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Abstract Background Circular RNAs (circRNA) have been shown to be involved in the pathogenesis of colorectal cancer (CRC). CircCTNNA1 was found to be one of the upregulated circRNAs in CRC. However, there are few studies on circCTNNA1, so it is necessary to carry out further studies. Methods The expression of circCTNNA1, microRNA (miR)-363-3p, and chemokine C-X-C motif ligand 5 (CXCL5) was detected by quantitative real-time PCR (qRT-PCR). The protein levels of CXCL5 and metastasis markers were measured using western blot (WB) analysis. Cell proliferation, apoptosis, cell cycle, migration, and invasion were determined by cell counting kit 8 (CCK8) assay, colony formation assay, flow cytometry, and transwell assay. The relationship between miR-363-3p and circCTNNA1 or CXCL5 was evaluated via dual-luciferase reporter assay and RNA immunoprecipitation assay. Animal study was performed to explore the function of circCTNNA1 on CRC tumorigenesis. Results CircCTNNA1 and CXCL5 were highly expressed in CRC. Knockdown of circCTNNA1 could inhibit the proliferation, cell cycle, metastasis, and promote the apoptosis of CRC cells. MiR-363-3p could be sponged by circCTNNA1, and the inhibition effect of circCTNNA1 silencing on CRC progression could be reversed by miR-363-3p inhibitor. Moreover, miR-363-3p could interact with CXCL5, and CXCL5 overexpression also could reverse the suppressive effect of miR-363-3p on CRC progression. Downregulation of circCTNNA1 also could hinder the tumor growth of CRC in vivo. Conclusion CircCTNNA1 enhanced CRC progression via regulating the miR-363-3p/CXCL5 axis.

Abstract Background Increasing evidence has demonstrated that long noncoding RNAs (lncRNAs) have regulatory functions in hepatocellular carcinoma (HCC). The link between lincSCRG1 and HCC remains unclear. Methods To explore the lincSCRG1 regulation axis, bioinformatics, RIP and luciferase reporter assay were performed. The expressions of lincSCRG1-miR26a-SKP2 were detected in HCC tissues and cell lines through qPCR and western blot. The functions of HCC cells were investigated through in vitro assays (MTT, colony formation, transwell and flow cytometry) and the inner effect of lincSCRG1-miR26a in vivo was evaluated by xenografts and liver metatstatic nude mice models. Results LincSCRG1 was found to be strongly elevated in human HCC tissues and cell lines. MiR26a and S phase kinase-related protein 2 (SKP2) were predicted as the target miRNA for lincSCRG1 and the target gene for miR26a with direct binding sites, respectively. LincSCRG1 was verified as a competing endogenous RNA (ceRNA) via negative regulation of miR26a and derepression of SKP2 in HCC cells. Both overexpression of lincSCRG1 (ov-lincSCRG1) and inhibition of miR26a (in-miR26a) obviously stimulated cellular viability, colony formation, migration and proliferation of S phase cells and also significantly increased the protein levels of cyclinD1, CDK4, MMP2/3/9, Vimentin, and N-cadherin or inhibited the protein level of E-cadherin of HCC cells, while knockdown of lincSCRG1 (sh-lincSCRG1) and upregulation of miR26a (mi-miR26a) had the opposite effects on HCC cells. Cotransfection of in-miR26a or overexpression of SKP2 (ov-SKP2) with sh-lincSCRG1 could rescue the anticancer functions of sh-lincSCRG1, including suppressing proliferation and migration of HCC cells. Additionally, sh-lincSCRG1 could effectively inhibit the growth of subcutaneous xenograft tumours and lung metastasis, while the anticancer effect of sh-lincSCRG1 could be reversed by cotransfection of in-miR26a. Conclusions LincSCRG1 acts as a ceRNA of miR26a to restrict its ability to derepress SKP2, thereby inducing the proliferation and migration of HCC cells in vitro and in vivo. Depletion of lincSCRG1 could be used as a potential therapeutic approach in HCC.

