Loss of Endometrial Plasticity in Recurrent Pregnancy Loss

© 2015 AlphaMed Press.Menstruation drives cyclic activation of endometrial progenitor cells, tissue regeneration, and maturation of stromal cells, which differentiate into specialized decidual cells prior to and during pregnancy. Aberrant responsiveness of human endometrial stromal cells (HESCs) to deciduogenic cues is strongly associated with recurrent pregnancy loss (RPL), suggesting a defect in cellular maturation. MeDIP-seq analysis of HESCs did not reveal gross perturbations in CpG methylation in RPL cultures, although quantitative differences were observed in or near genes that are frequently deregulated in vivo. However, RPL was associated with a marked reduction in methylation of defined CA-rich motifs located throughout the genome but enriched near telomeres. Non-CpG methylation is a hallmark of cellular multipotency. Congruently, we demonstrate that RPL is associated with a deficiency in endometrial clonogenic cell populations. Loss of epigenetic stemness features also correlated with intragenic CpG hypomethylation and reduced expression of HMGB2, coding high mobility group protein 2. We show that knockdown of this sequence-independent chromatin protein in HESCs promotes senescence and impairs decidualization, exemplified by blunted time-dependent secretome changes. Our findings indicate that stem cell deficiency and accelerated stromal senescence limit the differentiation capacity of the endometrium and predispose for pregnancy failure. Stem Cells 2016;34:346-356 Recurrent pregnancy loss is caused by endometrial stem cell deficiency, triggering heightened tissue senescence and impaired decidualization

The underground life of homeodomain-leucine zipper transcription factors

Roots are the anchorage organs of plants, responsible for water and nutrient uptake, exhibiting high plasticity. Root architecture is driven by the interactions of biomolecules, including transcription factors and hormones that are crucial players regulating root plasticity. Multiple transcription factor families are involved in root development; some, such as ARFs and LBDs, have been well characterized, whereas others remain less well investigated. In this review, we synthesize the current knowledge about the involvement of the large family of homeodomain-leucine zipper (HD-Zip) transcription factors in root development. This family is divided into four subfamilies (I–IV), mainly according to structural features, such as additional motifs aside from HD-Zip, as well as their size, gene structure, and expression patterns. We explored and analyzed public databases and the scientific literature regarding HD-Zip transcription factors in Arabidopsis and other species. Most members of the four HD-Zip subfamilies are expressed in specific cell types and several individuals from each group have assigned functions in root development. Notably, a high proportion of the studied proteins are part of intricate regulation pathways involved in primary and lateral root growth and development.Fil: Perotti, María Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Arce, Agustín Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Chan, Raquel Lia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentin

Learning Risk Preferences in Markov Decision Processes: an Application to the Fourth Down Decision in Football

For decades, National Football League (NFL) coaches' observed fourth down decisions have been largely inconsistent with prescriptions based on statistical models. In this paper, we develop a framework to explain this discrepancy using a novel inverse optimization approach. We model the fourth down decision and the subsequent sequence of plays in a game as a Markov decision process (MDP), the dynamics of which we estimate from NFL play-by-play data from the 2014 through 2022 seasons. We assume that coaches' observed decisions are optimal but that the risk preferences governing their decisions are unknown. This yields a novel inverse decision problem for which the optimality criterion, or risk measure, of the MDP is the estimand. Using the quantile function to parameterize risk, we estimate which quantile-optimal policy yields the coaches' observed decisions as minimally suboptimal. In general, we find that coaches' fourth-down behavior is consistent with optimizing low quantiles of the next-state value distribution, which corresponds to conservative risk preferences. We also find that coaches exhibit higher risk tolerances when making decisions in the opponent's half of the field than in their own, and that league average fourth down risk tolerances have increased over the seasons in our data.Comment: 33 pages, 9 figure

Assessing Global Surface Water Inundation Dynamics Using Combined Satellite Information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk

Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented

Microbial Contamination of Orthodontic Buccal Tubes from Manufacturers

This study aimed to test the sterility of new unused orthodontic buccal tubes received from manufacturers. Four different types of buccal tubes were used straight from the manufactures package without any additional sterilizing step. Of these buccal tubes tested, three genera of bacteria, implicated as opportunistic pathogens, namely Micrococcus luteus, Staphylococcus haemolyticus and Acinetobacter calcoaceticus were recovered from these buccal tubes. Our data showing microbial contamination on buccal tubes highlights the need of sterilization before clinical use. We also suggest that manufacturers should list the sterility state of orthodontic buccal tubes on their packaging or instructions stating the need for sterilization

A new approach to numerical characterisation of wear particle surfaces in three-dimensions for wear study

In the wear and tear process of synovial joints, wear particles generated and released from articular cartilage within the joints have surface topography and mechanical property which can be used to reveal wear conditions. Three-dimensional (3D) particle images acquired using laser scanning confocal microscopy (LSCM) contain appropriate surface information for quantitatively characterizing the surface morphology and changes to seek a further understanding of the wear process and wear features. This paper presents a new attempt on the 3D numerical characterisation of wear particle surfaces using the field and feature parameter sets which are defined in ISO/FDIS 25178-2. Based on the innovative pattern recognition capability, the feature parameters are, for the first time, employed for quantitative analysis of wear debris surface textures. Through performing parameter classification, ANOVA analysis and correlation analysis, typical changing trends of the surface transformation of the wear particles along with the severity of wear conditions and osteoarthritis (OA) have been observed. Moreover, the feature parameters have shown a significant sensitivity with the wear particle surfaces texture evolution under OA development. A correlation analysis of the numerical analysis results of cartilage surface texture variations and that of their wear particles has been conducted in this study. Key surface descriptors have been determined. Further research is needed to verify the above outcomes using clinic samples

The Multiscale Systems Immunology project: software for cell-based immunological simulation

<p>Abstract</p> <p>Background</p> <p>Computer simulations are of increasing importance in modeling biological phenomena. Their purpose is to predict behavior and guide future experiments. The aim of this project is to model the early immune response to vaccination by an agent based immune response simulation that incorporates realistic biophysics and intracellular dynamics, and which is sufficiently flexible to accurately model the multi-scale nature and complexity of the immune system, while maintaining the high performance critical to scientific computing.</p> <p>Results</p> <p>The Multiscale Systems Immunology (MSI) simulation framework is an object-oriented, modular simulation framework written in C++ and Python. The software implements a modular design that allows for flexible configuration of components and initialization of parameters, thus allowing simulations to be run that model processes occurring over different temporal and spatial scales.</p> <p>Conclusion</p> <p>MSI addresses the need for a flexible and high-performing agent based model of the immune system.</p