ERM) family. The ERM family includes three highly related actin filament binding proteins, Ezrin, Radixin, and Moesin. These three members share similar structural properties containing an N-terminal domain named FERM, a central helical linker region, and a C-terminal domain that mediates the interaction with F-actin. Ezrin protein is highly regulated through the conformational change between a closed, inactivate form and an open, active form. As a membrane-cytoskeleton linker protein, Ezrin facilitates numerous signal transductions in tumorigenesis and mediates diverse essential functions through interactions with a variety of growth factor receptors and adhesion molecules. Emerging evidence has demonstrated that Ezrin is an oncogene protein, as high levels of Ezrin are associated with metastatic behavior in various types of cancer. The diverse functions attributed to Ezrin and the understanding of how Ezrin drives the deadly process of metastasis are complex and often controversial. Here by reviewing recent findings across a wide spectrum of cancer types we will highlight the structures, protein interactions and oncogenic roles of Ezrin as well as the emerging therapeutic agents targeting Ezrin. This review provides a comprehensive framework to guide future studies of Ezrin and other ERM proteins in basic and clinical studies.

Abstract Background The diagnosis of paraduodenal hernia is still a challenge in clinical practice due to lacking of specific symptoms. Case presentation: An 83-yr-old male patient presented to our department due to severe abdominal pain for 8 h. Abdominal contrast enhanced computerized tomography (CT) scan indicated intussusception in the duodenum and the upper segment of jejunum, as well as internal hernia. He complaint of progression in the abdominal pain, and then laparoscope was carried out, which indicated left-sided paraduodenal hernia. Subsequently, the patient was transferred to celiotomy, during which slight ischemic changes were noticed in the intestinal canal. Meanwhile, a hernial orifice was noticed in the left orifice of the duodenum. Conclusions: In this case, we presented our experiences on the diagnosis of paraduodenal hernia and intussusception. Our study contributed to the understanding, early diagnosis and selection of surgical options for the surgeons.

The use of phytoremediation technology to remove heavy metal ions from aquatic environments or reduce their toxicity offers the possibility of restoring the ecological environment of polluted water bodies. Based on available literature on heavy metal absorption by aquatic plants, we conducted a meta-analysis to study the absorptive capacities of different plants as well as the factors that influence their Hg-absorption performance. Seventeen plant families, including Araceae, Haloragaceae, Hydrocharitaceae, and Poaceae, have a strong Hg-absorption capacity. The root systems of aquatic plants belonging to these families are primarily responsible for this remediation function, and only a limited proportion of Hg+ that enters a plant via the root system is transferred to other plant organs. Additionally, the diversity of plant life habits (e.g., floating, submerged, and emergent) and the water pH significantly influence the ability of plants to absorb Hg. It is expected that this study will provide a reference for the cultivation of aquatic plants for restoring the ecological environment of Hg-polluted water bodies.

Blind source extraction (BSE) is often posed as the maximization of a statistical criterion under a unitary constraint. This paper addresses the convergence problem of a kurtosis-based criterion in the presence of noise. We present the stationary points of such criterion, and show that these extrema are simplified as the minimum mean square error (MMSE) solutions with some approximations. Moreover, we introduce a robust preprocessing approach, which allows one to find the MMSE separation matrix up to an orthogonal factor. The excellent performance of the BSE algorithm based on this preprocessing approach shows that the analysis of the stationary point is reliable.

In this paper, a framework for inverse synthetic aperture radars (ISAR) imaging with wideband V-FM waveforms is investigated, where the dual-channel compressed-sensing-based dechirping (CS-D) algorithms are applied to achieve high-resolution range profiles (HRRPs) of moving targets. The final HRRPs are reconstructed via the synthesis of the two HRRPs recovered from the two independent channels with CS-D. The ISAR formation is achieved from the Fourier transform-based azimuth compression methods being applied to the rearranged 2-D array of HRRPs. Simulated trials of targets modeled as point scatterers are conducted and the final well-focused images demonstrate the effectiveness of the proposed dual-channel CS-D algorithm in ISAR imaging with wideband V-FM waveforms.

The results of angular and range estimation by conventional signal processing will be far away from true values in the case of unresolved targets due to the merged measurement. In this article, the authors propose a method to localise unresolved targets for frequency diverse array (FDA) radar. Adopting the configurations of subarray-based FDA and full-band FDA as transmitter and receiver, the transmit and receive beampatterns of system are both decoupled in range and angle demands. Furthermore, the receive beamwidth is only a quarter of transmit in range. With the wide transmit beam and narrow receive beam, the authors divide the resolution cell containing unresolved targets into four parts to process separately. A method of intra-pulse beam scanning is proposed to suppress the interference from the targets out of receive beam. Several simulation results demonstrate the effectiveness of the proposed method in the localisation of unresolved targets.

