Hierarchical Assembly Pathways of Spermine Induced Tubulin Conical-Spiral Architectures
The results are based on high quality cryo-TEM images, cutting edge synchrotron solution X-ray scattering measurements and state-of-the-art data analysis, using our home developed groundbreaking analysis software, D+. Expand abstract.
Tubulin dimers are flexible entities serving as building blocks for construction of cellular polymers essential for the cytoskeleton. The conformational state of the dimer dictates the exact formation of assembly and can be regulated by cellular factors including spermine. Using solution X-ray scattering and cryo-TEM measurements we studied the behavior of tubulin assembly in the presence of millimolar spermine concentrations. The results discovered novel structural architectures of tubulin polymers and revealing fascinating hierarchical self-associations based on unique tubulin conical-spiral (TCS) subunits. We followed the assembly pathways of tubulin dimers with different spermine concentrations, from milliseconds to days, and discovered multiple phase transitions with increasing spermine concentration. At 1 mM spermine, tubulin assembled into tubulin helical-pitch (THP) structures, resembling tubulin-rings. Above 1.5 mM spermine, tubulin assembled into TCS architectures. TCS is a unique tubulin assembly, serving as a new building block subunit. TCS assembled into different architectures . The predominant structure was TCS-tube (TCST) that further assembled in a remarkable antiparallel orientation which formed bundles with 2D-cubic and unique quasi-2D hexagonal lattices. Each TCST in the quasi-2D hexagonal lattice was surrounded by four antiparallel TCSTs and two parallel TCSTs. All the above assemblies have never been observed before. At higher spermine concentrations, tubulin assembled into twisted inverted tubulin tubules (ITTs). Here we also show for the first time, the hierarchical assembly pathways from tubulin dimer to each of the above structures, using time-resolved experiments with millisecond temporal resolution. We discovered that the structures that formed at low spermine concentrations were transient precursors of the structures formed at higher spermine concentrations. The results are based on high quality cryo-TEM images, cutting edge synchrotron solution X-ray scattering measurements and state-of-the-art data analysis, using our home developed groundbreaking analysis software, D+. The findings can be relevant to a broad research fields including studies which explore different arrangements of the cytoskeletal network, or studies exploring the attraction forces between proteins that dictate their mode of assembly and molecular designed self-assembly of natural and/or synthetic analogous.
Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
Experimental results show that our model achieves the state-of-the-art performance and our reasoning methods can also significantly improve existing baselines. Expand abstract.
Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity. This decoding method may not only cause the "many-to-one" problem but also neglect the coordinated nature of this task, that is, each alignment decision may highly correlate to the other decisions. In this paper, we introduce two coordinated reasoning methods, i.e., the Easy-to-Hard decoding strategy and joint entity alignment algorithm. Specifically, the Easy-to-Hard strategy first retrieves the model-confident alignments from the predicted results and then incorporates them as additional knowledge to resolve the remaining model-uncertain alignments. To achieve this, we further propose an enhanced alignment model that is built on the current state-of-the-art baseline. In addition, to address the many-to-one problem, we propose to jointly predict entity alignments so that the one-to-one constraint can be naturally incorporated into the alignment prediction. Experimental results show that our model achieves the state-of-the-art performance and our reasoning methods can also significantly improve existing baselines.
BATMAN: fast and accurate integration of single-cell RNA-Seq datasets via minimum-weight matching
Across multiple simulations and real datasets, we show that our method significantly outperforms state-of-the-art tools with respect to existing metrics for batch effects by up to 80% while retaining cell-to-cell relationships. Expand abstract.
Single-cell RNA-Sequencing (scRNA-Seq) is a set of technologies used to profile gene expression at the level of individual cells. Although the throughput of scRNA-Seq experiments is steadily growing in terms of the number of cells, large datasets are not yet commonly used due to prohibitively high costs. Integrating multiple datasets into one can improve power in scRNA-Seq experiments, and efficient integration is very important for downstream analyses such as identifying cell-type-specific eQTLs. State-of-the-art scRNA-Seq integration methods are based on the mutual nearest neighbors paradigm and fail to both correct for batch effects and maintain the local structure of the datasets. In this paper, we propose a novel scRNA-Seq dataset integration method called BATMAN (BATch integration via minimum-weight MAtchiNg). Across multiple simulations and real datasets, we show that our method significantly outperforms state-of-the-art tools with respect to existing metrics for batch effects by up to 80% while retaining cell-to-cell relationships. BATMAN is available at https://github.com/mandricigor/batman.
