Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections

The inference of genetic regulatory networks (GRNs) reveals how genes interact with each other. A few genes can regulate many genes as targets to control cell functions. We present new methods based on the order-1 vector autoregression (VAR1) for inferring GRNs from gene expression time series. The methods use the automatic relevance determination (ARD) to incorporate the regulatory hub structure into the estimation of VAR1 in a Bayesian framework. Several sparse approximation schemes are applied to the estimated regression weights or VAR1 model to generate the sparse weighted adjacency matrices representing the inferred GRNs. We apply the proposed and several widespread reference methods to infer GRNs with up to 100 genes using simulated, DREAM4 in silico and experimental E. coli gene expression time series. We show that the proposed methods are efficient on simulated hub GRNs and scale-free GRNs using short time series simulated by VAR1s and outperform reference methods on small-scale DREAM4 in silico GRNs and E. coli GRNs. They can utilize the known major regulatory hubs to improve the performance on larger DREAM4 in silico GRNs and E. coli GRNs. The impact of nonlinear time series data on the performance of proposed methods is discussed.

Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light

A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the image color channels. We introduce the block-greedy algorithm in a novel Convolutional Neural Network (CNN) proposed to normalize different color channels’ attenuation ratios and select regions with the lowest variance. We address the discontinuity associated with underwater images by transforming both local and global pixel values. We minimize energy in the proposed CNN via a novel Markov random field to smooth edges and improve the final underwater image features. A comparison of the performance of the proposed technique against existing state-of-the-art algorithms using entropy, Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), Underwater Image Colorfulness Measure (UICM), and Underwater Image Sharpness Measure (UISM) indicate better performance of the proposed approach in terms of average and consistency. As it concerns to averagely, UICM has higher values in the technique than the reference methods, which explainsits higher color balance. The μ values of UCIQE, UISM, and UICM of the proposed method supersede those of the existing techniques. The proposed noted a percent improvement of 0.4%, 4.8%, 9.7%, 5.1% and 7.2% in entropy, UCIQE, UIQM, UICM and UISM respectively compared to the best existing techniques. Consequently, dehazed images have sharp, colorful, and clear features in most images when compared to those resulting from the existing state-of-the-art methods. Stable σ values explain the consistency in visual analysis in terms of sharpness of color and clarity of features in most of the proposed image results when compared with reference methods. Our own assessment shows that only weakness of the proposed technique is that it only applies to underwater images. Future research could seek to establish edge strengthening without color saturation enhancement.

International Experience and Enlightenment of the Regularization Development of Stall Economy

Stall economy can promote the recovery of national economy, relieve the employment pressure of tens of thousands of people, increase people’s happiness, and so on. Many benefits brought by stall economy show its important position and function. In fact, stall economy should be a durable product, not a substitute. We should look at it from a long-term perspective, standardize it and develop it. Combined with the practices and policies of many countries on the standardization of street stall economy, this article puts forward the reference methods for the standardization of street stall economy in the future. The country, society and individuals can enjoy the wealth and value brought by stall economy in the future.

Point-of-care haemoglobin measurement in pooled capillary blood by a portable autoanalyser: comparison with venous blood haemoglobin measured by reference methods in cross-sectional and longitudinal studies

Population-based surveys commonly use point-of-care (POC) methods with capillary blood samples for estimating haemoglobin (Hb) concentrations; these estimates need to be validated by comparison with reference methods using venous blood. In a cross-sectional study in 748 participants (17-86y, 708 women, Hb: 5.1 to 18.2 g/dL) from Hyderabad, India, we validated Hb measured from a pooled capillary blood sample by a POC autoanalyser (Horiba ABX Micros 60OT, Hb-C-AA) by comparison with venous blood Hb measured by two reference methods: POC autoanalyser (Hb-V-AA) and cyanmethemoglobin method (Hb-V-CM). These comparisons also allowed estimation of blood sample related and equipment related differences in the Hb estimates. We also conducted a longitudinal study in 426 participants (17-21y) to measure differences in the Hb response to iron folate treatment by the capillary blood POC method compared to the reference methods. In the cross-sectional study, Bland Altman analyses showed trivial differences between source of blood (Hb-C-AA and Hb-V-AA; mean difference, limits of agreement: 0.1, -0.8 to 1.0 g/dL) and between analytical methods (Hb-V-AA and Hb-V-CM; mean difference, limits of agreement :< 0.1, −1.8 to 1.8 g/dL). Cross-sectional anaemia prevalence estimated using Hb-C-AA did not differ significantly from Hb-V-CM or Hb-V-AA. In the longitudinal study, the Hb increment in response to IFA intervention was not different when using Hb-C-AA (1.6 ± 1.7 g/dL) compared to Hb-V-AA (1.7± 1.7g/dL) and Hb-V-CM (1.7± 1.7 g/dL). The pooled capillary blood–autoanalyzer method (Hb-C-AA) offers a practical and accurate way forward for POC screening of anaemia.

