On the Design of a Photo Beauty Measurement Mechanism Based on Image Composition and Machine Learning

In this chapter, we propose a machine learning scheme on how to measure the beauty of a photo. Different from traditional measurements that focus on the quality of captured signals, the beauty of photos is based on high-level concepts from the knowledge of photo aesthetics. Because the concept of beauty is mostly defined by human being, the measurement must contain some knowledge obtained from them. Therefore, our measurement can be realized by a machine learning mechanism, which is trained by collected data from the human. There are several computational aesthetic manners used for building a photo beauty measurement system, including low-level feature extraction, image composition analysis, photo semantics parsing, and classification rule generation. Because the meaning of beauty may vary from different people, the personal preference is also taken into consideration. In this chapter, the performance of two computational aesthetic manners for the perception of beauty is evaluated, which are based on image composition analysis and low-level features to determine whether a photo meets the criterion of a professional photographing via different classifiers. The experimental results manifest that both decision tree and multilayer perceptron-based classifiers attain high accuracy of more than 90% for evaluation

Clinical features, acute complications, and outcome of Salmonella meningitis in children under one year of age in Taiwan

<p>Abstract</p> <p>Background</p> <p><it>Salmonella </it>meningitis remains a threat to children below two years of age in both developing and developed countries. However, information on such infections has not been well characterized. We analyzed data related to twelve years of experience in order to clarify the comprehensive features of <it>Salmonella </it>meningitis in our patients, including admission characteristics, acute complications, and long-term outcome.</p> <p>Methods</p> <p>The records of patients with spontaneous <it>Salmonella </it>meningitis from 1982 to 1994 were retrospectively reviewed. The long-term outcome was prospectively determined for survivors at school age by the developmental milestones reported by their parents and detailed neurological evaluation along with intelligence, hearing, visual, speech and language assessments.</p> <p>Results</p> <p>Of the twenty-four patients, seizures were noted in fifteen (63%) before admission and thirteen (54%) during hospitalization. Acute complications mainly included hydrocephalus (50%), subdural collection (42%), cerebral infarction (33%), ventriculitis (25%), empyema (13%), intracranial abscess (8%), and cranial nerve palsy (8%). Three patients (13%) died during the acute phase of <it>Salmonella </it>meningitis. The twenty-one survivors, on whom we followed up at school age, have sequelae consisting of language disorder (52%), motor disability (48%), intelligence quotient < 80 (43%), epilepsy (33%), sensorineural hearing loss (17%), visual deficits (10%), abducens nerve palsy (5%), microcephaly (5%), and hydrocephalus (5%). Overall, good outcome was noted in six (28.6%) of twenty-one survivors, mild sequelae in three (14.2%), moderate in six (28.6%), and severe in six (28.6%).</p> <p>Conclusion</p> <p><it>Salmonella </it>meningitis in neonates and infants had a wide spectrum of morbidity and acute complications, leading to a complicated hospital course and subsequently a high prevalence of permanent adverse outcome. Thus, early recognition of acute complications of <it>Salmonella </it>meningitis and a follow-up plan for early developmental assessment of survivors are vital.</p

On The Development of a Legal Penalty Prediction System for Drunk Driving Cases

Recent years, computer-aided penalty prediction have been promoted to gain people's trust to the judicial systems, especially in developing Chinese region. In this paper, we propose machine learning based models to predict the legal penalty of criminal cases. Particularly, we focus on drunk driving cases as they are frequent, and the regulations are clear. Unlike western text which words are separated by spaces, words in Chinese text are continuum. In our proposed method, we first use a word segmentation method to separate the Chinese words in text and apply a pre-trained model to convert words into vectors. In the vector space, words with similar meanings have short distance with each other. As the amount of each penalty varies greatly, resulting a data imbalance problem. Therefore, we adapt the Synthetic Minority Oversampling Technique (SMOTE) algorithm as a solution. Finally, we apply deep learning-based models, including Bi-GRU and TextCNN to perform penalty prediction, and compare their advantages and disadvantages.In the experimental result, for drunk driving case penalty prediction, our propose SMOTE + TextCNN solution can reach 73.96% of accuracy. If we allow the prediction to be plus or minus one month from the actual, the accuracy is 95.60%. As for the computation time, our proposed method can predict the penalty of 1,524 drunk driving cases per second.補正完畢國際富山市，日本JP

Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

[[abstract]]Gesture recognition is a human–computer interaction method, which is widely used for educational, medical, and entertainment purposes. Humans also use gestures to communicate with each other, and musical conducting uses gestures in this way. In musical conducting, conductors wave their hands to control the speed and strength of the music played. However, beginners may have a limited comprehension of the gestures and might not be able to properly follow the ensembles. Therefore, this paper proposes a real-time musical conducting gesture recognition system to help music players improve their performance. We used a single-depth camera to capture image inputs and establish a real-time dynamic gesture recognition system. The Kinect software development kit created a skeleton model by capturing the palm position. Different palm gestures were collected to develop training templates for musical conducting. The dynamic time warping algorithm was applied to recognize the different conducting gestures at various conducting speeds, thereby achieving real-time dynamic musical conducting gesture recognition. In the experiment, we used 5600 examples of three basic types of musical conducting gestures, including seven capturing angles and five performing speeds for evaluation. The experimental result showed that the average accuracy was 89.17% in 30 frames per second.[[notice]]補正完

Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm

[[abstract]]Insufficient physical activity is common in modern society. By estimating the energy expenditure (EE) of different physical activities, people can develop suitable exercise plans to improve their lifestyle quality. However, several limitations still exist in the related works. Therefore, the aim of this study is to propose an accurate EE estimation model based on depth camera data with physical activity classification to solve the limitations in the previous research. To decide the best location and amount of cameras of the EE estimation, three depth cameras were set at three locations, namely the side, rear side, and rear views, to obtain the kinematic data and EE estimation. Support vector machine was used for physical activity classification. Three EE estimation models, namely linear regression, multilayer perceptron (MLP), and convolutional neural network (CNN) models, were compared and determined the model with optimal performance in different experimental settings. The results have shown that if only one depth camera is available, optimal EE estimation can be obtained using the side view and MLP model. The mean absolute error (MAE), mean square error (MSE), and root MSE (RMSE) of the classification results under the aforementioned settings were 0.55, 0.66, and 0.81, respectively. If higher accuracy is required, two depth cameras can be set at the side and rear views, the CNN model can be used for light-to-moderate activities, and the MLP model can be used for vigorous activities. The RMSEs for estimating the EEs of standing, walking, and running were 0.19, 0.57, and 0.96, respectively. By applying the different models on different amounts of cameras, the optimal performance can be obtained, and this is also the first study to discuss the issue.[[notice]]補正完

KMUP-1 Ameliorates Ischemia-Induced Cardiomyocyte Apoptosis through the NO–cGMP–MAPK Signaling Pathways

