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The polygon mesh representation of 3D data exhibits great flexibility, fast rendering speed, and storage efficiency, which is widely preferred in various applications. However, given its unstructured graph representation, the direct generation of high-fidelity 3D meshes is challenging. Fortunately, with a pre-defined ordering strategy, 3D meshes can be represented as sequences, and the generation process can be seamlessly treated as an auto-regressive problem. In this paper, we validate the Neural Coordinate Field (NeurCF), an explicit coordinate representation with implicit neural embeddings, is a simple-yet-effective representation for large-scale sequential mesh modeling. After that, we present MeshXL, a family of generative pre-trained auto-regressive models, which addresses the process of 3D mesh generation with modern large language model approaches. Extensive experiments show that MeshXL is able to generate high-quality 3D meshes, and can also serve as foundation models for various down-stream applications.

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 releases $16,940$ textured 3D faces, captured from $847$ subjects and each with $20$ specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniform. These fine 3D facial models can be represented as a 3D morphable model for coarse 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 from most 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 at https://github.com/zhuhao-nju/facescape., Comment: Accepted to T-PAMI 2023; Extension of FaceScape(CVPR 2020); Code & data are available at https://github.com/zhuhao-nju/facescape

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able 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. The unprecedented dataset and code will be released to public for research purpose., Comment: Accepted to CVPR 2020

Anti-idiotypic antibodies recognize the antigenic determinants of an antibody, thus they can be used as surrogate antigens. Single-domain antibodies from camlid heavy-chain antibodies with the benefit features of small size, thermostability, and ease in expression, are leading candidates to produce anti-idiotypic antibodies. In this work, we constructed an antibody phage library from the mRNA of an alpaca immunized with an antiaflatoxin monoclonal antibody (mAb) 1C11. Three anti-idiotypic VHH antibodies were isolated and applied to immunoassay toward aflatoxin as a coating antigen. The best immunoassay developed with one of these VHH antibodies shows an IC50 of 0.16 ng/mL toward aflatoxin B1 and cross-reactivity toward aflatoxin B2, G1, and G2 of 90.4%, 54.4%, and 37.7%, respectively. The VHH-based immunoassay was successfully applied to the analysis of peanuts, corn, and rice, which are the predominant commodities regularly contaminated by aflatoxins. A good correlation (r(2) = 0.89) was found between the data obtained from the conventional ELISA and the ELISA based on a VHH coating antigen for the analysis of aflatoxins in peanuts and feedstuff. The use of biotechnology in developing the surrogate, the absence of standard aflatoxin and organic solvents in the synthesis procedures, and the reproducibility of the VHH antibody makes it an ideal strategy for replacing conventional synthesized antigens.

Weisun Huang,1 Weiwei Nie,1 Wenwen Zhang,2 Yanru Wang,1 Aiyu Zhu,1 Xiaoxiang Guan1,2 1Department of Medical Oncology, Jinling Hospital, School of Medicine, Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Medical Oncology, Jinling Hospital, Medical School of Nanjing University, Nanjing, People’s Republic of China Background: The ER signaling pathway plays a critical role in breast cancer. ER signaling pathway-related proteins, such as TRX, AR, and cyclin D1, may have an important function in breast cancer. However, the ways that they influence breast cancer development and progression are still unclear.Patients and methods: A total of 101 Chinese female patients diagnosed with invasive ductal breast adenocarcinoma were retrospectively enrolled in the study. The expression levels of TRX, AR, and cyclin D1 were detected by immunohistochemistry and analyzed via correlation with clinicopathological characteristics and the expression status of ER, PR, and HER2.Results: The expression status of TRX, AR, and cyclin D1 was not associated with the patient’s age, menopausal status, tumor size, or histological differentiation (P>0.05), but was positively correlated with ER and PR (P<0.001, respectively). Most (66/76, 86.8) TRX-positive patients were also HER2-positive (P=0.003). Of AR- or cyclin D1-positive patients, most had relatively earlier I–II tumor stage (P=0.005 and P=0.047, respectively) and no metastatic lymph node involvement (P=0.008 and P=0.005, respectively).Conclusion: TRX was found to be positively correlated with ER and PR expression, whereas it was negatively correlated with HER2 expression. In addition, we found that the positive expression of AR and cyclin D1 was correlated with lower TNM stage and fewer metastatic lymph nodes, and it was more common in ER-positive breast cancer than in the basal-like subtype. This may indicate that AR and cyclin D1 are good predictive a

