Archeological research on the Karacadag and a hieroglyphic Luwian inscription from Karaören

This essay presents a partial report of surveys on the Karacadaǧ (Konya), which have been carried out since 2016 due to the find of a fragment of a hieroglyphic Luwian inscription from the 13th century BC at the village of Karaören. The results of the survey allow a holistic understanding of the material and topographic conditions which led to the writing, re-use and then find of the inscription. The inscription is presented and a possible historical-geographical framework both of this and of other related texts is explained, whereby it seems probable that there was an important military-strategic border here. The survey and associated ethnographic research established the importance of the freshwater springs on the Karacadaǧ, as well as the continuous re-use of stones attesting a profound cultural memory that runs from the Hittite period through a populous Byzantine occupation up until modern applications by the inhabitants of the Karacadaǧ

The behavioral ecology of moral dilemmas: childhood unpredictability, but not harshness, predicts less deontological and utilitarian responding

Childhood unpredictability and harshness are associated with patterns of psychology and behavior that enable individuals to make the most of adverse environments. The current research assessed effects of childhood unpredictability and harshness on individual differences in sacrificial moral decision making. Six studies (N = 1,503) supported the hypothesis that childhood unpredictability, but not harshness, would be associated with fewer decisions to reject harm (consistent with deontological ethics) and to maximize overall outcomes (consistent with utilitarian ethics). These associations were not moderated by perceptions of current environmental unpredictability (Studies 3a and 3b) and were robust to potential confounds (religiosity, political conservativism, Big 5 personality traits, and social desirability; Study 5). The associations between childhood unpredictability and lower deontological and utilitarian tendencies were statistically mediated by low levels of empathic concern and poor-quality social relationships (Study 4). Findings are consistent with the possibility that early calibration to ecological unpredictability, but not harshness, undermines other-oriented psychological processes which, in turn, reduce moral concerns about harm and consequences for other people

Towards understanding the myometrial physiome: approaches for the construction of a virtual physiological uterus

Premature labour (PTL) is the single most significant factor contributing to neonatal morbidity in Europe with enormous attendant healthcare and social costs. Consequently, it remains a major challenge to alleviate the cause and impact of this condition. Our ability to improve the diagnosis and treatment of women most at risk of PTL is, however, actually hampered by an incomplete understanding of the ways in which the functions of the uterine myocyte are integrated to effect an appropriate biological response at the multicellular whole organ system. The level of organization required to co-ordinate labouring uterine contractile effort in time and space can be considered immense. There is a multitude of what might be considered mini-systems involved, each with their own regulatory feedback cycles, yet they each, in turn, will influence the behaviour of a related system. These include, but are not exclusive to, gestational-dependent regulation of transcription, translation, post-translational modifications, intracellular signaling dynamics, cell morphology, intercellular communication and tissue level morphology. We propose that in order to comprehend how these mini-systems integrate to facilitate uterine contraction during labour (preterm or term) we must, in concert with biological experimentation, construct detailed mathematical descriptions of our findings. This serves three purposes: firstly, providing a quantitative description of series of complex observations; secondly, proferring a database platform that informs further testable experimentation; thirdly, advancing towards the establishment of a virtual physiological uterus and in silico clinical diagnosis and treatment of PTL

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier

COVID-19 community spread and consequences for prison case rates

Background COVID-19 and mass incarceration are closely intertwined with prisons having COVID-19 case rates much higher than the general population. COVID-19 has highlighted the relationship between incarceration and health, but prior work has not explored how COVID-19 spread in communities have influenced case rates in prisons. Our objective was to understand the relationship between COVID-19 case rates in the general population and prisons located in the same county. Methods Using North Carolina’s (NC) Department of Health and Human Services data, this analysis examines all COVID-19 tests conducted in NC from June-August 2020. Using interrupted time series analysis, we assessed the relationship between substantial community spread (50/100,000 detected in the last seven days) and active COVID-19 case rates (cases detected in the past 14 days/100,000) within prisons. Results From June-August 2020, NC ordered 29,605 tests from prisons and detected 1,639 cases. The mean case rates were 215 and 427 per 100,000 in the general and incarcerated population, respectively. Once counties reached substantial COVID-19 spread, the COVID-19 prison case rate increased by 118.55 cases per 100,000 (95% CI: -3.71, 240.81). Conclusions Community COVID-19 spread contributes to COVID-19 case rates in prisons. In counties with prisons, community spread should be closely monitored. Stringent measures within prisons (e.g., vaccination) and decarceration should be prioritized to prevent COVID-19 outbreaks

