Regulation of TRPML1 by Lipids in Lysosomes.

TRPML1 is an inwardly-rectifying Ca2+-permeable channel in late endosomes and lysosomes (LELs). Loss-of-function mutations on the human TRPML1 gene cause a devastating pediatric neurodegenerative disease called type IV Mucolipidosis (ML4). Although it is well established that TRPML1 is involved in multiple late endocytic membrane trafficking processes, the regulatory mechanism for TRPML1 remains elusive. By directly patch-clamping endolysosomal membranes, we found that PI(3,5)P2, a low abundance endolysosome-specific phosphoinositide, potently and specifically activates TRPML1 in both heterologous and endogenous systems (Chapter 2). Lipid-protein binding assays showed that PI(3,5)P2 binds directly to the poly-basic region of the N terminus of TRPML1. Overexpression of TRPML1 rescued the enlarged vacuole phenotype in PI(3,5)P2-deficient cells, suggesting that TRPML1 is a downstream effector of PI(3,5)P2. Notably, this PI(3,5)P2-dependent regulation of TRPML1 is evolutionarily conserved. In budding yeast, the activity of a yeast functional TRPML homologue is also PI(3,5)P2-dependent. These results indicate that lysosomal PI(3,5)P2 is a physiological regulator of TRPML1, providing a previously unknown link between these two important regulators of intracellular membrane trafficking. In Chapter 3, a membrane-permeable small-molecule synthetic agonist for TRPMLs, Mucolipin-Synthetic Agonist 1 (ML-SA1), was identified. Electrophysiological results showed that ML-SA1 potently and specifically activates recombinant TRPMLs, as well as endogenous TRPML-like currents in many cell types. In addition, ML-SA1 evoked TRPML1-dependent Ca2+ release from endolysosomes in intact cells. ML-SA1 can therefore serve as a valuable tool for studying intracellular functions of TRPMLs. By taking advantage of ML-SA1, the activity of TRPML1 was examined under a pathological context (Chapter 4). TRPML1-mediated lysosomal Ca2+ release, measured using a genetically-encoded Ca2+ indicator (GCaMP3) attached directly to TRPML1, was dramatically reduced in Niemann-Pick (NP) disease cells. Patch-clamp analyses revealed that TRPML1 channel activity was inhibited by sphingomyelin, but potentiated by spingomyelinase. Importantly, increasing the expression/activity of TRPML1 was able to alleviate the lipid accumulation and trafficking defects in NPC cells. Our findings suggest that compromised channel activity of TRPML1 is the pathogenic cause for secondary lysosomal storage seen in many lysosomal storage disorders (LSDs). Thus manipulating TRPML1 channel activity by chemical agonists may provide therapeutic approaches not only for ML4, but also for other LSDs.PHDMolecular, Cellular and Developmental BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/96096/1/dongbiao_1.pd

Mucolipins: Intracellular TRPML1‐3 channels

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116338/1/feb2s0014579310000281.pd

Fault Diagnosis for Aircraft Hydraulic Systems via One-Dimensional Multichannel Convolution Neural Network

Detecting the faults in hydraulic systems in advance is difficult owing to the complexity associated with such systems. Hence, it is necessary to investigate the different fault modes and analyze the system reliability in order to establish a method for improving the reliability and security of hydraulic systems. To this end, this paper proposes a novel one-dimensional multichannel convolution neural network (1DMCCNN) for diagnosing fault modes. In this work, a landing gear hydraulic system was constructed with a normal model and a fault model; five types of faults were considered. Pressure signals were extracted from this hydraulic system, and the extracted signals were subsequently input into the convolution neural network (CNN) as multichannel data. Thereafter, the data were subjected to a one-dimensional convolution filter. The differences between channels were used to enhance features. The features obtained in this manner were compared for fault diagnoses. Furthermore, this proposed method was verified via simulations; the simulation results indicated that the precision of the 1DMCCNN was considerably higher than that of conventional machine learning algorithms

A Fault Diagnosis Method under Data Imbalance Based on Generative Adversarial Network and Long Short-Term Memory Algorithms for Aircraft Hydraulic System

