1,721,036 research outputs found
THE EFFECTS OF ALFAPROSTOL (PGF2a analogue) AND eCG ON REPRODUCTIVE PERFORMANCES IN POSTPARTUM RABBITS
No full text[EN] The aim of this study was to verify the effectiveness of post partum pharmacological oestrus synchronisation with alfaprostol or eCG in improving rabbit doe reproductive performances and to evaluate the incidence of pseudopregnancy in this species. One hundred and eighty commercial hybrid rabbit does, multiparous and lactating, were randomly divided into three groups (n=60) and treated on day 8 post partum (pp) as follows: Group A, 1 mL s.c. vitaminic solution with 200 mg alfaprostol, a synthetic analogue of PGF2a; Group B, 1 mL sc vitaminic solution with 20 I.U eCG; Group C (Control) 1 mL sc vitaminic solution. On day 11 pp (3 days after treatment), all the rabbits were artificially inseminated (AI), and injected with 0.8 mg of buserelin i.m. to induce ovulation. Concurrently, blood samples were taken for progesterone (P4) analysis by RIA with the following schedule: day 8 pp (before treatment), day 11 pp (before AI and the induction of ovulation) and day 16 pp (5 days after AI). Fertility was not influenced by hormonal treatments (71.7% and 71.2% respectively) compared to control (66.1%). Conversely, both PGF2a and eCG hormonal synchronisation treatments increased (P1 ng/mL). On day 5 after insemination 95.5% had P4 values, which attest to the presence of functional corpora lutea (CL) (P4>2 ng/mL). The results of this study show that as long as rabbitry is properly managed zootechnically and sanitarily, drugs such as alfaprostol and eCG, while not ameliorating fertility rate when used for post partum oestrus sychronisation, can increase litter size.Mollo, A.; Veronesi, M.; Battocchio, M.; Cairoli, F.; Brecchia, G.; Boiti, C. (2003). THE EFFECTS OF ALFAPROSTOL (PGF2a analogue) AND eCG ON REPRODUCTIVE PERFORMANCES IN POSTPARTUM RABBITS. World Rabbit Science. 11(2):63-74. https://doi.org/10.4995/wrs.2003.498SWORD637411
Neural Predictive Monitoring for Collective Adaptive Systems
Full text linkReliable bike-sharing systems can lead to numerous environmental, economic and social benefits and therefore play a central role in the effective development of smart cities. Bike-sharing models deal with spatially distributed stations and interact with an unpredictable environment, the users. Monitoring the trustworthiness of such a collective system is of paramount importance to ensure a good quality of the delivered service, but this task can become computationally demanding due to the complexity of the model under study. Neural Predictive Monitoring (NPM) [5], a neural-network learning-based approach to predictive monitoring (PM) with statistical guarantees, can be employed to preemptively detect violations of a specific requirement – e.g. a station has no more bikes available or a station is full. The computational efficiency of NPM makes PM applicable at runtime even on embedded devices with limited computational power. The goal of this paper is to demonstrate the applicability of NPM on collective adaptive systems such as bike-sharing systems. In particular, we first analyze the performance of NPM over a collective system evolving deterministically. Then, following [7], we tackle a more realistic scenario, where sensors allow only for partial observability and where the system evolves in a stochastic fashion. We evaluate the approach on multiple bike-sharing network topologies, obtaining highly accurate predictions and effective error detection rules
Neural Predictive Monitoring Under Partial Observability
Full text linkWe consider the problem of predictive monitoring (PM), i.e., predicting at runtime future violations of a system from the current state. We work under the most realistic settings where only partial and noisy observations of the state are available at runtime. Such settings directly affect the accuracy and reliability of the reachability predictions, jeopardizing the safety of the system. In this work, we present a learning-based method for PM that produces accurate and reliable reachability predictions despite partial observability (PO). We build on Neural Predictive Monitoring (NPM), a PM method that uses deep neural networks for approximating hybrid systems reachability, and extend it to the PO case. We propose and compare two solutions, an end-to-end approach, which directly operates on the rough observations, and a two-step approach, which introduces an intermediate state estimation step. Both solutions rely on conformal prediction to provide 1) probabilistic guarantees in the form of prediction regions and 2) sound estimates of predictive uncertainty. We use the latter to identify unreliable (and likely erroneous) predictions and to retrain and improve the monitors on these uncertain inputs (i.e., active learning). Our method results in highly accurate reachability predictions and error detection, as well as tight prediction regions with guaranteed coverage
Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees
Full text linkThis tutorial focuses on efficient methods to predictive mon- itoring (PM), the problem of detecting at runtime future violations of a given requirement from the current state of a system. While performing model checking at runtime would offer a precise solution to the PM prob- lem, it is generally computationally expensive. To address this scalabil- ity issue, several lightweight approaches based on machine learning have recently been proposed. These approaches work by learning an approxi- mate yet efficient surrogate (deep learning) model of the expensive model checker. A key challenge remains to ensure reliable predictions, especially in safety-critical applications.
