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Abstract Background The latest evidence indicates that ATP-binding cassette superfamily G member 2 (ABCG2) is critical in regulating lipid metabolism and mediating statin or cholesterol efflux. This study investigates whether the function variant loss within ABCG2 (rs2231142) impacts lipid levels and statin efficiency. Methods PubMed, Cochrane Library, Central, CINAHL, and ClinicalTrials.gov were searched until November 18, 2023. Results Fifteen studies (34,150 individuals) were included in the analysis. The A allele [Glu141Lys amino acid substitution was formed by a transversion from cytosine (C) to adenine (A)] of rs2231142 was linked to lower levels of high-density lipoprotein cholesterol (HDL-C), and higher levels of low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC). In addition, the A allele of rs2231142 substantially increased the lipid-lowering efficiency of rosuvastatin in Asian individuals with dyslipidemia. Subgroup analysis indicated that the impacts of rs2231142 on lipid levels and statin response were primarily in Asian individuals. Conclusions The ABCG2 rs2231142 loss of function variant significantly impacts lipid levels and statin efficiency. Preventive use of rosuvastatin may prevent the onset of coronary artery disease (CAD) in Asian individuals with dyslipidemia.

In this paper, the discontinuous dynamic behavior of a two-degree-of-freedom frictional collision system including intermediate elastic collision and unilateral elastic constraints subjected to periodic excitation is studied by using flow switching theory. In this system, given that the motion of each object might have a velocity that is either greater than or less than zero and each object experiences a periodic excitation force that has negative feedback, because the kinetic and static friction coefficients differ, the flow barrier manifests when the object's speed is zero. Based on the discontinuity or nonsmoothness of the oscillator's motion generated by elastic collision and friction, the motion states of the oscillator in the system are divided into 16 cases and the absolute and relative coordinates are used to define various boundaries and domains in the oscillator motion's phase space. On the basis of this, the G-function and system vector fields are used to propose the oscillator motion's switching rules at the displacement and velocity boundaries. Finally, some dynamic behaviors for the 2-DOF oscillator are demonstrated via numerical simulation of the oscillator's stick, grazing, sliding and periodic motions and the scene of sliding bifurcation. The mechanical system's optimization designs with friction and elastic collision will benefit from this investigation's findings.

Current shale gas well production capacity predictions primarily rely on analytical and numerical simulation methods, which necessitate extensive calculations and manual parameter tuning and produce lowly accurate predictions. Although employing neural networks yields highly accurate predictions, they can easily fall into local optima. This paper suggests a new way to use Cuckoo Search (CS)-optimized neural networks to make shale gas well production capacity predictions more accurate and to solve the problem of local optima. It aims to assist engineers in devising more effective development plans and production strategies, optimizing resource allocation, and reducing risk. The method first analyzes the factors influencing the production capacity of shale gas wells in a block located in western China through correlation coefficients. It identifies the main factors affecting the gas test absolute open flow as organic carbon content, small-layer passage rate, fracture pressure, acid volume, pump-in fluid volume, brittle mineral content in the rock, and rock density. Subsequently, we used the CS algorithm to conduct the global training of the neural network, avoiding the problem of local optima, and established a neural network model for predicting shale gas well production capacity optimized by the CS algorithm. A comparative analysis with other relevant methods demonstrates that the CS-optimized neural network model can accurately predict production capacity, enabling a more rational and effective exploitation of shale gas resources, which lower development costs and increase the economic returns of oil and gas fields. Compared to numerical simulation, SVM, and BP neural network algorithms, the CS-optimized BP neural network (CS-BP) exhibits significantly lower prediction error. Its correlation coefficient between predicted and actual values reaches as high as 0.9924. Verification experiments conducted on another shale gas well also demonstrate that, in comparison to the BP neural network algorithm, CS-BP offers superior prediction performance, with model validation showing a prediction error of only 0.05. This study can facilitate more rational and efficient exploitation of shale gas resources, reduce development costs, and enhance the economic benefits of oil and gas fields.

BackgroundIt has been shown that inflammation may be associated with cognitive impairment (CI). Diet modulates inflammation. However, there is currently a scarcity of epidemiological studies exploring the connection between the inflammatory potential of diet and CI. The objective was to investigate the correlation between the dietary inflammatory index (DII) and cognitive impairment in older adults in the United States.MethodsThe present investigation utilized a cross-sectional dataset obtained from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014. Dietary intake data was used to calculate DII scores, which were then used to categorize participants into quartiles. Participants’ cognitive function was assessed using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Individuals who scored in the lowest quartile on any of these tests were classified as exhibiting low cognitive performance. The association between DII and cognitive impairment was investigated by multivariate logistic regression, smooth curve fitting, and subgroup analysis.ResultsA total of 947 older adults were enrolled in the study. Following the adjustment of confounding variables, DII scores exhibited a significant and positive correlation with low cognitive performance, as measured by AFT (OR 1.15, 95% CI 1.02–1.28, p = 0.02) and the DSST (OR 1.38, 95% CI 1.13–1.68, p = 0.004). Compared with the lowest quartile of DII, the highest weighted odds ratio of cognitive impairment based on AFT was observed in the fourth quartile group (OR 1.89, 95% CI 1.05–3.38, p = 0.03). Similarly, a comparable pattern was evident in DSST (OR 4.30, 95% CI 1.85–9.98, p = 0.003). Additionally, the smooth curve fitting results showed a nonlinear relationship between DII and cognitive decline evaluated by DSST (p for nonlinearity = 0.016). No interaction effects between cognitive impairment and age or gender were observed in relation to all cognitive test scores.ConclusionThis research reveals a positive link between diet with higher inflammatory potential and cognitive decline among elderly individuals in America. However, additional studies on dietary interventions are necessary to explore the cause-and-effect relationship.

Abstract Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.

