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En-Yi Lin,1,2 Shao-Xi Hsu,1 Bing-Hua Wu,1 Yu-Chen Deng,1,3 Wei Wuli,1 Yuan-Sheng Li,3 Jui-Hao Lee,3 Shinn-Zong Lin,2,4 Horng-Jyh Harn,2,5 Tzyy-Wen Chiou1 1Department of Life Science and Graduate Institute of Biotechnology, National Dong Hwa University, Hualien, Taiwan; 2Bioinnovation Center, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; 3Everfront Biotech Inc, Taipei, Taiwan; 4Department of Neurosurgery, Hualien Tzu Chi Hospital, Hualien, Taiwan; 5Department of Pathology, Hualien Tzu Chi Hospital, Hualien, TaiwanCorrespondence: Tzyy-Wen Chiou, Department of Life Science, National Dong Hwa University, No. 1, Sec. 2, Da Hsueh Road, Shou-Feng, Hualien, Taiwan, Tel +886-3-890-3638, Email twchiou@gms.ndhu.edu.tw Horng-Jyh Harn, Department of Pathology, Hualien Tzu Chi Hospital, Hualien, Taiwan, Tel +886-3-856-1825, Email arthewduke@gmail.comPurpose: Exosomes are membrane vesicles secreted by various cells and play a crucial role in intercellular communication. They can be excellent delivery vehicles for oligonucleotide drugs, such as microRNAs, due to their high biocompatibility. MicroRNAs have been shown to be more stable when incorporated into exosomes; however, the lack of targeting and immune evasion is still the obstacle to the use of these microRNA-containing nanocarriers in clinical settings. Our goal was to produce functional exosomes loaded with target ligands, immune evasion ligand, and oligonucleotide drug through genetic engineering in order to achieve more precise medical effects.Methods: To address the problem, we designed engineered exosomes with exogenous cholecystokinin (CCK) or somatostatin (SST) as the targeting ligand to direct the exosomes to the brain, as well as transduced CD47 proteins to reduce the elimination or phagocytosis of the targeted exosomes. MicroRNA-29b-2 was the tested oligonucleotide drug for delivery because our previous research showed that this type of microRNA was capable of reducing presenilin 1 (PSEN1) gene expression a

Yu-Xiang Deng,1,* Yu-Jie Zhao,2,* Qiao-Hong Nong,3 Hong-Mei Qiu,1 Qiao-Li Guo,1 Hui Hu1 1Department of Thyroid and Breast Surgery, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518000, People’s Republic of China; 2Department of Radiotherapy, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518000, People’s Republic of China; 3Department of Oncology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hui Hu, Department of Thyroid and Breast Surgery, Peking University Shenzhen Hospital, 1120 Lianhua Road, Shenzhen, 518000, People’s Republic of China, Tel +86-755-83923333, Email Sapphiretjmu@163.comBackground: The immune system appears to play a crucial role in how breast cancer responds to chemotherapy. In this study, we investigated a peripheral marker of immune and inflammation named the neutrophil to albumin ratio (NAR) to explore its potential relationship with pathological complete response (pCR) in locally advanced breast cancer patients who underwent neoadjuvant chemotherapy (NAC).Methods: We conducted a retrospective analysis of 212 consecutive breast cancer patients who received NAC. The NAR was calculated by examining the complete blood cell count and albumin level in peripheral blood before starting NAC. Through ROC curve analysis, we determined the optimal cutoff value for NAR as 0.0877. We used Pearson’s chi-square test or Fisher’s exact test to evaluate the relationship between NAR and pCR, as well as other clinical and pathological characteristics. Logistic regression models were employed for univariate and multivariate analyses.Results: The results o

UNLABELLED: Wastewater treatment plants (WWTPs) are indispensable biotechnology facilities for modern cities and play an essential role in modern urban infrastructure by employing microorganisms to remove pollutants in wastewater, thus protecting public health and the environment. This study conducted a 13-month bacterial community survey of six full-scale WWTPs in Hong Kong with samples of influent, activated sludge (AS), and effluent to explore their synchronism and asynchronism of bacterial community. Besides, we compared AS results of six Hong Kong WWTPs with data from 1,186 AS amplicon data in 269 global WWTPs and a 9-year metagenomic sequencing survey of a Hong Kong WWTP. Our results showed the compositions of bacterial communities varied and the bacterial community structure of AS had obvious differences across Hong Kong WWTPs. The co-occurrence analysis identified 40 pairs of relationships that existed among Hong Kong WWTPs to show solid associations between two species and stochastic processes took large proportions for the bacterial community assembly of six WWTPs. The abundance and distribution of the functional bacteria in worldwide and Hong Kong WWTPs were examined and compared, and we found that ammonia-oxidizing bacteria had more diversity than nitrite-oxidizing bacteria. Besides, Hong Kong WWTPs could make great contributions to the genome mining of microbial dark matter in the global wanted list. Operational parameters had important effects on OTUs abundance, such as the temperature to the genera of Tetrasphaera, Gordonia and Nitrospira. All these results obtained from this study can deepen our understanding of the microbial ecology in WWTPs and provide foundations for further studies. IMPORTANCE: Wastewater treatment plants (WWTPs) are an indispensable component of modern cities, as they can remove pollutants in wastewater to prevent anthropogenic activities. Activated sludge (AS) is a fundamental wastewater treatment process and it harbors a highl

In this paper, we propose a novel learning approach for feed-forward one-shot 4D head avatar synthesis. Different from existing methods that often learn from reconstructing monocular videos guided by 3DMM, we employ pseudo multi-view videos to learn a 4D head synthesizer in a data-driven manner, avoiding reliance on inaccurate 3DMM reconstruction that could be detrimental to the synthesis performance. The key idea is to first learn a 3D head synthesizer using synthetic multi-view images to convert monocular real videos into multi-view ones, and then utilize the pseudo multi-view videos to learn a 4D head synthesizer via cross-view self-reenactment. By leveraging a simple vision transformer backbone with motion-aware cross-attentions, our method exhibits superior performance compared to previous methods in terms of reconstruction fidelity, geometry consistency, and motion control accuracy. We hope our method offers novel insights into integrating 3D priors with 2D supervisions for improved 4D head avatar creation., Comment: Project page: https://yudeng.github.io/Portrait4D-v2

Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included. Methods: Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical / healthcare / clinical", "un-certainty AND medical / healthcare / clinical", and "noise AND medical / healthcare / clinical". Results: A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided. Discussion: From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.

Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-annotated data to perform entity typing. Nevertheless, collecting such data in highly specialized science and engineering domains (e.g., software engineering and security) can be time-consuming and costly, without mentioning the domain gaps between training and inference data if the model needs to be applied to confidential datasets. In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i.e., those without seed entities). To solve this problem, we propose SEType which first enriches the weak supervision by finding more entities for each seen type from an unlabeled corpus using the contextualized representations of pre-trained language models. It then matches the enriched entities to unlabeled text to get pseudo-labeled samples and trains a textual entailment model that can make inferences for both seen and unseen types. Extensive experiments on two datasets covering four domains demonstrate the effectiveness of SEType in comparison with various baselines., Comment: 9 pages; Accepted to AAAI 2024 (Code: https://github.com/yuzhimanhua/SEType)

In this article, we consider the singular value asymptotics of compositions of compact linear operators mapping in the real Hilbert space of quadratically integrable functions over the unit interval. Specifically, the composition is given by the compact simple integration operator followed by the non-compact Ces`aro operator possessing a non-closed range. We show that the degree of ill-posedness of that composition is two, which means that the Ces`aro operator increases the degree of illposedness by the amount of one compared to the simple integration operator., Comment: 13 pages, 2 figures, submitted to conference proceedings

Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardiac shape modeling a more robust and broadly applicable tool that has processing times consistent with clinical workflows. Cardiovascular magnetic resonance (CMR) images from a cohort of 123 patients with repaired tetralogy of Fallot (rTOF) from two internal sites were used to train and validate each step in the automated pipeline. The complete automated pipeline was tested using CMR images from a cohort of 12 rTOF patients from an internal site and 18 rTOF patients from an external site. Manually and automatically generated shape models from the test set were compared using Euclidean projection distances, global ventricular measurements, and atlas-based shape mode scores. The mean absolute error (MAE) between manually and automatically generated shape models in the test set was similar to the voxel resolution of the original CMR images for end-diastolic models (MAE = 1.9 ± 0.5 mm) and end-systolic models (MAE = 2.1 ± 0.7 mm). Global ventricular measurements computed from automated models were in good agreement with those computed from manual models. The average mean absolute difference in shape mode Z-score between manually and automatically generated models was 0.5 standard deviations for the first 20 modes of a reference statistical shape atlas. Using deep learning, accurate three-dimensional, biventricular shape models can be reliably created. This fully automated end-to-end appro

Drawing on qualitative discourse analysis, this study conducted semi-structured interviews with seventeen sexual minorities, to investigate how the categorization mechanism generates institutional, interpersonal, and internalized stigmas and stressors against Chinese sexual minority youth, and how the categorization mechanism is represented in their contextualized lived experience. Heterosexual matrix and homonormativity hidden in Chinese traditions and ideologies are identified under the framework of queer linguistics. This study reveals that Chinese sexual minority youth are otherized as problematic out-groups on the textual level. They are negatively constructed as hypersexualized, pathologized, and dehumanized. On the micro-linguistic level, deixis, evaluations, and emotive words are used to classify in-groups and out-groups, show intimate and remote social distances, and express positive and negative attitudes. Results uncover that the categorization mechanism produces asymmetric relationships and power dynamics between heterosexual and non-heterosexual people, and between homonormative and non-homonormative sexual minorities.

Yuhan Wang,1,* Ping Zhang,1,* Yu Lv,1 Yu Deng,2 Ming Yao,3 Lin Wang,1 Guangtao Pan3 1College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei Province, People’s Republic of China; 2Department of Encephalopathy, Guangzhou Hospital of Integrated Traditional and West Medicine Affiliated to Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 3Department of Dermatology, Yancheng Hospital of Traditional Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Yancheng, Jiangsu Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guangtao Pan, Department of Dermatology, Yancheng Hospital of Traditional Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, No. 53, Renmin North Road, Tinghu District, Yancheng, Jiangsu Province, People’s Republic of China, Tel +86-17612738767, Email panguangtaowuhan@foxmail.comAbstract: This literature review systematically searched relevant literature from the past 10 years using various medical databases, including PubMed, Medline, Cochrane, China National Knowledge Infrastructure (CNKI), Wanfang, and Weipu. As a chronic inflammatory disease, psoriasis is associated with various cells and cytokines of the body’s immune system, making them more susceptible to various systemic comorbidities than the general population. At a time when conventional regimens are ineffective and there is a lack of personalized treatment options for such comorbidities, the development of biologics has revolutionized the treatment of psoriasis comorbidities, and a number of biologic combination therapies have been incorporated into first-line treatments in many countries. The focus of this review was to investigate the relationship between psoriasis and comorbidities, such as metabolic syndrome, psoriatic arthritis, cardiovascular diseases, mental disorders, infectious diseases, digestive syst

Wei Y, Liao Y, Deng Y, Zu Y, Zhao B, Li F. Onco Targets Ther. 2019;12:9005–9016. We, the Editors and Publisher of OncoTargets and Therapy, have retracted the following article. Following publication of the article, concerns were raised about the duplication of images from Figures 2, 5 and 7 with images from other unrelated articles. Specifically, The image for Figure 2B, PC9, NC inhibitor, has been duplicated with the image for Figure 2D, PANC1, Solasodine 10µg/mL, from Fan Y, Li Z, Wu L, et al. Solasodine, Isolated from Solanum sisymbriifolium Fruits, Has a Potent Anti-Tumor Activity Against Pancreatic Cancer. Drug Des Devel Ther. 2021;15:1509–1519. https://doi.org/10.2147/DDDT.S266746. Images for Figure 3A have been duplicated with images for Figure 4D from Wang M, Yu R, Ling X, et al. COPB2 promotes metastasis and inhibits apoptosis of lung adenocarcinoma cells through functioning as a target of miR-216a-3p JF. Translational Cancer Research. 2020; 9(4): 2648–2659. https://doi.org/10.21037/tcr.2020.02.65 and Figure 5B from Jia Yy, Yu Y, Li HJ. POSTN promotes proliferation and epithelial-mesenchymal transition in renal cell carcinoma through ILK/AKT/mTOR pathway. J Cancer. 2021;12(14):4183–4195. https://doi.org/10.7150/jca.51253. The image for Figure 5C, A549, miR-503+PDK1, has been duplicated with the image for Figure 3C, NB4 cell, inhibitors, from Wang D, Chen J, Ding Y, et al. miR-188-5p Promotes Tumor Growth by Targeting CD2AP Through PI3K/AKT/mTOR Signaling in Children with Acute Promyelocytic Leukemia. Onco Targets Ther. 2020;13:6681–6697. https://doi.org/10.2147/OTT.S244813. The authors did not respond to our queries and were unable to provide an explanation for the duplicated images or provide data for the study. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article and the authors were notified of this. We have b

