Neuron-type-specific signals for reward and punishment in the ventral tegmental area

Dopamine has a central role in motivation and reward. Dopaminergic neurons in the ventral tegmental area (VTA) signal the discrepancy between expected and actual rewards (that is, reward prediction error), but how they compute such signals is unknown. We recorded the activity of VTA neurons while mice associated different odour cues with appetitive and aversive outcomes. We found three types of neuron based on responses to odours and outcomes: approximately half of the neurons (type I, 52%) showed phasic excitation after reward-predicting odours and rewards in a manner consistent with reward prediction error coding; the other half of neurons showed persistent activity during the delay between odour and outcome that was modulated positively (type II, 31%) or negatively (type III, 18%) by the value of outcomes. Whereas the activity of type I neurons was sensitive to actual outcomes (that is, when the reward was delivered as expected compared to when it was unexpectedly omitted), the activity of type II and type III neurons was determined predominantly by reward-predicting odours. We 'tagged' dopaminergic and GABAergic neurons with the light-sensitive protein channelrhodopsin-2 and identified them based on their responses to optical stimulation while recording. All identified dopaminergic neurons were of type I and all GABAergic neurons were of type II. These results show that VTA GABAergic neurons signal expected reward, a key variable for dopaminergic neurons to calculate reward prediction error.     

Characterization of a carbohydrate transporter from symbiotic glomeromycotan fungi

The symbiotic relationships between mycorrhizal fungi and plants have an enormous impact on terrestrial ecosystems. Most common are the arbuscular mycorrhizas, formed by fungi belonging to the phylum Glomeromycota. Arbuscular mycorrhizal fungi facilitate the uptake of soil nutrients by plants and in exchange obtain carbohydrates, thus representing a large sink for atmospheric plant-fixed CO(2). However, how carbohydrates are transported through the symbiotic interface is still unknown. Here we report the characterization of the first known glomeromycotan monosaccharide transporter, GpMST1, by exploiting the unique symbiosis of a glomeromycotan fungus (Geosiphon pyriformis) with cyanobacteria. The GpMST1 gene has a very low GC content and contains six introns with unusual boundaries. GpMST1 possesses twelve predicted transmembrane domains and functions as a proton co-transporter with highest affinity for glucose, then mannose, galactose and fructose. It belongs to an as yet uncharacterized phylogenetic monosaccharide transporter clade. This initial characterization of a new transporter family involved in fungal symbiosis will lead to a better understanding of carbon flows in terrestrial environments.     

多参考预测链的时域可扩展性和抗错性

网络的带宽抖动和数据传输错误是影响视频传输的重要原因。该文提出了一种新的多参考帧图像预测的视频编码算法,即多参考预测链,使视频具有时域可扩展性和抗错性解决网络的这两个潜在问题。该算法中P帧之间的参考预测关系构成多条链状结构,使视频具有时域可扩展性。链间的不相关性使每个链可独立解码,从而增强了码流抗错性。该算法采用R-D框架动态的管理预测关系链,从而适合网络带宽的变化而获得最佳的终端视觉效果。仿真实验表明,采用多参考预测链编码的码流时域可扩展性的应用非常可行,且表现出较高的抗错性。     

Isolation of cDNA clones encoding T cell-specific membrane-associated proteins

Of 10 distinct cloned DNA copies of mRNAs expressed in T lymphocytes but not in B lymphocytes and associated with membrane-bound polysomes, one hybridizes to a region of the genome that has rearranged in a T-cell lymphoma and several T-cell hybridomas. These characteristics suggest that it encodes one chain of the elusive antigen receptor on the surface of T lymphocytes.     

Bit-Depth Scalable Coding Using a Perfect Picture and Adaptive Neighboring Filter

Bit-depth scalability is a new research field in the on-going scalable extension of the H.264/AVC (SVC) video coding standard. The key point is to accurately predict the enhancement layer, whose bit depth is 10 or more, from the 8 bit base layer. An improved inter-layer prediction scheme for bit-depth scalability was developed that ensures compatibility with the standard and improves the encoding efficiency. The scheme uses a “perfect” 8 bit picture with an adaptive neighbor filter whose coefficients are optimized by minimizing the block distortion between the 8 bit reconstructed picture and the “perfect” picture to achieve a more precise 10 bit prediction based on the filtered picture. Double arithmetic precision is used to further improve the encoding efficiency. Experimental results show that the scheme outperforms the recent joint video team (JVT) proposal in the joint scalable video model (JSVM).