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IntroductionWithin the maintenance phase of visual working memory (VWM), previous researchers presented retro-cues orienting to a probed dimension across all multidimension stimuli and found a robust dimension-based retro-cue benefit (RCB): VWM performance for cued dimension was better than no/neutral-cue baseline. This improvement is often attributed to the prioritization of information related to the focused dimension and the removal of information related to the unfocused dimension from VWM. However, it remains unclear whether the removal of the uncued dimension is necessary to observe this dimension-based RCB.MethodsIn the current study, we first manipulated the number of retro-cues to investigate this question. We used colored, oriented bars as stimuli and two sequential retro-cues oriented to different dimensions in the double-cue condition. The last presented cue in each trial was always valid. Therefore, the unfocused dimension in the first cue display was probed in double-cue trials. Experiment 1 adopted change detection tasks and three cue type conditions (no-cue, single-cue, double-cue). Experiment 2 divided the single-cue condition into early- and late- cue conditions, using recall tasks to elevated probe precision. Experiment 3 further added double-neutral and double-same cue types and eliminated the different influences of post-memory masks on each dimension respectively.ResultsResults across these experiments showed a robust pattern of no worse performances for the double-cue condition than for the single-cue condition.DiscussionBecause the dimension-based single cue benefit was observed especially in early-cue trials, we supposed that the dimension-based RCB does not require removing the unfocused dimension from VWM.

Road extraction from high-resolution remote sensing images has long been a focal and challenging research topic in the field of computer vision. Accurate extraction of road networks holds extensive practical value in various fields, such as urban planning, traffic monitoring, disaster response and environmental monitoring. With rapid development in the field of computational intelligence, particularly breakthroughs in deep learning technology, road extraction technology has made significant progress and innovation. This paper provides a systematic review of deep learning-based methods for road extraction from remote sensing images, focusing on analyzing the application of computational intelligence technologies in improving the precision and efficiency of road extraction. According to the type of annotated data, deep learning-based methods are categorized into fully supervised learning, semi-supervised learning, and unsupervised learning approaches, each further divided into more specific subcategories. They are comparatively analyzed based on their principles, advantages, and limitations. Additionally, this review summarizes the metrics used to evaluate the performance of road extraction models and the high-resolution remote sensing image datasets applied for road extraction. Finally, we discuss the main challenges and prospects for leveraging computational intelligence techniques to enhance the precision, automation, and intelligence of road network extraction.

Abstract Fine‐grained visual classification is challenging due to the inherently subtle intra‐class object variations. To solve this issue, a novel framework named channel attention and progressive multi‐granularity training network, is proposed. It first exploits meaningful feature maps through the channel attention module and captures multi‐granularity features by the progressive multi‐granularity training module. For each feature map, the channel attention module is proposed to explore channel‐wise correlation. This allows the model to re‐weight the channels of the feature map according to the impact of their semantic information on performance. Furthermore, the progressive multi‐granularity training module is introduced to fuse features cross multi‐granularity. And the fused features pay more attention to the subtle differences between images. The model can be trained efficiently in an end‐to‐end manner without bounding box or part annotations. Finally, comprehensive experiments are conducted to show that the method achieves state‐of‐the‐art performances on the CUB‐200‐2011, Stanford Cars, and FGVC‐Aircraft datasets. Ablation studies demonstrate the effectiveness of each part in our module.

Dimple surface texture with a matrix-distribution was prepared on the surface of 316L stainless steel by laser processing technology. Orthogonal experiment was established by taking as facters the geometrical parameters of dimple shape, area, and interval between two dimples. The effect of laser surface texturing on the tribological behavior of 316L stainless steel under dry friction was investigated. Range and variance analysis were used to analyze the mass loss of 316L stainless steel. The surface and worn morphologies of surface textured 316L stainless steel were characterized by scanning electron microscopy. The results show that the friction coefficients of surface textured 316L stainless steels were higher than that of the polished 316L stainless steel, which was attributed to the increase of surface roughness after laser processing. However, the mass loss values of surface textured 316L stainless steel samples were significantly lower in comparison with the polished 316L stainless steel because the obtained surface texture provided positive effects of capturing wear debris and reducing friction contact area, thus contributing to significant improvement in wear resistance of 316L stainless steel.

