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Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice. Plain Language Summary: Antarctic sea ice has changed significantly since 2016, leading to a higher demand for sea ice forecasts. However, forecasting Antarctic sea ice has not received enough attention. Limited observations and lack of understanding of ice‐related physical mechanisms result in significant errors in sea ice predictions in dynamical models, particularly at the subseasonal timescale. We utilized a deep‐learning model to predict Antarctic sea ice at this timescale to fill this gap. Results showed that the deep‐learning model performed skillfully 1–8 weeks in advance, with the Weddell Sea being the best‐predicted region and the West Pacific being the worst. Furthermore, our study found that the model significantly outperformed mainstream dynamic models and a conventional statistical model. These findings build a foundation for developing more advanced prediction models at high resolutions for operational applications. Key Points: A deep‐learning model outperforms dynamic models, filling a subseasonal Antarctic sea ice prediction gap by bypassing physical mechanismsUnlike a Markov model that predicts time series of fixed spatial patterns, our model can extract ice synchronized spatiotemporal evolutionsThe deep‐learning model captures climate signals in sea ice, delivering the best prediction in fall season and the Weddell Sea region [ABSTRACT FROM AUTHOR]

Unsupervised clustering is an essential step in identifying cell types from single‐cell RNA sequencing (scRNA‐seq) data. However, a common issue with unsupervised clustering models is that the optimization direction of the objective function and the final generated clustering labels in the absence of supervised information may be inconsistent or even arbitrary. To address this challenge, a dynamic ensemble pruning framework (DEPF) is proposed to identify and interpret single‐cell molecular heterogeneity. In particular, a silhouette coefficient‐based indicator is developed to determine the optimization direction of the bi‐objective function. In addition, a hierarchical autoencoder is employed to project the high‐dimensional data onto multiple low‐dimensional latent space sets, and then a clustering ensemble is produced in the latent space by the basic clustering algorithm. Following that, a bi‐objective fruit fly optimization algorithm is designed to prune dynamically the low‐quality basic clustering in the ensemble. Multiple experiments are conducted on 28 real scRNA‐seq datasets and one large real scRNA‐seq dataset from diverse platforms and species to validate the effectiveness of the DEPF. In addition, biological interpretability and transcriptional and post‐transcriptional regulatory are conducted to explore biological patterns from the cell types identified, which could provide novel insights into characterizing the mechanisms. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored. Results To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives. Availability and implementation The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Arecoline, the dominant alkaloid existing in areca nuts, is an addictive substance and classified as a Group 2B potential human carcinogen. Currently, the detection of arecoline is mostly dependent on chromatography-based approaches, which are time-consuming and expensive. We used arecaidine as a hapten to produce a highly specific monoclonal antibody (mAb) against arecoline. An indirect competitive enzyme-linked immunosorbent assay (icELISA) was developed using the mAb-A5H12, to detect arecoline in traditional Chinese medicines and fresh areca nuts. The icELISA indicated that the half maximum inhibition concentration (IC50) for arecoline was 67.9 ng/mL, with a working range of 10.1–502.6 ng/mL and a limit of detection (LOD) of 3.6 ng/mL. High-performance liquid chromatographic (HPLC) confirmed the accuracy and the working range of icELISA, suggesting that the icELISA approach based on the arecoline specific antibody could be a widely applicable and easy operation method in detection of arecoline in foods and medicines. [ABSTRACT FROM AUTHOR]

This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of efficient deep learning techniques running on cost-effective hardware, a number of methods have been developed to learn compact neural networks. Most of these works aim to slim down filters in different ways, e.g., investigating small, sparse or quantized filters. In contrast, we treat filters from an additive perspective. A series of secondary filters can be derived from a primary filter with the help of binary masks. These secondary filters all inherit in the primary filter without occupying more storage, but once been unfolded in computation they could significantly enhance the capability of the filter by integrating information extracted from different receptive fields. Besides spatial versatile filters, we additionally investigate versatile filters from the channel perspective. Binary masks can be further customized for different primary filters under orthogonal constraints. We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced. Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters, but require less memory and computation cost. [ABSTRACT FROM AUTHOR]

Deterioration trend prediction of hydropower units helps to detect abnormal conditions of hydropower units and can prevent early failures. The reliability and accuracy of the prediction results are crucial to ensure the safe operation of the units and promote the stable operation of the power system. In this paper, the long short-term neural network (LSTM) is introduced, a comprehensive deterioration index (CDI) trend prediction model based on the time–frequency domain is proposed, and the prediction accuracy of the situation trend of hydropower units is improved. Firstly, the time–domain health model (THM) is constructed with back-propagation neural network (BPNN) and condition parameters of active power, guide vane opening and blade opening and the time–domain indicators. Subsequently, a frequency-domain health model (FHM) is established based on ensemble empirical mode decomposition (EEMD), approximate entropy (ApEn), and k-means clustering algorithm. Later, the time–domain degradation index (TDI) is developed according to THM, the frequency-domain degradation index (FDI) is constructed according to FHM, and the CDI is calculated as a weighted sum by TDI and FDI. Finally, the prediction model of LSTM is proposed based on the CDI to achieve degradation trend prediction. In order to validate the effectiveness of the CDI and the accuracy of the prediction model, the vibration waveform dataset of a hydropower plant in China is taken as a case study and compared with four different prediction models. The results demonstrate that the proposed model outperforms other comparison models in terms of predicting accuracy and stability. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing is a transformative technology that enables us to study the heterogeneity of the tissue at the cellular level. Clustering is used as the key computational approach to group cells under the transcriptome profiles from single-cell RNA-seq data. However, accurate identification of distinct cell types is facing the challenge of high dimensionality, and it could cause uninformative clusters when clustering is directly applied on the original transcriptome. To address such challenge, an evolutionary multiobjective deep clustering (EMDC) algorithm is proposed to identify single-cell RNA-seq data in this study. First, EMDC removes redundant and irrelevant genes by applying the differential gene expression analysis to identify differentially expressed genes across biological conditions. After that, a deep autoencoder is proposed to project the high-dimensional data into different low-dimensional nonlinear embedding subspaces under different bottleneck layers. Then, the basic clustering algorithm is applied in those nonlinear embedding subspaces to generate some basic clustering results to produce the cluster ensemble. To lessen the unnecessary cost produced by those clusterings in the ensemble, the multiobjective evolutionary optimization is designed to prune the basic clustering results in the ensemble, unleashing its cell type discovery performance under three objective functions. Multiple experiments have been conducted on 30 synthetic single-cell RNA-seq datasets and six real single-cell RNA-seq datasets, which reveal that EMDC outperforms eight other clustering methods and three multiobjective optimization algorithms in cell type identification. In addition, we have conducted extensive comparisons to effectively demonstrate the impact of each component in our proposed EMDC. [ABSTRACT FROM AUTHOR]