An antibody-based analytical method for the detection of a chemical flame retardant using antibody fragments isolated from an alpaca has been developed. One specific chemical flame retardant congener, 2,2',4,4'-tetrabrominated diphenyl ether (BDE-47), is often the major poly-BDE (PBDE) congener present in human and environmental samples and that which is the most frequently detected. An alpaca was immunized with a surrogate of BDE-47 covalently attached to a carrier protein. The resulting mRNA coding for the variable domain of heavy-chain antibodies (VHH) were isolated, transcribed to cDNA, and cloned into a phagemid vector for phage display library construction. Selection of VHHs recognizing BDE-47 was achieved by panning under carefully modified conditions. The assay sensitivity for detecting BDE-47 was down to the part-per-billion (microgram per liter) level. Cross-reactivity analyses confirmed that this method was highly selective for BDE-47 and selected hydroxylated metabolites. When exposed to elevated temperatures, the camelid VHH antibodies retained more reactivity than a polyclonal antibody developed to the same target analyte. The use of this VHH antibody reagent immobilized onto a Au electrode for impedance biosensing demonstrates the increased versatility of VHH antibodies.

Two immuno-loop-mediated isothermal amplification assays (iLAMP) were developed by using a phage-borne peptide that was isolated from a cyclic eight-peptide phage library. One assay was used to screen eight organophosphorus (OP) pesticides with limits of detection (LOD) between 2 and 128 ng mL(-1). The iLAMP consisted of the competitive immuno-reaction coupled to the LAMP reaction for detection. This method provides positive results in the visual color of violet, while a negative response results in a sky blue color; therefore, the iLAMP allows one to rapidly detect analytes in yes or no fashion. We validated the iLAMP by detecting parathion-methyl, parathion, and fenitrothion in Chinese cabbage, apple, and greengrocery, and the detection results were consistent with the enzyme-linked immunosorbent assay (ELISA). In conclusion, the iLAMP is a simple, rapid, sensitive, and economical method for detecting OP pesticide residues in agro-products with no instrumental requirement.

Prostaglandins derived from the cyclooxygenase (COX) pathway and epoxyeicosatrienoic acids (EETs) from the cytochrome P450/soluble epoxide hydrolase (sEH) pathway are important eicosanoids that regulate angiogenesis and tumorigenesis. COX-2 inhibitors, which block the formation of prostaglandins, suppress tumor growth, whereas sEH inhibitors, which increase endogenous EETs, stimulate primary tumor growth and metastasis. However, the functional interactions of these two pathways in cancer are unknown. Using pharmacological inhibitors as probes, we show here that dual inhibition of COX-2 and sEH synergistically inhibits primary tumor growth and metastasis by suppressing tumor angiogenesis. COX-2/sEH dual pharmacological inhibitors also potently suppress primary tumor growth and metastasis by inhibiting tumor angiogenesis via selective inhibition of endothelial cell proliferation. These results demonstrate a critical interaction of these two lipid metabolism pathways on tumorigenesis and suggest dual inhibition of COX-2 and sEH as a potential therapeutic strategy for cancer therapy.

The NF2 tumor suppressor gene encodes an intracellular membrane-associated protein, called merlin, which belongs to the band 4.1 family of cytoskeleton-associated proteins that link cell surface glycoproteins to actin cytoskeleton. Merlin suppresses PI 3-kinase/Akt signaling through directly binding and inhibiting PIKE-L`s stimulatory activity on PI 3-kinase. In Nature Cell Biology paper (2007), we have demonstrated that Akt directly binds to and phosphorylates merlin on residues Thr 230 and Ser 315, which abolishes merlin NTD/CTD interactions and binding to merlin's effector protein PIKE-L and other binding partners. Furthermore, Akt-mediated phosphorylation leads to merlin degradation by ubiquitination. In Cancer Research paper (2009), we show Akt phosphorylation and PI( 3, 4, 5) P3 binding mediate the tumor suppressive activity of merlin. The extreme Nterminus of merlin directly interacts with phosphatidylinositols, for which the unfolded conformation is required. Moreover, Akt phosphorylation enhances merlin binding affinity to phosphatidylinositols and inhibits its pro-apoptotic actions. Further, Akt phosphorylation and phosphatidylinositols increase merlin binding to CD44. EGF treatment and Akt phosphorylation provoke merlin to aggregate in the ruffled plasma membrane and promote cell migration. Thus, these results suggest that PI 3-kinase signaling regulates merlin's tumor suppressive activity via both Akt phosphorylation and phosphatidylinositol lipids binding to merlin. Recently, we show that phosphorylation of merlin also regulates its sumoylation. The biological significance of this finding is under investigation., The original document contains color images.

