Effect of fiber on physiochemical properties of digesta and fecal microbial composition in pigs

This study was conducted to investigate the effects of different dietary fiber sources on physicochemical properties of digesta and fecal microbial composition in growing pigs. Seven castrated Yorkshire pigs with an initial weight of 24.8 kg (SD 3.0) fitted with a post valve t-caecum (PVTC) cannula were used in a change-over experiment with four periods and four diets. The diets consisted of a basal part and a fibre part, including either a soluble ((Chicory (CH) and Sugar beet pulp (SBP)) or an insoluble ((Wheat bran (WB) and Grass meal (GM)) fibre source. Each experimental period was two weeks and consisted of one week for adaptation to each diet followed by one collection week. Fecal sampling occurred the four first days of each collection period and the ileal digesta samples were collected during fifth and seventh day of the second week. Overall, the effect of various diets on ileal and fecal pH were significant (P 0.05). The effect of different type of dietary fiber on fecal and ileal dry matter (DM) was significant (P< 0.05). The pigs fed with SBP diet had highest fecal DM followed by pigs fed with WB diet. The pigs fed with GM and CH diets showed similar fecal DM which was lower than SBP and WB diets. The ileal DM was higher in the pigs fed with WB diet than the pigs fed with the other diets. The effect of different type of dietary fiber diet on fecal microbial composition was analyzed by Terminal Restriction Fraction Length Polymorphyism (T-RFLP) and showed significant differences among the diets, however the total diversity did not differ due to diet (P> 0.05). This study showed that pigs fed by CH, have more unique fecal microbial composition compared to the pigs fed with the other diets. TRF 160 and TRF 412 identified as Prevotella had higher relative abundance in pigs fed with the CH diet.TRF 275 identified as Megasphaera elsdenii had the highest relative abundance in pigs fed with WB diets. Generally, the effects of the studied fiber sources on physicochemical properties and gut microbiota seems to be ingredient specific. All animals stayed healthy on all diets which mean that all of our experimental dietary fiber sources can be used in pig nutrition at the inclusion level tested in our experiment

Il canale Cavour e le risaie: iconografia del paesaggio risicolo piemontese in trasformazione.

The research field is the landscape shaped from the early 19th century in the Piedmont plain among Vercelli, Novara and the Lomellina region after the Cavour canal construction (a hydraulic engineering work realized from the 1863 till the 1866 to mainly support the rice cultivation) whose effects have deeply characterized the agricultural landscape provoking cultural, social and architectural phenomena which became later a local landmark. Through the comparison between the cartography of the pre or post Cavour canal construction (from the teresian cadastre to the 21st century maps), the iconography, such us Gazzone or Ravello’s paintings, the first pictures from the Touring Italian Club Archive, or even some movies, as Riso Amaro and La Risaia, and the latest documentaries, the peculiar elements of this landscape have been identified in order to better understand this territory and be aware of which context features preserve in case of safeguard or valorization of this heritage

A Simple Approach for State-Action Abstraction using a Learned MDP Homomorphism

Animals are able to rapidly infer from limited experience when sets of state action pairs have equivalent reward and transition dynamics. On the other hand, modern reinforcement learning systems must painstakingly learn through trial and error that sets of state action pairs are value equivalent -- requiring an often prohibitively large amount of samples from their environment. MDP homomorphisms have been proposed that reduce the observed MDP of an environment to an abstract MDP, which can enable more sample efficient policy learning. Consequently, impressive improvements in sample efficiency have been achieved when a suitable MDP homomorphism can be constructed a priori -- usually by exploiting a practioner's knowledge of environment symmetries. We propose a novel approach to constructing a homomorphism in discrete action spaces, which uses a partial model of environment dynamics to infer which state action pairs lead to the same state -- reducing the size of the state-action space by a factor equal to the cardinality of the action space. We call this method equivalent effect abstraction. In a gridworld setting, we demonstrate empirically that equivalent effect abstraction can improve sample efficiency in a model-free setting and planning efficiency for modelbased approaches. Furthermore, we show on cartpole that our approach outperforms an existing method for learning homomorphisms, while using 33x less training data.Comment: Previously Presented at the Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) 202

Big-Loop Recurrence within the Hippocampal System Supports Integration of Information across Episodes

Recent evidence challenges the widely held view that the hippocampus is specialized for episodic memory, by demonstrating that it also underpins the integration of information across experiences. Contemporary computational theories propose that these two contrasting functions can be accomplished by big-loop recurrence, whereby the output of the system is recirculated back into the hippocampus. We use ultra-high-resolution fMRI to provide support for this hypothesis, by showing that retrieved information is presented as a new input on the superficial entorhinal cortex—driven by functional connectivity between the deep and superficial entorhinal layers. Further, the magnitude of this laminar connectivity correlated with inferential performance, demonstrating its importance for behavior. Our findings offer a novel perspective on information processing within the hippocampus and support a unifying framework in which the hippocampus captures higher-order structure across experiences, by creating a dynamic memory space from separate episodic codes for individual experiences

Chorus:A communication and processing architecture for distributed systems

Projet EUREC

Interpreting wde-band neural activity using convolutional neural networks

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity

Chorus:an architecture for distributed systems

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