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Motivation Single-cell DNA methylation sequencing can assay DNA methylation at single-cell resolution. However, incomplete coverage compromises related downstream analyses, outlining the importance of imputation techniques. With a rising number of cell samples in recent large datasets, scalable and efficient imputation models are critical to addressing the sparsity for genome-wide analyses. Results We proposed a novel graph-based deep learning approach to impute methylation matrices based on locus-aware neighboring subgraphs with locus-aware encoding orienting on one cell type. Merely using the CpGs methylation matrix, the obtained GraphCpG outperforms previous methods on datasets containing more than hundreds of cells and achieves competitive performance on smaller datasets, with subgraphs of predicted sites visualized by retrievable bipartite graphs. Besides better imputation performance with increasing cell number, it significantly reduces computation time and demonstrates improvement in downstream analysis. Availability and implementation The source code is freely available at https://github.com/yuzhong-deng/graphcpg.git. [ABSTRACT FROM AUTHOR]

Rutin, a natural flavonol glycoside, is widely present in plants and foods, such as black tea and wheat tea. The antioxidant and anti-inflammatory effects of flavonoids are well known. In this study, a new electrochemical rutin sensor was developed using multiwalled carbon nanotubes/aluminum-based metal–organic frameworks (MWCNT/CAU-1) (CAU−1, a type of Al-MOF) as the electrode modification material. The suspension of multiwalled carbon tubes was dropped on the surface of the GCE electrode to make MWCNT/GCEs, and CAU−1 was then attached to the electrode surface by electrodeposition. MWCNTs and CAU−1 were characterized using scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). Due to the synergistic effect of CAU−1 and MWCNT-COOH, the prepared sensor showed an ultrasensitive electrochemical response to rutin. Under optimized conditions, the sensor showed a linear relationship between 1.0 × 10−9~3.0 × 10−6 M with a detection limit of 6.7 × 10−10 M (S/N = 3). The sensor also showed satisfactory stability and accuracy in the detection of real samples. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) is a promising learning model due to its computational efficiency for discrete spike events. However, because of the binary output of spiking neurons, the standard backpropagation (BP) method is not suitable for deep SNN training. In this paper, we design a gradient-based spiking neuron named Relaxation Leaky Integrate-and-Fire (RLIF) neuron to enable the direct training of deep SNNs. Specifically, we develop a Re-weighted Straight-Through-Estimator (Re-STE) to model the RLIF neuron and SNNs. The Re-STE decomposes the output of the famous LIF neuron and generates a spike by iteratively re-weighting the membrane potential (MP). Then, the RLIF neuron is built, and the re-weighted MP provides a surrogate gradient for BP training. We theoretically prove that the Re-STE is an approximation of the STE technique with logarithmic activation. Such property guarantees that the designed RLIF neuron mitigates the mismatching gradient problem within limits. Finally, we design the RLIF-based VGG-like and ResNet-like SNNs for classification tasks. Experimental results show that the RLIF-based SNNs perform well on the popular benchmark datasets (MNIST, CIFAR10, CIFAR100, N-MNIST, CIFAR10-DVS, and DVS128-GESTURE) and achieve significant efficiencies in most cases. [ABSTRACT FROM AUTHOR]

With the rapid development of cities and the increasing complexity of distribution network systems, the cableization of transmission lines and the diversification of power users have brought new challenges to the supply reliability of distribution networks. Short-time outages caused by power outages and maintenance are one of the factors that affect the reliability of the power supply in the distribution network. The non-stop load transfer through a phase-shifting transformer (PST) operation can effectively improve the reliability of power supply to complex distribution systems. Considering the operation mode and structure form and fault type of the urban distribution network in China, comparing and analyzing the applicable scenarios of different neutral grounding methods, and based on the structure and zero sequence path characteristics of PST, an improved PST-based phase shifting and grounding transformer loop closing device with low power consumption is proposed. The fault characteristics of the PST-based loop closing device under the small current grounding system are also analyzed by the sequence component method, and, finally, the effectiveness of the phase-shifting and grounding transformer device is verified by simulation under PSCAD/EMTDC for fault routing and protection configuration of the urban distribution network in China. [ABSTRACT FROM AUTHOR]

Motivation Single-cell DNA methylation sequencing detects methylation levels with single-cell resolution, while this technology is upgrading our understanding of the regulation of gene expression through epigenetic modifications. Meanwhile, almost all current technologies suffer from the inherent problem of detecting low coverage of the number of CpGs. Therefore, addressing the inherent sparsity of raw data is essential for quantitative analysis of the whole genome. Results Here, we reported CaMelia, a CatBoost gradient boosting method for predicting the missing methylation states based on the locally paired similarity of intercellular methylation patterns. On real single-cell methylation datasets, CaMelia yielded significant imputation performance gains over previous methods. Furthermore, applying the imputed data to the downstream analysis of cell-type identification, we found that CaMelia helped to discover more intercellular differentially methylated loci that were masked by the sparsity in raw data, and the clustering results demonstrated that CaMelia could preserve cell-cell relationships and improve the identification of cell types and cell subpopulations. Availability and implementation Python code is available at https://github.com/JxTang-bioinformatics/CaMelia. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: The computational prediction of methylation levels at single CpG resolution is promising to explore the methylation levels of CpGs uncovered by existing array techniques, especially for the 450 K beadchip array data with huge reserves. General prediction models concentrate on improving the overall prediction accuracy for the bulk of CpG loci while neglecting whether each locus is precisely predicted. This leads to the limited application of the prediction results, especially when performing downstream analysis with high precision requirements. Results: Here we reported PretiMeth, a method for constructing precise prediction models for each single CpG locus. PretiMeth used a logistic regression algorithm to build a prediction model for each interested locus. Only one DNA methylation feature that shared the most similar methylation pattern with the CpG locus to be predicted was applied in the model. We found that PretiMeth outperformed other algorithms in the prediction accuracy, and kept robust across platforms and cell types. Furthermore, PretiMeth was applied to The Cancer Genome Atlas data (TCGA), the intensive analysis based on precise prediction results showed that several CpG loci and genes (differentially methylated between the tumor and normal samples) were worthy for further biological validation. Conclusion: The precise prediction of single CpG locus is important for both methylation array data expansion and downstream analysis of prediction results. PretiMeth achieved precise modeling for each CpG locus by using only one significant feature, which also suggested that our precise prediction models could be probably used for reference in the probe set design when the DNA methylation beadchip update. PretiMeth is provided as an open source tool via https://github.com/JxTang-bioinformatics/PretiMeth. [ABSTRACT FROM AUTHOR]