Yu Deng School of Educational Science, Zhaotong University, Zhaotong, 657000, People’s Republic of ChinaCorrespondence: Yu Deng, Email yu47005@outlook.comAim: This study aimed to investigate how perceived overqualification is linked to an individual’s career commitment among service sector employees in China. Additionally, it sought to examine the mediating role of career self-efficacy and the moderating effect of social support.Methods: This study collected data from 441 employees using a three-wave data collection design with a two-week gap between each round. Moreover, we employed partial least square structural equation modeling (PLS-SEM) to analyze the data.Results: The findings asserted that perceived overqualification was positively associated with employee career self-efficacy and commitment. Furthermore, career self-efficacy mediated the link between perceived overqualification and career commitment. The study also demonstrated that perceived overqualification and career self-efficacy were influenced by the level of social support received, with a stronger relationship observed when social support was high. These findings highlight the value of fostering social support and career self-efficacy among coworkers to increase overqualified employees’ commitment to their careers and provide valuable insights for organizations seeking to manage their talent pool effectively.Discussion: The study suggests that when employees perceive themselves as overqualified for their job, it can lead to a higher sense of career self-efficacy, which is the belief in one’s ability to perform job tasks effectively. This increased self-efficacy, in turn, can lead to a greater commitment to their career. Furthermore, fostering social support and building career self-efficacy can help organizations manage their overqualified pool effectively and improve employee satisfaction and productivity.Keywords: perceived overqualification, career self-efficacy, social support, career c

The paper presents a well-rounded experimental study on the flexural performance of Reinforced Concrete (RC) box girders strengthened with prestressed carbon fibre reinforced polymer (CFRP) plates. The motivation behind the study was twofold: the rising need for structural reinforcement of existing aged and heavily utilised hollow RC box girders, and the absence of prior attempts to integrate prestressed CFRP plate strengthening for those hollow girders. Previous experimental studies are scarce and fewer studies are focused on the combined prestress and thin-wall effects, such as prestress-related stress condensation and shear lag. However, experimental results are important in directing further analytical studies for hollow sections with more complex behaviours than solid sections since there is a need to predict the behaviour of the prestress-strengthened hollow RC structures for routine design. This pivotal experimental study aims to quantify the structural interactions initiated by prestress in hollow sections and evaluate the impact of age while promoting further analytical initiatives. In this study, two types of CFRP plates, ordinary CFRP and steel-wire-CFRP (SW-CFRP), were used on different specimen beams with varying prestressing levels, sizes of the CFRP plates, and pre-damaged states representing aged and over-used members. Their performance indexes, including cracking load, yield load, ultimate load, structural stiffness, ductility, and crack resistance, were tested and summarised in this paper. The CFRP plates of the eight specimen beams were prestressed to different levels (non-prestressed, and 30% and 40% of the CFRP plate's ultimate strength). The test results suggest that the crack load increased by 86% and 134%, when the specimens were enhanced with the combinations of 30% prestress level for the same CFRP cross-section, and 40% prestress level with a thicker CFRP plate, respectively. The flexural capacity also increased by 42% and 72%, and flexura

This paper presents a pioneering study that developed the first analytical model to analyse and design reinforced concrete (RC) box girders strengthened by prestressed CFRP plates. The proposed analytical model considered and addressed prestress loss, shear lag effect and failure modes under different design configurations at elastic, elastoplastic and plastic stages. An experimental study was also conducted to validate the proposed analytical model. The study was motivated by the increasing demand for the structural strengthening of aged and over-used hollow RC box girders in transport and other infrastructure systems, as well as the lack of previous attempts to incorporate prestressed CFRP plate strengthening for hollow RC beams. It is very common in many developing countries, the traffic flow increased dramatically due to the economic growth. The original design underestimated the traffic loads. When demolition and re-building may not always be the best option, thus, strengthening and enhanced maintenance have become promising alternatives. But the lack of existing analytical models that can guide the engineers to analyse and design this type of structures effective, has become an urgent need from the industry. In the experimental study, eight box girders with different types, cross-section sizes, and prestress levels were prepared and tested. Two samples were preloaded to create damaged beams before strengthening to simulate the aged or over-used members. The experimental results are in good agreement with the analytical prediction. The proposed analytical framework provides a comprehensive yet practical method for designing the prestressed CFRP strengthened RC box girders in bending and laid the foundation for further studies on shear and torsion behaviours. © 2023 The Author(s)

Generating images with both photorealism and multiview 3D consistency is crucial for 3D-aware GANs, yet existing methods struggle to achieve them simultaneously. Improving the photorealism via CNN-based 2D super-resolution can break the strict 3D consistency, while keeping the 3D consistency by learning high-resolution 3D representations for direct rendering often compromises image quality. In this paper, we propose a novel learning strategy, namely 3D-to-2D imitation, which enables a 3D-aware GAN to generate high-quality images while maintaining their strict 3D consistency, by letting the images synthesized by the generator's 3D rendering branch to mimic those generated by its 2D super-resolution branch. We also introduce 3D-aware convolutions into the generator for better 3D representation learning, which further improves the image generation quality. With the above strategies, our method reaches FID scores of 5.4 and 4.3 on FFHQ and AFHQ-v2 Cats, respectively, at 512x512 resolution, largely outperforming existing 3D-aware GANs using direct 3D rendering and coming very close to the previous state-of-the-art method that leverages 2D super-resolution. Project website: https://seanchenxy.github.io/Mimic3DWeb.

Introduction: Human adenovirus 7 (HAdV-7) is an important viral pathogen of severe pneumonia in children and a serious threat to health. Methods: A cohort of 45 pediatric patients diagnosed with HAdV-7-associated severe pneumonia and admitted to the Pediatric Intensive Care Unit at the Children's Hospital of Chongqing Medical University from May 2018 to January 2020 were included. Risk factors of death were analyzed by the Cox proportional risk mode with Clinical data, serum, and nasopharyngeal aspirate adenovirus load, Genome analysis, Olink proteomics, and cytokine profile between dead and surviving patients were also analyzed. Results: A total of 45 children with a median age of 12.0 months (interquartile range [IQR]: 6.5, 22.0) were included (female 14), including 14 (31.1%) who died. High serum viral load was an independent risk factor for mortality (hazard ratio [HR] = 2.16, 95% confidence interval [CI], 1.04-4.49, p = 0.039). BTB and CNC homology 1 (BACH1), interleukin-5 (IL-5), and IL-9 levels were significantly correlated with serum viral load (p = 0.0400, 0.0499, and 0.0290; r = 0.4663, 0.3339, and -0.3700, respectively), with significant differences between the dead and survival groups (p = 0.021, 0.001, and 0.021). Conclusions: Severe cytokine storm-associated high serum viral load after HAdV-7 infection may be the main mechanism responsible for poor prognosis in children., Funding Agencies|General Project of National Natural Science Foundation of China [32071123]; Chongqing Science and Health Joint project [2020FYYX148]; Ministry of Education Key Laboratory of Child Development and Disorders [YBRP-2021XX]; National Natural Science Foundation of China [32170139]; CQMU Program for Youth Innovation in Future Medicine [W0178]; Chongqing high-end medicine for young and middle-aged people Talent Program [CQYC202203091217]

