A Prediction Method for Evolution Effects of Software Architecture

Software architecture evolution may lead to architecture erosion, resulting in the increase of software maintenance cost, the deterioration of software quality, the decline of software performance, and so on. In order to avoid software architecture erosion, we should evaluate the evolution effect of software architecture in time. This paper proposes a prediction method for the evolution effects of software architecture based on BP network. Firstly, this method proposes four evolution principles and evaluates the overall evolution effects based on the combined measurements. Then, we extract the evolutionary activities from release notes. Finally, we establish a prediction model for evolution effect based on BP network. Experimental results show that the proposed method can be used to predict the evolution effect.

Plasma RIPK3 And HMGB1 Predict Severe COVID-19 Progression In ICU Patients: A Single-Center Cohort Study

Background: Severe progression of coronavirus disease 2019 (COVID‑19) causes respiratory failure and critical illness. Recently, these pathologies have been associated with necroptosis, a receptor‑interacting serine/threonine‑protein kinase 3 (RIPK3) dependent regulated form of inflammatory cell death. Investigations of indicator necroptosis proteins like RIPK3, mixed lineage kinase domain‑like pseudokinase (MLKL), receptor‑interacting serine/threonine‑protein kinases 1 (RIPK1), and high‑mobility group box 1 (HMGB1) in clinical COVID‑19 manifestations are lacking.Methods: A prospective prolonged cohort study including 46 intensive care unit (ICU) patients classified with moderate and severe COVID‑19 was conducted with daily measured plasma levels of indicator necroptosis proteins like RIPK3, MLKL, RIPK1, and HMGB1 by enzyme‑linked immunosorbent assay (ELISA). On this basis, a multiple logistic (regression) classification for the prediction of severe COVID‑19 progression was performed. Results: We found significantly elevated RIPK3, MLKL, HMGB1, and RIPK1 levels in COVID‑19 patients admitted to the ICU compared to healthy controls throughout the ongoing disease, indicating necroptotic processes. Above all, with combined measurements of RIPK3 and HMGB1 plasma levels, we were able to time‑independently predict COVID‑19 severity with 84% accuracy, 90% sensitivity, and 76% specificity.Conclusion: We suggest that HMGB1 and RIPK3 are potential biomarkers to identify high‑risk COVID‑19 patients and developed a classifier for COVID‑19 severity.

Metabolic turnover and dynamics of modified ribonucleosides by 13C labeling

Tandem mass spectrometry (MS/MS) is an accurate tool to assess modified ribonucleosides and their dynamics in mammalian cells. Yet, MS/MS quantification of lowly abundant modifications in non-ribosomal RNAs is unreliable, and the dynamic features of various modifications poorly understood. We developed a 13C labeling approach, 13C-dynamods, to quantify the turnover of base modifications in newly transcribed RNA. This turnover-based approach helped to resolve mRNA from ncRNA modifications in purified RNA or free ribonucleosides, and showed the distinct kinetics of N6-methyladenosine (m6A) versus 7-methylguanosine (m7G) in polyA+-purified RNA. We uncovered that N6,N6-dimethyladenosine (m62A) exhibits a distinct turnover in small RNAs and free ribonucleosides when compared to the known m62A-modified large rRNAs. Finally, combined measurements of turnover and abundance informed on the transcriptional versus posttranscriptional sensitivity of modified ncRNAs and mRNAs, respectively, to stress conditions. Thus, 13C-dynamods enables studies of origin of modified RNAs at steady-state and their dynamics under non-stationary conditions.

Pedestrian navigation: how can inertial measurment units assist smartphones?

This paper is devoted to construction of reference walking trajectories for developing pedestrian navigation algorithms for smartphones. Such trajectories can be used both for verification of classical algorithms of navigation or for application of machine learning technics. Reconstruction of closed trajectories based on data from foot-mounted inertial measurement units (IMU) is investigated. The advantages of the approach are the use of inexpensive sensors and the simplicity of the presented method. We propose algorithms for reconstruction of smooth 2D pedestrian trajectories based on measurements from a single IMU as well as on combined measurements from two IMU’s. Introduced algorithms are based on application of modified Kalman filter with an assumption of IMU having zero velocity when foot contacts the ground. In case of two measurement units, it is additionally assumed that the positions of the sensors cannot differ significantly from each other. The algorithms were tested on trajectories lasting from 1 to 10 minutes, passing indoors on horizontal surfaces. Obtained results were compared with high precision trajectories acquired with GNSS RTK receivers. Additionally, the process of inter-device time synchronization is investigated and detailed description of the experiments and used equipment is given. The dataset used for verification of proposed algorithms is freely available at: http://gartseev.ru/projects/rtj2021.

Ultraviolet absorption of contaminants in water

Contaminants in water were studied using ultraviolet absorption with light emitting diode and deuterium lamp sources, and a thresholding detector. The absorption spectra of potassium hydrogen pthalate, clothianidin, tryptophan, thiamethoxam, uric acid and metaldehyde were obtained in the range 200–360 nm. Only metaldehyde was not suitable for detection in this range. For the other contaminants, and mixtures of pairs of compounds, the transmitted signal could be approximately described with a simple spectral model of the source–absorption–detector system. Combined measurements at two wavelengths could allow relative concentrations in certain mixtures to be determined, and real-time absorption measurements were demonstrated in a flume.

Review of combined measurements of Higgs boson production and decay using up to 80 fb−1 of proton–proton collision data at s = 13 TeV collected with the ATLAS experiment

The combined measurements of Higgs boson production and decay are performed based on up to 80 fb[Formula: see text] of proton–proton collision data collected at [Formula: see text] = 13 TeV by the ATLAS detector. The overall signal strength, defined as measured signal yield divided by the Standard Model prediction, is [Formula: see text]. The inclusive and differential cross-sections of gluon–gluon fusion, vector-boson fusion, associated production with vector boson, and associated production with top quarks processes are also measured. The measurements are interpreted with leading-order motivated coupling strength modifiers. No significant deviations from the Standard Model predictions are observed.

CycleFlow quantifies cell-cycle heterogeneity in vivo

While the average cell-cycle length in a cell population can be derived from pulse-chase experiments, proliferative heterogeneity has been difficult to quantify. Here we describe CycleFlow, a broadly applicable method that applies Bayesian inference to combined measurements of EdU incorporation and DNA content. CycleFlow accurately quantifies the fraction of proliferating versus quiescent cells and the durations of cell-cycle phases of the proliferating cells in vitro and in vivo.