CD2^2: Fine-grained 3D Mesh Reconstruction with Twice Chamfer Distance

Monocular 3D reconstruction is to reconstruct the shape of object and its other information from a single RGB image. In 3D reconstruction, polygon mesh, with detailed surface information and low computational cost, is the most prevalent expression form obtained from deep learning models. However, the state-of-the-art schemes fail to directly generate well-structured meshes, and most of meshes have two severe problems Vertices Clustering (VC) and Illegal Twist (IT). By diving into the mesh deformation process, we pinpoint that the inappropriate usage of Chamfer Distance (CD) loss is the root causes of VC and IT problems in the training of deep learning model. In this paper, we initially demonstrate these two problems induced by CD loss with visual examples and quantitative analyses. Then, we propose a fine-grained reconstruction method CD$^2$ by employing Chamfer distance twice to perform a plausible and adaptive deformation. Extensive experiments on two 3D datasets and comparisons with five latest schemes demonstrate that our CD$^2$ directly generates well-structured meshes and outperforms others by alleviating VC and IT problems.Comment: under major review in TOM

Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction

Human-Object Interaction (HOI) detection focuses on human-centered visual relationship detection, which is a challenging task due to the complexity and diversity of image content. Unlike most recent HOI detection works that only rely on paired instance-level information in the union range, our proposed Spatial-aware Multilevel Parsing Network (SMPNet) uses a multi-level information detection strategy, including instance-level visual features of detected human-object pair, part-level related features of the human body, and scene-level features extracted by the graph neural network. After fusing the three levels of features, the HOI relationship is predicted. We validate our method on two public datasets, V-COCO and HICO-DET. Compared with prior works, our proposed method achieves the state-of-the-art results on both datasets in terms of mAProle, which demonstrates the effectiveness of our proposed multi-level information detection strategy

Small nucleolar RNA signatures as biomarkers for non-small-cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death. Early detection of NSCLC will improve its outcome. The current techniques for NSCLC early detection are either invasive or have low accuracy. Molecular analyses of clinical specimens present promising diagnostic approaches. Non-coding RNAs (ncRNAs) play an important role in tumorigenesis and could be developed as biomarkers for cancer. Here we aimed to develop small nucleolar RNAs (snoRNAs), a common class of ncRNAs, as biomarkers for NSCLC early detection. The study comprised three phases: (1) profiling snoRNA signatures in 22 NSCLC tissues and matched noncancerous lung tissues by GeneChip Array, (2) validating expressions of the signatures by RT-qPCR in the tissues, and (3) evaluating plasma expressions of the snoRNAs in 37 NSCLC patients, 26 patients with chronic obstructive pulmonary disease (COPD), and 22 healthy subjects.</p> <p>Results</p> <p>In the surgical tissues, six snoRNAs were identified, which were overexpressed in all tumour tissues compared with their normal counterparts. The overexpressions of the genes in tumors were confirmed by RT-qPCR. The snoRNAs were stably present and reliably detectable in plasma. Of the six genes, three (SNORD33, SNORD66 and SNORD76) displayed higher plasma expressions in NSCLC patients compared with the cancer-free individuals (All < 0.01). The use of the three genes produced 81.1% sensitivity and 95.8% specificity in distinguishing NSCLC patients from both normal and COPD subjects. The plasma snoRNA expressions were not associated with stages and histological types of NSCLC (All > 0.05).</p> <p>Conclusions</p> <p>The identified snoRNAs provide potential markers for NSCLC early detection.</p

Cooperative Stabilization of the [Pyridinium-CO2-Co] Adduct on a Metal-Organic Layer Enhances Electrocatalytic CO2 Reduction.

Pyridinium has been shown to be a cocatalyst for the electrochemical reduction of CO2 on metal and semiconductor electrodes, but its exact role has been difficult to elucidate. In this work, we create cooperative cobalt-protoporphyrin (CoPP) and pyridine/pyridinium (py/pyH+) catalytic sites on metal-organic layers (MOLs) for an electrocatalytic CO2 reduction reaction (CO2RR). Constructed from [Hf6(μ3-O)4(μ3-OH)4(HCO2)6] secondary building units (SBUs) and terpyridine-based tricarboxylate ligands, the MOL was postsynthetically functionalized with CoPP via carboxylate exchange with formate capping groups. The CoPP group and the pyridinium (pyH+) moiety on the MOL coactivate CO2 by forming the [pyH+--O2C-CoPP] adduct, which enhances the CO2RR and suppresses hydrogen evolution to afford a high CO/H2 selectivity of 11.8. Cooperative stabilization of the [pyH+--O2C-CoPP] intermediate led to a catalytic current density of 1314 mA/mgCo for CO production at -0.86 VRHE, which corresponds to a turnover frequency of 0.4 s-1

Diagnosis of lung cancer in individuals with solitary pulmonary nodules by plasma microRNA biomarkers

