Proper Normalization Research Articles

Stability evaluation and validation of appropriate reference genes for real-time PCR expression analysis of immune genes in the rohu (Labeo rohita) skin following argulosis

Argulosis is one of the most unrestrained economically significant freshwater fish ectoparasitic diseases. Proper selection or normalization of the best reference gene governs the accuracy of results of gene expression studies using real-time PCR. Earlier studies in rohu carp (Labeo rohita) have used reference genes without proper validation. Here, seven candidate reference genes viz., acidic ribosomal protein (ARP0), glyceraldehyde 3-phosphate dehydrogenase, RNA polymerase II (RPo), elongation factor1α (EF1α), α- tubulin (AT), ribosomal protein L 10, and β-actin were evaluated using four algorithms (geNorm, BestKeeper, NormFinder and ∆Ct) followed by a comprehensive gene expression analysis using skin tissue of rohu at varied time points of experimental Argulus siamensis infection. ARP0 and EF1α were found to be the most stable whereas RPo and AT were considered as least stable genes based on basal expression level and variation in expression levels. Validation of candidate reference genes was undertaken by looking into the expression of six immune-related genes using the two most stable and two least stable genes as housekeeping genes in Argulus-infected rohu skin at different time points of infection. An increased expression of immune genes indicated the role of inflammation and the immune modulation process at the site of attachment of parasites in governing infection.

Effect of aspect ratio on plasma response to resonant magnetic perturbations in tokamak devices

A systematic numerical study is carried out, computing and comparing the plasma response to the resonant magnetic perturbation (RMP) field, applied for controlling edge localized modes (ELMs), in a series of tokamak plasmas with varying aspect ratio and utilizing the MARS-F code. The aspect ratio is scanned either by varying the plasma major radius at a fixed minor radius or by varying the latter while fixing the former. Both approaches yield similar results when compared in terms of quantities with proper normalizations. In general, a non-monotonic dependence of the resonant response field (normalized by the vacuum counterpart) near the plasma edge is found with varying aspect ratio, indicating that a given ELM control coil current configuration strongly favors plasmas with a certain aspect ratio. This optimal aspect ratio, on the other hand, depends on the toroidal as well as poloidal (i.e., coil phasing) spectra of the applied RMP field. The equilibrium (edge) safety factor, the plasma shape, and the plasma toroidal flow are all fixed to ensure that the effects identified here are predominantly due to the plasma aspect ratio.

Competing effects of buoyancy-driven and electrothermal flows for Joule heating-induced transport in microchannels

Ionic fluids subjected to externally applied electric fields experience Joule heating, which increases with the increased electric field and ionic conductivity of the medium. Temperature gradients induced by Joule heating can create buoyancy-driven flows produced by local density changes, as well as electrothermal transport due to the temperature-dependent variations in fluid permittivity and conductivity. This manuscript considers Joule heating-induced transport in microchannels by a pair of electrodes under alternating current electric fields. Resulting buoyancy-driven and alternating current electrothermal (ACET) flows are investigated theoretically, numerically and experimentally. Proper normalizations of the governing equations led to the ratio of the electrothermal and buoyancy velocities, as a new non-dimensional parameter, which enabled the construction of a phase diagram that can predict the dominance of ACET and buoyancy-driven flows as a function of the channel size and electric field. Numerical results were used to verify the phase diagram in various height microchannels for different ionic conductivity fluids and electric fields, while the numerical results were validated using the micro-particle-image velocimetry technique. The results show that ACET flow prevails when the channel dimensions are small, and the electric potentials are high, whereas buoyancy-driven flow becomes dominant for larger channel heights. The present study brings insights into Joule heating-induced transport phenomena in microfluidic devices and provides a pathway for the design and utilization of ACET-based devices by properly considering the co-occurring buoyancy-driven flow.