We explore the splitting field F (X) for a simple connected graph X , which is the smallest field extension of Q that encompasses all eigenvalues of a specific adjacency matrix associated with X. The algebraic degree of X , denoted as [ F (X) : Q ], represents the extension degree of this field. Our study focuses on deriving both upper and lower bounds for the algebraic degrees of Cayley graphs over the dicyclic group and the semi-dihedral group. Furthermore, we provide detailed analysis on the algebraic degrees and the corresponding splitting fields for normal mixed Cayley graphs over these two groups. • The splitting field F (X) for a simple connected graph X , which is the smallest field extension of Q that encompasses all eigenvalues of a specific adjacency matrix associated with X. • The algebraic degree of X , denoted as [ F (X) : Q ], represents the extension degree of this field. • We derive both upper and lower bounds for the algebraic degrees of Cayley graphs over the dicyclic group and the semi-dihedral group. • We provide detailed analysis on the algebraic degrees and the corresponding splitting fields for normal mixed Cayley graphs over these two groups. • Our results are generalizations of integral graphs. [ABSTRACT FROM AUTHOR]

Spiking neural networks are efficient computation models for low-power environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful techniques for SNN training. Nevertheless, the spike-base BP training is slow and requires large memory costs, while ANN2SNN needs many inference steps to obtain good performance. In this paper, we propose an A ctivation C onsistency C oupled A NN- S NN (A C 2 AS) framework to train the SNN in a fast and memory-efficient way. The A C 2 AS consists of two components: (a) a weight-shared architecture between ANN and SNN and (b) spiking mapping units. Firstly, the architecture trains the weight-shared parameters on the ANN branch, resulting in fast training and low memory costs for SNN. Secondly, the spiking mapping units are designed to ensure that the activation values of the ANN are the spiking features. As a result, the activation consistency is guaranteed, and the classification error of the SNN can be optimized by training the ANN branch. Besides, we design an adaptive threshold adjustment (ATA) algorithm to decrease the firing of noisy spikes. Experiment results show that our A C 2 AS-based models perform well on the benchmark datasets (CIFAR10, CIFAR100, and Tiny-ImageNet). Moreover, the A C 2 AS achieves comparable accuracy under 0. 625 × time steps, 0. 377 × training time, 0. 27 × GPU memory costs, and 0. 33 × spike activities of the Spike-based BP model. The code is available at https://github.com/TJXTT/AC2ASNN. • Proposed the A C 2 AS framework for fast and memory efficient SNN training. • Designed an adaptive threshold adjustment algorithm and two spiking mapping units to ensure that the SNN can be equivalently represented by the ANN under the A C 2 AS framework. • The SNNs trained by A C 2 AS achieve considerable performance, sparse spike activity, and fast inference. [ABSTRACT FROM AUTHOR]

The human settlement environment is the concrete embodiment of measuring the sustainable development of tourism urbanization. Previous studies have preliminarily verified that there is a coupling relationship between tourism urbanization and human settlement environment. However, whether tourism urbanization can effectively improve human settlement environment, especially in the most vulnerable watershed area in China, the Yellow River Basin, is still unclear. Therefore, the purpose of this study is to explore the degree and direction of the impact of tourism urbanization on human settlement environment in 78 prefecture-level cities in the Yellow River Basin from 2005 to 2019 under the influence of nonlinear and spatial spillover effects. This paper uses the PSTR (panel smooth transition regression) model SDM (spatial Durbin) model to reveal the following results: First, tourism urbanization can significantly improve human settlement environment in the Yellow River Basin. After crossing the second threshold (0.434), the nonlinear effect of tourism urbanization on human settlement environment is the strongest, but only a very small number of cities cross the threshold. Secondly, the improvement of the level of tourism urbanization has effectively improved production environment and living environment of the region, but it has had a significant negative impact on ecological environment of the region. Finally, the combined effect of regional geographical proximity and economic interaction is conducive to the positive spatial spillover effect of tourism urbanization on production environment and living environment of surrounding areas of the Yellow River Basin. The current empirical results reveal the key role of tourism urbanization in improving human settlement environment in the Yellow River Basin, and clearly outline that the development of tourism urbanization should be consistent with the national strategy of ecological protection and high-quality development in the Yellow River Basin. [Display omitted] • Tourism urbanization is an effective way to improve human settlement environment. • Tourism urbanization has improved the production and living environment. • Tourism urbanization has a negative impact on ecological environment. • The human settlement environment in the watershed area should be paid attention to. • Tourism urbanization construction should be consistent with policy propositions. [ABSTRACT FROM AUTHOR]

Dopamine (DA), as an typical neurotransmitter, was believed to play important roles in addiction and some neurodegenerative diseases. Accurately and rapidly detection of DA is very valuable. In this study, a sensor based on 3D flake nickel oxide/ cobalt oxide@porous carbon nanosheets/carbon nanotubes/ electrochemical reduced of graphene oxide composites modified glassy carbon electrode (NiO/CoO@PCNs/CNTs/erGO/GCE) was constructed. The flake NiO/CoO@PCNs/CNTs were obtained by directly pyrolysis Ni/Co-MOF under nitrogen protection, which presented excellent electrochemical properties due to the synergistic catalytic effect and porous sheet structure. For the detection of DA, the NiO/CoO@PCNs/CNTs/erGO/GCE showed wider linear range (0.10 μM – 22.0 μM) and the detection limit (LOD) of DA was 0.045 μM, (S/N = 3). Additionally, the reaction sites and mechanism of DA were revealed by analyzing the electrochemical behavior with the help of density functional calculation. Moreover, the sensor showed satisfactory results in the determination of DA in real biological sample. [Display omitted] • The NiO/CoO@PCNs/CNTs/erGO nanocomposite was firstly prepared. • The electrochemical sensor based on the composite exhibited outstanding electrochemical performance for dopamine. • The sensor was successfully employed to detect dopamine in actual sample. [ABSTRACT FROM AUTHOR]

