Closest horizons of Hsp70 engagement to manage neurodegeneration

Our review seeks to elucidate the current state-of-the-art in studies of 70-kilodalton-weighed heat shock proteins (Hsp70) in neurodegenerative diseases (NDs). The family has already been shown to play a crucial role in pathological aggregation for a wide spectrum of brain pathologies. However, a slender boundary between a big body of fundamental data and its implementation has only recently been crossed. Currently, we are witnessing an anticipated advancement in the domain with dozens of studies published every month. In this review, we briefly summarize scattered results regarding the role of Hsp70 in the most common NDs including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). We also bridge translational studies and clinical trials to portray the output for medical practice. Available options to regulate Hsp70 activity in NDs are outlined, too

Closest horizons of Hsp70 engagement to manage neurodegeneration

Our review seeks to elucidate the current state-of-the-art in studies of 70-kilodalton-weighed heat shock proteins (Hsp70) in neurodegenerative diseases (NDs). The family has already been shown to play a crucial role in pathological aggregation for a wide spectrum of brain pathologies. However, a slender boundary between a big body of fundamental data and its implementation has only recently been crossed. Currently, we are witnessing an anticipated advancement in the domain with dozens of studies published every month. In this review, we briefly summarize scattered results regarding the role of Hsp70 in the most common NDs including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). We also bridge translational studies and clinical trials to portray the output for medical practice. Available options to regulate Hsp70 activity in NDs are outlined, too

Об определении уровня полезных сигналов при расшифровке магнитных и вихретоковых дефектограмм

To ensure traffic safety of railway transport, non-destructive testing of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. The paper is devoted to the problem of automatic determination of a threshold level of amplitudes of useful signals (from defects and structural elements of a railway track) during the analysis of defectograms (records) of magnetic and eddy current flaw detectors. A signal is considered useful (and is subject to further analysis) if a deviation of its value from an average of all signals is at least twice the threshold noise level of rails. The probability of obtaining a signal from a section without structural elements (a rail noise signal) is characterized by the normal distribution law. Thus, the rule of three sigma can be used to calculate the threshold noise level. And a signal is useful if its amplitude deviation from a sample mean exceeds twice the threshold noise level. The paper proposes an algorithm for finding the threshold level of a rail noise and gives its theoretical justification, and it also examines examples of its operation on several fragments of real magnetic and eddy current defectograms. Для обеспечения безопасности движения на железнодорожном транспорте регулярно проводится неразрушающий контроль рельсов с применением различных подходов и методов, включая методы магнитной и вихретоковой дефектоскопии. Статья посвящена задаче автоматического определения порогового уровня амплитуд полезных сигналов (от дефектов и конструктивных элементов рельсового пути) при расшифровке дефектограмм магнитных и вихретоковых дефектоскопов. Сигнал считается полезным (и подлежит дальнейшему анализу), если отклонение его значения от среднего значения всех сигналов как минимум в два раза превосходит пороговый уровень шума рельсов. Вероятность появления сигнала с некоторой амплитудой в бездефектных рельсах на участке без конструктивных элементов, т. е. являющегося рельсовым шумом, характеризуется законом нормального распределения. Таким образом, для вычисления порогового уровня шума может быть задействовано правило трех сигм. А удвоение порога шума дает уровень, превышение которого по амплитудному отклонению от выборочного среднего означает, что сигнал является полезным. В статье предлагается алгоритм нахождения порогового уровня шума рельсов и дается его теоретическое обоснование, а также рассматриваются примеры его работы на нескольких фрагментах реальных магнитных и вихретоковых дефектограмм.

Эффективный алгоритм определения уровня полезных сигналов при расшифровке магнитных и вихретоковых дефектограмм

