Three-dimensional and two-dimensional relationships of gangliogenesis with folliculogenesis in mature mouse ovary: a Golgi–Cox staining approach

The present study was set out to investigate two-dimensional (2D) and three-dimensional (3D) evaluations of ovarian nervous network development and the structural relationship between folliculogenesis and gangliogenesis in mouse ovaries. Adult mice ovarian tissue samples were collected from follicular and luteal phases after cardiac perfusion. Ovarian samples were stained by a Golgi–Cox protocol. Following staining, tissues were serially sectioned for imaging. Neural filaments and ganglia were present in the ovaries. In both 2D and 3D studies, an increase in the number and area of ganglia was seen during the follicular growth. The same pattern was also seen in corpora lutea development. However, in some cases such as ratio of ganglia number to follicle area, the ratio of ganglia area to follicular area, 2D findings were different compared with the 3D results. 3D analysis of ovarian gangliogenesis showed the possible direct effect of them on folliculogenesis. Golgi–Cox staining was used in this study for 3D evaluation in non-brain tissue. The results of 3D analysis of the present study showed that, in some cases, the information provided by 2D analysis does not match the reality of ovarian neuronal function. This confirmed the importance of 3D analysis for evaluation of ovarian functio

Characterising droughts in Central America with uncertain hydro-meteorological data

Central America is frequently affected by droughts that cause significant socio-economic and environmental problems. Drought characterisation, monitoring and forecasting are potentially useful to support water resource management. Drought indices are designed for these purposes, but their ability to characterise droughts depends on the characteristics of the regional climate and the quality of the available data. Local comprehensive and high-quality observational networks of meteorological and hydrological data are not available, which limits the choice of drought indices and makes it important to assess available datasets. This study evaluated which combinations of drought index and meteorological dataset were most suitable for characterising droughts in the region. We evaluated the standardised precipitation index (SPI), a modified version of the deciles index (DI), the standardised precipitation evapotranspiration index (SPEI) and the effective drought index (EDI). These were calculated using precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), the CRN073 dataset, the Climate Research Unit (CRU), ECMWF Reanalysis (ERA-Interim) and a regional station dataset, and temperature from the CRU and ERA-Interim datasets. The gridded meteorological precipitation datasets were compared to assess how well they captured key features of the regional climate. The performance of all the drought indices calculated with all the meteorological datasets was then evaluated against a drought index calculated using river discharge data. Results showed that the selection of database was more important than the selection of drought index and that the best combinations were the EDI and DI calculated with CHIRPS and CRN073. Results also highlighted the importance of including indices like SPEI for drought assessment in Central America.Universidad de Costa Rica/[805-B0-810]/UCR/Costa RicaUniversidad de Costa Rica/[805-A9-532]/UCR/Costa RicaUniversidad de Costa Rica/[805-B3-600]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-065]/UCR/Costa RicaUniversidad de Costa Rica/[805-B3-413]/UCR/Costa RicaUniversidad de Costa Rica/[805-B4-227]/UCR/Costa RicaUniversidad de Costa Rica/[805-B4-228]/UCR/Costa RicaUniversidad de Costa Rica/[805-B5-295]/UCR/Costa RicaUppsala University/[54100006]//SueciaMarie Curie Intra-European Fellowship/[No.329762]//EuropaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Físic

A Least-Squares Temporal Difference based method for solving resource allocation problems

Value function approximation has a central role in Approximate Dynamic Programming (ADP) to overcome the so-called curse of dimensionality associated to real stochastic processes. In this regard, we propose a novel Least-Squares Temporal Difference (LSTD) based method: the “Multi-trajectory Greedy LSTD” (MG-LSTD). It is an exploration-enhanced recursive LSTD algorithm with the policy improvement embedded within the LSTD iterations. It makes use of multi-trajectories Monte Carlo simulations in order to enhance the system state space exploration. This method is applied for solving resource allocation problems modeled via a constrained Stochastic Dynamic Programming (SDP) based framework. In particular, such problems are formulated as a set of parallel Birth–Death Processes (BDPs). Some operational scenarios are defined and solved to show the effectiveness of the proposed approach. Finally, we provide some experimental evidence on the MG-LSTD algorithm convergence properties in function of its key-parameters

Modelling and solving resource allocation problems via a dynamic programming approach

In this paper, resource allocation problems are formulated via a set of parallel birth–death processes (BDP). This way, we can model the fact that resources can be allocated to customers at different prices, and that customers can hold them as long as they like. More specifically, a discretisation approach is applied to model resource allocation problems as a set of discrete-time BDPs, which are then integrated into one Markov decision process. The stochastic dynamics of the resulting system are also investigated. As a result, revenue management becomes a stochastic decision-making problem, where price managers can propose suitable prices to the allocation requests such that the maximum expected total revenue is obtained at the end of a predefined finite time horizon. Stochastic Dynamic Programming is employed to solve the related optimisation problem with the support of an ad-hoc Matlab-based application. Several simulations are performed to prove the effectiveness of the proposed model and the optimisation approach

Recent insights into the roles of circular RNAs in human brain development and neurologic diseases

Circular RNAs (circRNAs) are a novel class of non-coding RNAs. They are single-stranded RNA transcripts characterized with a closed loop structure making them resistant to degrading enzymes. Recently, circRNAs have been suggested with regulatory roles in gene expression involved in controlling various biological processes. Notably, they have demonstrated abundance, dynamic expression, back-splicing events, and spatiotemporally regulation in the human brain. Accordingly, they are expected to be involved in brain functions and related diseases. Studies in animals and human brain have revealed differential expression of circRNAs in brain compartments. Interestingly, contributing roles of circRNAs in the regulation of central nervous system (CNS) development have been demonstrated in a number of studies. It has been proposed that circRNAs play role in substantial neurological functions like neurotransmitter-associated tasks, neural cells maturation, and functions of synapses. Furthermore, 3 main pathways have been identified in association with circRNAs's host genes including axon guidance, Wnt signaling, and transforming growth factor beta (TGF-β) signaling pathways, which are known to be involved in substantial functions like migration and differentiation of neurons and specification of axons, and thus play role in brain development. In this review, we have an overview to the biogenesis, biological functions of circRNAs, and particularly their roles in human brain development and the pathogenesis of neurodegenerative diseases including Alzheimer's diseases, multiple sclerosis, Parkinson's disease and brain tumors

microRNA-382 as a tumor suppressor? Roles in tumorigenesis and clinical significance

MicroRNAs (miRNAs) are small single-stranded RNAs belonging to a class of non-coding RNAs with an average length of 18-22 nucleotides. Although not able to encode any protein, miRNAs are vastly studied and found to play role in various human physiologic as well as pathological conditions. A huge number of miRNAs have been identified in human cells whose expression is straightly regulated with crucial biological functions, while this number is constantly increasing. miRNAs are particularly studied in cancers, where they either can act with oncogenic function (oncomiRs) or tumor-suppressors role (referred as tumor-suppressor/oncorepressor miRNAs). miR-382 is a well-studied miRNA, which is revealed to play regulatory roles in physiological processes like osteogenic differentiation, hematopoietic stem cell differentiation and normal hematopoiesis, and liver progenitor cell differentiation. Notably, miR-382 deregulation is reported in pathologic conditions, such as renal fibrosis, muscular dystrophies, Rett syndrome, epidural fibrosis, atrial fibrillation, amelogenesis imperfecta, oxidative stress, human immunodeficiency virus (HIV) replication, and various types of cancers. The majority of oncogenesis studies have claimed miR-382 downregulation in cancers and suppressor impact on malignant phenotype of cancer cells in vitro and in vivo, while a few studies suggest opposite findings. Given the putative role of this miRNA in regulation of oncogenesis, assessment of miR-382 expression is suggested in a several clinical investigations as a prognostic/diagnostic biomarker for cancer patients. In this review, we have an overview to recent studies evaluated the role of miR-382 in oncogenesis as well as its clinical potential