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We investigate experimentally and theoretically a system ruled by an intricate interplay between topology, nonlinearity, and spontaneous symmetry breaking. The experiment is based on a two-mode coherently-driven optical resonator where photons interact through the Kerr nonlinearity. In presence of a phase defect, the modal structure acquires a synthetic Möbius topology enabling the realization of spontaneous symmetry breaking in inherently bias-free conditions without fine tuning of parameters. Rigorous statistical tests confirm the robustness of the underlying symmetry protection, which manifests itself by a periodic alternation of the modes reminiscent of period-doubling. This dynamic also confers long term stability to various localized structures including domain walls, solitons, and breathers. Our findings are supported by an effective Hamiltonian model and have relevance to other systems of interacting bosons and to the Floquet engineering of quantum matter. They could also be beneficial to the implementation of coherent Ising machines., SCOPUS: ar.j, info:eu-repo/semantics/published

Gang Xu,1,2 Weiwei Gong,2,3 Shihong Dong,2 Guojiong Hu,2,4 Weizhen Tang,1 Hecheng Yu1 1Department of Rehabilitation Medicine, Affiliated Tenth People’s Hospital of Tongji University, Shanghai Tenth People’s Hospital, Shanghai, 200072, People’s Republic of China; 2Department of Rehabilitation Medicine, Tongji University School of Medicine, Shanghai, 200092, People’s Republic of China; 3Department of Rehabilitation Medicine, Shanghai First Rehabilitation Hospital, Shanghai, 200090, People’s Republic of China; 4Department of Rehabilitation Medicine, Affiliated Yangzhi Rehabilitation Hospital of Tongji University, Shanghai, 201619, People’s Republic of ChinaCorrespondence: Gang Xu, Department of Rehabilitation Medicine, Affiliated Tenth People’s Hospital of Tongji University, Shanghai Tenth People’s Hospital, Shanghai, 200072, People’s Republic of China, Tel +86 21-66306496, Fax +86 21-66301051, Email sykfsci@126.comPurpose: Mechanical allodynia is reportedly common during herpetic neuralgia. The purpose of this study was to establish a risk prediction model to predict the individual risk of allodynia in herpetic neuralgia.Methods: Three hundred and eighty-six patients with trunk herpetic neuralgia were divided into two regions, T2-5 and T6-11. The causality between allodynia and other factors was analyzed by a binary logistic regression model.Results: 42.2% of subjects had allodynia, 137 suffered from dynamic allodynia, and 110 with dynamic allodynia experienced local sweating. The following 5 items as predictors determined this model: local sweating (Odd Ratio =  27.57, P< 0.001), lesion location (Odd Ratio=2.46, P =0.017), pain intensity (Odd Ratio=1.38, P =0.020), pain duration (Odd Ratio=0.94, P =0.006), and local scars (Odd Ratio=0.07, P< 0.001). The presence and development of allodynia are associated with local sweating. The positive proportion of the Iodine-starch test between the T2-5 (50.0%) with the T6-11 (23.7%) had a stati

A dominant mutation in hnRNPA1 causes amyotrophic lateral sclerosis (ALS), but it is not known whether this mutation leads to motor neuron death through increased or decreased function. To elucidate the relationship between pathogenic hnRNPA1 mutation and its native function, we created novel transgenic rats that overexpressed wildtype rat hnRNPA1 exclusively in motor neurons. This targeted expression of wildtype hnRNPA1 caused severe motor neuron loss and subsequent denervation muscle atrophy in transgenic rats that recapitulated the characteristics of ALS. These findings demonstrate that the augmentation of hnRNPA1 expression suffices to trigger motor neuron degeneration and the manifestation of ALS-like phenotypes. It is reasonable to infer that an amplification of an as-yet undetermined hnRNPA1 function plays a pivotal role in the pathogenesis of familial ALS caused by pathogenic hnRNPA1 mutation.

Lei Meng,1,&ast; Jun Cao,1,2,&ast; Li Kang,3,&ast; Gang Xu,1 Da-Wei Yuan,1 Kang Li,1 Kun Zhu1 1Department of Surgical Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China; 2The Third Affiliated Hospital of Xi’an Medical University, Xi’an, 710068, People’s Republic of China; 3Department of Thoracic Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Kang Li; Kun Zhu, Department of Surgical Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China, Tel +86 13087566643, Email healthlee@xjtu.edu.cn; dr.zhkun@mail.xjtu.edu.cnObjective: Regimens of S-1-based adjuvant chemotherapy are of great significance in attenuating recurrence risk in postoperative patients with gastric cancer (GC). Kinase insert-domain receptor (KDR) gene plays an essential role in tumor growth and metastasis. This study aimed to investigate the implication of KDR genotyping on the therapeutic outcomes of patients with gastric cancer (GC) who received S-1-based adjuvant chemotherapy.Methods: A total of 169 postoperative GC with pathological staging of II and III and no metastasis who received S-1-based adjuvant chemotherapy were included retrospectively. Peripheral blood specimens were collected and prepared for KDR genotyping and KDR mRNA expression. Correlation between KDR genotype status and prognosis was performed using Kaplan–Meier survival analysis, and multivariate analysis was ultimately adopted using Cox regression analysis.Results: Median disease-free survival (DFS) of the 169 patients with GC was 5.1 years [95% confidence interval (CI): 4.25– 5.95] and median overall survival (OS) was 6.7 years (95% CI: 5.44– 7.96). Rs2071559 was located at the upstream region, and the prevalence among 169 patients with GC was

Qiang Guo,1,2,&ast; Xiao-Li Liu,3,&ast; Kui Zhai,4,&ast; Cheng Chen,1 Xi-Xian Ke,1 Jun Zhang,2 Gang Xu1 1Department of Thoracic Surgery, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, People’s Republic of China; 2Department of Cardiothoracic Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, People’s Republic of China; 3Department of Ultrasound, The People’s Hospital of Jianyang City, Jianyang, Sichuan, People’s Republic of China; 4Department of Thoracic Surgery, Xingyi People’s Hospital, Xinyi, Guizhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Gang Xu; Jun Zhang, Email xglhl333@163.com; 13508684276@139.comAbstract: Cancer was one of the common causes of death in the world, and it was increasing year by year. At present, Progestin and AdipoQ receptor family member 3 (PAQR3) was widely studied in cancer. It has been found that PAQR3 was down regulated in various cancers, such as the gastric cancer, osteosarcoma, glioma, hepatocellular carcinoma, acute lymphoblastic leukemia, laryngeal squamous cell carcinoma, esophageal cancer, breast cancer, non-small cell lung cancer, and colorectal cancer. The decreased expression of PAQR3 was associated with short overall survival and disease-free survival in patients with gastric cancer, hepatocellular carcinoma, laryngeal squamous cell carcinoma, esophageal cancer, breast cancer, and non-small cell lung cancer. PAQR3 could inhibit cancer progression by using the Ras/Raf/MEK/ERK, PI3/AKT, EMT and other mechanisms, and was negatively regulated by the miR-543, miR-15b-5p and miR-15b. The roles and signaling mechanisms of PAQR3, and the relationship between the expression of PAQR3 and prognosis in cancer progression are reviewed in this article, and provides new tumor marker and idea to guide cancer treatment.Keywords: PAQR3, overall survival, disease-free survival, tumor suppressor