EdgeNets:Edge Varying Graph Neural Networks
Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. Expand abstract.
Driven by the outstanding performance of neural networks in the structured Euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged as a parameterization to capture detail at the node level with a reduced number of parameters and complexity. Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. By extrapolating this strategy to more iterations between neighboring nodes, the EdgeNet learns edge- and neighbor-dependent weights to capture local detail. This is the most general local operation that a node can do and encompasses under one formulation all graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs). In writing different GNN architectures with a common language, EdgeNets highlight specific architecture advantages and limitations, while providing guidelines to improve their capacity without compromising their local implementation. For instance, we show that GCNNs have a parameter sharing structure that induces permutation equivariance. This can be an advantage or a limitation, depending on the application. When it is a limitation, we propose hybrid approaches and provide insights to develop several other solutions that promote parameter sharing without enforcing permutation equivariance. Another interesting conclusion is the unification of GCNNs and GATs -approaches that have been so far perceived as separate. In particular, we show that GATs are GCNNs on a graph that is learned from the features. This particularization opens the doors to develop alternative attention mechanisms for improving discriminatory power.
SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation
Our method also achieves state-of-the-art results on the two large public cardiac MRI image segmentation datasets of SUN09 and AC17. Expand abstract.
Medical image segmentation is a difficult but important task for many clinical operations such as cardiac bi-ventricular volume estimation. More recently, there has been a shift to utilizing deep learning and fully convolutional neural networks (CNNs) to perform image segmentation that has yielded state-of-the-art results in many public benchmark datasets. Despite the progress of deep learning in medical image segmentation, standard CNNs are still not fully adopted in clinical settings as they lack robustness and interpretability. Shapes are generally more meaningful features than solely textures of images, which are features regular CNNs learn, causing a lack of robustness. Likewise, previous works surrounding model interpretability have been focused on post hoc gradient-based saliency methods. However, gradient-based saliency methods typically require additional computations post hoc and have been shown to be unreliable for interpretability. Thus, we present a new architecture called Shape Attentive U-Net (SAUNet) which focuses on model interpretability and robustness. The proposed architecture attempts to address these limitations by the use of a secondary shape stream that captures rich shape-dependent information in parallel with the regular texture stream. Furthermore, we suggest multi-resolution saliency maps can be learned using our dual-attention decoder module which allows for multi-level interpretability and mitigates the need for additional computations post hoc. Our method also achieves state-of-the-art results on the two large public cardiac MRI image segmentation datasets of SUN09 and AC17.
Feedstock Recycling of Rubber – A Review on Devulcanization Technologies
This review article presents and update on the state-of-the art in rubber devulcanization. Expand abstract.
Vulcanized Rubber, as elastomer, is difficult to recycle. Today, the main end of life routes of tyres and other rubber products are landfilling, incineration in e.g. cement plants, and grinding to a fine powder, with huge quantities lacking sustainable recycling of this valuable material. Devulcanization, i.e. the breaking up of sulfur bonds by chemical, thermo-physical or biological means, is a promising route that has been investigated for more than 50 years. This review article presents and update on the state-of-the art in rubber devulcanization. This review article addresses established devulcanization technologies and novel processes described in the scientific and patent literatures. It is expected that the public discussion of environmental impacts of thermoplastics will soon spill over to thermosets and elastomers. Therefore, the industry needs to develop and market solutions proactively. Tyre recycling through devulcanization has a huge lever, since approx. 30 million tons of tyres are discarded annually.
Preprints.org
Rgb-d odometry and slam
In the final part, we provide a brief discussion on advanced research topics with the references to the state-of-the-art. Expand abstract.
