Control of Magnetizing Inrush Current in a Transformer by Means of Thyristors

When a transformer is energized, the inrush of abnormally high magnetizing current may be noted for a short time until normal flux conditions are established. This may cause the failure of a protective relay, so many preventives are usually accepted for the purpose of normal relay performance. The authors, instead, now have tried to control the inrush current itself, by means of the soft starting method using two reverse parallel thyristors. In this paper, the method to control the inrush current itself, is presented by the soft-starting method using thyristors. The experimental results of this Method verifies the good controlability of the transient magnetic flux of a transformer and then the availability of the control of magnetizing inrush current in the cases of a single phase connection and a three phase one

Light-induced silencing of neural activity in Rosa26 knock-in and BAC transgenic mice conditionally expressing the microbial halorhodopsin eNpHR3

An engineered light-inducible chloride pump, Natronomonas pharaonis halorhodopsin 3 (eNpHR3) enables temporally and spatially precise inhibition of genetically defined cell populations in the intact nervous tissues. In this report, we show the generation of new mouse strains that express eNpHR3-EYFP fusion proteins after Cre- and/or Flp-mediated recombination to optically inhibit neuronal activity. In these mouse strains, Cre/Flp recombination induced high levels of opsin expression. We confirmed their light-induced activities by brain slice whole-cell patch clamp experiments. eNpHR3-expressing neurons were optically hyperpolarized and silenced from firing action potentials. In prolonged silencing of action potentials, eNpHR3 was superior to eNpHR2, a former version of the engineered pump. Thus, these eNpHR3 mouse strains offer reliable genetic tools for light-induced inhibiting of neuronal activity in defined sets of neurons

Automated detection of retinal nonperfusion area caused by retinal vein occlusion

We aimed to assess the ability of deep learning (DL) and support vector machine (SVM) to detect a nonperfusion area (NPA) caused by retinal vein occlusion (RVO) with optical coherence tomography angiography (OCTA) images. The study included 322 OCTA images (normal: 148; NPA owing to RVO: 174 [128 branch RVO images and 46 central RVO images]). Training to construct the DL model using deep convolutional neural network (DNN) algorithms was provided using OCTA images. The SVM used a scikit-learn library with a radial basis function kernel. The area under the curve (AUC), sensitivity and specificity for detecting an NPA were examined. We compared the diagnostic ability (sensitivity, specificity and average required time) between the DNN, SVM and seven ophthalmologists. Heat maps were generated. With regard to the DNN, the mean AUC, sensitivity, specificity and average required time for distinguishing RVO OCTA images with an NPA from normal OCTA images were 0.986, 93.7%, 97.3% and 176.9 s, respectively. With regard to SVM, the mean AUC, sensitivity, and specificity were 0.880, 79.3%, and 81.1%, respectively. With regard to the seven ophthalmologists, the mean AUC, sensitivity, specificity and average required time were 0.962, 90.8%, 89.2%, and 700.6 s, respectively. The DNN focused on the foveal avascular zone and NPA in heat maps. The performance of the DNN was significantly better than that of SVM in all parameters (p < 0.01, all) and that of the ophthalmologists in AUC and specificity (p < 0.01, all). The combination of DL and OCTA images had high accuracy for the detection of an NPA, and it might be useful in clinical practice and retinal screening

Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images

Evaluating the discrimination ability of a deep convolution neural network for ultrawide-field pseudocolor imaging and ultrawide-field autofluorescence of retinitis pigmentosa. In total, the 373 ultrawide-field pseudocolor and ultrawide-field autofluorescence images (150, retinitis pigmentosa; 223, normal) obtained from the patients who visited the Department of Ophthalmology, Tsukazaki Hospital were used. Training with a convolutional neural network on these learning data objects was conducted. We examined the K-fold cross validation (K = 5). The mean area under the curve of the ultrawide-field pseudocolor group was 0.998 (95% confidence interval (CI) [0.9953–1.0]) and that of the ultrawide-field autofluorescence group was 1.0 (95% CI [0.9994–1.0]). The sensitivity and specificity of the ultrawide-field pseudocolor group were 99.3% (95% CI [96.3%–100.0%]) and 99.1% (95% CI [96.1%–99.7%]), and those of the ultrawide-field autofluorescence group were 100% (95% CI [97.6%–100%]) and 99.5% (95% CI [96.8%–99.9%]), respectively. Heatmaps were in accordance with the clinician’s observations. Using the proposed deep neural network model, retinitis pigmentosa can be distinguished from healthy eyes with high sensitivity and specificity on ultrawide-field pseudocolor and ultrawide-field autofluorescence images

Correlation between optic nerve head circulation and visual function before and after anti-VEGF therapy for central retinal vein occlusion : prospective, interventional case series

