Accounting for Sustainability in Asia: Stock Market Regulation and Reporting in Hong Kong and Singapore

10.1080/00130095.2018.1544461Economic Geography954362-38

Inferring direct regulatory targets from expression and genome location analyses: a comparison of transcription factor deletion and overexpression

BACKGROUND: Effects on gene expression due to environmental or genetic changes can be easily measured using microarrays. However, indirect effects on expression can be substantial. The indirect effects of a perturbation need to be distinguished from the direct effects if we are to understand the structure and behavior of regulatory networks. RESULTS: The most direct way to perturb a transcriptional network is to alter transcription factor activity. Here, for the first time, we compare expression changes and genomic binding in a simple regulon under conditions of both low and high transcription factor activity. Specifically, we assessed the effects on expression and binding due to deletion of the yeast LEU3 transcription factor gene and effects due to elevation of Leu3 activity. Leu3 activity was elevated through overexpression and the introduction of a mutation that renders the protein constitutively active. Genes that are bound and/or regulated by Leu3 under one or both conditions were characterized in terms of their functional annotations and their predicted potential to be bound by Leu3. We also assessed the evolutionary conservation of the predicted binding potential using a novel alignment-independent method. Both perturbations yield genes that are likely to be direct targets of Leu3, including most of the classically defined targets. Additional direct targets are identified by each of the methods. However, experimental and computational criteria suggest that most genes whose expression is affected by the Leu3 genotype are unlikely to be regulated by binding of the protein. CONCLUSION: Most genes that are differentially expressed by Leu3 are not direct targets despite the exceptional simplicity of the regulon, and the unusually direct nature of the perturbations investigated. These conclusions are reached through computational analyses that support and extend chromatin immunoprecipitation data on the identities of direct targets. These results have implications for the interpretation of expression experiments, especially in cases for which chromatin immunoprecipitation data are unavailable, incomplete, or ambiguous

H2A.Z facilitates access of active and repressive complexes to chromatin in embryonic stem cell self-renewal and differentiation.

SummaryChromatin modifications have been implicated in the self-renewal and differentiation of embryonic stem cells (ESCs). However, the function of histone variant H2A.Z in ESCs remains unclear. We show that H2A.Z is highly enriched at promoters and enhancers and is required for both efficient self-renewal and differentiation of murine ESCs. H2A.Z deposition leads to an abnormal nucleosome structure, decreased nucleosome occupancy, and increased chromatin accessibility. In self-renewing ESCs, knockdown of H2A.Z compromises OCT4 binding to its target genes and leads to decreased binding of MLL complexes to active genes and of PRC2 complex to repressed genes. During differentiation of ESCs, inhibition of H2A.Z also compromises RA-induced RARα binding, activation of differentiation markers, and the repression of pluripotency genes. We propose that H2A.Z mediates such contrasting activities by acting as a general facilitator that generates access for a variety of complexes, both activating and repressive

Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery

Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, and complex backgrounds, as well as the different camera sensors pose great challenges for current algorithms. In this work, we propose a new method consisting of a novel joint image cascade and feature pyramid network with multi-size convolution kernels to extract multi-scale strong and weak semantic features. These features are fed into rotation-based region proposal and region of interest networks to produce object detections. Finally, rotational non-maximum suppression is applied to remove redundant detections. During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on oriented bounding box detection tasks on the challenging DOTA dataset, outperforming all published methods by a large margin (+6% and +12% absolute improvement, respectively). Furthermore, it generalizes to two other datasets, NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines even when trained on DOTA. Our method can be deployed in multi-class object detection applications, regardless of the image and object scales and orientations, making it a great choice for unconstrained aerial and satellite imagery.Comment: ACCV 201

Rhythmic laser cue is beneficial for improving gait performance and reducing freezing of turning in Parkinson’s disease patients with freezing of gait

Background and aim: Gait time components in Parkinson’s disease (PD) patients such as step time, gait rhythmicity, symmetry, and coordination are exacerbated during turning. Freezing of gait (FOG) can be triggered off when such gait-timing deficiency exceeds a certain threshold. Whether laser visual cue could improve the impairments of gait time components and reduce freezing episodes in turning remains unclear. Different from continuous laser (CL), rhythmic laser (RL) cue could provide rhythmic temporal information. The aim of this study was to investigate the effect of RL and CL cue to identify which one was better at modulating gait time components and improving gait performance in turning. Methods: Twenty-three patients on dopaminergic medication performed the “8-shaped” turning task. Test conditions included no laser (NL), CL and RL cues. Gait parameters such as numbers of freezing episodes, the turning time, step time, gait arrhythmicity, asymmetry, and discoordination were assessed. Results: The numbers of freezing episodes, the turning time, step time and the gait arrhythmicity were significantly improved in RL cue compared with both CL and NL conditions, whereas no significant difference was found between CL and NL conditions. Gait asymmetry and discoordination did not show significant difference between the three conditions. Conclusion: Compared with CL cue, it seems that synchronization in RL cue might be beneficial in improving the background gait performance and reducing freezing in turning. For PD patients with FOG, RL cue might be promising when applied as an optional technique in gait rehabilitation. Keywords: Parkinson’s disease, freezing of gait, gait analysis, visual cue, lase

Machine learning framework for assessment of microbial factory performance

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model)