Abstract Infertility is defined as the failure to conceive after at least one year of unprotected intercourse. Long non-coding RNAs (lncRNAs) are transcripts that contain more than 200 nucleotides but do not convert into proteins. LncRNAs, particularly lncRNA H19, have been linked to the emergence and progression of various diseases. This review focuses on the role of H19 in infertility caused by polycystic ovary syndrome, endometriosis, uterine fibroids, diminished ovarian reserve, male factor, and assisted reproductive technology-related pathology, highlighting the potential of H19 as a molecular target for the future treatment of infertility.

BackgroundBased on the high prevalence and fatality rates associated with severe community-acquired pneumonia (SCAP), this study endeavored to construct an innovative nomogram for early identification of individuals at high risk of all-cause death within a 30-day period among SCAP patients receiving intensive care units (ICU) treatment.MethodsIn this single-center, retrospective study, 718 SCAP patients were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for the development of a predictive model. A total of 97 patients eligible for inclusion were included from Chongqing General Hospital, China between January 2020 and July 2023 for external validation. Clinical data and short-term prognosis were collected. Risk factors were determined using the least absolute shrinkage and selection operator (LASSO) and multiple logistic regression analysis. The model’s performance was evaluated through area under the curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsEight risk predictors, including age, presence of malignant cancer, heart rate, mean arterial pressure, albumin, blood urea nitrogen, prothrombin time, and lactate levels were adopted in a nomogram. The nomogram exhibited high predictive accuracy, with an AUC of 0.803 (95% CI: 0.756–0.845) in the training set, 0.756 (95% CI: 0.693–0.816) in the internal validation set, 0.778 (95% CI: 0.594–0.893) in the external validation set concerning 30-day mortality. Meanwhile, the nomogram demonstrated effective calibration through well-fitted calibration curves. DCA confirmed the clinical application value of the nomogram.ConclusionThis simple and reliable nomogram can help physicians assess the short-term prognosis of patients with SCAP quickly and effectively, and could potentially be adopted widely in clinical settings after more external validations.

BackgroundInflammation and lipid infiltration play crucial roles in the development of atherosclerosis. This study aimed to investigate the association between various complex indexes of blood cell types and lipid levels with the severity of coronary artery stenosis and their predictive value in coronary heart disease (CHD).MethodsThe retrospective study was conducted on 3,201 patients who underwent coronary angiography at the Department of Zhongnan Hospital of Wuhan University. The patients were divided into two groups: CHD group and non-CHD group. The CHD group was further classified into three subgroups (mild, moderate, severe) based on the tertiles of their Gensini score or SYNTAX score I. Various complex indexes of blood cell types and lipid levels were compared between the groups.ResultsIt revealed a positive correlation between all complex indexes and the severity of coronary artery stenosis. The systemic inflammation-response index/high-density lipoprotein cholesterol count (SIRI/HDL) exhibited the strongest correlation with both severity scores (Gensini score: r = 0.257, P

Cropland is a comprehensive system influenced by the natural environment and human activities. This article collects the data of cropland use, soil, and other geographic, social and economic factors in the study area and then uses the methods of system analysis, induction and deduction to propose a new research perspective for establishing a cognitive framework and analyzing cropland resources and their functions. The framework is used to assess the rapidly urbanizing region of Guangzhou and investigate the production, ecological, and living functions provided by cropland resources. Synergistic relationships between functions are analyzed using the hot and cold spot methods. The results indicate that the production function of cropland resources in Guangzhou is good, the ecological function is favorable, and the living function is relatively low. A synergistic relationship between the three functions is observed in 91% of areas of Guangzhou, whereas a balanced relationship occurs in some areas of the southern part of Zengcheng, the northwestern and northeastern parts of Conghua, and the western part of Nansha. This research provides guidance for managing cropland resources and ensuring their sustainable utilization.

Abstract The phosphatidylinositol 3-kinase (PI3K)/AKT signaling pathway plays an essential role in glucose metabolism, promoting glycolysis and resisting gluconeogenesis. PI3K/AKT signaling can directly alter glucose metabolism by phosphorylating several metabolic enzymes or regulators of nutrient transport. It can indirectly promote sustained aerobic glycolysis by increasing glucose transporters and glycolytic enzymes, which are mediated by downstream transcription factors. E3 ubiquitin ligase RING-finger proteins are mediators of protein post-translational modifications and include the cullin-RING ligase complexes, the tumor necrosis factor receptor-associated family, the tripartite motif family and etc. Some members of the RING family play critical roles in regulating cell signaling and are involved in the development and progression of various metabolic diseases, such as cancer, diabetes, and dyslipidemia. And with the progression of modern research, as a negative or active regulator, the RING-finger adaptor has been found to play an indispensable role in PI3K/AKT signaling. However, no reviews have comprehensively clarified the role of RING-finger E3 ligases in PI3K/AKT-mediated glucose metabolism. Therefore, in this review, we focus on the regulation and function of RING ligases in PI3K/AKT-mediated glucose metabolism to establish new insights into the prevention and treatment of metabolic diseases.

Abstract Aim This study aimed to synthesize qualitative evidence on experiences of patients with atrial fibrillation (AF) during the course of diagnosis and treatment. We addressed three main questions: (a) What were the experiences of patients with AF during the course of diagnosis and treatment? (b) How did they respond to and cope with the disease? (c) What were the requirements during disease management? Design In this study, qualitative evidence synthesis was performed using the Thomas and Harden method. Data Sources Electronic databases, including PubMed, the Cochrane Library, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature, the China Biomedical Database, the WanFang Database, Chinese National Knowledge Infrastructure and VIP, were searched. The databases were searched from inception to August 2021. Review Methods Two researchers independently selected studies using qualitative assessment and review instruments for quality evaluation and thematic synthesis for the data analysis. Results A total of 2627 studies were identified in the initial search and 15 studies were included. Five analytical themes were generated: ‘Diagnosing AF’; ‘The impact of AF on the patients’; ‘Self‐reorientation in the therapeutic process’; ‘Living with AF and QoL’; and ‘External support to facilitate coping strategies.’ Conclusions Our findings point out unique experiences of patients across the trajectory of AF related to delayed diagnosis, feelings of nonsupport, disappointment of repeated treatment failure and multiple distress associated with unpredictable symptoms. Future research and clinical practice are expected to improve the quality of medical diagnosis and treatment, optimize administrative strategy and provide diverse health support for patients with AF. Impact Understanding the experiences and needs of patients with AF in the entire disease process will inform future clinical practice in AF integrated management, which would be helpful in improving the professionalism and confidence of healthcare providers. In addition, our findings have implications for improving the effectiveness of AF diagnostic and treatment services. Patient or Public Contribution This paper presents a review of previous studies and did not involve patients or the public.

