3 research outputs found
Andrena transbaicalica Popov 1949
No full text19. Andrena transbaicalica Popov, 1949 (Figs 19a–e) Andrena transbaicalica Popov, 1949: 398, ♀, fig. 3. Type locality: Nerchinsk (Zabaikaisky Territory, Russia). Published (original) locality: Russia: Krasnoyarsk Terr., Yurty; Irkutsk Prov., Irkutsk; Chita PRov., Chita, Nerchinsk; Amur Prov., Kirma on amur; Bureya-Ussuri; Primorskaya Prov., Yakovlevka near Spassk; Zeleny Bor. Holotype: ♀, Sibiria, Nertschinsk [Russia, Zabaikalsky Terr., Nerchinsk, 51°59′N 116°35′E] // к. Ф. Моравица [Coll. of F. Morawitz] // transbaicalica sp. nov. // Andrena transbaicalica sp. n., ♀, V. Popov det. [1]936 // Holotypus Andrena transbaicalica Popov // Zoological Institute St. Petersburg INS_HYM_00002782. Paratypes: 1 ♀, Irkutsk, V.E. Yakovlev // Coll. A. Semenow-Tian-Shansky // Andrena transbaicalica sp. n., ♀, Cotype, V. Popov det. [1]936; 1 ♀, Чита, Кайдаловка, Забайк.[алье] [Chita, Kaidalovka River], 26-29. V.[1]912, Валуева [Valueva leg.] // Andr. transbaicalica sp. n., ♀, Cotype, V. Popov det. [1]936; 1 ♀, Кирма на Амуръ, БуреЯ-Уссури [Kirma on Amur, Bureya-Ussuri Rivers], Маакъ [Maak leg.] // 22.96 Andr. transbaicalica sp. n., ♀, Cotype, V. Popov det. [1]936; 1 ♀, Rковлевка, Спас.[ского] у.[еЗда], Уссур.[ийский] кр.[ай] [Yakovlevka, Ussury territory], 12.VII.[1]926, ДьЯконов, Филипьев [D’yakonov, Filip’ev leg.] // Пасека Квашука [apiary of Kvashuk] // Andr. transbaicalica sp. n., ♀, V. Cotype, V. Popov det. [1]936; 1 ♀, Зеленый бор, Прим.[орскаЯ] обл.[асть] [Zeleny Bor, Primorskiy Terr.] 27. VI.1913, П. Солдатов [P. Soldatov leg.] // Andr. transbaicalica sp. n., ♀, Cotype, V. Popov det. [1]936 // Paratypus Andrena transbaicalica Popov . Current status. Andrena (Plastandrena) transbaicalica Popov, 1949 (according to Xu & Tadauchi 2011: 65). Remark. Description of male: Hirashima 1957: 51, as Andrena astragalina Hirashima, 1957 (synonymized by Osytshnjuk 1995: 493). Distribution. Russia (Siberia, Far East), northern China, South Korea, Japan.Published as part of Astafurova, Yulia V., Proshchalykin, Maxim Yu. & Sidorov, Dmitry A., 2023, The type specimens of bees (Hymenoptera: Apoidea) deposited in the Zoological Institute of the Russian Academy of Sciences, St. Petersburg. Contribution VI. Family Andrenidae, genus Andrena Fabricius, 1775, taxa described by V. Popov, pp. 401-426 in Zootaxa 5301 (4) on pages 424-425, DOI: 10.11646/zootaxa.5301.4.1, http://zenodo.org/record/803595
KLASIFIKASI ALZHEIMER PADA CITRA MRI OTAK DENGAN CONVOLUTIONAL NEURAL NETWORK
No full textIn deep learning, Convolutional Neural Network (CNN) is an algorithm from Artificial Neural Network (ANN) which is generally used to analyze visual images. This algorithm can automatically extract important features from each image without human assistance, besides that the CNN algorithm is also more efficient than other neural network methods, especially in memory and complexity. In training, the algorithm will be given training data in the form of images that have been labeled so that the algorithm will be able to recognize the important characteristics of each of the labeled images. After the training stage, the trained algorithm will be given data validation in the form of an unlabeled image to be analyzed and classified. The algorithm will analyze the training and validation data for the specified number of epochs and provide information in the form of the level of accuracy of each epoch that is performed. Some that affect the level of accuracy include the type of optimizer, the pixel size of the input image, and the number of epochs. In this study, the CNN algorithm was used with a layer sequence made personally by the author. The research was conducted in a cloud-based Jupyter notebook environment called Google Colab. The dataset used in this study is the Alzheimer\u27s MRI Preprocessed Dataset which can be accessed by the public on the Kaggle website. The dataset consists of 6400 brain MRI scan images which are divided into four classes, namely: Non Demented, Very Mild Demented, Mild Demented, and Moderate Demented. As much as 20% of the dataset is used as data validation. In this study, the dataset will be analyzed by the CNN algorithm with several predetermined scenarios, then the accuracy of the training and validation data will be compared with each other to find the most optimal scenario. There are two input image pixel size scenarios to be compared, namely 128 x 128 pixels and 224 x 224 pixels. There are three types of optimizers that will be compared, namely Stochastic Gradient Descent (SGD), Adam, and RMSprop. From the research results, the most optimal type of optimizer to use with the architecture that has been made and the Alzheimer\u27s MRI Preprocessed Dataset is the Adam optimizer. Architectural training with an input size scenario of 224 x 224 pixels, seven epochs, and using the Adam optimizer achieves the most optimal accuracy rate, namely with a training data accuracy rate of 93.01% and a data validation accuracy rate of 94.45%. Architecture training with an input size scenario of 224 x 224 pixels and using the Adam optimizer achieves the most optimal number of epochs, namely achieving an accuracy level above 90% in just five epochs.
