ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity

<p>Abstract</p> <p>Background</p> <p>The analysis of mass spectra suggests that the existence of derivative peaks is strongly dependent on the intensity of the primary peaks. Peak selection from tandem mass spectrum is used to filter out noise and contaminant peaks. It is widely accepted that a valid primary peak tends to have high intensity and is accompanied by derivative peaks, including isotopic peaks, neutral loss peaks, and complementary peaks. Existing models for peak selection ignore the dependence between the existence of the derivative peaks and the intensity of the primary peaks. Simple models for peak selection assume that these two attributes are independent; however, this assumption is contrary to real data and prone to error.</p> <p>Results</p> <p>In this paper, we present a statistical model to quantitatively measure the dependence of the derivative peak's existence on the primary peak's intensity. Here, we propose a statistical model, named ProbPS, to capture the dependence in a quantitative manner and describe a statistical model for peak selection. Our results show that the quantitative understanding can successfully guide the peak selection process. By comparing ProbPS with AuDeNS we demonstrate the advantages of our method in both filtering out noise peaks and in improving <it>de novo </it>identification. In addition, we present a tag identification approach based on our peak selection method. Our results, using a test data set, suggest that our tag identification method (876 correct tags in 1000 spectra) outperforms PepNovoTag (790 correct tags in 1000 spectra).</p> <p>Conclusions</p> <p>We have shown that ProbPS improves the accuracy of peak selection which further enhances the performance of de novo sequencing and tag identification. Thus, our model saves valuable computation time and improving the accuracy of the results.</p

EVNet: An Explainable Deep Network for Dimension Reduction

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as image recognition, single-cell sequencing analysis, and biomarker discovery. However, contemporary parametric-free and parametric DR techniques suffer from several significant shortcomings, such as the inability to preserve global and local features and the pool generalization performance. On the other hand, regarding explainability, it is crucial to comprehend the embedding process, especially the contribution of each part to the embedding process, while understanding how each feature affects the embedding results that identify critical components and help diagnose the embedding process. To address these problems, we have developed a deep neural network method called EVNet, which provides not only excellent performance in structural maintainability but also explainability to the DR therein. EVNet starts with data augmentation and a manifold-based loss function to improve embedding performance. The explanation is based on saliency maps and aims to examine the trained EVNet parameters and contributions of components during the embedding process. The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability. The interactive visual interface makes it easier to illustrate the data features, compare different DR techniques, and investigate DR. An in-depth experimental comparison shows that EVNet consistently outperforms the state-of-the-art methods in both performance measures and explainability.Comment: 18 pages, 15 figures, accepted by TVC

Association of GSDMD with microvascular-ischemia reperfusion injury after ST-elevation myocardial infarction

ObjectivesLittle is known about the clinical prognosis of gasdermin D (GSDMD) in patients with ST-elevation myocardial infarction (STEMI). The purpose of this study was to investigate the association of GSDMD with microvascular injury, infarction size (IS), left ventricular ejection fraction (LVEF), and major adverse cardiac events (MACEs), in STEMI patients with primary percutaneous coronary intervention (pPCI).MethodsWe retrospectively analyzed 120 prospectively enrolled STEMI patients (median age 53 years, 80% men) treated with pPCI between 2020 and 2021 who underwent serum GSDMD assessment and cardiac magnetic resonance (CMR) within 48 h post-reperfusion; CMR was also performed at one year follow-up.ResultsMicrovascular obstruction was observed in 37 patients (31%). GSDMD concentrations ≧ median (13 ng/L) in patients were associated with a higher risk of microvascular obstruction and IMH (46% vs. 19%, P = 0.003; 31% vs. 13%, P = 0.02, respectively), as well as with a lower LVEF both in the acute phase after infarction (35% vs. 54%, P &lt; 0.001) and in the chronic phase (42% vs. 56%, P &lt; 0.001), larger IS in the acute (32% vs. 15%, P &lt; 0.001) and in the chronic phases (26% vs. 11%, P &lt; 0.001), and larger left ventricular volumes (119 ± 20 vs. 98 ± 14, P = 0.003) by CMR. Univariable and multivariable Cox regression analysis results showed that patients with GSDMD concentrations ≧ median (13 ng/L) had a higher incidence of MACE (P &lt; 0.05).ConclusionsHigh GSDMD concentrations in STEMI patients are associated with microvascular injury (including MVO and IMH), which is a powerful MACE predictor. Nevertheless, the therapeutic implications of this relation need further research

Red-fleshed apple flavonoid extract alleviates CCl4-induced liver injury in mice

In recent years, the global incidence of liver damage has increased. Despite the many known health benefits of red-fleshed apple flavonoids, their potential liver-protective effects have not yet been investigated. In this study, we analyzed the composition of red-fleshed apple flavonoid extract (RAFE) by high-performance liquid chromatography (HPLC). We then induced liver damage in mice with carbon tetrachloride (CCl4) and performed interventions with RAFE to analyze its effect on liver damage, using bifendate as a positive control. The results showed that catechin was the most abundant flavonoid in ‘XJ4’ RAFE (49.346 mg/100 g). In liver-injured mice, the liver coefficients converged to normal levels following RAFE intervention. Moreover, RAFE significantly reduced the enzymatic activity levels of glutamic oxaloacetic transaminase (ALT), glutamic alanine transaminase (AST), and alkaline phosphatase (ALP) in mouse serum. Furthermore, RAFE significantly increased the content or enzyme activity level of total glutathione, total antioxidant capacity, and superoxide dismutase, and significantly decreased the content of malondialdehyde in the liver of mice. In parallel, we performed histopathological observations of mouse livers for each group. The results showed that RAFE restored the pathological changes caused by CCl4 around the central hepatic vein in mice and resulted in tightly bound hepatocytes. The recovery effect of RAFE was dose-dependent in the liver tissue. Regarding intestinal microorganisms, we found that RAFE restored the microbial diversity in liver-injured mice, with a similar microbial composition in the RAFE intervention group and normal group. RAFE reduced the ratio of Firmicutes to Bacteroidetes, increased the levels of probiotic bacteria, such as Lactobacillus acidophilus, and Clostridium, and reduced the levels of harmful bacteria, such as Erysipelothrix Rosenbach. Therefore, RAFE ameliorated CCl4-induced liver damage by modulating the abundance and composition of intestinal microorganisms in mice. In conclusion, RAFE alleviated CCl4-induced liver damage in mice, with H-RAFE (5 mg kg–1) significantly improving liver damage in mice but M-RAFE (1 mg kg–1) significantly improving the imbalance of intestinal microorganisms in mice. Our research suggests that RAFE could be employed for the adjuvant treatment and prevention of liver damage, and may have important applications in food and medicine

Mass testing of the JUNO experiment 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%

Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the experiment is an unprecedented 3% energy resolution, guaranteed by its large active mass (20 kton) and the use of more than 20,000 20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution sampling electronics located very close to the PMTs. As the Front-End and Read-Out electronics is expected to continuously run underwater for 30 years, a reliable readout acquisition system capable of handling the timestamped data stream coming from the Large-PMTs and permitting to simultaneously monitor and operate remotely the inaccessible electronics had to be developed. In this contribution, the firmware and hardware implementation of the IPbus based readout protocol will be presented, together with the performances measured on final modules during the mass production of the electronics

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.Comment: 20 pages, 13 figure