Interaction of a Pulsar Wind with the Expanding Supernova Remnant

Recent HST observations of the Crab Nebula show filamentary structures that appear to originate from the Rayleigh-Taylor (R-T) instability operating on the supernova ejecta accelerated by the pulsar-driven wind. In order to understand the origin and formation of the filaments in the Crab Nebula, we study the interaction of a pulsar wind with the uniformly expanding supernova remnant by means of numerical simulation. By performing two-dimensional numerical simulations, we find three independent instabilities in the interaction region between the pulsar wind and the expanding supernova remnant. The most important instability develops as the shock driven by the pulsar bubble becomes accelerated ($r \propto t^{6/5}$). The instability produces pronounced filamentary structures that resemble the observed filaments in the Crab Nebula. Our numerical simulations can reproduce important observational features of the Crab Nebula. The high density heads in the R-T finger tips are produced because of the compressibility of the gas. The density of these heads is found to be about 10 times higher than other regions in the fingers. The mass contained in the R-T fingers is found to be $60$ of the total shocked mass and the kinetic energy within the R-T fingers is $55$ of the total kinetic energy of the shocked flow. The R-T fingers are found to accelerate with a slower rate than the shock front, which is consistent with the observations. By comparing our simulations and the observations, we infer that the some finger-like filaments (region F or G in Hester's observation) started to develop about 657 years ago.Comment: 16 pages, 9 figures, 1 table, accepted for Astrophysical Journa

A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows

The ability to replicate predictions by machine learning (ML) or artificial intelligence (AI) models and results in scientific workflows that incorporate such ML/AI predictions is driven by numerous factors. An uncertainty-aware metric that can quantitatively assess the reproducibility of quantities of interest (QoI) would contribute to the trustworthiness of results obtained from scientific workflows involving ML/AI models. In this article, we discuss how uncertainty quantification (UQ) in a Bayesian paradigm can provide a general and rigorous framework for quantifying reproducibility for complex scientific workflows. Such as framework has the potential to fill a critical gap that currently exists in ML/AI for scientific workflows, as it will enable researchers to determine the impact of ML/AI model prediction variability on the predictive outcomes of ML/AI-powered workflows. We expect that the envisioned framework will contribute to the design of more reproducible and trustworthy workflows for diverse scientific applications, and ultimately, accelerate scientific discoveries

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems and investigating the underlying mechanisms of biological functions and complex diseases. While existing databases provide curated biological data from literature and other sources, they are often incomplete and their maintenance is labor-intensive, necessitating alternative approaches. In this study, we propose to harness the capabilities of large language models to address these issues by automatically extracting such knowledge from the relevant scientific literature. Toward this goal, in this work, we investigate the effectiveness of different large language models in tasks that involve recognizing protein interactions, pathways, and gene regulatory relations. We thoroughly evaluate the performance of various models, highlight the significant findings, and discuss both the future opportunities and the remaining challenges associated with this approach. The code and data are available at: https://github.com/boxorange/BioIE-LLMComment: 10 pages, 3 figure

Radio Emission from a Young Supernova Remnant Interacting with an Interstellar Cloud: MHD Simulation with Relativistic Electrons

We present two-dimensional MHD simulations of the evolution of a young Type Ia supernova remnant during its interaction with an interstellar cloud of comparable size at impact. We include for the first time in such simulations explicit relativistic electron transport, including spectral information using a simple but effective scheme that follows their acceleration at shocks and subsequent transport. From this information we also model radio synchrotron emission, including spectra. The principal conclusions from these experiments are: 1) Independent of the cloud interaction, the SNR reverse shock can be an efficient site for particle acceleration in a young SNR. 2) At these early times the synchrotron spectral index due to electrons accelerated at the primary shocks should be close to 0.5 unless those shocks are modified by cosmic-ray pressures. However, interaction with the cloud generates regions of distinctly steeper spectra, which may complicate interpretation in terms of global dynamical models for SNR evolution. 3) The internal motions within the SNR become highly turbulent following the cloud interaction. 4) An initially uniform interstellar magnetic field is preferentially amplified along the magnetic equator of the SNR, primarily due to biased amplification by instabilities. Independent of the external field configuration, there is a net radial direction to this field inside the SNR. 5) Filamentary radio structures correlate well with magnetic filaments, while diffuse emission follows the electron distribution. 6) Interaction with the cloud enhances both the electron population and the radio emission.Comment: 29 pages of Latex generated text with 6 figures in gif format. Accepted for publication in the Astrophysical Journal. High resolution postscript figures can be obtained by anonymous ftp from ftp://ftp.msi.umn.edu/pub/users/twj/sn