<p>Abstract</p> <p>Background</p> <p>Making a definitive preoperative diagnosis of solitary pulmonary nodules (SPNs) found by CT has been a clinical challenge. We previously demonstrated that microRNAs (miRNAs) could be used as biomarkers for lung cancer diagnosis. Here we investigate whether plasma microRNAs are useful in identifying lung cancer among individuals with CT-detected SPNs.</p> <p>Methods</p> <p>By using quantitative reverse transcriptase PCR analysis, we first determine plasma expressions of five miRNAs in a training set of 32 patients with malignant SPNs, 33 subjects with benign SPNs, and 29 healthy smokers to define a panel of miRNAs that has high diagnostic efficiency for lung cancer. We then validate the miRNA panel in a testing set of 76 patients with malignant SPNs and 80 patients with benign SPNs.</p> <p>Results</p> <p>In the training set, miR-21 and miR-210 display higher plasma expression levels, whereas miR-486-5p has lower expression level in patients with malignant SPNs, as compared to subjects with benign SPNs and healthy controls (all P ≤ 0.001). A logistic regression model with the best prediction was built on the basis of miR-21, miR-210, and miR-486-5p. The three miRNAs used in combination produced the area under receiver operating characteristic curve at 0.86 in distinguishing lung tumors from benign SPNs with 75.00% sensitivity and 84.95% specificity. Validation of the miRNA panel in the testing set confirms their diagnostic value that yields significant improvement over any single one.</p> <p>Conclusions</p> <p>The plasma miRNAs provide potential circulating biomarkers for noninvasively diagnosing lung cancer among individuals with SPNs, and could be further evaluated in clinical trials.</p

Electrospun PLLA Nanofiber Scaffolds and Their Use in Combination with BMP-2 for Reconstruction of Bone Defects

Introduction Adequate migration and differentiation of mesenchymal stem cells is essential for regeneration of large bone defects. To achieve this, modern graft materials are becoming increasingly important. Among them, electrospun nanofiber scaffolds are a promising approach, because of their high physical porosity and potential to mimic the extracellular matrix (ECM). Materials and Methods The objective of the present study was to examine the impact of electrospun PLLA nanofiber scaffolds on bone formation in vivo, using a critical size rat calvarial defect model. In addition we analyzed whether direct incorporation of bone morphogenetic protein 2 (BMP-2) into nanofibers could enhance the osteoinductivity of the scaffolds. Two critical size calvarial defects (5 mm) were created in the parietal bones of adult male Sprague-Dawley rats. Defects were either (1) left unfilled, or treated with (2) bovine spongiosa, (3) PLLA scaffolds alone or (4) PLLA/BMP-2 scaffolds. Cranial CT-scans were taken at fixed intervals in vivo. Specimens obtained after euthanasia were processed for histology, histomorphometry and immunostaining (Osteocalcin, BMP-2 and Smad5). Results PLLA scaffolds were well colonized with cells after implantation, but only showed marginal ossification. PLLA/BMP-2 scaffolds showed much better bone regeneration and several ossification foci were observed throughout the defect. PLLA/BMP-2 scaffolds also stimulated significantly faster bone regeneration during the first eight weeks compared to bovine spongiosa. However, no significant differences between these two scaffolds could be observed after twelve weeks. Expression of osteogenic marker proteins in PLLA/BMP-2 scaffolds continuously increased throughout the observation period. After twelve weeks osteocalcin, BMP-2 and Smad5 were all significantly higher in the PLLA/BMP-2 group than in all other groups. Conclusion Electrospun PLLA nanofibers facilitate colonization of bone defects, while their use in combination with BMP-2 also increases bone regeneration in vivo and thus combines osteoconductivity of the scaffold with the ability to maintain an adequate osteogenic stimulus

SMF-GA: Optimized Multitask Allocation Algorithm in Urban Crowdsourced Transportation

Urban crowdsourced transportation, which can solve traffic problem within city, is a new scenario where citizens share vehicles to take passengers and packages while driving. Differing from the traditional location based crowdsourcing system (e.g., crowdsensing system), the task has to be completed with visiting two different locations (i.e., start and end points), so task allocation algorithms in crowdsensing cannot be leveraged in urban crowdsourced transportation directly. To solve this problem, we first prove that maximizing the crowdsourcing system’s profit (i.e., maximizing the total saved distance) is an NP-hard problem. We propose a heuristic greedy algorithm called Saving Most First (SMF) which is simple and effective in assigning tasks. Then, an optimized SMF based genetic algorithm (SMF-GA) is devised to jump out of the local optimal result. Finally, we demonstrate the performance of SMF and SMF-GA with extensive evaluations, based on a large scale real vehicle traces. The evaluation with large scale real dataset indicates that both SMF and SMF-GA algorithms outperform other benchmark algorithms in terms of saved distance, participant profits, etc

Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks

Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI) algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks