FreePSI: an alignment-free approach to estimating exon-inclusion ratios without a reference transcriptome.

Alternative splicing plays an important role in many cellular processes of eukaryotic organisms. The exon-inclusion ratio, also known as percent spliced in, is often regarded as one of the most effective measures of alternative splicing events. The existing methods for estimating exon-inclusion ratios at the genome scale all require the existence of a reference transcriptome. In this paper, we propose an alignment-free method, FreePSI, to perform genome-wide estimation of exon-inclusion ratios from RNA-Seq data without relying on the guidance of a reference transcriptome. It uses a novel probabilistic generative model based on k-mer profiles to quantify the exon-inclusion ratios at the genome scale and an efficient expectation-maximization algorithm based on a divide-and-conquer strategy and ultrafast conjugate gradient projection descent method to solve the model. We compare FreePSI with the existing methods on simulated and real RNA-seq data in terms of both accuracy and efficiency and show that it is able to achieve very good performance even though a reference transcriptome is not provided. Our results suggest that FreePSI may have important applications in performing alternative splicing analysis for organisms that do not have quality reference transcriptomes. FreePSI is implemented in C++ and freely available to the public on GitHub

Visual Concepts and Compositional Voting

It is very attractive to formulate vision in terms of pattern theory \cite{Mumford2010pattern}, where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is currently less successful than discriminative methods such as deep networks. Deep networks, however, are black-boxes which are hard to interpret and can easily be fooled by adding occluding objects. It is natural to wonder whether by better understanding deep networks we can extract building blocks which can be used to develop pattern theoretic models. This motivates us to study the internal representations of a deep network using vehicle images from the PASCAL3D+ dataset. We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles. To analyze this we annotate these vehicles by their semantic parts to create a new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised part detectors. We show that visual concepts perform fairly well but are outperformed by supervised discriminative methods such as Support Vector Machines (SVM). We next give a more detailed analysis of visual concepts and how they relate to semantic parts. Following this, we use the visual concepts as building blocks for a simple pattern theoretical model, which we call compositional voting. In this model several visual concepts combine to detect semantic parts. We show that this approach is significantly better than discriminative methods like SVM and deep networks trained specifically for semantic part detection. Finally, we return to studying occlusion by creating an annotated dataset with occlusion, called VehicleOcclusion, and show that compositional voting outperforms even deep networks when the amount of occlusion becomes large.Comment: It is accepted by Annals of Mathematical Sciences and Application

A Temporal Densely Connected Recurrent Network for Event-based Human Pose Estimation

Event camera is an emerging bio-inspired vision sensors that report per-pixel brightness changes asynchronously. It holds noticeable advantage of high dynamic range, high speed response, and low power budget that enable it to best capture local motions in uncontrolled environments. This motivates us to unlock the potential of event cameras for human pose estimation, as the human pose estimation with event cameras is rarely explored. Due to the novel paradigm shift from conventional frame-based cameras, however, event signals in a time interval contain very limited information, as event cameras can only capture the moving body parts and ignores those static body parts, resulting in some parts to be incomplete or even disappeared in the time interval. This paper proposes a novel densely connected recurrent architecture to address the problem of incomplete information. By this recurrent architecture, we can explicitly model not only the sequential but also non-sequential geometric consistency across time steps to accumulate information from previous frames to recover the entire human bodies, achieving a stable and accurate human pose estimation from event data. Moreover, to better evaluate our model, we collect a large scale multimodal event-based dataset that comes with human pose annotations, which is by far the most challenging one to the best of our knowledge. The experimental results on two public datasets and our own dataset demonstrate the effectiveness and strength of our approach. Code can be available online for facilitating the future research

Topologically Optimized Electrodes for Electroosmotic Actuation

Electroosmosis is one of the most used actuation mechanisms for the microfluidics in the current active lab-on-chip devices. It is generated on the induced charged microchannel walls in contact with an electrolyte solution. Electrode distribution plays the key role on providing the external electric field for electroosmosis, and determines the performance of electroosmotic microfluidics. Therefore, this paper proposes a topology optimization approach for the electrodes of electroosmotic microfluidics, where the electrode layout on the microchannel wall can be determined to achieve designer desired microfluidic performance. This topology optimization is carried out by implementing the interpolation of electric insulation and electric potential on the specified walls of microchannels. To demonstrate the capability of this approach, one typical electroosmotic device, i.e., electroosmotic micropump, is modeled with several electrode layouts derived. And this approach permits potential applications in chemicals and biochemistry due to its outstanding capability on determining the performance of electrokinetic microfluidics

Paeoniflorin and Albiflorin Attenuate Neuropathic Pain via MAPK Pathway in Chronic Constriction Injury Rats

Neuropathic pain remains as the most frequent cause of suffering and disability around the world. The isomers paeoniflorin (PF) and albiflorin (AF) are major constituents extracted from the roots of Paeonia (P.) lactiflora Pall. Neuroprotective effect of PF has been demonstrated in animal models of neuropathologies. However, only a few studies are related to the biological activities of AF and no report has been published on analgesic properties of AF about neuropathic pain to date. The aim of this study was to compare the effects of AF and PF against CCI-induced neuropathic pain in rat and explore the underlying mechanism. We had found that both PF and AF could inhibit the activation of p38 mitogen-activated protein kinase (p38 MAPK) pathway in spinal microglia and subsequent upregulated proinflammatory cytokines (interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α)). AF further displayed remarkable effects on inhibiting the activation of astrocytes, suppressing the overelevated expression of phosphorylation of c-Jun N-terminal kinases (p-JNK) in astrocytes, and decreasing the content of chemokine CXCL1 in the spinal cord. These results suggest that both PF and AF are potential therapeutic agents for neuropathic pain, which merit further investigation