Safe and stable operation of the aircraft hydraulic system is of great significance to the flight safety of an aircraft. Any fault may be a threat to flight safety and may lead to enormous economic losses and even human casualties. Hence, the normal status of the aircraft hydraulic system is large, but very few data samples relate to the fault status. This causes a data imbalance in the fault diagnosis of the aircraft hydraulic system, which directly affects the accuracy of aircraft fault diagnosis. To solve the data imbalance problem in the fault diagnosis of the aircraft hydraulic system, this paper proposes an improved GAN-LSTM algorithm by using the improved GAN method, which can stably and accurately generate high-quality simulated fault samples using a small number of fault data. First, the model of the aircraft hydraulic system was built using AMESim software, and the imbalanced fault data and normal status data were acquired. Then, the imbalanced data were used to train the GAN model until the system reached a Nash equilibrium. By comparing the time domain and frequency signal, it was found that the quality of the generated sample was highly similar to the real sample. Moreover, LSTM (long short-term memory) and some other data-driven intelligent fault diagnosis methods were used as classifiers. The accuracy of these fault diagnosis methods increased steadily when the number of fault samples was gradually increased until it reached a balance with the normal sample. Meanwhile, three different sample generation methods were compared and analyzed to find the method with the best data generation ability. Finally, the anti-noise performance of the LSTM-GAN method was analyzed; this model has superior noise immunity

An EMD-LSTM Deep Learning Method for Aircraft Hydraulic System Fault Diagnosis under Different Environmental Noises

Aircraft hydraulic fault diagnosis is an important technique in aircraft systems, as the hydraulic system is one of the key components of an aircraft. In aircraft hydraulic system fault diagnosis, complex environmental noises will lead to inaccurate results. To address the above problem, hydraulic system fault detection methods should be capable of noise resistance. Previous research has mainly focused on noise-free conditions and many effective approaches have been proposed; however, in real-world aircraft flying conditions, the aircraft hydraulic system often has strong and complex noises. The methods proposed may not have good fault detection results in such a noisy environment. According to the situation, this work focuses on aircraft hydraulic system fault classification under the influence of a hydraulic working environment with Gaussian white noise. In order to eliminate the noise interference and adapt to the actual noisy environment, a new aircraft hydraulic fault diagnostic method based on empirical mode deposition (EMD) and long short-term memory (LSTM) is presented. First, the hydraulic system is constructed by AMESIM. One normal state and five fault states are considered in this paper. Eight-channel signals of different states are collected for network training and testing. Second, the EMD method is used to obtain the different intrinsic mode functions (IMFs) of the signals. Third, principal component analysis (PCA) is used to obtain the main component of the IMFs. Fourth, three different LSTM methods are chosen to compare and the best structure that is chosen is the gate recurrent unit (GRU). After that, the network parameters are optimized. The results under different noise environments are given. Then, a comparison between the EMD-GRU with several different machine learning methods is considered, and the result shows that the method in this paper has a better anti-noise effect. Therefore, the proposed method is demonstrated to have a strong ability of fault diagnosis and classification under the working noises based on the simulation results

Activating mutations of the TRPML1 channel revealed by proline-scanning mutagenesis. The Journal of biological chemistry 284

Abstract The mucolipin TRP (TRPML) proteins are a family of endolysosomal cation channels with genetically established importance in man and rodent. Mutations of human TRPML1 cause type IV mucolipidosis (ML4), a devastating pediatric neurodegenerative disease. Our recent electrophysiological studies revealed that while a TRPML1-mediated current can only be recorded in late endosome and lysosome (LEL) using the lysosome patchclamp technique, a proline (Pro) substitution in TRPML1 (TRPML1 V432P ) results in a large whole-cell current. Thus, it remains unknown whether the large TRPML1 V432P -mediated current results from an increased surface expression (trafficking), elevated channel activity (gating), or both. Here we performed systemic Pro substitutions in a region previously implicated in the gating of various 6 transmembrane (TM) cation channels. We found that several Pro substitutions displayed gain-of-function (GOF) constitutive activities at both the plasma membrane (PM) and endolysosomal membranes. While wild-type TRPML1 and non-GOF Pro substitutions localized exclusively in LEL and were barely detectable in the PM, the GOF mutations with high constitutive activities were not restricted to LEL compartments, and most significantly, exhibited significant surface expression