• The eco-environment does not always deteriorate with the process of tourism urbanization. • Expanded the research of EKC theory from the perspective of tourism urbanization and eco-environment. • Introducing the gravitational model to explore the spatial connection of coupling coordination degrees. • More attention should be paid to tourism urbanization in underdeveloped areas. The deteriorating eco-environment is the key to restrict the development of tourism urbanization in the western region. Since the eco-environmental effects caused by tourism urbanization have complex interactions, how to coordinate the relationship between the two has become one of the most severe challenges for the sustainable development of western China and even the whole of China. Previous quantitative studies on the coupling coordination between tourism urbanization and eco-environment are limited because they ignore the interactive coercion relationship between complex systems and the spatial dynamic relationship of coupling coordination. Given this research gap, this research has constructed a new comprehensive evaluation index system of tourism urbanization and eco-environment. The interactive coercion model, the coupling coordination degree model and the gravity model are used to evaluate the interaction relationship, coupling coordination type and spatial connection strength between tourism urbanization and eco-environment in western China from 2005 to 2019. The main conclusions are as follows: (1) The level of tourism urbanization shows a rapid upward trend, and the eco-environment quality shows a fluctuating and low-speed upward trend; (2) The double exponential curve between tourism urbanization and eco-environment is an inverted U-shaped curve, and the empirical analysis confirms the Kuznets hypothesis; (3) The degree of coupling coordination has experienced a development process from "seriously unbalanced" to "superior balanced", and the interaction type has evolved into superior balanced-eco-environment lag; (4) The spatial connection of coupling coordination shows the characteristics of complication and gradient, and the interaction between provinces produces a pattern of adjacent associations. The results of this study are expected to implement an appropriate tourism urbanization development model in western China and provide decision support for the green and sustainable development of tourism urbanization and eco-environment in western China. [ABSTRACT FROM AUTHOR]

• A comprehensive framework for flood risk assessment is developed. • Spatiotemporal variability of flood risk and its drivers are dynamically analyzed. • Peak discharge, rainfall, age structure, reservoir, wetland, are main driving factors. • Built-up area, dike, financial investment are obvious drivers in some urbanized areas. • Spatially heterogeneous strategies should be fully considered in decision-making. Rapid urbanization and climate change can cause more extensive flood risk, in the absence of urgent and efficient adaptation measures. As the occurrence of floods varies with time and space, comprehensive and dynamical assessment of the spatiotemporal variability of flood risk and understanding of its drivers is vital for flood risk management. In this study, we developed a spatial multi-criteria analysis (SMCA) framework for quantifying the spatiotemporal dynamics of flood risk through a case study in a coastal watershed of southeastern China from 1990 to 2015. A comprehensive framework for flood risk assessment was constructed from the hazard, exposure, sensitivity and adaptive capacity components with 23 indicators. The results showed that the highest risk happened in the stage of 2006–2010, while the lowest risk stage was 2011–2015, with higher flood risk in the downstream areas of Jiulong River watershed (JRW). The contribution of each indicator reflects the difference in temporal, spatial and quantity aspects. The top 5 driving factors for JRW included: peak discharge, maximum daily rainfall, age structure, wetland, and reservoir. The risk perception showed a continuous growing impact on flood risk. However, some indicators only showed obvious contributions in the specified area: for example, the built-up expansion in Zhangzhou city; the increase of dike length and the improvement of dike standard in Xinluo district; and the increase of government financial investment in Zhangping and Liancheng district. This study demonstrates the well-performance of our proposed novel approach for flood risk assessment. Our results and conclusions are also of significance for policymakers to understand and point out the deficiencies in the current actions of flood adaption, and consequently develop more targeted and spatially-specific strategies for flood adaptation, in the context of climate change and rapid urbanization. [ABSTRACT FROM AUTHOR]

The article mainly deal with the block-transitive automorphism groups of 2 − (v , k , 1) designs. Let G = P G L 2 (q) for q a power of an odd prime number. We prove that if G acts block-transitively on a non-trivial 2 − (v , k , 1) design D then D is either a projective plane or 2 − (136 , 10 , 1) design. [ABSTRACT FROM AUTHOR]

Purpose: Incisional hernia is a common postoperative complication; however, few transcriptomic studies have been conducted on it. In this study, we used second-generation high-throughput sequencing to explore the pathogenesis and potential therapeutic targets of incisional hernias. Methods: Superficial fasciae were collected from 15 patients without hernia and 21 patients with an incisional hernia. High-throughput sequencing of the fascia was performed to generate an expression matrix. We analyzed the matrix to identify differentially expressed genes (DEGs) and performed gene ontology and enrichment analyses of these DEGs. Additionally, an external dataset was utilized to identify key DEGs. Results: We identified 1,823 DEGs closely associated with extracellular matrix (ECM) imbalance, bacterial inflammatory response, and fibrillar collagen trimerization. TNNT3, CMAY5, ATP1B4, ASB5, CILP, SIX4, FBN1 and FNDC5 were identified as key DEGs at the intersection of the two expression matrices. Moreover, non-alcoholic fatty liver disease-related, TNF, and IL-17 signaling pathways were identified as key enrichment pathways. Conclusions: We identified eight key DEGs and three pathways associated with incisional hernias. Our findings offer new insights into the pathogenesis of incisional hernias and highlight potential targets for their prevention and treatment. [ABSTRACT FROM AUTHOR]

