Observation of Seven Astrophysical Tau Neutrino Candidates with IceCube

We report on a measurement of astrophysical tau neutrinos with 9.7 yr of IceCube data. Using convolutional neural networks trained on images derived from simulated events, seven candidate ν_{τ} events were found with visible energies ranging from roughly 20 TeV to 1 PeV and a median expected parent ν_{τ} energy of about 200 TeV. Considering backgrounds from astrophysical and atmospheric neutrinos, and muons from π^{±}/K^{±} decays in atmospheric air showers, we obtain a total estimated background of about 0.5 events, dominated by non-ν_{τ} astrophysical neutrinos. Thus, we rule out the absence of astrophysical ν_{τ} at the 5σ level. The measured astrophysical ν_{τ} flux is consistent with expectations based on previously published IceCube astrophysical neutrino flux measurements and neutrino oscillations.

Validation and Responsiveness of the English version of the Chemotherapy-Induced Alopecia Distress Scale (CADS) in Breast Cancer Patients

Purpose

This study aimed to validate the chemotherapy-induced alopecia distress scale (CADS) in a diverse English-speaking population and patients with endocrine treatment- induced alopecia (EIA).

Objective

Chemotherapy and endocrine therapy commonly cause alopecia in breast cancer patients, leading to significant psychological and social challenges. The CADS was developed to assess the psychosocial impact of alopecia, but its generalizability beyond Korean patients requires further investigation.

Methods

Data from the CHANCE study ( NCT02530177 ), which focused on non-metastatic breast cancer, was used. The cohort included 256 patients, and CADS data were collected at baseline, six months after chemotherapy completion, or 12 months after initiating endocrine therapy. The CADS questionnaire comprised 17 items covering physical and emotional health, daily activities, and relationships. Reliability was assessed using Cronbach's alpha, and responsiveness was measured by effect size.

Results

The CADS exhibited good reliability, with a Cronbach's alpha of 0.91 for the overall score, indicating acceptable internal consistency in both chemotherapy (0.89) and endocrine therapy (0.86) groups. Longitudinal responsiveness was supported by an effect size of 0.49 between decreasing satisfaction with hair growth and increasing emotional distress. Cross-sectional validity was confirmed, with effect sizes of 0.91 and 0.92 for satisfaction with hair growth and emotional and activity domains, respectively.

Conclusion

The CADS is a valid and responsive tool for assessing the psychosocial impact of chemotherapy-induced alopecia and endocrine treatment-induced alopecia in a diverse Western patient population.

Feature extraction of time series data on functional near-infrared spectroscopy and comparison of deep learning performance for classifying patients with Alzheimer's-related mild cognitive impairment: a post-hoc analysis of a diagnostic interventional trial

OBJECTIVE

This study aimed to define a method of classifying patients with mild cognitive impairment caused by Alzheimer's disease by the retrieval of functional near-infrared spectroscopy (fNIRS) signal characteristics obtained during olfactory stimulation and the validation of deep learning findings.

PATIENTS AND METHODS

Participants were recruited for the study from March 02 and August 30, 2021. A total of 78 participants met the criteria for categorization. The Mini-Mental State Examination and the Seoul Neuropsychological Scale were used to distinguish between patients with mild Alzheimer's disease-related cognitive impairment and healthy controls. fNIRS data received during olfactory stimulation were used to create 1,680 time-series sample values. A total of 150 indices with a p-value ≤ 0.1 were used as deep learning features to construct the result values for 120 models accounting for all conceivable combinations of data ratios.

RESULTS

For this trial, 78 participants were recruited for the original intervention trial. The average accuracy of the 120 deep-learning models for classifying patients with Alzheimer's-related mild cognitive impairment ranged from 0.78 to 0.90. Sensitivity ranged from 0.88 to 0.96 for the 120 models, while specificity ranged from 0.86 to 0.94. The F1 scores ranged from 0.74 to 0.88. At 0.78 to 0.90, the precision and recall were equivalent.

