FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose.Comment: 14 pages, 13 figures, journal extension of FaceScape(CVPR 2020). arXiv admin note: substantial text overlap with arXiv:2003.1398

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients

Molecular characterization and expression of the related-male gene sox30 in the common carp Cyprinus carpio

The Sox (SRY-related HMG-box) family of transcription factors is involved in the regulation of embryonic development and determination of cell fate. Sox proteins serve as transcriptional regulators that are complexed with other proteins. For this study, we initially cloned and characterized the full-length cDNAs, DNA sequences, and 5′-flanking regions of the common carp Cyprinus carpio Sox30. The sequence analysis suggested that Ccsox30 carried a distinct HMG-box of the Sox family within Cyprinus carpio. Phylogenetic and gene structure analysis revealed that sox30 was homologous to mammalian Sox30, whereas chromosome synteny analysis demonstrated that the position of Cyprinus carpio Sox30 in the genome was different from that of other vertebrates. This might have been due to the split of the Sox30 flanking gene by several genes not yet found near the Ccsox30 in evolution, or because the genome sequencing data was not annotated. The results of Real Tim Quantitative-PCR (RT-qPCR) revealed that Sox30 expression was high in the testes, and the expression was traced in other tissues by researching the tissue distribution of C. carpio and ontogeny of Ccsox30 expression in the gonads. This expression pattern suggested that Ccsox30 may be involved in spermatogonial differentiation and spermatogenesis

Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

Objectives: The subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT) images. Methods: A dataset of 1222 patients with lung adenocarcinoma were retrospectively enrolled from three medical institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, invasive adenocarcinoma (IAC) with five predominant components were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category and 8-category. We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm. Then we established the prognostic models in lung adenocarcinoma patients with survival outcomes. The accuracy (ACC), area under ROC curves (AUCs) and C-index were primarily performed to evaluate the algorithms. Results: This study included a training set (n = 802) and two validation cohorts (internal, n = 196; external, n = 224). The ACC of deep radiomics algorithm in internal validation achieved 0.8776, 0.8061 in the 2-category, 3-category classification, respectively. Even in 8 classifications, the AUC ranged from 0.739 to 0.940 in internal set. Further, we constructed a prognosis model that C-index was 0.892(95% CI: 0.846–0.937) in internal validation set. Conclusions: The automated deep radiomics based triage system has achieved the great performance in the subtype classification and survival predictability in patients with CT-detected lung adenocarcinoma nodules, providing the clinical guide for treatment strategies

A simple, scalable approach to building a cross-platform transcriptome atlas

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows

Diversity of Circulating NKT Cells in Defense against Carbapenem-Resistant Klebsiella Pneumoniae Infection

Nosocomial infection caused by carbapenem-resistant Klebsiella pneumonia (CRKP) infection has become a global public health problem. Human NK and NKT cells in peripheral immune responses are recognized as occupying a critical role in anti-bacterial immunity. Through performed scRNA-seq on serial peripheral blood samples from 3 patients with CRKP undergoing colonization, infection, and recovery conditions, we were able to described the immune responses of NK and NKT cells during CRKP infection and identified a mechanism that could contribute to CRKP clearance. The central player of CRKP infection process appears to be the NKT subset and CD56hiNKT subset which maintained immune competence during CRKP colonization. With time, CRKP leads to the loss of NK and CD160hiNKT cells in peripheral blood, resulting in suppressed immune responses and increased susceptibility to opportunistic infection. In summary, our study identified a possible mechanism for the CRKP invasion and to decipher the clues behind the host immune response that influences CRKP infection pathogenesis

Habitat quality dynamics in China's first group of national parks in recent four decades: Evidence from land use and land cover changes