Background. Emerging evidence indicates tribbles homolog 1 (Trib1) protein may be involved in lipid metabolism regulation and coronary artery disease (CAD) pathogenesis. However, whether TRIB1 gene variants affect lipid levels and CAD remains elusive, this study is aimed at clarifying the effect of TRIB1 variants on lipid profile and CAD. Methods. By searching PubMed and Cochrane databases for studies published before December 18, 2022, a total of 108,831 individuals were included for the analysis. Results. The outcomes of the analysis on all individuals showed that the A allele carriers of rs17321515 and rs2954029 variants had higher low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) levels than the noncarriers. Consistently, a higher CAD risk was observed in the A allele carriers. Subgroup analysis indicated that increased LDL-C, TC, and CAD risk were observed in Asian population. Conclusions. Variants of TRIB1 (i.e., rs17321515 and rs2954029) may serve as causal genetic markers for dyslipidemia and CAD in Asian population.

Magnetocaloric effect (MCE) is one of the most promising features of molecular-based magnetic materials. We reported three Gd-based magnetic refrigerant materials, namely, Gd2(L)(NO3)(H2O)‧CH3CN‧H2O (1, H2L = (Z)-N-[(1E)-(2-hydroxy-3-methphenyl)methylidene]pyrazine-2-carbohydrazonic acid), {Gd6(L)6(CO3)2(CH3OH)2(H2O)3Cl}Cl‧4CH3CN (2), and Gd8(L)8(CO3)4(H2O)8‧2H2O (3). Complex 1 contains two GdIII ions linked by two η2:η1:η1:η1:μ2-L2- ligands, which are seven-coordinated in a capped trigonal prism, and complex 2 possesses six GdIII ions, contributing to a triangular prism configuration. For complex 3, eight GdIII ions form a distorted cube arrangement. Moreover, the large values of magnetic entropy in the three complexes prove to be excellent candidates as cryogenic magnetic coolants.

BackgroundSeveral 9p21.3 variants, such as rs1333049, rs4977574, rs10757274, rs10757278, and rs10811661, identified from recent genome-wide association studies (GWASs) are reported to be associated with coronary artery disease (CAD) susceptibility but independent of dyslipidemia. This study investigated whether these 9p21.3 variants influenced lipid profiles.Methods and resultsBy searching the PubMed and Cochrane databases, 101,099 individuals were included in the analysis. The consistent finding for the rs1333049 C allele on lipid profiles increased the triglyceride (TG) levels. Moreover, the rs4977574 G allele and the rs10757274 G allele, respectively, increased low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels. However, the rs10811661 C allele largely reduced LDL-C levels. Subgroup analyses indicated that the effects of the rs1333049 C allele, rs4977574 G allele, and rs10757274 G allele on lipid profiles were stronger in Whites compared with Asians. In contrast, the effect of the rs10811661 C allele on lipid profiles was stronger in Asians compared with Whites.ConclusionThe rs1333049 C allele, rs4977574 G allele, and rs10757274 G allele of lncRNA, and the rs10811661 G allele of CDKN2A/2B had a significant influence on lipid levels, which may help the understanding of the underlying mechanisms between 9p21.3 variants and CAD.

Greenhouse vegetable production provides significant quantities of vegetables throughout the year and improves farmers’ income. However, over-fertilization with mineral fertilizer causes soil secondary salinization and decreases the stability of the soil structure. To improve aggregate formation and decrease salt accumulation in the soil profile, bio-organic fertilizers (Protaetia brevitarsis larvae frass with Bacillus amyloliticus and/or Trichoderma harziensis) were applied to partially substitute mineral fertilizer in a salinized vegetable soil. Soil nutrient condition, aggregate stability, and salt movement in the soil profile were measured in a greenhouse double-cucumber system. The results showed that soil organic matter (SOM), total nitrogen (TN), and available phosphorus (AP) increased significantly under bio-organic fertilizer treatments compared with control. Soil electrical conductivity (EC) and total salt content (TSC) decreased by 15.74–24.20% and 19.15–29.05%, respectively, with bio-organic fertilizers (p < 0.05). Cl−, NO3−, and SO42− content under double inoculation with B. amyloliticus and T. harziensis reduced by 31.19%, 26.30%, and 53.11%, respectively, compared to CK (p < 0.05). In addition, double inoculation was more efficient in reducing nitrate content in the soil profile than single inoculation. Soil microaggregates of 0.25–0.053 mm increased by 75.87–78.51% with bio-fertilizers compared with control, and double inoculation was the best for aggregate formation. In conclusion, the inoculation of plant-growth-promoting and salt-tolerant microorganisms with high humic acid larvae frass can alleviate salinization in vegetable soil, enhance soil nutrient content, and improve the soil structure.

