The effects of androgens on lymphocyte infiltration into the porcine endometrium

Litter size is an important aspect of the swine industry and sows are often genetically selected for large litter sizes because of the economic importance of such a trait. Finding alternate ways to influence the number of pigs born, in addition to genetic manipulation could be of economic value to producers. Androgens, including testosterone and dihydrotestosterone (DHT) are used to increase a sow’s ovulation rate to maximize litter size. However, these hormones can have a negative effect on the health of the sow’s uterus. When DHT was used in high dosages, the uterine contents were opaque and infected which suggests that the hormone negatively influences the lymphocyte infiltration during proestrus (Cardenas et al. 2002). When pigs mate, the uterine environment is exposed to a large amount of foreign material that can potentially cause serious infections and limit the uterus’ ability to support growing embryos. Because of the volume of the ejaculate and the length of the uterine horns, swine rely on the lymphocyte infiltration that occurs during the proestrus period to fight off foreign material and bacteria that can compromise uterine health. In order to examine the effect that androgens have on the lymphocyte infiltration into the porcine endometrium, twelve gilts were divided into six groups and treated with either testosterone, dihydrotestosterone, testosterone plus flutamide, DHT plus flutamide, flutamide or oil from day 13 to day 18 of the estrus cycle. Then the number of lymphocytes in a section of each uterine horn was quantified. The concentration of lymphocytes that were observed in the endometrium of the gilts treated with DHT was significantly lower than in the control group. This is consistent with the occurrence of uterine infections and may explain the prevalence of infection in gilts that are treated with DHT

Attachment, stress, and self-efficacy while parenting children on the autism spectrum

The current study explored the relationship between parental perceptions of stress, self-efficacy, attachment, and child functioning level. Participants were parents of children with ASD enrolled in The Special Beginnings Program (SBP, N = 44) or receiving treatment as usual (TAU, N = 39). Hypotheses included that parental perceptions of child functioning level will be negatively correlated with stress and positively correlated with self-efficacy and attachment. In addition, that parental perceptions of stress will decrease and perceptions of attachment and self-efficacy would increase after Project ImPACT training and at follow-up more so for the parents in the SBP group compared to the TAU group. Results revealed child functioning level, attachment, and, self-efficacy are correlated and that child functioning level and parenting stress are negatively correlated. For all participants, regardless of group (SBP or TAU), perceptions of attachment and self-efficacy experienced a rebound to previous levels after first experiencing a decline from baseline. These results indicate that perceptions of child functioning level, attachment, and, self-efficacy are related. In addition, regardless of treatment group, participants experienced a reduction in their perceptions of stress. This is evidence that early intervention programs can be successful at addressing parents stress levels. Future research including a mediation model to explore if attachment or self-efficacy mediates stress is needed to better understand the direction of these variables. This would provide valuable information to early intervention programs as to which intervention services are most needed for parents and children to further child improvement

Rheinfelden AG, Weiherfeld (Rhe.013.1)

In einer landwirtschaftlich genutzten Fläche in der Nähe spätantiker Fundstellen wurde mit Ehrenamtlichen eine Sondiergrabung angelegt, um geomagnetische Anomalien zu überprüfen. Es ergaben sich deutliche Hinweise auf eine Ziegelei (Fehlbrände, Ofenbestandteile). Auf eine weitere Ausgrabung wurde verzichtet, da die Fundstelle nicht akut bedroht ist

Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty

We evaluate the robustness of a probabilistic formulation of system identification (ID) to sparse, noisy, and indirect data. Specifically, we compare estimators of future system behavior derived from the Bayesian posterior of a learning problem to several commonly used least squares-based optimization objectives used in system ID. Our comparisons indicate that the log posterior has improved geometric properties compared with the objective function surfaces of traditional methods that include differentially constrained least squares and least squares reconstructions of discrete time steppers like dynamic mode decomposition (DMD). These properties allow it to be both more sensitive to new data and less affected by multiple minima --- overall yielding a more robust approach. Our theoretical results indicate that least squares and regularized least squares methods like dynamic mode decomposition and sparse identification of nonlinear dynamics (SINDy) can be derived from the probabilistic formulation by assuming noiseless measurements. We also analyze the computational complexity of a Gaussian filter-based approximate marginal Markov Chain Monte Carlo scheme that we use to obtain the Bayesian posterior for both linear and nonlinear problems. We then empirically demonstrate that obtaining the marginal posterior of the parameter dynamics and making predictions by extracting optimal estimators (e.g., mean, median, mode) yields orders of magnitude improvement over the aforementioned approaches. We attribute this performance to the fact that the Bayesian approach captures parameter, model, and measurement uncertainties, whereas the other methods typically neglect at least one type of uncertainty