Abstract Homeobox B4 (HOXB4), which belongs to the homeobox (HOX) family, possesses transcription factor activity and has a crucial role in stem cell self-renewal and tumorigenesis. However, its biological function and exact mechanism in cervical cancer remain unknown. Here, we found that HOXB4 was markedly downregulated in cervical cancer. We demonstrated that HOXB4 obviously suppressed cervical cancer cell proliferation and tumorigenic potential in nude mice. Additionally, HOXB4-induced cell cycle arrest at the transition from the G0/G1 phase to the S phase. Conversely, loss of HOXB4 promoted cervical cancer cell growth both in vitro and in vivo. Bioinformatics analyses and mechanistic studies revealed that HOXB4 inhibited the activity of the Wnt/β-catenin signaling pathway by direct transcriptional repression of β-catenin. Furthermore, β-catenin re-expression rescued HOXB4-induced cervical cancer cell defects. Taken together, these findings suggested that HOXB4 directly transcriptional repressed β-catenin and subsequently inactivated the Wnt/β-catenin signaling pathway, leading to significant inhibition of cervical cancer cell growth and tumor formation.

Abstract Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) is a complicated maternally inherited disorder lacking of sensitive and specific biomarkers. The objective of this study was to investigate the serum neurofilament light chain (NfL) as a novel biomarker of neurological dysfunction in MELAS. Patients with different status of MELAS were enrolled in this study. The Mini-Mental State Examination (MMSE) was given to the participants to evaluate cognition status. Multiple functional MRI was performed on the participants. Blood samples were collected and the serum NfL concentrations were determined by the single-molecule array technology (Simoa). This study enrolled 23 patients with MELAS, 15 people in the acute attack phase of MELAS and 10 people in the remission phase, including 2 patients in both acute attack and remission phase. Sixteen healthy controls (HCs) were also enrolled. Serum NfL level increased significantly in patients with MELAS. Serum NfL level in the acute attack group (146.73 [120.91–411.31] pg/ml, median [IQR]) was higher than in the remission group (40.31 [19.54–151.05] pg/ml, median [IQR]) and HCs group (7.70 [6.13–9.78] pg/ml, median [IQR]) (p

To date, the rotational characteristics of the solar transition region remain unclear. In this work, by applying the flux modulation method to the images derived from the Solar Dynamics Observatory/Atmospheric Imaging Assembly between 2011 and 2022 at 304 Å wavelength, we have studied the rotation of the solar transition region, and the results obtained are as follows. The solar transition region rotates differentially, while, from the perspective of the entire time interval, the rotation coefficients A and B are 14.39 (±0.08) and −1.61 (±0.15), respectively, and we find no prominent asymmetry in the average rotation rate of the northern and southern hemispheres. The solar transition region rotates fastest during the solar cycle maximum, and the average rotation rate follows the overall trend of solar activity. Both the equatorial rotation rate (represented by coefficient A ) and the latitudinal gradient (represented by coefficient B ) of the solar transition region are smaller than that of the solar chromosphere and the corona, indicating the solar transition region rotates more slowly and more rigidly than the other two layers, and we speculate that the solar chromosphere and corona seem to restrain the rotation of the solar transition region at the same time.

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. The experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, and a high consistency between the region of detected clump and that of simulated clump, and the experiments demonstrate that FacetClumps is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises ^13 CO ( J = 1−0) emission line of the MWISP within 11.°7 ≤ l ≤ 13.°4, 0.°22 ≤ b ≤ 1.°05, and 5 km s ^−1 ≤ v ≤ 35 km s ^−1 and simulated clumps, reaches 90.2%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.

In the decade since its debut, the Mesocircuit Hypothesis (MH) has provided researchers a scaffolding for interpreting their findings by associating subcortical-cortical dysfunction with the loss and recovery of consciousness following severe brain injury. Here, we leverage new findings from human and rodent lesions, as well as chemo/optogenetic, tractography, and stimulation studies to propose the external segment of the globus pallidus (GPe) as an additional node in the MH, in hopes of increasing its explanatory power. Specifically, we discuss the anatomical and molecular mechanisms involving the GPe in sleep-wake control and propose a plausible mechanistic model explaining how the GPe can modulate cortical activity through its direct connections with the prefrontal cortex and thalamic reticular nucleus to initiate and maintain sleep. The inclusion of the GPe in the arousal circuitry has implications for understanding a range of phenomena, such as the effects of the adenosine (A2A) and dopamine (D2) receptors on sleep-wake cycles, the paradoxical effects of zolpidem in disorders of consciousness, and sleep disturbances in conditions such as Parkinson’s Disease.