Background: Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize (Zea mays) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time. Results: Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time. Conclusions: In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize. [ABSTRACT FROM AUTHOR]

Background: Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize (Zea mays) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time. Results: Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time. Conclusions: In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize. [ABSTRACT FROM AUTHOR]

Revealing the changes in the Fram Strait (FS) multiyear ice (MYI) export is crucial due to their climate relevance in the context of the loss rate of MYI being faster than that of the total ice in the Arctic. Here, we estimated winter (October–April) MYI area export through the FS over the last 2 decades by using updated MYI concentration data retrieved from active and passive microwave satellite observations. We divided the period into two regimes relative to the ice index: D1 (2002/03–2010/11) and D2 (2012/13–2019/20). The observed variations of winter MYI exports D2 were compared with those of the previous decade D1. The results show that the MYI area exports display strong interannual variability. A significant decrease in MYI export for the periods between D1 and D2 is noted. On average, the wintertime MYI area exports declined sharply by 22% from 3.82 × 105 km2 in D1 to 3.00 × 105 km2 in D2. In addition, the percentage of MYI in the total sea ice outflow through the FS (PCM) also decreased distinctly from 72% in D1 to 59% in D2. Statistics show that weekly sea ice drift across the strait can explain 76% of the MYI area export variability. Furthermore, the dominant atmospheric drivers contributing to the decline in MYI area export during D2 were examined. In the last decade (D2), the strengthened low pressure in the North Atlantic sector, combined with an eastward shift in the axis of dipole anomaly (DA), resulted in reduced MYI advection from the Beaufort Sea and Siberian Coast toward the FS. Moreover, weakened cyclonic activity south of the FS also contributed to the reduction in MYI export during D2. [ABSTRACT FROM AUTHOR]

Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the limited memory and computation resources. We aim to design efficient neural networks for heterogeneous devices including CPU and GPU, by exploiting the redundancy in feature maps, which has rarely been investigated in neural architecture design. For CPU-like devices, we propose a novel CPU-efficient Ghost (C-Ghost) module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed C-Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. C-Ghost bottlenecks are designed to stack C-Ghost modules, and then the lightweight C-GhostNet can be easily established. We further consider the efficient networks for GPU devices. Without involving too many GPU-inefficient operations (e.g., depth-wise convolution) in a building stage, we propose to utilize the stage-wise feature redundancy to formulate GPU-efficient Ghost (G-Ghost) stage structure. The features in a stage are split into two parts where the first part is processed using the original block with fewer output channels for generating intrinsic features, and the other are generated using cheap operations by exploiting stage-wise redundancy. Experiments conducted on benchmarks demonstrate the effectiveness of the proposed C-Ghost module and the G-Ghost stage. C-GhostNet and G-GhostNet can achieve the optimal trade-off of accuracy and latency for CPU and GPU, respectively. MindSpore code is available at https://gitee.com/mindspore/models/pulls/1809, and PyTorch code is available at https://github.com/huawei-noah/CV-Backbones. [ABSTRACT FROM AUTHOR]

In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Pacific-Arctic sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the sea ice concentration (SIC) anomaly correlation coefficient (ACC) between predictions and observations, increased by 32 % in the Bering Sea and 18 % in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. SIC trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. We find that subsurface ocean heat content (OHC) provides a crucial source of prediction skill in all seasons, especially in the cold season, and adding sea ice thickness (SIT) to the regional Markov model has a substantial contribution to the prediction skill in the warm season but a negative contribution in the cold season. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model. [ABSTRACT FROM AUTHOR]

Class-imbalanced biological datasets pose significant challenges in machine learning and data analysis tasks. Prior methods to handle imbalance rely on data oversampling, which increases computational costs and overfitting. While feature selection and ensemble learning are promising techniques, current applications in imbalanced contexts are limited. To address these challenges, we present a novel framework called Hybrid Sampling Nature-Inspired Optimization Ensemble (HSNOE) to enhance the identification of hidden responders in imbalanced biological datasets. Our contributions are three-fold: 1) A hybrid undersampling and oversampling technique to mitigate class-imbalance; 2) Integrate an ant colony optimization-based feature selection that identifies informative feature subsets; 3) An ensemble classifier integrating diverse models trained on optimized features to improve performance. The experiments conducted on the five biological datasets demonstrate that HSNOE exhibits more stable comprehensive performance across six evaluation metrics compared to ten benchmark methods. We also conducted a biological analysis specifically on the Pan-cancer dataset. Moreover, the HSNOE method has been made publicly available on GitHub. 1 1 https://github.com/wangxb96/HSNOE. • HSNOE can mitigate the effects of data skew when training the model. • A nature-inspired feature selection method is used to identify informative features. • HSNOE combines a nature-inspired optimization method to realize ensemble. • A case study was conducted to show the generalization capabilities of HSNOE. [ABSTRACT FROM AUTHOR]

Atmospheric circulation associated with the Arctic dipole (AD) pattern plays a crucial role in modulating the variations of summertime sea ice concentration (SIC) within the Pacific Arctic sector (PAS). Based on reanalysis data and satellite observations, we found that the impacts of atmospheric circulation associated with a positive AD (AD+) on SIC change over different regions of the PAS [including the East Siberian Sea (ESS), Beaufort and Chukchi Seas (BCS), and Canadian Arctic Archipelago (CAA)] are dependent on the phase shifts of Pacific decadal oscillation (PDO). Satellite observations reveal that SIC anomalies, influenced by AD+ during PDO− relative to that during PDO+, varies significantly in summer by 4.9%, −7.3%, and −6.4% over ESS, BCS, and CAA, respectively. Overall, the atmospheric anomalies over CAA and BCS in terms of specific humidity, air temperature, and thereby downward longwave radiation (DLR), are enhanced (weakened) in the atmospheric conditions associated with AD+ during PDO− (PDO+). In these two regions, the larger (smaller) increases in specific humidity and air temperature, associated with AD+ during PDO− (PDO+), are connected to the increased (decreased) poleward moisture flux, strengthened (weakened) convergence of moisture and heat flux, and in part to adiabatic heating. As a consequence, the DLR and surface net energy flux anomalies over the two regions are reinforced in the atmospheric scenarios associated with AD+ during PDO− compared with that during PDO+. Therefore, smaller SIC anomalies are identified over CAA and BCS in the cases related to AD+ during PDO− than during PDO+. Essentially, the changes of the DLR anomaly in CAA and BCS are in alignment with geopotential height anomalies, which are modulated by the anticyclonic circulation pattern in association with AD+ during varying PDO phases. In contrast, the SIC changes over ESS is primarily attributed to the variations in mechanical wind forcing and sea surface temperature (SST) anomalies. The cloud fraction anomalies associated with AD+ during different PDO phases are found not to be a significant contributor to the variations of sea ice anomaly in the studied regions. Given the oscillatory nature of PDO, we speculate that the recent shift to the PDO+ phase may temporarily slow the observed significant decline trend of the summertime SIC within PAS of the Arctic. [ABSTRACT FROM AUTHOR]

