Hubble-Lema\^itre fragmentation and the path to equilibrium of merger-driven cluster formation

This paper discusses a new method to generate self-coherent initial conditions for young substructured stellar cluster. The expansion of a uniform system allows stellar sub-structures (clumps) to grow from fragmentation modes by adiabatic cooling. We treat the system mass elements as stars, chosen according to a Salpeter mass function, and the time-evolution is performed with a collisional N-body integrator. This procedure allows to create a fully-coherent relation between the clumps' spatial distribution and the underlying velocity field. The cooling is driven by the gravitational field, as in a cosmological Hubble-Lema\^itre flow. The fragmented configuration has a `fractal'-like geometry but with a self-grown velocity field and mass profile. We compare the characteristics of the stellar population in clumps with that obtained from hydrodynamical simulations and find a remarkable correspondence between the two in terms of the stellar content and the degree of spatial mass-segregation. In the fragmented configuration, the IMF power index is ~0.3 lower in clumps in comparison to the field stellar population, in agreement with observations in the Milky Way. We follow in time the dynamical evolution of fully fragmented and sub-virial configurations, and find a soft collapse, leading rapidly to equilibrium (timescale of 1 Myr for a ~ 10^4 Msun system). The low-concentration equilibrium implies that the dynamical evolution including massive stars is less likely to induce direct collisions and the formation of exotic objects. Low-mass stars already ejected from merging clumps are depleted in the end-result stellar clusters, which harbour a top-heavy stellar mass function.Comment: 22 pages, accepted for publication in MNRA

Estimating Moterating effects in PLS-SEM andPLSc-SEM: interaction term gerneration*data treatment

When estimating moderating effects in partial least squares structural equation modeling (PLS-SEM), researchers can choose from a variety of approaches to model the influence of a moderator on a relationship between two constructs by generating different interaction terms. While prior research has evaluated the efficacy of these approaches in the context of PLS-SEM, the impact of different data treatment options on their performance in the context of standard PLS-SEM and consistent PLS-SEM (PLSc-SEM) is as yet unexplored. Our simulation study addresses these limitations and explores if the choice of approach and data treatment option has a pronounced impact on the methods’ parameter recovery. An empirical application substantiates these findings. Based on our results, we offer recommendations for researchers wishing to estimate moderating effects by means of PLS-SEM and PLSc-SEM

Consequences of altered eicosanoid patterns for nociceptive processing in mPGES-1-deficient mice

Cyclooxygenase-2 (COX-2)-dependent prostaglandin (PG) E2 synthesis in the spinal cord plays a major role in the development of inflammatory hyperalgesia and allodynia. Microsomal PGE2 synthase-1 (mPGES-1) isomerizes COX-2-derived PGH2 to PGE2. Here, we evaluated the effect of mPGES-1-deficiency on the noci-ceptive behavior in various models of nociception that depend on PGE2 synthesis. Surprisingly, in the COX-2-dependent zymosan-evoked hyperalgesia model, the nociceptive behavior was not reduced in mPGES-1-deficient mice despite a marked decrease of the spinal PGE2 synthesis. Similarly, the nociceptive behavior was unaltered in mPGES-1-deficient mice in the formalin test. Importantly, spinal cords and primary spinal cord cells derived from mPGES-1-deficient mice showed a redirection of the PGE2 synthesis to PGD2, PGF2α and 6-keto-PGF1α (stable metabolite of PGI2). Since the latter prostaglandins serve also as mediators of noci-ception they may compensate the loss of PGE2 synthesis in mPGES-1-deficient mice

Comparison of first-line and second-line terlipressin versus sole norepinephrine in fulminant ovine septic shock

The Surviving Sepsis Guidelines suggest the use of vasopressin in case of catecholamine-refractory septic shock. Terlipressin (TP) as a V-1-selective AVP analogue is a potential alternative, though data regarding the first-line administration in septic shock are scarce. The present study explored and compared the effects of first-line vs. second-line infusion of TP or sole norepinephrine regarding organ function, fluid and norepinephrine requirements and survival in fulminant ovine septic shock. Peritoneal sepsis was induced in 23 ewes after laparotomy and faecal withdrawal from the caecum. After onset of shock, causal and supportive sepsis therapy (antibiotics, peritoneal lavage, fluids and open-label norepinephrine) was performed in all animals. Concurrently, animals were randomized to receive 0.9% sodium chloride (control group) or TP (2 mu g.kg(-1).h(-1), first-line group) after shock onset. In the second-line TP group, TP (2 mu g.kg(-1).h(-1)) was started once norepinephrine requirements exceeded 0.5 mu g.kg(-1).min(-1). No significant differences were found between groups regarding survival, haemodynamics as well as fluid-and catecholamine-requirements. Kidney function and electron microscopic kidney injury were comparable between groups. In the present model of fulminant ovine septic shock, first-line TP infusion had no significant effect on fluid and norepinephrine requirements or organ dysfunction as compared to second-line TP infusion or placebo