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models. [ABSTRACT FROM AUTHOR]

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models. [ABSTRACT FROM AUTHOR]

The efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere. Plain Language Summary: The temporal and spatial distribution mechanism of physical parameters in near space is very complex. It is of great significance to carry out fine modeling and forecasting of atmospheric temperature profiles for spacecrafts in near space. In this study, taking a single‐station temperature profile in the stratosphere as the object, the mathematical mapping problem of temperature operator is realized by a new deep learning method (deep operator networks). This provides a new research perspective for fine spatiotemporal modeling of temperature profiles. The proposed temperature operator shows low cumulative error performance in forecasting low latitude temperature profiles, and is also suitable for high latitude temperature profiles and mid latitude wind profiles in the stratosphere. Key Points: A deep neural operator method is utilized to forecast hourly atmospheric temperature profiles in the stratosphereThe temperature profile operator network based on BiLSTM denotes lower cumulative error in multi‐step iterative forecastingThe proposed method exhibits potential performance in different locations and atmospheric variables [ABSTRACT FROM AUTHOR]

Abstract Background Little is known about the inter-relationship among fruit and vegetable intake, gut microbiota and metabolites, and type 2 diabetes (T2D) in human prospective cohort study. The aim of the present study was to investigate the prospective association of fruit and vegetable intake with human gut microbiota and to examine the relationship between fruit and vegetable-related gut microbiota and their related metabolites with type 2 diabetes (T2D) risk. Methods This study included 1879 middle-age elderly Chinese adults from Guangzhou Nutrition and Health Study (GNHS). Baseline dietary information was collected using a validated food frequency questionnaire (2008–2013). Fecal samples were collected at follow-up (2015–2019) and analyzed for 16S rRNA sequencing and targeted fecal metabolomics. Blood samples were collected and analyzed for glucose, insulin, and glycated hemoglobin. We used multivariable linear regression and logistic regression models to investigate the prospective associations of fruit and vegetable intake with gut microbiota and the association of the identified gut microbiota (fruit/vegetable-microbiota index) and their related fecal metabolites with T2D risk, respectively. Replications were performed in an independent cohort involving 6626 participants. Results In the GNHS, dietary fruit intake, but not vegetable, was prospectively associated with gut microbiota diversity and composition. The fruit-microbiota index (FMI, created from 31 identified microbial features) was positively associated with fruit intake (p

Long non-coding RNA(lncRNA)is defined as untranslatable(non-coding)RNA that is over 200 nucleotides and is transcribed from the human genome. With the successful completion of the human genome sequencing and mapping, in subsequent ENCODE study, we found that about 75% of the genome sequence can be transcribed into RNA, and most of the transcripts are non-coding RNA. New research in recent years has found that lncRNA is involved in many important physiological and pathological processes in the body, such as cell development, cell proliferation, cell differentiation, such as cell cycle regulation, cell metabolism, apoptosis, and reprogramming of induced peripheral stem cells. And epigenetic regulation and other biological functions, and differential expression play an important role in the occurrence of various human diseases, such as malignant tumors, inflammatory and immune diseases. Recent studies have shown that lncRNA is related to the pathogenesis of ophthalmic diseases. This article aims to review the latest progress between the abnormal expression of lncRNAs and eye diseases in recent years.