To ensure traffic safety of railway transport, non-destructive testing of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. An automatic analysis of large data sets (defectgrams) that come from the corresponding equipment is still an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks on defectograms. At the same time, under the conditions of significant volumes of incoming information, fast and efficient algorithms of data analysis are of most interest. This article is an addition to the previous article devoted to the problem of automatic determination of a threshold level of amplitudes of useful signals (from defects and structural elements of a railway track) during the analysis of defectograms (records) of magnetic and eddy current flaw detectors, which contains an algorithm for finding the threshold level of a rail noise and its theoretical justification with examples of its operation on several fragments of real magnetic and eddy current defectograms. The article presents a simple and effective implementation of the algorithm, which is successfully used in practice for the automatic analysis of magnetic and eddy current defectograms. Для обеспечения безопасности движения на железнодорожном транспорте регулярно проводится неразрушающий контроль рельсов с применением различных подходов и методов, включая методы магнитной и вихретоковой дефектоскопии. Актуальной задачей по-прежнему остается автоматический анализ больших массивов данных (дефектограмм), которые поступают от соответствующего оборудования. Под анализом понимается процесс определения по дефектограммам наличия дефектных участков наряду с выявлением конструктивных элементов рельсового пути. При этом в условиях значительных объемов поступающей на обработку информации наибольший интерес представляют быстрые и эффективные алгоритмы анализа данных. Данная статья является дополнением к предыдущей статье авторов, посвященной задаче автоматического определения порогового уровня амплитуд полезных сигналов при расшифровке дефектограмм магнитных и вихретоковых дефектоскопов, в которой был предложен алгоритм нахождения порогового уровня шума рельсов с его теоретическим обоснованием, а также рассматривались примеры работы алгоритма на фрагментах реальных магнитных и вихретоковых дефектограмм. В настоящей статье приводится простая и эффективная реализация этого алгоритма, которая с успехом применяется на практике при автоматическом анализе магнитных и вихретоковых дефектограмм

Применение нейронных сетей для распознавания конструктивных элементов рельсов на магнитных и вихретоковых дефектограммах

To ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. An automatic analysis of large data sets (defectgrams) that come from the corresponding equipment is an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks on defectograms. This article is devoted to the problem of recognition of rail structural element images in magnetic and eddy current defectograms. Three classes of rail track structural elements are considered: 1) a bolted joint with straight or beveled connection of rails, 2) a butt weld of rails, and 3) an aluminothermic weld of rails. Images that cannot be assigned to these three classes are conditionally considered as defects and are placed in a separate fourth class. For image recognition of structural elements in defectograms a neural network is applied. The neural network is implemented by using the open library TensorFlow. To this purpose each selected (picked out) area of a defectogram is converted into a graphic image in a grayscale with size of 20 x 39 pixels.Для обеспечения безопасности движения на железнодорожном транспорте регулярно проводится неразрушающий контроль рельсов с применением различных подходов и методов, включая методы магнитной и вихретоковой дефектоскопии. Актуальной задачей является автоматический анализ больших массивов данных (дефектограмм), которые поступают от соответствующего оборудования. Под анализом понимается процесс определения по дефектограммам наличия дефектных участков наряду с выявлением конструктивных элементов рельсового пути. Данная статья посвящена задаче распознавания образов конструктивных элементов железнодорожных рельсов по дефектограммам многоканальных магнитных и вихретоковых дефектоскопов. Рассматриваются три класса конструктивных элементов рельсового пути: 1) болтовой стык с прямым или скошенным соединением рельсов, 2) электроконтактная сварка рельсов и 3) алюмотермитная сварка рельсов. Образы, которые не могут быть отнесены к этим трем классам, условно считаются дефектами и выносятся в отдельный четвертый класс. Для распознавания образов конструктивных элементов на дефектограммах применяется нейронная сеть, реализованная в рамках открытой библиотеки TensorFlow. С этой целью каждая выделенная для анализа область дефектограммы преобразуется в графический образ в градации серого цвета размером 20 на 39 пикселей

Применение свёрточных нейронных сетей для распознавания длинных конструктивных элементов рельсов на вихретоковых дефектограммах

To ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including eddy-current flaw detection methods. An automatic analysis of large data sets (defectograms) that come from the corresponding equipment is an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks in defectograms. This article is devoted to the problem of recognizing images of long structural elements of rails in eddy-current defectograms. Two classes of rail track structural elements are considered: 1) rolling stock axle counters, 2) rail crossings. Long marks that cannot be assigned to these two classes are conditionally considered as defects and are placed in a separate third class. For image recognition of structural elements in defectograms a convolutional neural network is applied. The neural network is implemented by using the open library TensorFlow. To this purpose each selected (picked out) area of a defectogram is converted into a graphic image in a grayscale with size of 30 x 140 points.Для обеспечения безопасности движения на железнодорожном транспорте регулярно проводится неразрушающий контроль рельсов с применением различных подходов и методов, включая методы вихретоковой дефектоскопии. Актуальной задачей является автоматический анализ больших массивов данных (дефектограмм), которые поступают от соответствующего оборудования. Под анализом понимается процесс определения по дефектограммам наличия дефектных участков наряду с выявлением конструктивных элементов рельсового пути. Данная статья посвящена задаче распознавания образов длинных конструктивных элементов железнодорожных рельсов по дефектограммам многоканальных вихретоковых дефектоскопов. Рассматриваются два класса конструктивных элементов рельсового пути: 1) счётчики осей подвижного состава, 2) пересечения рельсовых путей. Длинные отметки, которые не могут быть отнесены к этим двум классам, условно считаются дефектами и выносятся в отдельный третий класс. Для распознавания образов конструктивных элементов на дефектограммах применяется свёрточная нейронная сеть, реализованная в рамках открытой библиотеки TensorFlow. С этой целью каждая выделенная для анализа область дефектограммы преобразуется в графический образ в градации серого цвета размером 30 на 140 точек

An Efficient Algorithm for Finding a Threshold of Useful Signals in the Analysis of Magnetic and Eddy Current Defectograms

To ensure traffic safety of railway transport, non-destructive testing of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. An automatic analysis of large data sets (defectgrams) that come from the corresponding equipment is still an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks on defectograms. At the same time, under the conditions of significant volumes of incoming information, fast and efficient algorithms of data analysis are of most interest. This article is an addition to the previous article devoted to the problem of automatic determination of a threshold level of amplitudes of useful signals (from defects and structural elements of a railway track) during the analysis of defectograms (records) of magnetic and eddy current flaw detectors, which contains an algorithm for finding the threshold level of a rail noise and its theoretical justification with examples of its operation on several fragments of real magnetic and eddy current defectograms. The article presents a simple and effective implementation of the algorithm, which is successfully used in practice for the automatic analysis of magnetic and eddy current defectograms

On Finding a Threshold of Useful Signals in the Analysis of Magnetic and Eddy Current Defectograms

To ensure traffic safety of railway transport, non-destructive testing of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. The paper is devoted to the problem of automatic determination of a threshold level of amplitudes of useful signals (from defects and structural elements of a railway track) during the analysis of defectograms (records) of magnetic and eddy current flaw detectors. A signal is considered useful (and is subject to further analysis) if a deviation of its value from an average of all signals is at least twice the threshold noise level of rails. The probability of obtaining a signal from a section without structural elements (a rail noise signal) is characterized by the normal distribution law. Thus, the rule of three sigma can be used to calculate the threshold noise level. And a signal is useful if its amplitude deviation from a sample mean exceeds twice the threshold noise level. The paper proposes an algorithm for finding the threshold level of a rail noise and gives its theoretical justification, and it also examines examples of its operation on several fragments of real magnetic and eddy current defectograms

Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy-Current Defectograms

To ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including eddy-current flaw detection methods. An automatic analysis of large data sets (defectograms) that come from the corresponding equipment is an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks in defectograms. This article is devoted to the problem of recognizing images of long structural elements of rails in eddy-current defectograms. Two classes of rail track structural elements are considered: 1) rolling stock axle counters, 2) rail crossings. Long marks that cannot be assigned to these two classes are conditionally considered as defects and are placed in a separate third class. For image recognition of structural elements in defectograms a convolutional neural network is applied. The neural network is implemented by using the open library TensorFlow. To this purpose each selected (picked out) area of a defectogram is converted into a graphic image in a grayscale with size of 30 x 140 points

Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current Defectograms

To ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. An automatic analysis of large data sets (defectgrams) that come from the corresponding equipment is an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks on defectograms. This article is devoted to the problem of recognition of rail structural element images in magnetic and eddy current defectograms. Three classes of rail track structural elements are considered: 1) a bolted joint with straight or beveled connection of rails, 2) a butt weld of rails, and 3) an aluminothermic weld of rails. Images that cannot be assigned to these three classes are conditionally considered as defects and are placed in a separate fourth class. For image recognition of structural elements in defectograms a neural network is applied. The neural network is implemented by using the open library TensorFlow. To this purpose each selected (picked out) area of a defectogram is converted into a graphic image in a grayscale with size of 20 x 39 pixels