84 research outputs found
Ab initio uncertainty quantification in scattering analysis of microscopy
Estimating parameters from data is a fundamental problem in physics,
customarily done by minimizing a loss function between a model and observed
statistics. In scattering-based analysis, researchers often employ their domain
expertise to select a specific range of wavevectors for analysis, a choice that
can vary depending on the specific case. We introduce another paradigm that
defines a probabilistic generative model from the beginning of data processing
and propagates the uncertainty for parameter estimation, termed ab initio
uncertainty quantification (AIUQ). As an illustrative example, we demonstrate
this approach with differential dynamic microscopy (DDM) that extracts
dynamical information through Fourier analysis at a selected range of
wavevectors. We first show that DDM is equivalent to fitting a temporal
variogram in the reciprocal space using a latent factor model as the generative
model. Then we derive the maximum marginal likelihood estimator, which
optimally weighs information at all wavevectors, therefore eliminating the need
to select the range of wavevectors. Furthermore, we substantially reduce the
computational cost by utilizing the generalized Schur algorithm for Toeplitz
covariances without approximation. Simulated studies validate that AIUQ
significantly improves estimation accuracy and enables model selection with
automated analysis. The utility of AIUQ is also demonstrated by three distinct
sets of experiments: first in an isotropic Newtonian fluid, pushing limits of
optically dense systems compared to multiple particle tracking; next in a
system undergoing a sol-gel transition, automating the determination of gelling
points and critical exponent; and lastly, in discerning anisotropic diffusive
behavior of colloids in a liquid crystal. These outcomes collectively
underscore AIUQ's versatility to capture system dynamics in an efficient and
automated manner
Analysis of Time Series Gene Expression and DNA Methylation Reveals the Molecular Features of Myocardial Infarction Progression
Myocardial infarction (MI) is one of the deadliest diseases in the world, and the changes at the molecular level after MI and the DNA methylation features are not clear. Understanding the molecular characteristics of the early stages of MI is of significance for the treatment of the disease. In this study, RNA-seq and MeDIP-seq were performed on heart tissue from mouse models at multiple time points (0 h, 10 min, 1, 6, 24, and 72 h) to explore genetic and epigenetic features that influence MI progression. Analysis based on a single point in time, the number of differentially expressed genes (DEGs) and differentially methylated regions (DMRs) increased with the time of myocardial infarction, using 0 h as a control group. Moreover, within 10 min of MI onset, the cells are mainly in immune response, and as the duration of MI increases, apoptosis begins to occur. Analysis based on time series data, the expression of 1012 genes was specifically downregulated, and these genes were associated with energy metabolism. The expression of 5806 genes was specifically upregulated, and these genes were associated with immune regulation, inflammation and apoptosis. Fourteen transcription factors were identified in the genes involved in apoptosis and inflammation, which may be potential drug targets. Analysis based on MeDIP-seq combined with RNA-seq methodology, focused on methylation at the promoter region. GO revealed that the downregulated genes with hypermethylation at 72 h were enriched in biological processes such as cardiac muscle contraction. In addition, the upregulated genes with hypomethylation at 72 h were enriched in biological processes, such as cell-cell adhesion, regulation of the apoptotic signaling pathway and regulation of angiogenesis. Among these genes, the Tnni3 gene was also present in the downregulated model. Hypermethylation of Tnni3 at 72 h after MI may be an important cause of exacerbation of MI
Challenges and Opportunities for Second-life Batteries: A Review of Key Technologies and Economy
Due to the increasing volume of Electric Vehicles in automotive markets and
the limited lifetime of onboard lithium-ion batteries (LIBs), the large-scale
retirement of LIBs is imminent. The battery packs retired from Electric
Vehicles still own 70%-80% of the initial capacity, thus having the potential
to be utilized in scenarios with lower energy and power requirements to
maximize the value of LIBs. However, spent batteries are commonly less reliable
than fresh batteries due to their degraded performance, thereby necessitating a
comprehensive assessment from safety and economic perspectives before further
utilization. To this end, this paper reviews the key technological and economic
aspects of second-life batteries (SLBs). Firstly, we introduce various
degradation models for first-life batteries and identify an opportunity to
combine physics-based theories with data-driven methods to establish
explainable models with physical laws that can be generalized. However,
degradation models specifically tailored to SLBs are currently absent.
Therefore, we analyze the applicability of existing battery degradation models
developed for first-life batteries in SLB applications. Secondly, we
investigate fast screening and regrouping techniques and discuss the regrouping
standards for the first time to guide the classification procedure and enhance
the performance and safety of SLBs. Thirdly, we scrutinize the economic
analysis of SLBs and summarize the potentially profitable applications.
Finally, we comprehensively examine and compare power electronics technologies
that can substantially improve the performance of SLBs, including
high-efficiency energy transformation technologies, active equalization
technologies, and technologies to improve reliability and safety
Potential of Core-Collapse Supernova Neutrino Detection at JUNO
JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve
Detection of the Diffuse Supernova Neutrino Background with JUNO
As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
Machine learning-based identification for ttH→invisible
To measure the Higgs→invisible BR in the ttH channel, we wish to improve signal to background ratio as larger as possible. One method worth considering is using machine learning to build a multi-class classifier to identify different types of events(ttH, ̅ and QCD are considered in this project). A multi-layer perceptron(MLP) was first introduced for it is simplicity and reliability. And the results show that the MLP classifier performs well to identify the signal (ttH) from the background(̅ and QCD), but was less effective at distinguishing different backgrounds. After that, recurrent neural network(RNN) was operated to our problem. The results indicate that the RNN can reliably discriminate different backgrounds as well. Therefore, machine learning may be a promising method in the research of ttH→invisible
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