Direct Conversion of Fibroblasts to Neurons by Reprogramming PTB-Regulated MicroRNA Circuits

SummaryThe induction of pluripotency or trans-differentiation of one cell type to another can be accomplished with cell-lineage-specific transcription factors. Here, we report that repression of a single RNA binding polypyrimidine-tract-binding (PTB) protein, which occurs during normal brain development via the action of miR-124, is sufficient to induce trans-differentiation of fibroblasts into functional neurons. Besides its traditional role in regulated splicing, we show that PTB has a previously undocumented function in the regulation of microRNA functions, suppressing or enhancing microRNA targeting by competitive binding on target mRNA or altering local RNA secondary structure. A key event during neuronal induction is the relief of PTB-mediated blockage of microRNA action on multiple components of the REST complex, thereby derepressing a large array of neuronal genes, including miR-124 and multiple neuronal-specific transcription factors, in nonneuronal cells. This converts a negative feedback loop to a positive one to elicit cellular reprogramming to the neuronal lineage

Reduced stomatal frequency with rising elevation for Kobresia royleana on the Tibetan Plateau

Knowledge about within-species variation in stomatal frequency with varying elevation at very high elevations is rare, which is crucial for us to understand how alpine plants are adapted to the extreme environment. Here, we focus on the variation in stomatal frequency in Kobresia royleana (Nees) Boeckeler (Cyperaceae, Cyperales) along two altitudinal transects (elevation ranges from 3723 m to 5081 m) in the center of the Tibetan Plateau. The result shows the stomatal density (SD) varied from 303 +/- 55.6 mm(-2) to 542 +/- 81.8 mm(-2), and stomatal index (SI) ranged from 21.0% to 29.6%. In contrast with most cases, an unexpected negative response of stomatal frequency to rising elevation was observed. Among abiotic factors, the growing season mean temperature and CO2 partial pressure significantly declined with increasing elevation, while the growing season precipitation did not vary. Therefore, the decreasing SD and SI were mainly due to the declining temperature rather than the decreasing CO2 partial pressure. Further, SD and SI were negatively related to leaf functional traits of specific leaf area (SLA), leaf nitrogen concentration (N) and stable carbon isotope ratios (delta C-13), and all these morphological and physiological traits tended to covary with rising elevation and declining temperature. Meanwhile, the increasing delta C-13, N and SLA with elevation seem to be strategies for alpine plants to cope with the low-temperature environments. Therefore, the observed covariance between stomatal frequency and leaf functional traits also suggests that the low temperature rather than low CO2 partial pressure mainly leads to the elevational pattern of stomatal frequency for this alpine species. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

Improving Entity Linking by Introducing Knowledge Graph Structure Information

Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most of the current methods are a combination of local and global models. The local model uses the local context information around the entity mention to independently resolve the ambiguity of each entity mention. The global model encourages thematic consistency across the target entities of all mentions in the document. However, the known global models calculate the correlation between entities from a semantic perspective, ignoring the correlation information between entities in nature. In this paper, we introduce knowledge graphs to enrich the correlation information between entities and propose an entity linking model that introduces the structural information of the knowledge graph (KGEL). The model can fully consider the relations between entities. To prove the importance of the knowledge graph structure, extensive experiments are conducted on multiple public datasets. Results illustrate that our model outperforms the baseline and achieves superior performance

Improving Entity Linking by Introducing Knowledge Graph Structure Information

Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most of the current methods are a combination of local and global models. The local model uses the local context information around the entity mention to independently resolve the ambiguity of each entity mention. The global model encourages thematic consistency across the target entities of all mentions in the document. However, the known global models calculate the correlation between entities from a semantic perspective, ignoring the correlation information between entities in nature. In this paper, we introduce knowledge graphs to enrich the correlation information between entities and propose an entity linking model that introduces the structural information of the knowledge graph (KGEL). The model can fully consider the relations between entities. To prove the importance of the knowledge graph structure, extensive experiments are conducted on multiple public datasets. Results illustrate that our model outperforms the baseline and achieves superior performance

Admittance spectroscopy of GeSi-based quantum dot systems: Experiment and Theory

A combined experimental and theoretical study is carried out in examining the important features of the admittance spectroscopy (AS) of self-assembled GeSi quantum dot (QD) systems. In the experimental component of the study, we measure the dependence o

An analysis of national action plans on antimicrobial resistance in Southeast Asia using a governance framework approach

10.1016/j.lanwpc.2020.100084The Lancet Regional Health - Western Pacific710008

No effect of monetary reward in a visual working memory task

Previous work has shown that humans distribute their visual working memory (VWM) resources flexibly across items: the higher the importance of an item, the better it is remembered. A related, but much less studied question is whether people also have control over the totalamount of VWM resource allocated to a task. Here, we approach this question by testing whether increasing monetary incentives results in better overall VWM performance. In three experiments, subjects performed a delayed-estimation task on the Amazon Turk platform. In the first two experiments, four groups of subjects received a bonus payment based on their performance, with the maximum bonus ranging from $0 to$10 between groups. We found no effect of the amount of bonus on intrinsic motivation or on VWM performance in either experiment. In the third experiment, reward was manipulated on a trial-by-trial basis using a within-subjects design. Again, no evidence was found that VWM performance depended on the magnitude of potential reward. These results suggest that encoding quality in visual working memory is insensitive to monetary reward, which has implications for resource-rational theories of VWM