Time-Integrated Neutrino Source Searches with 10 Years of IceCube Data

This Letter presents the results from pointlike neutrino source searches using ten years of IceCube data collected between April 6, 2008 and July 10, 2018. We evaluate the significance of an astrophysical signal from a pointlike source looking for an excess of clustered neutrino events with energies typically above ∼1  TeV among the background of atmospheric muons and neutrinos. We perform a full-sky scan, a search within a selected source catalog, a catalog population study, and three stacked Galactic catalog searches. The most significant point in the northern hemisphere from scanning the sky is coincident with the Seyfert II galaxy NGC 1068, which was included in the source catalog search. The excess at the coordinates of NGC 1068 is inconsistent with background expectations at the level of 2.9σ after accounting for statistical trials from the entire catalog. The combination of this result along with excesses observed at the coordinates of three other sources, including TXS 0506+056, suggests that, collectively, correlations with sources in the northern catalog are inconsistent with background at 3.3σ significance. The southern catalog is consistent with background. These results, all based on searches for a cumulative neutrino signal integrated over the 10 years of available data, motivate further study of these and similar sources, including time-dependent analyses, multimessenger correlations, and the possibility of stronger evidence with coming upgrades to the detector.

Venn-diaNet : venn diagram based network propagation analysis framework for comparing multiple biological experiments

BACKGROUND

The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system.

RESULTS

We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs.

CONCLUSIONS

We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet.

StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis

Background

Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the stress response are available in databases. With the data, an integrated analysis of multiple stresses is possible, which identifies stress-responsive genes with higher specificity because considering multiple stress can capture the effect of interference between stresses. To analyze such data, a machine learning model needs to be built.

Results

In this study, we developed StressGenePred, a neural network-based machine learning method, to integrate time-series transcriptome data of multiple stress types. StressGenePred is designed to detect single stress-specific biomarker genes by using a simple feature embedding method, a twin neural network model, and Confident Multiple Choice Learning (CMCL) loss. The twin neural network model consists of a biomarker gene discovery and a stress type prediction model that share the same logical layer to reduce training complexity. The CMCL loss is used to make the twin model select biomarker genes that respond specifically to a single stress. In experiments using Arabidopsis gene expression data for four major environmental stresses, such as heat, cold, salt, and drought, StressGenePred classified the types of stress more accurately than the limma feature embedding method and the support vector machine and random forest classification methods. In addition, StressGenePred discovered known stress-related genes with higher specificity than the Fisher method.

Conclusions

StressGenePred is a machine learning method for identifying stress-related genes and predicting stress types for an integrated analysis of multiple stress time-series transcriptome data. This method can be used to other phenotype-gene associated studies.

HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis

Background

Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples.

Results

HTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify “response order preserving DEGs” that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools.

Conclusions

HTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.

A protective mechanism of probiotic Lactobacillus against hepatic steatosis via reducing host intestinal fatty acid absorption

The gut microbiome has been known to contribute up to ~30% of the energy absorption of the host. Although various beneficial mechanisms of probiotics have been suggested for non-alcoholic fatty liver disease (NAFLD), whether and which probiotics impact the host's intestinal energy absorption have not yet been quantitatively studied. Here, we suggest a novel mechanism of probiotics against NAFLD, in which Lactobacillus rhamnosus GG, the most common probiotic, shares intestinal fatty acids and prevents the development of diet-induced hepatic steatosis. By using quantitative methods (radioactive tracers and LC-MS) under both in vitro and in vivo conditions, we found that bacteria and hosts competed for fatty acid absorption in the intestine, resulting in decreased weight gain, body fat mass, and hepatic lipid accumulation without differences in calorie intake and excretion in mice fed the probiotic bacteria.

Erythropoietin Protects Retina Against Ceramide 2-Induced Damage in Rat

BACKGROUND

Ceramide plays critical roles in cell proliferation, senescence and apoptosis, and is implicated in neurodegenerative diseases, etc. To clarify if ceramide plays some roles in retinal diseases, we established in vivo and in vitro retinal injury models with ceramide 2 (C2) treatment. In addition, Erythropoietin (EPO), which showed protective effects on retinal cells and blood-retinal barrier (BRB), was also tested for its protection and possible mechanism(s) in these models.

METHODS

Male Sprague-Dawley rats were divided into four groups, i.e., normal control, vehicle control, C2 treatment, and C2+EPO treatment. After intravitreal injection, the rats were examined for eye fundus, electroretinogram, histological study, and immunostaining, etc. In vitro, retinal neuronal cell line (R28) and the primary human retinal microvascular endothelial cells (HRMECs) were treated with C2, cell viability assay, transendothelial electrical resistance (TEER) and BRB-related molecules were studied to test the protective effect of EPO.