Existing one-shot 4D head synthesis methods usually learn from monocular videos with the aid of 3DMM reconstruction, yet the latter is evenly challenging which restricts them from reasonable 4D head synthesis. We present a method to learn one-shot 4D head synthesis via large-scale synthetic data. The key is to first learn a part-wise 4D generative model from monocular images via adversarial learning, to synthesize multi-view images of diverse identities and full motions as training data; then leverage a transformer-based animatable triplane reconstructor to learn 4D head reconstruction using the synthetic data. A novel learning strategy is enforced to enhance the generalizability to real images by disentangling the learning process of 3D reconstruction and reenactment. Experiments demonstrate our superiority over the prior art., Comment: CVPR24 camera ready version. Project page: https://yudeng.github.io/Portrait4D

In this study, our goal is to create interactive avatar agents that can autonomously plan and animate nuanced facial movements realistically, from both visual and behavioral perspectives. Given high-level inputs about the environment and agent profile, our framework harnesses LLMs to produce a series of detailed text descriptions of the avatar agents' facial motions. These descriptions are then processed by our task-agnostic driving engine into motion token sequences, which are subsequently converted into continuous motion embeddings that are further consumed by our standalone neural-based renderer to generate the final photorealistic avatar animations. These streamlined processes allow our framework to adapt to a variety of non-verbal avatar interactions, both monadic and dyadic. Our extensive study, which includes experiments on both newly compiled and existing datasets featuring two types of agents -- one capable of monadic interaction with the environment, and the other designed for dyadic conversation -- validates the effectiveness and versatility of our approach. To our knowledge, we advanced a leap step by combining LLMs and neural rendering for generalized non-verbal prediction and photo-realistic rendering of avatar agents., Comment: Project page: https://dorniwang.github.io/AgentAvatar_project

Tracking and modeling unknown rigid objects in the environment play a crucial role in autonomous unmanned systems and virtual-real interactive applications. However, many existing Simultaneous Localization, Mapping and Moving Object Tracking (SLAMMOT) methods focus solely on estimating specific object poses and lack estimation of object scales and are unable to effectively track unknown objects. In this paper, we propose a novel SLAM backend that unifies ego-motion tracking, rigid object motion tracking, and modeling within a joint optimization framework. In the perception part, we designed a pixel-level asynchronous object tracker (AOT) based on the Segment Anything Model (SAM) and DeAOT, enabling the tracker to effectively track target unknown objects guided by various predefined tasks and prompts. In the modeling part, we present a novel object-centric quadric parameterization to unify both static and dynamic object initialization and optimization. Subsequently, in the part of object state estimation, we propose a tightly coupled optimization model for object pose and scale estimation, incorporating hybrids constraints into a novel dual sliding window optimization framework for joint estimation. To our knowledge, we are the first to tightly couple object pose tracking with light-weight modeling of dynamic and static objects using quadric. We conduct qualitative and quantitative experiments on simulation datasets and real-world datasets, demonstrating the state-of-the-art robustness and accuracy in motion estimation and modeling. Our system showcases the potential application of object perception in complex dynamic scenes.

This study investigated how a group of twenty seven Wuhan citizens (coronavirus patients and their family member, medical staff, college students, social workers, teacher, journalist) employed metaphors to communicate about their experiences of COVID-19 pandemic through in-depth individual interviews. The analysis of metaphors captured the different kinds of emotional states and psychological conditions of the research participants, focusing on their mental imagery of COVID-19, extreme emotional experiences, and symbolic behaviors under the pandemic. The results show that multiple metaphors were used to construe emotionally-complex, isolating experiences of the COVID-19 pandemic. Most metaphorical narratives are grounded in embodied sensorimotor experiences such as body parts, batting, fighting, hit, weight, temperature, spatialization, motion, violence, light, and journey. Embodied metaphors were manifested in both verbal expressions and nonlinguistic behaviors (e.g., patients’ symptom of obsessive-compulsive behaviors). These results suggest that the bodily experiences of the pandemic, the environment, and the psychological factors combine to shape people’s metaphorical thinking processes.

This study used ERP method to investigate the processing of fictive motion and literal motion during natural languagecomprehension. A hypothesis is that the motion component of a verb is preserved in both literal and fictive motion con-structions (The army/The bridge crossed the river). However, the incorporation of a motion-event frame into fictive motionconstructions requires reanalysis or reconstruction both syntactically and semantically. The ERP results reveal that a P300effect on the subject NPs, a P600 effect on the motion verbs and an N400 modulation on the sentence-final complementNPs were uncovered in the processing of fictive motion constructions in relative to literal motion constructions. Theseresults suggest that the processing of fictive motion requires increased cognitive effort than the processing of literal motioncondition.

The operating conditions of functional materials usually involve varying stress fields, resulting in structural changes, whether intentional or undesirable. Complex multiscale microstructures including defects, domains, and new phases, can be induced by mechanical loading in functional materials, providing fundamental insight into the deformation process of the involved materials. On the other hand, these microstructures, if induced in a controllable fashion, can be used to tune the functional properties or to enhance certain performance. In situ nanomechanical tests conducted in scanning/transmission electron microscopes (STEM/TEM) provide a critical tool for understanding the microstructural evolution in functional materials. Here, select results on a variety of functional material systems in the field are presented, with a brief introduction into some newly developed multichannel experimental capabilities to demonstrate the impact of these techniques.

The Abstract is currently unavailable, due to the thesis being under Embargo.

Yu Yu,* Ligang Ding,* Yu Deng,* Hao Huang, Sijing Cheng, Chi Cai, Min Gu, Xuhua Chen, Xiaohui Ning, Hongxia Niu, Wei Hua Cardiac Arrhythmia Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei Hua, Cardiac Arrhythmia Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, People’s Republic of China, Email drhuaweifw@sina.comBackground: The joint association of atrial fibrillation (AF) and statin therapy with adverse outcomes in heart failure (HF) patients with cardiac resynchronization therapy (CRT) has not been fully investigated so far. The purpose of this study was to explore the independent and joint association of AF and statin therapy with adverse outcomes.Methods: Study patients were divided into four groups according to AF status and statin use: Non-AF/Statin, Non-AF/Non-Statin, AF/Statin, and AF/Non-Statin. Multivariate Cox proportional hazards regression models were used to evaluate the independent and joint association of AF and statin therapy with poor prognosis.Results: Among 685 CRT patients, there were 180 deaths (26.5%) and 198 HF hospitalization (29.6%) during the 14 years of follow-up. AF was associated with a 46% increased risk of all-cause mortality (HR, 1.46; 95% CI, 1.03– 2.07) and a 59% increased risk of HF hospitalization (HR, 1.59; 95% CI, 1.16– 2.20) than those without AF. However, statin therapy failed to improve the prognosis. In the joint analysis, compared with the Non-AF/Statin group, the AF/Non-Statin group suffered a higher risk of all-cause mortality (HR, 1.75; 95% CI, 1.04– 2.93) and HF hospitalization (HR, 1.76; 95% CI, 1.08– 2.86). Furthermore, adding AF to the traditional risk fa

This paper analyzes the inverse problem of deautoconvolution in the multi-dimensional case with respect to solution uniqueness and ill-posedness. Deautoconvolution means here the reconstruction of a real-valued $L^2$-function with support in the $n$-dimensional unit cube $[0,1]^n$ from observations of its autoconvolution either in the full data case (i.e. on $[0,2]^n$) or in the limited data case (i.e. on $[0,1]^n$). Based on multi-dimensional variants of the Titchmarsh convolution theorem due to Lions and Mikusi\'{n}ski, we prove in the full data case a twofoldness assertion, and in the limited data case uniqueness of non-negative solutions for which the origin belongs to the support. The latter assumption is also shown to be necessary for any uniqueness statement in the limited data case. A glimpse of rate results for regularized solutions completes the paper.

Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds. Entity set expansion in software-related domains such as StackOverflow can benefit many downstream tasks (e.g., software knowledge graph construction) and facilitate better IT operations and service management. Meanwhile, existing approaches are less concerned with two problems: (1) How to deal with multiple types of seed entities simultaneously? (2) How to leverage the power of pre-trained language models (PLMs)? Being aware of these two problems, in this paper, we study the entity set co-expansion task in StackOverflow, which extracts Library, OS, Application, and Language entities from StackOverflow question-answer threads. During the co-expansion process, we use PLMs to derive embeddings of candidate entities for calculating similarities between entities. Experimental results show that our proposed SECoExpan framework outperforms previous approaches significantly., Comment: 4 pages; Accepted to IEEE BigData 2022

We present a novel one-shot talking head synthesis method that achieves disentangled and fine-grained control over lip motion, eye gaze&blink, head pose, and emotional expression. We represent different motions via disentangled latent representations and leverage an image generator to synthesize talking heads from them. To effectively disentangle each motion factor, we propose a progressive disentangled representation learning strategy by separating the factors in a coarse-to-fine manner, where we first extract unified motion feature from the driving signal, and then isolate each fine-grained motion from the unified feature. We introduce motion-specific contrastive learning and regressing for non-emotional motions, and feature-level decorrelation and self-reconstruction for emotional expression, to fully utilize the inherent properties of each motion factor in unstructured video data to achieve disentanglement. Experiments show that our method provides high quality speech&lip-motion synchronization along with precise and disentangled control over multiple extra facial motions, which can hardly be achieved by previous methods., Comment: 18 pages

A key challenge for novel view synthesis of monocular portrait images is 3D consistency under continuous pose variations. Most existing methods rely on 2D generative models which often leads to obvious 3D inconsistency artifacts. We present a 3D-consistent novel view synthesis approach for monocular portrait images based on a recent proposed 3D-aware GAN, namely Generative Radiance Manifolds (GRAM), which has shown strong 3D consistency at multiview image generation of virtual subjects via the radiance manifolds representation. However, simply learning an encoder to map a real image into the latent space of GRAM can only reconstruct coarse radiance manifolds without faithful fine details, while improving the reconstruction fidelity via instance-specific optimization is time-consuming. We introduce a novel detail manifolds reconstructor to learn 3D-consistent fine details on the radiance manifolds from monocular images, and combine them with the coarse radiance manifolds for high-fidelity reconstruction. The 3D priors derived from the coarse radiance manifolds are used to regulate the learned details to ensure reasonable synthesized results at novel views. Trained on in-the-wild 2D images, our method achieves high-fidelity and 3D-consistent portrait synthesis largely outperforming the prior art., Comment: CVPR 2023 camera-ready version. Project page: https://yudeng.github.io/GRAMInverter

There is extensive mathematical literature on the inverse problem of deautoconvolution for a function with support in the unit interval $[0,1] \subset \mathbb R$, but little is known about the multidimensional situation. This article tries to fill this gap with analytical and numerical studies on the reconstruction of a real function of two real variables over the unit square from observations of its autoconvolution on $[0,2]^2 \subset \mathbb R^2$ (full data case) or on $[0,1]^2$ (limited data case). In an $L^2$-setting, twofoldness and uniqueness assertions are proven for the deautoconvolution problem in 2D. Moreover, its ill-posedness is characterized and illustrated. Extensive numerical case studies give an overview of the behaviour of stable approximate solutions to the two-dimensional deautoconvolution problem obtained by Tikhonov-type regularization with different penalties and the iteratively regularized Gauss-Newton method.

Although 2D generative models have made great progress in face image generation and animation, they often suffer from undesirable artifacts such as 3D inconsistency when rendering images from different camera viewpoints. This prevents them from synthesizing video animations indistinguishable from real ones. Recently, 3D-aware GANs extend 2D GANs for explicit disentanglement of camera pose by leveraging 3D scene representations. These methods can well preserve the 3D consistency of the generated images across different views, yet they cannot achieve fine-grained control over other attributes, among which facial expression control is arguably the most useful and desirable for face animation. In this paper, we propose an animatable 3D-aware GAN for multiview consistent face animation generation. The key idea is to decompose the 3D representation of the 3D-aware GAN into a template field and a deformation field, where the former represents different identities with a canonical expression, and the latter characterizes expression variations of each identity. To achieve meaningful control over facial expressions via deformation, we propose a 3D-level imitative learning scheme between the generator and a parametric 3D face model during adversarial training of the 3D-aware GAN. This helps our method achieve high-quality animatable face image generation with strong visual 3D consistency, even though trained with only unstructured 2D images. Extensive experiments demonstrate our superior performance over prior works. Project page: https://yuewuhkust.github.io/AniFaceGAN, Comment: Accepted by NeurIPS 2022. Project Page: https://yuewuhkust.github.io/AniFaceGAN

3D-aware generative models have demonstrated their superb performance to generate 3D neural radiance fields (NeRF) from a collection of monocular 2D images even for topology-varying object categories. However, these methods still lack the capability to separately control the shape and appearance of the objects in the generated radiance fields. In this paper, we propose a generative model for synthesizing radiance fields of topology-varying objects with disentangled shape and appearance variations. Our method generates deformable radiance fields, which builds the dense correspondence between the density fields of the objects and encodes their appearances in a shared template field. Our disentanglement is achieved in an unsupervised manner without introducing extra labels to previous 3D-aware GAN training. We also develop an effective image inversion scheme for reconstructing the radiance field of an object in a real monocular image and manipulating its shape and appearance. Experiments show that our method can successfully learn the generative model from unstructured monocular images and well disentangle the shape and appearance for objects (e.g., chairs) with large topological variance. The model trained on synthetic data can faithfully reconstruct the real object in a given single image and achieve high-quality texture and shape editing results., Comment: Accepted at Pacific Graphics 2022 & COMPUTER GRAPHICS Forum, Project Page: https://ziyuwang98.github.io/GDRF