ObjectivesTo develop and validate an efficient and automatically computational approach for stratifying glioma grades and predicting survival of lower-grade glioma (LGG) patients using an integration of state-of-the-art convolutional neural network (CNN) and radiomics.MethodThis retrospective study reviewed 470 preoperative MR images of glioma from BraTs public dataset (n=269) and Jinling hospital (n=201). A fully automated pipeline incorporating tumor segmentation and grading was developed, which can avoid variability and subjectivity of manual segmentations. First, an integrated approach by fusing CNN features and radiomics features was employed to stratify glioma grades. Then, a deep-radiomics signature based on the integrated approach for predicting survival of LGG patients was developed and subsequently validated in an independent cohort.ResultsThe performance of tumor segmentation achieved a Dice coefficient of 0.81. The intraclass correlation coefficients (ICCs) of the radiomics features between the segmentation network and physicians were all over 0.75. The performance of glioma grading based on integrated approach achieved the area under the curve (AUC) of 0.958, showing the effectiveness of the integrated approach. The multivariable Cox regression results demonstrated that the deep-radiomics signature remained an independent prognostic factor and the integrated nomogram showed significantly better performance than the clinical nomogram in predicting overall survival of LGG patients (C-index: 0.865 vs. 0.796, P=0.005).ConclusionThe proposed integrated approach can be noninvasively and efficiently applied in prediction of gliomas grade and survival. Moreover, our fully automated pipeline successfully achieved computerized segmentation instead of manual segmentation, which shows the potential to be a reproducible approach in clinical practice.

BackgroundRecent studies have shown that the 4G/5G insertion/deletion variant of SERPINE1 (rs1799889) is closely linked to coronary artery disease (CAD). This study aims to clarify the effects of the rs1799889 variant on lipid levels and to insight into the mechanisms underlying the rs1799889 variant and CAD.Methods and ResultsBy searching PubMed and the Cochrane databases for studies published before 31 October 2021, 40 studies conducted on a total of 13,117 subjects were included for the analysis. The consistent findings for the effects of the 5G allele of rs1799889 variant on lipid metabolism were the significantly decreased triglycerides (TG) [standardized mean difference (SMD) = –0.12, 95% CI = –0.21 to 0.03, P = 0.01], total cholesterol (TC) (SMD = –0.12, 95% CI = –0.17 to 0.06, P < 0.001), and low-density lipoprotein cholesterol (LDL-C) (SMD = –0.13, 95% CI = –0.23 to 0.03, P = 0.01) levels. Intriguingly, the significant effects of the rs1799889 variant on LDL-C (SMD = –0.15, 95% CI = –0.26 to 0.05, P < 0.01) and TC (SMD = –0.17, 95% CI = –0.27 to 0.07, P < 0.01) levels were primarily observed in the Asian population. However, the significant effect of the rs1799889 variant on high-density lipoprotein cholesterol (HDL-C) (SMD = 0.26, 95% CI = 0.03–0.48, P = 0.03) levels was detected only in female subjects.ConclusionThe rs1799889 variant of SERPINE1 is a protective genetic factor against CAD, the Asian population with the 5G allele of the rs1799889 variant may have a reduced CAD risk.

Retinopathy of prematurity and ischemic brain injury resulting in periventricular white matter damage are the main causes of visual impairment in premature infants. Accurate optic disc (OD) segmentation has important prognostic significance for the auxiliary diagnosis of the above two diseases of premature infants. Because of the complexity and non-uniform illumination and low contrast between background and the target area of the fundus images, the segmentation of OD for infants is challenging and rarely reported in the literature. In this article, to tackle these problems, we propose a novel attention fusion enhancement network (AFENet) for the accurate segmentation of OD in the fundus images of premature infants by fusing adjacent high-level semantic information and multiscale low-level detailed information from different levels based on encoder–decoder network. Specifically, we first design a dual-scale semantic enhancement (DsSE) module between the encoder and the decoder inspired by self-attention mechanism, which can enhance the semantic contextual information for the decoder by reconstructing skip connection. Then, to reduce the semantic gaps between the high-level and low-level features, a multiscale feature fusion (MsFF) module is developed to fuse multiple features of different levels at the top of encoder by using attention mechanism. Finally, the proposed AFENet was evaluated on the fundus images of preterm infants for OD segmentation, which shows that the proposed two modules are both promising. Based on the baseline (Res34UNet), using DsSE or MsFF module alone can increase Dice similarity coefficients by 1.51 and 1.70%, respectively, whereas the integration of the two modules together can increase 2.11%. Compared with other state-of-the-art segmentation methods, the proposed AFENet achieves a high segmentation performance.

BackgroundLeptin may have important implications in polycystic ovary syndrome (PCOS)-related metabolic disorders. However, the changes in serum leptin levels in patients with PCOS and its predictive value for PCOS remain obscure. We intend to analyze the association between leptin and PCOS in this study.Materials and MethodsThe study comprised 89 patients with PCOS and 139 individuals without PCOS. Each group was stratified as either normal- or hyper-fasting serum insulin (FSI), and lean or overweight/obese; and the patients were further categorized as normal- or hyper-androgenic. The validity of leptin toward the diagnosis of PCOS, or leptin combined with total testosterone, dehydroepiandrosterone sulfate (DHEAS), and free testosterone was estimated by receiver operating characteristic (ROC) curves, and correlations between paired variables was estimated by Spearman’s rank correlation coefficient. Associations between the clinical and metabolic variables and PCOS were analyzed via logistic regression.ResultsThe serum leptin levels of patients with PCOS were significantly higher than that of the control, and especially the PCOS in hyper-FSI, hyperandrogenimic and overweight/obese subgroups. The area under the ROC curve (AUC) of leptin was 74%, with cutoff value, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) 11.58 ng/mL, 77.5%, 62.6%, 57.0%, and 81.3%, respectively. Combined leptin and anti-Müllerian hormone (AMH) had the highest AUC (92.3%), excellent sensitivity (93.3%), moderate specificity (78.3%), PPV (73.5%) and NPV (94.8%). Serum leptin levels of the patients were correlated with the FSI, fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance (HOMA-IR), body mass index (BMI), and total testosterone levels. Elevated serum leptin was associated with a high risk of PCOS [P = 0.015; OR (95% CI) 1.128 (1.024–1.244)].ConclusionSubstantially elevated serum leptin is significantly associated with PCOS. These findings warrant further investigations into the function of leptin in the pathogenesis of PCOS.

