A nomogram for predicting the risk of intra-abdominal hypertension in critically ill patients based on ultrasound and clinical data.

Background: Intra-abdominal hypertension (IAH) is a common complication in critically ill patients. This study aimed to identify independent risk factors for IAH and generate a nomogram to distinguish IAH from non-IAH in these patients. Methods: We retrospectively analyzed 89 critically ill patients and divided them into an IAH group [intra-abdominal pressure (IAP) ≥12 mmHg] and a non-IAH group (IAP <12 mmHg) based on the IAP measured from their bladders. Ultrasound and clinical data were also measured. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for IAH. The correlation between IAP and independent risk factors was also assessed. Results: Of these 89 patients, 45 (51%) were diagnosed with IAH. Univariate analysis showed there were significant differences in the right renal resistance index (RRRI) of the interlobar artery, the right diaphragm thickening rate (RDTR), and lactic acid (Lac) between IAH and non-IAH groups (P<0.001). Multivariate logistic regression analysis revealed that increasing RRRI, RDTR, and Lactic acid (Lac) were independent risk factors for IAH (P=0.001, P=0.001, and P=0.039, respectively). IAP was significantly correlated with RRRI, RDTR, and Lac (r=0.741, r=-0.774, and r=0.396, respectively; P<0.001). The prediction model based on regression analysis results was expressed as follows: predictive score = -17.274 + 31.125 × RRRI - 29.074 × RDTR + 0.621 × Lac. Meanwhile, the IAH nomogram prediction model was established with an area under the receiver operating characteristic (ROC) curve of 0.956 (95% confidence interval: 0.909-1.000). The nomogram showed good calibration for IAH with the Hosmer-Lemeshow test (P=0.864) and was found to be applicable within a wide threshold probability range, especially that higher than 0.40. Conclusions: The noninvasive nomogram based on ultrasound and clinical data has good diagnostic efficiency and can predict the risk of IAH. This nomogram may provide valuable guidance for clinical interventions to reduce IAH morbidity and mortality in critically ill patients.

Structural insight into why S-linked glycosylation cannot adequately mimic the role of natural O-glycosylation.

There is an increasing interest in using S-glycosylation as a replacement for the more commonly occurring O-glycosylation, aiming to enhance the resistance of glycans against chemical hydrolysis and enzymatic degradation. However, previous studies have demonstrated that these two types of glycosylation exert distinct effects on protein properties and functions. In order to elucidate the structural basis behind the observed differences, we conducted a systematic and comparative analysis of 6 differently glycosylated forms of a model glycoprotein, CBM, using NMR spectroscopy and molecular dynamic simulations. Our findings revealed that the different stabilizing effects of S- and O-glycosylation could be attributed to altered hydrogen-bonding capability between the glycan and the polypeptide chain, and their diverse impacts on binding affinity could be elucidated by examining the interactions and motion dynamics of glycans in substrate-bound states. Overall, this study underscores the pivotal role of the glycosidic linkage in shaping the function of glycosylation and advises caution when switching glycosylation types in protein glycoengineering.

Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors.

We propose a new method for learning a generalized animatable neural human representation from a sparse set of multi-view imagery of multiple persons. The learned representation can be used to synthesize novel view images of an arbitrary person and further animate them with the user's pose control. While most existing methods can either generalize to new persons or synthesize animations with user control, none of them can achieve both at the same time. We attribute this accomplishment to the employment of a 3D proxy for a shared multi-person human model, and further the warping of the spaces of different poses to a shared canonical pose space, in which we learn a neural field and predict the person- and pose-dependent deformations, as well as appearance with the features extracted from input images. To cope with the complexity of the large variations in body shapes, poses, and clothing deformations, we design our neural human model with disentangled geometry and appearance. Furthermore, we utilize the image features both at the spatial point and on the surface points of the 3D proxy for predicting person- and pose-dependent properties. Experiments show that our method significantly outperforms the state-of-the-arts on both tasks.

Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis.