Zaijun Zou,1,2 Han Li,1,2 Gang Xu,1,3 Yunxiang Hu,2 Weiguo Zhang,1,3 Kang Tian1,3 1Department of Sports Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116011, People’s Republic of China; 2School of Graduates, Dalian Medical University, Dalian, Liaoning, 116000, People’s Republic of China; 3Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopaedic Disease, Dalian, Liaoning Province, 116011, People’s Republic of ChinaCorrespondence: Kang Tian; Weiguo Zhang, Tel +86-18098871513 ; +86-18041191111, Email dmu-tiankang@outlook.com; dlmedu126@126.comAbstract: Exosomes, as natural nanocarriers, characterized with low immunogenicity, non-cytotoxicity and targeted delivery capability, which have advantages over synthetic nanocarriers. Recently, exosomes have shown great potential as diagnostic markers for diseases and are also considered as a promising cell-free therapy. Engineered exosomes have significantly enhanced the efficacy and precision of delivering therapeutic agents, and are currently being extensively employed in targeted therapeutic investigations for various ailments, including oncology, inflammatory disorders, and degenerative conditions. Particularly, engineered exosomes enable therapeutic agent loading, targeted modification, evasion of MPS phagocytosis, intelligent control, and bioimaging, and have been developed as multifunctional nano-delivery platforms in recent years. The utilization of bioactive scaffolds that are loaded with exosome delivery has been shown to substantially augment retention, extend exosome release, and enhance efficacy. This approach has advanced from conventional hydrogels to nanocomposite hydrogels, nanofiber hydrogels, and 3D printing, resulting in superior physical and biological properties that effectively address the limitations of natural scaffolds. Additionally, plant-derived exosomes, which can participate in gut flora remodeling via oral administration, are co

Gang Xu,1 Weizhen Tang,1 Chaosheng Zhou,1 Jie Xu,1 Chao Cheng,1 Weiwei Gong,2,3 Shihong Dong,2 Yu Zhang1 1Department of Rehabilitation Medicine, Affiliated Tenth People’s Hospital of Tongji University, Shanghai Tenth People’s Hospital, Shanghai, 200072, People’s Republic of China; 2Department of Rehabilitation Medicine, Tongji University School of Medicine, Shanghai, 200092, People’s Republic of China; 3Department of Rehabilitation Medicine, Shanghai First Rehabilitation Hospital, Shanghai, 200090, People’s Republic of ChinaCorrespondence: Gang Xu, Department of Rehabilitation Medicine, Affiliated Tenth People’s Hospital of Tongji University, Shanghai Tenth People’s Hospital, 301 Middle Yanchang Road, Shanghai, 200072, People’s Republic of China, Tel +8621-66306496, Fax +8621-66301051, Email sykfsci@126.comPurpose: To evaluate the efficacy and pain fluctuations of methylcobalamin in combination with lidocaine local injection treatment for subacute herpetic neuralgia (SHN).Methods: Seventy-nine women (60.4 ± 2.7 years) with thoracic SHN were enrolled and randomized to receive a combination of methylcobalamin and lidocaine local injection (MI, N=40), or a combination of lidocaine patch 5% and oral methylcobalamin (PO, N=39) for four weeks. Repeated-measures analyses of variance were used to evaluate the effect on pain levels. Generalized estimation equations were used to analyze the cause-effect relationship between pain fluctuations and influencing factors.Results: At the treatment endpoint, the group, treatment time, and group interacted with treatment time effects of the pain scores and area were statistically significant (P< 0.001), The pain scores were 2.9 ± 0.9 (MI) and 4.3 ± 1.5 (PO). 80.00% (MI) or 28.21% (PO) of patients had pain scores ≤ 3, the odds ratio was 2.84 (95% CI: 1.68 to 4.79). The incidence of postherpetic neuralgia was 5.0% (2/40) at 3 months. Pain fluctuated repeatedly during treatment. The pain fluctuation increased fr

Gang Xu,1,2 Site Xu,3 Weiwei Gong,2,4 Shihong Dong,2 Hecheng Yu1 1Department of Rehabilitation Medicine, Affiliated Tenth People’s Hospital of Tongji University, Shanghai Tenth People’s Hospital, Shanghai, 20072, People’s Republic of China; 2Department of Rehabilitation Medicine, Tongji University School of Medicine, Shanghai, 200092, People’s Republic of China; 3Division of Information and Statistics, Affiliated Ruijin Hospital of Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China; 4Department of Rehabilitation Medicine, Shanghai First Rehabilitation Hospital, Shanghai, 200090, People’s Republic of ChinaCorrespondence: Gang Xu, Department of Rehabilitation Medicine, Affiliated Tenth People’s Hospital of Tongji University, Shanghai Tenth People’s Hospital, Shanghai, 20072, People’s Republic of China, Tel +8621-66306496, Fax +8621-66301051, Email sykfsci@126.comBackground: Herpes zoster (HZ) is a skin disease that can also cause virus-infectious peripheral neuropathies. Despite this, there is limited information on patient preferences for seeking medical attention for HZ and zoster-associated pain (ZAP). Our study aimed to evaluate how frequently patients with ZAP choose to visit neurologists for their symptoms.Methods: This study conducted a retrospective review of electronic health records in three general hospitals from January 2017 to June 2022. Using association rule mining, the study analyzed referral behaviors.Results: We identified 33,633 patients with 111,488 outpatient visits over 5.5 years. The study found that the majority of patients (74.77– 91.22%) visited dermatologists during their first outpatient visit, while only a small percentage (0.86– 1.47%) preferred to consult a neurologist. The proportion of patients referred to a specialist during their medical visit varied significantly between different specialties within the same hospital (p < 0.05) and even within the same specialty (p< 0.05

Neural radiance fields (NeRF) based methods have shown amazing performance in synthesizing 3D-consistent photographic images, but fail to generate multi-view portrait drawings. The key is that the basic assumption of these methods -- a surface point is consistent when rendered from different views -- doesn't hold for drawings. In a portrait drawing, the appearance of a facial point may changes when viewed from different angles. Besides, portrait drawings usually present little 3D information and suffer from insufficient training data. To combat this challenge, in this paper, we propose a Semantic-Aware GEnerator (SAGE) for synthesizing multi-view portrait drawings. Our motivation is that facial semantic labels are view-consistent and correlate with drawing techniques. We therefore propose to collaboratively synthesize multi-view semantic maps and the corresponding portrait drawings. To facilitate training, we design a semantic-aware domain translator, which generates portrait drawings based on features of photographic faces. In addition, use data augmentation via synthesis to mitigate collapsed results. We apply SAGE to synthesize multi-view portrait drawings in diverse artistic styles. Experimental results show that SAGE achieves significantly superior or highly competitive performance, compared to existing 3D-aware image synthesis methods. The codes are available at https://github.com/AiArt-HDU/SAGE., Comment: Accepted by IJCAI 2023

