Septo-Opticdysplasia with an Anterior Encephalocele and Intact Septum Pellucidum: A Case Report

ObjectiveThe diagnosis of de Morsier syndrome or septo-optic dysplasia is made on the basis of the diagnosis of optic nerve hypoplasia. Septo-optic dysplasia is defined by a variable combination of dysgenesis of midline brain structures including optic nerve hypoplasia and hypothalamic-pituitary dysfunction often associated with a wide variety of brain malformations of cortical development.The importance of direct ophthalmoscopy of optic nerve abnormalities is stressed, as well as of magnetic resonance imaging, which has become a guideline in the classification of  this syndrome This article reports a 19-year-old female with bilateral optic nerve  hypoplasia,anterior encephalocele and intact septum pellucidum. She was diagnosed with diabetes insipidus, short stature and the history of seizure

Improved key-rate bounds for practical decoy-state quantum-key-distribution systems

The decoy-state scheme is the most widely implemented quantum-key-distribution protocol in practice. In order to account for the finite-size key effects on the achievable secret key generation rate, a rigorous statistical fluctuation analysis is required. Originally, a heuristic Gaussian-approximation technique was used for this purpose, which, despite its analytical convenience, was not sufficiently rigorous. The fluctuation analysis has recently been made rigorous by using the Chernoff bound. There is a considerable gap, however, between the key-rate bounds obtained from these techniques and that obtained from the Gaussian assumption. Here we develop a tighter bound for the decoy-state method, which yields a smaller failure probability. This improvement results in a higher key rate and increases the maximum distance over which secure key exchange is possible. By optimizing the system parameters, our simulation results show that our method almost closes the gap between the two previously proposed techniques and achieves a performance similar to that of conventional Gaussian approximations

Improvements on “Secure multi-party quantum summation based on quantum Fourier transform”

Recently, a quantum multi-party summation protocol based on the quantum Fourier transform has been proposed (Yang et al. in Quantum Inf Process 17:129, 2018). The protocol claims to be secure against both outside and participant attacks. However, a closer look reveals that the player in charge of generating the required multi-partite entangled states can launch two kinds of attacks to learn about other parties’ private integer strings without being caught. In this paper, we present these attacks and propose countermeasures to make the protocol secure again. The improved protocol not only can resist these attacks but also remove the need for the quantum Fourier transform and encoding quantum operations by participants

Chemical composition and evaluation of antimicrobial properties of Rosmarinus officinalis L. essential oil

Preservatives used in the food industry are undergoing increasing scrutiny and reappraisal. There is therefore a renewed interest in the antimicrobial properties of herbs and spices. Rosemary (Rosmarinus officinalis L.) belonging to the Lamiaceae family, is a pleasant-smelling perennial herb. The antimicrobial activities of the R. officinalis oil against Leuconostoc mesenteroides (PTCC1591), Lactobacillus delbruekii (PTCC1333), Saccharomyces cerevisia (PTCC5269) and Candida krusei (PTCC 5295) were determined. The results indicate that among the tested microbes, the essential oil had a stronger inhibitory effect on the bacteria as compared to yeasts. Minimum inhibitory concentration (MIC) values for bacteria L. mesenteroides, L. delbruekii, S. cerevisia and C. krusei ranged between 0.5 and 1.5 mg/ml. The oil was analyzed by GC and GC/MS. The major components of R. officinalis oil were 1,8-cineole (23.14%), camphor (12.35%), α-pinene (9.87%), β-pinene (6.10%), borneol (5.61%), camphene (5.58%) and α-terpineol (4.30%), respectively. These results indicate the latent potency of essential oil of R. officinalis as a natural preservative in food products against L. mesenteroides, L. delbruekii, S. cerevisia and C.krusei.Key words: Rosmarinus officinalis L., essential oil, chemical composition, antimicrobial properties

Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis

Currently, there is an increasing global need for COVID-19 screening to help reduce the rate of infection and at-risk patient workload at hospitals. Smartphone-based screening for COVID-19 along with other respiratory illnesses offers excellent potential due to its rapid-rollout remote platform, user convenience, symptom tracking, comparatively low cost, and prompt result processing timeframe. In particular, speech-based analysis embedded in smartphone app technology can measure physiological effects relevant to COVID-19 screening that are not yet digitally available at scale in the healthcare field. Using a selection of the Sonde Health COVID-19 2020 dataset, this study examines the speech of COVID-19-negative participants exhibiting mild and moderate COVID-19-like symptoms as well as that of COVID-19-positive participants with mild to moderate symptoms. Our study investigates the classification potential of acoustic features (e.g., glottal, prosodic, spectral) from short-duration speech segments (e.g., held vowel, pataka phrase, nasal phrase) for automatic COVID-19 classification using machine learning. Experimental results indicate that certain feature-task combinations can produce COVID-19 classification accuracy of up to 80% as compared with using the all-acoustic feature baseline (68%). Further, with brute-forced n-best feature selection and speech task fusion, automatic COVID-19 classification accuracy of upwards of 82–86% was achieved, depending on whether the COVID-19-negative participant had mild or moderate COVID-19-like symptom severity