Abstract Unlike adult mammalian heart, zebrafish heart has a remarkable capacity to regenerate after injury. Previous study has shown Notch signaling activation in the endocardium is essential for regeneration of the myocardium and this activation is mediated by hemodynamic alteration after injury, however, the molecular mechanism has not been fully explored. In this study we demonstrated that blood flow change could be perceived and transmitted in a primary cilia dependent manner to control the hemodynamic responsive klf2 gene expression and subsequent activation of Notch signaling in the endocardium. First we showed that both homologues of human gene KLF2 in zebrafish, klf2a and klf2b, could respond to hemodynamic alteration and both were required for Notch signaling activation and heart regeneration. Further experiments indicated that the upregulation of klf2 gene expression was mediated by endocardial primary cilia. Overall, our findings reveal a novel aspect of mechanical shear stress signal in activating Notch pathway and regulating cardiac regeneration.

The time interval of the observational data changes irregularly because of the difference of sensors’ sampling rate, the communication delay and the target leaving observation region of the sensor sometimes. These problems of asynchronous observation data greatly reduce the tracking accuracy of the multi-sensors system. Therefore, asynchronous data fusion system is more practical than synchronous data fusion system, and worthier of study. By establishing an asynchronous track fusion model with irregular time interval of observation data and combining with the Track Quality with Multiple Model (TQMM), an asynchronous track fusion algorithm with information feedback is proposed, and the TQMM is used for weight allocation to improve the performance of the asynchronous multi-sensor fusion system. The simulation result shows that the algorithm has better tracking performance compared with other algorithms, so that this kind of problem of track-to-track fusion for asynchronous sensors is solved effectively.

ObjectivesPrevious studies showed that age is the most important factor that determines the outcome after embryo transfer (ET), with either in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI), regardless whether fresh or frozen cycles. The average cumulative live birth rate (CLBR) following a single ovarian stimulation cycle in women of advanced reproductive age (≥38 years of age) has been reported to be 22.6–34.1%. The purpose of this study is to compare the CLBR after a single ovarian stimulation cycle in women of different advanced reproductive age bracket (38/39, 40/41, 42/43 years of age or older), and to explore the factors (e.g., age, type of infertility, body mass index (BMI), ovarian stimulation protocols) associated with CLBR.MethodsThis retrospective analysis included all women of advanced reproductive age (38 years or older) undergoing IVF or ICSI at authors’ institute during a period from January 1, 2016 to December 31, 2018. The study protocol was approved by the Ethics Committee of the Second Hospital of Hebei Medical University (No. 2021-P045). Subjects with underlying diseases were excluded from analysis. The last follow-up was conducted in December 2020, with minimal 2-year follow-up.ResultsThe final analysis included 826 women (40.00 ± 2.10 years of age at the time of ovarian stimulation; n = 633 and 193 for IVF-ET and ICSI-ET, respectively). The number of women in each age bracket was: 424 for 38/39 y, 226 for 40/41 y, 118 for 42/43 y, and 58 for ≥44 y. The number of transferable embryos was 2 (interquartile range: 2,4) for 38/39 y, 2 (2,3) for 40/41 y, 2 (2,3) for 42/43 y, and 2 (1.75,3) for ≥44 y. The rate of fresh embryo transfer was comparable (62.03–72.58%) among the 4 age brackets. The average CLBR following a single cycle was 26.27% in the overall study population, 32.31% for 38/39 y, 26.99% for 40/41 y, 14.4% for 42/43 y, and 3.44% for ≥44 y (P

Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people.Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images.Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation.Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604.Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.

Corneal ulcer is a common leading cause of corneal blindness. It is difficult to accurately segment corneal ulcers due to the following problems: large differences in the pathological shapes between point-flaky and flaky corneal ulcers, blurred boundary, noise interference, and the lack of sufficient slit-lamp images with ground truth. To address these problems, in this paper, we proposed a novel semi-supervised multi-scale self-transformer generative adversarial network (Semi-MsST-GAN) that can leverage unlabeled images to improve the performance of corneal ulcer segmentation in fluorescein staining of slit-lamp images. Firstly, to improve the performance of segmenting the corneal ulcer regions with complex pathological features, we proposed a novel multi-scale self-transformer network (MsSTNet) as the MsST-GAN generator, which can guide the model to aggregate the low-level weak semantic features with the high-level strong semantic information and adaptively learn the spatial correlation in feature maps. Then, to further improve the segmentation performance by leveraging unlabeled data, the semi-supervised approach based on the proposed MsST-GAN was explored to solve the problem of the lack of slit-lamp images with corresponding ground truth. The proposed Semi-MsST-GAN was comprehensively evaluated on the public SUSTech-SYSU dataset, which contains 354 labeled and 358 unlabeled fluorescein staining slit-lamp images. The results showed that, compared with other state-of-the-art methods, our proposed method achieves better performance with comparable efficiency.

An increasing body of evidence connects non-alcoholic fatty liver disease (NAFLD) to hypertension. The objective of this systematic review and meta-analysis was to estimate the nature and magnitude of the association between NAFLD and hypertension. We systematically searched PubMed, Embase, Cochrane Library, and Web of Science for observational studies published up to May 1, 2021. Cohort studies that reported data on the association between NAFLD and incident hypertension or between hypertension and incident NAFLD were included. We used random-effects models to conduct meta-analysis on the measures of association from individual studies. A total of 11 studies were eligible for inclusion, among which 4 studies including 25,260 participants reported the association between hypertension and new-onset NAFLD. The presence of hypertension was significantly associated with an increased risk of incident NAFLD (HR 1.63, 95% CI: 1.41–1.88; I2 = 37.6%). On the other hand, 9 studies with data on 46,487 participants analyzed the effects of NAFLD on incident hypertension. Pooled analysis showed that the presence of NAFLD was significantly associated with an increased incidence of hypertension (HR 1.55, 95% CI: 1.29–1.87; I2 = 80.5%). There was significant heterogeneity among the studies in this analysis (p

Choroid neovascularization (CNV) is one of the blinding factors. The early detection and quantitative measurement of CNV are crucial for the establishment of subsequent treatment. Recently, many deep learning-based methods have been proposed for CNV segmentation. However, CNV is difficult to be segmented due to the complex structure of the surrounding retina. In this paper, we propose a novel dynamic multi-hierarchical weighting segmentation network (DW-Net) for the simultaneous segmentation of retinal layers and CNV. Specifically, the proposed network is composed of a residual aggregation encoder path for the selection of informative feature, a multi-hierarchical weighting connection for the fusion of detailed information and abstract information, and a dynamic decoder path. Comprehensive experimental results show that our proposed DW-Net achieves better performance than other state-of-the-art methods.