We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence, and executing context-aware feature augmentation. To achieve the latter, we use source features of the input and global style codes of the exemplar, as supplementary information, for decoding an image. Besides, we devise a novel contrastive style learning method, for acquire quality-discriminative style representations, which in turn benefit high-quality image generation. Experimental results show that our method, dubbed MATEBIT, performs considerably better than state-of-the-art methods, in diverse image translation tasks. The codes are available at \url{https://github.com/AiArt-HDU/MATEBIT}., Comment: Accepted by CVPR2023

Mesh offsetting plays an important role in discrete geometric processing. In this paper, we propose a parallel feature-preserving mesh offsetting framework with variable distance. Different from the traditional method based on distance and normal vector, a new calculation of offset position is proposed by using dynamic programming and quadratic programming, and the sharp feature can be preserved after offsetting. Instead of distance implicit field, a spatial coverage region represented by polyhedral for computing offsets is proposed. Our method can generate an offsetting model with smaller mesh size, and also can achieve high quality without gaps, holes, and self-intersections. Moreover, several acceleration techniques are proposed for the efficient mesh offsetting, such as the parallel computing with grid, AABB tree and rays computing. In order to show the efficiency and robustness of the proposed framework, we have tested our method on the quadmesh dataset, which is available at [https://www.quadmesh.cloud]. The source code of the proposed algorithm is available on GitHub at [https://github.com/iGame-Lab/PFPOffset].

Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labour-intensive labelling. In contrast, weakly supervised learning methods, which only require coarse-grained labels at the image level, can significantly reduce the labeling efforts. Unfortunately, while these methods perform reasonably well in slide-level prediction, their ability to locate cancerous regions, which is essential for many clinical applications, remains unsatisfactory. Previously, we proposed CAMEL, which achieves comparable results to those of fully supervised baselines in pixel-level segmentation. However, CAMEL requires 1,280x1,280 image-level binary annotations for positive WSIs. Here, we present CAMEL2, by introducing a threshold of the cancerous ratio for positive bags, it allows us to better utilize the information, consequently enabling us to scale up the image-level setting from 1,280x1,280 to 5,120x5,120 while maintaining the accuracy. Our results with various datasets, demonstrate that CAMEL2, with the help of 5,120x5,120 image-level binary annotations, which are easy to annotate, achieves comparable performance to that of a fully supervised baseline in both instance- and slide-level classifications., Comment: 41 pages, 13 figures, published in Advanced Intelligent Systems

The galvanostatic method is a commonly used accelerated corrosion method in studying the durability of concrete structures caused by steel corrosion. If this method is not adequately controlled, the ends of steel bars will be corroded seriously (this phenomenon is called end effect), which is not common in natural corrosion. How to effectively control the end effect during electrified corrosion is helpful to make the characteristics of electrified corrosion more similar to that of natural corrosion. In this paper, experimental and electromagnetic numerical simulation methods are used to study the influence of cathode and corrosion medium coverage area along the longitudinal direction on the end effect of steel bar before producing corrosion crack. The results showed that the wider the coverage area of the corrosive medium (NaCl solution with a mass concentration of 3%), the longer the corrosion area of reinforcement along the longitudinal direction, the more prone the end effect is. The change of cathode length has no noticeable effect on the corrosion area of reinforcement along the longitudinal direction when the corrosion medium coverage area is constant. Increasing the distance between the end of the corrosion medium coverage area and the end of the reinforcement can effectively avoid the end effect.

Acoustic emission (AE) technology is used to monitor the whole process of reinforced concrete rust expanding and cracking, and the accuracy of AE technology to distinguish the location and type of cracks in concrete rust expanding and cracking is studied and verified. The results show that the protective layer is accompanied by a large number of acoustic emission signals in the process of rust expansion and cracking, Moment tensor inversion can more accurately identify crack sources such as shear source, tensile source and mixed source; In the process of corrosion-induced expansion cracking, shear source cracks appear before tensile source cracks and mixed source cracks. It is feasible to use acoustic emission technology to locate and identify cracks.

To create an artificial structure to remarkably surpass the sensitivity, selectivity and speed of the olfaction system of animals is still a daunting challenge. Herein, we propose a core-sheath pillar (CSP) architecture with a perfect synergistic interface that effectively integrates the advantages of metal–organic frameworks and metal oxides to tackle the above-mentioned challenge. The sheath material, NH₂-MIL-125, can concentrate target analyte, nitro-explosives, by 10¹² times from its vapour. The perfect band-matched synergistic interface enables the TiO₂ core to effectively harvest and utilize visible light. At room temperature and under visible light, CSP (TiO₂, NH₂-MIL-125) shows an unexpected self-promoting analyte-sensing behaviour. Its experimentally reached limit of detection (～0.8 ppq, hexogeon) is 10³ times lower than the lowest one achieved by a sniffer dog or all sensing techniques without analyte pre-concentration. Moreover, the sensor exhibits excellent selectivity against commonly existing interferences, with a short response time of 0.14 min.

Kagome materials often host exotic quantum phases, including spin liquids, Chern gap, charge density wave, and superconductivity. Existing scanning microscopy studies of the kagome charge order have been limited to nonkagome surface layers. Here, we tunnel into the kagome lattice of FeGe to uncover features of the charge order. Our spectroscopic imaging identifies a 2×2 charge order in the magnetic kagome lattice, resembling that discovered in kagome superconductors. Spin mapping across steps of unit cell height demonstrates the existence of spin-polarized electrons with an antiferromagnetic stacking order. We further uncover the correlation between antiferromagnetism and charge order anisotropy, highlighting the unusual magnetic coupling of the charge order. Finally, we detect a pronounced edge state within the charge order energy gap, which is robust against the irregular shape fluctuations of the kagome lattice edges. We discuss our results with the theoretically considered topological features of the kagome charge order including unconventional magnetism and bulk-boundary correspondence.