Identification of materials in oil paintings through studies of correlations and ratios between the element line intensities during LIBS stratigraphy

Small fragments of the oil paintings “Emmaus Dinner” by Bellini (XVII century) and “Annunciation” by Brughi (XVIII century) were taken to laboratory because of insufficient information collected in-situ. At first, the samples were analyzed by SEM-EDX and FTIR Spectroscopy in order to identify the elemental and molecular composition of the surface layers, mostly covered by Ca-based patina (calcium oxalate and carbonates). Afterwards, the depth profiling by LIBS made possible to distinguish surface contaminations and retouches from the original pictorial materials. Stratigraphic variations of the chemical composition were established by a proper normalization of the element line intensities, necessary due to large changes of overall plasma intensity during ablation of heterogeneous samples. So obtained layer separations were confirmed by Principal Component Analysis (PCA). Studies of the correlations and ratios between intensities of the analytical element lines were effective to identify pigments and priming layers and, in some cases, to indicate also a possible material provenance. In the Bellini's painting we identified Natural Umber, Azurite, Green Earth, Goethite, Lead-Tin Yellow and Lead White, the latter detected in all examined fragments. The surface contamination by debris and retouches by modern pigments containing Ba, Cr and Zn were also detected. The presence of K and Na in the priming layer suggests the use of glass, commonly employed in the XVII century. In the Brughi's painting we identified a thin layer of Ultramarine Blue placed over Green Earth layer while the priming layer was mainly constituted by gypsum. On this painting, we also detected surface restoration by Ba based modern pigments.

Proper normalization removes nonlocal behavior of canonical-based thermodynamic entropy expressions

Equilibrium, canonical based expressions of entropy are known to exhibit non-local effects that can complicate statistical mechanical formulations and applications. Here, we demonstrate that proper normalization of the underlying probability density functions can remove non-local effects in entropy. Our analysis is carried out through third order in density function expansions and lead to results consistent with macroscopic thermodynamics, as required. In addition, we provide complete expressions for smaller dimensional systems that may not conform to the thermodynamic limit.

The beam stop as an intensity monitor

Free-electron lasers (FELs) provide unique possibilities in investigating matter down to femtosecond time and nanometer length scales, as well as in the regime of non-linear light-matter interaction. Due to the nature of FEL sources, the produced beam is significantly more unstable than beams produced by 3rd generation synchrotrons. As a result, pulse-resolved normalization of measurement data becomes essential and can be challenging. The intensity monitors permanently installed at a facility might indeed accurately measure the pulse intensities at a certain point of the beamline, but cannot precisely normalize experimental data. For example the impact of pointing instabilities and hence different clipping of the beam downstream on the way to the actual experiment is not reflected in the intensity measurement. Here, we show how the integral intensity of the FEL beam transmitted through the sample can be measured by photodiodes providing a proper normalization of measurement data.

Spatial integral of the solution to hyperbolic Anderson model with time-independent noise

In this article, we study the asymptotic behavior of the spatial integral of the solution to the hyperbolic Anderson model in dimension d≤2, as the domain of the integral gets large (for fixed time t). This equation is driven by a spatially homogeneous Gaussian noise, whose covariance function is either integrable, or is given by the Riesz kernel. The novelty is that the noise does not depend on time, which means that Itô’s martingale theory for stochastic integration cannot be used. Using a combination of Malliavin calculus with Stein’s method, we show that with proper normalization and centering, the spatial integral of the solution converges to a standard normal distribution, by estimating the speed of this convergence in the total variation distance. We also prove the corresponding functional limit theorem for the spatial integral process.