Yang Xi,1,* Yu-Qin Deng,1,2,* Han-Da Li,1 Wo-Er Jiao,1 Jin Chen,2 Jian-Jun Chen,3 Ze-Zhang Tao1,2 1Department of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 2Institute of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 3Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ze-Zhang Tao, Department of Otolaryngology, Head and Neck Surgery Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, 430060, Hubei, People’s Republic of China, Tel +86 13907141892, Fax +86 27 88043958, Email taozezhang696@163.com Jian-Jun Chen, Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Jianghan District, Wuhan, 430060, Hubei, People’s Republic of China, Tel +86 13659851719, Fax +86 27 85726300, Email ylly80331@163.comPurpose: To develop and evaluate the effectiveness/clinical application of an eosinophil cationic protein-myeloperoxidase (ECP-MPO) test paper before and after treatment in patients with allergic rhinitis (AR).Patients and Methods: We included 40 controls and 106 AR patients who were enrolled in the Allergy Clinic of Renmin Hospital of Wuhan University. Total IgE, specific IgE and skin prick test (SPT) were detected in all participants. AR patients were treated with oral cetirizine hydrochloride for 14 days. The ECP-MPO test paper results, nasal secretion smear and eosinophil counts, rhinoconjunctivitis total nasal symptom score (TNSS), quality of life questionnaire (RQLQ), visual analogue scale (VAS), serum Th1/Th2/Th17 cytokine, and chemokine data were collected pre- and post-treatment. ECP concentrations in nasal secretions were assessed by ELISA. P

Yu Deng,1 Xu Yang,1 Hongzhong Hua,1 Cong Zhang2 1Department of Pathology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China; 2Department of Pathology, Fuyang Hospital of Anhui Medical University, Fuyang, People’s Republic of ChinaCorrespondence: Cong Zhang, Department of Pathology, Fuyang Hospital of Anhui Medical University, Fuyang, People’s Republic of China, Email 657427787@qq.comPurpose: This study aimed to investigate the role of IGFBP5 in colorectal cancer (CRC) and the relationship between the expression of IGFBP5 and clinicopathological parameters in CRC patients.Patients and Methods: Immunohistochemical analysis was used to detect the expression of IGFBP5 and its correlation with clinicopathological parameters of CRC patients. Prognosis analysis, gene set enrichment analysis, and protein interaction network analysis were performed using bioinformatics analysis. The Genomics of Drug Sensitivity in Cancer (GDSC) dataset was used to analyze the correlation between the expression of IGFBP5 and drug resistance.Results: Immunohistochemical analysis revealed that the expression of IGFBP5 was significantly higher in CRC tissues than in para-cancerous tissues (P < 0.05). High expression of IGFBP5 was associated with tumor differentiation and the N stage of CRC (P < 0.05). Moreover, high expression of IGFBP5 predicted worse overall survival and disease-free survival in CRC patients (P < 0.05). The expression of IGFBP5 was associated with cell–matrix adhesion, extracellular matrix binding, and collagen binding (P < 0.05). Furthermore, IGFBP5 was involved in the Hedgehog signaling pathway and PI3K-Akt signaling pathway (P < 0.05). IGF1, IGF2, SPP1, LTBP1, and FAM20C were most closely related to IGFBP5.Conclusion: The expression of IGFBP5 is upregulated and associated with tumor differentiation, lymph node metastasis, drug resistance, and prognosis in CRC patients.Keywords: CRC, IGFBP5, IH

Recent works have shown that 3D-aware GANs trained on unstructured single image collections can generate multiview images of novel instances. The key underpinnings to achieve this are a 3D radiance field generator and a volume rendering process. However, existing methods either cannot generate high-resolution images (e.g., up to 256X256) due to the high computation cost of neural volume rendering, or rely on 2D CNNs for image-space upsampling which jeopardizes the 3D consistency across different views. This paper proposes a novel 3D-aware GAN that can generate high resolution images (up to 1024X1024) while keeping strict 3D consistency as in volume rendering. Our motivation is to achieve super-resolution directly in the 3D space to preserve 3D consistency. We avoid the otherwise prohibitively-expensive computation cost by applying 2D convolutions on a set of 2D radiance manifolds defined in the recent generative radiance manifold (GRAM) approach, and apply dedicated loss functions for effective GAN training at high resolution. Experiments on FFHQ and AFHQv2 datasets show that our method can produce high-quality 3D-consistent results that significantly outperform existing methods. It makes a significant step towards closing the gap between traditional 2D image generation and 3D-consistent free-view generation., Comment: ICCV2023 camera ready version (more results and method comparisons). Project page: https://jeffreyxiang.github.io/GRAM-HD

Background: Sorafenib is effective in treating hepatoma, but most patients develop resistance to it. STAT3 signaling has been implicated in sorafenib resistance. Artesunate (ART) and 20(R)-ginsenoside Rg3 (Rg3) have anti-hepatoma effects and can inhibit STAT3 signaling in cancer cells. This study aimed to evaluate the effects of Rg3 in combination with ART (Rg3-plus-ART) in overcoming sorafenib resistance, and to examine the involvement of STAT3 signaling in these effects. Methods: Sorafenib-resistant HepG2 cells (HepG2-SR) were used to evaluate the in vitro anti-hepatoma effects of Rg3-plus-ART. A HepG2-SR hepatoma-bearing BALB/c-nu/nu mouse model was used to assess the in vivo anti-hepatoma effects of Rg3-plus-ART. CCK-8 assays and Annexin V-FITC/PI double staining were used to examine cell proliferation and apoptosis, respectively. Immunoblotting was employed to examine protein levels. ROS generation was examined by measuring DCF-DA fluorescence. Results: Rg3-plus-ART synergistically reduced viability of, and evoked apoptosis in HepG2-SR cells, and suppressed HepG2-SR tumor growth in mice. Mechanistic studies revealed that Rg3-plus-ART inhibited activation/phosphorylation of Src and STAT3 in HepG2-SR cultures and tumors. The combination also decreased the STAT3 nuclear level and induced ROS production in HepG2-SR cultures. Furthermore, over-activation of STAT3 or removal of ROS diminished the anti-proliferative effects of Rg3-plus-ART, and removal of ROS diminished Rg3-plus-ART's inhibitory effects on STAT3 activation in HepG2-SR cells. Conclusions: Rg3-plus-ART overcomes sorafenib resistance in experimental models, and inhibition of Src/STAT3 signaling and modulation of ROS/STAT3 signaling contribute to the underlying mechanisms. This study provides a pharmacological basis for developing Rg3-plus-ART into a novel modality for treating sorafenib-resistant hepatoma. © 2021

This paper presents the experimental and analytical studies to investigate the effect of concentrated butt-joints on the flexural properties of laminated bamboo-timber flitch beam (BTFB, or bio-flitch beam). The experimental results reveal that the concentrated butt-joints significantly reduce the flexural strength of the BTFB. They also suggest that the failure mechanism of the BTFB with or without concentrated butt-joints are completely different but less evidence shows an obvious effect of the butt-joints on the modulus of elasticity in bending. In addition, laminated bamboo lumber with concentrated butt-joints is found to be unsuitable for structural applications. The analytical predictions are in close agreement with the experimental results that demonstrate the potentials of the proposed analytical model as a forensic investigation instrument to estimate the strength reduction of the structure, if the butt-jointed laminated bamboo was, unfortunately, used in construction. © The Author(s) 2021.