CONCLUSIONS

This trial using a deep-learning model found that the representative value extracted from the time series data of each channel could distinguish between healthy people and patients with mild cognitive impairment caused by Alzheimer's disease.

Observation of high-energy neutrinos from the Galactic plane

The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, is unknown. Because of deflection by interstellar magnetic fields, cosmic rays produced within the Milky Way arrive at Earth from random directions. However, cosmic rays interact with matter near their sources and during propagation, which produces high-energy neutrinos. We searched for neutrino emission using machine learning techniques applied to 10 years of data from the IceCube Neutrino Observatory. By comparing diffuse emission models to a background-only hypothesis, we identified neutrino emission from the Galactic plane at the 4.5σ level of significance. The signal is consistent with diffuse emission of neutrinos from the Milky Way but could also arise from a population of unresolved point sources.

Evidence for neutrino emission from the nearby active galaxy NGC 1068

A supermassive black hole, obscured by cosmic dust, powers the nearby active galaxy NGC 1068. Neutrinos, which rarely interact with matter, could provide information on the galaxy’s active core. We searched for neutrino emission from astrophysical objects using data recorded with the IceCube neutrino detector between 2011 and 2020. The positions of 110 known gamma-ray sources were individually searched for neutrino detections above atmospheric and cosmic backgrounds. We found that NGC 1068 has an excess of 79 − 20 + 22 neutrinos at tera–electron volt energies, with a global significance of 4.2σ, which we interpret as associated with the active galaxy. The flux of high-energy neutrinos that we measured from NGC 1068 is more than an order of magnitude higher than the upper limit on emissions of tera–electron volt gamma rays from this source.

Classification of Alzheimer's disease stage using machine learning for left and right oxygenation difference signals in the prefrontal cortex: a patient-level, single-group, diagnostic interventional trial

OBJECTIVE

Recent evidence shows that indicators testing conventional olfactory function have a high degree of similarity to cognitive function tests and the potential to diagnose early-stage Alzheimer's disease (AD). In this study, the efficacy of functional near-infrared spectroscopy time-series data obtained through olfactory stimulation was investigated as an early diagnostic tool for mild cognitive impairment in AD using random forest, a machine learning algorithm.

PATIENTS AND METHODS

We conducted a patient-level, single-group, diagnostic interventional trial using near-infrared signals measured during olfactory stimulation in the prefrontal cortex of 178 older adults ranging from normal to participants with AD as markers to discriminate AD stages. We first divided the participants into normal older adults, AD mild cognitive impairment, and AD groups using dementia diagnostic criteria such as the Mini-Mental State Examination and Seoul Neuropsychological Screening Battery. We compared the left and right oxygenation difference by calculating the relative oxygenation difference from the change in relative oxygen concentration.

RESULTS

A total of 168 participants met the eligibility criteria: 70 (41.6%) had normal cognitive function; 42 (25%) mild cognitive impairment; 21 (12.5%) mild AD; and 35 (20.8%) moderate AD. A random forest machine learning model was developed to predict the AD stage, with an area under the receiver operating characteristic curve of 90.7% for mild cognitive impairment and AD, 90.99% for mild cognitive impairment, and 93.34% for AD only.

CONCLUSIONS

Based on the classification of the oxygenation difference index of the left and right prefrontal cortices during olfactory stimulation through machine learning, we found that it was possible to detect early-stage mild cognitive impairment in AD. Our results highlight the potential for early AD diagnosis using near-infrared signals from the prefrontal cortex obtained upon olfactory stimulation. Moreover, the results showed high similarity to the existing cognitive function tests and high accuracy in AD stage classification.

Safe, Durable, and Sustainable Self-Powered Smart Contact Lenses

Smart contact lenses have the potential to serve as noninvasive healthcare devices or virtual displays. However, their implementation is limited by the lack of suitable power sources for microelectronic devices. This Article demonstrates smart contact lenses with fully embedded glucose fuel cells that are safe, flexible, and durable against deformations. These fuel cells produced stable power throughout the day or during intermittent use after storage for weeks. When the lenses were exposed to 0.05 mM glucose solution, a steady-state maximum power density of 4.4 μW/cm² was achieved by optimizing the chemistry and porous structure of the fuel cell components. Additionally, even after bending the lenses in half 100 times, the fuel cell performance was maintained without any mechanical failure. Lastly, when the fuel cells were connected to electroresponsive hydrogel capacitors, we could clearly distinguish between the tear glucose levels under normal and diabetic conditions through the naked eye.