To test whether KMUP-1 (7-[2-[4-(2-chlorophenyl) piperazinyl]ethyl]-1,3-dimethylxanthine) prevents myocardial ischemia-induced apoptosis, we examined KMUP-1-treated H9c2 cells culture. Recent attention has focused on the activation of nitric oxide (NO)-guanosine 3&#8217;, 5&#8217;cyclic monophosphate (cGMP)-protein kinase G (PKG) signaling pathway triggered by mitogen-activated protein kinase (MAPK) family, including extracellular-signal regulated kinase 1/2 (ERK1/2), c-Jun N-terminal kinase (JNK), and p38 in the mechanism of cardiac protection during ischemia-induced cell-death. We propose that KMUP-1 inhibits ischemia-induced apoptosis in H9c2 cells culture through these pathways. Cell viability was assessed using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay and apoptotic evaluation was conducted using DNA ladder assay and Hoechst 33342 staining. The level of intracellular calcium was detected using-Fura2-acetoxymethyl (Fura2-AM) staining, and mitochondrial calcium with Rhod 2-acetoxymethyl (Rhod 2-AM) staining under fluorescence microscopic observation. The expression of endothelium NO synthase (eNOS), inducible NO synthase (iNOS), soluble guanylate cyclase &#945;1 (sGC&#945;1), PKG, Bcl-2/Bax ratio, ERK1/2, p38, and JNK proteins were measured by Western blotting assay. KMUP-1 pretreatment improved cell viability and inhibited ischemia-induced apoptosis of H9c2 cells. Calcium overload both in the intracellular and mitochondrial sites was attenuated by KMUP-1 pretreatment. Moreover, KMUP-1 reduced intracellular reactive oxygen species (ROS), increased plasma NOx (nitrite and nitrate) level, and the expression of eNOS. Otherwise, the iNOS expression was downregulated. KMUP-1 pretreatment upregulated the expression of sGC&#945;1 and PKG protein. The ratio of Bcl-2/Bax expression was increased by the elevated level of Bcl2 and decreased level of Bax. In comparison with the ischemia group, KMUP-1 pretreatment groups reduced the expression of phosphorylated extracellular signal-regulated kinases ERK1/2, p-p38, and p-JNK as well. Therefore, KMUP-1 inhibits myocardial ischemia-induced apoptosis by restoration of cellular calcium influx through the mechanism of NO-cGMP-MAPK pathways

Takotsubo Syndrome Occurring after mRNA COVID-19 Vaccination in a Patient with Graves’ Disease

Cardiovascular events such as myocarditis following mRNA COVID-19 vaccination are increasing. We present a 67-year-old postmenopausal woman with Takotsubo Syndrome and Graves’ disease after mRNA COVID-19 vaccination. She developed chest pain and shortness of breath one week after vaccination. An electrocardiogram revealed ST elevation in the precordial leads. Coronary angiography revealed the absence of obstructive coronary artery disease, and the left ventriculography showed a typical feature with apical ballooning. Laboratory workup showed the elevation of free T4 and thyrotropin receptor antibodies. It was presumed that Takotsubo Syndrome and Graves’ disease were probably related to the COVID-19 mRNA vaccination. The patient was treated with low-dose bisoprolol, diuretics, carbimazole, and steroid and discharged uneventfully. The mRNA COVID-19 vaccination is still safe and effective to defend against COVID-19 pandemic. However, clinicians should be aware of the possible cardiovascular adverse events other than myocarditis following vaccination

Sustained Brown Fat Stimulation and Insulin Sensitization by a Humanized Bispecific Antibody Agonist for Fibroblast Growth Factor Receptor 1/βKlotho Complex

Dissipating excess calories as heat through therapeutic stimulation of brown adipose tissues (BAT) has been proposed as a potential treatment for obesity-linked disorders. Here, we describe the generation of a humanized effector-less bispecific antibody that activates fibroblast growth factor receptor (FGFR) 1/βKlotho complex, a common receptor for FGF21 and FGF19. Using this molecule, we show that antibody-mediated activation of FGFR1/βKlotho complex in mice induces sustained energy expenditure in BAT, browning of white adipose tissue, weight loss, and improvements in obesity-associated metabolic derangements including insulin resistance, hyperglycemia, dyslipidemia and hepatosteatosis. In mice and cynomolgus monkeys, FGFR1/βKlotho activation increased serum high-molecular-weight adiponectin, which appears to contribute over time by enhancing the amplitude of the metabolic benefits. At the same time, insulin sensitization by FGFR1/βKlotho activation occurs even before the onset of weight loss in a manner that is independent of adiponectin. Together, selective activation of FGFR1/βKlotho complex with a long acting therapeutic antibody represents an attractive approach for the treatment of type 2 diabetes and other obesity-linked disorders through enhanced energy expenditure, insulin sensitization and induction of high-molecular-weight adiponectin