Prenatal diagnosis of Caudal Regression Syndrome : a case report

BACKGROUND: Caudal regression is a rare syndrome which has a spectrum of congenital malformations ranging from simple anal atresia to absence of sacral, lumbar and possibly lower thoracic vertebrae, to the most severe form which is known as sirenomelia. Maternal diabetes, genetic predisposition and vascular hypoperfusion have been suggested as possible causative factors. CASE PRESENTATION: We report a case of caudal regression syndrome diagnosed in utero at 22 weeks' of gestation. Prenatal ultrasound examination revealed a sudden interruption of the spine and "frog-like" position of lower limbs. Termination of pregnancy and autopsy findings confirmed the diagnosis. CONCLUSION: Prenatal ultrasonographic diagnosis of caudal regression syndrome is possible at 22 weeks' of gestation by ultrasound examination

Controversy and Consensus on Indications for Sperm DNA Fragmentation Testing in Male Infertility: A Global Survey, Current Guidelines, and Expert Recommendations

Purpose: Sperm DNA fragmentation (SDF) testing was recently added to the sixth edition of the World Health Organization laboratory manual for the examination and processing of human semen. Many conditions and risk factors have been associated with elevated SDF; therefore, it is important to identify the population of infertile men who might benefit from this test. The purpose of this study was to investigate global practices related to indications for SDF testing, compare the relevant professional society guideline recommendations, and provide expert recommendations. Materials and Methods: Clinicians managing male infertility were invited to take part in a global online survey on SDF clinical practices. This was conducted following the CHERRIES checklist criteria. The responses were compared to professional society guideline recommendations related to SDF and the appropriate available evidence. Expert recommendations on indications for SDF testing were then formulated, and the Delphi method was used to reach consensus. Results: The survey was completed by 436 experts from 55 countries. Almost 75% of respondents test for SDF in all or some men with unexplained or idiopathic infertility, 39% order it routinely in the work-up of recurrent pregnancy loss (RPL), and 62.2% investigate SDF in smokers. While 47% of reproductive urologists test SDF to support the decision for varicocele repair surgery when conventional semen parameters are normal, significantly fewer general urologists (23%; p=0.008) do the same. Nearly 70% would assess SDF before assisted reproductive technologies (ART), either always or for certain conditions. Recurrent ART failure is a common indication for SDF testing. Very few society recommendations were found regarding SDF testing. Conclusions: This article presents the largest global survey on the indications for SDF testing in infertile men, and demonstrates diverse practices. Furthermore, it highlights the paucity of professional society guideline recommendations. Expert recommendations are proposed to help guide clinicians

Combat Mobile Evasive Malware via Skip-Gram-Based Malware Detection

Android malware detection is an important research topic in the security area. There are a variety of existing malware detection models based on static and dynamic malware analysis. However, most of these models are not very successful when it comes to evasive malware detection. In this study, we aimed to create a malware detection model based on a natural language model called skip-gram to detect evasive malware with the highest accuracy rate possible. In order to train and test our proposed model, we used an up-to-date malware dataset called Argus Android Malware Dataset (AMD) since the AMD contains various evasive malware families and detailed information about them. Meanwhile, for the benign samples, we used Comodo Android Benign Dataset. Our proposed model starts with extracting skip-gram-based features from instruction sequences of Android applications. Then it applies several machine learning algorithms to classify samples as benign or malware. We tested our proposed model with two different scenarios. In the first scenario, the random forest-based classifier performed with 95.64% detection accuracy on the entire dataset and 95% detection accuracy against evasive only samples. In the second scenario, we created a test dataset that contained zero-day malware samples only. For the training set, we did not use any sample that belongs to the malware families in the test set. The random forest-based model performed with 37.36% accuracy rate against zero-day malware. In addition, we compared our proposed model’s malware detection performance against several commercial antimalware applications using VirusTotal API. Our model outperformed 7 out of 10 antimalware applications and tied with one of them on the same test scenario

An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions

An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions