Generative Temporal Models with Spatial Memory for Partially Observed Environments

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments. In this work we introduce a novel action-conditioned generative model of such challenging environments. The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment. Low-dimensional spatial updates are computed using a state-space model that makes use of knowledge on the prior dynamics of the moving agent, and high-dimensional visual observations are modelled with a Variational Auto-Encoder. The result is a scalable architecture capable of performing coherent predictions over hundreds of time steps across a range of partially observed 2D and 3D environments.Comment: ICML 201

STABILITY OF ABNORMAL KARYOTYPES IN CELL CULTURE1

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dna replication patterns of canine chromosomes in vivo and in vitro

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The impact of liver disease: a leading cause of hospital admissions in people living vith HIV

Background: This study reviews recent trends of HIV inpatient admissions over 5 Infectious diseases Units in Liguria, in 2012. Patients and Methods: Five infectious diseases Units in Liguria, Italy, collected data on inpatient HIV admissions from January to December 2012, including patient demographic, discharge diagnosis, CD4 Tcell count, viral load (VL) and combined anti-retroviral treatment (cART). Results: Rate of patient admissions per 100 years was 6.12 (number=257), in 62.6% (n=161) of admissions a VL under 50 copies/ml was observed. Furthermore, 86.4% (n=222) of admissions were on active cART. Median age was 49 years. Mortality rate was 10.2%. Hepatitis C coinfection occurred in 64.6% of patients (n=166). The most common diagnosis was infectious diseases (29.1%), respiratory diseases (16.6%) and neoplasms (15.%). Chronic HCV infection and its complications (cirrhosis and hepatocellular carcinoma) accounted for 31% of all discharging diagnosis. Conclusions: The majority of inpatients admitted during 2012 in our Units were on cART and virologically suppressed. The complications of hepatitis C coinfection have a major impact on mortality rates and hospitalization rates in Italy. According to these observations, the availability of new drugs for chronic hepatitis C imposes a further effort to improve the quality of life of our patients

The effect of timing of oral meloxicam administration on physiological responses in calves after cautery dehorning with local anesthesia.

Abstract Dehorning is a painful husbandry procedure that is commonly performed in dairy calves. Parenteral meloxicam combined with local anesthesia mitigates the physiological and behavioral effects of dehorning in calves. The purpose of this study was to determine the influence of timing of oral meloxicam administration on physiological responses in calves after dehorning. Thirty Holstein bull calves, 8 to 10 wk of age (28–70kg), were randomly assigned to 1 of 3 treatment groups: placebo-treated control group (n=10), calves receiving meloxicam administered orally (1 mg/kg) in powdered milk replacer 12h before cautery dehorning (MEL-PRE; n=10), and calves receiving meloxicam administered as an oral bolus (1 mg/kg) at the time of dehorning (MEL-POST; n=10). Following cautery dehorning, blood samples were collected to measure cortisol, substance P (SP), haptoglobin, ex vivo prostaglandin E 2 (PgE 2 ) production after lipopolysaccharide stimulation and meloxicam concentrations. Maximum ocular temperature and mechanical nociceptive threshold (MNT) were also assessed. Data were analyzed using noncompartmental pharmacokinetic analysis and repeated measures ANOVA models. Mean peak meloxicam concentrations were 3.61±0 0.21 and 3.27±0.14μg/mL with average elimination half-lives of 38.62±5.87 and 35.81±6.26h for MEL-PRE and MEL-POST, respectively. Serum cortisol concentrations were lower in meloxicam-treated calves compared with control calves at 4h postdehorning. Substance P concentrations were significantly higher in control calves compared with meloxicam-treated calves at 120h after dehorning. Prostaglandin E 2 concentrations were lower in meloxicam-treated calves compared with control calves. Mechanical nociceptive threshold was higher in control calves at 1h after dehorning, but meloxicam-treated calves tended to have a higher MNT at 6h after dehorning. No effect of timing of meloxicam administration on serum cortisol concentrations, SP concentrations, haptoglobin concentrations, maximum ocular temperature, or MNT was observed. However, PgE 2 concentrations in MEL-PRE calves were similar to control calves after 12h postdehorning, whereas MEL-POST calves had lower PgE 2 concentrations for 3 d postdehorning. These findings support that meloxicam reduced cortisol, SP, and PgE 2 after dehorning, but only PgE 2 production was significantly affected by the timing of meloxicam administration

Goal-Driven Structured Argumentation for Patient Management in a Multimorbidity Setting

We use computational argumentation to both analyse and generate solutions for reasoning in multimorbidity about consistent recommendations, according to different patient-centric goals. Reasoning in this setting carries a complexity related to the multiple variables involved. These variables reflect the co-existing health conditions that should be considered when defining a proper therapy. However, current Clinical Decision Support Systems (CDSSs) are not equipped to deal with such a situation. They do not go beyond the straightforward application of the rules that build their knowledge base and simple interpretation of Computer-Interpretable Guidelines (CIGs). We provide a computational argumentation system equipped with goal-seeking mechanisms to combine independently generated recommendations, with the ability to resolve conflicts and generate explanations for its results. We also discuss its advantages over and relation to Multiple-criteria Decision-making (MCDM) in this particular setting.- (undefined