Sexual shape dimorphism and selection pressure on males in fossil ostracodes

Paleobiology ◽  
2017 ◽  
Vol 43 (3) ◽  
pp. 407-424 ◽  
Author(s):  
Tatsuhiko Yamaguchi ◽  
Rie Honda ◽  
Hiroki Matsui ◽  
Hiroshi Nishi
Keyword(s):  
Selection Pressure ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Temporal Changes ◽  
Length Ratio ◽  
Series Data ◽  
Sexually Dimorphic ◽  
Adult Males ◽  
Sexual Display ◽  
Sexual Shape Dimorphism

AbstractSexual dimorphism is thought to have evolved via selection on both sexes. Ostracodes display sexual shape dimorphism in adult valves; however, no previous studies have addressed temporal changes on evolutionary timescales or examined the relationships between sexual shape dimorphism and selection pressure and between sexual shape dimorphism and juvenile shape. Temporal changes in sexually dimorphic traits result from responses of these traits to selection pressure. Using the Gaussian mixture model for the height/length ratio, a valve-shape parameter, we identified sexual differences in the valve shape of Krithe dolichodeira s.l. from deep-sea sediments of the Paleocene (62.6–57.6 Ma) and estimated the proportion of females in the fossil populations at 11 time intervals. Because the proportion of females in a population is altered by the mortality rate of adult males, it is reflective of selection pressure on males. We attempted to correlate the height/length ratios between the sexes with the proportion of females, taking into consideration that the valve shape was not linked with the selection pressure on males. In time-series data of the height/length ratio, both sexes indicate no significant changes on evolutionary timescales, even though the sex ratio of the population changed from female skewed to male skewed during the late Paleocene. The sexual shape dimorphism was not driven by sexual selection. The static allometry between the height/length ratio and length indicates that the sexual shape dimorphism did not function for sexual display. The absence of change over time in the female allometric slope suggests that the evolution of valve shape was constrained by stasis.

scholarly journals Object carrying as socio-sexual display in an aquatic mammal

2008 ◽  
Vol 4 (3) ◽  
pp. 243-245 ◽  
Author(s):  
A.R Martin ◽  
V.M.F da Silva ◽  
P Rothery
Keyword(s):  
Social Groups ◽  
Sexually Dimorphic ◽  
Adult Males ◽  
Isolated Populations ◽  
Aquatic Mammal ◽  
Sexual Display ◽  
Adult Females ◽  
River Bed ◽  
Inia Geoffrensis ◽  
Dimorphic Species

Amazon river dolphins or botos ( Inia geoffrensis Blainville) were observed carrying objects in 221 social groups over a 3-year study period. Sticks, branches and clumps of grass were taken from the water surface and often repeatedly thrashed or thrown. Lumps of hard clay were collected from the river bed and held in the mouth while the carrier rose slowly above the surface and submerged again. Carriers were predominantly adult males and less often subadult males. Adult females and young dolphins rarely carried objects. Groups of dolphins in which object carrying occurred were differentially large and comprised a greater proportion of adult males and adult females. Aggression, mostly between adult males, was significantly associated with object carrying. The behaviour occurred year-round, with peaks in March and July. A plausible explanation of the results is that object carrying by adult males is aimed at females and is stimulated by the number of females in the group, while aggression is targeted at adult males and is stimulated by object carrying in the group. We infer that object carrying in this sexually dimorphic species is socio-sexual display. It is either of ancient origin or has evolved independently in several geographically isolated populations.

scholarly journals A METHOD OF TIME-SERIES CHANGE DETECTION USING FULL POLSAR IMAGES FROM DIFFERENT SENSORS

2018 ◽  
Vol XLII-3 ◽  
pp. 1127-1133
Author(s):  
W. Liu ◽  
J. Yang ◽  
J. Zhao ◽  
H. Shi ◽  
L. Yang
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Detection Methods ◽  
Series Data ◽  
Difference Image ◽  
Novel Method ◽  
Difference Images ◽  
The City

Most of the existing change detection methods using full polarimetric synthetic aperture radar (PolSAR) are limited to detecting change between two points in time. In this paper, a novel method was proposed to detect the change based on time-series data from different sensors. Firstly, the overall difference image of a time-series PolSAR was calculated by ominous statistic test. Secondly, difference images between any two images in different times ware acquired by R<sub>j</sub> statistic test. Generalized Gaussian mixture model (GGMM) was used to obtain time-series change detection maps in the last step for the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection by using the time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can detect the time-series change from different sensors.

scholarly journals Discovering Key Transcriptomic Regulators in Pancreatic Ductal Adenocarcinoma using Dirichlet Process Gaussian Mixture Model