Background: Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features. Methods: In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450 K methylation data were applied instead of the original 450 K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation. Results: Our selected gene features showed prediction accuracy close to 100% in the cross validation with fuzzy rule based classification model on 6 cancers from TCGA. The cross validation performance of our proposed model is similar to other integrative models or RNA-seq only model, while the prediction performance on independent data is obviously better than other 5 models. The gene signatures extracted with our fuzzy rule based integrative feature selection strategy were more robust, and had the potential to get better prediction results. Conclusion: The results indicated that the integration of expanded methylation data would cover more genes, and had greater capacity to retrieve the signature genes compared with the original 450 K methylation data. Also, the integration of the reported biomarkers was a promising way to improve the performance. PTCHD3 gene was selected as a discriminating gene in 3 out of the 6 cancers, which suggested that it might play important role in the cancer risk and would be worthy for the intensive investigation. [ABSTRACT FROM AUTHOR]

[Display omitted] • The conceptual framework of the interaction between TD and ESV was constructed. • The coupling coordination degree between TD and ESV in the YRB was evaluated. • The spatiotemporal evolution of coupling coordination between TD and ESV was analyzed. • Obstacle factors of CCD between TD and ESV were identified at the basin and city level. The rapid development of tourism has caused a series of impacts on the ecosystem. Although studies on the relationship between tourism development (TD) and the ecological environment are rich, few scholars have focused on the coupling coordination between them from the perspective of ecosystem services value (ESV). Given this research gap, we evaluated the levels of TD and ESV in 78 cities of the Yellow River Basin (YRB), China based on statistical and land-use data. Then, the coupling coordination degree (CCD) model, exploratory spatial data analysis (ESDA), the spatial variogram model, and the obstacle degree model were used to analyze the spatiotemporal evolution of coupling coordination between TD and ESV and its obstacle factors. The results show the following: (1) The comprehensive TD level shows an upward trend and presents significant spatial differentiation characteristics. The total ESV of the YRB shows a fluctuating upward trend, and the average ESV presents the spatial pattern of uplift in the middle reaches. (2) The comprehensive CCD of the YRB has gradually improved, but the gap between cities has widened. The global spatial autocorrelation of CCD is significantly positive, and the spatial variability has gradually increased. An "uplift area" in southern Shaanxi always exists and has gradually expanded to Shanxi and western Henan, while eastern Henan and western Shandong become a "subsidence area". The coupling coordination level has changed from uncoordinated to coordinated in most counties, but the problem of TD and ESV imbalance generally exists. (3) For the whole basin, TD is always the main obstacle factor at the system layer, and tourism scale and tourism economic benefit are the most significant obstacles at the category layer. At the city level, the proportion of cities where TD is the main obstacle factor exceeds 80% by 2019. Specifically, insufficient tourism scale and low tourism economic efficiency restrict the improvement of CCD in most cities. This research provides valuable references for studying the coupling coordination relationship between TU and ESV in big river basins worldwide. [ABSTRACT FROM AUTHOR]

Abstract The current growth in transportation-related greenhouse gas (GHG) emissions has been largely attributed to rapid urbanization, particularly for cities in developing countries. Studies on the contributing factors and analysis of the mechanisms by which they influence transportation GHG emissions could aid in better achievement of mitigation goals. Yet, comparative contributions of the different sources of these drivers have not been well quantitatively investigated. This study employs a wide range of indicators across urban form, socio-demographic characteristics and residents' travel attitudes. By integrating a questionnaire-based survey of 1125 household in 45 communities in Xiamen City, China, land-use-based urban form quantification, inventory-based GHG calculation, a path analysis model is built to identify the interactions among these indicators and investigate their comparative direct and indirect effects on residents' local transportation GHG emissions. It was found that many variables interactively influence the transportation GHG emissions, producing considerable effects, both direct and indirect. Urban form plays a leading role in transportation GHG emissions, in comparison to socio-demographics and travel attitudes. Population density, land use mix, road connectivity and bus accessibility, in urban form; education, in socio-demographics; and willingness to ride the bus, in travel attitudes; were found to have significant positive effects on reducing residents' local transportation GHG emissions. Urban density—characterized by population density here—is the primary influential factor, due not only to its large direct effects, but also to its wide indirect effects through its influence on other variables. The results of this study may help policy makers consider how they can effectively utilize these key indicators to formulate mitigations in the transportation sector, and particularly, how to design low-carbon-friendly urban forms, in urban planning. Graphical abstract Unlabelled Image Highlights • Effects of urban form, socio-demographics and travel attitudes on transportation GHG • Factors are interacted, producing direct and indirect effects on transportation GHG. • Urban form plays a leading role compared with socio-demographics and travel attitudes. • Population density is a primary factor due to its solid direct and indirect effects. • Interrelationships existed in factors should be fully considered in decision-making. [ABSTRACT FROM AUTHOR]

A novel pore size tuning method was proposed for the fabrication of TiO 2 multi-channel nanofiltration membrane from TiO 2 ultrafiltration substrate using in situ chemical deposition. Titanium isopropoxide and isopropanol were employed as the precursor and solvent, respectively. The effects of solvent, precursor, pore size of ultrafiltration substrate, as well as thermal treatment temperature, on the performance of TiO 2 multi-channel nanofiltration membrane were studied. The optimized TiO 2 multi-channel nanofiltration membrane, prepared from ceramic ultrafiltration substrate with an MWCO of approximately 5 kDa, showed a high pure water permeability of approximately 20 L m −2 h −1 bar −1 and a low MWCO of approximately 800 Da. The average membrane pore radius was tuned from 2.4 nm to 0.9 nm. The TiO 2 multi-channel nanofiltration membrane was successfully applied to the decolorization of sodium dehydroacetate. The decolorization rate was ~ 89.5%, and the sodium dehydroacetate recovery rate reached 98%, which is 2–3% higher than that obtained using the traditional activated carbon decolorization process. The in situ chemical deposition method has great potential as a novel and facile pore size tuning technique for ceramic ultrafiltration membrane for the fabrication of the ceramic multi-channel nanofiltration membranes, especially for large-scale production. [ABSTRACT FROM AUTHOR]

