Tropical Support Vector Machine and its Applications to Phylogenomics

Most data in genome-wide phylogenetic analysis (phylogenomics) is essentially multidimensional, posing a major challenge to human comprehension and computational analysis. Also, we can not directly apply statistical learning models in data science to a set of phylogenetic trees since the space of phylogenetic trees is not Euclidean. In fact, the space of phylogenetic trees is a tropical Grassmannian in terms of max-plus algebra. Therefore, to classify multi-locus data sets for phylogenetic analysis, we propose tropical support vector machines (SVMs). Like classical SVMs, a tropical SVM is a discriminative classifier defined by the tropical hyperplane which maximizes the minimum tropical distance from data points to itself in order to separate these data points into sectors (half-spaces) in the tropical projective torus. Both hard margin tropical SVMs and soft margin tropical SVMs can be formulated as linear programming problems. We focus on classifying two categories of data, and we study a simpler case by assuming the data points from the same category ideally stay in the same sector of a tropical separating hyperplane. For hard margin tropical SVMs, we prove the necessary and sufficient conditions for two categories of data points to be separated, and we show an explicit formula for the optimal value of the feasible linear programming problem. For soft margin tropical SVMs, we develop novel methods to compute an optimal tropical separating hyperplane. Computational experiments show our methods work well. We end this paper with open problems.Comment: 27 pages, 6 figures, 2 table

Sedimentation in the Three Gorges Dam and the future trend of Changjiang (Yangtze River) sediment flux to the sea

The Three Gorges Dam (TGD) on the upper Changjiang (Yangtze River), China, disrupts the continuity of Changjiang sediment delivery to downstream and coastal areas. In this study, which was based on 54 years of annual water and sediment data from the mainstream and major tributaries of Changjiang, sediment deposition induced by the TGD in 2003–2008 was quantified. Furthermore, we determined the theoretical trapping efficiency of the cascade reservoir upstream of the TGD. Its impact on Changjiang sediment flux in the coming decades is discussed. Results show that about 172 million tons (Mt) of sediment was trapped annually by the TGD in 2003–2008, with an averaged trapping efficiency of 75%. Most of the total sediment deposition, as induced by the TGD (88%), accumulated within the region between the TGD site and Cuntan. However, significant siltation (12% of the total sediment deposition) also occurred upstream of Cuntan as a consequence of the upstream extended backwater region of the TGD. Additionally, the Changjiang sediment flux entered a third downward step in 2001, prior to operation of the TGD. This mainly resulted from sediment reduction in the Jinshajiang tributary since the late 1990s. As the cascade reservoir is put into full operation, it could potentially trap 91% of the Jinshajiang sediment discharge and, therefore, the Jinshajiang sediment discharge would most likely further decrease to 14 Mt/yr in the coming decades. Consequently, the Changjiang sediment flux to the sea is expected to continuously decrease to below 90 Mt/yr in the near future, or only 18% of the amount observed in the 1950s. In the presence of low sediment discharge, profound impacts on the morphology of estuary, delta and coastal waters are expected

Macrophage CGI-58 Deficiency Activates ROS-Inflammasome Pathway to Promote Insulin Resistance in Mice

SummaryOvernutrition activates a proinflammatory program in macrophages to induce insulin resistance (IR), but its molecular mechanisms remain incompletely understood. Here, we show that saturated fatty acid and lipopolysaccharide, two factors implicated in high-fat diet (HFD)-induced IR, suppress macrophage CGI-58 expression. Macrophage-specific CGI-58 knockout (MaKO) in mice aggravates HFD-induced glucose intolerance and IR, which is associated with augmented systemic/tissue inflammation and proinflammatory activation of adipose tissue macrophages. CGI-58-deficient macrophages exhibit mitochondrial dysfunction due to defective peroxisome proliferator-activated receptor (PPAR)γ signaling. Consequently, they overproduce reactive oxygen species (ROS) to potentiate secretion of proinflammatory cytokines by activating NLRP3 inflammasome. Anti-ROS treatment or NLRP3 silencing prevents CGI-58-deficient macrophages from oversecreting proinflammatory cytokines and from inducing proinflammatory signaling and IR in the cocultured fat slices. Anti-ROS treatment also prevents exacerbation of inflammation and IR in HFD-fed MaKO mice. Our data thus establish CGI-58 as a suppressor of overnutrition-induced NLRP3 inflammasome activation in macrophages