Evaluation of Extraction Procedure of PCDD/Fs, PCBs and Chlorobenzenes from Activated Carbon Fibers (ACFs)

Active carbon-based sorbents are well known and are used in analytical chemistry. Activated carbon fibers (ACFs) are mainly used as abatement systems in industrial emission pollution control. The objective of this study was to extend the use of ACFs in analytical chemistry for the analysis of polychlorodibenzo-p-dioxins (PCDDs), polychlorodibenzofurans (PCDFs), dioxin-like polychlorobiphenyls (PCBs), and chlorobenzenes (CBs). For this purpose, the extraction efficiency was evaluated based on the QA/QC criteria defined by EPA/ISO reference methods on 13C-standards recovery rates. The procedures tested were ultrasonic assisted extraction (UAE), Soxhlet extraction (SE), accelerated solvent extraction (ASE), and microwave-assisted extraction (MAE). Each experiment was performed in triplicate to ensure the repeatability of the results, and a second extraction assessed the complete extraction. The comparison of the results of each set of experiments with the minimum requirements of the reference methods for each class of compounds led to SE being chosen as the best technique. SE with toluene resulted in a reduction of time and costs and with respect to the other investigated techniques. The present work demonstrated that ACFs can be used in environmental fields means of both prevention and control (exploiting the adsorbent characteristics) and for analytical purposes (exploiting the desorption) for the described chlorinated classes of pollutants.

Hydration dynamics gives the distinctive brown color in the "brown ring" nitrate test

The chemistry of the brown-ring test has been investigated for nearly a century. Though recent studies have focused on solid state structure determination and the measurement of spectra, mechanistic details and kinetics, the aspects of solution structure and dynamics remain unknown. From ab initio molecular dynamics simulations of the brown-ring complex in aqueous solution, we have identified that the classically established pseudo-octahedral [Fe(H2O)5(NO)]2+ complex is in equilibrium with a square-pyramidal [Fe(H2O)4(NO)]2+ complex through the exchange of one of the coordinated H2O molecules. We also find, using ab-initio multi-reference methods, that the mixture of these two complexes is what gives the distinctive brown coloration to the brown-ring test. We show that its UV-vis spectrum can be theoretically reproduced only by accounting these two species and not the [Fe(H2O)5(NO)]2+ complex alone. The energetics of the two complexes are also investigated with multi-reference methods.

A Machine Learning Approach for Gearbox System Fault Diagnosis

This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter’s prediction error. The obtained prediction error’s standard deviation is further processed with a support-vector machine to classify the gearbox’s condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings.

Cross-Validation of a Multiplex LC-MS/MS Method for Assaying mAbs Plasma Levels in Patients with Cancer: A GPCO-UNICANCER Study

Background: Different liquid chromatography tandem mass spectrometry (LC–MS/MS) methods have been published for quantification of monoclonal antibodies (mAbs) in plasma but thus far none allowed the simultaneous quantification of several mAbs, including immune checkpoint inhibitors. We developed and validated an original multiplex LC–MS/MS method using a ready-to-use kit to simultaneously assay 7 mAbs (i.e., bevacizumab, cetuximab, ipilimumab, nivolumab, pembrolizumab, rituximab and trastuzumab) in plasma. This method was next cross-validated with respective reference methods (ELISA or LC–MS/MS). Methods: The mAbXmise kit was used for mAb extraction and full-length stable-isotope-labeled antibodies as internal standards. The LC–MS/MS method was fully validated following current EMA guidelines. Each cross validation between reference methods and ours included 16–28 plasma samples from cancer patients. Results: The method was linear from 2 to 100 µg/mL for all mAbs. Inter- and intra-assay precision was <14.6% and accuracy was 90.1–111.1%. The mean absolute bias of measured concentrations between multiplex and reference methods was 10.6% (range 3.0–19.9%). Conclusions: We developed and cross-validated a simple, accurate and precise method that allows the assay of up to 7 mAbs. Furthermore, the present method is the first to offer a simultaneous quantification of three immune checkpoint inhibitors likely to be associated in patients.

fGAAM: A fast and resizable genetic algorithm with aggressive mutation for feature selection

The paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM) called fGAAM (fast GAAM) that significantly decreases the time needed to find feature subsets of a satisfactory classification accuracy. To demonstrate the time gains provided by fGAAM both algorithms were tested on eight datasets containing different number of features, classes, and examples. The fGAAM was also compared with four reference methods: the Holland GA with and without penalty term, Culling GA, and NSGA II. Results: (i) The fGAAM processing time was about 35% shorter than that of the original GAAM. (ii) The fGAAM was also 20 times quicker than two Holland GAs and 50 times quicker than NSGA II. (iii) For datasets of different number of features, classes, and examples, another number of individuals, stored for further processing, provided the highest acceleration. On average, the best results were obtained when individuals from the last 10 populations were stored (time acceleration: 36.39%) or when the number of individuals to be stored was calculated by the algorithm itself (time acceleration: 35.74%). (iv) The fGAAM was able to process all datasets used in the study, even those that, because of their high number of features, could not be processed by the two Holland GAs and NSGA II.