In this paper, a deep learning framework combined with isogeometric analysis (IGA for short) called IGA-Reuse-Net is proposed for efficient reuse of numerical simulation on a set of topology-consistent models. Compared with previous data-driven numerical simulation methods only for simple computational domains, our method can predict high-accuracy PDE solutions over topology-consistent geometries with complex boundaries. UNet3+ architecture with interlaced sparse self-attention (ISSA) module is used to enhance the performance of the network. In addition, we propose a new loss function that combines a coefficients loss and a numerical solution loss. Several training datasets with topology-consistent models are constructed for the proposed framework. To verify the effectiveness of our approach, two different types of Poisson equations with different source functions are solved on three datasets with different topologies. Our framework can achieve a good trade-off between accuracy and efficiency. It outperforms the physics-informed neural network (PINN for short) model and yields promising results of prediction., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Materials and Manufacturing

Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images. Recent networks mainly resort to image decomposition techniques with complex multi-branch architectures. However, the fixed decomposition techniques would largely restricts their power on versatile images. To exploit the potential power of decomposition mechanism, in this paper, we generalize it from the image domain to the broader feature domain. To this end, we propose a lightweight Feature Decomposition Aggregation Network (FDAN). In particular, we design a Feature Decomposition Block (FDB) to achieve learnable separation of detail and base feature maps, and develop a Hierarchical Feature Decomposition Group by cascading FDBs for powerful multi-level feature decomposition. Moreover, to better evaluate the comparison methods, we collect a large-scale dataset for joint SR-ITM, i.e., SRITM-4K, which provides versatile scenarios for robust model training and evaluation. Experimental results on two benchmark datasets demonstrate that our FDAN is efficient and outperforms state-of-the-art methods on joint SR-ITM. The code of our FDAN and the SRITM-4K dataset are available at https://github.com/CS-GangXu/FDAN., Comment: update the authors info and the article template

Ruihuan Du,1 Gang Xu,1,2 Xujue Bai,1 Zhonghai Li1,2 1Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian, People’s Republic of China; 2Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian, People’s Republic of ChinaCorrespondence: Zhonghai Li, The Third Department of the First Affiliated Hospital of Dalian Medical University, No. 5 Longbin Road, Development Zone, Dalian, 116011, People’s Republic of China, Tel +86-18098876419, Fax +86-411-83635963, Email lizhonghaispine@126.comAbstract: Facet joint osteoarthritis (OA) is the most frequent form of facet joint syndrome. Medical history, referred pain patterns, physical examination, and diagnostic imaging studies (standard radiographs, magnetic resonance imaging, computed tomography and single-photon emission computed tomography) may suggest but not confirm lumbar facet joint (LFJ) syndrome as a source of low back pain (LBP). However, the diagnosis and treatment of facet joint syndrome is still controversial and needs further study. It is widely acknowledged that block with local anesthetic is perhaps the most effective method to establish a diagnosis of pain from LFJ. Particularly, there are different rates of success among different populations selected for diagnostic block with various positive criteria. Currently, in addition to conservative treatments for pain such as painkillers, functional exercises, and massage, there are many other methods, including block, denervation of the nerves that innervate the joints by radiofrequency, freezing or endoscopy, and injections. Due to the limited duration of pain relief from neurolysis of medial branch, many scholars have recently turned their targets to dorsal roots and LFJ capsules. Therefore, we reviewed the latest research progress of facet joint syndrome from diagnosis to treatment.Keywords: low back pain, medial branch, block, radiofrequency, neurolysis

Aims/Introduction: β‐Cell dysfunction is a hallmark of type 2 diabetes. In a previous pilot study, we identified an association between genetic variants within the human DACH1 gene and young‐onset type 2 diabetes. Here, we characterized the function of dachb, the only dach homologue to be expressed in the pancreas, in developing zebrafish embryos. Materials and Methods: We injected one‐cell stage embryos with a dachb‐morpholino (MO) or with the dachb‐MO and dachb messenger ribonucleic acid, and determined the effect on the development of the pancreatic islet. We also carried out quantitative polymerase chain reaction and ribonucleic acid sequencing on the dachb‐MO group to determine the effect of dachb knockdown on gene expression. Results: MO‐mediated dachb knockdown resulted in impaired islet cell development, with a significant decrease in both the β‐cell and islet cell numbers. This islet developmental defect was rescued when embryos were co‐injected with dachb‐MO and dachb messenger ribonucleic acid. Knockdown of dachb was associated with a significant downregulation of the β‐cell specific marker gene, insa, and the somatostatin cell marker, sst2, as well as regulators of pancreas development, ptf1a, neuroD, pax6a and nkx6.1, and the cell cycle gene, insm1a. Furthermore, ribonucleic sequencing analysis showed an upregulation of genes enriched in the forkhead box O and mitogen‐activated protein kinase signaling pathways in the dachb‐MO group, when compared with the control groups. Conclusions: Together, our results suggest the possible role of dachb in islet development in zebrafish.of particle physics and cosmology. Using data from previous editions, plus 3,324 new measurements from 878 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. P

© 2020 Elsevier Ltd Sodium metal anodes (SMAs) are regarded as a good candidate for next-generation alkali metal batteries owing to the higher natural reserves and lower cost of sodium. Nevertheless, the challenging issues associated with SMAs, including the dendritic formation and the high reactivity of sodium metal, have significantly impeded the utilization of SMAs in the practical deployment of sodium metal batteries. Herein, we report that 2D graphitic carbon frameworks embedded with highly dispersive Sn nanoparticles (denoted as Sn/C) serve as a robust nucleation buffer layer to spatially guide the sodium deposition. The embedded Sn nanoparticles (NPs) are able to lower the nucleation barrier through in situ formed “sodiophilic” Na-Sn alloy. Meanwhile, the supporting carbon frameworks can effectively keep the functionality of nucleation sites for long-term cycling. As a result, the synergistic effects of these two components endow the cell with outstanding cyclability even at high areal capacities. The symmetric cells using the 2D Sn/C nucleation buffer layer can deliver a continuous plating/stripping cycles for nearly 3900 h with an average Coulombic efficiency (CE) of 99.63% at 4 mA h cm−2. Additionally, an ultrahigh sodium utilization can also be realized, as is evidenced by the stable cycling (up to 1272 h), large reversible capacity (4 mA h cm−2) and high depth of discharge (66.67%) of the symmetric cells with the nucleation buffer layer. It is found that the current density plays a greater influence than the reversible areal capacity on the SMA stability. This study provides a new insight on the role of nucleation buffer layer in the stabilization of SMAs.

Intertwining quantum order and non-trivial topology is at the frontier of condensed matter physics1-4. A charge-density-wave-like order with orbital currents has been proposed for achieving the quantum anomalous Hall effect5,6 in topological materials and for the hidden phase in cuprate high-temperature superconductors7,8. However, the experimental realization of such an order is challenging. Here we use high-resolution scanning tunnelling microscopy to discover an unconventional chiral charge order in a kagome material, KV3Sb5, with both a topological band structure and a superconducting ground state. Through both topography and spectroscopic imaging, we observe a robust 2 × 2 superlattice. Spectroscopically, an energy gap opens at the Fermi level, across which the 2 × 2 charge modulation exhibits an intensity reversal in real space, signalling charge ordering. At the impurity-pinning-free region, the strength of intrinsic charge modulations further exhibits chiral anisotropy with unusual magnetic field response. Theoretical analysis of our experiments suggests a tantalizing unconventional chiral charge density wave in the frustrated kagome lattice, which can not only lead to a large anomalous Hall effect with orbital magnetism, but also be a precursor of unconventional superconductivity.

