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Indoor positioning is an important component of location-based service. Moreover, it has extensive applications in areas such as the Internet of Things (IoT) and Artificial Intelligence (AI). As a new generation of the mobile communication signal, the introduction of technologies such as multi-carrier, massive multiple-input multiple-output (MIMO), and ultra-dense network (UDN) has enabled 5G NR signal to demonstrate unique technical advantages in the field of indoor positioning. In this paper, for indoor positioning, a subcarrier diversity precision ranging method based on a commercial 5G NR signal is proposed. According to the results of field experiments in the indoor environment, the proposed method significantly improves the ranging precision compared to the Reference Signal Receiving Power (RSRP)-based ranging method. When the diversity ratio is set to 0.25, the 95% cumulative distribution function (CDF) of the overall ranging error is less than 3 meters.

BACKGROUND: We used detailed information on patients with diabetes admitted to hospital to determine differences in clinical outcomes before and during the COVID-19 pandemic in the UK. METHODS: The study used electronic patient record data from Imperial College Healthcare NHS Trust. Hospital admission data for patients coded for diabetes was analysed over three time periods: pre-pandemic (31st January 2019-31st January 2020), Wave 1 (1st February 2020-30th June 2020), and Wave 2 (1st September 2020-30th April 2021). We compared clinical outcomes including glycaemia and length of stay. RESULTS: We analysed data obtained from 12,878, 4008 and 7189 hospital admissions during the three pre-specified time periods. The incidence of Level 1 and Level 2 hypoglycaemia was significantly higher during Waves 1 and 2 compared to the pre-pandemic period (25 % and 25.1 % vs. 22.9 % for Level 1 and 11.7 % and 11.5 % vs. 10.3 % for Level 2). The incidence of hyperglycaemia was also significantly higher during the two waves. The median hospital length of stay increased significantly (4.1[1.6, 9.8] and 4.0[1.4, 9.4] vs. 3.5[1.2, 9.2] days). CONCLUSIONS: During the COVID-19 pandemic in the UK, hospital in-patients with diabetes had a greater number of hypoglycaemic/hyperglycaemic episodes and an increased length of stay when compared to the pre-pandemic period. This highlights the necessity for a focus on improved diabetes care during further significant disruptions to healthcare systems and ensuring minimisation of the impact on in-patient diabetes services. SUMMARY: Diabetes is associated with poorer outcomes from COVID-19. However the glycaemic control of inpatients before and during the COVID-19 pandemic is unknown. We found the incidence of hypoglycaemia and hyperglycaemia was significantly higher during the pandemic highlighting the necessity for a focus on improved diabetes care during further pandemics.

Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, all of these distil genetic liability to a single number based on aggregation of an individual’s genome-wide alleles. This results in a key loss of information about an individual’s genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. Here we evaluate the performance of pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual, and we introduce a software, PRSet, for computing and analysing pathway PRSs. We find that pathway PRSs have similar power for evaluating pathway enrichment of GWAS signal as the leading methods, with the distinct advantage of providing estimates of pathway genetic liability at the individual-level. Exemplifying their utility, we demonstrate that pathway PRSs can stratify diseases into subtypes in the UK Biobank with substantially greater power than genome-wide PRSs. Compared to genome-wide PRSs, we expect pathway-based PRSs to offer greater insights into the heterogeneity of complex disease and treatment response, generate more biologically tractable therapeutic targets, and provide a more powerful path to precision medicine.

Histone H3 lysine‐9 di‐methylation (H3K9me2) and lysine‐27 tri‐methylation (H3K27me3) are linked to repression of gene expression, but the functions of repressive histone methylation dynamics during inflammatory responses remain enigmatic. Here, we report that lysine demethylases 7A (KDM7A) and 6A (UTX) play crucial roles in tumor necrosis factor (TNF)‐α signaling in endothelial cells (ECs), where they are regulated by a novel TNF‐α‐responsive microRNA, miR‐3679‐5p. TNF‐α rapidly induces co‐occupancy of KDM7A and UTX at nuclear factor kappa‐B (NF‐κB)‐associated elements in human ECs. KDM7A and UTX demethylate H3K9me2 and H3K27me3, respectively, and are both required for activation of NF‐κB‐dependent inflammatory genes. Chromosome conformation capture‐based methods furthermore uncover increased interactions between TNF‐α‐induced super enhancers at NF‐κB‐relevant loci, coinciding with KDM7A and UTX recruitments. Simultaneous pharmacological inhibition of KDM7A and UTX significantly reduces leukocyte adhesion in mice, establishing the biological and potential translational relevance of this mechanism. Collectively, these findings suggest that rapid erasure of repressive histone marks by KDM7A and UTX is essential for NF‐κB‐dependent regulation of genes that control inflammatory responses of ECs.

