Identification of multiple metabolic enzymes from mice cochleae tissue using a novel functional proteomics technology

A new type of technology in proteomics was developed in order to separate a complex protein mixture and analyze protein functions systematically. The technology combines the ability of two-dimensional gel electrophoresis (2-DE) to separate proteins with a protein elution plate (PEP) to recover active proteins for functional analysis and mass spectrometry (MS)-based identification. In order to demonstrate the feasibility of this functional proteomics approach, NADH and NADPH-dependent oxidases, major redox enzyme families, were identified from mice cochlear tissue after a specific drug treatment. By comparing the enzymatic activity between mice that were treated with a drug and a control group significant changes were observed. Using MS, five NADH-dependent oxidases were identified that showed highly altered enzymatic activities due to the drug treatment. In essence, the PEP technology allows for a systematic analysis of a large enzyme family from a complex proteome, providing insights in understanding the mechanism of drug treatment

Performance analysis of retrofitted tribo-corrosion test-rig for monitoring in situ oil condition

Oils and lubricants, once extracted after use from a mechanical system, can hardly be reused, and should be refurbished or replaced in most applications. New methods of in situ oil and lubricant efficiency monitoring systems have been introduced for a wide variety of mechanical systems, such as automobiles, aerospace aircrafts, ships, offshore wind turbines, and deep sea oil drilling rigs. These methods utilize electronic sensors to monitor the “byproduct effects” in a mechanical system that are not indicative of the actual remaining lifecycle and reliability of the oils. A reliable oil monitoring system should be able to monitor the wear rate and the corrosion rate of the tribo-pairs due to the inclusion of contaminants. The current study addresses this technological gap, and presents a novel design of a tribo-corrosion test rig for oils used in a dynamic system. A pin-on-disk tribometer test rig retrofitted with a three electrode-potentiostat corrosion monitoring system was used to analyze the corrosion and wear rate of a steel tribo-pair in industrial grade transmission oil. The effectiveness of the retrofitted test rig was analyzed by introducing various concentrations of contaminants in an oil medium that usually leads to a corrosive working environment. The results indicate that the retrofitted test rig can effectively monitor the in situ tribological performance of the oil in a controlled dynamic corrosive environment. It is a useful method to understand the wear–corrosion synergies for further experimental work, and to develop accurate predictive lifecycle assessment and prognostic models. The application of this system is expected to have economic benefits and help reduce the ecological oil waste footprint

Protocol: Streamline cloning of genes into binary vectors in Agrobacterium via the Gateway® TOPO vector system

<p>Abstract</p> <p>Background</p> <p>In plant functional genomic studies, gene cloning into binary vectors for plant transformation is a routine procedure. Traditionally, gene cloning has relied on restriction enzyme digestion and ligation. In recent years, however, Gateway^®cloning technology (Invitrogen Co.) has developed a fast and reliable alternative cloning methodology which uses a phage recombination strategy. While many Gateway- compatible vectors are available, we frequently encounter problems in which antibiotic resistance genes for bacterial selection are the same between recombinant vectors. Under these conditions, it is difficult, if not sometimes impossible, to use antibiotic resistance in selecting the desired transformants. We have, therefore, developed a practical procedure to solve this problem.</p> <p>Results</p> <p>An integrated protocol for cloning genes of interest from PCR to <it>Agrobacterium </it>transformants via the Gateway^®System was developed. The protocol takes advantage of unique characteristics of the replication origins of plasmids used and eliminates the necessity for restriction enzyme digestion in plasmid selections.</p> <p>Conclusion</p> <p>The protocol presented here is a streamlined procedure for fast and reliable cloning of genes of interest from PCR to <it>Agrobacterium </it>via the Gateway^®System. This protocol overcomes a key problem in which two recombinant vectors carry the same antibiotic selection marker. In addition, the protocol could be adapted for high-throughput applications.</p

Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults

Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors

Neuromodulatory systems exert profound influences on brain function. Understanding how these systems modify the operating mode of target circuits requires measuring spatiotemporally precise neuromodulator release. We developed dLight1, an intensity-based genetically encoded dopamine indicator, to enable optical recording of dopamine dynamics with high spatiotemporal resolution in behaving mice. We demonstrated the utility of dLight1 by imaging dopamine dynamics simultaneously with pharmacological manipulation, electrophysiological or optogenetic stimulation, and calcium imaging of local neuronal activity. dLight1 enabled chronic tracking of learning-induced changes in millisecond dopamine transients in striatum. Further, we used dLight1 to image spatially distinct, functionally heterogeneous dopamine transients relevant to learning and motor control in cortex. We also validated our sensor design platform for developing norepinephrine, serotonin, melatonin, and opioid neuropeptide indicators