We propose to use optimally ordered orthogonal neighbor-joining (O 3 NJ) trees as a new way to visually explore cluster structures and outliers in multi-dimensional data. Neighbor-joining (NJ) trees are widely used in biology, and their visual representation is similar to that of dendrograms. The core difference to dendrograms, however, is that NJ trees correctly encode distances between data points, resulting in trees with varying edge lengths. We optimize NJ trees for their use in visual analysis in two ways. First, we propose to use a novel leaf sorting algorithm that helps users to better interpret adjacencies and proximities within such a tree. Second, we provide a new method to visually distill the cluster tree from an ordered NJ tree. Numerical evaluation and three case studies illustrate the benefits of this approach for exploring multi-dimensional data in areas such as biology or image analysis.

Using Multi-Level Consistency Learning for Partial-to-Partial Point Cloud Registration.

Point cloud registration is a basic task in computer vision and computer graphics. Recently, deep learning-based end-to-end methods have made great progress in this field. One of the challenges of these methods is to deal with partial-to-partial registration tasks. In this work, we propose a novel end-to-end framework called MCLNet that makes full use of multi-level consistency for point cloud registration. First, the point-level consistency is exploited to prune points located outside overlapping regions. Second, we propose a multi-scale attention module to perform consistency learning at the correspondence-level for obtaining reliable correspondences. To further improve the accuracy of our method, we propose a novel scheme to estimate the transformation based on geometric consistency between correspondences. Compared to baseline methods, experimental results show that our method performs well on smaller-scale data, especially with exact matches. The reference time and memory footprint of our method are relatively balanced, which is more beneficial for practical applications.

MILO: Multi-Bounce Inverse Rendering for Indoor Scene With Light-Emitting Objects.

Recently, many advances in inverse rendering are achieved by high-dimensional lighting representations and differentiable rendering. However, multi-bounce lighting effects can hardly be handled correctly in scene editing using high-dimensional lighting representations, and light source model deviation and ambiguities exist in differentiable rendering methods. These problems limit the applications of inverse rendering. In this paper, we present a multi-bounce inverse rendering method based on Monte Carlo path tracing, to enable correct complex multi-bounce lighting effects rendering in scene editing. We propose a novel light source model that is more suitable for light source editing in indoor scenes, and design a specific neural network with corresponding disambiguation constraints to alleviate ambiguities during the inverse rendering. We evaluate our method on both synthetic and real indoor scenes through virtual object insertion, material editing, relighting tasks, and so on. The results demonstrate that our method achieves better photo-realistic quality.

Insights into the effect of protein glycosylation on carbohydrate substrate binding.

The role of glycosylation in the binding of glycoproteins to carbohydrate substrates has not been well understood. The present study addresses this knowledge gap by elucidating the links between the glycosylation patterns of a model glycoprotein, a Family 1 carbohydrate-binding module (TrCBM1), and the thermodynamic and structural properties of its binding to different carbohydrate substrates using isothermal titration calorimetry and computational simulation. The variations in glycosylation patterns cause a gradual transition of the binding to soluble cellohexaose from an entropy-driven process to an enthalpy-driven one, a trend closely correlated with the glycan-induced shift of the predominant binding force from hydrophobic interactions to hydrogen bonding. However, when binding to a large surface of solid cellulose, glycans on TrCBM1 have a more dispersed distribution and thus have less adverse impact on the hydrophobic interaction forces, leading to overall improved binding. Unexpectedly, our simulation results also suggest an evolutionary role of O-mannosylation in transforming the substrate binding features of TrCBM1 from those of type A CBMs to those of type B CBMs. Taken together, these findings provide new fundamental insights into the molecular basis of the role of glycosylation in protein-carbohydrate interactions and are expected to better facilitate further studies in this area.

Impact of N-Linked Glycosylation on Therapeutic Proteins.