In this paper, a novel deep learning framework is proposed for temporal super-resolution simulation of blood vessel flows, in which a high-temporal-resolution time-varying blood vessel flow simulation is generated from a low-temporal-resolution flow simulation result. In our framework, point-cloud is used to represent the complex blood vessel model, resistance-time aided PointNet model is proposed for extracting the time-space features of the time-varying flow field, and finally we can reconstruct the high-accuracy and high-resolution flow field through the Decoder module. In particular, the amplitude loss and the orientation loss of the velocity are proposed from the vector characteristics of the velocity. And the combination of these two metrics constitutes the final loss function for network training. Several examples are given to illustrate the effective and efficiency of the proposed framework for temporal super-resolution simulation of blood vessel flows.

Yuchao Zhang, Gang Xu, Ping Wang Department of Vascular Surgery, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian City, 223300, People’s Republic of ChinaCorrespondence: Ping Wang Tel/Fax +86-0517-80872149Email drwangping@126.comBackground: Recent researches have investigated the link between the rs1800796 polymorphism (− 572G/C) in the interleukin-6 (IL-6) gene and the risk of abdominal aortic aneurysm (AAA). However, no Chinese studies have addressed the association between the risk of AAA and this polymorphism.Methods: This case-control study included 153 AAA patients and 205 controls. Diabetes mellitus, hypertension, dyslipidemia, and coronary artery disease (CAD) status were collected. The AAA and control groups were 69.20 ± 7.56 and 68.50 ± 7.12 years old, respectively.Results: The analysis revealed that the G allele and GG genotype of the IL-6 rs1800796 polymorphism were remarkedly correlated with an elevated risk of AAA. The GG genotype displayed an association with the risk of AAA after adjustment for gender, body mass index, age, drinking, and smoking. Subgroup analyses indicated that this polymorphism elevated the risk of AAA among males, individuals aged ≥ 70 years, smokers, drinkers, individuals with a body mass index (BMI) ≥ 25 kg/m2, patients without diabetes, and patients with hypertension. Univariate and multivariate analyses indicated that smoking, hypertension, and the GG genotype (rs1800796 polymorphism) were independently related with the risk of AAA.Conclusion: The IL-6 rs1800796 polymorphism increases the risk of AAA. In addition, smoking, hypertension, and the GG genotype of the rs1800796 polymorphism are independent risk factors for AAA. Further studies of Chinese populations are needed to validate these findings.Keywords: IL-6, abdominal aortic aneurysm, rs1800796 polymorphism, risk factors

A key challenge in the task of human pose and shape estimation is occlusion, including self-occlusions, object-human occlusions, and inter-person occlusions. The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild. In this paper, we focus on the estimation of human pose and shape in the case of inter-person occlusions, while also handling object-human occlusions and self-occlusion. We propose a novel framework that synthesizes occlusion-aware silhouette and 2D keypoints data and directly regress to the SMPL pose and shape parameters. A neural 3D mesh renderer is exploited to enable silhouette supervision on the fly, which contributes to great improvements in shape estimation. In addition, keypoints-and-silhouette-driven training data in panoramic viewpoints are synthesized to compensate for the lack of viewpoint diversity in any existing dataset. Experimental results show that we are among the state-of-the-art on the 3DPW and 3DPW-Crowd datasets in terms of pose estimation accuracy. The proposed method evidently outperforms Mesh Transformer, 3DCrowdNet and ROMP in terms of shape estimation. Top performance is also achieved on SSP-3D in terms of shape prediction accuracy. Demo and code will be available at https://igame-lab.github.io/LASOR/.

This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets, which unrealistically assume that each image should contain at least one clear and uncluttered salient object. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. However, these models are still far from satisfactory when applied to real-world scenes. Based on our analyses, we propose a new high-quality dataset and update the previous saliency benchmark. Specifically, our dataset, called Salient Objects in Clutter~\textbf{(SOC)}, includes images with both salient and non-salient objects from several common object categories. In addition to object category annotations, each salient image is accompanied by attributes that reflect common challenges in common scenes, which can help provide deeper insight into the SOD problem. Further, with a given saliency encoder, e.g., the backbone network, existing saliency models are designed to achieve mapping from the training image set to the training ground-truth set. We, therefore, argue that improving the dataset can yield higher performance gains than focusing only on the decoder design. With this in mind, we investigate several dataset-enhancement strategies, including label smoothing to implicitly emphasize salient boundaries, random image augmentation to adapt saliency models to various scenarios, and self-supervised learning as a regularization strategy to learn from small datasets. Our extensive results demonstrate the effectiveness of these tricks. We also provide a comprehensive benchmark for SOD, which can be found in our repository: https://github.com/DengPingFan/SODBenchmark., Comment: 349 references, 20 pages, survey 201 models, benchmark 100 models. Online benchmark: https://github.com/DengPingFan/SODBenchmark

Space-time video super-resolution (STVSR) aims to increase the spatial and temporal resolutions of low-resolution and low-frame-rate videos. Recently, deformable convolution based methods have achieved promising STVSR performance, but they could only infer the intermediate frame pre-defined in the training stage. Besides, these methods undervalued the short-term motion cues among adjacent frames. In this paper, we propose a Temporal Modulation Network (TMNet) to interpolate arbitrary intermediate frame(s) with accurate high-resolution reconstruction. Specifically, we propose a Temporal Modulation Block (TMB) to modulate deformable convolution kernels for controllable feature interpolation. To well exploit the temporal information, we propose a Locally-temporal Feature Comparison (LFC) module, along with the Bi-directional Deformable ConvLSTM, to extract short-term and long-term motion cues in videos. Experiments on three benchmark datasets demonstrate that our TMNet outperforms previous STVSR methods. The code is available at https://github.com/CS-GangXu/TMNet., Comment: This paper is accepted at IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021

A textile fabric consists of countless parallel vertical yarns (warps) and horizontal yarns (wefts). While common looms can weave repetitive patterns, Jacquard looms can weave the patterns without repetition restrictions. A pattern in which the warps and wefts cross on a grid is defined in a binary matrix. The binary matrix can define which warp and weft is on top at each grid point of the Jacquard fabric. The process can be regarded as encoding from pattern to textile. In this work, we propose a decoding method that generates a binary pattern from a textile fabric that has been already woven. We could not use a deep neural network to learn the process based solely on the training set of patterns and observed fabric images. The crossing points in the observed image were not completely located on the grid points, so it was difficult to take a direct correspondence between the fabric images and the pattern represented by the matrix in the framework of deep learning. Therefore, we propose a method that can apply the framework of deep learning via the intermediate representation of patterns and images. We show how to convert a pattern into an intermediate representation and how to reconvert the output into a pattern and confirm its effectiveness. In this experiment, we confirmed that 93% of correct pattern was obtained by decoding the pattern from the actual fabric images and weaving them again.