Background: To determine the correlation between the optic nerve head (ONH) circulation determined by laser speckle flowgraphy and the best-corrected visual acuity or retinal sensitivity before and after intravitreal bevacizumab or ranibizumab for central retinal vein occlusion. Methods: Thirty-one eyes of 31 patients were treated with intravitreal bevacizumab or ranibizumab for macular edema due to a central retinal vein occlusion. The blood flow in the large vessels on the ONH, the best-corrected visual acuity, and retinal sensitivity were measured at the baseline, and at 1, 3, and 6 months after treatment. The arteriovenous passage time on fluorescein angiography was determined. The venous tortuosity index was calculated on color fundus photograph by dividing the length of the tortuous retinal vein by the chord length of the same segment. The blood flow was represented by the mean blur rate (MBR) determined by laser speckle flowgraphy. To exclude the influence of systemic circulation and blood flow in the ONH tissue, the corrected MBR was calculated as MBR of ONH vessel area – MBR of ONH tissue area in the affected eye divided by the vascular MBR – tissue MBR in the unaffected eye. Pearson’s correlation tests were used to determine the significance of correlations between the MBR and the best-corrected visual acuity, retinal sensitivity, arteriovenous passage time, or venous tortuosity index. Results: At the baseline, the corrected MBR was significantly correlated with the arteriovenous passage time and venous tortuosity index (r = -0.807, P < 0.001; r = -0.716, P < 0.001; respectively). The corrected MBR was significantly correlated with the best-corrected visual acuity and retinal sensitivity at the baseline, and at 1, 3, and 6 months (all P < 0.050). The corrected MBR at the baseline was significantly correlated with the best-corrected visual acuity at 6 months (r = -0.651, P < 0.001) and retinal sensitivity at 6 months (r = 0.485, P = 0.005). Conclusions: The pre-treatment blood flow velocity of ONH can be used as a predictive factor for the best-corrected visual acuity and retinal sensitivity after anti-VEGF therapy for central retinal vein occlusion. Trial registration: Trial Registration number: UMIN000009072. Date of registration: 10/15/2012

Accurate tomographic detection of myopic macular diseases

This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular lesions [e.g., myopic choroidal neovascularization (mCNV) and retinoschisis (RS)]. A total of 910 SS-OCT images were included in the study as follows and analyzed by k-fold cross-validation (k = 5) using DL's renowned model, Visual Geometry Group-16: nHM, 146 images; HM, 531 images; mCNV, 122 images; and RS, 111 images (n = 910). The binary classification of OCT images with or without myopic macular lesions; the binary classification of HM images and images with myopic macular lesions (i.e., mCNV and RS images); and the ternary classification of HM, mCNV, and RS images were examined. Additionally, sensitivity, specificity, and the area under the curve (AUC) for the binary classifications as well as the correct answer rate for ternary classification were examined. The classification results of OCT images with or without myopic macular lesions were as follows: AUC, 0.970; sensitivity, 90.6%; specificity, 94.2%. The classification results of HM images and images with myopic macular lesions were as follows: AUC, 1.000; sensitivity, 100.0%; specificity, 100.0%. The correct answer rate in the ternary classification of HM images, mCNV images, and RS images were as follows: HM images, 96.5%; mCNV images, 77.9%; and RS, 67.6% with mean, 88.9%.Using noninvasive, easy-to-obtain swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy. The study results suggest the possibility of conducting highly accurate screening of ocular diseases using artificial intelligence, which may improve the prevention of blindness and reduce workloads for ophthalmologists

Comparison of anterior chamber depth measurements by 3-dimensional optical coherence tomography, partial coherence interferometry biometry, Scheimpflug rotating camera imaging, and ultrasound biomicroscopy

PURPOSE: To evaluate the congruity of anterior chamber depth (ACD) measurements using 4 devices. SETTING: Saneikai Tsukazaki Hospital, Himeji City, Japan. DESIGN: Comparative case series. METHODS: In 1 eye of 42 healthy participants, the ACD was measured by 3-dimensional corneal and anterior segment optical coherence tomography (CAS-OCT), partial coherence interferometry (PCI), Scheimpflug imaging, and ultrasound biomicroscopy (UBM). The differences between the measurements were evaluated by 2-way analysis of variance and post hoc analysis. Agreement between the measurements was evaluated using Bland-Altman analysis. To evaluate the true ACD using PCI, the automatically calculated ACD minus the central corneal thickness measured by CAS-OCT was defined as PCI true. Two ACD measurements were also taken with CAS-OCT. RESULTS: The mean ACD was 3.72 mm G 0.23 (SD) (PCI), 3.18 G 0.23 mm (PCI true), 3.24 G 0.25 mm (Scheimpflug), 3.03G 0.25 mm (UBM), 3.14 G 0.24 mm (CAS-OCT auto), and 3.12 G 0.24 mm (CAS-OCT manual). A significant difference was observed between PCI biometry, Scheimpflug imaging, and UBM measurements and the other methods. Post hoc analysis showed no significant differences between PCI true and CAS-OCT auto or between the CAS-OCT auto and CAS-OCT manual. Strong correlations were observed between all measurements; however, Bland-Altman analysis showed good agreement only between PCI true and Scheimpflug imaging and between CAS-OCT auto and CAS OCT manual. CONCLUSION: The ACD measurements obtained from PCI biometry, Scheimpflug imaging, CAS-OCT, and UBM were significantly different and not interchangeable except for PCI true and CAS-OCT auto and CAS-OCT auto and CAS-OCT manual. Financial Disclosure: No author has a financial or proprietary interest in any material or method mentioned

Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy

Purpose We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, to detect treatment-naïve proliferative diabetic retinopathy (PDR). Methods We conducted training with the DCNN using 378 photographic images (132 PDR and 246 non-PDR) and constructed a deep learning model. The area under the curve (AUC), sensitivity, and specificity were examined. Result The constructed deep learning model demonstrated a high sensitivity of 94.7% and a high specificity of 97.2%, with an AUC of 0.969. Conclusion Our findings suggested that PDR could be diagnosed using wide-angle camera images and deep learning