5G NB-IoT via Low Density LEO Constellations

5G NB-IoT is seen as a key technology for providing truly ubiquitous, global 5G coverage (1.000.000 devices/km2) for machine type communications in the internet of things. A non-terrestrial network (NTN) variant of NB-IoT is being standardized in the 3GPP, which along with inexpensive and non-complex chip-sets enables the production of competitively priced IoT devices with truly global coverage. NB-IoT allows for narrowband single carrier transmissions in the uplink, which improves the uplink link-budget by as much as 16.8 dB over the 180 [kHz] downlink. This allows for a long range sufficient for ground to low earth orbit (LEO) communication without the need for complex and expensive antennas in the IoT devices. In this paper the feasibility of 5G NB-IoT in the context of low-density constellations of small-satellites carrying base-stations in LEO is analyzed and required adaptations to NB-IoT are discussed

Beacon Signalling for Expedited Cell Search Procedures in NTN NB-IoT

Three cellular standards have been considered for Non-Terrestrial Networks (NTN): NB-IoT, eMTC and NR, each having had features introduced to accommodate the challenges of the NTN case. In Terrestrial Networks (TNs), it is reasonable to expect continuous coverage when a UE is stationary within reach of a base-station (eNB) with rare exceptions of downtime due to failures or catastrophic events. The same continuity cannot be assumed in NTN for sparse eNB constellations or during the rollout of dense eNB constellations. Therefore, a feature of the NTN IoT protocols - NTN NB-IoT & NTN eMTC - is the support of discontinuous RAN coverage. Cell search is a core task of NTN UEs serviced by non-geostationary (NGSO) constellations. Initially, when UEs are booted up, unless a recent ephemeris has been provisioned to it, the UE must first discover a valid eNB by employing repeated cell searching. UEs will have to keep doing cell search each time they wish to access a cell again after losing or dropping connectivity. Intermittent coverage gaps, which occur in dense constellations due to system failures, during rollout or inherently in sparse constellations, exaggerate the number of cell search attempts required by a UE before finding an appropriate cell to camp on. These latter cases of intermittent coverage can be mitigated by the coverage prediction features for discontinuous coverage. In this paper, a beacon signal, which can be transmitted within the white-spaces of stand-alone NB-IoT, is introduced. The beacon signal is designed to expedite the cell search procedure in NTN NB-IoT in NGSO constellations by: (1) Allowing for easy and early detection of the presence of a cell, (2) encoding preliminary information for the UE to assess whether to continue cell search at that early point and (3) providing helpful information to the synchronisation procedure. The performance of the beacon signal is simulated and evaluations show a fair improvement over utilizing legacy synchronization signals for cell detection both in terms of speed and SNR

Diagnostic Yield of Genetic Testing in Young Patients With Atrioventricular Block of Unknown Cause

BACKGROUND: The cause of atrioventricular block (AVB) remains unknown in approximately half of young patients with the diagnosis. Although variants in several genes associated with cardiac conduction diseases have been identified, the contribution of genetic variants in younger patients with AVB is unknown. METHODS AND RESULTS: Using the Danish Pacemaker and Implantable Cardioverter Defibrillator (ICD) Registry, we identified all patients younger than 50 years receiving a pacemaker because of AVB in Denmark in the period from January 1, 1996 to December 31, 2015. From medical records, we identified patients with unknown cause of AVB at time of pacemaker implantation. These patients were invited to a genetic screening using a panel of 102 genes associated with inherited cardiac diseases. We identified 471 living patients with AVB of unknown cause, of whom 226 (48%) accepted participation. Median age at the time of pacemaker implantation was 39 years (interquartile range, 32–45 years), and 123 (54%) were men. We found pathogenic or likely pathogenic variants in genes associated with or possibly associated with AVB in 12 patients (5%). Most variants were found in the LMNA gene (n=5). LMNA variant carriers all had a family history of either AVB and/or sudden cardiac death. CONCLUSIONS: In young patients with AVB of unknown cause, we found a possible genetic cause in 1 out of 20 participating patients. Variants in the LMNA gene were most common and associated with a family history of AVB and/or sudden cardiac death, suggesting that genetic testing should be a part of the diagnostic workup in these patients to stratify risk and screen family members

Update 2016-2018 of the Nationwide Danish Fungaemia Surveillance Study:Epidemiologic Changes in a 15-Year Perspective