Neural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from the mel-spectrogram using vocoder such as WaveNet. Compared with traditional concatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust (i.e., some words are skipped or repeated) and lack of controllability (voice speed or prosody control). In this work, we propose a novel feed-forward network based on Transformer to generate mel-spectrogram in parallel for TTS. Specifically, we extract attention alignments from an encoder-decoder based teacher model for phoneme duration prediction, which is used by a length regulator to expand the source phoneme sequence to match the length of the target mel-spectrogram sequence for parallel mel-spectrogram generation. Experiments on the LJSpeech dataset show that our parallel model matches autoregressive models in terms of speech quality, nearly eliminates the problem of word skipping and repeating in particularly hard cases, and can adjust voice speed smoothly. Most importantly, compared with autoregressive Transformer TTS, our model speeds up mel-spectrogram generation by 270x and the end-to-end speech synthesis by 38x. Therefore, we call our model FastSpeech., Comment: Accepted by NeurIPS2019

The separated silicon-vacancy (SiV) photoluminescent diamond particles were synthesized on a silica optical fiber by hot filament chemical vapor deposition (HFCVD). The effects of the pre-treated method and chamber pressure on the microstructure and photoluminescence of the diamond particles were investigated. The results show that the diamond particles are homogeneously distributed on the surface of the optical fiber. With the chamber pressure increasing from 1.6 kPa to 3.5 kPa, the shape of the particles transforms from flake to circle, while the diamond particles cannot be deposited on the fiber with the pressure further increased to 4.5 kPa. The samples synthesized under 2.5 kPa chamber pressure are composed of diamond particles with size around 200–400 nm, exhibiting stronger SiV photoluminescence with the width of around 6 nm.

We study online social group dynamics based on how group members diverge in their online discussions. Previous studies mostly focused on the link structure to characterize social group dynamics, whereas the group behavior of content generation in discussions is not well understood. Particularly, we use Jensen-Shannon (JS) divergence to measure the divergence of topics in user-generated contents, and how it progresses over time. We study Twitter messages (tweets) in multiple real-world events (natural disasters and social activism) with different times and demographics. We also model structural and user features with guidance from two socio-psychological theories, social cohesion and social identity, to learn their implications on group discussion divergence. Those features show significant correlation with group discussion divergence. By leveraging them we are able to construct a classifier to predict the future increase or decrease in group discussion divergence, which achieves an area under the curve (AUC) of 0.84 and an F-1 score (harmonic mean of precision and recall) of 0.8. Our approach allows to systematically study collective diverging group behavior independent of group formation design. It can help to prioritize whom to engage with in communities for specific topics of needs during disaster response coordination, and for specific concerns and advocacy in the brand management.

The properties of large bipolarons in two and three dimensions are investigated by averaging over the relative wavefunction of the two electrons and using the Lee-Low-Pines-Huybrechts variational method. We obtain the ground-state (GS) and excited-state energies of the Fr\"{o}hlich bipolaron for the whole range of electron-phonon coupling constants. Furthermore, we calculate the energies of the first relaxed excited state (RES) and Franck-Condon (FC) excited state of the bipolaron. Compared with the FC state, the first RES has a lower energy. Our results for the GS and RES energies are lower than those obtained before by the Landau-Pekar method in the whole coupling regime., Comment: 19 pages, 5 figures (including 8 parts)

To determine why renal vasoconstriction elicited by periarterial nerve stimulation (RNS) at lower frequencies (< 4 Hz) is resistant to alpha-adrenergic receptor blockade in the rat kidney, we reevaluated the effect of alpha-receptor antagonists on the vasoconstrictor response to norepinephrine (NE) and to RNS and on the release of adrenergic transmitter. The alpha-receptor antagonist prazosin (PZ) at 0.2 and 7 nM reduced the vasoconstrictor response to NE, and 2.4 microM PZ abolished it. PZ (0.2 or 7 nM) reduced RNS-induced vasoconstriction without altering the fractional tritium overflow. PZ (2.4 microM) enhanced fractional tritium overflow and reduced the vasoconstrictor response to RNS at 2-10 Hz, but not at 0.5 or 1 Hz. The effect of 0.2 nM PZ to reduce RNS-induced vasoconstriction was reversed by increasing the concentration to 2.4 microM. Corynanthine (COR; 2.6 microM), a preferential alpha-receptor blocker, or phenoxybenzamine (PBZ; 30 nM) abolished the vasoconstrictor response to NE but only partially reduced response to RNS and enhanced the fractional tritium overflow. Rauwolscine (RW; 2.5 nM), a preferential alpha 2-receptor antagonist, did not alter the vasoconstrictor response to NE but potentiated RNS-induced vasoconstriction and fractional tritium overflow. RW (7.7 microM) inhibited NE-induced vasoconstriction but potentiated the vasoconstrictor response to RNS and fractional tritium overflow. PZ (7 nM) abolished the potentiation by RW and reduced the vasoconstrictor response to RNS. These data suggest that a component of RNS-induced vasoconstriction in the rat kidney is attributable to co-release of a nonadrenergic transmitter with NE. The diminished effect of alpha-receptor antagonists at higher concentrations (e.g., PZ 2.4 microM) to reduce RNS-induced vasoconstriction is caused by their prejunctional action to enhance co-release of the nonadrenergic transmitter.