Gap Junction Mediated miRNA Intercellular Transfer and Gene Regulation: A Novel Mechanism for Intercellular Genetic Communication

Intercellular genetic communication is an essential requirement for coordination of cell proliferation and differentiation and has an important role in many cellular processes. Gap junction channels possess large pore allowing passage of ions and small molecules between cells. MicroRNAs (miRNAs) are small regulatory RNAs that can regulate gene expression broadly. Here, we report that miRNAs can pass through gap junction channels in a connexin-dependent manner. Connexin43 (Cx43) had higher permeability, whereas Cx30 showed little permeability to miRNAs. In the tested connexin cell lines, the permeability to miRNAs demonstrated: Cx43 \u3e Cx26/30 \u3e Cx26 \u3e Cx31 \u3e Cx30 = Cx-null. However, consistent with a uniform structure of miRNAs, there was no significant difference in permeability to different miRNAs. The passage is efficient; the miRNA level in the recipient cells could be up to 30% of the donor level. Moreover, the transferred miRNA is functional and could regulate gene expression in neighboring cells. Connexin mutation and gap junctional blockers could eliminate this miRNA intercellular transfer and gene regulation. These data reveal a novel mechanism for intercellular genetic communication. Given that connexin expression is cell-specific, this connexin-dependent, miRNA intercellular genetic communication may play an important role in synchronizing and coordinating proliferation and differentiation of specific cell types during multicellular organ development

A Novel Unsupervised Graph Wavelet Autoencoder for Mechanical System Fault Detection

Reliable fault detection is an essential requirement for safe and efficient operation of complex mechanical systems in various industrial applications. Despite the abundance of existing approaches and the maturity of the fault detection research field, the interdependencies between condition monitoring data have often been overlooked. Recently, graph neural networks have been proposed as a solution for learning the interdependencies among data, and the graph autoencoder (GAE) architecture, similar to standard autoencoders, has gained widespread use in fault detection. However, both the GAE and the graph variational autoencoder (GVAE) have fixed receptive fields, limiting their ability to extract multiscale features and model performance. To overcome these limitations, we propose two graph neural network models: the graph wavelet autoencoder (GWAE), and the graph wavelet variational autoencoder (GWVAE). GWAE consists mainly of the spectral graph wavelet convolutional (SGWConv) encoder and a feature decoder, while GWVAE is the variational form of GWAE. The developed SGWConv is built upon the spectral graph wavelet transform which can realize multiscale feature extraction by decomposing the graph signal into one scaling function coefficient and several spectral graph wavelet coefficients. To achieve unsupervised mechanical system fault detection, we transform the collected system signals into PathGraph by considering the neighboring relationships of each data sample. Fault detection is then achieved by evaluating the reconstruction errors of normal and abnormal samples. We carried out experiments on two condition monitoring datasets collected from fuel control systems and one acoustic monitoring dataset from a valve. The results show that the proposed methods improve the performance by around 3%~4% compared to the comparison methods

Preclinical Models for Functional Precision Lung Cancer Research.

Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted therapies, immunotherapies, antibody-drug conjugates, and emerging investigational treatments. While traditional human lung cancer cell lines offer a basic framework for cancer research, they often lack the tumor heterogeneity and intricate tumor-stromal interactions necessary to accurately predict patient-specific clinical outcomes. Patient-derived xenografts (PDXs), however, retain the original tumors histopathology and genetic features, providing a more reliable model for predicting responses to systemic therapeutics, especially molecularly targeted therapies. For studying immunotherapies and antibody-drug conjugates, humanized PDX mouse models, syngeneic mouse models, and genetically engineered mouse models (GEMMs) are increasingly utilized. Despite their value, these in vivo models are costly, labor-intensive, and time-consuming. Recently, patient-derived lung cancer organoids (LCOs) have emerged as a promising in vitro tool for functional precision oncology studies. These LCOs demonstrate high success rates in growth and maintenance, accurately represent the histology and genomics of the original tumors and exhibit strong correlations with clinical treatment responses. Further supported by advancements in imaging, spatial and single-cell transcriptomics, proteomics, and artificial intelligence, these preclinical models are reshaping the landscape of drug development and functional precision lung cancer research. This integrated approach holds the potential to deliver increasingly accurate, personalized treatment strategies, ultimately enhancing patient outcomes in lung cancer