Abstract Background Interest in the interplay between host genetics and the gut microbiome in complex human diseases is increasing, with prior evidence mainly being derived from animal models. In addition, the shared and distinct microbiome features among complex human diseases remain largely unclear. Results This analysis was based on a Chinese population with 1475 participants. We estimated the SNP-based heritability, which suggested that Desulfovibrionaceae and Odoribacter had significant heritability estimates (0.456 and 0.476, respectively). We performed a microbiome genome-wide association study to identify host genetic variants associated with the gut microbiome. We then conducted bidirectional Mendelian randomization analyses to examine the potential causal associations between the gut microbiome and complex human diseases. We found that Saccharibacteria could potentially decrease the concentration of serum creatinine and increase the estimated glomerular filtration rate. On the other hand, atrial fibrillation, chronic kidney disease and prostate cancer, as predicted by host genetics, had potential causal effects on the abundance of some specific gut microbiota. For example, atrial fibrillation increased the abundance of Burkholderiales and Alcaligenaceae and decreased the abundance of Lachnobacterium, Bacteroides coprophilus, Barnesiellaceae, an undefined genus in the family Veillonellaceae and Mitsuokella. Further disease-microbiome feature analysis suggested that systemic lupus erythematosus and chronic myeloid leukaemia shared common gut microbiome features. Conclusions These results suggest that different complex human diseases share common and distinct gut microbiome features, which may help reshape our understanding of disease aetiology in humans. Video Abstract

Summary: Background: Calcium plays a role in a wide range of biological functions. Here we conducted a phenome-wide Mendelian randomisation (MR-PheWAS) analysis and a systematic review for MR studies to comprehensively investigate the health effects of serum calcium. Methods: One-hundred and thirty genetic variants strongly associated with serum calcium levels were used as instrumental variables. A phenome-wide association analysis (PheWAS) was conducted to examine the associations of genetically predicted serum calcium with 1473 distinct phenotypes in the UK Biobank including 339,197 individuals. Observed associations in PheWAS were further tested for replication in two-sample MR replication analysis. A systematic review for MR studies on serum calcium was performed to synthesize the published evidence and compare with the current MR-PheWAS findings. Findings: Higher genetically predicted calcium levels were associated with decreased risk of 5 diseases in dermatologic and musculoskeletal systems and increased risk of 17 diseases in circulatory, digestive, endocrine, genitourinary and immune systems. Eight associations were replicated in two-sample MR analysis. These included decreased risk of osteoarthritis and increased risk of coronary artery disease, myocardial infarction, coronary atherosclerosis, hyperparathyroidism, disorder of parathyroid gland, gout, and calculus of kidney and ureter with increased serum calcium. Systematic review of 25 MR studies provided supporting evidence on five out of the eight disease outcomes, while the increased risk of gout, hyperparathyroidism and disorder of parathyroid gland were novel findings. Interpretation: This study found wide-ranged health effects of high serum calcium, which suggests that the benefits and adversities of strategies promoting calcium intake should be assessed. Funding: ET is supported by a CRUK Career Development Fellowship (C31250/A22804). XL is supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province. SCL acknowledges research funding from the Swedish Heart Lung Foundation (Hjärt-Lungfonden, 20210351), the Swedish Research Council (Vetenskapsrådet, 2019-00977), and the Swedish Cancer Society (Cancerfonden).

This narrative review summarizes key concepts in dairy nutrition for supporting human health throughout the life course. Milk and dairy products have been a staple component of our diet for thousands of years and provide a wide range of important nutrients that are otherwise difficult to obtain from dairy-free diets. In this review, we provide a broad perspective on the nutritional roles of iodine and dairy protein in supporting human health during pregnancy and early life, childhood and adolescence, mid- and later-life. New methodologies to identify biomarkers of dairy intake via high-throughput mass spectrometry are discussed, and new concepts such as the role of the food matrix in dairy nutrition are introduced. Finally, future policy and research related to the consumption of dairy and non-dairy alternatives for health are discussed with a view to improving nutritional status across the lifespan.

