On LOS Contribution to Ultra-Dense Network

Ultra-dense networks (UDNs) are widely considered as an effective solution to greatly improve coverage by shortening the communication distance between user equipments (UEs) and base stations (BSs). The reality of UDN is that line-of-sight (LOS) communication becomes more likely to occur but this desirable result also complicates the performance analysis of random UDNs and puts an obstacle on the design and optimization of UDNs. The aim of this paper is to derive analytical results that take into account the phenomenon of having mixed LOS and non line-of-sight (NLOS) links in UDNs. In particular, the use of an arbitrary shaped thinning process to model the LOS wireless links allowed us to investigate a wide set of scenarios for what concerns the desired and interfering power levels. Our contribution is an accurate approximation in closed form for the success content delivery probability (SCDP) that decouples the contribution from LOS and NLOS links. Simulation results corroborate the accuracy of the proposed approximation

Joint Uncertainty Decoding for Noise Robust Subspace Gaussian Mixture Models

Abstract—Joint uncertainty decoding (JUD) is a model-based noise compensation technique for conventional Gaussian Mixture Model (GMM) based speech recognition systems. Unlike vector Taylor series (VTS) compensation which operates on the individual Gaussian components in an acoustic model, JUD clusters the Gaussian components into a smaller number of classes, sharing the compensation parameters for the set of Gaussians in a given class. This significantly reduces the computational cost. In this paper, we investigate noise compensation for subspace Gaussian mixture model (SGMM) based speech recognition systems using JUD. The total number of Gaussian components in an SGMM is typically very large. Therefore direct compensation of the individual Gaussian components, as performed by VTS, is computationally expensive. In this paper we show that JUDbased noise compensation can be successfully applied to SGMMs in a computationally efficient way. We evaluate the JUD/SGMM technique on the standard Aurora 4 corpus. Our experimental results indicate that the JUD/SGMM system results in lower word error rates compared with a conventional GMM system with either VTS-based or JUD-based noise compensation. Index Terms—subspace Gaussian mixture model, vector Taylor series, joint uncertainty decoding, noise robust ASR, Aurora

Harnessing technology and molecular analysis to understand the development of cardiovascular diseases in Asia: a prospective cohort study (SingHEART)

BACKGROUND: Cardiovascular disease (CVD) imposes much mortality and morbidity worldwide. The use of "deep learning", advancements in genomics, metabolomics, proteomics and devices like wearables have the potential to unearth new insights in the field of cardiology. Currently, in Asia, there are no studies that combine the use of conventional clinical information with these advanced technologies. We aim to harness these new technologies to understand the development of cardiovascular disease in Asia. METHODS: Singapore is a multi-ethnic country in Asia with well-represented diverse ethnicities including Chinese, Malays and Indians. The SingHEART study is the first technology driven multi-ethnic prospective population-based study of healthy Asians. Healthy male and female subjects aged 21-69 years old without any prior cardiovascular disease or diabetes mellitus will be recruited from the general population. All subjects are consented to undergo a detailed on-line questionnaire, basic blood investigations, resting and continuous electrocardiogram and blood pressure monitoring, activity and sleep tracking, calcium score, cardiac magnetic resonance imaging, whole genome sequencing and lipidomic analysis. Outcomes studied will include mortality and cause of mortality, myocardial infarction, stroke, malignancy, heart failure, and the development of co-morbidities. DISCUSSION: An initial target of 2500 patients has been set. From October 2015 to May 2017, an initial 683 subjects have been recruited and have completed the initial work-up the SingHEART project is the first contemporary population-based study in Asia that will include whole genome sequencing and deep phenotyping: including advanced imaging and wearable data, to better understand the development of cardiovascular disease across different ethnic groups in Asia

Triphasic Computed Tomography Enterography with Polyethylene Glycol to Detect Renal Cell Carcinoma Metastases to the Small Bowel

Enteroclysis was first used to diagnose small bowel obstruction in 1996. However, nasojejunal intubation required during enteroclysis causes discomfort to the patient. Triphasic computed tomography (CT) enterography, a noninvasive procedure that does not require intubation, was found to be an efficient method to diagnose small bowel lesions. We describe our experience of using triphasic CT enterography with polyethylene glycol (PEG) for diagnosing renal cell carcinoma (RCC) metastases to the small intestine. RCC can metastasize to many organs and can cause variable clinical presentations. We report the case of a 56-year-old man with RCC who had psoas muscle involvement and lung metastasis. The patient presented with melena and intermittent abdominal pain. Two conventional CT and small bowel series examinations had shown no obstructive lesion in the small intestine. However, triphasic CT enterography with PEG detected two enhanced masses suggestive of small bowel metastasis. The patient underwent laparotomy and segmental resection of the jejunum with primary anastomosis. Histologic examination was compatible with RCC. This is the first report where RCC metastasis to the small bowel was diagnosed using triphasic CT enterography. Our study emphasizes the importance of triphasic CT enterography in cases of obscure gastrointestinal bleeding, especially in patients suspected of having small bowel metastasis

Measurement batch differences and between-batch conversion of Alzheimer's disease cerebrospinal fluid biomarker values