Tropical Support Vector Machine and its Applications to Phylogenomics

Most data in genome-wide phylogenetic analysis (phylogenomics) is essentially multidimensional, posing a major challenge to human comprehension and computational analysis. Also, we can not directly apply statistical learning models in data science to a set of phylogenetic trees since the space of phylogenetic trees is not Euclidean. In fact, the space of phylogenetic trees is a tropical Grassmannian in terms of max-plus algebra. Therefore, to classify multi-locus data sets for phylogenetic analysis, we propose tropical support vector machines (SVMs). Like classical SVMs, a tropical SVM is a discriminative classifier defined by the tropical hyperplane which max- imizes the minimum tropical distance from data points to itself in order to separate these data points into sectors (half-spaces) in the tropical projective torus. Both hard margin tropical SVMs and soft margin tropical SVMs can be formulated as linear programming problems. We focus on classifying two categories of data, and we study a simpler case by assuming the data points from the same category ideally stay in the same sector of a tropical separating hyperplane. For hard margin tropical SVMs, we prove the necessary and sufficient conditions for two categories of data points to be separated, and we show an explicit formula for the optimal value of the feasible linear programming problem. For soft margin tropical SVMs, we develop novel methods to compute an optimal tropical separating hyperplane. Computational experiments show our methods work well. We end this paper with open problems

Tidal Channels in the Yellow River Estuary

<p>Tidal Channels extracted from GF-3 and Planetscope in the Yellow River Estuary</p&gt

Mapping Tidal Flats of the Bohai and Yellow Seas Using Time Series Sentinel-2 Images and Google Earth Engine

Tidal flats are one of the most productive ecosystems on Earth, providing essential ecological and economical services. Because of the increasing anthropogenic interruption and sea level rise, tidal flats are under great threat. However, updated and large-scale accurate tidal flat maps around the Bohai and Yellow Seas are still relatively rare, hindering the assessment and management of tidal flats. Based on time-series Sentinel-2 imagery and Google Earth Engine (GEE), we proposed a new method for tidal flat mapping with the Normalized Difference Water Index (NDWI) extremum composite around the Bohai and Yellow Seas. Tidal flats were derived from the differences of maximum and minimum water extent composites. Overall, 3477 images acquired from 1 Oct 2020 to 31 Oct 2021 produced a tidal flat map around the Bohai and Yellow Seas with an overall accuracy of 94.55% and total area of 546,360.2 ha. The resultant tidal flat map at 10 m resolution, currently one of the most updated products around the Bohai and Yellow Seas, could facilitate the process of sustainable policy making related to tidal flats and will help reveal the processes and mechanisms of its responses to natural and human disturbance

InSAR-Derived Coastal Subsidence Reveals New Inundation Scenarios Over the Yellow River Delta

Coastal subsidence exacerbates relative sea level rise (SLR) and increases the risk of coastal flooding. However, the contribution of local land subsidence (LLS) in the Yellow river delta (YRD) to the relative SLR remains unclear, leading to a gap in the understanding of future inundation scenarios. In this article, we firstly used five years of Sentinel-1 data to generate the high-accuracy coastal subsidence of the YRD. Radar interferometry (InSAR) results show that fast subsiding funnels larger than 50 mm/yr are mainly distributed in the brine mining clusters, and the maximum subsidence rate exceeds 300 mm/yr. We then proposed an inundation estimation method by combining extended seeded region growing model, InSAR-derived LLS and SLR. This method can effectively output the coastal inundation time series, quantify and characterize the changes of inundation area and depth without detailed hydrodynamic conditions. Moreover, we presented high spatiotemporal resolution inundation scenarios for the entire YRD, revealing that in the absence of control measures, annual subsidence of 19 mm/yr contributes at least three times more than that SLR to the increased flood risk in 2050 under the low greenhouse gas emissions scenario (SSP1-2.6). However, under the scenario of SSP5-8.5, 4611 km2 of land would be inundated by 2100 and coastal dams are extremely likely to be damaged. This article is expected to provide a practical and cost-effective alternative to understanding the contribution of coastal subsidence to the relative SLR, and for choosing when and how to mitigate land subsidence to prevent future coastal flooding in the delta

The 3rd workshop on sediment dynamics of muddy coasts and estuaries: An introduction and synthesis

International audienceThe workshop series on sediment dynamics of muddy coasts and estuaries focuses on international frontier issues and major social needs, such as land-sea interactions, estuarine and coastal monitoring, numerical simulation, sediment transport and its biogeochemical effects. The Third Workshop was held in Qingdao, China, in November 2018, hosted by the Institute of Estuarine and Coastal Studies, Ocean University of China. As a result of the Third Workshop, this special issue contains 18 papers with case studies of muddy coasts in the Bohai, Yellow and East China Seas in China and other regions worldwide. These papers represent the most recent advances in Chinese and international estuarine and coastal sediment research in the topics including 1) In-situ observations of sediment dynamics in muddy coasts and estuaries and satellite remote sensing; 2) Modelling of sediment transport and associated sedimentary processes; 3) Fluid mud transport and process in bottom boundary layer, and 4) Blue bay remediation action plan and coastal restoration

Periodic Oscillation of Sediment Transport Influenced by Winter Synoptic Events, Bohai Strait, China

Instruments on two bottom-mount platforms deployed in the Bohai Strait during a cruise from January 6–13, 2018 recorded an intense northerly wind event. The responses of hydrodynamic and hydrographical characteristics in Bohai Sea and Yellow Sea to the wind event were analyzed aided by the wind, wave, sea surface suspended sediment concentration and sea surface height datasets from open sources. It is shown that the strong wind event had a significant impact on the redistribution of sea surface height, regional wave conditions, regional circulations and the accompanying sediment transport pattern. Specifically, the sediment transport through the Bohai Strait may be divided into two chronological phases related to the wind event: (1) the enhanced sediment transport phase during the buildup and peak of the wind event when both the Northern Shandong Coastal Current and regional suspended sediment concentration were sharply increased; and (2) the relaxation phase when the northerly wind subsided or even reversed, accompanied by the enhanced Yellow Sea Warm Current with lowered suspended sediment concentration. Such results at synoptic scale would improve our capability of quantifying sediment exchange between the Bohai and Yellow sea, through the Bohai Strait and provide valuable reference for the study of other similar environments worldwide

Mapping Dynamic Turbidity Maximum Zone of the Yellow River Estuary from 38 Years of Landsat Imagery

Turbidity maximum zone (TMZ) plays a crucial role in estuarine ecosystems, exerting effects on erosion, environment evolution and socioeconomic activities in the coastal area. However, the long-term understanding of the TMZ in large river estuary such as the Yellow River estuary is still lacking. In this study, we focus on the TMZ distribution, variation and regulation mechanisms in the Yellow River estuary from different time scales. Based on time series Landsat images during the period 1984 to 2021 and Google Earth Engine (GEE), we proposed a TMZ extracting method in the Yellow River estuary to generate 322 TMZ maps. The overall accuracy of our algorithm reached 97.4%. The results show that there are clear decadal and seasonal TMZ variations during the 38-year period in the Yellow River estuary. Morphology, currents and wind speeds combined with seawater stratification have direct effects on TMZ at different time scales, while the direct impacts of tides and fluvial output of the Yellow River on TMZ are limited. In this article, the highly robust method provides a cost-effective alternative to accurately map the TMZ in global large river estuaries and systematically reveals the spatiotemporal evolution of TMZ, shedding light on the response mechanism of coastal geomorphology, marine ecological environment and biogeochemical cycle