Previous studies have revealed that melatonin could play a role in anti-osteoporosis and promoting osteogenesis. However, the effects of melatonin treatment on osteoporotic bone defect and the mechanism underlying the effects of melatonin on angiogenesis are still unclear. Our study was aimed to investigate the potential effects of melatonin on angiogenesis and osteoporotic bone defect. Bone marrow mesenchymal stem cells (BMSCs) were isolated from the femur and tibia of rats. The BMSC osteogenic ability was assessed using alkaline phosphatase (ALP) staining, alizarin red S staining, qRT-PCR, western blot, and immunofluorescence. BMSC-mediated angiogenic potentials were determined using qRT-PCR, western blot, enzyme-linked immunosorbent assay, immunofluorescence, scratch wound assay, transwell migration assay, and tube formation assay. Ovariectomized (OVX) rats with tibia defect were used to establish an osteoporotic bone defect model and then treated with melatonin. The effects of melatonin treatment on osteoporotic bone defect in OVX rats were analyzed using micro-CT, histology, sequential fluorescent labeling, and biomechanical test. Our study showed that melatonin promoted both osteogenesis and angiogenesis in vitro. BMSCs treated with melatonin indicated higher expression levels of osteogenesis-related markers [ALP, osteocalcin (OCN), runt-related transcription factor 2, and osterix] and angiogenesis-related markers [vascular endothelial growth factor (VEGF), angiopoietin-2, and angiopoietin-4] compared to the untreated group. Significantly, melatonin was not able to facilitate human umbilical vein endothelial cell angiogenesis directly, but it possessed the ability to promote BMSC-mediated angiogenesis by upregulating the VEGF levels. In addition, we further found that melatonin treatment increased bone mineralization and formation around the tibia defect in OVX rats compared with the control group. Immunohistochemical staining indicated higher expression levels of osteogenesis-related marker (OCN) and angiogenesis-related markers (VEGF and CD31) in the melatonin-treated OVX rats. Then, it showed that melatonin treatment also increased the bone strength of tibia defect in OVX rats, with increased ultimate load and stiffness, as performed by three-point bending test. In conclusion, our study demonstrated that melatonin could promote BMSC-mediated angiogenesis and promote osteogenesis–angiogenesis coupling. We further found that melatonin could accelerate osteoporotic bone repair by promoting osteogenesis and angiogenesis in OVX rats. These findings may provide evidence for the potential application of melatonin in osteoporotic bone defect.

The lack of efficient and cost-effective catalysts for hydrogen oxidation reaction (HOR) hampers the application of hydroxide exchange membrane fuel cells. Here, authors reported bimetallic MoNi4 and WNi4 nanoalloys with marked HOR activity in alkali, among which MoNi4 outperforms the Pt/C catalyst.

Objective To investigate the clinical features, endoscope diagnosis, and treatment of cases of colorectal foreign bodies. Methods A retrospective analysis of colorectal foreign body cases admitted to the Digestive Endoscope Center from June 2005 to September 2018 were completed. The information of gender, age, source and location of foreign bodies, complications, and methods of removal was collected so that characteristics and management experience were summarized. Results A total of 14 cases of colorectal foreign bodies were included, among which there were 10 males (71%) with an average age of 49 years old (21-80 years old). The origin of foreign body included iatrogenic (5 cases), oral (3 cases), anal (5 cases) and one case of unknown reason. As to the position of foreign bodies, iatrogenic and oral foreign bodies might distribute in the whole colon, while anal foreign bodies located in sigmoid-rectum mainly. In total 6 cases suffered from foreign body-related complications, including mucosal injury (1 case), intestinal wall injury (2 cases), intestinal obstruction (2 cases) and liver abscess (1 case). We attempted to remove foreign bodies under endoscopy in 12 cases, with success in 11 cases; the instruments used were foreign body forceps (7 cases) and snares (5 cases). The main reason of failure was big size of foreign bodies. Conclusions The distribution of colorectal foreign bodies is related to its origin; colorectal foreign bodies may cause colonic mucosal injury, intestinal obstruction or intestinal perforation, and even severe peritoneal infection; when attempting to remove colorectal foreign body under endoscope, foreign body forceps and snares can be used. After the foreign body's removal, it is necessary to be alert of the colon perforation.

Abstract. Background. The catheter-tissue contact force (CF) is one of the significant determinants of lesion size and thus has a considerable impact on the effectiveness of ablation procedures. This study aimed to evaluate the impact of CF on the lesion size during right ventricular outflow tract (RVOT) ablation in a swine model. Methods. Twelve Guangxi Bama miniature male pigs weighing 40 to 50 kg were studied. After general anesthesia, a ThermoCool SmartTouch contact-sensing ablation catheter was introduced to the RVOT via the femoral vein under the guidance of the CARTO 3 system. The local ventricular voltage amplitude and impedance were measured using different CF levels. We randomly divided the animals into the following four groups according to the different CF levels: group A (3–9 g); group B (10–19 g); group C (20–29 g); and group D (30–39 g). Radiofrequency ablations were performed at three points in the free wall and septum of the RVOT in power control mode at 30 W for 30 s while maintaining the saline irrigation rate at 17 mL/min. At the end of the procedures, the maximum depth, surface diameter, and lesion volume were measured and recorded. A linear regression analysis was performed to determine the relationship between continuous variables. Results. A total of 72 ablation lesions were created in the RVOT of the 12 Bama pigs. The maximum depth, surface diameter, and volume of the lesions measured were well correlated with the CF (free wall: β = 0.105, β = 0.162, β = 3.355, respectively, P