Parallel architecture of CNN‐bidirectional LSTMs for implied volatility forecast

AbstractWe propose a new forecast method based on artificial neural networks (ANNs), ensemble CNN‐BiLSTM, which is an ensemble of three CNN‐BiLSTMs constructed with the combination of Convolution Neural Network (CNN) and Bidirectional Long Short‐Term Memory (BiLSTM). The new forecast method effectively handles the strong long memory serial dependence feature of the daily VXN by the ensemble CNN‐BiLSTM together with proper normalization and batch size. The long memory features arising from time‐dependent mean and variance are largely reduced by normalizing the data with local mean and local standard deviation (SD). The batch size is determined by the optimal block length of the moving block bootstrap which reflects the long memory. The ensemble CNN‐BiLSTM concentrates on 1‐day, 1‐week, and 2‐week features of the normalized VXN data. An out‐of‐sample forecast comparison reveals that (i) the proposed ensemble CNN‐BiLSTM has better forecast performance than the autoregressive model, DNN, LSTM, BiLSTM, and individual CNN‐BiLSTMs; (ii) the local mean‐SD normalization has superior forecast performance to the standard global mean‐SD normalization; (iii) and the optimal block length improves the forecast performance over a batch size considered in the literature.

Evaluation of Optimal Reference Genes for qRT-PCR Analysis in Hyphantria cunea (Drury).

Simple SummaryReverse transcription-quantitative polymerase chain reaction (qRT-PCR) has rapidly become the most accessible and widely-applied method for quantifying gene transcription. Stable reference genes need to be normalized to facilitate gene expression studies and obtain more accurate qRT-PCR data. In this research, eight reference genes from different functional categories and gene families were used to assess the stability level of gene expression under different conditions in H. cunea. Finally, the expression pattern of the target gene serine proteases (HcSP1) was evaluated under different conditions to verify the precision and reliability of the results. This study provides a comprehensive list of suitable reference genes for analyses and optional reference genes for other gene function research in H. cunea.The relative quantification of gene expression is mainly achieved through reverse transcription-quantitative PCR (qRT-PCR); however, its reliability and precision rely on proper data normalization using one or more optimal reference genes. Hyphantria cunea (Drury) has been an invasive pest of forest trees, ornamental plants, and fruit trees in China for many years. Currently, the molecular physiological role of reference genes in H. cunea is unclear, which hinders functional gene study. Therefore, eight common reference genes, RPS26, RPL13, UBI, AK, RPS15, EIF4A, β-actin, α-tub, were selected to evaluate levels of gene expression stability when subjected to varied experimental conditions, including developmental stage and gender, different tissues, larvae reared on different hosts and different larval density. The geNorm, BestKeeper, ΔCt method, and NormFinder statistical algorithms were used to normalize gene transcription data. Furthermore, the stability/suitability of these candidates was ranked overall by RefFinder. This study provides a comprehensive evaluation of reference genes in H. cunea and could help select reference genes for other Lepidoptera species.

Asymptotic analysis of higher-order scattering transform of Gaussian processes

We analyze the scattering transform with the quadratic nonlinearity (STQN) of Gaussian processes without depth limitation. STQN is a nonlinear transform that involves a sequential interlacing convolution and nonlinear operators, which is motivated to model the deep convolutional neural network. We prove that with a proper normalization, the output of STQN converges to a chi-square process with one degree of freedom in the finite dimensional distribution sense, and we provide a total variation distance control of this convergence at each time that converges to zero at an exponential rate. To show these, we derive a recursive formula to represent the intricate nonlinearity of STQN by a linear combination of Wiener chaos, and then apply the Malliavin calculus and Stein’s method to achieve the goal.

Experimental investigation of ion recombination in a locally made low-voltage ionization chamber

In this study, the weak alpha radiation sources and the beta radiation by the effect of them on the electrical conductivity of air inside the ionization chamber are studied by measuring the produced current. The aim of this study is to make an ionization chamber to measure the ionization caused by weak radioactive sources which produce the electrical current at the order of femto- to pico-amperes and try to estimate the ion recombination coefficient at low ion densities. A parallel-plate ionization chamber with proper shielding and noise reduction circuits are made which measure data and sent them to a personal computer by USB port. The current-voltage characteristics of the ion chamber are measured when different radioactive sources are placed inside the chamber at the different separation of electrodes. The recombination constant obtained is estimated equal to 1.18 × 10−6 cm3/s. It is shown that current-voltage characteristic for ion chamber with different electrode separation lies on a unique curve by a proper normalization in the pico- and femto-ampere current range.