Heterodera avenae, as an obligate endoparasite, causes severe yield loss in wheat (Triticum aestivum). Investigation on the mechanisms how H. avenae perceives wheat roots is limited. Here, the attractiveness of root exudates from eight plant genotypes to H. avenae were evaluated on agar plates. Results showed that the attraction of H. avenae to the root exudates from the non-host Brachypodium distachyon variety Bd21-3 was the highest, approximately 50 infective second-stage juveniles (J2s) per plate, followed by that from three H. avenae-susceptible wheat varieties, Zhengmai9023, Yanmai84 and Xiangmai25, as well as the resistant one of Xinyuan958, whereas the lowest attractive activity was observed in the two H. avenae-resistant wheat varieties, Xianmai20 (approximately 12 J2s/plate) and Liangxing66 (approximately 11 J2s/plate). Then Bd21-3, Zhengmai9023 and Heng4399 were selected for further assays as their different attractiveness and resistance to H. avenae, and attractants for H. avenae in their root exudates were characterized to be heat-labile and low-molecular compounds (LM) by behavioral bioassay. Based on these properties of the attractants, a principle of identifying attractants for H. avenae was set up. Then LM of six root exudates from the three plants with and without heating were separated and analyzed by HPLC-MS. Finally, dihydroxyacetone (DHA), methylprednisolone succinate, embelin and diethylpropionin in the root exudates were identified to be putative attractants for H. avenae according to the principle, and the attraction of DHA to H. avenae was validated by behavioral bioassay on agar. Our study enhances the recognition to the orientation mechanism of H. avenae towards wheat roots. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNNs) are successfully used in a number of applications. However, their storage and computational requirements have largely prevented their widespread use on mobile devices. Here we present a series of approaches for compressing and speeding up CNNs in the frequency domain, which focuses not only on smaller weights but on all the weights and their underlying connections. By treating convolution filters as images, we decompose their representations in the frequency domain as common parts (i.e., cluster centers) shared by other similar filters and their individual private parts (i.e., individual residuals). A large number of low-energy frequency coefficients in both parts can be discarded to produce high compression without significantly compression romising accuracy. Furthermore, we explore a data-driven method for removing redundancies in both spatial and frequency domains, which allows us to discard more useless weights by keeping similar accuracies. After obtaining the optimal sparse CNN in the frequency domain, we relax the computational burden of convolution operations in CNNs by linearly combining the convolution responses of discrete cosine transform (DCT) bases. The compression and speed-up ratios of the proposed algorithm are thoroughly analyzed and evaluated on benchmark image datasets to demonstrate its superiority over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A novel L-shaped composite shear wall composed of a series of columns was proposed. • An experimental investigation of L-shaped composite shear walls under cyclic lateral and axial loads was conducted. • A 3D finite element model, considering improved confinement, plastic hardening, and damage accumulation, can simulate seismic behavior well. • Plastic stress distributed methods were proposed to predict the lateral load strength of an L-shaped steel composite shear wall. Behavior of L-shaped multi-partition steel–concrete composite shear walls (MP-SC-CSWs) subjected to the cyclic lateral load is experimentally and numerically evaluated. Such composite shear walls possess proper capacity, stiffness, and ductility, as the steel tube strengthened by inner steel plates considerably confines the infill concrete. Four large-scale L-shaped MP-SC-CSW specimens were designed, fabricated, and tested. The L-shaped specimens were exposed to lateral hysteretic loading and axial compression loading. The experimental parameters were axial load ratios and height-to-depth ratios (wall aspect ratios). The experimental results indicated that the L-shaped MP-SC-CSWs had high lateral capacities, energy dissipations, and good ductility. Based on the experimental observations, specimens failed in flexure mode. Increasing the axial load ratio enhances the capacity to dissipate energy but decreases the ductility. The tested specimens could meet the drift requirement of the specification even when subjected to a high design axial load ratio of 0.76. Additionally, the wall aspect ratios had little influence on the ductility. 3D nonlinear finite element models of tested specimens were established and verified with test results to conduct further numerical studies. A simplified method based on the plastic stress distribution was developed and confirmed by the experimental and finite element analysis to calculate the flexural capacities of MP-SC-CSWs. [ABSTRACT FROM AUTHOR]

Graphical abstract Solution-processed Cu-doped NiO x nanoparticles is an efficient hole transport layer in polymer solar cells. The Cu doping enhances the electrical conductivity and improves the interface contacts with the active layer, thereby improving the device efficiency. Abstract Polymer solar cells (PSCs) are considered promising energy power suppliers due to their light weight, printability, low-energy fabrication and roll-to-roll processability. Recently, the solution-processed NiO x nanoparticles have been a desirable interfacial material for hole transport in the PSCs, instead of organic semiconductors. However, pure NiO x films restrain the high performance of PSCs due to their poor electrical characteristics caused by the localized orbital distribution at the top of valence band. Therefore, metal ion doping has been explored as a method to endow NiO x nanoparticles with the appropriate electrical characteristics. Herein, we applied solution-processed Cu-doped NiO x (Cu:NiO x) nanoparticles as an efficient hole transport layer (HTL) in PSCs. The Cu-doped NiO x enhanced the electrical conductivity of the material and improved the interface contact with the active layer, which remarkably facilitated the hole extraction and effectively suppressed the carrier recombination at the interface. Thus, a higher power conversion efficiency of 7.05%, corresponding to an approximately 30% efficiency improvement compared with that of a pristine NiO x interlayer (5.44%) in poly[N- 9′'-hepta-decanyl-2,7-carbazolealt-5,5-(4′,7′-di-2-thienyl-2′,1′,3′-ben-zothiadiazole)]:[6,6]-phenyl-C 71 -butyric acid methyl ester (PCDTBT:PC 71 BM)-based PSCs, was achieved by the proposed device. The developed solution-processed Cu:NiO x nanoparticles may be an excellent alternative for interfacial materials in PSCs or other optoelectronic devices requiring HTLs. [ABSTRACT FROM AUTHOR]