Search for Unstable Sterile Neutrinos with the IceCube Neutrino Observatory

We present a search for an unstable sterile neutrino by looking for a resonant signal in eight years of atmospheric ν_{μ} data collected from 2011 to 2019 at the IceCube Neutrino Observatory. Both the (stable) three-neutrino and the 3+1 sterile neutrino models are disfavored relative to the unstable sterile neutrino model, though with p values of 2.8% and 0.81%, respectively, we do not observe evidence for 3+1 neutrinos with neutrino decay. The best-fit parameters for the sterile neutrino with decay model from this study are Δm_{41}^{2}=6.7_{-2.5}^{+3.9}  eV^{2}, sin^{2}2θ_{24}=0.33_{-0.17}^{+0.20}, and g^{2}=2.5π±1.5π, where g is the decay-mediating coupling. The preferred regions of the 3+1+decay model from short-baseline oscillation searches are excluded at 90% C.L.

Strong Constraints on Neutrino Nonstandard Interactions from TeV-Scale ν_{μ} Disappearance at IceCube

We report a search for nonstandard neutrino interactions (NSI) using eight years of TeV-scale atmospheric muon neutrino data from the IceCube Neutrino Observatory. By reconstructing incident energies and zenith angles for atmospheric neutrino events, this analysis presents unified confidence intervals for the NSI parameter ε_{μτ}. The best-fit value is consistent with no NSI at a p value of 25.2%. With a 90% confidence interval of -0.0041≤ε_{μτ}≤0.0031 along the real axis and similar strength in the complex plane, this result is the strongest constraint on any NSI parameter from any oscillation channel to date.

Search for Relativistic Magnetic Monopoles with Eight Years of IceCube Data

We present an all-sky 90% confidence level upper limit on the cosmic flux of relativistic magnetic monopoles using 2886 days of IceCube data. The analysis was optimized for monopole speeds between 0.750c and 0.995c, without any explicit restriction on the monopole mass. We constrain the flux of relativistic cosmic magnetic monopoles to a level below 2.0×10^{-19}  cm^{-2} s^{-1} sr^{-1} over the majority of the targeted speed range. This result constitutes the most strict upper limit to date for magnetic monopoles with β≳0.8 and up to β∼0.995 and fills the gap between existing limits on the cosmic flux of nonrelativistic and ultrarelativistic magnetic monopoles.

Average SAR prediction, validation, and evaluation for a compact MR scanner head-sized RF coil

A recently developed compact 3 T (C3T) MRI scanner with high performance gradients [1, 2] has a dedicated radiofrequency (RF) transmit coil that exposes only the head, neck and a small portion of the upper body region during head-first scanning. Due to the unique coil geometry and patient positioning, the established SAR model used for a conventional whole-body scanner cannot be directly translated to the C3T. Here a specific absorption rate (SAR) estimation and validation framework was developed and used to implement a dedicated and accurate SAR prediction model for the C3T. Two different SAR prediction models for the C3T were defined and evaluated: one based on an anatomically derived exposed mass, and one using a fixed anatomical position located caudally to the RF coil to determine the exposed mass. After coil modeling and virtual human body simulation, the designed SAR prediction model was implemented on the C3T and verified with calorimetry and in vivo scan power monitoring. The fixed-demarcation exposed mass model was selected as appropriate exposed mass region to accurately estimate the SAR deposition in the patient on the C3T.