As part of a national surveillance programme initiated in 2004, fungal blood isolates from 2016–2018 underwent species identification and EUCAST susceptibility testing. The epidemiology was described and compared to data from previous years. In 2016–2018, 1454 unique isolates were included. The fungaemia rate was 8.13/100,000 inhabitants compared to 8.64, 9.03, and 8.38 in 2004–2007, 2008–2011, and 2012–2015, respectively. Half of the cases (52.8%) involved patients 60–79 years old and the incidence was highest in males ≥70 years old. Candida albicans accounted for 42.1% of all isolates and Candida glabrata for 32.1%. C. albicans was more frequent in males (p = 0.03) and C. glabrata in females (p = 0.03). During the four periods, the proportion of C. albicans decreased (p < 0.001), and C. glabrata increased (p < 0.001). Consequently, fluconazole susceptibility gradually decreased from 68.5% to 59.0% (p < 0.001). Acquired fluconazole resistance was found in 4.6% Candida isolates in 2016–2018. Acquired echinocandin resistance increased during the four periods 0.0%, 0.6%, 1.7% to 1.5% (p < 0.0001). Sixteen echinocandin-resistant isolates from 2016–2018 harboured well-known FKS resistance-mutations and one echinocandin-resistant C. albicans had an FKS mutation outside the hotspot (P1354P/S) of unknown importance. In C. glabrata specifically, echinocandin resistance was detected in 12/460 (2.6%) in 2016–2018 whereas multidrug-class resistance was rare (1/460 isolates (0.2%)). Since the increase in incidence during 2004–2011, the incidence has stabilised. In contrast, the species distribution has changed gradually over the 15 years, with increased C. glabrata at the expense of C. albicans. The consequent decreased fluconazole susceptibility and the emergence of acquired echinocandin resistance complicates the management of fungaemia and calls for antifungal drug development

Identification and analysis of miRNAs in human breast cancer and teratoma samples using deep sequencing

<p>Abstract</p> <p>Background</p> <p>MiRNAs play important roles in cellular control and in various disease states such as cancers, where they may serve as markers or possibly even therapeutics. Identifying the whole repertoire of miRNAs and understanding their expression patterns is therefore an important goal.</p> <p>Methods</p> <p>Here we describe the analysis of 454 pyrosequencing of small RNA from four different tissues: Breast cancer, normal adjacent breast, and two teratoma cell lines. We developed a pipeline for identifying new miRNAs, emphasizing extracting and retaining as much data as possible from even noisy sequencing data. We investigated differential expression of miRNAs in the breast cancer and normal adjacent breast samples, and systematically examined the mature sequence end variability of miRNA compared to non-miRNA loci.</p> <p>Results</p> <p>We identified five novel miRNAs, as well as two putative alternative precursors for known miRNAs. Several miRNAs were differentially expressed between the breast cancer and normal breast samples. The end variability was shown to be significantly different between miRNA and non-miRNA loci.</p> <p>Conclusion</p> <p>Pyrosequencing of small RNAs, together with a computational pipeline, can be used to identify miRNAs in tumor and other tissues. Measures of miRNA end variability may in the future be incorporated into the discovery pipeline as a discriminatory feature. Breast cancer samples show a distinct miRNA expression profile compared to normal adjacent breast.</p

Influence of Phthalates on <i>in vitro</i> Innate and Adaptive Immune Responses

<div><p>Phthalates are a group of endocrine disrupting chemicals, suspected to influence the immune system. The aim of this study was to investigate the influence of phthalates on cytokine secretion from human peripheral blood mononuclear cells. <i>Escherichia coli</i> lipopolysaccharide and phytohemagglutinin-P were used for stimulation of monocytes/macrophages and T cells, respectively. Cells were exposed for 20 to 22 hours to either di-ethyl, di-n-butyl or mono-n-butyl phthalate at two different concentrations. Both diesters were metabolised to their respective monoester and influenced cytokine secretion from both monocytes/macrophages and T cells in a similar pattern: the secretion of interleukin (IL)-6, IL-10 and the chemokine CXCL8 by monocytes/macrophages was enhanced, while tumour necrosis factor (TNF)-α secretion by monocytes/macrophages was impaired, as was the secretion of IL-2 and IL-4, TNF-α and interferon-γ by T cells. The investigated phthalate monoester also influenced cytokine secretion from monocytes/macrophages similar to that of the diesters. In T cells, however, the effect of the monoester was different compared to the diesters. The influence of the phthalates on the cytokine secretion did not seem to be a result of cell death. Thus, results indicate that both human innate and adaptive immunity is influenced <i>in vitro</i> by phthalates, and that phthalates therefore may affect cell differentiation and regenerative and inflammatory processes <i>in vivo</i>.</p></div

Influence of phthalates on T-cell responses.

<p>MNC cultures were stimulated with phytohemagglutinin-P and exposed to di-ethyl phthalate (DEP), di-n-butyl phthalate (DnBP) or mono-n-butyl phthalate (MnBP), at two different concentrations, for 20–22 hours. The resulting production of IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ are shown as ratios to the respective ethanol control. The red dashed line indicates the level of the ethanol controls (ratio = 1). * = p<0.05 compared to ethanol control, # = p<0.05 compared to low phthalate exposure (0.1 μM).</p