N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

Thank you very much for reading statistical parametric mapping the analysis of functional brain images. As you may know, people have search numerous times for their chosen novels like this statistical parametric mapping the analysis of functional brain images, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some infectious bugs inside their desktop computer.

Statistical Parametric Mapping The Analysis Of Functional Brain Images

ERK1/2 MAP kinases: structure, function, and regulation.

Following pathogen infection or tissue damage, the stimulation of pattern recognition receptors on the cell surface and in the cytoplasm of innate immune cells activates members of each of the major mitogen-activated protein kinase (MAPK) subfamilies--the extracellular signal-regulated kinase (ERK), p38 and Jun N-terminal kinase (JNK) subfamilies. In conjunction with the activation of nuclear factor-κB and interferon-regulatory factor transcription factors, MAPK activation induces the expression of multiple genes that together regulate the inflammatory response. In this Review, we discuss our current knowledge about the regulation and the function of MAPKs in innate immunity, as well as the importance of negative feedback loops in limiting MAPK activity to prevent host tissue damage. We also examine how pathogens have evolved complex mechanisms to manipulate MAPK activation to increase their virulence. Finally, we consider the potential of the pharmacological targeting of MAPK pathways to treat autoimmune and inflammatory diseases.

Mitogen-activated protein kinases in innate immunity

Jun N-terminal kinases or JNKs play a critical role in death receptor-initiated extrinsic as well as mitochondrial intrinsic apoptotic pathways. JNKs activate apoptotic signaling by the upregulation of pro-apoptotic genes through the transactivation of specific transcription factors or by directly modulating the activities of mitochondrial pro- and antiapoptotic proteins through distinct phosphorylation events. This review analyses our present understanding of the role of JNK in apoptotic signaling and the various mechanisms by which JNK promotes apoptosis.

JNK signaling in apoptosis.

Mitogen-activated protein kinases (MAPKs) regulate critical signaling pathways involved in cell proliferation, differentiation and apoptosis. Recent studies have shown that a novel class of scaffold proteins mediates the structural and functional organization of the three-tier MAPK module. By linking the MAP3K, MAP2K and MAPK into a multienzyme complex, these MAPK-specific scaffold proteins provide an insulated physical conduit through which signals from the respective MAPK can be transmitted to the appropriate spatiotemporal cellular loci. Scaffold proteins play a determinant role in modulating the signaling strength of their cognate MAPK module by regulating the signal amplitude and duration. The scaffold proteins themselves are finely regulated resulting in dynamic intra- and inter-molecular interactions that can modulate the signaling outputs of MAPK modules. This review focuses on defining the diverse mechanisms by which these scaffold proteins interact with their respective MAPK modules and the role of such interactions in the spatiotemporal organization as well as context-specific signaling of the different MAPK modules.

/pdf/scaffold-proteins-of-map-kinase-modules-1czrn4hhlw.pdf

Scaffold proteins of MAP-kinase modules

Recently, distributed word embeddings trained by neural language models are commonly used for text classification with Convolutional Neural Networks (CNNs). In this paper, we propose a novel neural language model, Topic-based Skip-gram, to learn topic-based word embeddings for biomedical literature indexing with CNNs. Topic-based Skip-gram leverages textual content with topic models, e.g., Latent Dirichlet Allocation (LDA), to capture precise topic-based word relationship and then integrate it into distributed word embedding learning. We then describe two multimodal CNN architectures, which are able to employ different kinds of word embeddings at the same time for text classification. Through extensive experiments conducted on several real-world datasets, we demonstrate that combination of our Topic-based Skip-gram and multimodal CNN architectures outperforms state-of-the-art methods in biomedical literature indexing, clinical note annotation and general textual benchmark dataset classification.

Text Classification with Topic-based Word Embedding and Convolutional Neural Networks

Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors. In this paper, we propose a novel 3D shape representation learning approach, Directionally Convolutional Network (DCN), to solve the shape segmentation problem. DCN extends convolution operations from images to the surface mesh of 3D shapes. With DCN, we learn effective shape representations from raw geometric features, i.e., face normals and distances, to achieve robust segmentation. More specifically, a two-stream segmentation framework is proposed: one stream is made up by the proposed DCN with the face normals as the input, and the other stream is implemented by a neural network with the face distance histogram as the input. The learned shape representations from the two streams are fused by an element-wise product. Finally, Conditional Random Field (CRF) is applied to optimize the segmentation. Through extensive experiments conducted on benchmark datasets, we demonstrate that our approach outperforms the current state-of-the-arts (both classic and deep learning-based) on a large variety of 3D shapes.

Directionally Convolutional Networks for 3D Shape Segmentation

Convolutional neural networks (CNNs) have recently been used in biomedical imaging applications with great success. In this paper, we investigated the classification performance of CNN models on diffusion weighted imaging (DWI) streamlines defined by functional MRI (fMRI) and electrical stimulation mapping (ESM). To learn a set of discriminative and interpretable features from the extremely unbalanced dataset, we evaluated different CNN architectures with multiple loss functions (e.g., focal loss and center loss) and a soft attention mechanism and compared our models with current state-of-the-art methods. Through extensive experiments on streamlines collected from 70 healthy children and 70 children with focal epilepsy, we demonstrated that our deep CNN model with focal and central losses and soft attention outperforms all existing models in the literature and provides clinically acceptable accuracy (73%–100%) for the objective detection of functionally important white matter pathways, including ESM determined eloquent areas such as primary motors, aphasia, speech arrest, auditory, and visual functions. The findings of this paper encourage further investigations to determine if DWI-CNN analysis can serve as a noninvasive diagnostic tool during pediatric presurgical planning by estimating not only the location of essential cortices at the gyral level but also the underlying fibers connecting these cortical areas to minimize or predict postsurgical functional deficits. This paper translates an advanced CNN model to clinical practice in the pediatric population where currently available approaches (e.g., ESM and fMRI) are suboptimal. The implementation will be released at https://github.com/HaotianMXu/Brain-fiber-classification-using-CNNs .

https://par.nsf.gov/servlets/purl/10127084

Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery Using Diffusion Tractography and Convolutional Neural Networks

Single-cell analysis is pivotal to deciphering complex phenomena like heterogeneity, bistability, and asynchronous oscillations, where a population ensemble cannot represent individual behaviors. Bulk cell-free systems, despite having unique advantages of manipulation and characterization of biochemical networks, lack the essential single-cell information to understand a class of out-of-steady-state dynamics including cell cycles. Here, by encapsulating Xenopus egg extracts in water-in-oil microemulsions, we developed artificial cells that are adjustable in sizes and periods, sustain mitotic oscillations for over 30 cycles, and function in forms from the simplest cytoplasmic-only to the more complicated ones involving nuclear dynamics, mimicking real cells. Such innate flexibility and robustness make it key to studying clock properties like tunability and stochasticity. Our results also highlight energy as an important regulator of cell cycles. We demonstrate a simple, powerful, and likely generalizable strategy of integrating strengths of single-cell approaches into conventional in vitro systems to study complex clock functions.

https://cdn.elifesciences.org/articles/33549/elife-33549-v1.pdf

Haotian Xu

Papers

Scaffold proteins of MAP-kinase modules

Text Classification with Topic-based Word Embedding and Convolutional Neural Networks

Directionally Convolutional Networks for 3D Shape Segmentation

Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery Using Diffusion Tractography and Convolutional Neural Networks

A robust and tunable mitotic oscillator in artificial cells