Abstract Background The early life risk factors of childhood obesity among preterm infants are unclear and little is known about the influence of the feeding practices. We aimed to identify early life risk factors for childhood overweight/obesity among preterm infants and to determine feeding practices that could modify the identified risk factors. Methods A total of 338,413 mother-child pairs were enrolled in the Jiaxing Birth Cohort (1999 to 2013), and 2125 eligible singleton preterm born children were included for analyses. We obtained data on health examination, anthropometric measurement, lifestyle, and dietary habits of each participant at their visits to clinics. An interpretable machine learning-based analytic framework was used to identify early life predictors for childhood overweight/obesity, and Poisson regression was used to examine the associations between feeding practices and the identified leading predictor. Results Of the eligible 2125 preterm infants (863 [40.6%] girls), 274 (12.9%) developed overweight/obesity at age 4–7 years. We summarized early life variables into 25 features and identified two most important features as predictors for childhood overweight/obesity: trajectory of infant BMI (body mass index) Z-score change during the first year of corrected age and maternal BMI at enrollment. According to the impacts of different BMI Z-score trajectories on the outcome, we classified this feature into the favored and unfavored trajectories. Compared with early introduction of solid foods (≤ 3 months of corrected age), introducing solid foods after 6 months of corrected age was significantly associated with 11% lower risk (risk ratio, 0.89; 95% CI, 0.82 to 0.97) of being in the unfavored trajectory. Conclusions The trajectory of BMI Z-score change within the first year of life is the most important predictor for childhood overweight/obesity among preterm infants. Introducing solid foods after 6 months of corrected age is a recommended feeding practice for mitigating the risk of being in the unfavored trajectory.

Automatically recognizing and tracking dynamic targets on the sea is an important task for intelligent navigation, which is the prerequisite and foundation of the realization of autonomous ships. Nowadays, the radar is a typical perception system which is used to detect targets, but the radar echo cannot depict the target’s shape and appearance, which affects the decision-making ability of the ship collision avoidance. Therefore, visual perception system based on camera video is very useful for further supporting the autonomous ship navigational system. However, ship’s recognition and tracking has been a challenge task in the navigational application field due to the long distance detection and the ship itself motion. An effective and stable approach is required to resolve this problem. In this paper, a novel ship recognition and tracking system is proposed by using the deep learning framework. In this framework, the deep residual network and cross-layer jump connection policy are employed to extract the advanced ship features which help enhance the classification accuracy, thus improves the performance of the object recognition. Experimentally, the superiority of the proposed ship recognition and tracking system was confirmed by comparing it with state of-the-art algorithms on a large number of ship video datasets.

Noble-metal-free catalysts often show stability issues in acidic media due to structural degradation. Here authors show that phase-mixed engineering of cobalt diselenide electrocatalysts can enable greater covalency of Co-Se bonds and improve robustness for catalyzing hydrogen evolution in acid.