Luminance-Depth Reconstruction From Compressed Time-of-Flight Histograms

Single photon avalanche diodes (SPADs) combined with high-frequency time-to-digital converters (TDCs) enable the estimation of photon Time-of-Flight (ToF) for active 3D-depth imaging. Nevertheless, SPAD sensors still face hardware limitations due to a complex pixel readout design and a large amount of data collected by way of pixel-wise histograms. The intrinsic high background illumination (BI) also remains a challenging issue for the related depth reconstruction algorithms. Using a physically-relevant SPAD sensor model, this work tackles these issues by implementing a pixel-wise ToF histogram compressive sensing (CS) with a specific deep generative model based reconstruction. It demonstrates a possible reduction of hardware design constraints while reaching a depth inference root mean square error below 16 centimeters regardless of BI (50–1050 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{W}\mathrm{/}{\mathrm{2}}^{2}$</tex-math></inline-formula> ) and distance (20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{m}$</tex-math></inline-formula> ), at a compression ratio (CR) of 10% (32 CS measurements). In addition, this paper introduces a novel multimodal reconstruction from SPAD data, enabling joint depth and luminance estimations. Indeed, since ToF histogram raw data gathers multiple physical scene characteristics, we propose a two-part deep generative model (DGM) capable of inferring Super-Resolved depth maps and normalized luminance images, independently from the average scene BI. Our key contributions related to the DGM topology design are the introduction of proper normalization layers with a learned pile-up effect compensation, multidimensional-multiscale filtering and the concatenation of Softmax-ReLU activation functions to capture both peak-position and relative amplitude features. Numerically, depth and luminance maps reconstructions of natural scenes respectively reach more than 30 dB and 25 dB PSNRs for any CR higher than 2.5%.

Analysis of Data Normalization in Decision Making Process for ICU's Patients During the Pandemic

Multi Criteria Decision Making (MCDM) helps decision makers to select the best solution among the set of alternatives with respect to the criteria which have different scales. Selection of the best solution by comparison between alternatives in most MCDM problems needs the normalizing process that maps criteria into the same scale. Hence, using different normalization techniques produces a different ranking of alternatives and has influence effects on the final ranking. So, selecting a more suitable technique for the MCDM problem leads to more accurate ranking results. Considering the current pandemic and the importance of efficient usage of clinical staff and ICU's bed, using more proper normalization techniques assists decision makers to rank and prioritize ICU's patients with less error and facilitates resource allocation in ICU. This study introduces an assessment framework using different metrics to ensure a more proper normalization technique for the chosen MCDM method.

Assessing Normalization Techniques for Simple Additive Weighting Method

One of the current topics of attention in data analysis is the selection of the best normalization technique in the aggregation process when using Multi-Criteria Decision Making (MCDM) methods for solving decision problems. This is particularly critical in complex collaborative decision-making systems dealing with a large variety of heterogeneous data sources. Using different normalization techniques may result in different rankings of alternatives. So, enhancing the accuracy of the final ranking of alternatives could be achieved by selecting the most proper normalization techniques for each MCDM decision problem. In this direction, several attempts have been carried out, however, the lack of coherence and lack of a robust assessment framework persist. This situation encouraged the authors to propose an assessment framework that is enriched with several metrics for the evaluation of different normalization techniques in MCDM problems with the focus on partner/supplier selection in collaborative networks. As an illustration of the approach, in this work we assess different normalization techniques with the Simple Additive Weighting (SAW) method using metrics from the proposed assessment framework and select the most adequate technique for a small case study that is borrowed from literature. The suggested approach contributes to increasing the accuracy of final results for MCDM methods.

Dynamical Field Inference and Supersymmetry.