A fractal model for reserve estimation, FMRE-CA, was developed based on the concentration-area model which is used to describe the spatial distribution of the mineralisation variables such as orebody thickness and grade thickness. Using the demarcation values obtained in the concentration-area model, the ore body is spatially divided into several parts with different value ranges and spatial variation of the mineralisation variables. The local ore reserves in the different parts are estimated based on the FMRE-CA, and the global reserve is obtained by addition of the local reserves. In the FMRE-CA the spatial variation of the mineralisation variables in a local reserve is characterised by a fractal dimension, with a greater fractal dimension denoting more variation. Unlike traditional reserve estimations based on discrete and linear functions, the FMRE-CA is established via a continuous and nonlinear function, the calculation process is easier and the way in which the local reserves are delimitated is different. In contrast to a fractal model for reserve estimation based on the number-size model, FMRE-NS, FMRE-CA is capable of estimating the local reserves. Case studies are presented for an Au orebody in Southwest Yunnan and two bauxite orebodies in Western Guangxi, China, which show that the global reserves calculated using FMRE-CA are similar to those derived from FMRE-NS and the traditional geometric block method., A fractal model for reserve estimation, FMRE-CA, was developed based on the concentration-area model which is used to describe the spatial distribution of the mineralisation variables such as orebody thickness and grade thickness. Using the demarcation values obtained in the concentration-area model, the ore body is spatially divided into several parts with different value ranges and spatial variation of the mineralisation variables. The local ore reserves in the different parts are estimated based on the FMRE-CA, and the global reserve is obtained by addition of the local reserves. In the FMRE-CA the spatial variation of the mineralisation variables in a local reserve is characterised by a fractal dimension, with a greater fractal dimension denoting more variation. Unlike traditional reserve estimations based on discrete and linear functions, the FMRE-CA is established via a continuous and nonlinear function, the calculation process is easier and the way in which the local reserves are delimitated is different. In contrast to a fractal model for reserve estimation based on the number-size model, FMRE-NS, FMRE-CA is capable of estimating the local reserves. Case studies are presented for an Au orebody in Southwest Yunnan and two bauxite orebodies in Western Guangxi, China, which show that the global reserves calculated using FMRE-CA are similar to those derived from FMRE-NS and the traditional geometric block method.

The Heqing area in the Sanjiang ore belt includes the Beiya Au polymetallic ore field inthe SW, which is related to one of the many alkaline porphyries, as well as the central Emeishan belt of volcanic mafic rocks and a series of sedimentary rocks. Principal component analysis was used to classify 39 elements in stream sediment samples from a 1:200 000 geochemical survey into four groups. Two fractal models, the concentration-area model and the number-size model, were used to determine the thresholds for the representative elements in the four groups. The thresholds obtained from the two models were similar and showed that the element concentration distributions could be divided into 3 segments, each of which was mainly correlated to one type of rock. Various geological events could be characterised by the different fractal models of element distribution. Porphyry-related hydrothermal activation tended to result in enrichment of the Au-Pb-Zn-Ag group of elements, hydrothermal activity in the Emeishan large igneous province could be correlated with enrichment of the Cu-Co-Mn-P-V-Fe group and the sedimentary rocks are more likely to result in high concentrations of the Li-F-U group than porphyry or mafic rocks., The Heqing area in the Sanjiang ore belt includes the Beiya Au polymetallic ore field inthe SW, which is related to one of the many alkaline porphyries, as well as the central Emeishan belt of volcanic mafic rocks and a series of sedimentary rocks. Principal component analysis was used to classify 39 elements in stream sediment samples from a 1:200 000 geochemical survey into four groups. Two fractal models, the concentration-area model and the number-size model, were used to determine the thresholds for the representative elements in the four groups. The thresholds obtained from the two models were similar and showed that the element concentration distributions could be divided into 3 segments, each of which was mainly correlated to one type of rock. Various geological events could be characterised by the different fractal models of element distribution. Porphyry-related hydrothermal activation tended to result in enrichment of the Au-Pb-Zn-Ag group of elements, hydrothermal activity in the Emeishan large igneous province could be correlated with enrichment of the Cu-Co-Mn-P-V-Fe group and the sedimentary rocks are more likely to result in high concentrations of the Li-F-U group than porphyry or mafic rocks.