scholarly journals ŌvSim: a Simulation of the Population Dynamics of Mammalian Ovarian Follicles

2015 ◽  
Author(s):  
Joshua Johnson ◽  
Xin Chen ◽  
Xiao Xu ◽  
John W. Emerson
Keyword(s):  
Markov Chain Model ◽  
Ovarian Follicles ◽  
Chain Model ◽  
Follicle Growth ◽  
Daily Growth ◽  
Growth And Survival ◽  
Primordial Follicles ◽  
Developmental States ◽  
Reproductive Life ◽  
Growth Activation

AbstractNo two ovaries are alike, and indeed, the same ovary can change its architecture from day to day. This is because ovarian follicles are present in different numbers, positions, and states of maturation throughout reproductive life. All possible developmental states of follicles can be represented at any time, along with follicles that have committed to death (termed follicle atresia). Static histological and whole-mount imaging approaches allow snapshots of what is occurring within ovaries, but our views of dynamic follicle growth and death have been limited to these tools. We present a simple Markov chain model of the complex mouse ovary, called “ŌvSim”. In the model, follicles can exist in one of three Markov states with stationary probabilities, Hold (growth arrest), Grow, and Die. The probability that individual primordial follicles can growth activate daily, the fraction of granulosa cells that survive as follicles grow, and the probability that individual follicles can commit to atresia daily are user definable parameters. When the probability of daily growth activation is stationary at 0.005, the probability of atresia for all follicles is near 0.1, and the probability of granulosa cell survival is modeled around 0.88, ŌvSim simulates the growth and fate of each of the approximately 3000 postpubertal mouse ovarian follicles in a fashion that approximates actual biological measurements (e.g., follicle counts). ŌvSim thus offers a starting platform to simulate mammalian ovaries and to explore factors that might impact follicle development and global organ function.Author SummaryŌvSim is a computer simulation of the dynamic growth of mouse ovarian follicles. The program is offered as the beginning of a research and teaching platform to model asynchronous follicle growth and survival or death.

scholarly journals In vitro development of mechanically and enzymatically isolated cat ovarian follicles

2021 ◽  
Vol 2 (1) ◽  
pp. 35-46
Author(s):  
Jennifer B Nagashima ◽  
Andrea M Hill ◽  
Nucharin Songsasen
Keyword(s):  
Fertility Preservation ◽  
Mammalian Species ◽  
Ovarian Follicles ◽  
Theca Cell ◽  
Follicle Development ◽  
Follicle Growth ◽  
Growth And Survival ◽  
Isolation Methods ◽  
Mechanical Isolation

Graphical Abstract Isolation of ovarian follicles is a key step in culture systems for large mammalian species to promote the continued growth of follicles beyond the preantral stage in fertility preservation efforts. Still, mechanical isolation methods are user-skill dependent and time-consuming, whereas enzymatic strategies carry increased risk of damaging theca cell layers and the basement membranes. Here, we sought to determine an optimal method to rescue domestic cat (Felis catus) early antral and antral stage follicles from ovarian tissue and to evaluate the influence of isolation strategy on follicle development, survival, and gene expression during 14 days of in vitro culture in alginate hydrogel. Mechanical isolation was compared with 90 min digestion in 0.7 and 1.4 Wünsch units/mL Liberase blendzyme (0.7L and 1.4L, respectively). Mechanical isolation resulted in improved follicle growth and survival, and better antral cavity and theca cell maintenance in vitro, compared with 1.4L (P < 0.05) but displayed higher levels of apoptosis after incubation compared with enzymatically isolated follicles. However, differences in follicle growth and survival were not apparent until 7+ days in vitro. Expressions of CYP19A1, GDF9, LHR, or VEGFA were similar among isolation-strategies. Cultured follicles from all isolation methods displayed reduced STAR expression compared with freshly isolated follicles obtained mechanically or via 0.7L, suggesting that prolonged culture resulted in loss of theca cell presence and/or function. In sum, early antral and antral stage follicle development in vitro is significantly influenced by isolation strategy but not necessarily observable in the absence of extended culture. These results indicate that additional care must be taken in follicle isolation optimizations for genome rescue and fertility preservation efforts. Lay summary The ovary contains hundreds of eggs with only a select few developing from an immature stage through to ovulation over the course of an animal's lifetime. Rescue of eggs from this pool, and the ability to grow them in culture to a mature stage, would be incredibly valuable for fertility preservation efforts in both humans and endangered species. Currently, the isolation of ovarian follicles (eggs with their surrounding helper cells) is a key step in culture systems for large mammalian species, to promote continued growth. Yet, isolation methods may affect the follicle’s future developmental capacity. We evaluated two isolation strategies, mechanical micro-dissection (needle/scalpel blade) and enzymatic digestion (using Liberase blendzyme) on ovaries of domestic cats obtained via routine spay procedures. Mechanically isolated follicles displayed improved growth, survival, and indications of developmental competence in 14-day culture, compared with high concentration (1.4 Wünsch units/mL) enzyme-isolated follicles. However, mechanical isolation was not different from low (0.7 Wünsch units/mL) enzyme for these metrics, or for expression of key genes indicative of follicular cell functions. Further, differences in follicle growth/survival were not apparent until 7+ days in culture. Thus, ovarian follicle isolation strategies influence developmental potential in culture, and extended culture will be required to identify optimal methods for fertility preservation efforts.

