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Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful. The perception of odorant stimuli is one such application as olfaction is the least understood among all the other senses. In this study, we employ machine learning based algorithms and data analytics to address the efficacy of using a data-driven approach to predict the perceptual attributes of an odorant namely the odorant characters (OC) of “sweet” and “musky”. We first analyze a psychophysical dataset containing perceptual ratings of 55 subjects to reveal patterns in the ratings given by subjects. We then use the data to train several machine learning algorithms such as random forest, gradient boosting and support vector machine for prediction of the odor characters and report the structural features correlating well with the odor characters based on the optimal model. Furthermore, we analyze the impact of the data quality on the performance of the models by comparing the semantic descriptors generally associated with a given odorant to its perception by majority of the subjects. The study presents a methodology for developing models for odor perception and provides insights on the perception of odorants by untrained human subjects and the effect of the inherent bias in the perception data on the model performance. The models and methodology developed here could be used for predicting odor characters of new odorants.

Music enthusiasts are growing exponentially and based on this, many songs are being introduced to the market and stored in signal music libraries. Due to this development emotion recognition model from music contents has received increasing attention in today’s world. Of these technologies, a novel Music Emotion Recognition (MER) system is introduced to meet the ever-increasing demand for easy and efficient access to music information. Even though this system was well-developed it lacks in maintaining accuracy of the system and finds difficulty in predicting multi-label emotion type. To address these shortcomings, in this research article, a novel MER system is developed by inter-linking the pre-processing, feature extraction and classification steps. Initially, pre-processing step is employed to convert larger audio files into smaller audio frames. Afterwards, music related temporal, spectral and energy features are extracted for those pre-processed frames which are subjected to the proposed gradient descent based Spiking Neural Network (SNN) classifier. While learning SNN, it is important to determine the optimal weight values for reducing the training error so that gradient descent optimization approach is adopted. To prove the effectiveness of proposed research, proposed model is compared with conventional classification algorithms. The proposed methodology was experimentally tested using various evaluation metrics and it achieves 94.55% accuracy. Hence the proposed methodology attains a good accuracy measure and outperforms well than other algorithms.

Ideally, ripe fruits offer appropriate nutritional content and best quality in terms of taste and flavour. Prediction of ripe climacteric fruits acts as the main marketing indicator for quality from the consumer perspective and thus renders it a genuine industrial concern for all the stakeholders of the fruit supply chain. However, the building of fruit-specific individual model for the prediction of ripeness level remains an existing challenge due to the scarcity of sufficient labeled experimental data for each fruit. This paper describes the development of generic AI models based on the similarity in physico-chemical degradation phenomena of climacteric fruits for prediction of ‘unripe’ and ‘ripe’ levels using ‘zero-shot’ transfer learning techniques. Experiments were performed on a variety of climacteric and non-climacteric fruits, and it was observed that transfer learning works better for fruits within a cluster (climacteric fruits) as compared to across clusters (climacteric to non-climacteric fruits). The main contributions of this work are two-fold (i) Using domain knowledge of food chemistry to label the data in terms of age of the fruit, (ii) We hypothesize and prove that the zero-shot transfer learning works better within a set of fruits, sharing similar degradation chemistry depicted by their visual properties like black spot formations, wrinkles, discoloration, etc. The best models trained on banana, papaya and mango dataset resulted in s zero-shot transfer learned accuracies in the range of 70 to 82 for unknown climacteric fruits. To the best of our knowledge, this is the first study to demonstrate the same.

Aesthetic comes under the branch of cognitive science, and it plays a significant role in demonstrating the psychology of humans. Videos or images that are more colourful attracts lots of audiences. At the same time, these colour characteristic helps in finding the mood of a person whether they are happy, sad, anger, fear etc. Many approaches such as machine learning technique, neural network were designed earlier for achieving mood detection in appealing video. However, effective detection with enhanced accuracy was not attained in conventional methods. For overcoming these drawbacks, Deep Convolutional Neural Network (DCNN) based approach is designed in this proposed work to perform aesthetic classification and mood detection. At the beginning of the process video is converted into frames and key frame extraction is performed using histogram method. Then, foreground portion is separated from the background using object detection technique based on Gaussian Mixture Model (GMM). After that, extraction of low level, high level features and aesthetic classification is done using DCNN. Finally, using pleasing video the mood detection is done based on color features. The proposed method is implemented and its performances are evaluated using measures including accuracy, precision, recall, and F1 score whose values are 77.4, 77.4 77.9 and 77.4%. Based on this proposed approach automated and effective aesthetic classification can be achieved along with human mood detection.

Liddle syndrome is an autosomal dominant genetic condition that causes hypertension and hypokalemia due to a gain-of-function mutation in the SCNN1B or SCNN1G genes which code for the epithelial sodium channel in the kidney. This leads to increased sodium and water reabsorption causing hypertension. We report a case of a 27-year-old pregnant woman who was admitted for hypertension and hypokalemia and later diagnosed and treated for Liddle syndrome using amiloride. Maintaining a high suspicion of Liddle syndrome in pregnancy is essential in such cases to be able to adequately and effectively treat the hypertension. Due to physiological effects of pregnancy, the dose of amiloride may need to be increased as gestational age progresses up to a maximum dose of 30 mg orally per day.

