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OBJECTIVES To investigate health-related quality of life in patients with idiopathic inflammatory myopathies (IIMs) compared with those with non-IIM autoimmune rheumatic diseases (AIRDs), non-rheumatic autoimmune diseases (nrAIDs) and without autoimmune diseases (controls) using Patient-Reported Outcome Measurement Information System (PROMIS) instrument data obtained from the second COVID-19 vaccination in autoimmune disease (COVAD-2) e-survey database. METHODS Demographics, diagnosis, comorbidities, disease activity, treatments and PROMIS instrument data were analysed. Primary outcomes were PROMIS Global Physical Health (GPH) and Global Mental Health (GMH) scores. Factors affecting GPH and GMH scores in IIMs were identified using multivariable regression analysis. RESULTS We analysed responses from 1582 IIM, 4700 non-IIM AIRD and 545 nrAID patients and 3675 controls gathered through 23 May 2022. The median GPH scores were the lowest in IIM and non-IIM AIRD patients {13 [interquartile range (IQR) 10-15] IIMs vs 13 [11-15] non-IIM AIRDs vs 15 [13-17] nrAIDs vs 17 [15-18] controls, P < 0.001}. The median GMH scores in IIM patients were also significantly lower compared with those without autoimmune diseases [13 (IQR 10-15) IIMs vs 15 (13-17) controls, P < 0.001]. Inclusion body myositis, comorbidities, active disease and glucocorticoid use were the determinants of lower GPH scores, whereas overlap myositis, interstitial lung disease, depression, active disease, lower PROMIS Physical Function 10a and higher PROMIS Fatigue 4a scores were associated with lower GMH scores in IIM patients. CONCLUSION Both physical and mental health are significantly impaired in IIM patients, particularly in those with comorbidities and increased fatigue, emphasizing the importance of patient-reported experiences and optimized multidisciplinary care to enhance well-being in people with IIMs.

Taxonomy expansion is the process of incorporating a large number of additional nodes (i.e., "queries") into an existing taxonomy (i.e., "seed"), with the most important step being the selection of appropriate positions for each query. Enormous efforts have been made by exploring the seed's structure. However, existing approaches are deficient in their mining of structural information in two ways: poor modeling of the hierarchical semantics and failure to capture directionality of is-a relation. This paper seeks to address these issues by explicitly denoting each node as the combination of inherited feature (i.e., structural part) and incremental feature (i.e., supplementary part). Specifically, the inherited feature originates from "parent" nodes and is weighted by an inheritance factor. With this node representation, the hierarchy of semantics in taxonomies (i.e., the inheritance and accumulation of features from "parent" to "child") could be embodied. Additionally, based on this representation, the directionality of is-a relation could be easily translated into the irreversible inheritance of features. Inspired by the Darmois-Skitovich Theorem, we implement this irreversibility by a non-Gaussian constraint on the supplementary feature. A log-likelihood learning objective is further utilized to optimize the proposed model (dubbed DNG), whereby the required non-Gaussianity is also theoretically ensured. Extensive experimental results on two real-world datasets verify the superiority of DNG relative to several strong baselines., Comment: 7figures

In this work, we describe the design and implementation of a reusable tool named KnowledgeZooClient targeting the construction, as a crowd-sourced effort, of a knowledge graph for Android apps. KnowledgeZooClient is made up of two modules: (1) the Metadata Extraction Module (MEM), which aims at extracting metadata from Android apps and (2) the Metadata Integration Module (MIM) for importing and integrating extracted metadata into a graph database. The usefulness of KnowledgeZooClient is demonstrated via an exclusive knowledge graph called KnowledgeZoo, which contains information on over 500,000 apps already and still keeps growing. Interested users can already benefit from KnowledgeZoo by writing advanced search queries so as to collect targeted app samples.

In this work, we describe the design and implementation of a reusable tool named KnowledgeZooClient targeting the construction, as a crowd-sourced effort, of a knowledge graph for Android apps. KnowledgeZooClient is made up of two modules: (1) the Metadata Extraction Module (MEM), which aims at extracting metadata from Android apps and (2) the Metadata Integration Module (MIM) for importing and integrating extracted metadata into a graph database. The usefulness of KnowledgeZooClient is demonstrated via an exclusive knowledge graph called KnowledgeZoo, which contains information on over 500,000 apps already and still keeps growing. Interested users can already benefit from KnowledgeZoo by writing advanced search queries so as to collect targeted app samples.