Segmentation of PLS-Path Models by Iterative Reweighted Regressions

Uncovering unobserved heterogeneity is a requirement to obtain valid results when using the structural equation modeling (SEM) method with empirical data. Conventional segmentation methods usually fail in SEM since they account for the observations but not the latent variables and their relationships in the structural model. This research introduces a new segmentation approach to variance-based SEM. The iterative reweighted regressions segmentation method for PLS (PLS-IRRS) effectively identifies segments in data sets. In comparison with existing alternatives, PLS-IRRS is multiple times faster while delivering the same quality of results. We believe that PLS-IRRS has the potential to become one of the primary choices to address the critical issue of unobserved heterogeneity in PLS-SE

The effect of endometriosis on live birth rate and other reproductive outcomes in ART cycles: a cohort study

Study question: What is the effect of endometriosis compared to unexplained subfertility on live birth rate in women undergoing IVF and embryo transfer (ET)? Summary answer: Endometriosis decreases live birth rate in women undergoing IVF-ET treatment, particularly with increasing severity of the disease. What is known already: Endometriosis affects up to 50% of women seeking fertility treatment and is known to reduce fecundity. There remains a debate as to effects of endometriosis on the outcomes of IVF treatment, with live birth being a secondary outcome or not reported in most studies. Study design, size, duration: A retrospective cohort study analyzing data of IVF treatment cycles from January 2000 to December 2014 was carried out. Participants/materials, setting, methods: Women with endometriosis (n = 531) and women with unexplained subfertility (n = 737) undergoing a first cycle of IVF-ET in a tertiary fertility treatment center were included in the study. The primary outcome was live birth. Other outcome measures were response to ovarian stimulation, embryo development and implantation rate. Bivariate and multivariate logistic regression analysis was performed and differences compared using Chi squared test of Student’s t-test as appropriate. Main results and the role of chance: Women with endometriosis had 24% less likelihood of a live birth when compared to those with unexplained subfertility [odds ratio (OR) 0.76 (95% CI, 0.59–0.98) P = 0.035]. This effect became more apparent with increasing severity of endometriosis. Using multivariable logistic regression analysis, the trend for lower live birth rate remained but did not reach statistical significance [adjusted OR 0.76 (95% CI 0.56–1.03), P = 0.078]. Women with endometriosis were as likely as those with unexplained subfertility to have a singleton live birth when two embryos were transferred as opposed to a single ET [OR 1.38 (95% CI 0.73–2.62), P = 0.32 and OR 3.22 (95% CI 1.7–6.05), P = 0.0003, respectively]. Compared to women with unexplained subfertility, those with endometriosis had fewer oocytes retrieved [(10.54 (95% CI 10.13–0.95) and 9.15 (95% CI 8.69–9.6), respectively], lower blastocyst transfer [OR 0.24 (95% CI 0.12–0.5), P = 0.0001] and a significantly reduced implantation rate [OR 0.73 (0.58–0.92), P = 0.007]. Limitations reasons for caution: The study is limited by a retrospective design. By limiting the study to a single ET cycle, it was not possible to assess the cumulative outcome including use of all frozen embryos. Wider implications of the findings: Endometriosis has similar phenotypes among women in different populations and would be expected to have a similar effect on fertility. These results are therefore generalizable to other populations of women. Study funding/competing interest(s): None. Trial registration number: Not applicable

Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats

A large proportion of information systems research is concerned with developing and testing models pertaining to complex cognition, behaviors, and outcomes of individuals, teams, organizations, and other social systems that are involved in the development, implementation, and utilization of information technology. Given the complexity of these social and behavioral phenomena, heterogeneity is likely to exist in the samples used in IS studies. While researchers now routinely address observed heterogeneity by introducing moderators, a priori groupings, and contextual factors in their research models, they have not examined how unobserved heterogeneity may affect their findings. We describe why unobserved heterogeneity threatens different types of validity and use simulations to demonstrate that unobserved heterogeneity biases parameter estimates, thereby leading to Type I and Type II errors. We also review different methods that can be used to uncover unobserved heterogeneity in structural equation models. While methods to uncover unobserved heterogeneity in covariance-based structural equation models (CB-SEM) are relatively advanced, the methods for partial least squares (PLS) path models are limited and have relied on an extension of mixture regression—finite mixture partial least squares (FIMIX-PLS) and distance measure-based methods—that have mismatches with some characteristics of PLS path modeling. We propose a new method—prediction-oriented segmentation (PLS-POS)—to overcome the limitations of FIMIX-PLS and other distance measure-based methods and conduct extensive simulations to evaluate the ability of PLS-POS and FIMIX-PLS to discover unobserved heterogeneity in both structural and measurement models. Our results show that both PLS-POS and FIMIX-PLS perform well in discovering unobserved heterogeneity in structural paths when the measures are reflective and that PLS-POS also performs well in discovering unobserved heterogeneity in formative measures. We propose an unobserved heterogeneity discovery (UHD) process that researchers can apply to (1) avert validity threats by uncovering unobserved heterogeneity and (2) elaborate on theory by turning unobserved heterogeneity into observed heterogeneity, thereby expanding theory through the integration of new moderator or contextual variables