2020 ◽  
Author(s):  
Sk Md Mosaddek Hossain ◽  
Aanzil Akram Halsana ◽  
Lutfunnesa Khatun ◽  
Sumanta Ray ◽  
Anirban Mukhopadhyay
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Mixture Model ◽  
Dirichlet Process ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Ductal Adenocarcinoma ◽  
Series Data ◽  
Main Text

ABSTRACTPancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer (PC), late detection of which leads to its therapeutic failure. This study aims to find out key regulatory genes and their impact on the progression of the disease helping the etiology of the disease which is still largely unknown. We leverage the landmark advantages of time-series gene expression data of this disease, and thereby the identified key regulators capture the characteristics of gene activity patterns in the progression of the cancer. We have identified the key modules and predicted gene functions of top genes from the compiled gene association network (GAN). Here, we have used the natural cubic spline regression model (splineTimeR) to identify differentially expressed genes (DEG) from the PDAC microarray time-series data downloaded from gene expression omnibus (GEO). First, we have identified key transcriptomic regulators (TR) and DNA binding transcription factors (DbTF). Subsequently, the Dirichlet process and Gaussian process (DPGP) mixture model is utilized to identify the key gene modules. A variation of the partial correlation method is utilized to analyze GAN, which is followed by a process of gene function prediction from the network. Finally, a panel of key genes related to PDAC is highlighted from each of the analyses performed.Please note: Abbreviations should be introduced at the first mention in the main text – no abbreviations lists. Suggested structure of main text (not enforced) is provided below.

scholarly journals Temporal ordering of omics and multiomic events inferred from time series data

2020 ◽  
Author(s):  
Sandeep Kaur ◽  
Timothy J. Peters ◽  
Pengyi Yang ◽  
Laurence Don Wai Luu ◽  
Jenny Vuong ◽  
...  
Keyword(s):  
Time Series ◽  
Prior Knowledge ◽  
Temporal Resolution ◽  
Time Series Data ◽  
Temporal Changes ◽  
Series Data ◽  
Analysis Method ◽  
Current Analysis ◽  
Event Ordering ◽  
Analysis Of Time Series

AbstractTemporal changes in omics events can now be routinely measured, however current analysis methods are often inadequate, especially for multiomics experiments. We report a novel analysis method that can infer event ordering at better temporal resolution than the experiment, and integrates omic events into two concise visualizations (event maps and sparklines). Testing our method gave results well-correlated with prior knowledge and indicated it streamlines analysis of time-series data.

scholarly journals Expectation-maximization analysis of spatial time series

2007 ◽  
Vol 14 (1) ◽  
pp. 73-77 ◽  
Author(s):  
K. W. Smith ◽  
A. L. Aretxabaleta
Keyword(s):  
Time Series ◽  
Expectation Maximization ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Sea Surface ◽  
Series Data ◽  
Eof Analysis ◽  
Orthogonal Function ◽  
The Em Algorithm ◽  
Em Method

Abstract. Expectation maximization (EM) is used to estimate the parameters of a Gaussian Mixture Model for spatial time series data. The method is presented as an alternative and complement to Empirical Orthogonal Function (EOF) analysis. The resulting weights, associating time points with component distributions, are used to distinguish physical regimes. The method is applied to equatorial Pacific sea surface temperature data from the TAO/TRITON mooring time series. Effectively, the EM algorithm partitions the time series into El Niño, La Niña and normal conditions. The EM method leads to a clearer interpretation of the variability associated with each regime than the basic EOF analysis.

scholarly journals Temporal changes in species composition affect a ubiquitous species’ views of patch quality

2019 ◽  
Author(s):  
Ellen K. Bledsoe ◽  
S. K. Morgan Ernest
Keyword(s):  
Time Series ◽  
Species Composition ◽  
Energy Use ◽  
Time Series Data ◽  
Patch Dynamics ◽  
Temporal Changes ◽  
Series Data ◽  
Patch Quality ◽  
Experimental Time Series ◽  
Rodent Community

AbstractMetacommunity theory, particularly the patch dynamics archetype, suggests that an organism’s perspective of patch quality can depend solely on the local competitive environment. Across landscapes, however, shifts in species composition often co-occur with shifts in habitat, making it difficult to disentangle the role of competitors and environment on assessments of patch quality. Using 26 years of rodent community time-series data, we show that perception of patch quality by a small, ubiquitous granivore (Chaetodipus penicillatus) shifted with both spatial and temporal changes in species composition. In the mid-1990s,C. baileyi, a novel competitor, colonized and the study site.C. penicillatuspatch preference shifted with increasing abundance ofC. baileyi, including corresponding changes in estimated survival, probability of movement between patches, and the arrival of new individuals in patches. Changes in energy use on patches due to the establishment ofC. baileyipoint to a potential mechanism for the differences in patch quality perceived byC. penicillatus. These results demonstrate that experimental time-series data can be used to examine how changes in species composition and, specifically, changes in the competitive landscape, can affect perception of patch quality and patch preference.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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