Intrahepatic cholestasis of pregnancy is seldom associated with significant vitamin K deficiency. We report a case of a 16-year-old primigravid patient at 24 weeks and 3 days of gestation who presented with pruritus, hematuria, and preterm labor. Laboratory work-up showed severe coagulopathy with Prothrombin Time (PT) of 117.8 seconds, International Normalized Ratio (INR) of 10.34, and elevated transaminases suggestive of intrahepatic cholestasis of pregnancy. Her serum vitamin K level was undetectable (<0.1 nMol/L). Initial therapy consisted of intramuscular replacement of vitamin K and administration of fresh frozen plasma. Her hematuria and preterm labor resolved and she was discharged. She presented in active labor and delivered at 27 weeks and 1 day. Her bile acids (93 μ/L) and INR (2.32) had worsened. She delivered a male infant, 1150 grams with Apgar scores 7 and 9. The newborn received 0.5 mg of intramuscular vitamin K shortly after delivery but went on to develop bilateral grade III intraventricular hemorrhages by day 5. Intrahepatic cholestasis in pregnancy and nutrition issues were identified as the main risk factors for the severe coagulopathy of this patient. This case underlines the importance of evaluation of possible severe coagulopathy in patients with intrahepatic cholestasis of pregnancy in order to avoid serious maternal or fetal adverse outcomes.

Preamble Need for developing case definitions and guidelines for data collection, analysis, and presentation for fetal growth restriction as an adverse event following immunization Fetuses that fail to meet their growth potential in utero are at risk for adverse antenatal and postnatal events such as stillbirth, preterm birth, and adverse neonatal and long-term health outcomes [1-5]. [...]antenatal recognition and monitoring of fetal growth restriction (FGR) is an important component of prenatal care [6-8]. [...]the Brighton case definition of FGR will focus on use of a combination of B-mode and Doppler ultrasound technology to establish the diagnosis of FGR. [...]to avoid selection bias, a restrictive time interval from immunization to onset of FGR should not be an integral part of such a definition. [...]we would like to acknowledge the Global Alignment of Immunization Safety Assessment in Pregnancy (GAIA) project, funded by the Bill and Melinda Gates Foundation.

According to the International Diabetes Federation (IDF), about 16.8% of live-births are born to women with hyperglycemia in pregnancy [1]. Ideally, studies that examine the effect of vaccines on glucose tolerance would include a time period close to the vaccination administration, perhaps 0-14days. Since this definition is not currently in use the studies included in this review have not limited the time between vaccination and diagnosis of GDM. [...]to avoid selection bias, a restrictive time interval from immunization to onset of GDM should not be an integral part of such a definition. [...]we would like to acknowledge the Global Alignment of Immunization Safety Assessment in Pregnancy (GAIA) project, funded by the Bill and Melinda Gates Foundation.

Objective: To investigate dynamic changes in myometrial thickness during the third stage of labor. Methods: Myometrial thickness was measured using ultrasound at one-minute time intervals during the third stage of labor in the mid-region of the upper and lower uterine segments in 151 patients including: women with a long third stage of labor (n = 30), postpartum hemorrhage (n = 4), preterm delivery (n = 7) and clinical chorioamnionitis (n = 4). Differences between myometrial thickness of the uterine segments and as a function of time were evaluated. Results: There was a significant linear increase in the mean myometrial thickness of the upper uterine segments, as well as a significant linear decrease in the mean myometrial thickness of the lower uterine segments until the expulsion of the placenta (p < 0.001). The ratio of the measurements of the upper to the lower uterine segments increased significantly as a function of time (p < 0.0001). In women with postpartum hemorrhage, preterm delivery, and clinical chorioamnionitis, an uncoordinated pattern among the uterine segments was observed. Conclusion: A well-coordinated activity between the upper and lower uterine segments is demonstrated in normal placental delivery. In some clinical conditions this pattern is not observed, increasing the time for placental delivery and the risk of postpartum hemorrhage.

Mirror artifacts are produced by the reflection of ultrasound waves after they propagate through a structure and encounter a strong and smooth interface capable of acting as a mirror. Ultrasound waves bounce back and forth between the mirroring interface and the reflective object and then eventually return to the transducer. The typical display of the mirror artifact consists of two similar structures separated and at similar distances from the reflective interface. We report a mirror artifact in a patient with a singleton gestation at 18 weeks. The image was interpreted as consistent with a twin gestation using transabdominal and transvaginal ultrasound. The differential diagnosis consisted of an abdominal heterotopic pregnancy. The presence of synchronized but opposite movements of both fetuses, and the blurred image of the second fetus, suggested a mirror artifact. The reflective surface was created by the interface located between a distended rectosigmoid filled with gas and the posterior uterine wall. Mirror artifacts can lead to diagnostic errors. This case illustrates how a distended rectosigmoid colon can generate an image that simulates either a twin gestation or an abdominal heterotopic pregnancy.