Therapeutic proteins have unique advantages over small-molecule drugs in the treatment of various diseases, such as higher target specificity, stronger pharmacological efficacy and relatively low side effects. These advantages make them increasingly valued in drug development and clinical practice. However, although highly valued, the intrinsic limitations in their physical, chemical and pharmacological properties often restrict their wider applications. As one of the most important post-translational modifications, glycosylation has been shown to exert positive effects on many properties of proteins, including molecular stability, and pharmacodynamic and pharmacokinetic characteristics. Glycoengineering, which involves changing the glycosylation patterns of proteins, is therefore expected to be an effective means of overcoming the problems of therapeutic proteins. In this review, we summarize recent efforts and advances in the glycoengineering of erythropoietin and IgG monoclonal antibodies, with the goals of illustrating the importance of this strategy in improving the performance of therapeutic proteins and providing a brief overview of how glycoengineering is applied to protein-based drugs.

DO-Conv: Depthwise Over-Parameterized Convolutional Layer.

Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise convolution, where each input channel is convolved with a different 2D kernel. The composition of the two convolutions constitutes an over-parameterization, since it adds learnable parameters, while the resulting linear operation can be expressed by a single convolution layer. We refer to this depthwise over-parameterized convolutional layer as DO-Conv, which is a novel way of over-parameterization. We show with extensive experiments that the mere replacement of conventional convolutional layers with DO-Conv layers boosts the performance of CNNs on many classical vision tasks, such as image classification, detection, and segmentation. Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization. As DO-Conv introduces performance gains without incurring any computational complexity increase for inference, we advocate it as an alternative to the conventional convolutional layer. We open sourced an implementation of DO-Conv in Tensorflow, PyTorch and GluonCV at https://github.com/yangyanli/DO-Conv.

A General Decoupled Learning Framework for Parameterized Image Operators.

Many different deep networks have been used to approximate, accelerate or improve traditional image operators. Among these traditional operators, many contain parameters which need to be tweaked to obtain the satisfactory results, which we refer to as "parameterized image operators". However, most existing deep networks trained for these operators are only designed for one specific parameter configuration, which does not meet the needs of real scenarios that usually require flexible parameters settings. To overcome this limitation, we propose a new decoupled learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network. The learned algorithm is formed as another network, namely the weight learning network, which can be end-to-end jointly trained with the base network. Experiments demonstrate that the proposed framework can be successfully applied to many traditional parameterized image operators. To accelerate the parameter tuning for practical scenarios, the proposed framework can be further extended to dynamically change the weights of only one single layer of the base network while sharing most computation cost. We demonstrate that this cheap parameter-tuning extension of the proposed decoupled learning framework even outperforms the state-of-the-art alternative approaches.

Implicit Multidimensional Projection of Local Subspaces.

We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases.

Iterative Local-Global Collaboration Learning towards One-Shot Video Person Re-Identification.

Video person re-identification (video Re-ID) plays an important role in surveillance video analysis and has gained increasing attention recently. However, existing supervised methods require vast labeled identities across cameras, resulting in poor scalability in practical applications. Although some unsupervised approaches have been exploited for video Re-ID, they are still in their infancy due to the complex nature of learning discriminative features on unlabelled data. In this paper, we focus on one-shot video Re-ID and present an iterative local-global collaboration learning approach to learning robust and discriminative person representations. Specifically, it jointly considers the global video information and local frame sequence information to better capture the diverse appearance of the person for feature learning and pseudo-label estimation. Moreover, as the cross-entropy loss may induce the model to focus on identity-irrelevant factors, we introduce the variational information bottleneck as a regularization term to train the model together. It can help filter undesirable information and characterize subtle differences among persons. Since accuracy cannot always be guaranteed for pseudo-labels, we adopt a dynamic selection strategy to select part of pseudo-labeled data with higher confidence to update the training set and re-train the learning model. During training, our method iteratively executes the feature learning, pseudo-label estimation, and dynamic sample selection until all the unlabeled data have been seen. Extensive experiments on two public datasets, i.e., DukeMTMC-VideoReID and MARS, have verified the superiority of our model to several cutting-edge competitors.

Synthesis, Characterization and Anti-hepatoma Activity of New Hederagenin Derivatives.