• Using the number of structural layers as a main basis, develop an in-depth classification of wave plate types based on material and design structure. • The pros and drawbacks of various wave plates and bandwidth materials are clearly contrasted in the form of the tables. • The current state of the terahertz achromatic wideband wave plates is outlined, as are the major ideas for designing a wideband dispersion-engineering wave plate. • The significance of metamaterials in the design of terahertz wave plates is discussed, as well as the prospects for future development. The electromagnetic wave in the terahertz region shows many promising characteristics, such as non-ionization, "fingerprint" spectrum, sensitivity to weak resonance, strong penetration to non-polar substances, and so on. This study reviews the recent achievements of terahertz achromatic broadband polarization wave plates. The key parameters to evaluate the performance of terahertz polarized wave plates are described briefly. The relevant works, and benefits and drawbacks of terahertz wave plates based on diverse materials and designs, notably metamaterial wave plates, are summarized based on unique material attributes and design methodologies. Additionally, the current state of the terahertz achromatic broadband wave plate is generalized, along with the role of metamaterial in the design of terahertz wave plate, the innovative interdisciplinary path of terahertz metamaterial polarization is explained, and future research prospects are explored. [ABSTRACT FROM AUTHOR]

• Assessing disaster losses at the watershed scale is a challenging task. • Quantitative assessment of flood damage can help managers grasp the basin conditions. • The flood risk curve helps watershed develop flood protection standards. • This article presents a combined riverine flood risk model to assess flood damage. Climate change and rapid urbanization have exacerbated the occurrence and impact of floods. It is essential to carry out a quantitative flood risk assessment and manage the flood risk before a disaster occurs. This article presents a combined riverine flood risk model to obtain the exceedance probability loss (EPL) curve and expected annual damage (EAD) under the current climate. This model includes a rapid flood model and a flood damage model. It aims to simulate the flood risk and evaluate the flood damage at 10-, 30-, 50-, 100-, and 200-year return period events. The results show that: (1) The total inundation areas will sharply increase when the flood return periods are over 30 years. (2) The EAD is 1,476 million dollars in the Jiulong River Basin (JRB). When the flood return period is over 30 years, the total damage increases sharply. (3) The flood risk in the lower reaches of the JRB is higher than in the upper reaches when the flood event is beyond a 20-year return period. (4) Industrial sector damage is the largest, followed by tertiary industry, transportation, construction, agriculture, and infrastructure. The combined model chain can quickly assess the flood risk in the river basin and judge the flood risk in a watershed compared to the national level. In addition, it can be applied to other river basins, and it will provide actionable information for future flood risk management. [ABSTRACT FROM AUTHOR]

A fabric hydrogel composite hernia mesh with biomechanical and mesh-tissue interface dual compliance is designed for scarless abdominal wall reconstruction. The mesh is composed of a polyester knitted fabric and chitosan-polyacrylamide hydrogel complex. The mechanical properties of the composite mesh are adjusted to resemble the human abdominal wall by alkali treatment of chitosan to achieve good biomechanical compliance. An adhesion group is introduced into the composite mesh that forms covalent bonds with tissue, eliminating the need for suturing, reducing stress concentration at the fixation site, achieving mesh-tissue interface compliance, and improving the simplicity of the operation. Covalent bonds and hydrogen bonds make the mesh have strong adhesion (70.1 ± 3.2 kPa) and repeatable (four times) robust adhesiveness. In vivo experiments using a rabbit abdominal wall defect model demonstrate quick adhesiveness and excellent functional reconstruction. Biomechanical and mesh-tissue interface dual compliance allow the tissue to regenerate an intact abdominal wall structure. The adhesive fabric hydrogel composite hernia mesh offers significant clinical values for repairing abdominal wall defects and provides design ideas for repairing other load-bearing soft tissues. [ABSTRACT FROM AUTHOR]

A generation algorithm is used to optimize a tunable optical wavelength selection switch (WSS) which is based on a cascaded silicon microring resonator. This method can help us find the optimal inter-ring coupling coefficients which can lead to smaller ripple and better wavelength selectivity over a predefined pass band range. In this paper, we validate this method by a real design and report the results of several, such GA-based WSS optimization. The results show that the GA method performs well even under the limit of physical properties of a silicon ring resonator. [ABSTRACT FROM AUTHOR]

The application of traditional deep learning methods for intelligent fault diagnosis is limited by the distribution discrepancy of the unlabeled data collected under different working conditions. Transfer learning can break through this limitation by generalizing a model trained on the source domain with massive labeled data to solve the fault diagnosis problem in the target domain with unlabeled data. The current transfer learning methods focus on directly measuring and minimizing the distribution discrepancy of the features between the two domains. These methods may confront difficulties when the distributions between the domains are complex and heterogeneous, and may cause the incorrect alignment of the same class data with the greatest distribution discrepancy across the two domains. In this paper, a deep transfer learning method with inter-domain decision discrepancy minimization (InDo-DDM) is proposed. The proposed method directly measures and minimizes the discrepancy of the decision result matrixes to facilitate the minimization of the distribution discrepancy between the two-domain data. With the proposed domain indicator, the InDo-DDM can find the greatest decision discrepancy and better align the data with the greatest distribution discrepancy. Additionally, the measurement of the decision discrepancy can be more precise and robust by introducing the nuclear-norm to avoid the fallible data classification near the decision boundary caused by the intra-batch imbalance. Extensive experiments in three different scenarios with two datasets from Case Western Reserve University (CWRU) and one dataset from Prognostic and Health Management (PHM) Data Challenge revealed that the InDo-DDM outperformed the other widely used methods. [ABSTRACT FROM AUTHOR]