Identification of BRAF p. V600E-Mutant and Wild-Type by MR Imaging in Pleomorphic Xanthoastrocytoma and Anaplastic Pleomorphic Xanthoastrocytoma

BACKGROUND AND PURPOSE

Compared with BRAF p. V600E wild-type pleomorphic xanthoastrocytoma, BRAF p. V600E-mutant pleomorphic xanthoastrocytoma showed a higher survival rate. In this study, we focused on finding preoperative MR imaging differences between BRAF p. V600E mutant and wild-type in pleomorphic xanthoastrocytoma and anaplastic pleomorphic xanthoastrocytoma.

MATERIALS AND METHODS

Twenty-three patients with pathologically confirmed pleomorphic xanthoastrocytoma or anaplastic pleomorphic xanthoastrocytoma in our hospital were retrospectively analyzed from January 2015 to December 2020. They were divided into a BRAF p. V600E-mutant group (including 6 pleomorphic xanthoastrocytomas and 5 anaplastic pleomorphic xanthoastrocytomas) and a wild-type group (including 8 pleomorphic xanthoastrocytomas and 4 anaplastic pleomorphic xanthoastrocytomas). The preoperative MR imaging characteristics of these groups were statistically compared.

RESULTS

The wild-type pleomorphic xanthoastrocytoma group presented with more aggressive conventional and advanced MR imaging features than the mutant pleomorphic xanthoastrocytoma group, including greater mean maximum tumor diameter (3.1 [SD, 0.9] cm versus 1.7 [SD, 0.4 ] cm, P < .05), more frequent heterogeneous contrast enhancement of solid portions (100% versus 0%, P < .001), more obvious peritumoral edema (mean, [2.1 SD, 0.7] cm versus 0.6 [SD, 0.2] cm, P < .01), and lower mean minimum relative ADC (896 [SD, 86] versus 988 [SD, 73], P < .05) and mean relative ADC (1060 [SD, 159] versus 1248 [SD, 116], P < .05) on DWI. However, there was no significant difference in either conventional or advanced MR imaging features between the wild-type anaplastic pleomorphic xanthoastrocytoma group and the mutant anaplastic pleomorphic xanthoastrocytoma group.

CONCLUSIONS

Neurosurgeons should carefully interpret MR images before an operation and select appropriate surgical strategies according to genotype prediction.

A Microfluidic Model Artery for Studying the Mechanobiology of Endothelial Cells

Recent vascular mechanobiology studies find that endothelial cells (ECs) convert multiple mechanical forces into functional responses in a nonadditive way, suggesting that signaling pathways such as those regulating cytoskeleton may be shared among the processes of converting individual forces. However, previous in vitro EC-culture platforms are inherent with extraneous mechanical components, which may saturate or insufficiently activate the shared signaling pathways and accordingly, may misguide EC mechanobiological responses being investigated. Here, a more physiologically relevant model artery is reported that accurately reproduces most of the mechanical forces found in vivo, which can be individually varied in any combination to pathological levels to achieve diseased states. Arterial geometries of normal and diseased states are also realized. By mimicking mechanical microenvironments of early-stage atherosclerosis, it is demonstrated that the elevated levels of the different types of stress experienced by ECs strongly correlate with the disruption of barrier integrity, suggesting that boundaries of an initial lesion could be sites for efficient disease progression.

Microfluidic valvular chips and a numerical lymphatic vessel model for the study of lymph transport characteristics

Lymph transport inside lymphatic vessels is highly complex and not yet fully understood. So far, a consensus has not been reached among existing analytical models on how spatiotemporal coordination of contracting adjacent lymphangions affects lymph transport. To understand complex lymph transport, we created a novel microfluidic valvular chip with flexible bicuspid valves and segmental pneumatic pumps based on a microfluidic device with an inside 3D structure made of hydrogels. Inside the chip, water moved unidirectionally when the microfluidic channel was locally compressed, with its velocity profile closely resembling the waveform of lymph observed in vivo. Furthermore, for a systematic and mechanistic study, we constructed a numerical model based on fluid-structure interaction and validated the model via demonstration of similarities in water transport characteristics between the model and the chip. Using this model, we examined various mechanical and time-dependent parameters, such as period, phase delay, sequence, and strength of contractions, valve compliance, fluid viscosity, and pressure differences, for their effects on water transport. Although our model is simplified, it enabled a parametric study that helped clarify the mechano-temporal correlations between compressions of adjacent chambers via transmissions of hydrodynamic forces, which regulate complex lymph transport. Moreover, our chip demonstrated technical advances that enable unidirectional discrete movement of fluid in the picoliter range by phenumatic pumping. The velocity profile is also similar to the pulse waveform of arteries under pathological conditions such as increased aortic stiffness, allowing our chip to be used for in vitro mechanobiology studies of endothelial cells.

