NMR Spectra Denoising with Vandermonde Constraints

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. However, NMR signals are easily contaminated by noise during the data acquisition, which can affect subsequent quantitative analysis. Therefore, denoising NMR signals has been a long-time concern. In this work, we propose an optimization model-based iterative denoising method, CHORD-V, by treating the time-domain NMR signal as damped exponentials and maintaining the exponential signal form with a Vandermonde factorization. Results on both synthetic and realistic NMR data show that CHORD-V has a superior denoising performance over typical Cadzow and rQRd methods, and the state-of-the-art CHORD method. CHORD-V restores low-intensity spectral peaks more accurately, especially when the noise is relatively high.Comment: 10 pages, 9 figure

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal

Objective: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen for training compared to the end-to-end deep learning method. Results: Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. Conclusion: This study provides an intelligent, reliable and robust MRS quantification. Significance: QNet is the first LLS quantification aided by deep learning

Impact of the COVID-19 lockdown on air pollution in an industrial city in Northeastern China

Many studies in China investigated how the lockdown following the coronavirus disease 2019 substantially affected air quality; however, few were conducted in Northeastern China. Here, the changes in six criteria air pollutants, including particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3), were investigated in Shenyang from January to May 2015–2020. Compared with the pre-lockdown, the mass concentrations of PM2.5, PM10, SO2, NO2, and CO during the lockdown decreased by 40.3% to 48.6%, indicating a positive impact of lockdown policies on reducing pollutant emissions. The responses of PM2.5, PM10, and CO to the lockdown measures in downtown areas were more sensitive than in the suburbs. However, the O3 concentration showed the opposite trend, attributed to the drop in NOx and particulate matters. Compared to the same period in 2015–2019, the proportion of days with good air quality increased from 63.2% to 77.2% during the lockdown and Shenyang experienced no severe pollution. Our results suggest that reducing human activities can improve air quality; however, coordinated control policies of O3, PM2.5, and NO2 are imperative.

Long Non-Coding RNA as a Potential Biomarker for Canine Tumors

Cancer is the leading cause of death in both humans and companion animals. Long non-coding RNA (lncRNA) plays a crucial role in the progression of various types of cancers in humans, involving tumor proliferation, metastasis, angiogenesis, and signaling pathways, and acts as a potential biomarker for diagnosis and targeted treatment. However, research on lncRNAs related to canine tumors is in an early stage. Dogs have long been considered a promising natural model for human disease. This article summarizes the molecular function of lncRNAs as novel biomarkers in various types of canine tumors, providing new insights into canine tumor diagnosis and treatment. Further research on the function and mechanism of lncRNAs is needed, which will benefit both human and veterinary medicine

Anti-Diabetic Atherosclerosis by Inhibiting High Glucose-Induced Vascular Smooth Muscle Cell Proliferation via Pin1/BRD4 Pathway

Background and purpose. Vascular smooth muscle cells (VSMC) proliferation and migration is the important pathological process of diabetic atherosclerosis. Bromine domain protein 4 (BRD4) is involved in cell proliferation and inflammatory disease. Pin1 enhances BRD4 stability and its transcriptional activity. This study aimed to explore the possible mechanism of Pin1/BRD4 in diabetic atherosclerosis. Methods. Diabetic Apoe-/- mice induced by streptozotocin were treated with vehicle, the Pin1 inhibitor juglone, or the BRD4 inhibitor JQ1 for 3 weeks. VSMCs were pretreated with juglone, JQ1, or vehicle for 45 min, and then exposed to high glucose for 48 h. Hematoxylin–eosin staining was performed to assess atherosclerotic plaques of the thoracic aorta. Western blotting was used to detect expression levels of Pin1, BRD4, cyclin D1, and matrix metalloproteinase-9 (MMP-9) in the thoracic aorta and VSMCs. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and transwell assay were used to measure proliferation and migration of VSMCs. Results. Juglone and JQ1 significantly improved atherosclerosis of diabetic Apoe-/- mice and reduced high glucose-induced VSMC proliferation and migration. Cyclin D1 and MMP-9 levels in the thoracic aorta were lower in diabetic Apoe-/- mice treated with juglone and JQ1 compared with vehicle-treated diabetic Apoe-/- mice. Additionally, BRD4 protein expression in high glucose-induced VSMCs was inhibited by juglone and JQ1. Upregulation of Pin1 expression by transduction of the Pin1 plasmid vector promoted BRD4 expression induced by high glucose, and stimulated proliferation and migration of VSMCs. Conclusions. Inhibition of Pin1/BRD4 pathway may improve diabetic atherosclerosis by inhibiting proliferation and migration of VSMCs