Deep learning based methods are attractive and meaningful in the field of fault diagnosis recently. However, the sample size of different faults may be imbalanced in practical application scenarios, which results in performance degeneration. The optimization of data representation could be an effective way to alleviate such phenomenon. In this paper, a deep learning framework considering time series and distance metric called LSTM-Quadruplet Deep Metric Learning(LSTM-QDM) model is proposed. It maps original space to a feature space where the distribution of imbalance faults is more distinguishable. A novel quadruplet data pair is designed which adds a minor sample from imbalanced classes into traditional data pair. Based on such data pair, a quadruplet loss function is proposed to increase the distance between the imbalanced classes and other classes, and its combination with softmax loss would improve the representation ability and classification performance simultaneously. The proposed data pair and loss function encourage the full mining of imbalanced data in the training process. The experiments are carried out on two open-source datasets including TE and CWRU, and the fault diagnosis performance is validated under different imbalanced conditions. The experimental results indicate that our proposed model is effective and robust in the imbalanced fault diagnosis task. [ABSTRACT FROM AUTHOR]

Venous thromboembolism (VTE) is an important postoperative complication. We investigated and analyzed the current inguinal hernia treatment methods and occurrence of early postoperative VTE in Chinese adults. This study involved data for patients with inguinal hernia hospitalized in 58 general hospitals in mainland China from January 1st, 2017 to December 31st, 2017. Data were retrospectively analyzed using a questionnaire. After data inputting and cleaning, we stratified and statistically analyzed patients' data using Caprini scores to create a high-, middle-, and low-risk group. A total of 14,322 patients with inguinal hernia were admitted to the 58 participating hospitals. After data collation and cleaning, 13,886 patients (97.0%) met our inclusion and exclusion criteria. The percentages of laparoscopic surgery and open surgery were 51.2% and 48.8%, respectively. 16 VTEs occurred during the hospitalization, accounting for 0.1% of all adverse events (95% confidence interval (CI) 0.11–0.13). The incidence of VTE was 0.2% (95% CI 0.18–0.2) in the high-risk group and 0.02% (95% CI 0.01–0.03) in the middle-risk group, based on Caprini scoring, with a significant difference (p < 0.0001). No VTE occurred in the low-risk group. Only 3,250 (23.4%) patients underwent Caprini risk assessment regarding treatment, with 13.2% receiving any prevention and only 1.2% receiving appropriate prevention. The treatment of inguinal hernia in Chinese adults has progressed somewhat; however, the evaluation and prevention of perioperative VTE was seriously neglected, in our study, and the incidence of postoperative VTE was underestimated postoperatively. Risk factors continue to be inadequately considered. [ABSTRACT FROM AUTHOR]

Background: Determination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which can possibly lead to cancer. Due to the involuted biochemical mechanism of DNA methylation, obtaining a precise prediction is a considerably tough challenge. Existing approaches have yielded good predictions, but the methods either need to combine plenty of features and prerequisites or deal with only hypermethylation and hypomethylation. Results: In this paper, we propose a deep learning method for prediction of the genome-wide DNA methylation, in which the Methylation Regression is implemented by Convolutional Neural Networks (MRCNN). Through minimizing the continuous loss function, experiments show that our model is convergent and more precise than the state-of-art method (DeepCpG) according to results of the evaluation. MRCNN also achieves the discovery of de novo motifs by analysis of features from the training process. Conclusions: Genome-wide DNA methylation could be evaluated based on the corresponding local DNA sequences of target CpG loci. With the autonomous learning pattern of deep learning, MRCNN enables accurate predictions of genome-wide DNA methylation status without predefined features and discovers some de novo methylation-related motifs that match known motifs by extracting sequence patterns. [ABSTRACT FROM AUTHOR]

A novel superhydrophilic hybrid scaffold was created by electrospinning a mixture of poly(L-lactic acid-co-ε-caprolactone) and formulated fibrinogen. The hybrid scaffolds possess the combined benefits of each individual component, such as moderate mechanical strength and excellent biocompatibility. In vitro studies also revealed that endothelial cells seeded on the hybrid scaffolds achieved a relatively high level of cell attachment after three days of culture and a significant increase in the proliferation rate after seven days of culture, compared with pure fibrinogen or poly(L-lactic acid-co-ε-caprolactone) scaffolds. A comparative study of hybrid and pure poly(L-lactic acid-co-ε-caprolactone) patches was performed in an abdominal wall defect model in rats. In both groups, implants degraded by six months, but muscle reconstruction was only observed in the hybrid patch group. [ABSTRACT FROM AUTHOR]

Deep learning-based fault diagnosis models constructed from imbalanced datasets would meet severe performance degradation when the number of samples for fault classes is much smaller than the normal category. Recent feature-learning-based methods have shown that learning the discriminative feature representation helps construct the well-performing fault classifier in imbalanced fault diagnosis. However, these methods have limited discriminative feature extraction abilities when applying them to a more practical but not well-studied class-imbalance scenario, where only the normal condition has a large amount of data while sample sizes of all fault classes are small. To address this issue, a novel feature-learning-based method called Class-aware Supervised Contrastive Learning (CA-SupCon) is proposed. Supervised contrastive learning (SupCon) is adopted for the first time in imbalanced fault diagnosis to optimize the feature difference between any two classes by leveraging category information. Additionally, a class-aware sampler (CA) is designed to rebalance data distribution within each mini-batch during training, which improves the ability of SupCon to enlarge the feature distance between any two minority fault conditions. By effectively integrating SupCon and CA, the proposed CA-SupCon framework can obtain a more discriminative feature space with better intra-class compactness and inter-class separability, and achieves good performance under the above class-imbalance scenario. Extensive experiments on two open-source datasets demonstrate the effectiveness of the proposed method. Code is available at https://github.com/JiyangZhang-UESTC/CA-SupCon. [ABSTRACT FROM AUTHOR]