BACKGROUND: Based on the biological significance of hederagenin-type saponins found in our previous investigation, a series of new hederagenin derivatives were designed and synthesized. METHODS: Their in vitro antiproliferative activities were evaluated against the HepG2 liver cancer cell line and normal cell line L929 by MTT assay. RESULTS: The preliminary bioassay results demonstrated that all the tested compounds 1-7 showed potent anti-hepatoma activities, and some compounds exhibited better effects than 5-fluorouracil against human hepatocellular carcinoma HepG2 cell line. Furthermore, compound 5 showed a significant antihepatoma activity against HepG2 cells with an IC50 value of 1.88 µM. Besides, all of the tested compounds showed a low cytotoxic effect against the normal cell line L929. CONCLUSION: All the compounds 1-7 displayed superior selectivity against human hepatocellular carcinoma HepG2 cell line, and the results suggest that the structural modifications of C ring on the hederagenin backbone are vital for modulating anti-hepatoma activities.

Sensitized Molecular Triplet and Triplet Excimer Emission in Two-Dimensional Hybrid Perovskites.

Two-dimensional (2D) organic-inorganic hybrid perovskites are promising materials for next-generation optoelectronic devices owning to their structural and functional versatility and enhanced ambient stability. Recent studies have started to focus on engineering the molecular properties of the organic cations to induce inorganic-to-organic energy/charge transfer for new functionalities, yet many puzzles regarding the inorganic-organic interaction mechanisms remain to be resolved. Here we fabricate 2D lead halide perovskites containing naphthalene methylamine (NMA) cations to study naphthalene triplet sensitization by inorganic excitons. We find that triplet sensitization proceeds via a two-step mechanism initiated by subpicosecond hole transfer from the inorganic layer to naphthalene. We also provide spectroscopic evidence for triplet excimer formation, i.e., the association between triplet and ground state molecules. The intensity ratio between the excimer and triplet emissions can be tuned via the percentage of the NMA cations in the organic layer, offering a route to tunable white-light emitters using 2D hybrid perovskites.

A distinctive mitochondrion-targeting, in situ-activatable near-infrared fluorescent probe for visualizing sulfur dioxide derivatives and their fluctuations in vivo.

Sulfur dioxide derivatives are intimately involved in some physiological processes in organisms, and high levels of these substances can cause many diseases. Herein, we rationally prepared a mitochondrion-targeting, in situ-activatable near-infrared (NIR) fluorescent probe (DCQN) by coupling 2-(3,5,5-trimethylcyclohex-2-enylidene)malononitrile with 3-quinolinium carboxaldehyde. DCQN displayed a NIR fluorescence turn-on signal to indicate the presence of HSO3-, along with a considerable hyperchromic shift from light yellow to purple via a 1,4-nucleophilic addition reaction. We were able to use DCQN to instantaneously and quantitatively determine the concentration of HSO3- with high specificity, a low detection limit (24 nM), a large Stokes shift (∼110 nm), and a high contrast ratio. Moreover, DCQN displayed good mitochondrion-targeting abilities and was in situ-activated by HSO3- to produce NIR fluorescence for imaging HSO3- in the mitochondria of live breast cancer cells. Furthermore, DCQN was used to monitor HSO3- in zebrafish with a high contrast ratio.

Neural Multimodal Cooperative Learning Toward Micro-Video Understanding.

The prevailing characteristics of micro-videos result in the less descriptive power of each modality. The micro-video representations, several pioneer efforts proposed, are limited in implicitly exploring the consistency between different modality information but ignore the complementarity. In this paper, we focus on how to explicitly separate the consistent features and the complementary features from the mixed information and harness their combination to improve the expressiveness of each modality. Toward this end, we present a neural multimodal cooperative learning (NMCL) model to split the consistent component and the complementary component by a novel relation-aware attention mechanism. Specifically, the computed attention score can be used to measure the correlation between the features extracted from different modalities. Then, a threshold is learned for each modality to distinguish the consistent and complementary features according to the score. Thereafter, we integrate the consistent parts to enhance the representations and supplement the complementary ones to reinforce the information in each modality. As to the problem of redundant information, which may cause overfitting and is hard to distinguish, we devise an attention network to dynamically capture the features which closely related the category and output a discriminative representation for prediction. The experimental results on a real-world micro-video dataset show that the NMCL outperforms the state-of-the-art methods. Further studies verify the effectiveness and cooperative effects brought by the attentive mechanism.