The emergence of modern RGB-D sensors had a significant impact in many application fields, including robotics, augmented reality (AR) and 3D scanning. They are low-cost, low-power and low-size alternatives to traditional range sensors such as LiDAR. Moreover, unlike RGB cameras, RGB-D sensors provide the additional depth information that removes the need of frame-by-frame triangulation for 3D scene reconstruction. These merits have made them very popular in mobile robotics and AR, where it is of great interest to estimate ego-motion and 3D scene structure. Such spatial understanding can enable robots to navigate autonomously without collisions and allow users to insert virtual entities consistent with the image stream. In this chapter, we review common formulations of odometry and Simultaneous Localization and Mapping (known by its acronym SLAM) using RGB-D stream input. The two topics are closely related, as the former aims to track the incremental camera motion with respect to a local map of the scene, and the latter to jointly estimate the camera trajectory and the global map with consistency. In both cases, the standard approaches minimize a cost function using nonlinear optimization techniques. This chapter consists of three main parts: In the first part, we introduce the basic concept of odometry and SLAM and motivate the use of RGB-D sensors. We also give mathematical preliminaries relevant to most odometry and SLAM algorithms. In the second part, we detail the three main components of SLAM systems: camera pose tracking, scene mapping and loop closing. For each component, we describe different approaches proposed in the literature. In the final part, we provide a brief discussion on advanced research topics with the references to the state-of-the-art.
Accelerating the Registration of Image Sequences by Spatio-temporal Multilevel Strategies
The acceleration is achieved by a factor of 2.5 on average, over the state-of-the-art multilevel methods on three examined optical coherence tomography datasets. Expand abstract.
Multilevel strategies are an integral part of many image registration algorithms. These strategies are very well-known for avoiding undesirable local minima, providing an outstanding initial guess, and reducing overall computation time. State-of-the-art multilevel strategies build a hierarchy of discretization in the spatial dimensions. In this paper, we present a spatio-temporal strategy, where we introduce a hierarchical discretization in the temporal dimension at each spatial level. This strategy is suitable for a motion estimation problem where the motion is assumed smooth over time. Our strategy exploits the temporal smoothness among image frames by following a predictor-corrector approach. The strategy predicts the motion by a novel interpolation method and later corrects it by registration. The prediction step provides a good initial guess for the correction step, hence reduces the overall computational time for registration. The acceleration is achieved by a factor of 2.5 on average, over the state-of-the-art multilevel methods on three examined optical coherence tomography datasets.
True Nonlinear Dynamics from Incomplete Networks
We study nonlinear dynamics on complex networks. Expand abstract.
We study nonlinear dynamics on complex networks. Each vertex $i$ has a state $x_i$ which evolves according to a networked dynamics to a steady-state $x_i^*$. We develop fundamental tools to learn the true steady-state of a small part of the network, without knowing the full network. A naive approach and the current state-of-the-art is to follow the dynamics of the observed partial network to local equilibrium. This dramatically fails to extract the true steady state. We use a mean-field approach to map the dynamics of the unseen part of the network to a single node, which allows us to recover accurate estimates of steady-state on as few as 5 observed vertices in domains ranging from ecology to social networks to gene regulation. Incomplete networks are the norm in practice, and we offer new ways to think about nonlinear dynamics when only sparse information is available.
MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recogntion
By plugging MixTConv into the conventional 2D CNN backbone ResNet-50, we further propose an efficient and effective network architecture named MSTNet for action recognition, and achieve state-of-the-art results on multiple benchmarks. Expand abstract.
To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolution into a conventional 2D CNN backbone. However, they all exploit 1D temporal convolution of fixed kernel size (i.e., 3) in the network building block, thus have suboptimal temporal modeling capability to handle both long-term and short-term actions. To address this problem, we first investigate the impacts of different kernel sizes for the 1D temporal convolutional filters. Then, we propose a simple yet efficient operation called Mixed Temporal Convolution (MixTConv), which consists of multiple depthwise 1D convolutional filters with different kernel sizes. By plugging MixTConv into the conventional 2D CNN backbone ResNet-50, we further propose an efficient and effective network architecture named MSTNet for action recognition, and achieve state-of-the-art results on multiple benchmarks.