Uncovering the mechanisms of virus infection and assembly is crucial for preventing the spread of viruses and treating viral disease. The technique of single-virus tracking (SVT), also known as ...single-virus tracing, allows one to follow individual viruses at different parts of their life cycle and thereby provides dynamic insights into fundamental processes of viruses occurring in live cells. SVT is typically based on fluorescence imaging and reveals insights into previously unreported infection mechanisms. In this review article, we provide the readers a broad overview of the SVT technique. We first summarize recent advances in SVT, from the choice of fluorescent labels and labeling strategies to imaging implementation and analytical methodologies. We then describe representative applications in detail to elucidate how SVT serves as a valuable tool in virological research. Finally, we present our perspectives regarding the future possibilities and challenges of SVT.
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This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate the objective function into the ...group-wise least square loss regularized by low rank and sparsity with respect to two latent variables, model parameters and grouping information, for joint optimization. To handle this non-convex optimization, we decompose it into two sub-tasks, multi-task learning and task relatedness discovery. First, we convert this non-convex objective function into the convex formulation by fixing the latent grouping information. This new objective function focuses on multitask learning by strengthening the shared-action relationship and action-specific feature learning. Second, we leverage the learned model parameters for the task relatedness measure and clustering. In this way, HC-MTL can attain both optimal action models and group discovery by alternating iteratively. The proposed method is validated on three kinds of challenging datasets, including six realistic action datasets (Hollywood2, YouTube, UCF Sports, UCF50, HMDB51 & UCF101), two constrained datasets (KTH & TJU), and two multi-view datasets (MV-TJU & IXMAS). The extensive experimental results show that: 1) HC-MTL can produce competing performances to the state of the arts for action recognition and grouping; 2) HC-MTL can overcome the difficulty in heuristic action grouping simply based on human knowledge; 3) HC-MTL can avoid the possible inconsistency between the subjective action grouping depending on human knowledge and objective action grouping based on the feature subspace distributions of multiple actions. Comparison with the popular clustered multi-task learning further reveals that the discovered latent relatedness by HC-MTL aids inducing the group-wise multi-task learning and boosts the performance. To the best of our knowledge, ours is the first work that breaks the assumption that all actions are either independent for individual learning or correlated for joint modeling and proposes HC-MTL for automated, joint action grouping and modeling.
The formation of atherosclerotic plaques is the root cause of various cardiovascular diseases (CVDs). Effective CVD interventions thus call for precise identification of the plaques to aid clinical ...assessment, diagnosis, and treatment of such diseases. In this study, we introduce a dual‐target sequentially activated fluorescence reporting system, termed in‐sequence high‐specificity dual‐reporter unlocking (iSHERLOCK), to precisely identify the atherosclerotic plaques in vivo and ex vivo. ISHERLOCK was achieved by creating a three‐in‐one fluorescent probe that permits highly specific and sensitive detection of lipid droplets and hypochlorous acid via “off‐on” and ratiometric readouts, respectively. Based on this format, the upregulated lipid accumulation and oxidative stress—the two hallmarks of atherosclerosis (AS)—were specifically measured in the atherosclerotic plaques, breaking through the barrier of precise tissue biopsy of AS and thus aiding effective CVD stewardship.
An in‐sequence high‐specificity dual‐reporter unlocking platform, termed iSHERLOCK, is delicately designed to detect lipid droplets and HClO via fluorescent “off–on” and ratiometric readouts, respectively. Based on iSHERLOCK, the two hallmarks of atherosclerosis including upregulated lipid accumulation and oxidative stress were quantitatively measured in the plaques.
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Image-text retrieval is a fundamental and vital task in multi-media retrieval and has received growing attention since it connects heterogeneous data. Previous methods that perform well on image-text ...retrieval mainly focus on the interaction between image regions and text words. But these approaches lack joint exploration of characteristics and contexts of regions and words, which will cause semantic confusion of similar objects and loss of contextual understanding. To address these issues, a dual-level representation enhancement network (DREN) is proposed to strength the characteristic and contextual representations by innovative block-level and instance-level representation enhancement modules, respectively. The block-level module focuses on mining the potential relations between multiple blocks within each instance representation, while the instance-level module concentrates on learning the contextual relations between different instances. To facilitate the accurate matching of image-text pairs, we propose the graph correlation inference and weighted adaptive filtering to conduct the local and global matching between image-text pairs. Extensive experiments on two challenging datasets (i.e., Flickr30K and MSCOCO) verify the superiority of our method for image-text retrieval.