Objective: This study aims to explore the influence of patient activation (PA) and relational aspects on the quality of life (QoL) in patients with Atrial Fibrillation (AF) for developing measures to improve PA and QoL.Methods: A cross-sectional study was undertaken in 2021 among 190 AF patients in Nanjing, China. Research instruments included a self-designed social-demographic characteristics scale, the Patient Activation Measure (PAM), the Atrial Fibrillation Effect on Quality of Life (AFEQT). The data analysis was performed using IBM SPSS 25.0. Spearman correlation analysis, multiple linear regression analysis, and Wilcoxon rank-sum tests were used to assess the association accordingly.Results: The average AFEQT score for the 190 AF patients was 69.32 ± 14.52. The distribution of activation Levels 1, 2, 3, and 4, were where 4.7, 34.2, 47.4, and 13.7%, respectively. The multiple linear regression analysis revealed that patient activation, work status, and cardiac rehabilitation of AF patients predicted AF-related QoL (β = 0.270, −0.205, and 0.183, respectively; all P < 0.05). The influences of PA level on subdimensions of AF-related QoL were as follows: symptoms, daily activities and treatment concern.Conclusion: The level of QoL of patients with AF was moderate. Higher levels of patient activation in those with AF were associated with milder symptoms, more positive daily activities and fewer treatment concern. Based on our findings, we suggest that healthcare personnel should encourage AF patients to take active participation in cardiac rehabilitation, disease self-management and foster progression of PA level. Future research is warranted to develop tailor-made interventions aimed at the activation level.

Background: The plasminogen activator inhibitor-1 (PAI-1) was found in many types of tumor cells, which involved in tumorigenesis. Some studies investigated the associations between PAI-1 polymorphisms and various cancers, but the results were inconsistent. So this study did a meta-analysis to assess the strength of relationship between PAI-1 and cancer. Methods: Articles that meet the requirements were searched from PubMed, EMBASE, MEDLINE, Scopus, CNKI, Wanfang and SinoMed electronic databases before June 17th 2021. Stata version 11.2 was performed to merge the odds ratios (ORs) values and calculate 95% confidence intervals (CIs). Stratified analyses were assessed on the basis of types of cancer, ethnicity and source of the control group. Heterogeneity and sensitivity analysis were tested, and publication bias was also estimated. A meta-regression analysis was applied to explore sources of heterogeneity. The false-positive report probabilities (FPRP) and the Bayesian False Discovery Probability (BFDP) test were used to assess the credibility of statistically significant associations. Results: Ultimately, in this study, 33 eligible reports were included with 9550 cases and 10431 controls for the rs1799889 polymorphism, 5 reports with 2705 cases and 3168 controls for the rs2227631 polymorphism, and 4 reports with 2799 cases and 4011 controls for the rs2227667 polymorphism. The ORs and 95% CIs showed a statistically significant relationship between rs1799889 4G>5G polymorphism and cancer risk, especially in feminine cancer. The term refers to cancers that occur in the female reproductive system, such as ovarian, breast, endometrial and cervical cancer. Moreover, there was no association observed for the PAI-1 promoter A>G polymorphism (rs2227631 and rs2227667). In further subgroup analyses of 4G>5G polymorphism (rs1799889), an increased susceptibility to cancer was observed in Caucasians group and some types of cancer groups. Conclusions: This article comes to a conclusion that the rs1799889 polymorphism might help to increase the risk of cancer; moreover, the susceptibility to feminine cancer is more evident.

The disturbance in gut microbiota composition and metabolism has been implicated in the process of pathogenic bacteria infection. However, the characteristics of the microbiota and the metabolic interaction of commensals–host during pathogen invasion remain more than vague. In this study, the potential associations of gut microbes with disturbed lipid metabolism in mice upon carbapenem-resistant Escherichia coli (CRE) infection were explored by the biochemical and multi-omics approaches including metagenomics, metabolomics and lipidomics, and then the key metabolites–reaction–enzyme–gene interaction network was constructed. Results showed that intestinal Erysipelotrichaceae family was strongly associated with the hepatic total cholesterol and HDL-cholesterol, as well as a few sera and fecal metabolites involved in lipid metabolism such as 24, 25-dihydrolanosterol. A high-coverage lipidomic analysis further demonstrated that a total of 529 lipid molecules was significantly enriched and 520 were depleted in the liver of mice infected with CRE. Among them, 35 lipid species showed high correlations (|r| > 0.8 and p < 0.05) with the Erysipelotrichaceae family, including phosphatidylglycerol (42:2), phosphatidylglycerol (42:3), phosphatidylglycerol (38:5), phosphatidylcholine (42:4), ceramide (d17:1/16:0), ceramide (d18:1/16:0) and diacylglycerol (20:2), with correlation coefficients higher than 0.9. In conclusion, the systematic multi-omics study improved the understanding of the complicated connection between the microbiota and the host during pathogen invasion, which thereby is expected to lead to the future discovery and establishment of novel control strategies for CRE infection.

BackgroundAdvanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories. ObjectiveWe aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide. MethodsData of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient, and Spearman correlation coefficient. ResultsOur models can accurately predict the daily new confirmed cases of COVID-19 in most countries and territories. Of the 215 countries and territories under study, 198 (92.1%) had MAEs 0.8. For the 215 countries and territories, the mean MAE was 5.42 (range 0.26-15.32), the mean RMSE was 9.27 (range 1.81-24.40), the mean Pearson correlation coefficient was 0.89 (range 0.08-0.99), and the mean Spearman correlation coefficient was 0.84 (range 0.2-1.00). ConclusionsBy integrating previous incidence and Google Trends data, our machine learning algorithm was able to predict the incidence of COVID-19 in most individual countries and territories accurately 7 days ahead.