This paper presents the experimental and analytical studies to investigate the effect of concentrated butt-joints on the flexural properties of laminated bamboo-timber flitch beam (BTFB, or bio-flitch beam). The experimental results reveal that the concentrated butt-joints significantly reduce the flexural strength of the BTFB. They also suggest that the failure mechanism of the BTFB with or without concentrated butt-joints are completely different but less evidence shows an obvious effect of the butt-joints on the modulus of elasticity in bending. In addition, laminated bamboo lumber with concentrated butt-joints is found to be unsuitable for structural applications. The analytical predictions are in close agreement with the experimental results that demonstrate the potentials of the proposed analytical model as a forensic investigation instrument to estimate the strength reduction of the structure, if the butt-jointed laminated bamboo was, unfortunately, used in construction. © The Author(s) 2021.

Background: Sorafenib is effective in treating hepatoma, but most patients develop resistance to it. STAT3 signaling has been implicated in sorafenib resistance. Artesunate (ART) and 20(R)-ginsenoside Rg3 (Rg3) have anti-hepatoma effects and can inhibit STAT3 signaling in cancer cells. This study aimed to evaluate the effects of Rg3 in combination with ART (Rg3-plus-ART) in overcoming sorafenib resistance, and to examine the involvement of STAT3 signaling in these effects. Methods: Sorafenib-resistant HepG2 cells (HepG2-SR) were used to evaluate the in vitro anti-hepatoma effects of Rg3-plus-ART. A HepG2-SR hepatoma-bearing BALB/c-nu/nu mouse model was used to assess the in vivo anti-hepatoma effects of Rg3-plus-ART. CCK-8 assays and Annexin V-FITC/PI double staining were used to examine cell proliferation and apoptosis, respectively. Immunoblotting was employed to examine protein levels. ROS generation was examined by measuring DCF-DA fluorescence. Results: Rg3-plus-ART synergistically reduced viability of, and evoked apoptosis in HepG2-SR cells, and suppressed HepG2-SR tumor growth in mice. Mechanistic studies revealed that Rg3-plus-ART inhibited activation/phosphorylation of Src and STAT3 in HepG2-SR cultures and tumors. The combination also decreased the STAT3 nuclear level and induced ROS production in HepG2-SR cultures. Furthermore, over-activation of STAT3 or removal of ROS diminished the anti-proliferative effects of Rg3-plus-ART, and removal of ROS diminished Rg3-plus-ART's inhibitory effects on STAT3 activation in HepG2-SR cells. Conclusions: Rg3-plus-ART overcomes sorafenib resistance in experimental models, and inhibition of Src/STAT3 signaling and modulation of ROS/STAT3 signaling contribute to the underlying mechanisms. This study provides a pharmacological basis for developing Rg3-plus-ART into a novel modality for treating sorafenib-resistant hepatoma. © 2021

The paper presents a new multi-material topology optimization method with a novel adjoint sensitivity analysis that can accommodate not only multiple plasticity but also multiple hardening models for individual materials in composite structures. Based on the proposed method, an integrated framework is developed which details the nonlinear finite element analysis, sensitivity analysis and optimization procedure. The proposed method and framework are implemented and illustrated by three numerical examples presented in this paper. An in-depth analysis of the numerical results has revealed the significant impact of the selection of plasticity and hardening model on the results of topology. © 2021, Crown.

The platinum palladium concentrate from copper anode slime was used as raw material. According to its mineral characteristics, HCl was selected as leaching agent to remove the main base metals such as Bi, Fe and enrich the precious metals such as Au, Ag, Pt and Pd. The composition distribution of Bi(III) and Fe(III) in the hydrochloric acid system was plotted by thermodynamic calculation. The influences of the concentrations of HCl and Cl-, reaction temperature and time, and other factors on leaching efficiencies of Bi and Fe were investigated experimentally. The results show that the leaching efficiencies of Bi and Fe are 96.24% and 93.08%, respectively, when the HCl concentration is 2 mol/l, no chloride ion is added, the liquid-solid ratio is 20:1 and at 50 degrees C for 1 h. Bi (III) and Fe (III) in the leaching solution are mainly in the forms of [BiCl6]3- and [FeCl2]+ complexes. The main components of the leaching residue are Ag, Ti, Fe and Te, with the contents of 20.22%, 15.25%, 5.87% and 4.57%. The grades of Au, Pt and Pd are 2 930 g/t, 1 980 g/t and 1 330 g/t. The SEM images show that the leached residue is granular with uneven size. The EDS images show that the leached residue contains Fe, Ti, Ag and other metal elements. The results of XRD show that the main phase of the residue is AgCl, which realises the separation of impurities and the enrichment of precious metals., The platinum palladium concentrate from copper anode slime was used as raw material. According to its mineral characteristics, HCl was selected as leaching agent to remove the main base metals such as Bi, Fe and enrich the precious metals such as Au, Ag, Pt and Pd. The composition distribution of Bi(III) and Fe(III) in the hydrochloric acid system was plotted by thermodynamic calculation. The influences of the concentrations of HCl and Cl-, reaction temperature and time, and other factors on leaching efficiencies of Bi and Fe were investigated experimentally. The results show that the leaching efficiencies of Bi and Fe are 96.24% and 93.08%, respectively, when the HCl concentration is 2 mol/l, no chloride ion is added, the liquid-solid ratio is 20:1 and at 50 degrees C for 1 h. Bi (III) and Fe (III) in the leaching solution are mainly in the forms of [BiCl6]3- and [FeCl2]+ complexes. The main components of the leaching residue are Ag, Ti, Fe and Te, with the contents of 20.22%, 15.25%, 5.87% and 4.57%. The grades of Au, Pt and Pd are 2 930 g/t, 1 980 g/t and 1 330 g/t. The SEM images show that the leached residue is granular with uneven size. The EDS images show that the leached residue contains Fe, Ti, Ag and other metal elements. The results of XRD show that the main phase of the residue is AgCl, which realises the separation of impurities and the enrichment of precious metals.