Image captioning is one of the primary goals in computer vision which aims to automatically generate natural descriptions for images. Intuitively, human visual system can notice some stimulating ...regions at first glance, and then volitionally focus on interesting objects within the region. For example, to generate a free-form sentence about "boy-catch-baseball", the visual region involving "boy" and "baseball" could be first attended and then guide the salient object discovery for the word-by-word generation. Till now, previous captioning works mainly rely on the object-wise modeling and ignore the rich regional patterns. To mitigate the drawback, this paper proposes the region-aware interaction learning method, which aims to explicitly capture the semantic correlations in the region and object dimensions for the word inference. First, given an image, we extract a set of regions which contain diverse objects and their relations. Second, we present the spatial-GCN interaction refining structure which can establish the connection between regions and objects to effectively capture contextual information. Third, we design the dual-attention interaction inference procedure, which enables attention to be calculated in region and object dimensions jointly for the word generation. Specifically, the guidance mechanism is proposed to selectively emphasize semantic inter-dependencies from region to object attentions. Extensive experiments on the MSCOCO dataset demonstrate the superiority of the proposed method. Additional ablation studies and visualization further validate its effectiveness.
Multi-Class Incremental Learning (MCIL) aims to learn new concepts by incrementally updating a model trained on previous concepts. However, there is an inherent trade-off to effectively learning new ...concepts without catastrophic forgetting of previous ones. To alleviate this issue, it has been proposed to keep around a few examples of the previous concepts but the effectiveness of this approach heavily depends on the representativeness of these examples. This paper proposes a novel and automatic framework we call mnemonics, where we parameterize exemplars and make them optimizable in an end-to-end manner. We train the framework through bilevel optimizations, i.e., model-level and exemplar-level. We conduct extensive experiments on three MCIL benchmarks, CIFAR-100, ImageNet-Subset and ImageNet, and show that using mnemonics exemplars can surpass the state-of-the-art by a large margin. Interestingly and quite intriguingly, the mnemonics exemplars tend to be on the boundaries between different classes.
Image captioning aims at understanding various semantic concepts (e.g., objects and relationships) from an image and integrating them in a sentence-level description. Hence, it is necessary to learn ...the interaction among these concepts. If we define the context of the interaction to be involved in the subject-predicate-object triplet, most current methods only focus on the single triplet for the first-order interaction to generate sentences. Intuitively, we humans are able to perceive the high-order interaction among concepts from two or more triplets to describe an image. For example, when we see the triplets man-cutting-sandwich and man-with-knife , it is natural to integrate and predict the sentence man cutting sandwich with knife . This depends on the high-order interaction between cutting and knife in different triplets. Therefore, exploiting high-order interaction is expected to benefit image captioning and focus on reasoning. In this paper, we introduce the novel high-order interaction learning method over detected objects and relationships for image captioning under the umbrella of the encoder-decoder framework. We first extract a set of object and relationship features in an image. During the encoding stage, the interactive refining network is proposed to learn high-order representations by modeling intra- and inter-object feature interaction in the self-attention fashion. During the decoding stage, the interactive fusion network is proposed to integrate object and relationship information by strengthening their high-order interaction based on language context for sentence generation. In this way, we learn the object-relationship dependencies in different stages, which can provide abundant cues for both visual understanding and caption generation. Extensive experiments show that the proposed method can achieve competitive performances against the state-of-the-art methods on MSCOCO dataset. Additional ablation studies further validate its effectiveness.