105110^{51} Ergs: The Evolution of Shell Supernova Remnants

This paper reports on a workshop hosted by the University of Minnesota, March 23-26, 1997. It addressed fundamental dynamical issues associated with the evolution of shell supernova remnants and the relationships between supernova remnants and their environments. The workshop considered, in addition to classical shell SNRs, dynamical issues involving X-ray filled composite remnants and pulsar driven shells, such as that in the Crab Nebula. Approximately 75 participants with wide ranging interests attended the workshop. An even larger community helped through extensive on-line debates prior to the meeting. Each of the several sessions, organized mostly around chronological labels, also addressed some underlying, general physical themes: How are SNR dynamics and structures modified by the character of the CSM and the ISM and vice versa? How are magnetic fields generated in SNRs and how do magnetic fields influence SNRs? Where and how are cosmic-rays (electrons and ions) produced in SNRs and how does their presence influence or reveal SNR dynamics? How does SNR blast energy partition into various components over time and what controls conversion between components? In lieu of a proceedings volume, we present here a synopsis of the workshop in the form of brief summaries of the workshop sessions. The sharpest impressions from the workshop were the crucial and under-appreciated roles that environments have on SNR appearance and dynamics and the critical need for broad-based studies to understand these beautiful, but enigmatic objects. \\Comment: 54 pages text, no figures, Latex (aasms4.sty). submitted to the PAS

Enhanced stochastic optimization algorithm for finding effective multi-target therapeutics

<p>Abstract</p> <p>Background</p> <p>For treating a complex disease such as cancer, we need effective means to control the biological network that underlies the disease. However, biological networks are typically robust to external perturbations, making it difficult to beneficially alter the network dynamics by controlling a single target. In fact, multi-target therapeutics is often more effective compared to monotherapies, and combinatory drugs are commonly used these days for treating various diseases. A practical challenge in combination therapy is that the number of possible drug combinations increases exponentially, which makes the prediction of the optimal drug combination a difficult combinatorial optimization problem. Recently, a stochastic optimization algorithm called the Gur Game algorithm was proposed for drug optimization, which was shown to be very efficient in finding potent drug combinations.</p> <p>Results</p> <p>In this paper, we propose a novel stochastic optimization algorithm that can be used for effective optimization of combinatory drugs. The proposed algorithm analyzes how the concentration change of a specific drug affects the overall drug response, thereby making an informed guess on how the concentration should be updated to improve the drug response. We evaluated the performance of the proposed algorithm based on various drug response functions, and compared it with the Gur Game algorithm.</p> <p>Conclusions</p> <p>Numerical experiments clearly show that the proposed algorithm significantly outperforms the original Gur Game algorithm, in terms of reliability and efficiency. This enhanced optimization algorithm can provide an effective framework for identifying potent drug combinations that lead to optimal drug response.</p

Clinical Outcomes and Adverse Events of Gastric Endoscopic Submucosal Dissection of the Mid to Upper Stomach under General Anesthesia and Monitored Anesthetic Care