Dynamic multimodal holograms of conjugated organogels via dithering mask lithography

Polymeric materials have been used to realize optical systems that, through periodic variations of their structural or optical properties, interact with light-generating holographic signals. Complex holographic systems can also be dynamically controlled through exposure to external stimuli, yet they usually contain only a single type of holographic mode. Here, we report a conjugated organogel that reversibly displays three modes of holograms in a single architecture. Using dithering mask lithography, we realized two-dimensional patterns with varying cross-linking densities on a conjugated polydiacetylene. In protic solvents, the organogel contracts anisotropically to develop optical and structural heterogeneities along the third dimension, displaying holograms in the form of three-dimensional full parallax signals, both in fluorescence and bright-field microscopy imaging. In aprotic solvents, these heterogeneities diminish as organogels expand, recovering the two-dimensional periodicity to display a third hologram mode based on iridescent structural colours. Our study presents a next-generation hologram manufacturing method for multilevel encryption technologies.

[Short-term effect of sodium zirconium cyclosilicate on potassium lowering in chronic kidney disease patients with hyperkalemia]

Objective: To investigate the efficacy of sodium zirconium cyclosilicate on emergency correction of hyperkalemia in chronic kidney disease patients. Methods: Patients with chronic kidney disease who were admitted to the Department of Nephrology of the First Affiliated Hospital of Zhengzhou University from May to June 2020 were selected. Those who had hyperkalemia and took sodium zirconium cyclosilicate powder were finally included. The patient's clinical data and laboratory results were collected. Results: A total of 24 results were included from 21 patients. The age of patients was (48.9±13.5) years old. Fourteen patients were male, and 7 patients were female. After 2 hours of administration, the venous potassium level decreased from (5.85±0.52) mmol/L to (5.15±0.43) mmol/L (P<0.001, n=21), with an average decline of (0.71±0.43) mmol/L. Meanwhile, the arterial potassium level decreased from (5.50±0.40) mmol/L to (4.88±0.33) mmol/L (P<0.001, n=10), with an average decline of (0.62±0.29) mmol/L. Based on the initial venous potassium level, the patients were further divided into three groups. The average potassium decrease in<5.5 mmol/L group (4 patients), 5.5-<6.0 mmol/L group (11 patients) and ≥6.0 mmol/L group (6 patients) was (0.46±0.26) mmol/L, (0.62±0.38) mmol/L and (1.04±0.45) mmol/L, respectively. There was statistically significant difference of potassium reduction among the three groups (P=0.045). Moreover, the extent of potassium reduction was positively correlated with baseline venous potassium level (r=0.603, P=0.004, n=21). The study did not reveal any treatment-related adverse event. Conclusion: Sodium zirconium cyclosilicate powder can rapidly and effectively reduce the serum potassium level in chronic kidney disease patients with hyperkalemia.

eV-Scale Sterile Neutrino Search Using Eight Years of Atmospheric Muon Neutrino Data from the IceCube Neutrino Observatory

The results of a 3+1 sterile neutrino search using eight years of data from the IceCube Neutrino Observatory are presented. A total of 305 735 muon neutrino events are analyzed in reconstructed energy-zenith space to test for signatures of a matter-enhanced oscillation that would occur given a sterile neutrino state with a mass-squared differences between 0.01 and 100  eV^{2}. The best-fit point is found to be at sin^{2}(2θ_{24})=0.10 and Δm_{41}^{2}=4.5  eV^{2}, which is consistent with the no sterile neutrino hypothesis with a p value of 8.0%.