Deep learning has made significant progress in many fields such as image identification, speech recognition and natural language processing, especially in the field of computer vision. The better performance of the neural network often built on deeper, wider network structure, more network parameters and more storage and often computational expensive. As a result, it is hard to deploy neural network to mobile and embedded devices. Therefore, compressing of convolutional neural networks is very necessary and practical. In this paper, we propose a channel pruning algorithm for depth-wise separable convolution units and introduce a new channel selection algorithm based on information gain and a method for quickly recovering network performance after pruning. The proposed method is implemented on MobileNet and validated on several popular datasets. The experimental results show that our method can achieve better experimental results on several image classification datasets, and also achieve good detection results on the PASCAL VOC image detection dataset.

BackgroundGastroesophageal junction (GEJ) was one of the most common malignant tumors. However, the value of clinicopathological features in predicting the prognosis of postoperative patients with GEJ cancer and without distant metastasis was still unclear.MethodsThe 3425 GEJ patients diagnosed and underwent surgical resection without distant metastasis in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015 were enrolled,and they were randomly divided into training and validation cohorts with 7:3 ratio. Univariate and multivariate Cox regression analysis were used to determine the predictive factors that constituted the nomogram. The predictive accuracy and discriminability of Nomogram were determined by the area under the curve (AUC), C index, and calibration curve, and the influence of various factors on prognosis was explored.Results2,400 patients were designed as training cohort and 1025 patients were designed as validation cohort. The percentages of the distribution of demographic and clinicopathological characteristics in the training and validation cohorts tended to be the same. In the training cohort, multivariate Cox regression analysis revealed that the age, tumor grade, T stage and N stage were independent prognostic risk factors for patients with GEJ cancer without distant metastasis. The C index of nomogram model was 0.667. The AUC of the receiver operating characteristic (ROC) analysis for 3- and 5-year overall survival (OS) were 0.704 and 0.71, respectively. The calibration curve of 3- and 5-year OS after operation showed that there was the best consistency between nomogram prediction and actual observation. In the validation cohort, the C index of nomogram model, the AUC of 3- and 5-year OS, and the calibration curve were similar to the training cohort.ConclusionsNomogram could evaluate the prognosis of patients with GEJ cancer who underwent surgical resection without distant metastasis.

Unlike mammals, zebrafish can regenerate injured hearts even in the adult stage. Cardiac regeneration requires the coordination of cardiomyocyte (CM) proliferation and migration. The TGF-β/Smad3 signaling pathway has been implicated in cardiac regeneration, but the molecular mechanisms by which this pathway regulates CM proliferation and migration have not been fully illustrated. Here, we investigated the function of TGF-β/Smad3 signaling in a zebrafish model of ventricular ablation. Multiple components of this pathway were upregulated/activated after injury. Utilizing a specific inhibitor of Smad3, we detected an increased ratio of unrecovered hearts. Transcriptomic analysis suggested that the TGF-β/Smad3 signaling pathway could affect CM proliferation and migration. Further analysis demonstrated that the CM cell cycle was disrupted and the epithelial–mesenchymal transition (EMT)-like response was impaired, which limited cardiac regeneration. Altogether, our study reveals an important function of TGF-β/Smad3 signaling in CM cell cycle progression and EMT process during zebrafish ventricle regeneration.

Multiperson activity recognition is a pivotal branch as well as a challenging topic of human action recognition research. This paper adopts a hybrid learning model to the spatio-temporal relationship and occlusion relationship among multiple people. Initially, this paper builds up an active multiperson interaction relationship estimation framework model to capture interpersonal spatio-temporal relation. This model incorporates the interaction relationship estimation framework with the multiperson relationship network. On this ground, it automatically learns from the human-computer interaction dataset in an end-to-end manner and performs reasoning with standard matrix operations. Secondly, this paper proposed an adaptive occlusion state behavior recognition method derived from the semantic knowledge model to ravel out the concern of occlusion and self-occlusion in human action recognition. Then, Petri Nets are used to recognize multiperson interactive actions. This model has been through extensive experiments on the TV interaction dataset, Vlog dataset, AVA dataset, and MLB-YouTube dataset, experimental results have proved that the recognition performance of this model is superior than the other available models. This paper prospects and summarizes the estimation framework of the interaction relationship and occlusion semantic-knowledge relationship. Experimental results suggest that the proposed method in the paper could capture the discriminative relation information for multiperson interactive activity recognition, which further validates the efficiency of the hybrid learning model.

Long non-coding RNAs (lncRNAs) regulate plant responses to abiotic stresses. However, the short reads produced by second-generation sequencing technology make it difficult to accurately explore full-length transcripts, limiting the study of lncRNAs. In this study, we used third-generation long-read sequencing technology with the PacBio Sequel and Illumina platform to explore the role of lncRNAs in the heat stress response of Populus x canadensis Moench trees. We using 382,034,416 short reads to correct 4,297,179 long reads by resulted in 66,657 full-length transcripts, representing 33,840 genes. Then, 753 putative lncRNAs were identified, including 658 sense lncRNAs (87.38%), 41 long intervening/intergenic non-coding RNAs (lincRNAs) (5.44%), 12 antisense lncRNAs (1.59%), and 42 sense intronic lncRNAs (5.58%). Using the criteria | log2FC| ≥ 1 and q-value < 0.05, 3,493 genes and 78 lncRNAs were differentially expressed under the heat treatment. Furthermore, 923 genes were detected as targets of 43 differently expressed lncRNAs by cis regulation. Functional annotation demonstrated that these target genes were related to unfolded protein binding, response to stress, protein folding, and response to stimulus. Lastly, we identified a lncRNA–gene interaction network consisting of four lncRNAs and six genes [Heat Shock Protein 82 (HSP82), HSP83, Disease Resistance Protein 27 (DRL27), DnaJ family protein (DNJH), and two other predicted protein-coding genes], which showed that lncRNAs could regulate HSP family genes in response to heat stress in Populus. Therefore, our third-generation sequencing has improved the description of the P. canadensis transcriptome. The potential lncRNAs and HSP family genes identified here present a genetic resource to improve our understanding of the heat-adaptation mechanisms of trees.

