Changes in life-style after liver transplantation

Sixty-five pediatric patients who received liver transplants between May 1981 and May 1984 were observed for as many as 5 years and examined for changes in life-style. Children were less frequently hospitalized, spent less time hospitalized, required fewer medications, and generally had excellent liver and renal function after hepatic transplantation as compared with their pretransplantation status. Most children were in age-appropriate and standard school classes or were only 1 year behind. Cognitive abilities remained unchanged. Children improved in gross motor function and patients' behavior significantly improved according to parents' perceptions. Enuresis was more prevalent, however, than in the population of children who had not received liver transplants. Parental divorce rates were no greater than those reported for other families with chronically ill children. Overall, objective changes in life-style as well as parents' perceptions of behavior of children appear to be improved after liver transplantation

Dedifferentiation of neurons precedes tumor gormation in lola mutants

The ability to reprogram differentiated cells into a pluripotent state has revealed that the differentiated state is plastic and reversible. It is evident, therefore, that mechanisms must be in place to maintain cells in a differentiated state. Transcription factors that specify neuronal characteristics have been well studied, but less is known about the mechanisms that prevent neurons from dedifferentiating to a multipotent, stem cell-like state. Here, we identify Lola as a transcription factor that is required to maintain neurons in a differentiated state. We show that Lola represses neural stem cell genes and cell-cycle genes in postmitotic neurons. In lola mutants, neurons dedifferentiate, turn on neural stem cell genes, and begin to divide, forming tumors. Thus, neurons rather than stem cells or intermediate progenitors are the tumor-initiating cells in lola mutants

The NetHack learning environment

Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL environments are either sufficiently complex or based on fast simulation, they are rarely both. Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack. We argue that NetHack is sufficiently complex to drive long-term research on problems such as exploration, planning, skill acquisition, and language-conditioned RL, while dramatically reducing the computational resources required to gather a large amount of experience. We compare NLE and its task suite to existing alternatives, and discuss why it is an ideal medium for testing the robustness and systematic generalization of RL agents. We demonstrate empirical success for early stages of the game using a distributed Deep RL baseline and Random Network Distillation exploration, alongside qualitative analysis of various agents trained in the environment. NLE is open source and available at https://github.com/facebookresearch/nle

Language Models as Knowledge Bases?

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as "fill-in-the-blank" cloze statements. Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches. The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems. The code to reproduce our analysis is available at https://github.com/facebookresearch/LAMA

Cellular, molecular and functional characterisation of YAC transgenic mouse models of Friedreich Ataxia

Copyright © 2014 Anjomani Virmouni et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.Background - Friedreich ataxia (FRDA) is an autosomal recessive neurodegenerative disorder, caused by a GAA repeat expansion mutation within intron 1 of the FXN gene. We have previously established and performed preliminary characterisation of several human FXN yeast artificial chromosome (YAC) transgenic FRDA mouse models containing GAA repeat expansions, Y47R (9 GAA repeats), YG8R (90 and 190 GAA repeats) and YG22R (190 GAA repeats). Methodology/Principal Findings - We now report extended cellular, molecular and functional characterisation of these FXN YAC transgenic mouse models. FXN transgene copy number analysis of the FRDA mice demonstrated that the YG22R and Y47R lines each have a single copy of the FXN transgene while the YG8R line has two copies. Single integration sites of all transgenes were confirmed by fluorescence in situ hybridisation (FISH) analysis of metaphase and interphase chromosomes. We identified significant functional deficits, together with a degree of glucose intolerance and insulin hypersensitivity, in YG8R and YG22R FRDA mice compared to Y47R and wild-type control mice. We also confirmed increased somatic GAA repeat instability in the cerebellum and brain of YG22R and YG8R mice, together with significantly reduced levels of FXN mRNA and protein in the brain and liver of YG8R and YG22R compared to Y47R. Conclusions/Significance - Together these studies provide a detailed characterisation of our GAA repeat expansion-based YAC transgenic FRDA mouse models that will help investigations of FRDA disease mechanisms and therapy.European Union, Ataxia UK and FARA

Electrophysiological Signatures of Spatial Boundaries in the Human Subiculum.

Environmental boundaries play a crucial role in spatial navigation and memory across a wide range of distantly related species. In rodents, boundary representations have been identified at the single-cell level in the subiculum and entorhinal cortex of the hippocampal formation. Although studies of hippocampal function and spatial behavior suggest that similar representations might exist in humans, boundary-related neural activity has not been identified electrophysiologically in humans until now. To address this gap in the literature, we analyzed intracranial recordings from the hippocampal formation of surgical epilepsy patients (of both sexes) while they performed a virtual spatial navigation task and compared the power in three frequency bands (1-4, 4-10, and 30-90 Hz) for target locations near and far from the environmental boundaries. Our results suggest that encoding locations near boundaries elicited stronger theta oscillations than for target locations near the center of the environment and that this difference cannot be explained by variables such as trial length, speed, movement, or performance. These findings provide direct evidence of boundary-dependent neural activity localized in humans to the subiculum, the homolog of the hippocampal subregion in which most boundary cells are found in rodents, and indicate that this system can represent attended locations that rather than the position of one\u27s own body

Evolution of Landau Levels into Edge States at an Atomically Sharp Edge in Graphene

The quantum-Hall-effect (QHE) occurs in topologically-ordered states of two-dimensional (2d) electron-systems in which an insulating bulk-state coexists with protected 1d conducting edge-states. Owing to a unique topologically imposed edge-bulk correspondence these edge-states are endowed with universal properties such as fractionally-charged quasiparticles and interference-patterns, which make them indispensable components for QH-based quantum-computation and other applications. The precise edge-bulk correspondence, conjectured theoretically in the limit of sharp edges, is difficult to realize in conventional semiconductor-based electron systems where soft boundaries lead to edge-state reconstruction. Using scanning-tunneling microscopy and spectroscopy to follow the spatial evolution of bulk Landau-levels towards a zigzag edge of graphene supported above a graphite substrate we demonstrate that in this system it is possible to realize atomically sharp edges with no edge-state reconstruction. Our results single out graphene as a system where the edge-state structure can be controlled and the universal properties directly probed.Comment: 16 pages, 4 figure

MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions

Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) has emerged as an alternative to automatically optimize such control strategies. Research so far has primarily considered RL interfaces which block the sender while an agent considers its next action. This is largely an artifact of building on top of frameworks designed for RL in games (e.g. OpenAI Gym). However, this does not translate to real-world networking environments, where a network sender waiting on a policy without sending data leads to under-utilization of bandwidth. We instead propose to formulate congestion control with an asynchronous RL agent that handles delayed actions. We present MVFST-RL, a scalable framework for congestion control in the QUIC transport protocol that leverages state-of-the-art in asynchronous RL training with off-policy correction. We analyze modeling improvements to mitigate the deviation from Markovian dynamics, and evaluate our method on emulated networks from the Pantheon benchmark platform. The source code is publicly available at https://github.com/facebookresearch/mvfst-rl