Characteristics of the Diffuse Astrophysical Electron and Tau Neutrino Flux with Six Years of IceCube High Energy Cascade Data

We report on the first measurement of the astrophysical neutrino flux using particle showers (cascades) in IceCube data from 2010-2015. Assuming standard oscillations, the astrophysical neutrinos in this dedicated cascade sample are dominated (∼90%) by electron and tau flavors. The flux, observed in the sensitive energy range from 16 TeV to 2.6 PeV, is consistent with a single power-law model as expected from Fermi-type acceleration of high energy particles at astrophysical sources. We find the flux spectral index to be γ=2.53±0.07 and a flux normalization for each neutrino flavor of ϕ_{astro}=1.66_{-0.27}^{+0.25} at E_{0}=100  TeV, in agreement with IceCube's complementary muon neutrino results and with all-neutrino flavor fit results. In the measured energy range we reject spectral indices γ≤2.28 at ≥3σ significance level. Because of high neutrino energy resolution and low atmospheric neutrino backgrounds, this analysis provides the most detailed characterization of the neutrino flux at energies below ∼100  TeV compared to previous IceCube results. Results from fits assuming more complex neutrino flux models suggest a flux softening at high energies and a flux hardening at low energies (p value ≥0.06). The sizable and smooth flux measured below ∼100  TeV remains a puzzle. In order to not violate the isotropic diffuse gamma-ray background as measured by the Fermi Large Area Telescope, it suggests the existence of astrophysical neutrino sources characterized by dense environments which are opaque to gamma rays.

OP0202 STRESS-ACTIVATED MIR-204 GOVERNS SENESCENT PHENOTYPES OF CHONDROCYTES TO PROMOTE OSTEOARTHRITIS DEVELOPMENT

Background:

A progressive loss of cartilage matrix leads to the development of osteoarthritis (OA). Matrix homeostasis is disturbed in OA cartilage as the result of reduced production of cartilage-specific matrix and increased secretion of catabolic mediators by chondrocytes. Chondrocyte senescence is a crucial cellular event contributing to such imbalance in matrix metabolism during OA development.

Objectives:

We sought to identify a previously unknown, senescence-associated signaling pathway in chondrocytes linked to major OA cartilage manifestations such as PG loss and cartilage degeneration.

Methods:

We particularly aimed to screen miRNAs whose inhibition could effectively modulate senescent phenotypes of chondrocytes to treat OA. We investigated the regulatory mechanisms of miR-204 under various stress-eliciting stimuli in primary cultured human and mouse chondrocytes. We examined the in vivo effects of miR-204 overexpression and its antagonism in surgically induced OA mouse models. DMM surgery was used to induce posttraumatic OA in 12-week-old mice. Small RNAs were delivered to mouse knee joints by intra-articular injection. Various OA manifestations including cartilage destruction, subchondral bone sclerosis, osteophyte maturity, and synovial inflammation in mice were histologically inspected.

Results:

We identify miR-204 as a senescence-associated microRNA (miRNA) which is markedly upregulated in OA cartilage. The upregulated miR-204 simultaneously targets multiple components of the sulfated proteoglycan (PG) biosynthesis pathway, effectively shutting down PG anabolism. Ectopic expression of the miR-204 in joints triggers spontaneous cartilage loss and OA development, whereas inhibition of miR-204 ameliorates experimental OA, with concomitant recovery of PG synthesis and suppression of inflammatory senescence-associated secretory phenotype (SASP) factors in cartilage

Conclusion:

we unravel a stress-activated senescence pathway that underlies disrupted matrix homeostasis in OA cartilage.

References:

[1]O. H. Jeon, C. Kim, R.-M. Laberge, M. Demaria, S. Rathod, A. P. Vasserot, J. W. Chung, D. H. Kim, Y. Poon, N. David, D. J. Baker, J. M. van Deursen, J. Campisi, J. H. Elisseeff, Local clearance of senescent cells attenuates the development of post-traumatic osteoarthritis and creates a pro-regenerative environment. Nat. Med. 23, 775–781 (2017)

Disclosure of Interests:

None declared

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.