Phytoplankton and Carbon Dynamics in the Estuarine-Coastal Waters of the Northern Gulf of Mexico from Field Data and Ocean Color Remote Sensing

In this study, phytoplankton community and carbon dynamics were examined in the optically complex estuarine-coastal regions of the northern Gulf of Mexico (nGOM) from field and satellite ocean color observations. As part of this study, bio-optical ocean color algorithms for i) dissolved organic carbon (DOC), ii) phytoplankton pigment composition, iii) adaptive estimation of Chl a and iv) phytoplankton size fractions were developed to facilitate the study of biogeochemical cycling in the nGOM. The phytoplankton based algorithms were applied to Sentinel 3A/B-OLCI oean color data to assess phytoplankton community dynamics to extreme river discharge conditions as well as hurricanes in the nGOM. This study revealed that the effects of hurricanes on phytoplankton community dynamics were dependent on background nutrient conditions, as well as the intensity, track and translational speed of storms: 1) Strong flooding associated with Hurricane Harvey (2017) shifted the dominance of phytoplankton community in Galveston Bay from cyanobacteria and dinoflagellate to diatom and chlorophyte; 2) high levels of organic matter delivered from estuaries to shelf waters after Hurricane Michael (2018) fueled a red tide mixed with coccolithophore bloom in the nGoM; 3) the physical and chemical environment after hurricanes are favorable for the growth and dominance of coccolithophores in shelf waters. Further, microphytoplankton mainly controlled by freshwater inflows showed dominance in estuaries of the nGoM, with highest/lowest values observed in spring/fall. In comparison, phytoplankton size fraction (PSF) dynamics in the midshelf and offshore waters of the nGoM are strongly influenced by Loop Current (LC) expansion, and eddy shedding with highest picophytoplankton fraction observed in the warm waters of LC. DOC dynamics was studied using an empirical algorithm that was developed and applied to multiple satellite sensors (Landsat 5 TM and MODIS-Aqua) to assess multi-decadal (1985-2012) DOC trends in Barataria Basin. The linkages between DOC and environmenal variations were investigated. The relationships between satellite-derived DOC and land cover variations (1985–2011) derived from Landsat-5 TM supervised classification indicate soil loss in the salt marsh to be an important DOC source in the wetland-estuary system, and overall strong land use/land loss impact on the long-term DOC trends in the Barataria Basin

A Review of Hypoxia in the Chesapeake Bay Under Climate Change

The Chesapeake Bay is the largest estuary in the United States, with high ecological and economic values. However, hypoxia occurs in the Chesapeake Bay every summer and threatens the ecosystem of the Bay. The seasonal hypoxia in the Chesapeake Bay is caused by the organic matter decompositions, intensive water stratification, and other biological and physical factors/processes. Under the stress of global climate change, Chesapeake Bay will likely experience severer hypoxia in the future. This case is because climate change affects water temperature, sea level, precipitation, river discharge, and wind strength, and consequently impacts the formation of hypoxia in the Bay. Most of the previous studies explore the effects of climate change on hypoxia in the Chesapeake Bay through qualitative discussions. Few of them quantified and predicted the impacts. This paper attempts to provide recommendations for further studies to quantify and predict the effects of climate change on hypoxia in the Bay. For further studies, it is recommended to use hydrodynamic-biogeochemical models and multimodel climate projections. More studies are needed for investigating the impacts of sea-level rise on hypoxia in the Bay and the wind changes caused by climate change. Studies could explore the effects of climate change from both hypoxic volume and hypoxic duration. Moreover, studies could take atmospheric dissolved inorganic nitrogen (DIN) and coastal DIN as variables into consideration to study the impacts of climate change. More studies are needed for understanding, quantifying, and predicting the impacts of climate change on hypoxia in the Chesapeake Bay, which will help to improve the management of Chesapeake Bay

A hierarchical semantic segmentation framework for computer vision-based bridge damage detection

Computer vision-based damage detection using remote cameras and unmanned aerial vehicles (UAVs) enables efficient and low-cost bridge health monitoring that reduces labor costs and the needs for sensor installation and maintenance. By leveraging recent semantic image segmentation approaches, we are able to find regions of critical structural components and recognize damage at the pixel level using images as the only input. However, existing methods perform poorly when detecting small damages (e.g., cracks and exposed rebars) and thin objects with limited image samples, especially when the components of interest are highly imbalanced. To this end, this paper introduces a semantic segmentation framework that imposes the hierarchical semantic relationship between component category and damage types. For example, certain concrete cracks only present on bridge columns and therefore the non-column region will be masked out when detecting such damages. In this way, the damage detection model could focus on learning features from possible damaged regions only and avoid the effects of other irrelevant regions. We also utilize multi-scale augmentation that provides views with different scales that preserves contextual information of each image without losing the ability of handling small and thin objects. Furthermore, the proposed framework employs important sampling that repeatedly samples images containing rare components (e.g., railway sleeper and exposed rebars) to provide more data samples, which addresses the imbalanced data challenge

Effects of Personality on Trading Performance in Social Trading Platforms

Social trading platforms offer opportunities for amateur investors to copy professional traders’ behavior. However, past studies on behavioral finance have largely neglected the role of personality in shaping traders’ behavior. To this end, we aim to scrutinize the effects of leader traders’ personality on their trading behaviors and subsequent performance on social trading platforms. Particularly, we employ the Myers–Briggs Type Indicator (MBTI) personality classification scheme to delineate leader traders’ personality into the four dimensions of Extraversion-Introversion (E-I), Sensing-Intuition (S-N), Thinking-Feeling (T-F), and Judging-Perceiving (J-P). Next, we draw on machine learning techniques to advance a novel text-based approach for extracting the personality dimensions of leader traders automatically. Analytical results attest to the impact of personality dimensions on trading behavior and that of trading behavior on performance. Findings from this study yield insights for both social trading platforms and followers by identifying profitable leader traders based on their personality

