Nitrogen deprivation-induced de novo transcriptomic profiling of the oleaginous green alga Botryococcus braunii 779

AbstractTo assess the effect of nitrogen deprivation (ND), a moderately growing A-race Botryococcus braunii subisolate 779 was subjected to nitrogen deprivation for 3days. De novo transcriptome was assembled and annotated by using Trinity software and Basic Local Alignment Search Tools (BLAST), respectively. Comparative analysis indicates that transcriptomes of A-races differ from those of B-races. Furthermore, majority of the homologous ESTs in A-race but not B-race transcriptomes were unknown sequences. Upon ND, level of photosynthetic transcripts, but not photosynthetic efficiency was downregulated. Unlike hydrocarbon contents, ESTs involved in hydrocarbon biosynthesis were not upregulated. Taken together, our results imply that A- and B-races belong to different B. braunii subspecies. Upon ND, excess photosynthetic transcripts are recycled for nitrogen; and hydrocarbon accumulation is not via de novo biosynthesis. Here we describe in details the data contents and analytic methodologies associated with the data uploaded to Gene Expression Omnibus (accession number GSE71296)

GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing

Particle Swarm Optimization (PSO) is a population-based stochastic search technique for solving optimization problems, which has been proven to be effective in a wide range of applications. However, the computational efficiency on large-scale problems is still unsatisfactory. A graph drawing is a pictorial representation of the vertices and edges of a graph. Two PSO heuristic procedures, one serial and the other parallel, are developed for undirected graph drawing. Each particle corresponds to a different layout of the graph. The particle fitness is defined based on the concept of the energy in the force-directed method. The serial PSO procedure is executed on a CPU and the parallel PSO procedure is executed on a GPU. Two PSO procedures have different data structures and strategies. The performance of the proposed methods is evaluated through several different graphs. The experimental results show that the two PSO procedures are both as effective as the force-directed method, and the parallel procedure is more advantageous than the serial procedure for larger graphs

Weak-value-amplification analysis beyond the AAV limit of weak measurements

The weak-value (WV) measurement proposed by Aharonov, Albert and Vaidman (AAV) has attracted a great deal of interest in connection with quantum metrology. In this work, we extend the analysis beyond the AAV limit and obtain a few main results. (i) We obtain non-perturbative result for the signal-to-noise ratio (SNR). In contrast to the AAV's prediction, we find that the SNR asymptotically gets worse when the AAV's WV $A_w$ becomes large, i.e., in the case $g|A_w|^2>>1$, where $g$ is the measurement strength. (ii) With the increase of $g$ (but also small), we find that the SNR is comparable to the result under the AAV limit, while both can reach -- actually the former can slightly exceed -- the SNR of the standard measurement. However, along a further increase of $g$, the WV technique will become less efficient than the standard measurement, despite that the postselection probability is increased. (iii) We find that the Fisher information can characterize the estimate precision qualitatively well as the SNR, yet their difference will become more prominent with the increase of $g$. (iv) We carry out analytic expressions of the SNR in the presence of technical noises and illustrate the particular advantage of the imaginary WV measurement. The non-perturbative result of the SNR manifests a favorable range of the noise strength and allows an optimal determination.Comment: 10 pages, 6figure

An unstructured-grid, finite-volume sea ice model : development, validation, and application

Author Posting. © American Geophysical Union, 2011. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 116 (2011): C00D04, doi:10.1029/2010JC006688.A sea ice model was developed by converting the Community Ice Code (CICE) into an unstructured-grid, finite-volume version (named UG-CICE). The governing equations were discretized with flux forms over control volumes in the computational domain configured with nonoverlapped triangular meshes in the horizontal and solved using a second-order accurate finite-volume solver. Implementing UG-CICE into the Arctic Ocean finite-volume community ocean model provides a new unstructured-grid, MPI-parallelized model system to resolve the ice-ocean interaction dynamics that frequently occur over complex irregular coastal geometries and steep bottom slopes. UG-CICE was first validated for three benchmark test problems to ensure its capability of repeating the ice dynamics features found in CICE and then for sea ice simulation in the Arctic Ocean under climatologic forcing conditions. The model-data comparison results demonstrate that UG-CICE is robust enough to simulate the seasonal variability of the sea ice concentration, ice coverage, and ice drifting in the Arctic Ocean and adjacent coastal regions.This work was supported by the NSF Arctic Program for projects with grant numbers of ARC0712903, ARC0732084, and ARC0804029. The Arctic Ocean Model Intercomparison Project (AOMIP) has provided an important guidance for model improvements and ocean studies under coordinated experiments activities. We would like to thank AOMIP PI Proshutinsky for his valuable suggestions and comments on the ice dynamics. His contribution is supported by ARC0800400 and ARC0712848. The development of FVCOM was supported by the Massachusetts Marine Fisheries Institute NOAA grants DOC/NOAA/ NA04NMF4720332 and DOC/NOAA/NA05NMF4721131; the NSF Ocean Science Program for projects of OCE‐0234545, OCE‐0227679, OCE‐ 0606928, OCE‐0712903, OCE‐0726851, and OCE‐0814505; MIT Sea Grant funds (2006‐RC‐103 and 2010‐R/RC‐116); and NOAA NERACOOS Program for the UMASS team. G. Gao was also supported by the Chinese NSF Arctic Ocean grant under contract 40476007. C. Chen’s contribution was also supported by Shanghai Ocean University International Cooperation Program (A‐2302‐10‐0003), the Program of Science and Technology Commission of Shanghai Municipality (09320503700), the Leading Academic Discipline Project of Shanghai Municipal Education Commission (J50702), and Zhi jiang Scholar and 111 project funds of the State Key Laboratory for Estuarine and Coastal Research, East China Normal University (ECNU)

Combination of sonic wave velocity, density and electrical resistivity for joint estimation of gas-hydrate reservoir parameters and their uncertainties

Gas-hydrate saturation and porosity are the most crucial reservoir parameters for gas-hydrate resource assessment. Numerous academics have put forward elastic and electrical petrophysical models for calculating the saturation and porosity of gas-hydrate. However, owing to the limitations of a single petrophysical model, the estimation of gas-hydrate saturation and porosity using single elastic or electrical measurement data appears to be inconsistent and uncertain. In this study, the sonic wave velocity, density and resistivity well log data are combined with a Bayesian linear inversion method for the simultaneous estimation of gas-hydrate saturation and porosity. The sonic wave velocity, density and resistivity data of the Shenhu area in the South China Sea are used to estimate the gas-hydrate saturation and porosity. To validate the accuracy of this method, the estimation results are compared with the saturation obtained from pore water chemistry and porosity obtained from density logs. The well log data examples show that the joint estimation method not only provides a rapid estimation of the gas-hydrate reservoir parameters but also improves the accuracy of results and determines their uncertainty.Document Type: Original articleCited as: Zhang, X., Li, Q., Li, L., Fan, Q., Geng, J. Combination of sonic wave velocity, density and electrical resistivity for joint estimation of gas-hydrate reservoir parameters and their uncertainties. Advances in Geo-Energy Research, 2023, 10(2): 133-140. https://doi.org/10.46690/ager.2023.11.0

SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website:https://github.com/7ohnKeats/SCVCNet.Comment: 12 page

A dike–groyne algorithm in a terrain-following coordinate ocean model (FVCOM) : development, validation and application

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Ocean Modelling 47 (2012): 26-40, doi:10.1016/j.ocemod.2012.01.006.A dike-groyne module is developed and implemented into the unstructured-grid, three dimensional primitive equation Finite-Volume Coastal Ocean Model (FVCOM) for the study of the hydrodynamics around human-made construction in the coastal area. The unstructured-grid finite-volume flux discrete algorithm makes this module capable of realistically including narrow-width dikes and groynes with free exchange in the upper column and solid blocking in the lower column in a terrain-following coordinate system. This algorithm used in the module is validated for idealized cases with emerged and/or submerged dikes and a coastal seawall where either analytical solutions or laboratory experiments are available for comparison. As an example, this module is applied to the Changjiang Estuary where a dike-groyne structure was constructed in the Deep Waterway channel in the inner shelf of the East China Sea (ECS). Driven by the same forcing under given initial and boundary conditions, a comparison was made for model-predicted flow and salinity via observations between dike-groyne and bed-conforming slope algorithms. The results show that with realistic resolution of water transport above and below the dike-groyne structures, the new method provides more accurate results. FVCOM with this MPI-architecture parallelized dike-groyne module provides a new tool for ocean engineering and inundation applications in coastal regions with dike, seawall and/or dam structures.J. Ge and P. Ding have been supported by the Fund for Creative Research Groups of NSFC (No. 41021064), the PhD Program Scholarship Fund (2009010) of East China Normal University (ECNU), and the State Scholarship Fund from China Scholarship Council. C. Chen, J. Qi and R. C. Beardsley have been funded by the Northeast Regional Association of Coastal Ocean Observing Systems (NERACOOS), the IOOS/SURA Super-Regional Coastal Modeling Testbed, MIT Sea Grant NA06OAR4170019 and 571000271, and NSF grants OCE0606928, OCE0712903, OCE0732084, OCE0726851, OCE0814505, and OCE0804029

Huber Kalman Filter for Wi-Fi based Vehicle Driver\u27s Respiration Detection

The use of breath detection in vehicles can reduce the number of vehicular accidents caused by drivers in poor physical condition. Prior studies of contactless respiration detection mainly targeted a static person. However, there are emerging applications to sense a driver, with emphasis on contactless methods. For example, being able to detect a driver\u27s respiration while driving by using a vehicular Wi-Fi system can significantly enhance driving safety. The sensing system can be mounted on the back of the driver\u27s seat, and it can sense the tiny chest displacement of the driver via Wi-Fi signals. The body displacement and car vibrations could introduce significant noise in the sensed signal. The noise then needs to be filtered to obtain the driver\u27s respiration. In this work, the noise in the sensed signal is proposed to be reduced using a Huber Kalman filter to restore the original respiration curve. Through several experiments in terms of different drivers, different car models, multiple passengers, and abnormal breathing, we demonstrate the accuracy and robustness of the Huber Kalman filter in driver\u27s respiration

Seasonal and interannual variability of the Arctic sea ice : a comparison between AO-FVCOM and observations

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 121 (2016): 8320–8350, doi:10.1002/2016JC011841.A high-resolution (up to 2 km), unstructured-grid, fully ice-sea coupled Arctic Ocean Finite-Volume Community Ocean Model (AO-FVCOM) was used to simulate the sea ice in the Arctic over the period 1978–2014. The spatial-varying horizontal model resolution was designed to better resolve both topographic and baroclinic dynamics scales over the Arctic slope and narrow straits. The model-simulated sea ice was in good agreement with available observed sea ice extent, concentration, drift velocity and thickness, not only in seasonal and interannual variability but also in spatial distribution. Compared with six other Arctic Ocean models (ECCO2, GSFC, INMOM, ORCA, NAME, and UW), the AO-FVCOM-simulated ice thickness showed a higher mean correlation coefficient of ∼0.63 and a smaller residual with observations. Model-produced ice drift speed and direction errors varied with wind speed: the speed and direction errors increased and decreased as the wind speed increased, respectively. Efforts were made to examine the influences of parameterizations of air-ice external and ice-water interfacial stresses on the model-produced bias. The ice drift direction was more sensitive to air-ice drag coefficients and turning angles than the ice drift speed. Increasing or decreasing either 10% in water-ice drag coefficient or 10° in water-ice turning angle did not show a significant influence on the ice drift velocity simulation results although the sea ice drift speed was more sensitive to these two parameters than the sea ice drift direction. Using the COARE 4.0-derived parameterization of air-water drag coefficient for wind stress did not significantly influence the ice drift velocity simulation.This work was supported by NSF grants OCE-1203393 for the UMASSD team and PLR-1203643 for R. C. Beardsley.2017-05-2