We review our recent work on predictive monitoring, one of the first to propose learning-based approximations for CPS verification of tem- poral logic specifications and the first in this context to apply conformal prediction (CP) for rigorous uncertainty quantification. These CP-based uncertainty estimators offer statistical guarantees regarding the gener- alization error of the learning model, and they can be used to deter- mine unreliable predictions that should be rejected. In this tutorial, we present a general and comprehensive framework summarizing our app- roach to the predictive monitoring of CPSs, examining in detail several variants determined by three main dimensions: system dynamics (deter- ministic, non-deterministic, stochastic), state observability, and seman- tics of requirements’ satisfaction (Boolean or quantitative)
Abstraction of Markov Population Dynamics via Generative Adversarial Nets
No full textMarkov Population Models are a widespread formalism used to model the dynamics of complex systems, with applications in Systems Biology and many other fields. The associated Markov stochastic process in continuous time is often analyzed by simulation, which can be costly for large or stiff systems, particularly when a massive number of simulations has to be performed (e.g. in a multi-scale model). A strategy to reduce computational load is to abstract the population model, replacing it with a simpler stochastic model, faster to simulate. Here we pursue this idea, building on previous works and constructing a generator capable of producing stochastic trajectories in continuous space and discrete time. This generator is learned automatically from simulations of the original model in a Generative Adversarial setting. Compared to previous works, which rely on deep neural networks and Dirichlet processes, we explore the use of state of the art generative models, which are flexible enough to learn a full trajectory rather than a single transition kernel
Valutazione dell’efficacia di un GnRH analogo nella terapia delle cisti ovariche della bovina mediante l’andamento del quadro endocrino (LH, progesterone, 17B- estradiolo).
No full textCertified Guidance for Planning with Deep Generative Models
No full textDeep generative models, such as generative adversarial networks (GANs) and score-based diffusion models, have recently emerged as powerful tools for planning tasks and behavior synthesis in autonomous systems. Various guidance strategies have been introduced to steer the generative process toward outputs that are more likely to satisfy the planning objectives. These strategies avoid the need for model retraining but do not provide any guarantee that the generated outputs will satisfy the desired planning objectives. To address this limitation, we introduce certified guidance, an approach that modifies a generative model - without retraining it - into a new model guaranteed to satisfy a given specification with probability 1. We focus on Signal Temporal Logic (STL) specifications, which are rich enough to describe non-trivial planning tasks. Our approach leverages neural network verification techniques to systematically explore the generative models' latent spaces, identifying latent regions that are certifiably correct w.r.t. the STL property of interest. We evaluate the effectiveness of our method on four planning benchmarks using GANs and diffusion models. Our results confirm that certified guidance produces generative models that are always correct, unlike existing (non-certified) guidance methods
Long-Term Effect of eCG or Biostimulation on Reproductive Performances in the Rabbit Doe
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