RESULTS

Intravitreal C2-treatment caused significant vision loss in rats, as reflected by reduced b-wave amplitude, increased TUNEL positive cells and GFAP immunostaining in retina. Another major retinal injury observed was BRB breakdown following C2- treatment. Such C2-induced injuries were further confirmed by in vitro study. When HRMECs were treated with C2, the TEER was significantly reduced. The mechanisms for C2 to induce such injuries might be through evidently increased expressions of the related molecules like plasmalemma vesicle-associated protein (PLVAP or PV-1), ecto- 5'-nucleotidase (CD73) and intercellular adhesion molecule-1 (ICAM-1), as observed in C2-treated R28 cells. All these injuries induced by C2 were significantly prevented by EPO both in vivo and in vitro, and its protective mechanisms here might be, in addition to neuroprotective, closely related to its maintenance of BRB integrity, through reducing the expressions of PV-1, CD73 and ICAM-1.

CONCLUSION

C2 could induce severe retinal injury, and such injuries could be effectively prevented by EPO treatment.

Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal

BACKGROUND

In this study, we propose a method for automatically predicting atrial fibrillation (AF) based on convolutional neural network (CNN) using a short-term normal electrocardiogram (ECG) signal.

METHODS

We designed a CNN model and optimized it by dropout and normalization. One-dimensional convolution, max-pooling, and fully-connected multiple perceptron were used to analyze the short-term normal ECG. The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model. The training and test sets consisted of the two AF and one normal dataset from the MIT-BIH database.

RESULTS

The proposed CNN model for the automatic prediction of AF achieved a high performance with a sensitivity of 98.6%, a specificity of 98.7%, and an accuracy of 98.7%.

CONCLUSION

The results show the possibility of automatically predicting AF based on the CNN model using a short-term normal ECG signal. The proposed CNN model for the automatic prediction of AF can be a helpful tool for the early diagnosis of AF in healthcare fields.

Recent results from the KASCADE-Grande data analysis

KASCADE, together with its extension KASCADE-Grande measured individual air showers of cosmic rays in the primary energy range of 100 TeV to 1 EeV. The data collection was fully completed at the end of 2013 and the experiment was dismantled. However, the data analysis is still in progress. Recently, we published a new result on upper limits to the flux of ultra-high energy gamma rays, which set constraints on some fundamental astrophysical models. We also use the data to investigate the validity of the new hadronic interactions models like SIBYLL version 2.3c or EPOS-LHC. In addition, we updated and improved the webbased platform of the KASCADE Cosmic Ray Data Centre (KCDC), where now the data from KASCADE and KASCADE-Grande of more than 20 years measurements is available, including corresponding Monte-Carlo simulated events based on three different hadronic interaction models. In this contribution, recent results from KASCADE-Grande and the update of KCDC is briefly discussed.

Study of themuon content of high-energy air showers with KASCADE-Grande

In this work, we report measurements on the muon content (Eth > 230 MeV) of extensive air showers (EAS) induced by cosmic rays with primary energy from 10 PeV up to 1 EeV performed with the KASCADE-Grande experiment. The measurements are confronted with SIBYLL 2.3. The results are focused on the dependence of the total muon number and the lateral density distribution of muons in EAS on the zenith angle and the total number of charged particles in the shower. We also present updated results of a detailed study of the attenuation length of shower muons, which reveal a deviation between the measured data and the predictions of the post-LHC hadronic interaction models SIBYLL 2.3, QGSJET-II-04 and EPOS-LHC.

Summary of the main results of the KASCADE and KASCADE-Grande experiments

The KASCADE and KASCADE-Grande experiments operated in KIT-Campus North, Karlsruhe (Germany) from 1993 to 2012. The two experiments studied primary cosmic rays in the energy range from 1014 eV to 1018 eV, investigating the change of slope of the spectrum detected at 2 - 4 × 1015 eV, the so called knee. We briefly review the performance of the experiments and then the main results obtained in the operation of both experiments: the test of hadronic interaction models, the all particle primary spectrum, the elemental composition of primary cosmic rays (with the first claim of a knee-like feature of the heavy primaries spectrum) and the search for large scale anisotropies.

Dynamic Interplay between Transport and Reaction Kinetics of Luminophores on the Operation of AC-Driven Electrochemiluminescence Devices

Electrochemiluminescence (ECL) involves light emission accompanied by a series of electrochemical processes on luminophores, which has been recently exploited in a new light-emitting device platform, referred to as the ECL device (ECLD). Here, we investigate the influence of the transport of the ECL luminophores and their reaction kinetics on the emission properties of alternating current-voltage-driven ECLDs. A model based on the diffusion and reaction rate equations is developed to predict the operational frequency ( f)-dependent luminance properties of the ECLD. It is found that more frequent generation of the redox precursors with a shorter time interval enhances their probability of encountering each other, and therefore the luminance of the device increases with increasing f initially. The luminance at a higher f, however, is suppressed eventually due to the decreased rate of the electrode reactions. Using the model, the influence of diffusion and reaction rates on the performance of an ECLD is analyzed separately and systematically. The results provide insight on the operation of this emerging class of a light-emitting device platform.