In this study, cone calorimeter and thermogravimetric analyses were used to simulate the asphalt combustion process under the conditions of fire radiation and programmed temperature increase. The gaseous compositions and release rules were analyzed by infrared spectroscopy to investigate the influence of hydrated lime on the smoke suppression mechanism in the asphalt combustion process. The experimental results show that hydrated lime can promote the asphalt mastic surface to form a barrier layer during the combustion process. This barrier layer can reduce the burning intensity of asphalt. Although the compositions of gaseous products do not change much, the rates of CO production and smoke release are decreased. In addition, hydrated lime is alkaline and can thus neutralize acidic gases such as SO2 and reduce the toxicity of gaseous products. With the addition of 40 wt.% hydrated lime, the total smoke release and the CO release rate both decrease by more than 20% relative to the addition of the same amount of limestone fillers and decrease by more than 10% relative to the addition of the same amount of magnesium hydroxide flame retardant. [ABSTRACT FROM AUTHOR]

• A new type of l -shaped steel–concrete composite shear wall using multi-partition steel tube was proposed. • The behavior under the eccentric load was investigated by the means of experiments and finite element analysis. • The proposed model using improved constitutional relationship of concrete simulated the eccentric behavior reasonably. • Simplified formulas were proposed to predict the N - M curves, which could consider the influence of load angle and slenderness. An l -shaped composite shear wall, comprised of a multi-partition steel tube and concrete, is developed to improve the behavior of the l -shaped component. It possesses high load-resisting strength and good ductility as it makes better use of steel and concrete. Eleven samples were tested under eccentric load to determine the impacts of the depth, breadth, and load angle. The experimental results showed that the eccentric strength of the specimen under a load angle π was higher than that of the specimen under a load angle of 0. The specimens failed in out-of-plane and in-plane flexural modes when the depth-to-width ratios were different. Then, the finite element (FE) model using an improved constitutional relation of concrete was established to analyze the eccentric mechanism of the l -shaped composite shear walls. Furthermore, parametric analysis using the FE model was taken to determine the key factors of eccentric behavior. Compared to the design methods specified in EC4, AISC, and CECS, it was found that the calculated capacity for the stub was underestimated while the calculated capacity of the slender components was overestimated. The simplified formulas were presented for predicting the N - M curves based on the experimental and FE analysis and it could conservatively include the influence of the load angle and the relative slenderness. [ABSTRACT FROM AUTHOR]

Here we study the extreme visual recovery problem, in which over 90% of pixel values in a given image are missing. Existing low rank-based algorithms are only effective for recovering data with at most 90% missing values. Thus, we exploit visual data’s smoothness property to help solve this challenging extreme visual recovery problem. Based on the discrete cosine transform (DCT), we propose a novel DCT regularizer that involves all pixels and produces smooth estimations in any view. Our theoretical analysis shows that the total variation regularizer, which only achieves local smoothness, is a special case of the proposed DCT regularizer. We also develop a new visual recovery algorithm by minimizing the DCT regularizer and nuclear norm to achieve a more visually pleasing estimation. Experimental results on a benchmark image data set demonstrate that the proposed approach is superior to the state-of-the-art methods in terms of peak signal-to-noise ratio and structural similarity. [ABSTRACT FROM AUTHOR]

The transition set semantics (Wang and Jiao, LNCS 6128:84-103, ) partitions the Petri net behaviors in a canonical way such that behaviors in an equivalence class have the same canonical transition set sequence. This article extends the semantics in two ways: firstly, the semantics is parameterized by the basic relation on the structural transitions to define different variants; secondly, the semantics for the infinite firing sequences of the net is defined. We prove that these extensions still preserve the well-definedness, soundness and completeness of the semantics. Furthermore, we show how to recognize some infinite sequences called back-loops in the view of this new semantics. [ABSTRACT FROM AUTHOR]

The research has analyzed the axial behavior of an innovative L-shaped composite column. The L-shaped composite columns could work as series rectangular composite columns, which could take advantage of the steel tubes and the inner concrete. Seven specimens have been tested to analyze the failure progress, load-carrying capacity and failure mode. Main parameters were the length-to-width ratio and length-to-depth ratio. The shear failure mode accompanied by local buckling appeared for the tested specimens. The results showed that such columns possessed high strength and good ductility. A finite element model was established and verified by the experimental results. Results showed that the finite element model could simulate the axial behavior of the L-shaped composite columns well. Parametric analysis was conducted to analyze the influence on the axial behavior of composite columns. Furthermore, a theoretical model was proposed to calculate the axial strength of such component and was in good agreement with the experimental results. A simplified formula has been proposed for determining the axial strength according to the results of parametric analysis of FEA and the theoretical model. In addition, the calculated results based on design methods specified in EC4, CECS and AISC have been compared with the experimental results, the results of theoretical model and the results of simplified formula. Among them, the results of EC4 were the closest to the experimental results. [Display omitted] • An innovative multi-partition steel-concrete composite column is proposed. • The axial experiments and finite element analysis have been done to study the axial behavior. • A simplified method has been proposed for the design of the axial strength. • An improved stability factor is added in the simplified formula. [ABSTRACT FROM AUTHOR]