Knowledge on evolving physical fields is of paramount importance in science, technology, and economics. Dynamical field inference (DFI) addresses the problem of reconstructing a stochastically-driven, dynamically-evolving field from finite data. It relies on information field theory (IFT), the information theory for fields. Here, the relations of DFI, IFT, and the recently developed supersymmetric theory of stochastics (STS) are established in a pedagogical discussion. In IFT, field expectation values can be calculated from the partition function of the full space-time inference problem. The partition function of the inference problem invokes a functional Dirac function to guarantee the dynamics, as well as a field-dependent functional determinant, to establish proper normalization, both impeding the necessary evaluation of the path integral over all field configurations. STS replaces these problematic expressions via the introduction of fermionic ghost and bosonic Lagrange fields, respectively. The action of these fields has a supersymmetry, which means there exists an exchange operation between bosons and fermions that leaves the system invariant. In contrast to this, measurements of the dynamical fields do not adhere to this supersymmetry. The supersymmetry can also be broken spontaneously, in which case the system evolves chaotically. This affects the predictability of the system and thereby makes DFI more challenging. We investigate the interplay of measurement constraints with the non-linear chaotic dynamics of a simplified, illustrative system with the help of Feynman diagrams and show that the Fermionic corrections are essential to obtain the correct posterior statistics over system trajectories.

Evaluation of endogenous miRNA reference genes across different zebrafish strains, developmental stages and kidney disease models

The majority of kidney diseases arise from the loss of podocytes and from morphological changes of their highly complex foot process architecture, which inevitably leads to a reduced kidney filtration and total loss of kidney function. It could have been shown that microRNAs (miRs) play a pivotal role in the pathogenesis of podocyte-associated kidney diseases. Due to their fully functioning pronephric kidney, larval zebrafish have become a popular vertebrate model, to study kidney diseases in vivo. Unfortunately, there is no consensus about a proper normalization strategy of RT-qPCR-based miRNA expression data in zebrafish. In this study we analyzed 9 preselected candidates dre-miR-92a-3p, dre-miR-206-3p, dre-miR-99-1, dre-miR-92b-3p, dre-miR-363-3p, dre-let-7e, dre-miR-454a, dre-miR-30c-5p, dre-miR-126a-5p for their capability as endogenous reference genes in zebrafish experiments. Expression levels of potential candidates were measured in 3 different zebrafish strains, different developmental stages, and in different kidney disease models by RT-qPCR. Expression values were analyzed with NormFinder, BestKeeper, GeNorm, and DeltaCt and were tested for inter-group differences. All candidates show an abundant expression throughout all samples and relatively high stability. The most stable candidate without significant inter-group differences was dre-miR-92b-3p making it a suitable endogenous reference gene for RT-qPCR-based miR expression zebrafish studies.

Nonlinear rheological behavior of telechelic ionomers with the distribution of ionic stickers at the ends

This study examines the nonlinear rheology properties of telechelic ionomers with connecting ions at the chain ends. Our previous studies revealed that these telechelic ionomers relaxed in a hierarchical way depending on the average number of connecting ionic groups per chain end m. The terminal relaxation time increased with m because the dissociation was slower for the chain end with more connecting ions. In this study, the stress growth and damping functions are examined for these telechelic ionomer samples and a reference conventional telechelic sample having strictly one ion per chain end, both based on poly(isobutyl acrylate). During both the steady shear and elongation measurements, the stress overshoots are observed, and the corresponding strains at the stress overshoots, γpeak and εH,peak, are insensitive to the Weissenberg number Wi for Wi≫1. This feature suggests that γpeak or εH,peak are critical strains for the induction of dissociation, which is rationalized by the phenomenological analysis of the yielding stress based on the von Mises criterion. The approximate time-strain separability was surprisingly observed for the telechelic ionomer networks with a moderate m, which may be due to the fact that, although the high strain leads to a rupture of strand, the network structure has been maintained and its terminal relaxation is governed by the ionic dissociation (at least in the strain range of our measurements). These arguments are supported by the proper normalization of damping functions obtained for different samples using γ/γpeak as the horizontal axis.