Background/Aims Endoscopic submucosal dissection (ESD) of gastric tumors in the mid-to-upper stomach is a technically challenging procedure. This study compared the therapeutic outcomes and adverse events of ESD of tumors in the mid-to-upper stomach performed under general anesthesia (GA) or monitored anesthesia care (MAC). Methods Between 2012 and 2018, 674 patients underwent ESD for gastric tumors in the midbody, high body, fundus, or cardia (100 patients received GA; 574 received MAC). The outcomes of the propensity score (PS)-matched (1:1) patients receiving either GA or MAC were analyzed. Results The PS matching identified 94 patients who received GA and 94 patients who received MAC. Both groups showed high rates ofen bloc resection (GA, 95.7%; MAC, 97.9%; p=0.68) and complete resection (GA, 81.9%; MAC, 84.0%; p=0.14). There were no significant differences between the rates of adverse events (GA, 16.0%; MAC, 8.5%; p=0.18) in the anesthetic groups. Logistic regression analysis indicated that the method of anesthesia did not affect the rates of complete resection or adverse events. Conclusions ESD of tumors in the mid-to-upper stomach at our high-volume center had good outcomes, regardless of the method of anesthesia. Our results demonstrate no differences between the efficacies and safety of ESD performed under MAC and GA

Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network

BACKGROUND: Discovering robust markers for cancer prognosis based on gene expression data is an important yet challenging problem in translational bioinformatics. By integrating additional information in biological pathways or a protein-protein interaction (PPI) network, we can find better biomarkers that lead to more accurate and reproducible prognostic predictions. In fact, recent studies have shown that, “modular markers,” that integrate multiple genes with potential interactions can improve disease classification and also provide better understanding of the disease mechanisms. RESULTS: In this work, we propose a novel algorithm for finding robust and effective subnetwork markers that can accurately predict cancer prognosis. To simultaneously discover multiple synergistic subnetwork markers in a human PPI network, we build on our previous work that uses affinity propagation, an efficient clustering algorithm based on a message-passing scheme. Using affinity propagation, we identify potential subnetwork markers that consist of discriminative genes that display coherent expression patterns and whose protein products are closely located on the PPI network. Furthermore, we incorporate the topological information from the PPI network to evaluate the potential of a given set of proteins to be involved in a functional module. Primarily, we adopt widely made assumptions that densely connected subnetworks may likely be potential functional modules and that proteins that are not directly connected but interact with similar sets of other proteins may share similar functionalities. CONCLUSIONS: Incorporating topological attributes based on these assumptions can enhance the prediction of potential subnetwork markers. We evaluate the performance of the proposed subnetwork marker identification method by performing classification experiments using multiple independent breast cancer gene expression datasets and PPI networks. We show that our method leads to the discovery of robust subnetwork markers that can improve cancer classification. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1224-1) contains supplementary material, which is available to authorized users

Probabilistic reconstruction of the tumor progression process in gene regulatory networks in the presence of uncertainty

<p>Abstract</p> <p>Background</p> <p>Accumulation of gene mutations in cells is known to be responsible for tumor progression, driving it from benign states to malignant states. However, previous studies have shown that the detailed sequence of gene mutations, or the steps in tumor progression, may vary from tumor to tumor, making it difficult to infer the exact path that a given type of tumor may have taken.</p> <p>Results</p> <p>In this paper, we propose an effective probabilistic algorithm for reconstructing the tumor progression process based on partial knowledge of the underlying gene regulatory network and the steady state distribution of the gene expression values in a given tumor. We take the BNp (Boolean networks with pertubation) framework to model the gene regulatory networks. We assume that the true network is not exactly known but we are given an uncertainty class of networks that contains the true network. This network uncertainty class arises from our partial knowledge of the true network, typically represented as a set of local pathways that are embedded in the global network. Given the SSD of the cancerous network, we aim to simultaneously identify the true normal (healthy) network and the set of gene mutations that drove the network into the cancerous state. This is achieved by analyzing the effect of gene mutation on the SSD of a gene regulatory network. At each step, the proposed algorithm reduces the uncertainty class by keeping only those networks whose SSDs get close enough to the cancerous SSD as a result of additional gene mutation. These steps are repeated until we can find the best candidate for the true network and the most probable path of tumor progression.</p> <p>Conclusions</p> <p>Simulation results based on both synthetic networks and networks constructed from actual pathway knowledge show that the proposed algorithm can identify the normal network and the actual path of tumor progression with high probability. The algorithm is also robust to model mismatch and allows us to control the trade-off between efficiency and accuracy.</p