2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Sodium metal is an ideal anode material for metal rechargeable batteries, owing to its high theoretical capacity (1166 mAh g−1), low cost, and earth-abundance. However, the dendritic growth upon Na plating, stemming from unstable solid electrolyte interphase (SEI) film, is a major and most notable problem. Here, a sodium benzenedithiolate (PhS2Na2)-rich protection layer is synthesized in situ on sodium by a facile method that effectively prevents dendrite growth in the carbonate electrolyte, leading to stabilized sodium metal electrodeposition for 400 cycles (800 h) of repeated plating/stripping at a current density of 1 mA cm−2. The organic salt, PhS2Na2, is found to be a critical component in the protection layer. This finding opens up a new and promising avenue, based on organic sodium slats, to stabilize sodium metals with a protection layer.

In many cases, the computing resources are limited without the benefit from GPU, especially in the edge devices of IoT enabled systems. It may not be easy to implement complex AI models in edge devices. The Universal Approximation Theorem states that a shallow neural network (SNN) can represent any nonlinear function. However, how fat is an SNN enough to solve a nonlinear decision-making problem in edge devices? In this paper, we focus on the learnability and robustness of SNNs, obtained by a greedy tight force heuristic algorithm (performance driven BP) and a loose force meta-heuristic algorithm (a variant of PSO). Two groups of experiments are conducted to examine the learnability and the robustness of SNNs with Sigmoid activation, learned/optimised by KPI-PDBPs and KPI-VPSOs, where, KPIs (key performance indicators: error (ERR), accuracy (ACC) and $F_1$ score) are the objectives, driving the searching process. An incremental approach is applied to examine the impact of hidden neuron numbers on the performance of SNNs, learned/optimised by KPI-PDBPs and KPI-VPSOs. From the engineering prospective, all sensors are well justified for a specific task. Hence, all sensor readings should be strongly correlated to the target. Therefore, the structure of an SNN should depend on the dimensions of a problem space. The experimental results show that the number of hidden neurons up to the dimension number of a problem space is enough; the learnability of SNNs, produced by KPI-PDBP, is better than that of SNNs, optimized by KPI-VPSO, regarding the performance and learning time on the training data sets; the robustness of SNNs learned by KPI-PDBPs and KPI-VPSOs depends on the data sets; and comparing with other classic machine learning models, ACC-PDBPs win for almost all tested data sets., Comment: 36 pages, 21 figues for corresponding eps files. Neural Comput & Applic (2021)

Nowadays, interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition. During the InSAR processing, phase denoising of interferogram is a mandatory step for topography mapping and deformation monitoring. Over the last three decades, a large number of effective algorithms have been developed to do efforts on this topic. In this paper, we give a comprehensive overview of InSAR phase denoising methods, classifying the established and emerging algorithms into four main categories. The first two parts refer to the categories of traditional local filters and transformed-domain filters, respectively. The third part focuses on the category of nonlocal (NL) filters, considering their outstanding performances. Latter, some advanced methods based on new concept of signal processing are also introduced to show their potentials in this field. Moreover, several popular phase denoising methods are illustrated and compared by performing the numerical experiments using both simulated and measured data. The purpose of this paper is intended to provide necessary guideline and inspiration to related researchers by promoting the architecture development of InSAR signal processing., Comment: G. Xu, Y. Gao, J. Li and M. Xing, "InSAR Phase Denoising: A Review of Current Technologies and Future Directions," IEEE Geoscience and Remote Sensing Magazine, vol. 8, no. 2, pp. 64-82, June 2020. DOI 10.1109/MGRS.2019.2955120

Sodium metal anode (SMA) is one of the most favored choices for the next-generation rechargeable battery technologies owing to its low cost and natural abundance. However, the poor reversibility resulted from dendrite growth and formation of unstable solid electrolyte interphase has significantly hindered the practical application of SMAs. Herein, we report that a nucleation buffer layer comprising elaborately designed core-shell C@Sb nanoparticles (NPs) enables the homogeneous electrochemical deposition of sodium metal for long-term cycling. These C@Sb NPs can increase active sites for initial sodium nucleation through Sb-Na alloy cores and keep these cores stable through carbon shells. The assembled cells with this nucleation layer can deliver continuously repeated sodium plating/stripping cycles for nearly 6000 h at a high areal capacity of 4 mA h cm-2 with an average Coulombic efficiency 99.7%. This ingenious structure design of alloy-based nucleation agent opens up a promising avenue to stabilize sodium metal with targeted properties.

Wang X, Fang H, Xu G, et al. Diabetes Metab Syndr Obes. 2020;13:1061–1075.The authors have advised the affiliations listed for Hui Fang and the correspondence address on page 1061 are incorrect. The text “Hui Fang2” in the author list should read “Hui Fang1,2”. The correspondence address is as follows.Correspondence: Hui FangDepartment of Internal Medicine, Hebei Medical University,361 Zhongshan East Road, Shijiazhuang, Hebei 050017,People’s Republic of ChinaTel +86-13831581838Fax +8631186266193Email fanghui2018@126.comThe authors apologize for these errors.Read the original article&nbsp

Xiaoxiao Wang, 1 Hui Fang, 2 Gang Xu, 3 Ying Yang, 2 Ruizhe Xu, 4 Qiang Liu, 5 Xiangyu Xue, 5 Jiaqi Liu, 5 Hezhi Wang 6 1Department of Internal Medicine, Hebei Medical University, Shijiazhuang, Hebei 050017, People’s Republic of China; 2Second Department of Endocrinology, Tangshan Gongren Hospital, Tangshan, Hebei 063000, People’s Republic of China; 3Department of Burns and Orthopedics, Tangshan Gongren Hospital, Tangshan, Hebei 063000, People’s Republic of China; 4Department of Clinical Medicine, Tangshan Vocational and Technical College, Tangshan, Hebei, 063000, People’s Republic of China; 5Department of Internal Medicine, North China University of Science and Technology, Tangshan, Hebei 063000, People’s Republic of China; 6Department of Surgery, Hebei Medical University, Shijiazhuang 050017, People’s Republic of ChinaCorrespondence: Hui FangSecond Department of Endocrinology, Tangshan Gongren Hospital, 27 Wenhua Road, Lubei District, Tangshan, Hebei 063000, People’s Republic of ChinaTel +86-13831581838Fax +00863152814801Email fanghui2018@126.comPurpose: This study aimed to determine whether the natural antioxidant resveratrol (RSV) prevents type 2 diabetes mellitus (T2DM)-induced cognitive impairment and to explore whether redox-associated factor nuclear factor erythroid 2-related factor 2 (Nrf2) plays a critical role in the neuroprotective effect of RSV.Materials and Methods: We established a T2DM model with 8-week-old male ICR mice by administration of a high-fat diet for 2 months and low-dose streptozotocin for 3 days. Then, diabetic and age-matched control mice were treated with or without RSV for 4 months every other day and subjected to the Morris water maze test. After the mice were euthanized, whole brains were sectioned for Nissl staining and immunofluorescence labeling. Hippocampal sections were observed by transmission electron microscopy to evaluate the ultrastructure of sy