scholarly journals Anti-Müllerian Hormone Attenuates the Effects of FSH on Follicle Development in the Mouse Ovary

Endocrinology ◽  
2001 ◽  
Vol 142 (11) ◽  
pp. 4891-4899 ◽  
Author(s):  
Alexandra L. L. Durlinger ◽  
Maria J. G. Gruijters ◽  
Piet Kramer ◽  
Bas Karels ◽  
T. Rajendra Kumar ◽  
...  
Keyword(s):  
Ovarian Follicle ◽  
Inhibitory Effect ◽  
Ovarian Follicles ◽  
Follicle Development ◽  
Wild Type ◽  
Follicle Growth ◽  
Primordial Follicles ◽  
Ovarian Follicle Development

Abstract Although ovarian follicle growth is under the influence of many growth factors and hormones of which FSH remains one of the most prominent regulators. Therefore, factors affecting the sensitivity of ovarian follicles to FSH are also important for follicle growth. The aim of the present study was to investigate whether anti-Müllerian hormone (AMH) has an inhibitory effect on follicle growth by decreasing the sensitivity of ovarian follicles to FSH. Furthermore, the combined action of AMH and FSH on ovarian follicle development was examined. Three different experiments were performed. Using an in vitro follicle culture system it was shown that FSH-stimulated preantral follicle growth is attenuated in the presence of AMH. This observation was confirmed by an in vivo experiment showing that in immature AMH-deficient females, more follicles start to grow under the influence of exogenous FSH than in their wild-type littermates. In a third experiment, examination of the follicle population of 4-month-old wild-type, FSHβ-, AMH-, and AMH-/FSHβ-deficient females revealed that loss of FSH expression has no impact on the number of primordial and preantral follicles, but the loss of inhibitory action of AMH on the recruitment of primordial follicles in AMH-deficient mice is increased in the absence of FSH. In conclusion, these studies show that AMH inhibits FSH-stimulated follicle growth in the mouse, suggesting that AMH is one of the factors determining the sensitivity of ovarian follicles for FSH and that AMH is a dominant regulator of early follicle growth.

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Performance Analysis of Synchronous Multi-Radio Multi-Link MAC Protocols in IEEE 802.11be Extremely High Throughput WLANs

2020 ◽  
Vol 11 (1) ◽  
pp. 317
Author(s):  
Taewon Song ◽  
Taeyoon Kim
Keyword(s):  
Ieee 802.11 ◽  
Markov Chain Model ◽  
Careful Consideration ◽  
Mac Protocols ◽  
Media Access Control ◽  
Chain Model ◽  
Distributed Coordination ◽  
Media Access ◽  
Medium Access ◽  
Coordination Function

The representative media access control (MAC) mechanism of IEEE 802.11 is a distributed coordination function (DCF), which operates based on carrier-sense multiple access with collision avoidance (CSMA/CA) with binary exponential backoff. The next amendment of IEEE 802.11 being developed for future Wi-Fi by the task group-be is called IEEE 802.11be, where the multi-link operation is mainly discussed when it comes to MAC layer operation. The multi-link operation discussed in IEEE 802.11be allows multi-link devices to establish multiple links and operate them simultaneously. Since the medium access on a link may affect the other links, and the conventional MAC mechanism has just taken account of a single link, the DCF should be used after careful consideration for multi-link operation. In this paper, we summarize the DCFs being reviewed to support the multi-radio multi-link operation in IEEE 802.11be and analyze their performance using the Markov chain model. Throughout the extensive performance evaluation, we summarize each MAC protocol’s pros and cons and discuss essential findings of the candidate MAC protocols.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

Sign in / Sign up

Export Citation Format

Share Document