Autographa californica Multiple Nucleopolyhedrovirus ac76 Is Involved in Intranuclear Microvesicle Formation▿

In this study, we characterized Autographa californica multiple nucleopolyhedrovirus (AcMNPV) orf76 (ac76), which is a highly conserved gene of unknown function in lepidopteran baculoviruses. Transcriptional analysis of ac76 revealed that transcription of multiple overlapping multicistronic transcripts initiates from a canonical TAAG late-transcription start motif but terminates at different 3′ ends at 24 h postinfection in AcMNPV-infected Sf9 cells. To investigate the role of ac76 in the baculovirus life cycle, an ac76-knockout virus was constructed using an AcMNPV bacmid system. Microscopy, titration assays, and Western blot analysis demonstrated that the resulting ac76-knockout virus was unable to produce budded viruses. Quantitative real-time PCR analysis demonstrated that ac76 deletion did not affect viral DNA synthesis. Electron microscopy showed that virus-induced intranuclear microvesicles as well as occlusion-derived virions were never observed in cells transfected with the ac76-knockout virus. Confocal microscopy analysis revealed that Ac76 was predominantly localized to the ring zone of nuclei during the late phase of infection. This suggests that ac76 plays a role in intranuclear microvesicle formation. To the best of our knowledge, this is the first baculovirus gene identified to be involved in intranuclear microvesicle formation

Research Advancements in Swine Wastewater Treatment and Resource-Based Safe Utilization Management Technology Model Construction

Swine wastewater contains large amounts of organic matter, nutrients, toxic metal elements, and antibiotics. If it is directly discharged or not properly treated, it poses a significant threat to the environment and human health. Currently, the management of swine wastewater has become a focus of social attention, and it adopts a dual-track parallel model of standard discharge supplemented by resource utilization. If treated properly, it can achieve the recycling of water resources and promote the effective recovery of resources. Based on the pollution characteristics of swine wastewater, this paper analyzes its impact on the environment, society, and the economy in detail and expounds on the research progress of swine wastewater treatment technology. From the perspective of resource utilization and recycling of anaerobic digestion liquid (biogas slurry) from swine wastewater and the carrying capacity of the soil environment and cumulative ecological environmental risks, this study explores new development trends and application prospects for swine wastewater treatment technology

A Systematic Overview of Android Malware Detection

Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. To stay ahead of other similar review work attempting to deal with the serious security problem of the Android environment, this work not only summarizes the approaches in the malware classification phase but also lays emphasis on the Android feature selection algorithm and presents some areas neglected in previous works in the field of Android malware detection, like limitations and commonly applied datasets in machine learning-based models. In this paper, the Android OS environment, feature selection, classification models, and confronted challenges of machine learning detection are described in detail. Based on the brief introduction to Android background knowledge, feature selection methods are elaborated from key perspectives as feature extraction, raw data preprocessing, valid feature subsets selection, and machine learning-based selection models. For the algorithms of the malware classification, machine learning methods are categorized according to different standards to present an all-around view. Furthermore, this paper focuses on the study of deterioration problems and evasion attacks in machine learning detectors

Autographa californica Multiple Nucleopolyhedrovirus 38K Is a Novel Nucleocapsid Protein That Interacts with VP1054, VP39, VP80, and Itself▿ †

