Monocular BEV Perception of Road Scenes Via Front-to-Top View Projection.

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency.

An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer.

Introduction: We aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features. Methods: Ultrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve. Results: We found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value. Discussion: The Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.

LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors.

We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only using single LiDAR and 4 IMUs, which are set up conveniently and worn lightly. Specifically, to fully utilize the global geometry information captured by LiDAR and local dynamic motions captured by IMUs, we design a two-stage pose estimator in a coarse-to-fine manner, where point clouds provide the coarse body shape and IMU measurements optimize the local actions. Furthermore, considering the translation deviation caused by the view-dependent partial point cloud, we propose a pose-guided translation corrector. It predicts the offset between captured points and the real root locations, which makes the consecutive movements and trajectories more precise and natural. Moreover, we collect a LiDAR-IMU multi-modal mocap dataset, LIPD, with diverse human actions in long-range scenarios. Extensive quantitative and qualitative experiments on LIPD and other open datasets all demonstrate the capability of our approach for compelling motion capture in large-scale scenarios, which outperforms other methods by an obvious margin. We will release our code and captured dataset to stimulate future research.

Decision Fusion Networks for Image Classification.

Convolutional neural networks, in which each layer receives features from the previous layer(s) and then aggregates/abstracts higher level features from them, are widely adopted for image classification. To avoid information loss during feature aggregation/abstraction and fully utilize lower layer features, we propose a novel decision fusion module (DFM) for making an intermediate decision based on the features in the current layer and then fuse its results with the original features before passing them to the next layers. This decision is devised to determine an auxiliary category corresponding to the category at a higher hierarchical level, which can, thus, serve as category-coherent guidance for later layers. Therefore, by stacking a collection of DFMs into a classification network, the generated decision fusion network is explicitly formulated to progressively aggregate/abstract more discriminative features guided by these decisions and then refine the decisions based on the newly generated features in a layer-by-layer manner. Comprehensive results on four benchmarks validate that the proposed DFM can bring significant improvements for various common classification networks at a minimal additional computational cost and are superior to the state-of-the-art decision fusion-based methods. In addition, we demonstrate the generalization ability of the DFM to object detection and semantic segmentation.

[Nonspecific adsorption evaluation and general minimization strategy in peptide analysis based on ultra-performance liquid chromatography-mass spectrometry].

Proteomics technology is being increasingly used in the development of novel therapeutic peptides and protein drugs, and also in the intensive search for clinical biomacromolecule diagnostic biomarkers. Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) is a rapid method to analyze peptides and proteins in low abundance. However, the nonspecific adsorption properties of peptides may lead to the loss or interference of the analytes throughout the analytical process. This unique nonspecific adsorption property is the main reason for the false negative and false positive results obtained through quantification, as well as for the poor precision, accuracy, linear range, and sensitivity, all of which impose significant challenges in the development of analytical methods. Accordingly, a general strategy was established to evaluate and reduce the negative impact of the nonspecific adsorption of peptides on UPLC-MS analysis. In this study, bovine serum albumin (BSA) was used as a model protein to explore the correlation between the physicochemical properties of 50 peptides obtained by the enzymatic digestion of BSA, as well as the degree of nonspecific adsorption. First, these peptides were classified into four categories according to their response and the degree of adsorption in the pretreatment containers and LC system. Next, the factors influencing the adsorption of 12 Class Ⅱ peptides, which were highly responsive and susceptible to adsorption, were systematically studied in terms of several aspects, including: (1) time-dependent adsorption on centrifuge tubes of three kinds (Protein-LoBind polypropylene tube and two types of polypropylene tubes); (2) time-dependent adsorption on sample vials of three kinds (Protein-LoBind polypropylene vial, polypropylene vial, and glass vial); (3) carryovers on chromatographic columns with six different stationary phases (Polar C18, Cortecs C18+, PFP, BEH C18, CSH C18, and BEH C8); (4) carryovers at different chromatographic gradients (2%B-30%B, 2%B-40%B, 2%B-50%B, and 2%B-60%B within 3 min), flow rates (0.2, 0.3, and 0.4 mL/min), and column temperatures (30, 40, 50, and 60 ℃); and (5) carryovers using different washing needle solutions (0.2% formic acid in 10% acetonitrile and 0.2% formic acid in 90% acetonitrile). The results showed that parameters such as the HPLC index and amino acid length of peptides were significantly correlated with their degree of adsorption (p<0.05), However, the above parameters can only explain the adsorption degree of 30% of the peptides. The use of the modified polypropylene material resulted in higher recovery (recovery rate>80% within 24 h) of the peptide solution during storage or pretreatment. During protein/peptide pretreatment and storage, good overall recoveries (recovery rate>80% within 24 h) were obtained using centrifuge tubes and sample vials made of the modified polypropylene material. Analysis and optimization of the LC conditions revealed that the use of the C8 chromatographic column, a high flow rate (0.4 mL/min), slow gradient (2%B-50%B within 3 min), and strong needle solution (0.2% formic acid in 90% acetonitrile) could minimize the carryover. However, the effect of the column temperature on the carryover varied considerably from peptide to peptide, and hence, requires further analysis for specific peptides. The combined optimization of the above experimental conditions resulted in minimal (approximately 1/150) or no adsorption of the Class Ⅱ peptides that were susceptible to adsorption in the analytical process. In this study, a workflow was designed to standardize the procedures for evaluating and reducing peptide adsorption. Detailed data were collected to elucidate the key risk factors and corresponding general mechanism of nonspecific adsorption throughout the analysis. Thus, this study serves as a reference for the development of analytical methods for peptides and proteins with different physicochemical properties. In future work, the risk factors should be assessed during the development and validation of protein-based macromolecular analysis methods. In conclusion, it is important to implement adequate and appropriate measures to ensure risk elimination or minimization.

Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection.

Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the current methods are time-consuming and suffer from large biases in landmark localization, leading to unreliable diagnosis results. In this work, we propose a novel Structure-Aware Long Short-Term Memory framework (SA-LSTM) for efficient and accurate 3D landmark detection. To reduce the computational burden, SA-LSTM is designed in two stages. It first locates the coarse landmarks via heatmap regression on a down-sampled CBCT volume and then progressively refines landmarks by attentive offset regression using multi-resolution cropped patches. To boost accuracy, SA-LSTM captures global-local dependence among the cropping patches via self-attention. Specifically, a novel graph attention module implicitly encodes the landmark's global structure to rationalize the predicted position. Moreover, a novel attention-gated module recursively filters irrelevant local features and maintains high-confident local predictions for aggregating the final result. Experiments conducted on an in-house dataset and a public dataset show that our method outperforms state-of-the-art methods, achieving 1.64 mm and 2.37 mm average errors, respectively. Furthermore, our method is very efficient, taking only 0.5 seconds for inferring the whole CBCT volume of resolution 768×768×576 .

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception.

State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, etc.) often project the point clouds to 2D space and then process them via 2D convolution. Although this cooperation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. An important reason is the property of the outdoor point cloud, namely sparsity and varying density. Motivated by this investigation, we propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties. The proposed model acts as a backbone and the learned features from this model can be used for downstream tasks such as point cloud semantic and panoptic segmentation or 3D detection. In this paper, we benchmark our model on these three tasks. For semantic segmentation, we evaluate the proposed model on several large-scale datasets, i.e., SemanticKITTI, nuScenes and A2D2. Our method achieves the state-of-the-art on the leaderboard of SemanticKITTI (both single-scan and multi-scan challenge), and significantly outperforms existing methods on nuScenes and A2D2 dataset. Furthermore, the proposed 3D framework also shows strong performance and good generalization on LiDAR panoptic segmentation and LiDAR 3D detection.

Self-Supervised Point Set Local Descriptors for Point Cloud Registration.

Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and selection of patches, which make the model hard to migrate to new scenarios. In this work, we learn local registration descriptors for point clouds in a self-supervised manner. In each iteration of the training, the input of the network is merely one unlabeled point cloud. Thus, the whole training requires no manual annotation and manual selection of patches. In addition, we propose to involve keypoint sampling into the pipeline, which further improves the performance of our model. Our experiments demonstrate the capability of our self-supervised local descriptor to achieve even better performance than the supervised model, while being easier to train and requiring no data labeling.

Effects of dietary live yeast Hanseniaspora opuntiae C21 on the immune and disease resistance against Vibrio splendidus infection in juvenile sea cucumber Apostichopus japonicus.