How to recover geometric transformations is one of the most challenging issues in image registration. To alleviate the effect of large geometric distortion in multimodal remote sensing image registration, a scale and rotate transform prediction net is proposed in this paper. First, to reduce the scale between the reference and sensed images, the image scale regression module is constructed via CNN feature extraction and FFT correlation, and the scale of sensed image can be recovered roughly. Second, the rotation estimate module is developed for predicting the rotation angles between the reference and the scale-recovered images. Finally, to obtain the accurate registration results, LoFTR is employed to match the geometric-recovered images. The proposed registration network was evaluated on GoogleEarth, HRMS, VIS-NIR and UAV datasets with contrast differences and geometric distortions. The experimental results show that the number of correct matches of our model reached 74.6%, and the RMSE of the registration results achieved 1.236, which is superior to the related methods.

Monogalactosyldiacylglycerol (MGDG) is the most abundant polar lipid in thylakoid membrane, wherein it plays critical roles related to thylakoid membrane assembly and function in diatoms. However, diatom MGDG biosynthesis has not been fully characterized. In this study, we investigated the role of a novel MGDG synthase (ptMGD2), which is one of the key enzymes for MGDG biosynthesis, in the model diatom Phaeodactylum tricornutum. An analysis of subcellular localization demonstrated that the ptMGD2 is mainly localized in plastids. Gene disruption by gene editing of ptMGD2 resulted in delayed growth, decrease in oxygen evolution rate, reduced MGDG and digalactosyldiacylglycerol (using MGDG as the substrate) content as well as lipid remodeling. Considered together, these observations provide novel insights into the importance of ptMGD2 for regulating MGDG biosynthesis and its potential roles in biotechnical application of Phaeodactylum.

Titanium and titanium alloys have been restricted in tribology-related field, because of their disadvantages, such as low surface hardness, high friction coefficient and poor wear resistance. Various surface modification techniques have been applied to improve their deficiencies. As a new technology of severe surface plastic deformation, ultrasonic nanocrystal surface modification (UNSM) technique has been widely used to realize the surface treatment of various materials, and also has attracted wide attention in many engineering fields. This review begins with a brief introductionof the basic concept and the modification mechanism of the UNSM technique. The specific applications of the UNSM technique for improving the tribological and fatigue properties of titanium and its alloys were reviewed and summarized.

In recent years, skeleton-based action recognition, graph convolutional networks, have achieved remarkable performance. In these existing works, the features of all nodes in the neighbor set are aggregated into the updated features of the root node, while these features are located in the same feature channel determined by the same 1 × 1 convolution filter. This may not be optimal for capturing the features of spatial dimensions among adjacent vertices effectively. Besides, the effect of feature channels that are independent of the current action on the performance of the model is rarely investigated in existing methods. In this paper, we propose cross-channel graph convolutional networks for skeleton-based action recognition. The features fusion mechanism in our network is cross-channel, i.e, the updated feature of the root node is derived from different feature channels. Because different feature channels come from different 1 × 1 convolution filters, the cross-channel fusion mechanism significantly improves the ability of the model to capture local features among adjacent vertices. Moreover, by introducing a channel attention mechanism to our model, we suppress the influence of feature channels unrelated to action recognition on model performance, which improves the robustness of the model against the feature channels independent of the current action. Extensive experiments on the two large-scale datasets, NTU-RGB+D and KineticsSkeleton, demonstrate that the performance of our model exceeds the current mainstream methods.

The accurate detection and extraction of roads using remote sensing technology are crucial to the development of the transportation industry and intelligent perception tasks. Recently, in view of the advantages of CNNs in feature extraction, its related road extraction methods have been proposed successively. However, due to the limitation of kernel size, they perform less effectively at capturing long-range information and global context, which are crucial for road targets distributed over long distances and highly structured. To deal with this problem, a novel model named RoadFormer with a Swin Transformer as the backbone is developed in this paper. Firstly, to extract long-range information effectively, a Swin Transformer multi-scale encoder is adopted in our model. Secondly, to enhance the feature representation capability of the model, we design an innovative bottleneck module, in which the spatial and channel separable convolution is employed to obtain fine-grained and globe features, and then a dilated block is connected after the spatial convolution module to capture more integrated road structures. Finally, a lightweight decoder consisting of transposed convolution and skip connection generates the final extraction results. Extensive experimental results confirm the advantages of RoadFormer on the Deepglobe and Massachusetts datasets. The comparative results of visualization and quantification demonstrate that our model outperforms comparable methods.

