NMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease

AbstractThe neuromuscular junction (NMJ) is the peripheral synapse formed between a motor neuron axon terminal and a muscle fibre. NMJs are thought to be the primary site of peripheral pathology in many neuromuscular diseases, but innervation/denervation status is often assessed qualitatively with poor systematic criteria across studies, and separately from 3D morphological structure. Here, we describe the development of ‘NMJ-Analyser’, to comprehensively screen the morphology of NMJs and their corresponding innervation status automatically. NMJ-Analyser generates 29 biologically relevant features to quantitatively define healthy and aberrant neuromuscular synapses and applies machine learning to diagnose NMJ degeneration. We validated this framework in longitudinal analyses of wildtype mice, as well as in four different neuromuscular disease models: three for amyotrophic lateral sclerosis (ALS) and one for peripheral neuropathy. We showed that structural changes at the NMJ initially occur in the nerve terminal of mutant TDP43 and FUS ALS models. Using a machine learning algorithm, healthy and aberrant neuromuscular synapses are identified with 95% accuracy, with 88% sensitivity and 97% specificity. Our results validate NMJ-Analyser as a robust platform for systematic and structural screening of NMJs, and pave the way for transferrable, and cross-comparison and high-throughput studies in neuromuscular diseases.

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Design of English text-to-speech conversion algorithm based on machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189238 ◽

2020 ◽

pp. 1-12

Author(s):

Li Dongmei

Keyword(s):

Machine Learning ◽

Speech Synthesis ◽

Feature Recognition ◽

Learning Algorithm ◽

Morphological Structure ◽

English Text ◽

Text To Speech ◽

Part Of Speech ◽

Modern Computer ◽

Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

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Motor Unit Number Estimation in Neuromuscular Disease

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/s0317167100008568 ◽

2008 ◽

Vol 35 (2) ◽

pp. 153-159 ◽

Cited By ~ 22

Author(s):

Omid Rashidipour ◽

K. Ming Chan

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Motor Unit ◽

Neuromuscular Disease ◽

Muscular Atrophy ◽

Neuromuscular Diseases ◽

Neuronal Loss ◽

Response To Treatment ◽

Motor Unit Number Estimation ◽

Electrophysiological Method ◽

Lateral Sclerosis

Motor unit number estimation (MUNE) is an electrophysiological method designed to quantify motor unit loss in target muscles of interest. Most of the techniques are noninvasive and are therefore well suited for longitudinal monitoring. In this brief review, we describe the more commonly used techniques and their applications in amyotrophic lateral sclerosis, poliomyelitis, spinal muscular atrophy and hereditary sensorimotor neuropathies. Findings in some of these studies offer important pathophysiological insights. Since conventional electrophysiologic methods are not sensible measures of motor neuronal loss, MUNE could play a potentially important role in the diagnosis, monitoring of disease progression and response to treatment in neuromuscular diseases in which motor unit loss is a major feature.

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La Risonanza Magnetica muscolare nelle malattie neuromuscolari

Rivista di Neuroradiologia ◽

10.1177/197140090301600333 ◽

2003 ◽

Vol 16 (3) ◽

pp. 503-510

Author(s):

A. Pichiecchio ◽

C. Cini ◽

M.G. Egitto ◽

A. Berardinelli ◽

E. Mercuri ◽

...

Keyword(s):

Magnetic Resonance ◽

Characteristic Feature ◽

Neuromuscular Disease ◽

Structural Changes ◽

Neuromuscular Diseases ◽

Risonanza Magnetica ◽

Muscle Involvement ◽

Mr Scanning

We describe the main magnetic resonance features of neuromuscular diseases based on a series of 345 patients undergoing muscle MR scans. The study aimed to establish the use of this method in the diagnosis of neuromuscular disease, namely its ability to distinguish neurogenic forms from primary myopathy by analysing the location, extension and distribution of structural changes. A selective muscle involvement was encountered in some myogenic diseases and can be considered a characteristic feature of different diseases and hence an important aid in diagnosis. We propose that MR scanning be included among the instrumental tests required in the diagnosis of neuromuscular disease.

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treeClust improves protein co-regulation analysis due to robust selectivity for close linear relationships

10.1101/578971 ◽

2019 ◽

Cited By ~ 1

Author(s):

Georg Kustatscher ◽

Piotr Grabowski ◽

Juri Rappsilber

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Synthetic Data ◽

Transcriptome Profiling ◽

Machine Learning Algorithms ◽

Proteomics Data ◽

Unsupervised Machine Learning ◽

Biologically Relevant ◽

Linear Relationships ◽

Improved Performance

Gene co-expression analysis is a widespread method to identify the potential biological function of uncharacterised genes. Recent evidence suggests that proteome profiling may provide more accurate results than transcriptome profiling. However, it is unclear which statistical measure is best suited to detect proteins that are co-regulated. We have previously shown that expression similarities calculated using treeClust, an unsupervised machine-learning algorithm, outperformed correlation-based analysis of a large proteomics dataset. The reason for this improvement is unknown. Here we systematically explore the characteristics of treeClust similarities. Leveraging synthetic data, we find that tree-based similarities are exceptionally robust against outliers and detect only close-fitting, linear protein – protein associations. We then use proteomics data to demonstrate that both of these features contribute to the improved performance of treeClust relative to Pearson, Spearman and robust correlation. Our results suggest that, for large proteomics datasets, unsupervised machine-learning algorithms such as treeClust may significantly improve the detection of biologically relevant protein – protein associations relative to correlation metrics.

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Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

10.1055/s-0038-1660728 ◽

2018 ◽

Author(s):

C.H.B. van Niftrik ◽

F. van der Wouden ◽

V. Staartjes ◽

J. Fierstra ◽

M. Stienen ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Tumor Surgery ◽

Early Complications ◽

Brain Tumor Surgery

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Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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