Strong 3D Printing by TPMS Injection.

3D printed objects are rapidly becoming prevalent in science, technology and daily life. An important question is how to obtain strong and durable 3D models using standard printing techniques. This question is often translated to computing smartly designed interior structures that provide strong support and yield resistant 3D models. In this paper we suggest a combination between 3D printing and material injection to achieve strong 3D printed objects. We utilize triply periodic minimal surfaces (TPMS) to define novel interior support structures. TPMS are closed form and can be computed in a simple and straightforward manner. Since TPMS are smooth and connected, we utilize them to define channels that adequately distribute injected materials in the shape interior. To account for weak regions, TPMS channels are locally optimized according to the shape stress field. After the object is printed, we simply inject the TPMS channels with materials that solidify and yield a strong inner structure that supports the shape. Our method allows injecting a wide range of materials in an object interior in a fast and easy manner. Results demonstrate the efficiency of strong printing by combining 3D printing and injection together.

Mass-Driven Topology-Aware Curve Skeleton Extraction from Incomplete Point Clouds.

We introduce a mass-driven curve skeleton as a curve skeleton representation for 3D point cloud data. The mass-driven curve skeleton presents geometric properties and mass distribution of a curve skeleton simultaneously. The computation of the mass-driven curve skeleton is formulated as a minimization of Wasserstein distance, with an entropic regularization term, between mass distributions of point clouds and curve skeletons. Assuming that the mass of one sampling point should be transported to a line-like structure, a topology-aware rough curve skeleton is extracted via the optimal transport plan. A Dirichlet energy regularization term is then used to obtain a smooth curve skeleton via geometric optimization. Given that rough curve skeleton extraction does not depend on complete point clouds, our algorithm can be directly applied to curve skeleton extraction from incomplete point clouds. We demonstrate that a mass-driven curve skeleton can be directly applied to an unoriented raw point scan with significant noise, outliers and large areas of missing data. In comparison with state-of-the-art methods on curve skeleton extraction, the performance of the proposed mass-driven curve skeleton is more robust in terms of extracting a correct topology.

A Recursive Subdivision Technique for Sampling Multi-class Scatterplots.

We present a non-uniform recursive sampling technique for multi-class scatterplots, with the specific goal of faithfully presenting relative data and class densities, while preserving major outliers in the plots. Our technique is based on a customized binary kd-tree, in which leaf nodes are created by recursively subdividing the underlying multi-class density map. By backtracking, we merge leaf nodes until they encompass points of all classes for our subsequently applied outlier-aware multi-class sampling strategy. A quantitative evaluation shows that our approach can better preserve outliers and at the same time relative densities in multi-class scatterplots compared to the previous approaches, several case studies demonstrate the effectiveness of our approach in exploring complex and real world data.

Interactive Structure-aware Blending of Diverse Edge Bundling Visualizations.

Many edge bundling techniques (i.e., data simplification as a support for data visualization and decision making) exist but they are not directly applicable to any kind of dataset and their parameters are often too abstract and difficult to set up. As a result, this hinders the user ability to create efficient aggregated visualizations. To address these issues, we investigated a novel way of handling visual aggregation with a task-driven and user-centered approach. Given a graph, our approach produces a decluttered view as follows: first, the user investigates different edge bundling results and specifies areas, where certain edge bundling techniques would provide user-desired results. Second, our system then computes a smooth and structural preserving transition between these specified areas. Lastly, the user can further fine-tune the global visualization with a direct manipulation technique to remove the local ambiguity and to apply different visual deformations. In this paper, we provide details for our design rationale and implementation. Also, we show how our algorithm gives more suitable results compared to current edge bundling techniques, and in the end, we provide concrete instances of usages, where the algorithm combines various edge bundling results to support diverse data exploration and visualizations.