Background: Microbial communities in both natural and applied settings reliably carry out myriads of functions, yet how stable these taxonomically diverse assemblages can be and what causes them to transition between states remains poorly understood. We studied monthly activated sludge (AS) samples collected over 9 years from a full-scale wastewater treatment plant to answer how complex AS communities evolve in the long term and how the community functions change when there is a disturbance in operational parameters. Results: Here, we show that a microbial community in activated sludge (AS) system fluctuated around a stable average for 3 years but was then abruptly pushed into an alternative stable state by a simple transient disturbance (bleaching). While the taxonomic composition rapidly turned into a new state following the disturbance, the metabolic profile of the community and system performance remained remarkably stable. A total of 920 metagenome-assembled genomes (MAGs), representing approximately 70% of the community in the studied AS ecosystem, were recovered from the 97 monthly AS metagenomes. Comparative genomic analysis revealed an increased ability to aggregate in the cohorts of MAGs with correlated dynamics that are dominant after the bleaching event. Fine-scale analysis of dynamics also revealed cohorts that dominated during different periods and showed successional dynamics on seasonal and longer time scales due to temperature fluctuation and gradual changes in mean residence time in the reactor, respectively. Conclusions: Our work highlights that communities can assume different stable states under highly similar environmental conditions and that a specific disturbance threshold may lead to a rapid shift in community composition. [MediaObject not available: see fulltext.]

Li Yu, Yu-Hong Deng, Yuan-Hui Huang, Hai-Jin Ke, Yong Guo, Jie-Ling Wu Department of Children’s Health Care, Guangdong Women and Children Hospital, Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of ChinaCorrespondence: Jie-Ling Wu; Yong GuoDepartment of Children’s Health Care, Guangdong Women and Children Hospital, Guangzhou Medical University, Guangzhou, 511400, Guangdong, People’s Republic of ChinaTel +86 20 39151521Email jieling3861@163.com; geyong084@163.comPurpose: Evidence on the role of the gut microbiota in atopic dermatitis is inconsistent as human intestinal microbiota is influenced by geography. This cross-sectional study therefore aimed to compare differences in the gut microbiota of infants with atopic dermatitis and healthy infants in Guangzhou, China, by analyzing their stool.Patients and Methods: The composition of the intestinal microbiota was analyzed from the stool samples of 20 infants with atopic dermatitis (AD group) and 25 healthy infants (non-AD group) (1– 6 months old), using full-length 16S rRNA gene sequencing. The Wilcoxon test was used to analyze the relative abundance of bacteria by phylum, family, genus, and species between groups; microbial community richness and diversity were compared between the two groups.Results: There were no significant differences in the microbial community richness and diversity between the two groups. At the phylum level, 11 bacterial phyla were found; most sequences belonged to one of the three dominant bacterial phyla – Firmicutes, Proteobacteria, and Bacteroidetes. The top 10 microbes at the phylum, family, and genus levels showed no significant changes in their composition within the gut microbiota between the AD and non-AD groups. A decrease in the ratio of the Streptococcus genus was found in atopic dermatitis group when compared with healthy controls (p=0.048).Conclusion: A decrease in the abundance of Streptococcus was

In Taiwan, combustible wood mostly comes from waste pallets and scrap packaging materials discarded by factories, which produced a total of 278,067 tons of waste wood in 2019. In this study, the heat value of waste wood was 18.3 ± 1.07 MJ kg–1. The measured volatile fraction was 76.5 ± 7.34%, the fixed carbon was 15.7 ± 3.19%, the ash content was 2.96 ± 2.45%, and the moisture content was 21.6 ± 10.2%. The proportions of the elemental constituents in the waste wood were 45.3 ± 4.95%, 46.9 ± 3.94%, 5.9 ± 0.44%, 0.21 ± 0.17%, 0.29 ± 0.26%, and 0.02 ± 0.02% for carbon, oxygen, hydrogen, sulfur, nitrogen, and chlorine, respectively. The average boiler capacity was 11.5 ± 6.84 ton hr–1, the average fuel consumption of the boilers was 1.47 ± 1.81 ton hr–1, the average operating temperature of the boilers was 853 ± 228°C, the average steam generation of the boilers was 7.63 ± 5.97 ton hr–1, and the average exhaust flow rate was 246.6 ± 200.9 m3 min–1. The main air pollution control systems used in the waste wood combustion boilers were systems combining a cyclone, a baghouse and a scrubber (37.8%), a cyclone and a baghouse (28.4%), a cyclone and a scrubber (10.2%), and systems using a baghouse only (9.8%). Based on our fuel consumption data, the air pollutant emission factors were 0.71 ± 1.44 kg per ton of wood for PM, 0.86 ± 1.47 kg per ton of wood for SOx, and 5.24 ± 9.56 kg per ton of wood for NOx. In July 2022, new emission standards for boilers will be implemented, and emission reductions of at least 30% for PM, 35% for NOx and 7% for SO2 will be required.

Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data. We developed four algorithms: a rule-based algorithm using only structured data (baseline algorithm) and three algorithms using different NLP models. The three NLP models are based on regularized logistic regression and use different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components respectively. The baseline algorithm and the best performed NLP algorithm were external validated on a dataset from Vanderbilt University Medical Center (VUMC). Our best performing NLP model incorporating features from both structured data, regular expression concepts, and mapped CUIs improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.62 vs 0.96) datasets compared to the baseline lupus nephritis algorithm.

3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses. Recent works have shown promising results by training neural radiance field (NeRF) generators on unstructured 2D images, but still can not generate highly-realistic images with fine details. A critical reason is that the high memory and computation cost of volumetric representation learning greatly restricts the number of point samples for radiance integration during training. Deficient sampling not only limits the expressive power of the generator to handle fine details but also impedes effective GAN training due to the noise caused by unstable Monte Carlo sampling. We propose a novel approach that regulates point sampling and radiance field learning on 2D manifolds, embodied as a set of learned implicit surfaces in the 3D volume. For each viewing ray, we calculate ray-surface intersections and accumulate their radiance generated by the network. By training and rendering such radiance manifolds, our generator can produce high quality images with realistic fine details and strong visual 3D consistency., Comment: CVPR2022 Oral. Project page: https://yudeng.github.io/GRAM

The standard RSA relies on multiple big-number modular exponentiation operations and longer key-length is required for better protection. This imposes a hefty time penalty for encryption and decryption. In this study, we analyzed and developed an improved parallel algorithm (PMKRSA) based on the idea of splitting the plaintext into multiple chunks and encrypt the chunks using multiple key-pairs. The algorithm in our new scheme is so natural for parallelized implementation that we also investigated its parallelization in a GPU environment. In the following, the structure of our new scheme is outlined, and its correctness is proved mathematically. Then, with the algorithm implemented and optimized on both CPU and CPU+GPU platforms, we showed that our algorithm shortens the computational time considerably, and it has a security advantage over the standard RSA as it is invulnerable to the common attacks. Finally, we also proved the feasibility of using our algorithm to encrypt large files through simulation. The results show that over the set of file size: 1 MB, 10 MB, 25 MB, 50 MB, 100 MB, the average encryption and decryption time of the CPU version is 0.2476s and 9.4476s, and for the CPU+GPU version, it is 0.0009s and 0.0618s, respectively.

Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human--computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

© 2017 Elsevier Inc. We prove Strichartz estimates over large time scales for the Schrödinger equation set on irrational tori. They are optimal for Lebesgue exponents p>6.