Pisatin is a fungistatic isoflavonoid produced by garden pea. Field isolates of the ascomycete Nectria haematococca mating population VI (anamorph: Fusarium solani) that are highly virulent on pea have been found to possess the PDA1 gene encoding a pisatin detoxifying activity. Expression of PDA1 is specifically and highly induced by exposure of mycelia to pisatin. A pisatin-responsive DNA-binding activity has previously been identified with properties suggestive of a transcriptional regulator of PDA1. In this study, the sequence determinants for binding this pisatin-responsive factor (PRF) were localized to a 14-bp region through analysis of sequence alterations that reduced PRF binding. Using a homologous in vitro transcription system, a transcriptional activator of PDA1 was shown to be present in mycelial extracts that shared the sequence specificity characteristic of the PRF, indicating function of the DNA-binding protein in transcriptional control. A 70-kDa protein was shown to be a DNA-binding component of PRF by three independent assays for DNA-binding proteins: Southwestern (DNA-protein) blotting, UV-crosslinking, and binding to immobilized DNA. These results characterize a transcriptional activator acting on the PDA1 promoter that is responsive to a host-specific compound and provide insight into the regulation of fungal genes in response to plant flavonoids.

Hydrocarbon leaks in large scale LNG and gas processing and handling facilities present a risk to health and safety, as well as a negative impact on the environment. Existing autonomous leak detection systems rely primarily on point and path detection. This technology requires dispersed gas to physically contact the point sensors or move between two path detectors. Manual leak checks are also performed periodically by checking one component at a time using a flame ionization detector (FID) or optical gas imager, however the size and complexity of these facilities means that smaller leaks may go undetected for extended periods of time and unintended releases may occur when plant personnel are not present. ExxonMobil Research Qatar Ltd. and Providence Photonics LLC have developed the IntelliRed™ Remote Gas Detection system that integrates computer vision algorithms and infrared (IR) optical gas imaging technology to autonomously scan for and identify leaks. The IntelliRed™ system utilizes a unique mid-wave IR (MWIR) imager and a computer vision algorithm that analyzes the video output from the IR imager to determine the presence of hydrocarbon plumes. Most hydrocarbons have strong absorbance peaks in a narrow MWIR region. The algorithm takes advantage of the difference in contrast between a hydrocarbon plume and the background in an IR imager. The algorithm compares sequentially collected IR images and uses a multi-stage confirmation process to model the behavior of the plume and confirm the detection. It has multiple filters that mitigate interferences such as humans, vehicles, and trees. After extended pilot deployments, the IntelliRed™ technology has been qualified and is commercially available for leak detection and environmental applications. An innovative differential infrared (DIR) camera design now offers the possibility of lower detection limits and higher immunity to false alarms for the IntelliRed™ system. The design employs two cooled MWIR sensors with a common optical path. The infrared energy from the scene is divided using a beam splitter, focusing a spatially registered image on each sensor. The spectral filtering for the two sensors is chosen so that one sensor can visualize a hydrocarbon plume while the second sensor cannot. A synchronized system clock for the two sensors ensures that the frames are temporally aligned. The result is a camera which produces both spatially and temporally aligned frames with the plume present in one frame but absent in the other. A differential image is produced by comparing the two sensors frame by frame, providing a robust filter for common interferences such as steam and dust plumes. The DIR camera design requires new computer vision techniques to exploit the information provided by the reference sensor. Results are compared to other autonomous hydrocarbon detection technologies, including single sensor IntelliRed™ technology. New applications enabled by the DIR camera design are discussed, including aerial pipeline surveys. The technology is currently being qualified with DIR based IntelliRed™ pilot deployments underway in Qatar and the United States.

The ball punch test technique was used to evaluate the conventional tensile and impact properties of the tempered martensitic steel EUROFER97 from room temperature down to liquid nitrogen temperature. The testing was carried out on unirradiated material only with small disks, 3 mm in diameter and 0.25 mm in thickness. For comparison, tensile tests were also performed over the same temperature range. Correlations between the load at the plastic bending initiation and the maximum load of the punch tests with the yield stress and the ultimate tensile stress of the tension tests could be established. The temperature dependence of the specific fracture energy of the punch test was used to define a ductile-brittle transition temperature (DBTT) and to correlate this with the DBTT measured from impact Charpy on KLST specimens. The results are compared with other available correlations done in the past on other ferritic steels. (C) 2002 Elsevier Science B.V. All rights reserved.