Background: Townes-Brocks syndrome (TBS) is a rare genetic disorder characterized by imperforate anus, dysplastic ears, thumb malformations, and other abnormalities. Previous studies have revealed that mutations in the SALL1 gene can disrupt normal development, resulting in the characteristic features of Townes-Brocks syndrome. Spalt-like transcription factors (SALLs) are highly conserved proteins that play important roles in various cellular processes, including embryonic development, cell differentiation, and cell survival. Over 400 different variants or mutations have been reported in the SALL1 gene in individuals with TBS. Most of these variants lead to the formation of premature termination codons (PTCs), also known as nonsense mutations. The majority of these PTCs occur in a specific region of the SALL1 gene called the "hotspot region", which is particularly susceptible to mutation. Methods: In this study, we conducted whole-exome sequencing on a three-generation Chinese family with anorectal malformations. Results: We identified a novel heterozygous mutation (chr16:51175376:c.757 C > T p.Gln253*) in the SALL1 gene. Molecular analysis revealed a heterozygous C to T transition at nucleotide position 757 in exon 2 of the SALL1 (NM_002968) gene. This mutation is predicted to result in the substitution of the Gln253 codon with a premature stop codon (p.Gln253*). The glutamine-rich domain forms a long alpha helix, enabling the mutant protein to interact with the wild-type SALL1 protein. This interaction may result in steric hindrance effects on the wild-type SALL1 protein. Conclusions: Our findings have expanded the mutation database of the SALL1 gene, which is significant for genetic counseling and clinical surveillance in the affected family. Furthermore, our study enhances the understanding of Townes-Brocks syndrome and has the potential to improve its diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Motivation Thanks to the development of high-throughput sequencing technologies, massive amounts of various biomolecular data have been accumulated to revolutionize the study of genomics and molecular biology. One of the main challenges in analyzing this biomolecular data is to cluster their subtypes into subpopulations to facilitate subsequent downstream analysis. Recently, many clustering methods have been developed to address the biomolecular data. However, the computational methods often suffer from many limitations such as high dimensionality, data heterogeneity and noise. Results In our study, we develop a novel Graph-based Multiple Hierarchical Consensus Clustering (GMHCC) method with an unsupervised graph-based feature ranking (FR) and a graph-based linking method to explore the multiple hierarchical information of the underlying partitions of the consensus clustering for multiple types of biomolecular data. Indeed, we first propose to use a graph-based unsupervised FR model to measure each feature by building a graph over pairwise features and then providing each feature with a rank. Subsequently, to maintain the diversity and robustness of basic partitions (BPs), we propose multiple diverse feature subsets to generate several BPs and then explore the hierarchical structures of the multiple BPs by refining the global consensus function. Finally, we develop a new graph-based linking method, which explicitly considers the relationships between clusters to generate the final partition. Experiments on multiple types of biomolecular data including 35 cancer gene expression datasets and eight single-cell RNA-seq datasets validate the effectiveness of our method over several state-of-the-art consensus clustering approaches. Furthermore, differential gene analysis, gene ontology enrichment analysis and KEGG pathway analysis are conducted, providing novel insights into cell developmental lineages and characterization mechanisms. Availability and implementation The source code is available at GitHub: https://github.com/yifuLu/GMHCC. The software and the supporting data can be downloaded from: https://figshare.com/articles/software/GMHCC/17111291. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks a superior flight path in a complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability to address complex optimization problems. However, different SI algorithms present various performances for UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of different SI algorithms for UCAV path-planning research. In the experiment, twelve algorithms that published in major journals and conference proceedings are surveyed and then applied to UCAV path-planning. Moreover, to demonstrate the performance of different algorithms in further, we design different scales of problem cases for those comparative algorithms. The experimental results show that UCAV can find the safe path to avoid the threats efficiently based on most SI algorithms. In particular, the Spider Monkey Optimization is more effective and robust than other algorithms in handling the UCAV path-planning problem. The analysis from different perspectives contributes to highlight trends and open issues in the field of UCAVs. [ABSTRACT FROM AUTHOR]

Aliphatic aldehydes are a class of organic compounds containing aldehyde groups, which are widespread, and closely related to people's daily life and health. In this work, a series of terpenes based hydrophobic deep eutectic solvents were designed and synthesized using hexafluoroisopropanol as hydrogen bond donor and menthol/thymol as hydrogen bond acceptor. Then they are used as extraction solvent in dispersive liquid-liquid microextraction for extracting and determining seven aliphatic aldehydes from drinking water and alcoholic beverage combined with high performance liquid chromatography-ultraviolet. Due to the fact that these hydrophobic deep eutectic solvents are liquid at the room temperature, a density greater than that of water, a lower viscosity (≤26.10 mPa s, 25 °C), after extraction and centrifugation, the microvolume DES-rich phase in the bottom is convenient for collection and direct analysis without further dissolution or dilution with organic solvents. Some factors affecting the extraction recovery were optimized by one-variable-at-a-time and response surface methodology. Under the optimal conditions, the enrichment factors for the seven aliphatic aldehydes were 48–56. The method had good performance: linear ranges of 1.0–200, 0.5–200, 0.2–200, 0.4–400, 1.0–400, 0.4–400 and 0.4–400 μg L−1 for seven aliphatic aldehydes (r 2 ≥ 0.9949), limits of detection of 0.1–0.5 μg L−1, intra-day and inter-day precisions <4.9%. The recoveries of seven aliphatic aldehydes ranged from 76.0 to 119.0%. The proposed dispersive liquid-liquid microextraction method is simple, rapid, highly efficient, and green, which effectively reduces the amount of toxic chemical reagents used and their impact on the environment. Rapid and efficient detection of aliphatic aldehydes helps ensure a healthy diet and has great application prospects in food safety analysis. [Display omitted] • Terpenes based HDESs with low viscosity and high density were designed and prepared. • A terpenes HDESs based DLLME was established for enrichment of aliphatic aldehydes. • The DES-rich phase in the bottom is easy to be collected and directly analyzed by HPLC. • The method is simple, rapid, effective and green for detecting aliphatic aldehydes. • The green profile of the method was assessed by AES and AGREE. [ABSTRACT FROM AUTHOR]

Haploinsufficiency, wherein a single allele is not enough to maintain normal functions, can lead to many diseases including cancers and neurodevelopmental disorders. Recently, computational methods for identifying haploinsufficiency have been developed. However, most of those computational methods suffer from study bias, experimental noise and instability, resulting in unsatisfactory identification of haploinsufficient genes. To address those challenges, we propose a deep forest model, called HaForest, to identify haploinsufficient genes. The multiscale scanning is proposed to extract local contextual representations from input features under Linear Discriminant Analysis. After that, the cascade forest structure is applied to obtain the concatenated features directly by integrating decision-tree-based forests. Meanwhile, to exploit the complex dependency structure among haploinsufficient genes, the LightGBM library is embedded into HaForest to reveal the highly expressive features. To validate the effectiveness of our method, we compared it to several computational methods and four deep learning algorithms on five epigenomic data sets. The results reveal that HaForest achieves superior performance over the other algorithms, demonstrating its unique and complementary performance in identifying haploinsufficient genes. The standalone tool is available at https://github.com/yangyn533/HaForest. [ABSTRACT FROM AUTHOR]

The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several critical limitations there, such as high-dimensionality, data sparsity and model performance. Given the central importance and broad impact of precision oncology, we propose nature-inspired deep Ras activation pan-cancer (NatDRAP), a deep neural network (DNN) model, to address those restrictions for the identification of hidden responders. In this study, we develop the nature-inspired deep learning model that integrates bulk RNA sequencing, copy number and mutation data from PanCanAltas to detect pan-cancer Ras pathway activation. In NatDRAP, we propose to synergize the nature-inspired artificial bee colony algorithm with different gradient-based optimizers in one framework for optimizing DNNs in a collaborative manner. Multiple experiments were conducted on 33 different cancer types across PanCanAtlas. The experimental results demonstrate that the proposed NatDRAP can provide superior performance over other benchmark methods with strong robustness towards diagnosing RAS aberrant pathway activity across different cancer types. In addition, gene ontology enrichment and pathological analysis are conducted to reveal novel insights into the RAS aberrant pathway activity identification and characterization. NatDRAP is written in Python and available at https://github.com/lixt314/NatDRAP1. [ABSTRACT FROM AUTHOR]