Kaempferol (KA) belongs to the group of flavonoids with versatile biological activities, which has attracted much attention. Herein, a novel KA electrochemical sensor was proposed based on glass carbon electrode modified with MIL-100(Fe)-multi-walled-carbon nanotubes/poly(3,4-ethylenedioxythiophene) (MIL-100(Fe)-MWCNTs/PEDOT) composite, which were characterized by X-ray diffraction spectroscopy and scanning electron microscopy. MWCNTs decorated with in situ green synthesized MIL-100(Fe) showed excellent electrochemical sensing performance. For detecting KA with DPV model, the MIL-100(Fe)-MWCNTs/PEDOT/GCE showed a linear range of 50–1950 nM under optimal conditions, and the corresponding detection limit (LOD) was 13.2 nM (S/N = 3). In addition, the sensor exhibited satisfactory sensitivity and accuracy for detecting KA in real samples (XinDaKang tablet). [Display omitted] • A novel nanocomposite of MIL-100(Fe)-MWCNTs/PEDOT was firstly prepared. • MIL-100(Fe) has been successfully prepared by a facile, high-yield and green synthetic route. • The electrochemical sensor based on the composites exhibited outstanding electrochemical performance for kaempferol. [ABSTRACT FROM AUTHOR]

Polypropylene (PP) mesh has been one of the most frequently selected materials for abdominal wall hernia (AWH) repair in the past decades. However, the repair effectiveness is often compromised by the high incidence of adverse events. Improvements are highly desirable to reduce unwanted visceral adhesion and increase tissue integration. Herein, we developed a PP-based composite mesh combined with alginate-assistant nanofiber membrane (NFM) by assembling a large-pore lightweight PP mesh, a NFM barrier consisting of poly (lactic-co-glycolic acid) (PLGA) and polycaprolactone (PCL), and an alginate hydrogel (AH). In vitro studies revealed that the resulting meshes had excellent structural stability, mechanical properties, and biocompatibility. The NFM barriers were firmly attached to the PP substrates, with AH effectively eliminating the delamination without unwanted damage to the nanofiber barrier. In vivo experiments showed that incorporating AH-assistant NFM into PP prostheses could prevent visceral adhesion in an effective manner and promote mesh integration into surrounding tissues from the abdominal wall. These results prove a novel facile and successful strategy to endow prosthetic meshes with superior anti-adhesion capability and tissue compatibility for repairing AWH. [Display omitted] • Nanofiber membranes (NFM) composed of PLGA and PCL were proved to be a promising physical barrier. • The resulting NFM barrier combining alginate layer was integrated into PP substrate to produce new composite meshes. • The obtained composite mesh showed efficient anti-adhesion capability and superior tissue compatibility. • Combining alginate-assistant NFM barrier and PP mesh provides a strategy to customize anti-adhesive hernia meshes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nanofibers containing composite meshes with different polypropylene substrates were fabricated. • In vitro and in vivo studies on polypropylene meshes before and after incorporation with nanofibers were conducted. • Polypropylene prostheses play a dominant role in mechanical support, while affecting cell proliferation and growth. • Lightweight polypropylene-based composite mesh showed superior anti-adhesion effects and tissue compatibility. Electrospun nanofibrous membranes (NFM) can serve as a physical barrier, employed to confer polypropylene (PP) mesh with anti-adhesion capability for repairing abdominal wall hernias. Still, the feasibility of NFM combined with different PP meshes was poorly researched. In this study, nanofiber containing composite meshes with three PP substrates were produced by integrating the NFM barrier compositing poly (lactic-co-glycolic acid) and polycaprolactone (PLGA/PCL) into each PP mesh. Tensile mechanical test results revealed that the PP component played a dominant role in the mechanical support, and this structure has not deteriorated in any case during the process. In vitro cell studies showed that the PP mesh with a higher density was significantly beneficial for cell proliferation within 3 days of seeding, while the one with lower density exhibited notable cell proliferation after a culture of 5 days. All the meshes had excellent biocompatibility. Moreover, an intraperitoneal implantation rabbit model was used to assess the anti-adhesion effects and tissue compatibility. The results revealed that the NFM barrier could be incorporated into the lightweight PP mesh to yield the composite mesh with superior anti-adhesion property and tissue compatibility. [ABSTRACT FROM AUTHOR]

Coastal flood risk facing urban communities is increasing due to the compound impacts of sea level rise (SLR) and urban growth. Accurate information on future dynamic risk of coastal flooding and its physical and socio-economic drivers is essential for planning effective and targeted adaptation. However, the current scenarios regarding urban development are insufficient to predict the future dynamic spatial distribution of urban socio-economic and physical growth, hindering the exact and dynamic assessment of future coastal flood risk. Moreover, the relative contributions of SLR and urban development to the future dynamics of city-level coastal flood risk have not been examined. Therefore, here we developed a land use-based urban growth scenario for the temporally and spatially explicit simulation of future urban growth in terms of buildings, roads and electrical facilities, and combined it with a local temporally explicit SLR scenario, into a "hazard-exposure-vulnerability" model, for the purpose of dynamic risk assessment of coastal flooding on the urban built environment in Xiamen, China by 2050, and accordingly examined the relative contributions of SLR and urban growth. Our results demonstrated a good performance of the land use-based urban growth model. SLR and urban growth will increase future coastal flood risk (represented by the expected annual damage, EAD) in varying degrees, but their combination is projected to exacerbate future flood damages significantly, indicating that considering either SLR or urban growth in isolation will underestimate future coastal flood risk. Moreover, the relative contributions of SLR and urban growth are dynamic over time, depending on the rate of SLR and the stage of urbanization: for example, the change in coastal flood risk is mainly attributable to urban growth in the phase of rapid urbanization before 2033 in Xiamen, yet the contribution of urban growth then becomes less important compared to SLR, when urbanization slows down, while SLR reaches a threshold after accelerating growth. The approach and results of our study can be considered a decision support tool, to help decision makers assess future coastal flood risk and detect the relative importance of each driving force, in order to employ land use planning, nature-based solutions and engineering protection, to achieve sustainable and resilient coastal cities. [Display omitted] • Urban growth and sea level rise are integrated, for city-level coastal flood risk assessment. • Combination of urban growth and sea level rise drive significant increases in coastal flood risk. • Accounting for urban growth and sea level rise separately underestimates coastal flood risk. • Relative contributions of urban growth and sea level rise to coastal flood risk are dynamic. • Flood-resilient coasts need smart land use planning, green and grey integrated solutions. [ABSTRACT FROM AUTHOR]