Massive MIMO systems promise high spectrum efficiency by deploying M ≫ 1 antennas at the base station (BS). However, to achieve the full gain provided by massive MIMO, the BS requires M radio ...frequency (RF) chains, which are expensive. This motivates us to consider RF-chain limited massive MIMO systems with M antennas but only S ≪ M RF chains. We propose a two-stage precoding scheme to efficiently exploit the large spatial degree of freedom (DoF) gain in massive MIMO systems with limited RF chains and reduced channel state information (CSI) signaling overhead. In this scheme, the MIMO precoder is partitioned into a high-dimensional phase only RF precoder followed by a low-dimensional baseband precoder. The RF precoder is adaptive to the spatial correlation matrices for inter-cluster interference mitigation. The baseband precoder is adaptive to the reduced dimensional "effective" CSI for intra-cluster spatial multiplexing. We formulate the two stage precoding problem such that the minimum (weighted) average data rate of users is maximized under the phase only constraint on the RF precoder and the limited RF chain constraint. This is a combinatorial optimization problem which is in general NP-hard. We propose a low complexity solution based on a novel bi-convex approximation approach. Simulations show that the proposed design has significant gain over various baselines.
Multi-view matching is an important but a challenging task in view-based 3D model retrieval. To address this challenge, we propose an original multi-modal clique graph (MCG) matching method in this ...paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi-modal feature space and hyper-edges that link pairwise cliques. Moreover, we propose an image set-based clique/edgewise similarity measure to address the issue of the set-to-set distance measure, which is the core problem in MCG matching. The proposed MCG provides the following benefits: 1) preserves the local and global attributes of a graph with the designed structure; 2) eliminates redundant and noisy information by strengthening inliers while suppressing outliers; and 3) avoids the difficulty of defining high-order attributes and solving hyper-graph matching. We validate the MCG-based 3D model retrieval using three popular single-modal data sets and one novel multi-modal data set. Extensive experiments show the superiority of the proposed method through comparisons. Moreover, we contribute a novel real-world 3D object data set, the multi-view RGB-D object data set. To the best of our knowledge, it is the largest real-world 3D object data set containing multi-modal and multi-view information.
Many plasma membrane (PM) functions depend on the cholesterol concentration in the PM in strikingly nonlinear, cooperative ways: fully functional in the presence of physiological cholesterol levels ...(35~45 mol%), and nonfunctional below 25 mol% cholesterol; namely, still in the presence of high concentrations of cholesterol. This suggests the involvement of cholesterol‐based complexes/domains formed cooperatively. In this review, by examining the results obtained by using fluorescent lipid analogs and avoiding the trap of circular logic, often found in the raft literature, we point out the fundamental similarities of liquid‐ordered (Lo)‐phase domains in giant unilamellar vesicles, Lo‐phase‐like domains formed at lower temperatures in giant PM vesicles, and detergent‐resistant membranes: these domains are formed by cooperative interactions of cholesterol, saturated acyl chains, and unsaturated acyl chains, in the presence of >25 mol% cholesterol. The literature contains evidence, indicating that the domains formed by the same basic cooperative molecular interactions exist and play essential roles in signal transduction in the PM. Therefore, as a working definition, we propose that raft domains in the PM are liquid‐like molecular complexes/domains formed by cooperative interactions of cholesterol with saturated acyl chains as well as unsaturated acyl chains, due to saturated acyl chains' weak multiple accommodating interactions with cholesterol and cholesterol's low miscibility with unsaturated acyl chains and TM proteins. Molecules move within raft domains and exchange with those in the bulk PM. We provide a logically established collection of fluorescent lipid probes that preferentially partition into raft and non‐raft domains, as defined here, in the PM.
A working definition of raft domains in the plasma membrane (PM) is proposed, based on cooperative interactions of cholesterol with saturated acyl chains as well as unsaturated acyl chains, found in giant unilamellar vesicles, giant PM vesicles, which resemble the PMs after the removal of the actin‐based membrane skeleton, cold‐detergent‐treated PMs, and single‐molecule imaging data obtained in the PM. Furthermore, we provide a logically established list of fluorescent lipid probes that preferentially partition into raft/non‐raft domains in the PM.
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