Avian leukosis virus subgroup J (ALV-J) infection can cause tumors and immunosuppression in infected chickens. Macrophages play a central role in host defense against invading pathogens. In this study, we discovered an interesting phenomenon: ALV-J replication is weakened from 3 hours post-infection (hpi) to 36 hpi, which was verified using Western blotting and RT-PCR. To further investigate the interaction between ALV-J and macrophages, transcriptome analysis was performed to analyze the host genes’ function in chicken primary monocyte-derived macrophages (MDM). Compared to the uninfected control, 624 up-regulated differentially expressed genes (DEG) and 341 down-regulated DEG at 3 hpi, and 174 up-regulated DEG and 87 down-regulated DEG at 36 hpi were identified in chicken MDM, respectively. ALV-J infection induced strong innate immune responses in chicken MDM at 3 hpi, instead of 36 hpi, according to the analysis results of Gene Ontology and KEGG pathway. Importantly, the host factors, such as up-regulated MIP-3α, IL-1β, iNOS, K60, IRG1, CH25H, NFKBIZ, lysozyme and OASL were involved in the host defense response during the course of ALV-J infection. On the contrary, up-regulated EX-FABP, IL4I1, COX-2, NFKBIA, TNFAIP3 and the Jak STAT pathway inhibitors including CISH, SOCS1 and SOCS3 are beneficial to ALV-J survival in chicken macrophages. We speculated that ALV-J tropism for macrophages helps to establish a latent infection in chicken MDM from 6 to 36 hpi. The present study provides a comprehensive view of the interactions between macrophages and ALV-J. It suggests the mechanisms of defense of chicken macrophages against ALV-J invasion and how ALV-J escape the host innate immune responses.

Heart failure remains a major source of late morbidity and mortality after myocardial infarction (MI). The temporospatial presence of activated fibroblasts in the injured myocardium predicts the quality of cardiac remodeling after MI. Therefore, monitoring of activated fibroblasts is of great interest for studying cardiac remodeling after MI. Fibroblast activation protein (FAP) expression is upregulated in activated fibroblasts. This study investigated the feasibility of imaging activated fibroblasts with a new 68Ga-labeled FAP inhibitor (68Ga-FAPI-04) for PET imaging of fibroblast activation in a preclinical model of MI. Methods: MI and sham-operated rats were scanned with 68Ga-FAPI-04 PET/CT (1, 3, 6, 14, 23, and 30 d after MI) and with 18F-FDG (3 d after MI). Dynamic 68Ga-FAPI-04 PET and blocking studies were performed on MI rats 7 d after coronary ligation. After in vivo scans, the animals were euthanized and their hearts harvested for ex vivo analyses. Cryosections were prepared for autoradiography, hematoxylin and eosin (H&E), and immunofluorescence staining. Results: 68Ga-FAPI-04 uptake in the injured myocardium peaked on day 6 after coronary ligation. The tracer accumulated intensely in the MI territory, as identified by decreased 18F-FDG uptake and confirmed by PET/MR and H&E staining. Autoradiography and H&E staining of cross-sections revealed that 68Ga-FAPI-04 accumulated mainly at the border zone of the infarcted myocardium. In contrast, there was only minimal uptake in the infarct of the blocked rats, comparable to the uptake in the remote noninfarcted myocardium (PET image-derived ratio of infarct uptake to remote uptake: 6 ± 2). Immunofluorescence staining confirmed the presence of FAP-positive myofibroblasts in the injured myocardium. Morphometric analysis of the whole-heart sections demonstrated 3- and 8-fold higher FAP-positive fibroblast density in the border zone than in the infarct center and remote area, respectively. Conclusion: 68

Modern Knowledge Graphs such as DBPedia contain significant information regarding Named Entities and the logical relationships which exist between them. Twitter on the other hand, contains important information on the popularity and frequency with which these entities are mentioned and discussed in combination with one another. In this paper we investigate whether these two sources of information can be used to complement and explain one another. In particular, we would like to know whether the logical relationships (a.k.a. semantic paths) which exist between pairs of known entities can help to explain the frequency with which those entities co-occur with one another in Twitter. To do this we train attranking function over semantic paths between pairs of entities. The aim of the ranker is to identify the path that most likely explains why a particular pair of entities have appeared together in a particular tweet. We train the ranking model using a number of lexical, graph-embedding and popularity-based features over semantic paths containing a single intermediate entity and demonstrate the efficacy of the model for determining why pairs of entities occur together in tweets. © 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.

Modern Knowledge Graphs such as DBPedia contain significant information regarding Named Entities and the logical relationships which exist between them. Twitter on the other hand, contains important information on the popularity and frequency with which these entities are mentioned and discussed in combination with one another. In this paper we investigate whether these two sources of information can be used to complement and explain one another. In particular, we would like to know whether the logical relationships (a.k.a. semantic paths) which exist between pairs of known entities can help to explain the frequency with which those entities co-occur with one another in Twitter. To do this we train attranking function over semantic paths between pairs of entities. The aim of the ranker is to identify the path that most likely explains why a particular pair of entities have appeared together in a particular tweet. We train the ranking model using a number of lexical, graph-embedding and popularity-based features over semantic paths containing a single intermediate entity and demonstrate the efficacy of the model for determining why pairs of entities occur together in tweets. © 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.

Modern Knowledge Graphs such as DBPedia contain significant information regarding Named Entities and the logical relationships which exist between them. Twitter on the other hand, contains important information on the popularity and frequency with which these entities are mentioned and discussed in combination with one another. In this paper we investigate whether these two sources of information can be used to complement and explain one another. In particular, we would like to know whether the logical relationships (a.k.a. semantic paths) which exist between pairs of known entities can help to explain the frequency with which those entities co-occur with one another in Twitter. To do this we train attranking function over semantic paths between pairs of entities. The aim of the ranker is to identify the path that most likely explains why a particular pair of entities have appeared together in a particular tweet. We train the ranking model using a number of lexical, graph-embedding and popularity-based features over semantic paths containing a single intermediate entity and demonstrate the efficacy of the model for determining why pairs of entities occur together in tweets. © 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.

Accelerating the inference of a trained DNN is a well studied subject. In this paper we switch the focus to the training of DNNs. The training phase is compute intensive, demands complicated data communication, and contains multiple levels of data dependencies and parallelism. This paper presents an algorithm/architecture space exploration of efficient accelerators to achieve better network convergence rates and higher energy efficiency for training DNNs. We further demonstrate that an architecture with hierarchical support for collective communication semantics provides flexibility in training various networks performing both stochastic and batched gradient descent based techniques. Our results suggest that smaller networks favor non-batched techniques while performance for larger networks is higher using batched operations. At 45nm technology, CATERPILLAR achieves performance efficiencies of 177 GFLOPS/W at over 80% utilization for SGD training on small networks and 211 GFLOPS/W at over 90% utilization for pipelined SGD/CP training on larger networks using a total area of 103.2 mm$^2$ and 178.9 mm$^2$ respectively., Comment: ASAP 2017: The 28th Annual IEEE International Conference on Application-specific Systems, Architectures and Processors

Xuechao Liu,1,2,* Pengfei Xu,1,3,* Haibo Qiu,1,2,* Jianjun Liu,1,2 Shangxiang Chen,1,2 Dazhi Xu,1,2 Wei Li,1,2 Youqing Zhan,1,2 Yuanfang Li,1,2 Yingbo Chen,1,2 Zhiwei Zhou,1,2 Xiaowei Sun1,2 1Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 2Department of Gastric and Pancreatic Surgery, 3Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China *These authors contributed equally to this work Purpose: We sought to determine whether human epidermal growth factor receptor 2 (HER2) and vascular endothelial growth factor (VEGF) expression were independent prognostic factors for gastric cancer (GC).Patients and methods: A total of 678 consecutive patients with GC undergoing curative surgery between October 2010 and December 2012 had resected tissue examined for HER2 and VEGF expression using immunohistochemistry. Immunohistochemical expression of HER2 was analyzed using the DAKO-HercepTest™ and scored according to published reports. VEGF expression was calculated by multiplying the score for the percentage of positive cells by the intensity score. We defined positive expression as a score of 1+, 2+, or 3+, and a score of 0 was defined as negative expression. We compared these results to clinicopathological characteristics, including overall survival (OS).Results: Multivariate analysis revealed that HER2 expression was independently associated with shorter OS (hazard ratio [HR], 1.55; 95% confidence interval [CI], 1.10–2.18; P=0.01) and with higher tumor–nodes–metastasis stage (HR, 3.88; 95% CI, 2.67–5.64; P<0.001) in patients with GC. VEGF expression was not associated with OS (HR, 1.25; 95% CI, 0.86–1.82; P=0.24). HER2 expression was still identified as an independent prognostic factor in Stage II–III p

Este artículo documenta la experiencia de China en la transformación de un sistema de producción de alimentos centralmente planificado a uno basado en el sistema de mercado. La reforma ha progresado en tres etapas: a) aumento de los precios agrícolas pagados por el Estado; b) introducción de los mecanismos de mercado; y c) avances hacia la realización total de operaciones de mercado. Esta serie de reformas incrementó la producción de alimentos y fortaleció la seguridad alimentaria nacional. Las reformas de precios agrícolas en China durante las tres décadas pasadas sugieren que las políticas correctas y las instituciones son esenciales para resolver los problemas alimentarios.

Este artículo documenta la experiencia de China en la transformación de un sistema de producción de alimentos centralmente planificado a uno basado en el sistema de mercado. La reforma ha progresado en tres etapas: a) aumento de los precios agrícolas pagados por el Estado; b) introducción de los mecanismos de mercado; y c) avances hacia la realización total de operaciones de mercado. Esta serie de reformas incrementó la producción de alimentos y fortaleció la seguridad alimentaria nacional. Las reformas de precios agrícolas en China durante las tres décadas pasadas sugieren que las políticas correctas y las instituciones son esenciales para resolver los problemas alimentarios.