The flame retardancy of asphalt binders with layered double hydroxides (LDHs) was investigated using limiting oxygen index (LOI) and cone calorimeter tests. The flame-retardant mechanism of the LDHs was also studied with thermogravimetry and differential scanning calorimetry (TG–DSC), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The cone calorimeter testing results indicated that 2 wt.% of the LDHs can decease the peak heat and smoke release rate of asphalt binders. Because a low dose of LDHs can be well dispersed in asphalt binder and favor the formation of polyaromatic structures during combustion, the thermal oxidation resistance and compactness of the char layer can be improved. The LOI of asphalt binder can be increased and the heat and smoke release during combustion can be decreased with 25 wt.% LDHs. The decomposition of LDHs can absorb the heat release of the initial two stages of asphalt combustion and reduce the burning rate of asphalt. Due to the loss of loosely bound water in the LDHs during the blending process and the decrease of dispersibility at a high LDH dose, the improvement of thermal stability is limited. [ABSTRACT FROM AUTHOR]

Baffin Bay serves as a huge reservoir of sea ice which would provide the solid freshwater sources to the seas downstream. By employing satellite-derived sea ice motion and concentration fields, we obtain a nearly 40-year-long record (1978–1979 to 2016–2017) of the sea ice area flux through key fluxgates of Baffin Bay. Based on the estimates, the Baffin Bay sea ice area budget in terms of inflow and outflow are quantified and possible causes for its interannual variations and trends are analyzed. On average, the annual (September–August) inflows through the northern gate and Lancaster Sound are on the order of 205.8(±74.7)×103 km 2 and 55.2(±17.8)×103 km 2. In particular, a comparison with published results seems to suggest that about 75 %–85 % of the inflow through the northern gates is newly formed ice produced in the recurring North Water Polynya (NOW), in addition to the inflow via Nares Strait and Jones Sound. Meanwhile, the mean outflow via the southern gate approaches 394.3(±110.2)×103 km 2. The distinct interannual variability for ice area flux through the northern gate and southern gate is partly explained by wind forcing associated with cross-gate sea level pressure difference, with correlations of 0.62 and 0.68, respectively. Also, significant increasing trends are found for the annual sea ice area flux through the three gates, amounting to 38.9×103 , 82.2×103 , and 7.5×103 km 2 decade -1 for the northern gate, southern gate, and Lancaster Sound. These trends are chiefly related to the increasing ice motion, which is associated with thinner ice owing to the warmer climate (i.e., higher surface air temperature and shortened freezing period) and increased air and water drag coefficients over the past decades. [ABSTRACT FROM AUTHOR]

In recent years, contrastive learning has made great progress in the field of computer vision, which shows great potential in action representation learning. Current contrastive learning methods usually employ contrastive loss function to learn video motion representation, which attend to capturing the similar background appearance but ignore similar motion information. This problem is called background bias, which restricts the model from exploring motion patterns. The background bias can be divided into wide background bias and micro background bias. The wide background bias refers to the statistically significant background bias, while the micro background bias refers to the background bias directly interacting with the moving object. To tackle these problems, this paper first proposes a semi-negative pair merging foreground–background, which mainly decouples the dynamic factor with obvious motion and the static factor with stable invariants in the video frame sequence. Then the dynamic factor of the original video is fused with other static factors to obtain a random background image where the foreground is more similar than the background to solve the wide background bias. Secondly, a pixel-level motion aware representation decomposition module calculates the pixel-level intensity variations from the feature space of adjacent frames, and these variations are further accumulated to obtain the salient map that focuses on the boundaries of moving objects, so the model attends to the motion pattern rather than the background to solve the micro background bias. Furthermore, a new dual loss function is proposed based on joint wide and micro background bias to better capture both static and dynamic features. The heat map shows that the proposed method effectively can solve background bias and achieve better performance in public datasets compared with other existing methods. [ABSTRACT FROM AUTHOR]

T-shaped welded multi-partition composite members, which are made of several rectangular concrete-filled steel tubes, are proposed to speed up the fabrication procedure. Seven half-scale T-shaped specimens were tested by considering the section height-to-width ratio and slenderness ratio. In the test, a slight torsion angle was measured when the instability failure along the asymmetric axis occurred, indicating the specimens had good torsion resistance. When the relative slenderness ratio of the specimen was larger than 0.44, the specimens failed due to the instability along the asymmetric axis. Parametric numerical analysis is conducted by considering the slenderness ratio, cross-section dimension, steel ratio, and material strength. The results show that the strength of concrete negatively impacts the ductility of the specimens, leading to a decrease of 43%, while the strength of the material positively influences the stability of the specimens. The formulas from different traditional codes are verified by the parametric numerical analysis results, indicating most codes can conservatively predict the sectional bearing capacity, but overestimate the stability coefficient of the specimens by neglecting the reduction effect of the excessive height-to-depth ratio. Recommendations are provided for calculating the stability coefficient under axial compression applicable to T-shaped welded multi-partition composite members. • Axial compression of T-shaped welded multi-partition composite members was studied through experiments and FE analysis. • The measured torsion angle confirms the good torsional capacity of T-shaped composite members. • Both sectional bearing capacity and stability bearing capacity have been discussed. • A suggestion for calculating stability coefficients is proposed, which is suitable for T-shaped columns and shear walls. [ABSTRACT FROM AUTHOR]

The satellite observations unveiled that the July sea ice extent of the Arctic shrank to the lowest value, since 1979, in 2020 with a major ice retreat in the Eurasian shelf seas including Kara, Laptev, and East Siberian seas. Based on the ERA-5 reanalysis products, we explored the impacts of warm and moist air-mass transport on this extreme event. The results revealed that anomalously high energy and moisture converged into these regions in the spring months (April to June) of 2020, leading to a burst of high moisture content and warming within the atmospheric column. The convergence is accompanied by local enhanced downward longwave surface radiation and turbulent fluxes, which is favorable for initiating an early melt onset in the region with severe ice loss. Once the melt begins, solar radiation plays a decisive role in leading to further sea ice depletion due to ice–albedo positive feedback. The typical trajectories of the synoptic cyclones that occurred on the Eurasian side in spring 2020 agree well with the path of atmospheric flow. Assessments suggest that variations in characteristics of the spring cyclones are conducive to the severe melt of sea ice. We argue that large-scale atmospheric circulation and synoptic cyclones acted in concert to trigger the exceptional poleward transport of total energy and moisture from April to June to cause this record minimum of sea ice extent in the following July. [ABSTRACT FROM AUTHOR]