Phenol is a ubiquitous pollutant and can contaminate natural water resources. Hence, the removal of phenol from wastewater is of significant importance. A series of biological methods were used to remove phenol based on the natural ability of microorganisms to degrade phenol, but the tolerance mechanism of phenol-degraded strains to phenol are not very clear. Morphological observation on Candida tropicalis showed that phenol caused the reactive oxygen species (ROS) accumulation, damaging the mitochondrial and the endoplasmic reticulum. On the basis of transcriptome data and cell wall susceptibility analysis, it was found that C. tropicalis prevented phenol-caused cell damage through improvement of cell wall resistance, maintenance of high-fidelity DNA replication, intracellular protein homeostasis, organelle integrity, and kept the intracellular phenol concentration at a low level through cell-wall remodeling and removal of excess phenol via MDR/MXR transporters. The knowledge obtained will promote the genetic modification of yeast strains in general to tolerate the high concentrations of phenol and improve their efficiency of phenol degradation.

Both MicroRNAs and HMGB1 took part in pathological process of myocardial I/R injury though several signaling pathways. We hypothesized that mircoRNA451 (miR-451), a group of small non-coding RNAs, could improve this injury by inhibiting HMGB1. Male SD rats were randomly distributed into 5 groups and subjected to I/R process. After 24 hours of reperfusion injury, the serum content of CK and LDH, the content of MDA in tissue and activity of SOD were detected; The infarcted areas were defined by TTC staining and Evans Blue; TUNEL staining and cleaved-Caspase 3 were used to test apoptosis; HMGB1 was detected by real-time fluorescence quantitative PCR and Western Blotting. Compared with the I/R and I/R+Ad-GFP group, upregulation of miR-451 could reduce the infarcted areas, cardiomyocytes apoptosis index, expression of cleaved-caspase 3 and content of CK and LDH significantly(P

Introduction: Alzheimer’s disease (AD) is the most common progressive neurodegenerative disorder in the elderly, which will eventually lead to dementia without an effective precaution and treatment. As a typical complex disease, the mechanism of AD’s occurrence and development still lacks sufficient understanding. Research design and methods: In this study, we aim to directly analyze the relationship between DNA variants and phenotypes based on the whole genome sequencing data. Firstly, to enhance the biological meanings of our study, we annotate the deleterious variants and mapped them to nearest protein coding genes. Then, to eliminate the redundant features and reduce the burden of downstream analysis, a multi-objective evaluation strategy based on entropy theory is applied for ranking all candidate genes. Finally, we use multi-classifier XGBoost for classifying unbalanced data composed with 46 AD samples, 483 mild cognitive impairment (MCI) samples and 279 cognitive normal (CN) samples. Results: The experimental results on real whole genome sequencing data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) show that our method not only has satisfactory classification performance but also finds significance correlation between AD and RIN3, a known susceptibility gene of AD. In addition, pathway enrichment analysis was carried out using the top 20 feature genes, and three pathways were confirmed to be significantly related to the formation of AD. Conclusions: From the experimental results, we demonstrated that the efficacy of our proposed method has practical significance.

In tight fractured reservoirs, oil in matrices is mainly explored due to mass transfer mechanisms during the pressure depletion process. In the modeling of mass transfer in fractured reservoirs using the dual porosity concept, the shape factor is the most important parameter and should be described accurately. However, the current shape factors are not suited for tight oil reservoir simulation because the characteristics of tight oil reservoirs are not taken into account. In order to solve this problem, a new mass transfer function for tight fractured oil reservoirs is proposed by introducing a new time-related correction factor which could consider not only the existence of the boundary layer in nano-microscale throats in tight porous media but also the heterogeneous pressure distribution in matrix blocks. In addition, special contact relations between matrix and fracture are included. The correction factor presented in this study is verified using the experimental data and numerical simulation results. Data analysis results demonstrate that the lower and slower the pressure propagation velocity, the longer the duration time of unsteady flow compared to conventional reservoirs. Therefore, in the calculation of mass transfer flow in tight oil reservoirs, the unsteady flow between fracture and matrix cannot be ignored.

We investigate experimentally and numerically the scattering properties of liquid gallium nanoparticles coupled to a thin gold or silver film. The gallium nanoparticles are excited either directly by using inclined white light or indirectly by surface plasmon polaritons generated on the surface of the gold/silver film. In the former case, the scattering spectrum is always dominated by a scattering peak at ∼540 nm with a long-wavelength shoulder which is redshifted with increasing diameter of the gallium nanoparticle. Under the excitation of the surface plasmon polaritons, optical resonances with much narrower linewidths, which are dependent on the incidence angle of the white light, appear in the scattering spectra. In this case, the scattering spectrum depends weakly on the diameter of the gallium nanoparticle but the radiation pattern exhibits a strong dependence. In addition, a significant enhancement of electric field is expected in the gap region between the gallium nanoparticles and the gold film based on numerical simulation. As compared with the gallium nanoparticle coupled to the gold film which exhibit mainly yellow and orange colors, vivid scattering light spanning the visible light spectrum can be achieved in the gallium nanoparticles coupled to the silver film by simply varying the incidence angle. Gallium nanoparticles coupled to thin metal films may find potential applications in light–matter interaction and color display.

A new genus and species, Undulopsychopsis alexi gen. et sp. n., is described from the Early Cretaceous Yixian Formation of western Liaoning Province, China. This genus is probably most closely related to the Asian Cretaceous genus Kagapsychops Fujiyama, 1978. The family affinity of Undulopsychopsis gen. n. is discussed. The genus is preliminarily assigned to Psychopsidae, although it shares some character states with Osmylopsychopidae (e.g., crossveins are very scarce; Rs1 and 1A are multi-branched).