Mining complex data in highly streaming environments

Data is growing at a rapid rate because of advanced hardware and software technologies and platforms such as e-health systems, sensor networks, and social media. One of the challenging problems is storing, processing and transferring this big data in an efficient and effective way. One solution to tackle these challenges is to construct synopsis by means of data summarization techniques. Motivated by the fact that without summarization, processing, analyzing and communicating this vast amount of data is inefficient, this thesis introduces new summarization frameworks with the main goals of reducing communication costs and accelerating data mining processes in different application scenarios. Specifically, we study the following big data summarizaion techniques:(i) dimensionality reduction;(ii)clustering,and(iii)histogram, considering their importance and wide use in various areas and domains. In our work, we propose three different frameworks using these summarization techniques to cover three different aspects of big data:"Volume","Velocity"and"Variety" in centralized and decentralized platforms. We use dimensionality reduction techniques for summarizing large 2D-arrays, clustering and histograms for processing multiple data streams. With respect to the importance and rapid growth of emerging e-health applications such as tele-radiology and tele-medicine that require fast, low cost, and often lossless access to massive amounts of medical images and data over band limited channels,our first framework attempts to summarize streams of large volume medical images (e.g. X-rays) for the purpose of compression. Significant amounts of correlation and redundancy exist across different medical images. These can be extracted and used as a data summary to achieve better compression, and consequently less storage and less communication overheads on the network. We propose a novel memory-assisted compression framework as a learning-based universal coding, which can be used to complement any existing algorithm to further eliminate redundancies/similarities across images. This approach is motivated by the fact that, often in medical applications, massive amounts of correlated images from the same family are available as training data for learning the dependencies and deriving appropriate reference or synopses models. The models can then be used for compression of any new image from the same family. In particular, dimensionality reduction techniques such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) are applied on a set of images from training data to form the required reference models. The proposed memory-assisted compression allows each image to be processed independently of other images, and hence allows individual image access and transmission. In the second part of our work,we investigate the problem of summarizing distributed multidimensional data streams using clustering. We devise a distributed clustering framework, DistClusTree, that extends the centralized ClusTree approach. The main difficulty in distributed clustering is balancing communication costs and clustering quality. We tackle this in DistClusTree through combining spatial index summaries and online tracking for efficient local and global incremental clustering. We demonstrate through extensive experiments the efficacy of the framework in terms of communication costs and approximate clustering quality. In the last part, we use a multidimensional index structure to merge distributed summaries in the form of a centralized histogram as another widely used summarization technique with the application in approximate range query answering. In this thesis, we propose the index-based Distributed Mergeable Summaries (iDMS) framework based on kd-trees that addresses these challenges with data generative models of Gaussian mixture models (GMMs) and a Generative Adversarial Network (GAN). iDMS maintains a global approximate kd-tree at a central site via GMMs or GANs upon new arrivals of streaming data at local sites. Experimental results validate the effectiveness and efficiency of iDMS against baseline distributed settings in terms of approximation error and communication costs

The Effects of Calcium, Vitamins D and K co-Supplementation on Markers of Insulin Metabolism and Lipid Profiles in Vitamin D-Deficient Women with Polycystic Ovary Syndrome

Background Data on the effects of calcium, vitamins D and K co-supplementation on markers of insulin metabolism and lipid profiles among vitamin D-deficient women with polycystic ovary syndrome (PCOS) are scarce. Objective This study was done to determine the effects of calcium, vitamins D and K co-supplementation on markers of insulin metabolism and lipid profiles in vitamin D-deficient women with PCOS. Methods This randomized double-blind, placebo-controlled trial was conducted among 55 vitamin D-deficient women diagnosed with PCOS aged 18–40 years old. Subjects were randomly assigned into 2 groups to intake either 500 mg calcium, 200 IU vitamin D and 90 µg vitamin K supplements (n=28) or placebo (n=27) twice a day for 8 weeks. Results After the 8-week intervention, compared with the placebo, joint calcium, vitamins D and K supplementation resulted in significant decreases in serum insulin concentrations (−1.9±3.5 vs. +1.8±6.6 µIU/mL, P=0.01), homeostasis model of assessment-estimated insulin resistance (−0.4±0.7 vs. +0.4±1.4, P=0.01), homeostasis model of assessment-estimated b cell function (−7.9±14.7 vs. +7.0±30.3, P=0.02) and a significant increase in quantitative insulin sensitivity check index (+0.01±0.01 vs. −0.008±0.03, P=0.01). In addition, significant decreases in serum triglycerides (−23.4±71.3 vs. +9.9±39.5 mg/dL, P=0.03) and VLDL-cholesterol levels (−4.7±14.3 vs. +2.0±7.9 mg/dL, P=0.03) was observed following supplementation with combined calcium, vitamins D and K compared with the placebo. Conclusion Overall, calcium, vitamins D and K co-supplementation for 8 weeks among vitamin D-deficient women with PCOS had beneficial effects on markers of insulin metabolism, serum triglycerides and VLDL-cholesterol levels

Effect of enhanced external counterpulsation and cardiac rehabilitation on quality of life, plasma nitric oxide, endothelin 1 and high sensitive CRP in patients with coronary artery disease: A pilot study

Objective To investigate the effect of enhanced external counterpulsation (EECP) on plasma nitric oxide (NO), Endothelin 1 (ET1), high sensitive C-reactive protein (HSCRP) and quality of life (QoL) in patients with coronary artery disease (CAD).Methods We conducted a pilot randomized clinical trial in order to evaluate plasma NO, ET1, HSCRP and QoL before and after twenty sessions of EECP (group A) and cardiac rehabilitation (CR, group B) in 42 patients with CAD (21 in each group).Results Forty-two patients (33 male and 9 female) were included in the study. The mean age was 58.2±10 years. The mean HSCRP was 1.52±0.7 in the EECP group and it was reduced to 1.27±0.4 after intervention. The reduction in HSCRP was not statistically significant in EECP and CR groups with p=0.33 and p=0.27, respectively. There was not significant improvement of NO, ET1, and QoL in the EECP and CR groups shortly after therapy (p>0.05).Conclusion Although the short-term EECP treatment in CAD patients improved HSCRP, NO, ET1, and QoL compared with the baseline those improvements are not statistically significant. Further studies are necessary with large study groups and more sessions. © 2015 by Korean Academy of Rehabilitation Medicine