Sentence Specified Dynamic Video Thumbnail Generation

With the tremendous growth of videos over the Internet, video thumbnails, providing video content previews, are becoming increasingly crucial to influencing users' online searching experiences. Conventional video thumbnails are generated once purely based on the visual characteristics of videos, and then displayed as requested. Hence, such video thumbnails, without considering the users' searching intentions, cannot provide a meaningful snapshot of the video contents that users concern. In this paper, we define a distinctively new task, namely sentence specified dynamic video thumbnail generation, where the generated thumbnails not only provide a concise preview of the original video contents but also dynamically relate to the users' searching intentions with semantic correspondences to the users' query sentences. To tackle such a challenging task, we propose a novel graph convolved video thumbnail pointer (GTP). Specifically, GTP leverages a sentence specified video graph convolutional network to model both the sentence-video semantic interaction and the internal video relationships incorporated with the sentence information, based on which a temporal conditioned pointer network is then introduced to sequentially generate the sentence specified video thumbnails. Moreover, we annotate a new dataset based on ActivityNet Captions for the proposed new task, which consists of 10,000+ video-sentence pairs with each accompanied by an annotated sentence specified video thumbnail. We demonstrate that our proposed GTP outperforms several baseline methods on the created dataset, and thus believe that our initial results along with the release of the new dataset will inspire further research on sentence specified dynamic video thumbnail generation. Dataset and code are available at https://github.com/yytzsy/GTP

County-level CO2 emissions and sequestration in China during 1997–2017

With the implementation of China’s top-down CO2 emissions reduction strategy, the regional differences should be considered. As the most basic governmental unit in China, counties could better capture the regional heterogeneity than provinces and prefecture-level city, and county-level CO2 emissions could be used for the development of strategic policies tailored to local conditions. However, most of the previous accounts of CO2 emissions in China have only focused on the national, provincial, or city levels, owing to limited methods and smaller-scale data. In this study, a particle swarm optimization-back propagation (PSO-BP) algorithm was employed to unify the scale of DMSP/OLS and NPP/VIIRS satellite imagery and estimate the CO2 emissions in 2,735 Chinese counties during 1997–2017. Moreover, as vegetation has a significant ability to sequester and reduce CO2 emissions, we calculated the county-level carbon sequestration value of terrestrial vegetation. The results presented here can contribute to existing data gaps and enable the development of strategies to reduce CO2 emissions in China

An international Delphi consensus statement on metabolic dysfunction-associated fatty liver disease and risk of chronic kidney disease

Background: With the rising global prevalence of fatty liver disease related to metabolic dysfunction, the association of this common liver condition with chronic kidney disease (CKD) has become increasingly evident. In 2020, the more inclusive term metabolic dysfunction-associated fatty liver disease (MAFLD) was proposed to replace the term non-alcoholic fatty liver disease (NAFLD). The observed association between MAFLD and CKD and our understanding that CKD can be a consequence of underlying metabolic dysfunction support the notion that individuals with MAFLD are at higher risk of having and developing CKD compared with those without MAFLD. However, to date, there is no appropriate guidance on CKD in individuals with MAFLD. Furthermore, there has been little attention paid to the link between MAFLD and CKD in the Nephrology community. Methods and Results: Using a Delphi-based approach, a multidisciplinary panel of 50 international experts from 26 countries reached a consensus on some of the open research questions regarding the link between MAFLD and CKD. Conclusions: This Delphi-based consensus statement provided guidance on the epidemiology, mechanisms, management and treatment of MAFLD and CKD, as well as the relationship between the severity of MAFLD and risk of CKD, which establish a framework for the early prevention and management of these two common and interconnected diseases.Fil: Sun, Dan Qin. Jiangnan University Medical Center; China. Nantong University; ChinaFil: Targher, Giovanni. Azienda Ospedaliera Universitaria Integrata Verona; ItaliaFil: Byrne, Christopher D.. University of Southampton; Reino UnidoFil: Wheeler, David C.. University College London; Estados UnidosFil: Wong, Vincent Wai Sun. Chinese University of Hong Kong; ChinaFil: Fan, Jian Gao. Shanghai Jiao Tong University; ChinaFil: Tilg, Herbert. Medical University Innsbruck; AustriaFil: Yuan, Wei Jie. Shanghai Jiao Tong University; ChinaFil: Wanner, Christoph. Würzburg University Clinic; AlemaniaFil: Gao, Xin. Fudan University; ChinaFil: Long, Michelle T.. Boston University School of Medicine; Estados UnidosFil: Kanbay, Mehmet. Koc University School of Medicine; TurquíaFil: Nguyen, Mindie H.. Stanford University Medical Center; Estados UnidosFil: Navaneethan, Sankar D.. Baylor College of Medicine; Estados UnidosFil: Yilmaz, Yusuf. Marmara University; Turquía. Recep Tayyip Erdoğan University; TurquíaFil: Huang, Yuli. Southern Medical University; ChinaFil: Gani, Rino A.. Universitas Indonesia; IndonesiaFil: Marzuillo, Pierluigi. Università della Campania “Luigi Vanvitelli”; ItaliaFil: Boursier, Jérôme. Angers University; FranciaFil: Zhang, Huijie. Southern Medical University; ChinaFil: Jung, Chan Young. Yonsei University; Corea del SurFil: Chai, Jin. Army Medical University; ChinaFil: Valenti, Luca. Università degli Studi di Milano; ItaliaFil: Papatheodoridis, George. Kapodistrian University of Athens; GreciaFil: Sookoian, Silvia Cristina. Centro de Investigacion Traslacional En Salud (cenitres) ; Facultad de Cs. de la Salud ; Universidad Maimonides; . Universidad Abierta Interamericana; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chunsun, Dai. Nanjing Medical University; ChinaFil: Eslam, Mohammed. University of Sydney; AustraliaFil: Wei, Lai. Tsinghua University; ChinaFil: George, Jacob. University of Sydney; AustraliaFil: Zheng, Ming Hua. Wenzhou Medical University; Chin

An international Delphi consensus statement on metabolic dysfunction-associated fatty liver disease and risk of chronic kidney disease

BACKGROUND: With the rising global prevalence of fatty liver disease related to metabolic dysfunction, the association of this common liver condition with chronic kidney disease (CKD) has become increasingly evident. In 2020, the more inclusive term metabolic dysfunction-associated fatty liver disease (MAFLD) was proposed to replace the term non-alcoholic fatty liver disease (NAFLD). The observed association between MAFLD and CKD and our understanding that CKD can be a consequence of underlying metabolic dysfunction support the notion that individuals with MAFLD are at higher risk of having and developing CKD compared with those without MAFLD. However, to date, there is no appropriate guidance on CKD in individuals with MAFLD. Furthermore, there has been little attention paid to the link between MAFLD and CKD in the Nephrology community. METHODS AND RESULTS: Using a Delphi-based approach, a multidisciplinary panel of 50 international experts from 26 countries reached a consensus on some of the open research questions regarding the link between MAFLD and CKD. CONCLUSIONS: This Delphi-based consensus statement provided guidance on the epidemiology, mechanisms, management and treatment of MAFLD and CKD, as well as the relationship between the severity of MAFLD and risk of CKD, which establish a framework for the early prevention and management of these two common and interconnected diseases

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Automatic Function Selection for Large Scale Salient Object Detection

Robust detection of a large dictionary of salient objects in natural image database is of fundamental importance to image retrieval systems. We review three popular frameworks for salient object detection, i.e., segmentation-based method, grid-based method and part-based method and discuss their advantages and limitations. We argue that using these frameworks individually is generally not enough to handle a large number of salient object classes accurately because of the intrinsic diversity of salient object features. Motivated by this observation, we have proposed a new system which combines the merits of these frameworks into one single hybrid system. The system automatically selects the appropriate modeling method for each individual object class using J measure and shape variance. We conduct comparison experiments on two popular image dataset – Corel and LabelMe. Empirical results have shown that the proposed hybrid method is more general and can handle much more salient object classes in a robust manner