This manuscript describes the effects of alterations in biochemical composition on structural morphology and the mechanical behaviour of elementary and technical fibres of hemp used for composite applications. First, the strength and apparent Young's modulus distribution of technical fibres of hemp of 96 hemp samples, corresponding to 32 different hemp accessions cultivated in 3 locations, were analysed using Weibull distribution. From these, 2 samples (one with high and one with low fibre strength) were selected for further analysis. Next, full-field strain measurement at the micro-scale during tensile loading was used for evaluating both, the stress-strain behaviour at a global scale and the local mechanical behaviour heterogeneity at a micro-scale, along elementary and technical fibres of hemp. At the composite level, the local behaviour of each phase of the composite (fibre and matrix) and of the fibre/matrix interphase during a transversal 3 point bending test were characterized. Results show that the strength of technical fibres of hemp is highly dependent on the shear strength between elementary fibres, which itself is related to the biochemical composition of the middle lamellae. A correlation between the strength of a technical fibre and their elementary fibres was also observed. At the composite level, the relation of the composite mechanical behaviour (Young's modulus and strength) with the technical or elementary fibre mechanical behaviour is complex and might depend on the combination of multiple factors such as the matrix (thermoset or thermoplastic), or the technical fibre sample employed (weak or strong) and the level of fibre-matrix wetting (impregnation) and adhesion.

A simple and straightforward synthetic approach for carbon nanodots (C-dots) is proposed. The strategy is based on a one-step hydrothermal chemical reduction with thiourea and urea, leading to high quantum yield C-dots. The obtained C-dots are well-dispersed with a uniform size and a graphite-like structure. A synergistic reduction mechanism was investigated using Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. The findings show that using both thiourea and urea during the one-pot synthesis enhances the luminescence of the generated C-dots. Moreover, the prepared C-dots have a high distribution of functional groups on their surface. In this work, C-dots proved to be a suitable nanomaterial for imaging of bacteria and exhibit potential for application in bioimaging thanks to their low cytotoxicity.

A deep understanding of the molecular interactions of carbon nanodots with biomacromolecules is essential for wider applications of carbon nanodots both in vitro and in vivo. Herein, nitrogen and sulfur co-doped carbon dots (N,S-CDs) with a quantum yield of 16% were synthesized by a 1-step hydrothermal method. The N,S-CDs exhibited a good dispersion, with a graphite-like structure, along with the fluorescence lifetime of approximately 7.50ns. Findings showed that the fluorescence of the N,S-CDs was effectively quenched by bovine hemoglobin as a result of the static fluorescence quenching. The mentioned quenching mechanism was investigated by the Stern-Volmer equation, temperature-dependent quenching, and fluorescence lifetime measurements. The binding constants, number of binding sites, and the binding average distance between the energy donor N,S-CDs and acceptor bovine hemoglobin were calculated as well. These findings will provide for valuable insights on the future bioapplications of N,S-CDs.

Most treatments of decentralized estimation rely on some form of track fusion, in which local track estimates and their associated covariances are communicated. This implies a great deal of communication; and it was recently proposed that by an intelligent quantization directly of measurements, the communication needs could be considerably cut. However, several issues were not discussed. The first of these is that estimation with quantized measurements requires an update with a non-Gaussian distribution, reflecting the uncertainty within the quantization "bin."; In general this would be a difficult task for dynamic estimation, but Markov-chain Monte-Carlo (MCMC, and specifically here particle filtering) techniques appear quite appropriate since the resulting system is, in essence, a nonlinear filter. The second issue is that in a realistic sensor network it is to be expected that measurements should arrive out-of-sequence. Again, a particle filter is appropriate, since the recent literature has reported particle filter modifications that accommodate nonlinear-filter updates based on new past measurements, with the need to refilter obviated. We show results that indicate a compander/particle-filter combination is a natural fit, and specifically that quite good performance is achievable with only 2-3 bits per dimension per observation. The third issue is that intelligent quantization requires that both sensor and fuser share an understanding of the quantization rule. In dynamic estimation this is a problem since both quantizer and fuser are working with only partial information; if measurements arrive out-of-sequence the problem is compounded. We therefore suggest architectures, and comment on their suitabilities for the task. A scheme based on delta-modulation appears to be promising.

We report fabrication of the lead silicate microstructured fibers (MOFs) with core holes as small as 20nm, the smallest holes fabricated within the core of an optical fiber to date. We show that light confinement and average mode intensity within such holes are strongly dependent on the hole size. Light confinement within 80nm and 250nm core hole within the fabricated MOFs has been experimentally characterized using Scanning Near-field Optical Microscopy (SNOM). Refereed/Peer-reviewed