Introduction: Batch differences in cerebrospinal fluid (CSF) biomarker measurement can introduce bias into analyses for Alzheimer's disease studies. We evaluated and adjusted for batch differences using statistical methods. Methods: A total of 792 CSF samples from 528 participants were assayed in three batches for 12 biomarkers and 3 biomarker ratios. Batch differences were assessed using Bland-Altman plot, paired t test, Pitman-Morgan test, and linear regression. Generalized linear models were applied to convert CSF values between batches. Results: We found statistically significant batch differences for all biomarkers and ratios, except that neurofilament light was comparable between batches 1 and 2. The conversion models generally had high R2 except for converting P-tau between batches 1 and 3. Discussion: Between-batch conversion allows harmonized CSF values to be used in the same analysis. Such method may be applied to adjust for other sources of variability in measuring CSF or other types of biomarkers

The exoskeletons are here

It is a fantastic time for the field of robotic exoskeletons. Recent advances in actuators, sensors, materials, batteries, and computer processors have given new hope to creating the exoskeletons of yesteryear's science fiction. While the most common goal of an exoskeleton is to provide superhuman strength or endurance, scientists and engineers around the world are building exoskeletons with a wide range of diverse purposes. Exoskeletons can help patients with neurological disabilities improve their motor performance by providing task specific practice. Exoskeletons can help physiologists better understand how the human body works by providing a novel experimental perturbation. Exoskeletons can even help power mobile phones, music players, and other portable electronic devices by siphoning mechanical work performed during human locomotion. This special thematic series on robotic lower limb exoskeletons and orthoses includes eight papers presenting novel contributions to the field. The collective message of the papers is that robotic exoskeletons will contribute in many ways to the future benefit of humankind, and that future is not that distant

First documented cure of a suggestive exogenous reinfection in polymyositis with same but multidrug resistant M. tuberculosis

BACKGROUND: MDR Mycobacterium tuberculosis is the major cause of treatment failure in tuberculosis patients, especially in immunosuppressed. We described a young polymyositis patient on immunosuppressive therapy who was started with antituberculosis therapy as a susceptible strain of M. tuberculosis was isolated from a single cutaneous abscess in his neck and from regional lymph nodes. CASE PRESENTATION: He had non-reactive miliary tuberculosis and multiple cutaneous abscesses 6 months later with the same strain, which was resistant this time to 9 antituberculosis drugs. We described clinical presentation, radiological and laboratory work-up, treatment and follow-up as the patient was cured after 1.5 years with 6 antituberculosis drugs. CONCLUSION: To our knowledge, this is the first reported case where an immunosuppressed patient with suggestive exogenous reinfection within 6 months with the same but MDR strain of M. tuberculosis was cured. Intense management and regular follow up were important since the patient was a potent source of MDR M. tuberculosis infection and there was limited choice for therapy

Analysis of the Origin and Evolutionary History of HIV-1 CRF28_BF and CRF29_BF Reveals a Decreasing Prevalence in the AIDS Epidemic of Brazil

HIV-1 subtype B and subtype F are prevalent in the AIDS epidemic of Brazil. Recombinations between these subtypes have generated at least four BF circulating recombinant forms (CRFs). CRF28_BF and CRF29_BF are among the first two BF recombinants being identified in Brazil and they contributed significantly to the epidemic. However, the evolution and demographic histories of the CRFs are unclear.A collection of gag and pol sequences sampled within Brazil was screened for CRF28_BF-like and CRF29_BF-like recombination patterns. A Bayesian coalescent framework was employed to delineate the phylogenetic, divergence time and population dynamics of the virus having CRF28_BF-like and CRF29_BF-like genotype. These recombinants were phylogenetically related to each other and formed a well-supported monophyletic clade dated to 1988-1989. The effective number of infections by these recombinants grew exponentially over a five-year period after their emergence, but then decreased toward the present following a logistic model of population growth. The demographic pattern of both recombinants closely resembles those previously reported for CRF31_BC.We revealed that HIV-1 recombinants of the CRF28_BF/CRF29_BF clade are still circulating in the Brazilian population. These recombinants did not exhibit a strong founder effect and showed a decreasing prevalence in the AIDS epidemic of Brazil. Our data suggested that multiple URFs may also play a role in shaping the epidemic of recombinant BF HIV-1 in the region

High-resolution digital phenotypes from consumer wearables and their applications in machine learning of cardiometabolic risk markers: cohort study

Background: Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized. Objective: We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk. Methods: We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events. Results: We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers. Conclusions: High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management

Metallothionein in human oesophagus, Barrett's epithelium and adenocarcinoma

The potential of the metal-binding protein, metallothionein, in assessing the progression of normal oesophagus through Barrett's to adenocarcinoma was investigated. Metallothionein was quantitatively determined in resected tissues from patients undergoing oesophagectomy for high grade dysplasia/adenocarcinoma and in biopsies from patients with Barrett's syndrome. In 10 cancer patients, metallothionein concentrations in adenocarcinoma were not significantly different from normal oesophagus, although six had elevated metallothionein concentrations in the metaplastic tissue bordering the adenocarcinoma. In 17 out of 20 non-cancer patients with Barrett's epithelium, metallothionein was significantly increased by 108% (P<0.004). There was no association between the metallothionein levels in Barrett's epithelium and the presence of inflammatory cells, metaplasia or dysplasia. Metallothionein is a marker of progression from normal to Barrett's epithelium but is not increased in oesophageal adenocarcinoma