The interplay between electronic correlations and topological protection may offer a rich avenue for discovering emergent quantum phenomena in condensed matter. However, electronic correlations have so far been little investigated in Weyl semimetals (WSMs) by experiments. Here, we report a combined optical spectroscopy and theoretical calculation study on the strength and effect of electronic correlations in a magnet Co3Sn2S2. The electronic kinetic energy estimated from our optical data is about half of that obtained from single-particle ab initio calculations in the ferromagnetic ground state, which indicates intermediate-strength electronic correlations in this system. Furthermore, comparing the energy and side-slope ratios between the interband-transition peaks at high energies in the experimental and single-particle-calculation-derived optical conductivity spectra with the bandwidth-renormalization factors obtained by many-body calculations enables us to estimate the Coulomb-interaction strength (U ∼ 4 eV) in Co3Sn2S2. Besides, a sharp experimental optical conductivity peak at low energy, which is absent in the single-particle-calculation-derived spectrum but is consistent with the optical conductivity peaks obtained by many-body calculations with U ∼ 4 eV, indicates that an electronic band connecting the two Weyl cones is flattened by electronic correlations and emerges near the Fermi energy in Co3Sn2S2. Our work paves the way for exploring flat-band-generated quantum phenomena in WSMs.

Hypobaric hypoxia (HH) exposure can cause serious brain injury as well as life-threatening cerebral edema in severe cases. Previous studies on the mechanisms of HH-induced brain injury have been conducted primarily using non-primate animal models that are genetically distant to humans, thus hindering the development of disease treatment. Here, we report that cynomolgus monkeys ( Macacafascicularis) exposed to acute HH developed human-like HH syndrome involving severe brain injury and abnormal behavior. Transcriptome profiling of white blood cells and brain tissue from monkeys exposed to increasing altitude revealed the central role of the HIF-1 and other novel signaling pathways, such as the vitamin D receptor (VDR) signaling pathway, in co-regulating HH-induced inflammation processes. We also observed profound transcriptomic alterations in brains after exposure to acute HH, including the activation of angiogenesis and impairment of aerobic respiration and protein folding processes, which likely underlie the pathological effects of HH-induced brain injury. Administration of progesterone (PROG) and steroid neuroprotectant 5α-androst-3β,5,6β-triol (TRIOL) significantly attenuated brain injuries and rescued the transcriptomic changes induced by acute HH. Functional investigation of the affected genes suggested that these two neuroprotectants protect the brain by targeting different pathways, with PROG enhancing erythropoiesis and TRIOL suppressing glutamate-induced excitotoxicity. Thus, this study advances our understanding of the pathology induced by acute HH and provides potential compounds for the development of neuroprotectant drugs for therapeutic treatment.

We have performed a multi-wavelength study toward a quasi-sinusoidal filament (CFG028.68-0.28). A new large-scale (CO)-C-12 J = 3 - 2 map was obtained from the China-Cologne Observation for SubMillimeter Astronomy (CCOSMA) 3m radio telescope. Based on the ATLASGAL catalog, we have identified 27 dust clumps in the filament. Through the relationship between the mass and radius of these clumps, 67% of these clumps are dense and massive enough to potentially form massive stars. The obtained CFE is similar to 11% in the filament. The filament has a linear mass density of similar to 305 M(circle dot)pc(-1), which is smaller than its critical mass to length ratio. This suggests that the external pressure from the neighboring H II regions may help prevent the filament from dispersing under the effects of turbulence. Comparing the energy injection from outflows and H II regions in the filament, the ionization feedback from the H II regions can help maintain the observed turbulence.

The interplay between electronic correlations and topological protection may offer a rich avenue for discovering emergent quantum phenomena in condensed matter. However, electronic correlations have so far been little investigated in Weyl semimetals (WSMs) by experiments. Here, we report a combined optical spectroscopy and theoretical calculation study on the strength of electronic correlations in a kagome magnet Co3Sn2S2 and the influence of electronic correlations on its WSM state expected within a single-particle picture. The electronic kinetic energy estimated from our optical data is about half of that obtained from single-particle ab initio calculations, which indicates intermediate-strength electronic correlations in this system. Furthermore, comparing the energy ratios between the interband-transition peaks at high energies in the experimental and single-particle-ab-initio-calculation derived optical conductivity spectra with the electronic bandwidth renormalization factors obtained by many-body calculations enables us to estimate the Coulomb-interaction strength (U ~ 4 eV) of electronic correlations in Co3Sn2S2. Our many-body calculations with U ~ 4 eV show that a WSM state, which is characterized by bulk Weyl cones and surface Fermi arcs, survives in this correlated electron system. More importantly, a sharp experimental optical conductivity peak at low energy, which is absent in the single-particle-ab-initio-calculation-derived optical conductivity spectrum but is consistent with the optical conductivity peaks obtained by many-body calculations, indicates that an electronic band connecting the two Weyl cones is flattened by electronic correlations and emerges near the Fermi energy in Co3Sn2S2. Our work not only provides a paradigm for identifying WSM phase in correlated materials, but also paves the way for exploring flat-band-generated quantum phenomena in WSMs.

Numerous urban indicators scale with population in a power law across cities, but whether the cross-sectional scaling law is applicable to the temporal growth of individual cities is unclear. Here we first find two paradoxical scaling relationships that urban built-up area sub-linearly scales with population across cities, but super-linearly scales with population over time in most individual cities because urban land expands faster than population grows. Different cities have diverse temporal scaling exponents and one city even has opposite temporal scaling regimes during two periods, strongly supporting the absence of single temporal scaling and further illustrating the failure of cross-sectional urban scaling in predicting temporal growth of cities. We propose a conceptual model that can clarify the essential difference and also connections between the cross-sectional scaling law and temporal trajectories of cities. Our model shows that cities have an extra growth of built-up area over time besides the supposed growth predicted by the cross-sectional scaling law. Disparities of extra growth among different-sized cities change the cross-sectional scaling exponent. Further analyses of GDP and other indicators confirm the contradiction between cross-sectional and temporal scaling relationships and the validity of the conceptual model. Our findings may open a new avenue towards the science of cities.