Oocyte maturation and fertilization are fundamental processes for successful human reproduction, and abnormalities in these processes will cause infertility. Recently, we identified biallelic mutations in CDC20 that are responsible for human oocyte maturation arrest, fertilization failure, and early embryonic development arrest. In this study, we screened for further CDC20 mutations in a new cohort of patients with abnormalities in oocyte maturation, fertilization, and early embryonic development. Through whole-exome sequencing, we identified the four novel mutations c.887G > A (p. Arg296Gln), c.964C > T (p.Arg322∗), c.1155G > C (p.Trp385Cys), and c.330 + 1G > A (p. Glu111Ilefs∗36) and one previously reported mutation c.965G > A (p.Arg322Gln) in CDC20 in four infertile individuals from three independent families. The patients had different phenotypes of oocyte maturation arrest and fertilization failure resulting from the different mutations. This study confirms our previous research and expands the spectrum of known mutations in CDC20, providing new evidence supporting the function of CDC20 in the genetic etiology of female infertility characterized by oocyte maturation arrest and fertilization failure.

Multimodal remote sensing image registration is a prerequisite for comprehensive application of remote sensing image data. However, inconsistent imaging environment and conditions often lead to obvious geometric deformations and significant contrast differences between multimodal remote sensing images, which makes the common feature extraction extremely difficult, resulting in their registration still being a challenging task. To address this issue, a robust local statistics-based registration framework is proposed, and the constructed descriptors are invariant to contrast changes and geometric transformations induced by imaging conditions. Firstly, maximum phase congruency of local frequency information is performed by optimizing the control parameters. Then, salient feature points are located according to the phase congruency response map. Subsequently, the geometric and contrast invariant descriptors are constructed based on a joint local frequency information map that combines Log-Gabor filter responses over multiple scales and orientations. Finally, image matching is achieved by finding the corresponding descriptors; image registration is further completed by calculating the transformation between the corresponding feature points. The proposed registration framework was evaluated on four different multimodal image datasets with varying degrees of contrast differences and geometric deformations. Experimental results demonstrated that our method outperformed several state-of-the-art methods in terms of robustness and precision, confirming its effectiveness.

Abstract This paper introduces the regular decoupling field to study the existence and uniqueness of solutions of two-point boundary value problems for a class of ordinary differential equations which can be derived from the maximum principle in optimal control theory. The monotonicity conditions used to guarantee the existence and uniqueness of such equations are initially a special case of the regular decoupling field method. More generally, in case of the homogeneous equations, this paper generalizes the application scope of the monotonicity conditions method by using the linear transformation method. In addition, the linear transformation method can be used to handle the situation where the monotonicity conditions and regular decoupling field method cannot be directly applied. These two methods overall develop the well-posedness theory of two-point boundary value problems which has potential applications in optimal control and partial differential equation theory.

Image classification has been an incredibly active research topic in recent years with widespread applications. Researchers have put forward many remarkable techniques and semi-supervised learning (SSL) is one among them. However, due to not taking the relationship of samples among different classes in consideration, previous approaches cannot often get a clear decision boundary. In this paper, we propose an improved classification model on the basis of SSL. First, we adopt a deformable partbased model to capture a stable global structure and salient objects, and then, we find a better decision boundary by our classification algorithm-based on an improved ensemble projection (IEP). Our IEP exploits the weighted average method. To evaluate the effectiveness of our approach, we do experiments not only with the LandUse-21 (L-21) data set, but also with an architecture style data set. Experimental results show that our approach is capable of achieving the state-of-the-art performance on the two data sets. For each class in L-21 data set, when 50 images are randomly chosen as training images, the multi-class average precision increases to 97.63%. Besides, for the architecture style data set, we achieve the best result with about 80% accuracy and have about a 10% improvement over the previous best work. Although there are a small number of labeled data used to train, we get the satisfactory performance.

Standard classification tasks have already achieved good results in computer vision. However, the task of Architectural style classification yet faces many challenges, since the rich inter-class relationships between different styles may disturb the classification accuracy. To better classify buildings, we propose a feature extraction module based on image preprocessed with Deformable Part-based Models (DPM). Specifically, we first use DPM to remove elements that are not related to classification, and capture representative elements of buildings, and then these elements are sent to our feature extraction module. In our feature extraction module, we adopt our improved ensemble projection method to maximize the inter-class distance and minimize the intra-class distance to find the common features in the same style and differences among different styles. Finally, the performances of several classifiers are tested and the best one of SVM classifier is selected to output the ultimate accuracy. Experimental results show that our approach achieves promising performance and is superior to previous methods.