Smart plugs: A low cost solution for programmable control of domestic loads

Balancing energy demand and production is becoming a more and more challenging task for energy utilities. This is due to a number of different reasons among which the larger penetration of renewable energies which are more difficult to predict and the meagre availability of financial resources to upgrade the existing power grid. While the traditional solution is to dynamically adapt energy production to follow the time-varying demand, a new trend is to drive the demand itself by means of Direct Load Control (DLC). In this paper we consider a scenario where DLC functionalities are deployed at a large set of small deferrable energy loads, like appliances of residential users. The required additional intelligence and communication capabilities may be introduced through smart plugs, without the need to replace older 'dumb' appliances. Smart plugs are inserted between the appliances plugs and the power sockets and directly connected to the Internet. An open software architecture allows to abstract the hardware sensors and actuators integrated in the plug and to easily program different load control applications

Robust identification of non-autonomous dynamical systems using stochastic dynamics models

This paper considers the problem of system identification (ID) of linear and nonlinear non-autonomous systems from noisy and sparse data. We propose and analyze an objective function derived from a Bayesian formulation for learning a hidden Markov model with stochastic dynamics. We then analyze this objective function in the context of several state-of-the-art approaches for both linear and nonlinear system ID. In the former, we analyze least squares approaches for Markov parameter estimation, and in the latter, we analyze the multiple shooting approach. We demonstrate the limitations of the optimization problems posed by these existing methods by showing that they can be seen as special cases of the proposed optimization objective under certain simplifying assumptions: conditional independence of data and zero model error. Furthermore, we observe that our proposed approach has improved smoothness and inherent regularization that make it well-suited for system ID and provide mathematical explanations for these characteristics' origins. Finally, numerical simulations demonstrate a mean squared error over 8.7 times lower compared to multiple shooting when data are noisy and/or sparse. Moreover, the proposed approach can identify accurate and generalizable models even when there are more parameters than data or when the underlying system exhibits chaotic behavior

Water authorities' pricing strategies to recover supply costs in the absence of water metering for irrigated agriculture

Most of the irrigated agricultural regions in Europe are supplied by surface irrigation networks managed by local water authorities (WAs). Under such conditions,WAs are not able to fully monitor water usage and farmers have an information advantage vis-a-vis theWA. This results in the water authority suffering 'pricing failure' if it decides to apply an incentive pricing strategy (tariffs proportional to the alleged water uses). Indeed, farmers could exploit their information advantage by behaving in an opportunistic manner, withdrawing more water than declared, and ultimately paying less than they should. This situation could also undermine the efficacy and the efficiency of theWA incentive pricing strategies. This paper analyses incentive water pricing schemes under asymmetric information by the means of a Principal-Agent model. The Agency problem between the WA and farmers is addressed by introducing a monitoring strategy that would enable the WA to detect farms action. In doing so, we compare incentive strategies with flat rate water pricing and investigate under what conditions the WA might provide/not provide incentive water pricing in the absence of water metering

Assessing the potential economic viability of precision irrigation: A theoretical analysis and pilot empirical evaluation

The present study explores the value generated by the use of information to rationalize the use of water resources in agriculture. The study introduces the value of information concept in the field of irrigation developing a theoretical assessment framework to evaluate whether the introduction of "Precision Irrigation" (PI) practices can improve expectations on income. This is supported by a Stakeholders consultation and by a numerical example, using secondary data and crop growth models. The study reveals that the value generated with the transition to PI varies with pedo-climate, economic, technological and other conditions, and it depends on the initial status of the farmer's information environment. These factors affect the prerequisite needed to make viable PI. To foster the adoption of PI, stakeholders envisaged the need to set up free meteorological information and advisory service that supports farmers in using PI, as well as other type of instruments. The paper concludes that the profitability of adoption and the relevant impact on the environment cannot be considered as generally given, but must be evaluated case by case justifying (or not) the activation of specific agricultural policy measures supporting PI practices to target regions

The value of information for the management of water resources in agriculture: Assessing the economic viability of new methods to schedule irrigation

Abstract This study develops a methodology to assess the comparative advantages of new methods to plan irrigation with respect to prevailing existing irrigation practices. The methodology consists of a comparative cost-benefit analysis based on the Value of Information approach that makes it possible to analyse whether an improvement in the information available to farmers generates economic benefits. The method is applied to the problem of comparing computer irrigation models (providing irrigation advice based on measurements, water balance models and weather predictions) and prevailing irrigation practices (at times based on soil and plant observations, or on advanced technologies) in estimating and predicting crop water requirements, in pilot experiments located in four different European regions. The results reveal that the introduction of the alternative method improves the performance of irrigation practices in Mediterranean regions that are characterised by high weather variability and for those crops for which the consequences of failing to meet predictions are relatively low (i.e. tomato instead of maize, drip irrigated crops instead of sprinkler irrigated crops). Under favourable conditions, the use of the alternative technology generates a 0–20% increase in gross margin and a 10–30% water saving with respect to prevailing existing irrigation practices. The study concludes by addressing the conditions that justify the use of advanced information systems to schedule irrigation interventions and by offering some policy recommendations to drive their uptake. These include subsidising research at the evaluation stage and public investments aimed at knowledge creation (weather and shallow water table monitoring stations) and knowledge sharing (counselling) at the adoption stage