Texture profile analysis reveals a stiffer ovarian cortex after testosterone therapy : a pilot study

Purpose: The importance of the surrounding ovarian stromal cells and extracellular matrix in the development and maturation of follicles has recently gained attention. An aberrant extracellular matrix has been described in ovaries of patients with polycystic ovary syndrome where a more rigid structural environment, possibly induced by endogenous testosterone, impairs normal folliculogenesis. In this context, we describe the textural parameters of the ovarian cortex of transgender men after prolonged testosterone administration compared to the textural parameters of the non-exposed ovarian cortex originating from female oncological patients. Methods: Texture profile analysis (TPA) was performed on ovarian cortex (5 x 5 mm) of oncological and transgender patients in order to measure stiffness, hardness, cohesiveness, and springiness of the ovarian cortex (LRXplus universal testing system). Statistical analysis was performed using repeated measurements mixed models and the Spearman rank order correlation test (IBM SPSS Statistics 23). Results: A total of 36 frozen-thawed cortical strips (5 x 5 mm) were subjected to TPA. The superficial part of cortex fragments originating from transgender persons (fragments < 1.4 mm; N = 10) appeared to be significantly stiffer compared to cortex derived from oncology patients (fragments < 1.4 mm; N = 7) (6.78 +/- 1.38 N/mm versus 5.41 +/- 0.9 N/mm respectively, p = 0.036). Conclusions: This is the first application of TPA in ovarian cortex to study the physical properties. Comparing the physical properties, we objectively describe an increased cortical stiffness in the most outer part of the ovarian cortex following prolonged testosterone administration in transgender men compared to the ovarian cortex of oncological patients. This preliminary and novel approach could be the start of future research to understand the physical properties of ovarian tissue

Blastocyst transfer for all? : Higher cumulative live birth chance in a blastocyst-stage transfer policy compared to a cleavage-stage transfer policy

Background: In an unselected patient population, what is the cumulative live birth rate per oocyte collection cycle in a blastocyst-stage transfer policy compared to a cleavage-stage transfer policy? Methods: A retrospective cohort analysis of 1656 IVF and ICSI cycles was performed in two timeframes between January 2010 and December 2016. Transfer was scheduled, either on day 3 (n=729) or on day 5 (n=927). In this study, the main outcome measure was cumulative live birth rate per oocyte collection cycle including fresh and frozen embryo transfers in both groups. Results: The cumulative live birth rates per oocyte collection cycle were comparable between patients with cleavage-stage transfers (day 3 group) and those with blastocyst-stage transfers (day 5 group) (23.7% versus 25.5%, respectively; p = 0.42). After controlling for confounders, there was a 34% increased chance of live birth with blastocyst-stage transfer policy compared with cleavage-stage transfer policy (odds ratio (OR) =1.34; 95% confidence interval (CI), 1.051 to 1.704; p = 0.018). Conclusion: In an unselected patient cohort, the cumulative live birth chance per oocyte collection cycle is higher in a blastocyst-stage transfer policy compared to a cleavage-stage transfer policy

One Network, Many Robots: Generative Graphical Inverse Kinematics

Quickly and reliably finding accurate inverse kinematics (IK) solutions remains a challenging problem for robotic manipulation. Existing numerical solvers are broadly applicable, but rely on local search techniques to manage highly nonconvex objective functions. Recently, learning-based approaches have shown promise as a means to generate fast and accurate IK results; learned solvers can easily be integrated with other learning algorithms in end-to-end systems. However, learning-based methods have an Achilles' heel: each robot of interest requires a specialized model which must be trained from scratch. To address this key shortcoming, we investigate a novel distance-geometric robot representation coupled with a graph structure that allows us to leverage the flexibility of graph neural networks (GNNs). We use this approach to train the first learned generative graphical inverse kinematics (GGIK) solver that is, crucially, "robot-agnostic"-a single model is able to provide IK solutions for a variety of different robots. Additionally, the generative nature of GGIK allows the solver to produce a large number of diverse solutions in parallel with minimal additional computation time, making it appropriate for applications such as sampling-based motion planning. Finally, GGIK can complement local IK solvers by providing reliable initializations. These advantages, as well as the ability to use task-relevant priors and to continuously improve with new data, suggest that GGIK has the potential to be a key component of flexible, learning-based robotic manipulation systems

A macaque connectome for large-scale network simulations in TheVirtualBrain

© 2019, The Author(s). Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data

Impact of 6 month conjugated equine estrogen versus estradiol-treatment on biomarkers and enriched gene sets in healthy mammary tissue of non-human primates.