The combustion mechanism and gaseous products of asphalt binder at five different oxygen concentrations (21%, 18%, 15%, 12%, and 10%) were studied by thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR). The evolution of the functional groups and microscopic morphology of condensed phase were also analyzed by infrared spectroscopy (IR) and scanning electron microscopy (SEM). The semi quantitative research was realized by calculating the absorption peak area of IR, proximate and ultimate analyses. Results show that the oxygen concentration influences both the pyrolysis and combustion stages of asphalt, and the influence on the combustion of heavy components is more significant. With the decrease of oxygen concentration, the residual mass after the combustion of asphalt binder increases. Additionally, the temperature zone of combustion shifts higher, and a thermal reaction lag effect becomes more obvious. The concentration of oxygen only changes the concentration of gaseous products, instead of the types. Moreover, the concentrations of CO 2 , CO, H 2 O, SO 2 , etc. all decrease, and there is the biggest drop in CO 2. The low oxygen concentration inhibits the dehydrogenation reaction of asphalt in the aerobic pyrolysis stage, while the complete combustion of asphalt residues is inhibited in the combustion of heavy components stage. These will all provide more scientific basis for the analysis of asphalt combustion under low oxygen conditions in tunnel fires. • Combustion mechanism of asphalt binder under different oxygen concentration was discussed. • Dynamic evolution of volatiles released from asphalt binder combustion at different oxygen concentration was described. • Evolution of functional groups in the condensed phase of asphalt binder was analyzed. • Combustion mechanism of asphalt binder under low oxygen conditions was further understood. [ABSTRACT FROM AUTHOR]

This study experimentally and numerically explores the axial-compression behaviours of tapered recycled aggregate concrete-filled high-strength double skin steel tubular (RAC-FHDST) stub column. Twelve specimens were fabricated and tested, considering the effects of tapered angle, hollow ratio and replacement level of coarse recycled aggregate (CRA). The experimental results, consisting of deformation patterns, axial load versus strain responses and confining effect from external high-strength steel tube were obtained and analyzed. Failure mode showed that all stub columns exhibited good ductile behaviour. The elastic stiffness and ultimate strength of RAC-FHDSTs decreased with rising tapered angles, while the ductile index increased. As the replacement levels of CRA varied from 0% to 100%, the elastic stiffness and ductility index obviously reduced. Larger confining effect from outer steel tube to RAC occurred close to the top section of specimens, and the occurrence of confinement was advanced with decreasing hollow ratio and increasing CRA content. Additionally, detailed numerical models were constructed and validated using experiment results. The validated finite element (FE) models were then employed to analyze the working mechanism of specimen, encompassing the load distributions and the interaction between the sandwiched RAC and steel tubes. Afterwards, a parametric study was performed to reveal the influences of essential parameters on the axial compressive performance. Finally, the existing design approaches to normal CFST and CFDST stub columns were employed to assess their applicability for estimating the ultimate strengths of tapered RAC-FHDSTs. • Axial-load tests conducted on 12 RAC-FHDST stub columns. • Effects of tapered angle, hollow ratio and CRA content on the axial response of RAC-FHDSTs. • Confinement effect from outer high-strength steel tube. • The applicability of existing design provisions to tapered RAC-FHDSTs. [ABSTRACT FROM AUTHOR]

Perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), as emerging endocrine-disrupting chemicals (EDCs), pose adverse effects on aquatic organisms. Conventional ecological risk assessment (ERA) not fully considering the mode of toxicity action of PFOS and PFOA, may result in an underestimation of risks and confuse decision-makers. In the study, we developed species sensitivity weighted distribution (SSWD) models based on adverse outcome pathway (AOP) networks for deriving predicted no-effect concentrations (PNECs). Three kinds of weighting criteria (intraspecies variation, trophic level abundance, and data quality) and weighted log-normal distribution methods were adopted. The developed models considered the inter/intraspecies variation and integrated nontraditional endpoints of endocrine-disrupting effects. The PNECs of endocrine disruption effects were derived as 2.52 μg/L (95% confidence intervals 0.667–9.85 μg/L) for PFOS and 18.7 μg/L (5.40–71.0 μg/L) for PFOA, which were more conservative than those derived from the SSD method and were comparable with the values in the literature based on the chronic toxicity data. For PFOS, the effect of growth and development was the most sensitive; however, for PFOA, the effect of reproduction was the most sensitive in the effects of growth and development, reproduction, biochemistry and genetics, and survival. The endocrine-disrupting effects of PFOS and PFOA are significant and need to be fully recognized in the ERA. This study provided an ERA framework that can improve the ecological relevance and reduce the uncertainty of PNECs of EDCs. [Display omitted] • SSWD considers the inter/intraspecies variation and data quality of toxicity. • AOP network integrates endocrine disruption effects of muti-biological levels. • PNECs of endocrine disruption effects as 2.52 (PFOS) and 18.7 (PFOA) μg/L. • SSWD-based PNECs are more conservative than those from SSD models. • AOP-SSWD improves ecological relevance and reduce uncertainty of PNEC. [ABSTRACT FROM AUTHOR]

• Impact-resistant behaviours of steel–concrete composite wall with recycled aggregate concrete. • Time histories of impact force and deformation, and failure mode. • Effects of CRA content, material strength, steel plate ratio, tie-bar spacing, impact energy and axial load ratio on the impact response. • Single degree of freedom approach used to predict the displacement response. This paper investigates the impact-resistant behaviours of steel–concrete (SC) composite walls experimentally and numerically, which consist of two outer steel plates and infilled recycled aggregate concrete (RAC). Totally eight SC walls were designed and fabricated with different coarse recycled aggregate (CRA) content and steel plate thicknesses. Drop-hammer impact testing was first performed to obtain the failure mode, impact force and deformation. Test results indicated that the impact resistance behaviour of SC walls enhanced considerably with a decrease in CRA content. The tie bars combined with studs were effective to avoid the separation of steel plate from the concrete core. Afterwards, the finite element (FE) models were developed and calibrated, and then used to obtain more information on the full-range motion response, development of stress and strain, and energy absorption. In addition, the influences of different parameters, including the CRA content, material strength, steel plate ratio, tie-bar spacing, impact energy and axial load ratio on the impact resistance performance were assessed. Finally, the displacement response of SC composite walls with RAC was predicted using single-degree of freedom (SDOF) approach. [ABSTRACT FROM AUTHOR]