Background There is controversy about associations between total dietary fatty acids, their classes (saturated fatty acids [SFAs], monounsaturated fatty acids, and polyunsaturated fatty acids), and risk of coronary heart disease (CHD). Specifically, the relevance of food sources of SFAs to CHD associations is uncertain. Methods and Results We conducted a case‐cohort study involving 10 529 incident CHD cases and a random subcohort of 16 730 adults selected from a cohort of 385 747 participants in 9 countries of the EPIC (European Prospective Investigation into Cancer and Nutrition) study. We estimated multivariable adjusted country‐specific hazard ratios (HRs) and 95% CIs per 5% of energy intake from dietary fatty acids, with and without isocaloric macronutrient substitutions, using Prentice‐weighted Cox regression models and pooled results using random‐effects meta‐analysis. We found no evidence for associations of the consumption of total or fatty acid classes with CHD, regardless of macronutrient substitutions. In analyses considering food sources, CHD incidence was lower per 1% higher energy intake of SFAs from yogurt (HR, 0.93 [95% CI, 0.88–0.99]), cheese (HR, 0.98 [95% CI, 0.96–1.00]), and fish (HR, 0.87 [95% CI, 0.75–1.00]), but higher for SFAs from red meat (HR, 1.07 [95% CI, 1.02–1.12]) and butter (HR, 1.02 [95% CI, 1.00–1.04]). Conclusions This observational study found no strong associations of total fatty acids, SFAs, monounsaturated fatty acids, and polyunsaturated fatty acids, with incident CHD. By contrast, we found associations of SFAs with CHD in opposite directions dependent on the food source. These findings should be further confirmed, but support public health recommendations to consider food sources alongside the macronutrients they contain, and suggest the importance of the overall food matrix.

Abstract PM2.5 is a major component of air pollution in China and has a serious threat to public health. It is very important to quantify spatial characteristics of the health effects caused by outdoor PM2.5 exposure. This study analyzed the spatial distribution of PM2.5 concentration (45.9 μg/m3 national average in 2016) and premature mortality attributed to PM2.5 in cities at the prefectural level and above in China in 2016. Using the Global Exposure Mortality Model (GEMM), the total premature mortality in China was estimated to be 1.55 million persons, and the per capita mortality was 11.2 per 10,000 persons in the year 2016, resulting in higher estimates compared to the integrated exposure‐response model. We assessed the premature mortality attributed to PM2.5 through common diseases, including ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and lower respiratory infections (LRI). The premature mortality due to IHD and CEV accounted for 68.5% of the total mortality, and the per capita mortality (per 10,000 persons) for all ages due to IHD was 3.86, the highest among diseases. For the spatial distribution of disease‐specific premature mortality, the top two highest absolute numbers of premature mortality associated with IHD, CEV, LC, and LRI, respectively, were found in Chongqing and Beijing. In 338 cities of China, we have found a significant positive spatial autocorrelation of per capita premature mortality, indicating the necessity of coordinated regional governance for an efficient control of PM2.5.

Purpose: To qualitatively and quantitatively compare the image quality and diagnostic performance of turbo gradient and spin echo PROPELLER diffusion-weighted imaging (TGSE-PROPELLER-DWI) vs. readout-segmented echo-planar imaging (rs-EPI) in the evaluation of orbital tumors.Materials and Methods: A total of 43 patients with suspected orbital tumors were enrolled to perform the two DWIs with comparable spatial resolution on 3T. The overall image qualities, geometric distortions, susceptibility artifacts, and lesion conspicuities were scored by using a four-point scale (1, poor; 4, excellent). Quantitative measurements, including contrast-to-noise ratios (CNRs), apparent diffusion coefficients (ADCs), geometric distortion rates (GDRs), and lesion sizes, were calculated and compared. The two ADCs for differentiating malignant from benign orbital tumors were evaluated. Wilcoxon signed-rank test, Kappa statistic, and receiver operating characteristics (ROC) curves were used.Results: TGSE-PROPELLER-DWI performed superior in all subjective scores and quantitative GDR evaluation than rs-EPI (p < 0.001), and excellent interobserver agreement was obtained for Kappa value ranging from 0.876 to 1.000. ADClesion of TGSE-PROPELLER-DWI was significantly higher than those of rs-EPI (p < 0.001). Mean ADC of malignant tumors was significantly lower than that of benign tumors both in two DWIs. However, the AUC for differentiating malignant and benign tumors showed no significant difference in the two DWIs (0.860 vs. 0.854, p = 0.7448). Sensitivity and specificity could achieve 92.86% and 72.73% for TGSE-PROPELLER-DWI with a cutoff value of 1.23 × 10–3 mm2/s, and 85.71% and 81.82% for rs-EPI with a cutoff value of 0.99 × 10–3 mm2/s.Conclusion: Compared with rs-EPI, TGSE-PROPELLER-DWI showed minimized geometric distortion and susceptibility artifacts significantly improved the image quality for orbital tumors and achieved comparable diagnostic performance in differentiating malignant and benign orbital tumors.