International audienc

二聚酸抗磨剂对航空煤油润滑性的影响Effects of dimer acid antiwear agent on lubricity of jet fuel

为考察二聚酸抗磨剂对加氢精制航空煤油润滑性的改善作用，同时验证不同二聚酸抗磨剂的改善效果，分别采用桐油、棉籽油及妥尔油酸为原料进行了二聚酸的合成与精制，利用液相色谱仪、红外光谱仪、核磁共振波谱仪等验证了桐油基、棉籽油基及妥尔油酸基二聚酸的分子结构，采用球柱润滑性评定仪（BOCLE）法和高频往复试验机（HFRR）法考察了3种不同原料来源的二聚酸对航空煤油润滑性的改善作用，用显微镜分析试验球的磨痕形貌，并提出抗磨机制。结果表明：随着二聚酸添加量的不断增大，磨痕直径、表面划痕及深度不断减小；在BOCLE法中，当二聚酸添加量为20 mg/L时，3种二聚酸均可使航空煤油润滑性磨痕直径从0.92 mm降到0.65 mm以下，满足GB 6537—2018《3号喷气燃料》要求；在HFRR法中，当二聚酸添加量为50 mg/L时，3种二聚酸均可使润滑性磨痕直径降到550 μm 以下，平均摩擦系数降到0.265以下，平均成膜率达到24%以上，且平均摩擦系数和平均成膜率的变化规律与二聚酸对航空煤油润滑性改善规律一致；制备的二聚酸均具有长碳链和羧基官能团，羧基通过强电负性吸附在金属表面，羧基之间的氢键以及非极性端通过范德华力等使二聚酸分子形成平行分子簇，进而在金属表面形成有效的润滑膜，阻止两摩擦副之间发生直接接触与摩擦。综上，3种二聚酸的润滑能力相当，均显示出良好的润滑性能。 In order to investigate the improvement of lubricity of dimer acid antiwear agents on jet fuel and verify the improvement effects of different dimer acids antiwear agents,dimer acids were synthesized and refined from tung oil, cottonseed oil and tall oil fatty acids, respectively, and their molecular structures were verified by liquid chromatography, FT-IR spectrometer and NMR spectrometer. The effects of cottonseed oil-based dimer acid, tung oil-based dimer acid and tall oil fatty acids-based dimer acid on the lubricity of jet fuel were investigated by BOCLE and HFRR tests. The wear scar morphology of the test ball was analyzed by measuring microscope and the antiwear mechanism was proposed. The results showed that with the increase of the dosage of dimer acid, the wear scar diameter, surface scratch and depth decreased. In BOCLE test, 20 mg/L dosage of dimer acids from three different raw materials could reduce the wear scar diameter of jet fuel from 0.92 mm to less than 0.65 mm ,meeting the requirement of GB 6537-2018 No.3 jet fuel. In HFRR test, when the dosage of three dimer acids was 50 mg/L, the wear scar diameter of lubricity and the average friction coefficient could be reduced below 550 μm and 0.265 respectively, and the average film formation rate could reach above 24%. The variation of the average friction coefficient and average film formation rate was consistent with the improvement rule of dimer acid on the lubricity of jet fuel. The dimer acids prepared had long carbon chains and carboxyl functional groups. The carboxyl group was adsorbed on the metal surface through strong electronegativity, the hydrogen bond with carboxyl groups and the non-polar end through Van Der Waals force made the dimer acid molecules form parallel molecular clusters, and then formed an effective lubrication film on the metal surface to prevent direct contact and friction between the two friction pairs. In conclusion, the three dimer acids have the uniform lubrication ability, and show good lubrication performance

An integrated analysis of human myeloid cells identifies gaps in in vitro models of in vivo biology

The Stemformatics myeloid atlas is an integrated transcriptome atlas of human macrophages and dendritic cells that systematically compares freshly isolated tissue-resident, cultured, and pluripotent stem cell-derived myeloid cells. Three classes of tissue-resident macrophage were identified: Kupffer cells and microglia; monocyte-associated; and tumor-associated macrophages. Culture had a major impact on all primary cell phenotypes. Pluripotent stem cell-derived macrophages were characterized by atypical expression of collagen and a highly efferocytotic phenotype. Myeloid subsets, and phenotypes associated with derivation, were reproducible across experimental series including data projected from single-cell studies, demonstrating that the atlas provides a robust reference for myeloid phenotypes. Implementation in Stemformatics.org allows users to visualize patterns of sample grouping or gene expression for user-selected conditions and supports temporary upload of your own microarray or RNA sequencing samples, including single-cell data, to benchmark against the atlas

Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes

Abstract Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy