Parameter uncertainty and sensitivity evaluation of copula-based multivariate hydroclimatic risk assessment

Extensive uncertainties exist in hydroclimatic risk analysis. Especially in multivariate hydrologic risk inferences, uncertainties in individual hydroclimatic extremes such as floods and their dependence structure may lead to bias and uncertainty in future hydrologic risk predictions. In this study, a parameter uncertainty and sensitivity evaluation (PUSE) framework is proposed to quantify parameter uncertainties and then reveal their contributions to the multivariate hydroclimatic risk predictions. The predictive risks are finally generated by “integrating†the values over the posterior distributions of the parameters. The proposed approach was applied for bivariate risk analysis of compound floods at the Xiangxi River to characterize the concurrence probabilities of flood peaks and volumes. The results demonstrate that the proposed approach can quantify uncertainties in a copula-based multivariate risk analysis and characterize effects and contributions of parameters in marginal and dependence structures on the multivariate hydroclimatic risk predictions. In terms of the bivariate risk for flood peak and volume at the Xiangxi River, uncertainties in model parameters would lead to noticeable uncertainties even for moderate floods. The performances of the copula model for flood peak-volume at Xiangxi River are mainly affected by the uncertainties in location parameters of the two individual flood variables. Also, parameter uncertainty in the dependence structure (i.e., copula) would also poses explicit impacts on performance of the copula-based risk analyses model. These uncertainties would result into higher bivariate predictive risks than the values obtained by “optimal/deterministic†predictions. This indicates that uncertain- ties are required to be considered to provide reliable multivariate hydroclimatic risk predictions.National Key Research and Development Plan (2016YFA0601502), and the Royal Society International Exchanges Program (No. IES\R2\202075)

A coupled ensemble filtering and probabilistic collocation approach for uncertainty quantification of hydrological models

Natural Science Foundation of China (Nos. 51190095 and 51225904) and the Program for Innovative Research Team in University (IRT1127)

Immunomodulatory Effects of Bone Marrow-Derived Mesenchymal Stem Cells in a Swine Hemi-Facial Allotransplantation Model

BACKGROUND: In this study, we investigated whether the infusion of bone marrow-derived mesenchymal stem cells (MSCs), combined with transient immunosuppressant treatment, could suppress allograft rejection and modulate T-cell regulation in a swine orthotopic hemi-facial composite tissue allotransplantation (CTA) model. METHODOLOGY/PRINCIPAL FINDINGS: Outbred miniature swine underwent hemi-facial allotransplantation (day 0). Group-I (n = 5) consisted of untreated control animals. Group-II (n = 3) animals received MSCs alone (given on days -1, +1, +3, +7, +14, and +21). Group-III (n = 3) animals received CsA (days 0 to +28). Group-IV (n = 5) animals received CsA (days 0 to +28) and MSCs (days -1, +1, +3, +7, +14, and +21). The transplanted face tissue was observed daily for signs of rejection. Biopsies of donor tissues and recipient blood sample were obtained at specified predetermined times (per 2 weeks post-transplant) or at the time of clinically evident rejection. Our results indicated that the MSC-CsA group had significantly prolonged allograft survival compared to the other groups (P<0.001). Histological examination of the MSC-CsA group displayed the lowest degree of rejection in alloskin and lymphoid gland tissues. TNF-α expression in circulating blood revealed significant suppression in the MSC and MSC-CsA treatment groups, as compared to that in controls. IHC staining showed CD45 and IL-6 expression were significantly decreased in MSC-CsA treatment groups compared to controls. The number of CD4+/CD25+ regulatory T-cells and IL-10 expressions in the circulating blood significantly increased in the MSC-CsA group compared to the other groups. IHC staining of alloskin tissue biopsies revealed a significant increase in the numbers of foxp3(+)T-cells and TGF-β1 positive cells in the MSC-CsA group compared to the other groups. CONCLUSIONS: These results demonstrate that MSCs significantly prolong hemifacial CTA survival. Our data indicate the MSCs did not only suppress inflammation and acute rejection of CTA, but also modulate T-cell regulation and related cytokines expression

Sexual Propagation of Pteris Vittata L. Influenced by pH, Calcium, and Temperature

National High-tech Program (863 Program) of China 2007AA061001;Foundation of the Ministry of Agricultural Key Laboratory of Plant Nutrition and Nutrient CyclingWe aimed to optimize germination and growth conditions of the arsenic hyperaccumulating fern, Pteris vittata L. Pot experiments were carried out to investigate the effects of soil pH, soil calcium (Ca) concentration, and temperature on the sexual propagation of P. vittata. At 25 degrees C, germination was both accelerated and increased by high soil pH and Ca concentration. Spores of P. vittata did not germinate on medium with a pH of 4.6. Amending strongly acid soils with 27.5 or 40 mol/g Ca(OH)2 significantly improved the growth rate during both the germination phase and the gametophyte phase. Amending strongly acid soils with NaOH (55 mol/g) promoted germination, but did not affect subsequent growth. Among the different temperature, germination and growth rates were higher at 25 degrees C than at 20 degrees C or 30 degrees C. The distribution of P. vittata in China might be influenced by its requirement for high pH and high Ca concentration in the soil, and appropriate growth temperature to complete sexual propagation. These results provided important information for improving breeding conditions of P. vitatta and will be helpful for extending the range of areas in which P. vittata can be used for phytoremediation

Development of a copula-based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

National Natural Science Foundation of China, the National Key Research and Development Plan, and the Natural Sciences and Engineering Research Council of Canad

Repeatability of Corneal Elevation Maps in Keratoconus Patients Using the Tomography Matching Method

To assess repeatability of corneal tomography in successive measurements by Pentacam in keratoconus (KC) and normal eyes based on the Iterative Closest Point (ICP) algorithm. The study involved 143 keratoconic and 143 matched normal eyes. ICP algorithm was used to estimate six single and combined misalignment (CM) parameters, the root mean square (RMS) of the difference in elevation data pre (PreICP-RMS) and post (PosICP-RMS) tomography matching. Corneal keratometry, expressed in the form of M, J0 and J45 (power vector analysis parameters), was used to evaluate the effect of misalignment on corneal curvature measurements. The PreICP-RMS and PosICP-RMS were statistically higher (P < 0.01) in KC than normal eyes. CM increased significantly (p = 0.00), more in KC (16.76 ± 20.88 μm) than in normal eyes (5.43 ± 4.08 μm). PreICP-RMS, PosICP-RMS and CM were correlated with keratoconus grade (p < 0.05). Corneal astigmatism J0 was different (p = 0.01) for the second tomography measurements with misalignment consideration (−1.11 ± 2.35 D) or not (−1.18 ± 2.35 D), while M and J45 kept similar. KC corneas consistently show higher misalignments between successive tomography measurements and lower repeatability compared with healthy eyes. The influence of misalignment is evidently clearer in the estimation of astigmatism than spherical curvature. These higher errors appear correlated with KC progression

Multimodal Fake News Detection with Textual, Visual and Semantic Information

[EN] Recent years have seen a rapid growth in the number of fake news that are posted online. Fake news detection is very challenging since they are usually created to contain a mixture of false and real information and images that have been manipulated that confuses the readers. In this paper, we propose a multimodal system with the aim to di erentiate between fake and real posts. Our system is based on a neural network and combines textual, visual and semantic information. The textual information is extracted from the content of the post, the visual one from the image that is associated with the post and the semantic refers to the similarity between the image and the text of the post. We conduct our experiments on three standard real world collections and we show the importance of those features on detecting fake news.Anastasia Giachanou is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). Guobiao Zhang is funded by China Scholarship Council (CSC) from the Ministry of Education of P.R. China. The work of Paolo Rosso is partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31)Giachanou, A.; Zhang, G.; Rosso, P. (2020). Multimodal Fake News Detection with Textual, Visual and Semantic Information. Springer. 30-38. https://doi.org/10.1007/978-3-030-58323-1_3S3038Boididou, C., et al.: Verifying multimedia use at MediaEval 2015. In: MediaEval 2015 Workshop, pp. 235–237 (2015)Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: WWW 2011, pp. 675–684 (2011)Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR 2017, pp. 1251–1258 (2017)Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: ICWSM 2017 (2017)Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009, pp. 248–255 (2009)Ghanem, B., Rosso, P., Rangel, F.: An emotional analysis of false information in social media and news articles. ACM Trans. Internet Technol. (TOIT) 20(2), 1–18 (2020)Giachanou, A., Gonzalo, J., Mele, I., Crestani, F.: Sentiment propagation for predicting reputation polarity. In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 226–238. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_18Giachanou, A., Ríssola, E.A., Ghanem, B., Crestani, F., Rosso, P.: The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. In: Métais, E., Meziane, F., Horacek, H., Cimiano, P. (eds.) NLDB 2020. LNCS, vol. 12089, pp. 181–192. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51310-8_17Giachanou, A., Rosso, P., Crestani, F.: Leveraging emotional signals for credibility detection. In: SIGIR 2019, pp. 877–880 (2019)He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR 2016, pp. 770–778 (2016)Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C 41(6), 765–781 (2011)Khattar, D., Goud, J.S., Gupta, M., Varma, V.: MVAE: multimodal variational autoencoder for fake news detection. In: WWW 2019, pp. 2915–2921 (2019)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)Popat, K., Mukherjee, S., Yates, A., Weikum, G.: DeClarE: debunking fake news and false claims using evidence-aware deep learning. In: EMNLP 2018, pp. 22–32 (2018)Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: EMNLP 2017, pp. 2931–2937 (2017)Shu, K., Wang, S., Liu, H.: Understanding user profiles on social media for fake news detection. In: MIPR 2018, pp. 430–435 (2018)Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media. arXiv:1809.01286 (2018)Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR 2016, pp. 2818–2826 (2016)Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)Wang, Y., et al.: EANN: event adversarial neural networks for multi-modal fake news detection. In: KDD 2018, pp. 849–857 (2018)Zhao, Z., et al.: An image-text consistency driven multimodal sentiment analysis approach for social media. Inf. Process. Manag. 56(6), 102097 (2019)Zlatkova, D., Nakov, P., Koychev, I.: Fact-checking meets fauxtography: verifying claims about images. In: EMNLP-IJCNLP 2019, pp. 2099–2108 (2019

Management of Drinking Water Source in Rural Communities under Climate Change

In rural communities where central public water supply systems can hardly reach, the acquisition and management of safe drinking water sources are challenging due to population growth, environmental pollution, and climate change. Numerous endeavours have been made over the past several decades to help rural communities manage drinking water sources and obtain safe drinking water under climate change, which are summarized in this review. Firstly, the crises of rural drinking water safety under climate change are overviewed based on the extensive investigation of recent studies on rural water security. Second, the sustainable management of rural drinking water sources are systematically reviewed, mainly focusing on issues of water quality assessments, drinking water quantity and quality improvement, system maintenance and community management, and decision making in rural regions across the world. Finally, knowledge gaps of recent endeavors are highlighted, emerging threats and complications to water security under climate change are identified and perspectives for future works are discussed.This research was supported by the Natural Science and Engineering Research Council of Canada, and the National Foreign Expert Project (G2021111016L and (G2021111017L)

Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods

Natural Science Foundation of China; National Key Research and Development Plan; Natural Sciences and Engineering Research Council of Canada

Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method

This research was supported by the Natural Science Foundation of China (Nos. 51190095 and 51225904) and the Program for Innovative Research Team in University (IRT1127)