The increase in atmospheric CO2 caused by land use and land cover change (LUCC) is one of the drivers of the global climate. As one of the most typical high-urbanization areas, the ecological conflicts occurring in Guangdong Province warrant urgent attention. A growing body of evidence suggests LUCC could guide the future ecosystem carbon storage, but most LUCC simulations are simply based on model results without full consistency with the actual situation. Fully combined with the territorial spatial planning project and based on the land use pattern in 2010 and 2020, we have used the Markov and Patch-generating Land Use Simulation (PLUS) model to simulate the future four land use scenarios: the Business as Usual (BU), Ecological Protection (EP), Farmland Protection (FP), and Economic Development (ED) scenario, and the ecosystem carbon storage was assessed by the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The results show that the built-up area experience further expansion in all scenarios, the largest scale happened in ED and the smallest in FP. Besides, the forest area in the EP scenario is the largest, while the land use pattern developed based on the previous circumstances in the BU scenario. Furthermore, the carbon storage plunged from 1619.21 Tg C in 2010 to 1606.60 Tg C in 2020, with a total decrease of 12.61 Tg C. Urban expansion caused 79.83% of total carbon losses, of which 31.56% came from farmland. In 2030, the carbon storage dropped in all scenarios, and their storage amount has a relationship of FP > BU > EP > ED. To better resolve the ecological problems and conserve ecosystem carbon storage, not only ecological protection but also the protection of the land near the city such as farmland protection strategies must be considered.

Multi-cell cooperative control can be competent for the current increasingly complex biomedical experiments, greatly improving the efficiency of cell manipulation experiments. At present, this kind of multi-cell cooperative control algorithm is becoming more and more important. In this study, holographic optical tweezers are used to capture multiple cells, and a cell manipulation controller is designed based on the Fuzzy Brain Emotional Learning (FBEL) neural network. Firstly, the dynamic model of trapping yeast cells by optical tweezers is analyzed. The distance between the trap position and the cell position is constrained to avoid cell detachment due to the trap moving too fast. Then, the design cell manipulation controller is relied upon to realize single transport trajectory tracking control. Finally, a multi-cell cooperative control algorithm is designed, and combined with the cell manipulation controller, a multi-cell cooperative controller based on the FBEL neural network is formed. The error between the cell position and the desired trajectory is the input of the multi-cell cooperative controller. The output of the multi-cell cooperative controller is the optical trap position, which is used to realize the cooperative control of multiple cells by holographic optical tweezers. The simulation results showed that the multi-cell cooperative controller based on the FBEL neural network can effectively control multiple yeast cells and quickly converge the cell formation, while ensuring a higher control accuracy than other traditional cell manipulation controllers. It provides a new solution for the efficient and precise automatic manipulation of multiple cells.

In industry, early fault signals of rolling bearings are submerged in strong background noise, causing a low signal-to-noise ratio (SNR) and difficult diagnosis. This paper proposes a fault feature extraction method based on an optimized Laplacian wavelet dictionary (LWD) and the feature symbol search (FSS) algorithm to extract early fault characteristic frequencies of bearings under low SNR. As the morphological parameters of the Laplace wavelet dictionary and sparse coefficients are not easy to obtain, this method uses the adaptive empirical wavelet transform (AEWT) to determine the morphological parameters of the Laplace wavelet. Firstly, AEWT is applied to obtain the different frequency components, and the combination index is utilized for optimal component selection. Then, the morphological parameters of LWD are determined by AEWT processing, by which the overcomplete dictionary that best matches the signal can be obtained. Finally, the optimal sparse representation of the component signal in the dictionary is calculated by FSS, which helps to achieve sparse denoising and enhance the impact features. The effectiveness of the method is verified by simulation. The effectiveness and advantages of LWDFSS-AEWT are verified by experiment in comparison with methods such as fast spectral kurtosis (FSK), correlation filtering (CF), shift-invariant sparse coding (SISC), base pursuit denoising (BPDN) and wavelet packet transform Kurtogram (WPT Kurtogram).