A feeding experiment was conducted to determine effects of Hanseniaspora opuntiae C21 on immune response and disease resistance against Vibrio splendidus infection in juvenile sea cucumbers Apostichopus japonicus. Sea cucumbers were fed with either diets containing C21 at 10(4), 10(5) and 10(6) CFU g(-1) feed or a control diet for 30-50 days, respectively. After feeding for 30 days and 45 days, five sea cucumbers from each tank were sampled for immunological analyses. Results indicated that C21 significantly improved the phagocytic activity in coelomocytes of sea cucumbers (P < 0.05). Moreover, C21 administration significantly enhanced lysozyme (LSZ), phenoloxidase activity (PO), total nitric oxide synthase (T-NOS), superoxide dismutase (SOD), alkaline phosphatase (AKP) and acid phosphatase (ACP) activities in coelomic fluid, and LSZ, T-NOS, AKP and ACP activities in coelomocytes lysate supernatant (CLS) of sea cucumbers (P < 0.05). After feeding for 45 days, 10 sea cucumbers from each dose group were challenged with V. splendidus NB13. Cumulative incidence and mortality of sea cucumbers fed with C21 were found to be lower than those of control group. After feeding for 50 days, sea cucumbers in 10(4) CFU g(-1) C21 treatment and control tanks were subjected to acute salinity changes (from 30 to 20) for 24 h in the laboratory, and the immunological parameters were measured to evaluate the immune capacities of the A. japonicus. Phagocytic, LAZ and T-NOS activities of C21-treated group were higher than those of control group, indicating that salinity stress tolerance of sea cucumber was enhanced by C21. The present results showed that a diet supplemented with C21 could stimulate the immune system of juvenile A. japonicus thus enhancing their resistance against V. splendidus.

[Culture optimization and characterization of an alginate-lyase from Pseudoalteromonas sp. LJ1].

OBJECTIVES: The present work aimed to optimize the culture conditions to produce extracellular alginate-lyase by Pseudoalteromonas sp. LJ1. METHODS: A bacterial alginate-lyase producing strain LJ1 was isolated from Laminaria japonica by enrichment culture technique. The strain was identified based on phenotypic characters, fatty acid compositions and 16S rRNA gene sequencing. Culture conditions were optimized to produce the extracellular alginate-lyase by the single factor and orthogonal tests. RESULTS: Strain LJ1 was identified as Pseudoalteromonas sp.. The optimal medium components were: sodium alginate 3 g/L, (NH4)2SO4 3 g/L, NaCl 20 g/L, KH2PO4 0.1 g/L, CaCl2 0.1 g/L; The optimal culture conditions were: 25 ml medium in 250 mL Erlenmeyer flask, inoculum's volume 3%, shaking speed of 150 r/min, initial pH 7.5, at 28 degrees C for 24 h. The enzyme exhibited maximal activity at pH 7.6, 40 degrees C and NaCl 0.3 mol/L. The enzyme activity was improved by Mg2+, and inhibited by Co2+ and Zn2+ at 1 mol/L. CONCLUSIONS: Strain LJ1 was a novel alginate-lyase producing bacterium of Pseudoalteromonas.

Optimization of kappa-carrageenase production by Pseudoalteromonas sp. AJ5.

OBJECTIVES: To optimize the culture conditions of Pseudoalteromonas sp. AJ5 for a higher production of extracellular kappa-carrageenase. METHODS: A kappa-carrageenan-degrading bacterium AJ5, capable of utilizing kappa-carrageenan as sole source of carbon and energy, was isolated from the intestine of holothurian Apostichopus japonicus by enrichment culture technique. The strain was identified as the genus Pseudoalteromonas sp. according to its morphological and physiological characterization and 16S rRNA gene analysis. Culture conditions for the bacterium were standardized for the maximal productivity of the extracellular kappa-carrageenase by the single factor and orthogonal tests. RESULTS: According to the single factor test, the optimal culture conditions were: 75 mL medium in 250 mL Erlenmeyer flask, shaking speed of 150 r/min, inoculum's volume 7%, and pH 8.0. Based on the single factor and orthogonal tests the optimal medium components were: kappa-carrageenan (1 g/L), beef extract (2 g/L ), NaCl (20 g/L), K2HPO4.3H2O (1 g/L), MgSO4.7H2O (0.5 g/L), MnCl2.4H2O (0.2 g/L), FePO4.4H2O (0.01 g/L), with the incubation temperature and time of 28 degrees C and 28 h. CONCLUSION: Pseudoalteromonas sp. AJ5 secreted an extracellular kappa-carrageenase. Under the optimal culture conditions, four-fold increase in kappa-carrageenase activity was achieved as compared to the control.