Incipient cavitating flows around two axisymmetric bodies with blunt and conical headforms respectively are investigated both experimentally and numerically. The Lagrangian coherent structures (LCS) defined by the ridges of the finite-time Lyapunov exponent (FTLE) method are employed to investigate flow structures and the motion of incipient cavities. The partially averaged Navier–Stokes (PANS) method is evaluated by comparison with experimental data, and it is found that the PANS model with parameter fk = 0.2 (where fk is the ratio of unresolved to total kinetic energy) gives good predictions of the scale of the separation region and the time evolution of the morphology of incipient cavitation. The incipient cavities exhibit a hairpin-shaped structure, traveling arbitrarily and unattached to the body surface. During the evolution process, the incipient cavities move downstream, with some circumferential motion. The FTLE contours and the trajectories of tracer particles reveal significant circumferential flow in the detached vortex structures around the two cylinders. The greater the distance downstream from the cylinder head, the more pronounced is the circumferential motion. Furthermore, it is found that the motion of the incipient cavities is closely related to local flow behavior. The circumferential flow around the blunt-headed cylinder is stronger than that around the conical-headed cylinder. This provides a reasonable explanation for the more pronounced circumferential motion of incipient cavities around the blunt-headed cylinder.

Thermoelectric material SnSe has aroused world-wide interests in the past years, and its inherent strong lattice anharmonicity is regarded as a crucial factor for its outstanding thermoelectric performance. However, the understanding of lattice anharmonicity in SnSe system remains inadequate, especially regarding how phonon dynamics are affected by this behavior. In this work, we present a comprehensive study of lattice dynamics on Na _0.003 Sn _0.997 Se _0.9 S _0.1 by means of neutron total scattering, inelastic neutron scattering, Raman spectroscopy as well as frozen-phonon calculations. Lattice anharmonicity is evidenced by pair distribution function, inelastic neutron scattering and Raman measurements. By separating the effects of thermal expansion and multi-phonon scattering, we found that the latter is very significant in high-energy optical phonon modes. The strong temperature-dependence of these phonon modes indicate the anharmonicity in this system. Moreover, our data reveals that the linewidths of high-energy optical phonons become broadened with mild doping of sulfur. Our studies suggest that the thermoelectric performance of SnSe could be further enhanced by reducing the contributions of high-energy optical phonon modes to the lattice thermal conductivity via phonon engineering.

Hepatocellular carcinoma (HCC) is a common malignancy associated with a high risk of recurrence and metastasis and a poor prognosis. Here, we examined the involvement of the pseudokinase Tribbles 1 (TRIB1), a scaffold protein associated with several malignancies, in HCC and investigated the underlying mechanisms. TRIB1 was upregulated in HCC tissues and cell lines in correlation with low levels of p53. TRIB1 gain and loss of function experiments indicated that TRIB1 promoted HCC cell viability concomitant with the downregulation of p53, and induced HCC cell migration, invasion, and epithelial-mesenchymal transition. TRIB1 was identified as a target of microRNA-23a (miR-23a), and miR-23a overexpression downregulated TRIB1 and upregulated p53 in HCC cells. Ectopic expression of TRIB1 upregulated β-catenin and its effectors c-myc and MMP-7 in a p53-dependent manner. TRIB1 silencing inhibited tumor growth and promoted apoptosis in vivo via a mechanism that would involve the modulation of p53 and β-catenin signaling. The present results indicate that TRIB1 promotes HCC tumorigenesis and invasiveness via a feedback loop that involves the modulation of its expression by miR-23a with the likely downregulation of p53, and suggest the involvement of the β-catenin signaling pathway. These findings suggest potential targets for the treatment of HCC and therefore merit further investigation.

V-FM waveforms, composed of two chirp signals with the opposite slopes, can also achieve high range resolution with wide bandwidth via intrapulse frequency modulation. In this paper, a framework for inverse synthetic aperture radar (ISAR) imaging of moving targets with V-FM waveforms is investigated, where the range compression of the received signals is achieved by the dual-channel dechirping and the azimuth compression is done via the traditional Fourier transform (FT). The two corresponding reconstructed temporary high-resolution range profiles (HRRPs) from the double channels are synthesized for the HRRPs of the target, in which one is flipped from left to right and added to the other. Then the final HRRPs are arranged into a two-dimensional (2D) array and the azimuth compression is done via FT to achieve the ISAR imaging after the motion compensation. Simulated trials, adopting the scattering center modeling of the Yak-42 plane, are used to validate the correctness of the analyses and the finally well-focused images greatly support the effectiveness of V-FM waveforms in ISAR imaging.