Research progress on the relationship between fibroblast growth factor 23 and chronic kidney disease

Chronic kidney disease(CKD)is now a global public health problem. In chronic kidney disease(CKD)patients，almost all have complications such as calcium and phosphorus metabolism disorders，hyperparathyroidism，cardiovascular disease，anemia，and inflammation，which seriously affect the progress and prognosis of CKD. Fibroblast growth factor 23(FGF23) is a bone-derived hormone that regulates the metabolism of phosphate and vitamin D. In the past，FGF23 was generally considered to play only an important role in the regulation of calcium and phosphorus metabolism. In recent years FGF23has been found to be associated with the occurrence or progression of various CKD complications. This opens up new horizons for studying the role of FGF23 in the course of chronic kidney disease. FGF23 is expected to become a new therapeutic target in the future，improving the prognosis of patients with CKD. This article will review the biological characteristics of FGF23 and its role in the progression of CKD.  And briefly discuss its potential future role in chronic kidney disease

Controlling the Amount of Verbatim Copying in Abstractive Summarization

An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstraction. Human editors can usually exercise control over copying, resulting in summaries that are more extractive than abstractive, or vice versa. However, it remains poorly understood whether modern neural abstractive summarizers can provide the same flexibility, i.e., learning from single reference summaries to generate multiple summary hypotheses with varying degrees of copying. In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones. We frame the task of summarization as language modeling and exploit alternative mechanisms to generate summary hypotheses. Our method allows for control over copying during both training and decoding stages of a neural summarization model. Through extensive experiments we illustrate the significance of our proposed method on controlling the amount of verbatim copying and achieve competitive results over strong baselines. Our analysis further reveals interesting and unobvious facts.Comment: AAAI 2020 (Main Technical Track

Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection

Current video object detection (VOD) models often encounter issues with over-aggregation due to redundant aggregation strategies, which perform feature aggregation on every frame. This results in suboptimal performance and increased computational complexity. In this work, we propose an image-level Object Detection Difficulty (ODD) metric to quantify the difficulty of detecting objects in a given image. The derived ODD scores can be used in the VOD process to mitigate over-aggregation. Specifically, we train an ODD predictor as an auxiliary head of a still-image object detector to compute the ODD score for each image based on the discrepancies between detection results and ground-truth bounding boxes. The ODD score enhances the VOD system in two ways: 1) it enables the VOD system to select superior global reference frames, thereby improving overall accuracy; and 2) it serves as an indicator in the newly designed ODD Scheduler to eliminate the aggregation of frames that are easy to detect, thus accelerating the VOD process. Comprehensive experiments demonstrate that, when utilized for selecting global reference frames, ODD-VOD consistently enhances the accuracy of Global-frame-based VOD models. When employed for acceleration, ODD-VOD consistently improves the frames per second (FPS) by an average of 73.3% across 8 different VOD models without sacrificing accuracy. When combined, ODD-VOD attains state-of-the-art performance when competing with many VOD methods in both accuracy and speed. Our work represents a significant advancement towards making VOD more practical for real-world applications.Comment: 11 pages, 6 figures, accepted by ACM MM202

Astragalus Polysaccharides Lowers Plasma Cholesterol through Mechanisms Distinct from Statins

To determine the efficacy and underlying mechanism of Astragalus polysaccharides (APS) on plasma lipids in hypercholesterolemia hamsters. The effect of APS (0.25g/kg/d) on plasma and liver lipids, fecal bile acids and neutral sterol, cholesterol absorption and synthesis, HMG-CoA reductase activity, and gene and protein expressions in the liver and small intestine was investigated in twenty-four hypercholesterolemia hamsters. Treatment periods lasted for three months. APS significantly lowered plasma total cholesterol by 45.8%, triglycerides by 30%, and low-density lipoprotein-cholesterol by 47.4%, comparable to simvastatin. Further examinations revealed that APS reduced total cholesterol and triglycerides in the liver, increased fecal bile acid and neutral sterol excretion, inhibited cholesterol absorption, and by contrast, increased hepatic cholesterol synthesis and HMG-CoA reductase activity. Plasma total cholesterol or low-density lipoprotein-cholesterol levels were significantly correlated with cholesterol absorption rates. APS up-regulated cholesterol-7α-hydroxylase and LDL-receptor gene expressions. These new findings identify APS as a potential natural cholesterol lowering agent, working through mechanisms distinct from statins

Unraveling the Relationship between Content Design and Kinesthetic Learning on Communities of Practice Platforms

As a variant of the sharing economy, Communities of Practice (CoP) platforms have allowed kinesthetic learners to acquire skillsets corresponding to their interests for immediate or future use in practice. However, the impact of digital learning content design on kinesthetic learning remains underexplored in the field of information systems. We hence extend prior research by advancing content richness and structure clarity as antecedents affecting kinesthetic learners’ digestibility of contents, culminating in differential kinesthetic learning effects. To substantiate our arguments, we collected data from a leading Chinese recipe sharing platform. Whereas content richness was measured in terms of readability, verb richness, and prototypicality, structure clarity was operationalized as block structure, block quantity, and block regularity. Employing a machine learning model, we simulated and tested learners’ digestibility of image content embodied within recipes. Plans for future research beyond the current study are also discussed