It has been shown that the Autographa californica multiple nucleopolyhedrovirus (AcMNPV) 38K (ac98) is required for nucleocapsid assembly. However, the exact role of 38K in nucleocapsid assembly remains unknown. In the present study, we investigated the relationship between 38K and the nucleocapsid. Western blotting using polyclonal antibodies raised against 38K revealed that 38K was expressed in the late phase of infection in AcMNPV-infected Spodoptera frugiperda cells and copurified with budded virus (BV) and occlusion-derived virus (ODV). Biochemical fractionation of BV and ODV into the nucleocapsid and envelope components followed by Western blotting showed that 38K was associated with the nucleocapsids. Immunoelectron microscopic analysis revealed that 38K was specifically localized to the nucleocapsids in infected cells and appeared to be distributed over the cylindrical capsid sheath of nucleocapsid. Yeast two-hybrid assays were performed to examine potential interactions between 38K and nine known nucleocapsid shell-associated proteins (PP78/83, PCNA, VP1054, FP25, VLF-1, VP39, BV/ODV-C42, VP80, and P24), three non-nucleocapsid shell-associated proteins (P6.9, PP31, and BV/ODV-E26), and itself. The results revealed that 38K interacted with the nucleocapsid proteins VP1054, VP39, VP80, and 38K itself. These interactions were confirmed by coimmunoprecipitation assays in vivo. These data demonstrate that 38K is a novel nucleocapsid protein and provide a rationale for why 38K is essential for nucleocapsid assembly

Table_2_PROS1 shapes the immune-suppressive tumor microenvironment and predicts poor prognosis in glioma.docx

BackgroundGlioma is the most malignant cancer in the brain. As a major vitamin-K-dependent protein in the central nervous system, PROS1 not only plays a vital role in blood coagulation, and some studies have found that it was associated with tumor immune infiltration. However, the prognostic significance of PROS1 in glioma and the underlying mechanism of PROS1 in shaping the tumor immune microenvironment (TIME) remains unclear.MethodsThe raw data (including RNA-seq, sgRNA-seq, clinicopathological variables and prognosis, and survival data) were acquired from public databases, including TCGA, GEPIA, CGGA, TIMER, GEO, UALCAN, and CancerSEA. GO enrichment and KEGG pathway analyses were performed using “cluster profiler” package and visualized by the “ggplot2” package. GSEA was conducted using R package “cluster profiler”. Tumor immune estimation resource (TIMER) and spearman correlation analysis were applied to evaluate the associations between infiltration levels of immune cells and the expression of PROS1. qRT-PCR and WB were used to assay the expression of PROS1. Wound-healing assay, transwell chambers assays, and CCK-8 assays, were performed to assess migration and proliferation. ROC and KM curves were constructed to determine prognostic significance of PROS1 in glioma.ResultsThe level of PROS1 expression was significantly increased in glioma in comparison to normal tissue, which was further certificated by qRT-PCR and WB in LN-229 and U-87MG glioma cells. High expression of PROS1 positively correlated with inflammation, EMT, and invasion identified by CancerSEA, which was also proved by downregulation of PROS1 could suppress cells migration, and proliferation in LN-229 and U-87MG glioma cells. GO and KEGG analysis suggested that PROS1 was involved in disease of immune system and T cell antigen receptor pathway. Immune cell infiltration analysis showed that expression of PROS1 was negatively associated with pDC and NK CD56 bright cells while positively correlated with Macrophages, Neutrophils in glioma. Immune and stromal scores analysis indicated that PROS1 was positively associated with immune score. The high level of PROS1 resulted in an immune suppressive TIME via the recruitment of immunosuppressive molecules. In addition, Increased expression of PROS1 was correlated with T-cell exhaustion, M2 polarization, poor Overall-Survival (OS) in glioma. And it was significantly related to tumor histological level, age, primary therapy outcome. The results of our experiment and various bioinformatics approaches validated that PROS1 was a valuable poor prognostic marker.ConclusionIncreased expression of PROS1 was correlated with malignant phenotype and associated with poor prognosis in glioma. Besides, PROS1 could be a possible biomarker and potential immunotherapeutic target through promoting the glioma immunosuppressive microenvironment and inducing tumor-associated macrophages M2 polarization.</p