Nonparametric nonlinear model predictive control

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

COVID-19 in corrections: Quarantine of incarcerated people

Carceral settings in the United States have been the source of many single site COVID-19 outbreaks. Quarantine is a strategy used to mitigate the spread of COVID-19 in correctional settings, and specific quarantine practices differ state to state. To better understand how states are using quarantine in prisons, we reviewed each state’s definition of quarantine and compared each state’s definition to the Centers for Disease Control’s (CDC) definition and recommendations for quarantine in jails and prisons. Most prison systems, 45 of 53, define quarantine, but definitions vary widely. No state published definitions of quarantine that align with all CDC recommendations, and only 9 states provide quarantine data. In these states, the highest recorded quarantine rate occurred in Ohio in May 2020 at 843 per 1,000. It is necessary for prison systems to standardize their definitions of quarantine and to utilize quarantine practices in accordance with CDC recommendations. In addition, data transparency is needed to better understand the use of quarantine and its effectiveness at mitigating COVID-19 outbreaks in carceral settings

Energy Linearity and Resolution of the ATLAS Electromagnetic Barrel Calorimeter in an Electron Test-Beam

A module of the ATLAS electromagnetic barrel liquid argon calorimeter was exposed to the CERN electron test-beam at the H8 beam line upgraded for precision momentum measurement. The available energies of the electron beam ranged from 10 to 245 GeV. The electron beam impinged at one point corresponding to a pseudo-rapidity of eta=0.687 and an azimuthal angle of phi=0.28 in the ATLAS coordinate system. A detailed study of several effects biasing the electron energy measurement allowed an energy reconstruction procedure to be developed that ensures a good linearity and a good resolution. Use is made of detailed Monte Carlo simulations based on Geant which describe the longitudinal and transverse shower profiles as well as the energy distributions. For electron energies between 15 GeV and 180 GeV the deviation of the measured incident electron energy over the beam energy is within 0.1%. The systematic uncertainty of the measurement is about 0.1% at low energies and negligible at high energies. The energy resolution is found to be about 10% sqrt(E) for the sampling term and about 0.2% for the local constant term

Comparison of labour induction with misoprostol and dinoprostone and characterization of uterine response based on electrohysterogram

[EN] Objective: The objective of this study is to compare the uterine activity response between women administered dinoprostone (prostaglandin E2) and misoprostol (prostaglandin E1) for induction of labour (IOL) by analysing not only the traditional obstetric data but also the parameters extracted from uterine electrohysterogram (EHG). Methods: Two cohorts were defined: misoprostol (25-mg vaginal tablets; 251 women) and dinoprostone cohort (10 mg vaginal inserts; 249 women). All the mothers were induced by a medical indication of a Bishop Score < ¿ 6. Results: The misoprostol cohort was associated with a shorter time to achieve active labour (p ¿ .017) and vaginal delivery (p ¿ .009) and with a higher percentage of vaginal delivery in less than 24 h in mothers with a very unfavourable cervix score (risk ratio (RR): 1.41, IC95% 1.17¿1.69, p ¿ .002). Successful inductions with misoprostol showed EHG parameter values significantly higher than basal state for amplitude and pseudo Montevideo units (PMU) 60¿ after drug administration, while spectral parameters significantly increased after 150¿. This response was not observed in failed inductions. In the successful dinoprostone group, the duration and number of contractions increased significantly after 120¿, PMU did so after 180¿, and no significant differences were found for spectral parameters, possibly due to the slower pharmacokinetics of this drug. Conclusion: Successful inductions of labour by misoprostol are associated with earlier effective contractions than in labours induced by dinoprostone.This work was partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under grant [DPI2015-68397-R] and by the company Bial SA.Benalcazar-Parra, C.; Monfort-Orti, R.; Ye Lin, Y.; Prats-Boluda, G.; Alberola Rubio, J.; Perales Marín, AJ.; Garcia-Casado, J. (2019). Comparison of labour induction with misoprostol and dinoprostone and characterization of uterine response based on electrohysterogram. The Journal of Maternal-Fetal & Neonatal Medicine. 32(10):1586-1594. https://doi.org/10.1080/14767058.2017.1410791S15861594321