Keywords: CNN, Alzheimer\u27s, accuracy, optimizer, optimal.
Daftar Pustaka
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[5] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
[6] Mendez, M. F. (2006). The accurate diagnosis of early-onset dementia. The International Journal of Psychiatry in Medicine, 36(4), 401-412.
[7] Mortimer, J. A., Borenstein, A. R., Gosche, K. M., & Snowdon, D. A. (2005). Very early detection of Alzheimer neuropathology and the role of brain reserve in modifying its clinical expression. Journal of geriatric psychiatry and neurology, 18(4), 218-223.
[8] National Institute for Health and Clinical Excellence. (2006, November). Dementia: Quick Reference Guide. Diambil kembali darihttps://web.archive.org/web/20080227161412/http://www.nice.org.uk/nicemedia/pdf/CG042quickrefguide.pdf.
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[11] Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and computers in simulation, 177, 232-243
Воздействие высокой концентрации оксида азота на оксигенаторы аппаратов искусственного кровообращения (экспериментальное исследование)
No full textThe aim of the study. To study the effect of high nitric oxide concentrations on hollow polypropylene fibers of oxygenators.Materials and methods. The study was conducted in two stages. At the first stage, we evaluated the stability of oxygenator membrane made of hollow polypropylene fibers after six hours of exposure to air-oxygen mixture containing NO at 500 parts per million, or 500 pro pro mille (ppm) concentration, using mass spectrometry and infrared spectroscopy. At the second stage, an experiment with cardiopulmonary bypass (CPB) was conducted on 10 pigs. In the study group (n=5) animals sweep gas was supplied to the oxygenator as an air-oxygen mixture with NO at 100 ppm. In the control group animals (n=5) an air-oxygen mixture was used without NO. The CPB lasted for 4 hours, followed by observation for 12 hours. NO, NO2 (at the inlet and outlet of the oxygenator), and the dynamics of methemoglobin were evaluated. After weaning of animals from CPB, the oxygenators were tested for leakproofness, and scanning electron microscopy (SEM) was performed.Results. The oxygenator made of polypropylene hollow fibers retained its gas transfer parameters after six hours of exposure to air-oxygen mixture containing NO at 500 ppm. Based on IR-Fourier spectroscopy findings, NO did not affect structural integrity of polypropylene membranes. NO added to gas mixture at 100 ppm did not increase NO2 to toxic level of 2 ppm in 91% of control tests during 4 hours CPB in pigs; mean value was 1.58 ± 0.28 ppm. Methemoglobin concentration did not exceed the upper limit of permissible level (3%), and there were no statistically significant differences with the control group. All tested oxygenators have passed the leakproofness test. According to SEM findings, larger amounts of fibrin deposits were found in the control group oxygenators vs study group.Conclusion. There were no negative effects of NO at 500 ppm concentration on the oxygenator membrane made of hollow polypropylene fibers. NO at 100 ppm in a gas-mixture supplied to oxygenators did not lead to an exceedance of safe NO2 and methemoglobin concentrations in an animal model. Reduced fibrin deposits on hollow fibers of polypropylene oxygenator membranes were observed when with NO at a level of 100 ppm was added to a gas mixture. Цель исследования. Изучить воздействие высоких концентраций оксида азота на полипропиленовые полые волокна оксигенаторов.Материалы и методы. Исследование провели в два этапа. На первом этапе с помощью масс-спектрометрии и инфракрасной спектроскопии выполнили оценку стабильности мембраны оксигенатора из полых волокон полипропилена после шестичасового воздействия воздушно-кислородной смеси, содержащей NO в концентрации 500 пропромилле, или 500 частей на миллион – parts per million (ppm). На втором этапе провели эксперимент на 10 свиньях с подключением аппарата искусственного кровообращения (ИК). Животным основной группы (n=5) в оксигенатор подавали воздушно-кислородную смесь, содержащую NO в концентрации 100 ppm. Животным контрольной группы (n=5) в оксигенатор подавали воздушно-кислородную смесь без NO. Процедура ИК длилась 4 часа, затем следовало наблюдение в течение 12 часов. Оценивали NO, NO2 (на входе и выходе из оксигенатора), динамику метгемоглобина. После отключения от ИК оксигенаторы тестировали на герметичность, а также выполняли сканирующую электронную микроскопию (СЭМ).Результаты. Оксигенатор из полипропиленовых полых волокон сохранял свои газотранспортные характеристики после шестичасового воздействия воздушно-кислородной смеси с добавлением NO в концентрации 500 ppm. По данным ИК-Фурье спектроскопии показали, что NO не влияет на структуру мембран из полипропилена. Добавление NO в дозировке 100 ppm во время 4 часов ИК у свиней не сопровождалось повышением концентрации NO2 до токсичного уровня 2 ppm в 91% измерений: среднее значение составило 1,58 ± 0,28 ppm. Концентрация метгемоглобина не превышала верхнего предела допустимых значений (3%), не обнаружили каких-либо статистически значимых различий при сравнении с группой контроля. Все исследуемые оксигенаторы выдержали тестирование на герметичность. По результатам СЭМ оксигенаторы группы контроля характеризовались большим количеством отложений фибрина, чем оксигенаторы основной группы.Заключение. Негативного воздействия NO в концентрации 500 ppm на мембраны оксигенаторов из полых волокон полипропилена не обнаружили. Подача в оксигенатор NO в концентрации 100 ppm NO2 не приводила к превышению безопасного содержания NO2 и метгемоглобина в эксперименте на животных. Выявили снижение образования отложений фибрина на полых волокнах мембран оксигенаторов из полипропилена при подаче NO в концентрации 100 ppm
