Proteomic analyses reveal distinct chromatin-associated and soluble transcription factor complexes.

The current knowledge on how transcription factors (TFs), the ultimate targets and executors of cellular signalling pathways, are regulated by protein-protein interactions remains limited. Here, we performed proteomics analyses of soluble and chromatin-associated complexes of 56 TFs, including the targets of many signalling pathways involved in development and cancer, and 37 members of the Forkhead box (FOX) TF family. Using tandem affinity purification followed by mass spectrometry (TAP/MS), we performed 214 purifications and identified 2,156 high-confident protein-protein interactions. We found that most TFs form very distinct protein complexes on and off chromatin. Using this data set, we categorized the transcription-related or unrelated regulators for general or specific TFs. Our study offers a valuable resource of protein-protein interaction networks for a large number of TFs and underscores the general principle that TFs form distinct location-specific protein complexes that are associated with the different regulation and diverse functions of these TFs

Analysis on Wind Environment in Winter of Different Rural Courtyard Layout in the Northeast

AbstractThrough to survey the northeast region in China, summarized four kinds of typical rural courtyard layout forms. Using Fluent software to simulate the wind velocity of different layout forms, combined with the assessment criteria of wind velocity ratio, considering factors such as the wind shadow area and the numbers of eddy current, to analyze advantages and disadvantages of wind environment in existing courtyard

A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier

AbstractBased on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method which couples adaptive resonance theory (ART) network to reconstruct a multi-class classifier. Different coupling strategies to reconstruct a multi-class classifier from binary SVM classifiers are compared with application to fault diagnosis of transmission line. Majority voting, a mixture matrix and self-organizing map (SOM) network are compared in reconstructing the global classification decision. In order to evaluate the method’s efficiency, one-against-all, decision directed acyclic graph (DDAG) and decision-tree (DT) algorithm based SVM are compared too. The comparison is done with simulations and the best method is validated with experimental data

Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes. To address these problems, we propose a novel deep learning-based framework for interactive segmentation by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine-tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised (without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine-tuning. We applied this framework to two applications: 2D segmentation of multiple organs from fetal MR slices, where only two types of these organs were annotated for training; and 3D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only tumor cores in one MR sequence were annotated for training. Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.Comment: 11 pages, 11 figure

Direct measurement of neutrons induced in lead by cosmic muons at a shallow underground site

Neutron production in lead by cosmic muons has been studied with a Gadolinium doped liquid scintillator detector. The detector was installed next to the Muon-Induced Neutron Indirect Detection EXperiment (MINIDEX), permanently located in the T\"ubingen shallow underground laboratory where the mean muon energy is approximately 7 GeV. The MINIDEX plastic scintillators were used to tag muons; the neutrons were detected through neutron capture and neutron-induced nuclear recoil signals in the liquid scintillator detector. Results on the rates of observed neutron captures and nuclear recoils are presented and compared to predictions from GEANT4-9.6 and GEANT4-10.3. The predicted rates are significantly too low for both versions of GEANT4. For neutron capture events, the observation exceeds the predictions by factors of $1.65\,\pm\,0.02\,\textrm{(stat.)}\,\pm\,0.07\,\textrm{(syst.)}$ and $2.58\,\pm\,0.03\,\textrm{(stat.)}\,\pm\,0.11\,\textrm{(syst.)}$ for GEANT4-9.6 and GEANT4-10.3, respectively. For neutron nuclear recoil events, which require neutron energies above approximately 5 MeV, the factors are even larger, $2.22\,\pm\,0.05\,\textrm{(stat.)}\,\pm\,0.25\,\textrm{(syst.)}$ and $3.76\,\pm\,0.09\,\textrm{(stat.)}\,\pm\,0.41\,\textrm{(syst.)}$, respectively. Also presented is the first statistically significant measurement of the spectrum of neutrons induced by cosmic muons in lead between 5 and 40 MeV. It was obtained by unfolding the nuclear recoil spectrum. The observed neutron spectrum is harder than predicted by GEANT4. An investigation of the distribution of the time difference between muon tags and nuclear recoil signals confirms the validity of the unfolding procedure and shows that GEANT4 cannot properly describe the time distribution of nuclear recoil events. In general, the description of the data is worse for GEANT4-10.3 than for GEANT4-9.6.Comment: 29 pages, 22 figures, 4 table