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers. OPEs have been released into various environments (e.g., water, sediments, dust and air, and soil). To investigate the occurrence and distribution of OPEs in various environments in China, this review collects and discusses the published scientific studies in this field. Chlorinated OPEs, as flame retardants, are the predominant OPEs found in the environment. The analysis of data revealed large concentration variations among microenvironments, including inflowing river water (range: 0.69–10.62 µgL−1), sediments (range: 0.0197–0.234 µg/g), dust (range: 8.706–34.872 µg/g), and open recycling sites’ soil (range: 0.122–2.1 µg/g). Moreover, OPEs can be detected in the air and biota. We highlight the overall view regarding environmental levels of OPEs in different matrices as a starting point to monitor trends for China. The levels of OPEs in the water, sediment, dust, and air of China are still low. However, dust samples from electronic waste workshop sites were more contaminated. Human activities, pesticides, electronics, furniture, paint, plastics and textiles, and wastewater plants are the dominant sources of OPEs. Human exposure routes to OPEs mainly include dermal contact, dust ingestion, inhalation, and dietary intake. The low level of ecological risk and risk to human health indicated a limited threat from OPEs. Furthermore, current challenges and perspectives for future studies are prospected. A criteria inventory of OPEs reflecting the levels of OPEs contamination association among different microenvironments, emerging OPEs, and potential impact of OPEs on human health, particularly for children are needed in China for better investigation.

Recent progress on skeleton-based action recognition has been substantial, benefiting mostly from the explosive development of Graph Convolutional Networks (GCN). However, prevailing GCN-based methods may not effectively capture the global co-occurrence features among joints and the local spatial structure features composed of adjacent bones. They also ignore the effect of channels unrelated to action recognition on model performance. Accordingly, to address these issues, we propose a Global Co-occurrence feature and Local Spatial feature learning model (GCLS) consisting of two branches. The first branch, based on the Vertex Attention Mechanism branch (VAM-branch), captures the global co-occurrence feature of actions effectively; the second, based on the Cross-kernel Feature Fusion branch (CFF-branch), extracts local spatial structure features composed of adjacent bones and restrains the channels unrelated to action recognition. Extensive experiments on two large-scale datasets, NTU-RGB+D and Kinetics, demonstrate that GCLS achieves the best performance when compared to the mainstream approaches.

Jasmonates (JAs) and abscisic acid (ABA) are phytohormones known play important roles in plant response and adaptation to various abiotic stresses including salinity, drought, wounding, and cold. JAZ (JASMONATE ZIM-domain) proteins have been reported to play negative roles in JA signaling. However, direct evidence is still lacking that JAZ proteins regulate drought resistance. In this study, OsJAZ1 was investigated for its role in drought resistance in rice. Expression of OsJAZ1 was strongly responsive to JA treatment, and it was slightly responsive to ABA, salicylic acid, and abiotic stresses including drought, salinity, and cold. The OsJAZ1-overexpression rice plants were more sensitive to drought stress treatment than the wild-type (WT) rice Zhonghua 11 (ZH11) at both the seedling and reproductive stages, while the jaz1 T-DNA insertion mutant plants showed increased drought tolerance compared to the WT plants. The OsJAZ1-overexpression plants were hyposensitive to MeJA and ABA, whereas the jaz1 mutant plants were hypersensitive to MeJA and ABA. In addition, there were significant differences in shoot and root length between the OsJAZ1 transgenic and WT plants under the MeJA and ABA treatments. A subcellular localization assay indicated that OsJAZ1 was localized in both the nucleus and cytoplasm. Transcriptome profiling analysis by RNA-seq revealed that the expression levels of many genes in the ABA and JA signaling pathways exhibited significant differences between the OsJAZ1-overexpression plants and WT ZH11 under drought stress treatment. Quantitative real-time PCR confirmed the expression profiles of some of the differentially expressed genes, including OsNCED4, OsLEA3, RAB21, OsbHLH006, OsbHLH148, OsDREB1A, OsDREB1B, SNAC1, and OsCCD1. These results together suggest that OsJAZ1 plays a role in regulating the drought resistance of rice partially via the ABA and JA pathways.

Sperm with normal morphology and motility are essential for successful fertilization, and the strong attachment of the sperm head-tail coupling apparatus to the nuclear envelope during spermatogenesis is required to ensure the integrity of sperm for capacitation and fertilization. Here, we report that Arrdc5 is associated with spermatogenesis. The Arrdc5 knockout mouse model showed male infertility characterized by a high bent-head rate and reduced motility in sperm, which led to capacitation defects and subsequent fertilization failure. Through mass spectrometry, we found that ARRDC5 affects spermatogenesis by affecting NDC1 and SUN5. We further found that ARRDC5 might affect the vesicle-trafficking protein SEC22A-mediated transport and localization of NDC1, SUN5 and other head-tail coupling apparatus-related proteins that are responsible for initiating the attachment of the sperm head and tail. We finally performed intracytoplasmic sperm injection as a way to explore therapeutic strategies. Our findings demonstrate the essential role and the underlying molecular mechanism of ARRDC5 in anchoring the sperm head to the tail during spermatogenesis. [ABSTRACT FROM AUTHOR]