Discovering Power Laws in Entity Length

This paper presents a discovery that the length of the entities in various datasets follows a family of scale-free power law distributions. The concept of entity here broadly includes the named entity, entity mention, time expression, aspect term, and domain-specific entity that are well investigated in natural language processing and related areas. The entity length denotes the number of words in an entity. The power law distributions in entity length possess the scale-free property and have well-defined means and finite variances. We explain the phenomenon of power laws in entity length by the principle of least effort in communication and the preferential mechanism

Neural networks in FPGAs

As FPGAs have increasingly become denser and faster, they are being utilized for many applications, including the implementation of neural networks. Ideally, FPGA implementations, being directly in hardware and having parallelism, will have performance advantages over software on conventional machines. But there is a great deal to be done to make the most of FPGAs and to prove their worth in implementing neural networks, especially in view of past failures in the implementation of neurocomputers. This paper looks at some of the relevant issues

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

Baum_et_al_2019_Supplementary_Figures.pdf: Supplementary Figures S1 and S2. Legends are included under each figure. sbm-for-correlation-based-networks-master.zip: Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks

Neutrophils infected with highly virulent influenza H3N2 virus exhibit augmented early cell death and rapid induction of type I interferon signaling pathways

AbstractWe developed a model of influenza virus infection of neutrophils by inducing differentiation of the MPRO promyelocytic cell line. After 5days of differentiation, about 20–30% of mature neutrophils could be detected. Only a fraction of neutrophils were infected by highly virulent influenza (HVI) virus, but were unable to support active viral replication compared with MDCK cells. HVI infection of neutrophils augmented early and late apoptosis as indicated by annexin V and TUNEL assays. Comparison between the global transcriptomic responses of neutrophils to HVI and low virulent influenza (LVI) revealed that the IFN regulatory factor and IFN signaling pathways were the most significantly overrepresented pathways, with activation of related genes in HVI as early as 3h. Relatively consistent results were obtained by real-time RT-PCR of selected genes associated with the type I IFN pathway. Early after HVI infection, comparatively enhanced expression of apoptosis-related genes was also elicited

Improving signal-to-noise ratio of structured light microscopy based on photon reassignment

In this paper, we report a method for 3D visualization of a biological specimen utilizing a structured light wide-field microscopic imaging system. This method improves on existing structured light imaging modalities by reassigning fluorescence photons generated from off-focal plane excitation, improving in-focus signal strength. Utilizing a maximum likelihood approach, we identify the most likely fluorophore distribution in 3D that will produce the observed image stacks under structured and uniform illumination using an iterative maximization algorithm. Our results show the optical sectioning capability of tissue specimens while mostly preserving image stack photon count, which is usually not achievable with other existing structured light imaging methods

Rosa26-GFP Direct Repeat (RaDR-GFP) Mice Reveal Tissue- and Age-Dependence of Homologous Recombination in Mammals In Vivo

Homologous recombination (HR) is critical for the repair of double strand breaks and broken replication forks. Although HR is mostly error free, inherent or environmental conditions that either suppress or induce HR cause genomic instability. Despite its importance in carcinogenesis, due to limitations in our ability to detect HR in vivo, little is known about HR in mammalian tissues. Here, we describe a mouse model in which a direct repeat HR substrate is targeted to the ubiquitously expressed Rosa26 locus. In the Rosa26 Direct Repeat-GFP (RaDR-GFP) mice, HR between two truncated EGFP expression cassettes can yield a fluorescent signal. In-house image analysis software provides a rapid method for quantifying recombination events within intact tissues, and the frequency of recombinant cells can be evaluated by flow cytometry. A comparison among 11 tissues shows that the frequency of recombinant cells varies by more than two orders of magnitude among tissues, wherein HR in the brain is the lowest. Additionally, de novo recombination events accumulate with age in the colon, showing that this mouse model can be used to study the impact of chronic exposures on genomic stability. Exposure to N-methyl-N-nitrosourea, an alkylating agent similar to the cancer chemotherapeutic temozolomide, shows that the colon, liver and pancreas are susceptible to DNA damage-induced HR. Finally, histological analysis of the underlying cell types reveals that pancreatic acinar cells and liver hepatocytes undergo HR and also that HR can be specifically detected in colonic somatic stem cells. Taken together, the RaDR-GFP mouse model provides new understanding of how tissue and age impact susceptibility to HR, and enables future studies of genetic, environmental and physiological factors that modulate HR in mammals.National Institutes of Health (U.S.) (Program Project Grant P01-CA026731)National Institutes of Health (U.S.) (R33-CA112151)National Institute of Environmental Health Sciences (P30-ES002109)Singapore-MIT Alliance for Research and Technology CenterNational Institutes of Health (U.S.) (P41-EB015871)National Cancer Institute (U.S.) (P30-CA014051

Sub-population analysis based on temporal features of high content images

Background: High content screening techniques are increasingly used to understand the regulation and progression of cell motility. The demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods. Results: In this paper, we present sub-populations analysis of cells at the tissue level by using dynamic features of the cells. We used active contour without edges for segmentation of cells, which preserves the cell morphology, and autoregressive modeling to model cell trajectories. The sub-populations were obtained by clustering static, dynamic and a combination of both features. We were able to identify three unique sub-populations in combined clustering. Conclusion: We report a novel method to identify sub-populations using kinetic features and demonstrate that these features improve sub-population analysis at the tissue level. These advances will facilitate the application of high content screening data analysis to new and complex biological problems.Computation and Systems Biology Programme of Singapore--Massachusetts Institute of Technology Allianc