OBJECTIVE To identify distinctly regulated gene markers and enriched gene sets in breast tissue of cynomolgus monkeys (Macaca fascicularis) treated for six months with either conjugated equine estrogens (CEE) or estradiol (E2) by analysis of corresponding mRNA levels of genes associated with breast development, carcinogenesis, apoptosis and immune regulation. Additionally, translation of three nuclear markers was analyzed. METHODS RNA from breast biopsies and necropsies was isolated from two independent study trials from Ethun et al. (CEE) and Foth et al. (E2) after 6 month of treatment duration. RNA was subjected to qRT-PCR and MicroArray analysis. Immunohistochemical stainings were performed for the estrogen receptor alpha subunit (ERa), the progesterone receptor (PGR) and the proliferation marker Ki67. RESULTS We identified a total of 36 distinctly enriched gene sets. Thirty-one were found in the CEE treatment group and five were found in the E2 treatment group, with no overlap. Furthermore, two individual genes IGFBP1 and SGK493 were upregulated in CEE treated animals. Additional targeted qRT-PCR analysis of ten specific estrogen-related genes showed upregulation of three genes (TFF1, PGR and GREB1) after CEE treatment, respectively one gene (TFF1) after E2 treatment. Immunohistochemical stains of breast biopsies showed a significant increase in expression of the PGR marker after CEE treatment. CONCLUSIONS In this study we identified enriched gene sets possibly induced by CEE or E2 treatment in various processes associated with cancer biology and immunology. This preliminary translational data supports the concept that different estrogen types have different effects on healthy breast tissue and may help generate hypotheses for future research

Mechanisms of Comorbidities Associated With the Metabolic Syndrome: Insights from the JCR:LA-cp Corpulent Rat Strain

Obesity and its metabolic complications have emerged as the epidemic of the new millennia. The use of obese rodent models continues to be a productive component of efforts to understand the concomitant metabolic complications of this disease. In 1978, the JCR:LA-cp rat model was developed with an autosomal recessive corpulent (cp) trait resulting from a premature stop codon in the extracellular domain of the leptin receptor. Rats that are heterozygous for the cp trait are lean-prone, while those that are homozygous (cp/cp) spontaneously display the pathophysiology of obesity as well as a metabolic syndrome (MetS)-like phenotype. Over the years, there have been formidable scientific contributions that have originated from this rat model, much of which has been reviewed extensively up to 2008. The premise of these earlier studies focused on characterizing the pathophysiology of MetS-like phenotype that was spontaneously apparent in this model. The purpose of this review is to highlight areas of recent advancement made possible by this model including; emerging appreciation of the thrifty gene hypothesis in the context of obesity, the concept of how chronic inflammation may drive obesogenesis, the impact of acute forms of inflammation to the brain and periphery during chronic obesity, the role of dysfunctional insulin metabolism on lipid metabolism and vascular damage, and the mechanistic basis for altered vascular function as well as novel parallels between the human condition and the female JCR:LA-cp rat as a model for polycystic ovary disease (PCOS)

Salmonella typhimurium fecal shedding following Salmonella choleraesuis-thyphimurium vaccination via drinking water and subsequent challenge

Salmonella typhimurium (ST) is a primary cause of enteritis and subclinical production losses in growing or finishing swine. Due to the zoonotic potential, intervention programs for ST have been established attempting to reduce carcass contamination. The objective of this study was to evaluate Salmonella fecal shedding of pigs vaccinated with a commercial, avirulent live culture (ALC) Salmonella Choleraesuis-Typhimurium vaccine when challenged with virulent ST

Late Bronze Age Food Storage in Lower Cerovačka Cave, Croatia

This paper presents new archaeobotanical data from the Lower Cerovačka Cave located in Dalmatia, Croatia. At the site a high density of carbonized plant remains was recovered, indicating the remnants of a burnt crop store dating to the Late Bronze Age. Overall, the assemblage is dominated by lentil (Lens culinaris) and free-threshing wheat (Triticum aestivum/durum), and to a lesser extent, emmer (Triticum dicoccum), einkorn (Triticum monococcum), spelt (Triticum spelta) and broomcorn millet (Panicum miliaceum). In general, the large botanical collection from Lower Cerovačka Cave fits with what is already known about Bronze Age agriculture in Croatia, yet the unique nature of this site brings to the fore questions around storage practices and the use of caves in prehistory

Identification and assessment of water quality risks associated with sludge supernatant recycling in the presence of cyanobacteria.

This study focussed on the fate of cyanobacteria cells and associated metabolites during the sludge management processes that follow the conventional drinking water treatment train. The topic is of importance, as the release of metabolites during sludge treatment may pose a risk to water quality if supernatant is recycled to the head of the plant. The study of the kinetics of cell damage and metabolite release into the supernatant is complicated by simultaneous and rapid natural removal processes. In this study, the release of organic material from cyanobacterial sludge was monitored simultaneously with secondary metabolites (Microcystins (MCs), cylindrosperopsin (CYN), and geosmin (GSM)) as an additional parameter to aid in understanding the range of processes occurring in sludge. Only GSM produced by Dolichospermum circinale was found to represent a low risk, as the compound is readily degraded. In contrast, the metabolites CYN and MC were shown to increase in concentration during simulated sludge treatment, suggesting that this could occur within full scale sludge treatment facilities with a range of cyanobacteria species, metabolites and water quality. A generic risk matrix was developed, incorporating the type of cyanobacteria, metabolite production, and the treatment