Coastal cities, most of them experiencing growing population and rapid urbanization, are facing reactive nitrogen (Nr) pollution crisis and are considered as Nr hotspots worldwide. Increased human activities generate drastic effects on the nitrogen (N) flows of coastal cities. Nevertheless, the N flows of coastal cities are not clearly understood, and the existing city-scale N flow models cannot depict the detailed N flows in coastal cities. Here, we developed a NI trogen MO del for CO astal ci T y (NIMOCOT) which includes four processes and 14 subsystems and used the Material Flow Analysis (MFA) method to model detailed N flows by taking Xiamen as a case study. The results showed that total N inputs to and outputs from Xiamen in 2015 were 403.8 and 201.7 Gg respectively, with half of total N inputs accumulated in the city. The top two N inputs were N embodied in nonfood goods consumption by households and fossil fuel combustion, accounting for 48.5% and 38.5% of total N inputs to Xiamen respectively. After city internal consumption, 109.5 Gg NO x were emitted mainly from energy and industrial subsystems, accounting for 93.2% of total gaseous Nr emissions to the atmosphere. In the typical coastal city, shipping contributed to 34.0% of the total gaseous Nr emissions to the atmosphere in the transportation sector, ranked after highway (58.0%). Moreover, the largest Nr contributor to the hydrosphere came from riparian import (56.0%) which has a significant impact on the hydrosphere of Xiamen. Our results indicated that enhancing the remove ratio of NO x emissions during fossil fuel consumption, and strengthening watershed managements to low riparian N imports from upstream will be useful for reducing N contaminants in environment of Xiamen, and NIMOCOT model is suitable for tracking the key N pollutant sources and could help to make decisions on cutting associate pollutants in coastal cities. Unlabelled Image • A comprehensive nitrogen model for a coastal city (NIMOCOT) is developed. • Unobserved nearby sea system is taken into account in nitrogen model for a coastal city (NIMOCOT) • The riparian N from upstream has a significant effect on the nearby sea subsystem in Xiamen. • Shipping plays an important role in transportation energy consumption and releases a larger Nr to the atmosphere. • NIMOCOT can serve as an effective guide for the mitigation of Nr pollution in a typical coastal city. [ABSTRACT FROM AUTHOR]

"Internet + waste electrical and electronic equipment (WEEE) collection" business ecosystems have attracted considerable attention from industry circles, and they are widely used in practice. However, the construction mechanism of "Internet + WEEE collection" business ecosystems has not been explored. In this paper, based on the mode of entry, "Internet + WEEE collection" platforms are divided into two types: platforms derived from an Internet company (PDICs) and platforms derived from a recycling company (PDRCs), and two typical corresponding platforms are analyzed using a double-case study to explore the interaction and mechanism between dominant platform capability, symbiotic strategy and business ecosystem construction. The results show the following: (1) different types of "Internet + WEEE collection" platforms have different dominant platform capabilities and then construct different business ecosystems based on their capabilities. PDICs take digital capability as the dominant platform capability and prefer to form an embedded business ecosystem, while PDRCs usually take network capability as the dominant platform capability and try to build a central business ecosystem. (2) PDICs and PDRCs prefer different symbiotic strategies, and the strategies affect the construction of business ecosystems. The strategy of mutualism symbiosis is adopted by PDICs, and it promotes them to build an embedded business ecosystem. PDRCs prefer the strategy of predation symbiosis, and they choose to construct a central business ecosystem. (3) Dominant platform capability can also affect the construction of business ecosystems through the symbiotic strategy. This study contributes to revealing the internal logic of "Internet + WEEE collection" business ecosystems construction in theory and helps platforms achieve sustainable development in practice. [ABSTRACT FROM AUTHOR]

Cities play a significant role in climate change mitigation and adaptation. Urban land planning shapes the urban form and is considered to be an effective approach for climate change mitigation and adaptation. Yet, there is little knowledge about what urban forms can reduce both greenhouse gas (GHG) emissions and climate stresses while considering trade-offs between them. Here, we investigate the role of urban land use in both climate change mitigation and adaptation. In particular, we assess quantitatively the competition between strategies for mitigation and adaptation and identify potential win-win solutions in land use responses. Using a coastal city as a case study, we find that the land use strategies for unilateral mitigation or adaptation can cause contradicting consequences with respect to the reductions in GHG emissions and climate stresses, i.e. reductions in GHGs could increase climate stresses or vice versa. Poorly integrated strategies potentially may compromise international efforts to meet the Climate Action in the Sustainable Development Goals. Properly integrated mitigation and adaptation strategies, or climate-sensitive land use planning, however, can lead to win-win outcomes and eventually achieve co-benefits. Yet, any co-benefits will gradually diminish if there is a delay in climate-sensitive land use planning, implying growing GHGs and intensified climate stresses. Our analysis indicates that integrating climate change mitigation and adaptation in urban land use needs to be enacted as soon as possible: any delays in implementation reduce the window to act to maximize the co-benefits. • Investigating the trade-offs between mitigation and adaptation in urban land use • Land use strategies for unilateral mitigation or adaptation can cause contradicting consequences. • Urban land use integrating mitigation and adaptation can lead to win-win outcomes. • Win-win outcomes will diminish if climate-sensitive land use planning is delayed. • Integrating mitigation and adaptation in urban land use need to be enacted now. [ABSTRACT FROM AUTHOR]