Background: Left atrial (LA) and left atrial appendage (LAA) dysfunction has been demonstrated to contribute to atrial fibrillation (AF)-related stroke. However, usefulness of LA and LAA mechanics has not been fully compared. We sought to investigate the association of LA and LAA mechanics with stroke and to compare their diagnostic values in the risk stratification of stroke in patients with nonvalvular AF.Methods: A total of 208 consecutive patients with AF (63.58 ± 10.37 years, 63.9% male,57.7% persistent AF) who underwent echocardiography before catheter ablation were prospectively enrolled. Speckle-tracking was used to measure LA and LAA global longitudinal strain (GLS). LA and LAA mechanical dispersions (MD) were defined as the standard deviation (SD) of time to peak positive strain corrected by the R-R interval.Results: Patients with prior stroke/ transient ischemic attack (TIA) (n = 31) had significantly higher LA and LAA MD than those without (n = 177) (11.56 ± 4.38% vs. 8.43 ± 3.44%, 15.15 ± 5.46% vs. 10.94 ± 4.40%, both P < 0.01). In multivariable analysis, LA and LAA MD were independently associated with stroke/TIA (odds ratio, 1.18-1.29, 1.19-1.22, respectively, both P < 0.01), providing incremental values over clinical and standard echocardiographic parameters. In a subgroup analysis, LA MD was more useful than LAA MD in patients with normal LA volumes, while LAA MD was superior to LA MD in patients with LA enlargement.Conclusions: Higher LA and LAA mechanical dispersion are independently associated with stroke/TIA in AF patients and had incremental values over clinical and conventional echocardiographic parameters. What's more, priorities of dispersion assessment are different depending on patients' LA size. [ABSTRACT FROM AUTHOR]

The Pacific sector of the Arctic Ocean (PA, hereafter) is a region sensitive to climate change. Given the alarming changes in sea ice cover during recent years, knowledge of sea ice loss with respect to ice advection and melting processes has become critical. With satellite-derived products from the National Snow and Ice Center (NSIDC), a 38-year record (1979–2016) of the loss in sea ice area in summer within the Pacific-Arctic (PA) sector due to the two processes is obtained. The average sea ice outflow from the PA to the Atlantic-Arctic (AA) Ocean during the summer season (June–September) reaches 0.173×106 km 2 , which corresponds to approximately 34 % of the mean annual export (October to September). Over the investigated period, a positive trend of 0.004×106 km 2 yr -1 is also observed for the outflow field in summer. The mean estimate of sea ice retreat within the PA associated with summer melting is 1.66×106 km 2 , with a positive trend of 0.053×106 km 2 yr -1. As a result, the increasing trends of ice retreat caused by outflow and melting together contribute to a stronger decrease in sea ice coverage within the PA (0.057×106 km 2 yr -1) in summer. In percentage terms, the melting process accounts for 90.4 % of the sea ice retreat in the PA in summer, whereas the remaining 9.6 % is explained by the outflow process, on average. Moreover, our analysis suggests that the connections are relatively strong (R=0.63), moderate (R=-0.46), and weak (R=-0.24) between retreat of sea ice and the winds associated with the dipole anomaly (DA), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO), respectively. The DA participates by impacting both the advection (R=0.74) and melting (R=0.55) processes, whereas the NAO affects the melting process (R=-0.46). [ABSTRACT FROM AUTHOR]

Materials and structures with enhanced energy-absorbing and impact perception capabilities are widely deployed for crash mitigation, protective packaging of sensitive elements, and personal impact protection. However, conventional lightweight structures with a monolithic constitutive material cannot simultaneously achieve exceptional energy-absorbing capacity and electromechanical sensitivity. Here we proposed a new class of architected ceramic-polymer composites with improved energy-absorbing capacity and piezoelectric performance. An interconnected porous lead zirconate titanate ([Pb(Zr 0.52 Ti 0.48)O 3 ], PZT) skeleton with uniformly distributed cellular-like pores in the transverse section and directionally aligned porous structure in the longitudinal section was fabricated using a facial camphene-templated freeze-casting method. Subsequently, the polymeric polydimethylsiloxane (PDMS) was impregnated into the skeleton to form the three-dimensional (3-D) interpenetrating-phase piezoelectric composite (IP3C). The as-fabricated interpenetrating architecture with each phase interconnected has endowed the proposed IP3C with an unprecedented combination of mechanical-damping (energy-absorption efficiency ∼ 7.71 MJ m−3) and electromechanical-conversion (piezoelectric constant d 33 ∼ 146 pC N−1) properties, which are 9 times and 7 times higher than the conventional counterpart 0–3 piezoelectric composite, respectively. As evidenced by numerical simulations, this remarkable enhancement is attributed to the high stress transfer efficiency within the IP3C, which is intrinsically controlled by the rationally designed interpenetrating architecture. The findings reported here demonstrate that multifunction, e.g., exceptional energy absorption and high sensitivity, can be achieved in one composite with architecture design, thereby driving forward and expanding the fundamental understanding in the area of multifunctional materials in hostile loading environments. • An interconnected porous lead zirconate titanate ([Pb(Zr 0.52 Ti 0.48)O 3 ], PZT) skeleton with uniformly distributed cellular-like pores in the transverse section and directionally aligned porous structure in the longitudinal section was fabricated using a facial camphene-templated freeze-casting method. • The as-fabricated interpenetrating architecture with each phase is interconnected has endowed the proposed IP3C with an unprecedented combination of mechanical-damping and electromechanical-conversion properties, which are 9 times and 7 times higher than the conventional counterpart 0–3 piezoelectric composite, respectively. • The findings reported here demonstrate that multifunction, e.g., exceptional energy absorption and high sensitivity, can be achieved in one composite with architecture design, thereby driving forward and expanding the fundamental understanding in the area of multifunctional materials in hostile loading environments. [ABSTRACT FROM AUTHOR]

Auxin regulates diverse aspects of growth and development. Furthermore, polar auxin transport, which is mediated by the PIN-FORMED (PIN) and AUXIN1/LIKE-AUX (AUX/LAX) proteins, plays a crucial role in auxin distribution. In this study, six PIN and four AUX/LAX genes were identified in ramie (Boehmeria nivea L.). We used qRT-PCR to characterize and analyze the two gene families, including phylogenetic relationships, intron/exon structures, cis-elements, subcellular localization, and the expression patterns in different tissues. The expression of these genes in response to indole-3-acetic acid (IAA) treatment and drought stress was also assessed; the results indicate that most of the BnAUX/LAX and BnPIN genes were regulated as a result of IAA treatment and drought stress. Our study provides insights into ramie auxin transporters and lays the foundation for further analysis of their biological functions in ramie fiber development and adaptation to environmental stresses. [ABSTRACT FROM AUTHOR]