Making External Validation Valid for Molecular Classifier Development.

PURPOSEAccurate assessment of a molecular classifier that guides patient care is of paramount importance in precision oncology. Recent years have seen an increasing use of external validation for such assessment. However, little is known about how it is affected by ubiquitous unwanted variations in test data because of disparate experimental handling and by the use of data normalization for alleviating such variations.METHODSIn this paper, we studied these issues using two microarray data sets for the same set of tumor samples and additional data simulated by resampling under various levels of signal-to-noise ratio and different designs for array-to-sample allocation.RESULTSWe showed that (1) unwanted variations can lead to biased classifier assessment and (2) data normalization mitigates the bias to varying extents depending on the specific method used. In particular, frozen normalization methods for test data outperform their conventional forms in terms of both reducing the bias in accuracy estimation and increasing robustness to handling effects. We make available our benchmarking tool as an R package on GitHub for performing such evaluation on additional methods for normalization and classification.CONCLUSIONOur findings thus highlight the importance of proper test-data normalization for valid assessment by external validation and call for caution on the choice of normalization method for molecular classifier development.

CoBRA: Containerized Bioinformatics Workflow for Reproducible ChIP/ATAC-seq Analysis

Chromatin immunoprecipitation sequencing (ChIP-seq) and the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) have become essential technologies to effectively measure protein–DNA interactions and chromatin accessibility. However, there is a need for a scalable and reproducible pipeline that incorporates proper normalization between samples, correction of copy number variations, and integration of new downstream analysis tools. Here we present Containerized Bioinformatics workflow for Reproducible ChIP/ATAC-seq Analysis (CoBRA), a modularized computational workflow which quantifies ChIP-seq and ATAC-seq peak regions and performs unsupervised and supervised analyses. CoBRA provides a comprehensive state-of-the-art ChIP-seq and ATAC-seq analysis pipeline that can be used by scientists with limited computational experience. This enables researchers to gain rapid insight into protein–DNA interactions and chromatin accessibility through sample clustering, differential peak calling, motif enrichment, comparison of sites to a reference database, and pathway analysis. CoBRA is publicly available online at https://bitbucket.org/cfce/cobra

DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network

Non-invasive whole-brain scans aid the diagnosis of neuropsychiatric disorder diseases such as autism, dementia, and brain cancer. The assessable analysis for autism spectrum disorders (ASD) is rationally challenging due to the limitations of publicly available datasets. For diagnostic or prognostic tools, functional Magnetic Resonance Imaging (fMRI) exposed affirmation to the biomarkers in neuroimaging research because of fMRI pickup inherent connectivity between the brain and regions. There are profound studies in ASD with introducing machine learning or deep learning methods that have manifested advanced steps for ASD predictions based on fMRI data. However, utmost antecedent models have an inadequacy in their capacity to manipulate performance metrics such as accuracy, precision, recall, and F1-score. To overcome these problems, we proposed an avant-garde DarkASDNet, which has the competence to extract features from a lower level to a higher level and bring out promising results. In this work, we considered 3D fMRI data to predict binary classification between ASD and typical control (TC). Firstly, we pre-processed the 3D fMRI data by adopting proper slice time correction and normalization. Then, we introduced a novel DarkASDNet which surpassed the benchmark accuracy for the classification of ASD. Our model's outcomes unveil that our proposed method established state-of-the-art accuracy of 94.70% to classify ASD vs. TC in ABIDE-I, NYU dataset. Finally, we contemplated our model by performing evaluation metrics including precision, recall, F1-score, ROC curve, and AUC score, and legitimize by distinguishing with recent literature descriptions to vindicate our outcomes. The proposed DarkASDNet architecture provides a novel benchmark approach for ASD classification using fMRI processed data.