Histopathology image analysis plays a critical role in cancer diagnosis and treatment. To automatically segment the cancerous regions, fully supervised segmentation algorithms require labor-intensive and time-consuming labeling at the pixel level. In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels. Using multiple instance learning (MIL)-based label enrichment, CAMEL splits the image into latticed instances and automatically generates instance-level labels. After label enrichment, the instance-level labels are further assigned to the corresponding pixels, producing the approximate pixel-level labels and making fully supervised training of segmentation models possible. CAMEL achieves comparable performance with the fully supervised approaches in both instance-level classification and pixel-level segmentation on CAMELYON16 and a colorectal adenoma dataset. Moreover, the generality of the automatic labeling methodology may benefit future weakly supervised learning studies for histopathology image analysis., Comment: 10 pages, 9 figures, accepted by ICCV 2019

Homework grading is critical to evaluate teaching quality and effect. However, it is usually time-consuming to grade the homework manually. In automatic homework grading scenario, many optical mark reader (OMR)-based solutions which require specific equipments have been proposed. Although many of them can achieve relatively high accuracy, they are less convenient for users. In contrast, with the popularity of smart phones, the automatic grading system which depends on the image photographed by phones becomes more available. In practice, due to different photographing angles or uneven papers, images may be distorted. Moreover, most of images are photographed under complex backgrounds, making answer areas detection more difficult. To solve these problems, we propose BAGS, an automatic homework grading system which can effectively locate and recognize handwritten answers. In BAGS, all the answers would be written above the answer area underlines (AAU), and we use two segmentation networks based on DeepLabv3+ to locate the answer areas. Then, we use the characters recognition part to recognize students' answers. Finally, the grading part is designed for the comparison between the recognized answers and the standard ones. In our test, BAGS correctly locates and recognizes the handwritten answers in 91% of total answer areas.

In this paper, we propose an improved singularity structure simplification method for hexahedral (hex) meshes using a weighted ranking approach. In previous work, the selection of to-be-collapsed base complex sheets/chords is only based on their thickness, which will introduce a few closed-loops and cause an early termination of simplification and a slow convergence rate. In this paper, a new weighted ranking function is proposed by combining the valence prediction function of local singularity structure, shape quality metric of elements and the width of base complex sheets/chords together. Adaptive refinement and local optimization are also introduced to improve the uniformity and aspect ratio of mesh elements. Compared to thickness ranking methods, our weighted ranking approach can yield a simpler singularity structure with fewer base-complex components, while achieving comparable Hausdorff distance ratio and better mesh quality. Comparisons on a hex-mesh dataset are performed to demonstrate the effectiveness of the proposed method.

The Internet of Things (IoT) enables environmental monitoring by collecting data from sensing devices, including cameras and microphones. The popularity of smartphones enables mobile users to communicate and collect data from their surrounding sensing devices. The mobile devices can obtain useful environmental data from nearby sensors through short-range communication such as Bluetooth. Nevertheless, the limited contact time and the wireless capacity constrain the amount of data to be collected. With the increasing amount of multimedia big data such as videos and pictures from cameras, it is crucial for mobile users to collect prioritized data that can maximize their data utility. In this paper, we propose a distributed algorithm to provide information-centric ubiquitous data collection of multimedia big data by mobile users in the IoT. The algorithm can handle transmissions of multimedia big data recorded by the surrounding cameras and sensors, and prioritize the transmissions of the most important and relevant data. The mobile users construct data collection trees adaptively according to their dynamic moving speeds and the value of information carried by the multimedia and sensor data. The distributed algorithm can support smooth data collection and coordination of multiple mobile users. We provide both numerical analysis and extensive simulations to evaluate the information value, energy efficiency and scalability of our solution. The results showed that our distributed algorithm can improve the value of information up to 50% and reduce energy consumption to half compared with existing approach. Our algorithm also scales perfectly well with increasing number of mobile users and dynamic moving speeds.

Background: Evidence suggests the possible impact of ambient high temperature on fetal growth and birth outcomes. However, little is known about the relative impact of exposure to heat and cold and the possible vulnerable window during pregnancy. Methods: Data on a total of 237,585 pregnant women from January 1st, 2001 to December 31st, 2010 were acquired from the Queensland Health, Australia. Daily data on meteorological factors, including ambient temperature, relative humidity, barometric pressure, and air pollutants, such as PM10, SO2, NO2, and O3, were obtained from relevant government agencies. This study was to examine the associations of maternal exposure to ambient temperature (high and low temperatures, in early vs. late pregnancy) with the duration of gestation and birth weight. Results: A J-shaped association between minimum temperature at conception and duration of gestation was observed after adjusting for seasonality and other confounders. Compared to women who were exposed to the minimum temperature of 15–20 °C in the first gestational week, exposure to the minimum temperature of > 20 °C significantly increased the duration of gestation by 0.029 weeks (95% CI: 0.008, 0.049). A cumulative effect was found when exposure across the first four weeks was examined. There was an inverted U-shaped relationship between minimum temperature at delivery and the duration of gestation. Compared to women exposed to 15–20 °C, exposure to minimum temperature of > 20 °C and ≤ 10 °C was associated with a shortened gestation by 0.030 weeks (95% CI: -0.052, − 0.008) and 0.018 weeks (95% CI: -0.057, − 0.004), respectively. By contrast, an inverse relationship between maximum temperature and birth weight was observed. Compared to exposure to the maximum temperature of > 30 °C in the last week of pregnancy, maternal exposure to 20–25 °C and < 20 °C significantly increased birth weight by 0.011 kg (95% CI: 0.008, 0.018) and 0.018 kg (95% CI: 0.010, 0.031), respectively. Simil

This paper presents an approach to generalize the concept of isogeometric analysis (IGA) by allowing different spaces for parameterization of the computational domain and for approximation of the solution field. The method inherits the main advantage of isogeometric analysis, i.e. preserves the original, exact CAD geometry (for example, given by NURBS), but allows pairing it with an approximation space which is more suitable/flexible for analysis, for example, T-splines, LR-splines, (truncated) hierarchical B-splines, and PHT-splines. This generalization offers the advantage of adaptive local refinement without the need to re-parameterize the domain, and therefore without weakening the link with the CAD model. We demonstrate the use of the method with different choices of the geometry and field splines, and show that, despite the failure of the standard patch test, the optimum convergence rate is achieved for non-nested spaces.

In this paper, we propose a general framework for constructing IGA-suitable planar B-spline parameterizations from given complex CAD boundaries. Instead of the computational domain bounded by four B-spline curves, planar domains with high genus and more complex boundary curves are considered. Firstly, some pre-processing operations including B´ezier extraction and subdivision are performed on each boundary curve in order to generate a high-quality planar parameterization; then a robust planar domain partition framework is proposed to construct high-quality patch-meshing results with few singularities from the discrete boundary formed by connecting the end points of the resulting boundary segments. After the topology information generation of quadrilateral decomposition, the optimal placement of interior B´ezier curves corresponding to the interior edges of the quadrangulation is constructed by a global optimization method to achieve a patch-partition with high quality. Finally, after the imposition of C1/G1-continuity constraints on the interface of neighboring Bezier patches with respect to each quad in the quadrangulation, the high-quality Bezier patch parameterization is obtained by a local optimization method to achieve uniform and orthogonal iso-parametric structures while keeping the continuity conditions between patches. The efficiency and robustness of the proposed method are demonstrated by several examples which are compared to results obtained by the skeleton-based parameterization approach.