First occurrence of the problematic vetulicolian Skeemella clavula in the Cambrian Marjum Formation of Utah, USA

The Cambrian Marjum Formation of western Utah (USA) preserves a diverse soft-bodied fauna from the upper Drumian that is slightly younger than the well-known Burgess Shale. While the Marjum is dominated by arthropods, animals belonging to a variety of phyla have been found. Here, we document the second occurrence of the rare, enigmatic taxon Skeemella clavula, which was previously thought to be restricted to the Pierson Cove Formation of the Drum Mountains. The occurrence in the Marjum represents a new preservational setting, as well as a slightly younger deposit. The new specimens also expand the number of known specimens to three. In addition, they improve understanding of the morphology of this representative of the problematic phylum Vetulicolia

No differences in value-based decision-making due to use of oral contraceptives

Fluctuating ovarian hormones have been shown to affect decision-making processes in women. While emerging evidence suggests effects of endogenous ovarian hormones such as estradiol and progesterone on value-based decision-making in women, the impact of exogenous synthetic hormones, as in most oral contraceptives, is not clear. In a between-subjects design, we assessed measures of value-based decision-making in three groups of women aged 18 to 29 years, during (1) active oral contraceptive intake (N = 22), (2) the early follicular phase of the natural menstrual cycle (N = 20), and (3) the periovulatory phase of the natural menstrual cycle (N = 20). Estradiol, progesterone, testosterone, and sex-hormone binding globulin levels were assessed in all groups via blood samples. We used a test battery which measured different facets of value-based decision-making: delay discounting, risk-aversion, risk-seeking, and loss aversion. While hormonal levels did show the expected patterns for the three groups, there were no differences in value-based decision-making parameters. Consequently, Bayes factors showed conclusive evidence in support of the null hypothesis. We conclude that women on oral contraceptives show no differences in value-based decision-making compared to the early follicular and periovulatory natural menstrual cycle phases. Copyright © 2022 Lewis, Kimmig, Kroemer, Pooseh, Smolka, Sacher and Derntl

Anti-Mullerian-Hormone during pregnancy and peripartum using the new Beckman Coulter AMH Gen II Assay

Background: AMH levels determined by the conventional AMH assay declined during pregnancy and postpartum. A new Beckman Coulter AMH Gen II assay removes the potentially assay-interfering complement which is activated in pregnancy. The aim of this study was to evaluate if the decline of AMH levels in the serum of pregnant women during the course of pregnancy and peripartum was assay-dependent and thus artificial. Methods: In this cross-sectional study prepartal blood samples were collected from 62 patients (median age 30.6 years [interquartile range: 25.6 - 34.5]) in the third trimester of pregnancy and again 1–4 days after delivery between 2011 and 2012. In another cohort of 11 patients (median age 34.1 years [interquartile range: 32.6 - 37.8]) blood samples were taken in different trimesters of pregnancy between 1995 and 2001. The conventional and the modified AMH assay were performed in the same patient serum samples. We used the conventional and the modified AMH-Gen-II ELISA (Beckman Coulter, Immunotech, Webster, USA) for the assessment of AMH levels. The Wilcoxon signed rank test was used for determining differences between AMH levels pre- and postpartum. The method of Bland and Altman was applied for analyzing the agreement of both methods for determining AMH levels. Results: AMH values peripartum were lower than those expected in fertile non-pregnant women of comparable age. An overall mean difference of 0.44 ng/ml was observed between the conventional and the modified assay. Measurements with the modified assay showed a significant decline of postpartal levels compared with prepartal levels which is consistent with values obtained using the conventional assay (both p < 0.00001). Compared to the longitudinal measurements of AMH levels determined using the conventional assay, AMH levels obtained using the modified assay suggest a steeper decline of values during the course of pregnancy. Conclusion: By comparing the conventional assay for AMH determination with the modified assay the present study confirmed that AMH levels decline during the course of pregnancy and early after delivery

Combining Stochastic Constraint Optimization and Probabilistic Programming

Algorithms and the Foundations of Software technolog

Probabilistic (logic) programming concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years

Generating Random Logic Programs Using Constraint Programming

Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to generate random logic programs are limited to propositional programs and often impose stringent syntactic restrictions. We present a novel approach to generating random logic programs and random probabilistic logic programs using constraint programming, introducing a new constraint to control the independence structure of the underlying probability distribution. We also provide a combinatorial argument for the correctness of the model, show how the model scales with parameter values, and use the model to compare probabilistic inference algorithms across a range of synthetic problems. Our model allows inference algorithm developers to evaluate and compare the algorithms across a wide range of instances, providing a detailed picture of their (comparative) strengths and weaknesses.Comment: This is an extended version of the paper published in CP 202

Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains

The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp concerns itself with questions about the expressiveness of formal languages for capturing knowledge about the world, together with proof systems for reasoning from such knowledge bases. The learning camp attempts to generalize from examples about partial descriptions about the world. In AI, historically, these camps have loosely divided the development of the field, but advances in cross-over areas such as statistical relational learning, neuro-symbolic systems, and high-level control have illustrated that the dichotomy is not very constructive, and perhaps even ill-formed. In this article, we survey work that provides further evidence for the connections between logic and learning. Our narrative is structured in terms of three strands: logic versus learning, machine learning for logic, and logic for machine learning, but naturally, there is considerable overlap. We place an emphasis on the following "sore" point: there is a common misconception that logic is for discrete properties, whereas probability theory and machine learning, more generally, is for continuous properties. We report on results that challenge this view on the limitations of logic, and expose the role that logic can play for learning in infinite domains