Evaluation of the fetal heart at 11-13 + 6 weeks of gestation is indicated for women with a family history of congenital heart defects (CHD), a previous child with CDH, or an ultrasound finding associated with cardiac anomalies. The accuracy for early detection of CHD is highly related to the experience of the operator. The 4-chamber view and outflow tracts are the most important planes for identification of an abnormal heart, and can be obtained in the majority of fetuses from 11 weeks of gestation onward. Transvaginal ultrasound is the preferred route for fetal cardiac examination prior to 12 weeks of gestation, whereas, after 12 weeks, the fetal heart can be reliably evaluated by transabdominal ultrasound. Cardiac defects, such as ventricular septal defects, tetralogy of Fallot, Ebstein's anomaly, or cardiac tumors, are unlikely to be identified at ≤14 weeks of gestation. Additional ultrasound techniques such as spatiotemporal image correlation and the evaluation of volumes by a fetal-heart expert can improve the detection of congenital heart disease. The evaluation of the fetal cardiac function at 11-13 + 6 weeks of gestation can be useful for early identification of fetuses at risk of anemia due to hemoglobinopathies, such as hemoglobin Bart's disease.
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.
Personality is a combination of all the attributes—behavioral, temperamental, emotional, and mental—that characterizes a unique individual. Ability to identify personalities of people has always been of great interest to the researchers due to its importance. It continues to find highly useful applications in many domains. Owing to the increasing popularity of online social networks, researchers have started looking into the possibility of predicting a user's personality from his online social networking profile, which serves as a rich source of textual as well as non-textual content published by users. In the process of creating social networking profiles, users reveal a lot about themselves both in what they share and how they say it. Studies suggest that the online social networking websites are, in fact, a relevant and valid means of communicating personality. In this article, we review these various studies reported in literature toward identification of personality using online social networks. To the best of our knowledge, this is the first reported survey of its kind at the time of submission. We hope that our contribution, especially in summarizing the previous findings and in identifying the directions for future research in this area, would encourage researchers to do more work in this budding area.
HBD-1 is a physiological constituent of amniotic fluid that is increased in midtrimester during normal pregnancy and in the presence of culturable microorganisms in the amniotic cavity. These findings provide insight into the soluble host defense mechanisms against intra-amniotic infection.
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.
Cameras constantly capture and track facial images and videos on cell phones, webcams etc. In the past decade, facial expression classification and recognition has been the topic of interest as facial expression analysis has a wide range of applications such as intelligent tutoring system, systems for psychological studies etc. This study reviews the latest advances in the algorithms and techniques used in distinct phases of real-time facial expression recognition. Though there are state-of-art approaches to address facial expression identification in real-time, many issues such as subjectivity-removal, occlusion, pose, low resolution, scale, variations in illumination level and identification of baseline frame still remain unaddressed. Attempts to deal with such issues for higher accuracy lead to a trade-off in efficiency. Furthermore, the goal of this study is to elaborate on these issues and highlight the solutions provided by the current approaches. This survey has helped the authors to understand that there is a need for a better strategy to address these issues without having to trade-off performance in real-time.
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