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A robust transmission scheme for intelligent reflecting surface (IRS) aided cognitive radio systems is designed to provide security against internal eavesdropping. By considering the secondary receiver as an internal eavesdropper, a total transmit power (TTP) minimization problem is formulated. Through a novel optimization strategy which jointly considers the transmit beamforming vector at the primary transmitter, the transmit beamforming vector at the secondary transmitter and the phase shifts at the IRS, a solution to the formulated problem is presented. By considering perfect channel state information (CSI) and imperfect CSI scenarios, two optimization algorithms are proposed both aiming at reducing the TTP. For the former scenario, instead of applying the commonly used inner approximation (IA) algorithm which cannot handle maximum allowable leaked rate constraint, a novel penalty-based IA algorithm is proposed. For the latter scenario, a non-convex robust optimization problem is formulated. Because of its non-convex nature, a novel upper-bounding technique and S-procedure are used to transform it into a more tractable form which is then solved by deriving of a penalty convex-concave procedure based alternating optimization algorithm. Performance results show that, as compared to other baseline schemes, the proposed algorithms significantly reduce the TTP.

For absolute protein quantification using nuclear magnetic resonance (NMR) spectroscopy, we considered proteins as homopolymers and effective amino acid (AA) residues (AAREff) as monomer units. For diverse classes of proteins, we determined the AAREffmolecular weight as 111.5 ± 3.2 Da and the number of hydrogens per AA as 7.8 ± 0.2. Their ratio of 14.3 ± 0.3 (g/LP)/(mol/LH) remains constant across various protein classes and is equivalent to Kjeldahl’s nitrogen-to-protein conversion constant of 5.78 ± 0.29 gN/gP. By analogy to the Kjeldahl method, we suggest that the total integral of a 1H NMR solution protein spectrum could be used for total protein quantification. We synthesized low-resolution protein spectra from the weighted sums of individual AA spectra and compared them with experimental spectra. In the methyl region, the ratio of the protein mass to the total number of protons in the synthetic spectra (corrected for the chemical shift mismatch) was ∼1 (mg/mL)/mM, which agrees with an earlier reported experimental ratio for urine (1.05 ± 0.06 (mg/mL)/mM). For human blood plasma, in the methyl region, we found empirical ratios of 1.115 ± 0.006 (mg/mL)/mM (using 96 patient samples) and 1.121 ± 0.011 (mg/mL)/mM for the NIST plasma standard. This numerical agreement points to universal conversion constants, i.e., protein mixtures with unknown compositions could be quantified without the need for calibration standards by measuring the millimolar proton concentration within the methyl region of the NMR spectrum using the same conversion constant.

Critical Infrastructures (CIs), which serve as the foundation of our modern society, are facing increasing risks from cyber threats, physical attacks, and natural disasters. Additionally, the interdependencies between CIs throughout their operational lifespan can also significantly impact their integrity and safety. As a result, enhancing the resilience of CIs has emerged as a top priority for many countries, including the European Union. This involves not only understanding the threats/attacks themselves but also gaining knowledge about the areas and infrastructures that could potentially be affected. A European Union-funded project named PRECINCT (Preparedness and Resilience Enforcement for Critical INfrastructure Cascading Cyber-Physical Threats), under the Horizon 2020 program, tries to connect private and public stakeholders of CIs in a specific geographical area. The key objective of this project is to establish a common cyber-physical security management approach that will ensure the protection of both citizens and infrastructures, creating a secure territory. This paper presents the components of PRECINCT, including a directory of PRECINCT Critical Infrastructure Protection (CIP) blueprints. These blueprints support CI communities in designing integrated ecosystems, operating and replicating PRECINCT components (or toolkits). The integration enables coordinated security and resilience management, incorporating improved 'installation-specific' security solutions. Additionally, Serious Games (SG), and Digital Twins (DT) are a significant part of this project, serving as a novel vulnerability evaluation method for analysing complicated multi-system cascading effects in the PRECINCT Living Labs (LLs). The use of SG supports the concentrated advancement of innovative resilience enhancement services.

Composite estimation in repeated surveys with rotating panels refers to methods of estimation that exploit correlations in the data in the sample overlap between survey times to improve the precision of estimates. In this article a novel approach to composite estimation is proposed, in which composite regression estimators of current totals for a number of key variables are generated from a simultaneous calibration of the sampling weights of the overlapping samples of the current and previous survey time. In this procedure, in addition to the usual calibration to known population totals, differences of estimates for the key variables based on the full sample and the common sample from the two consecutive times are calibrated to each other. The resulting multivariate composite regression estimator, which is constructed as an approximate best linear unbiased estimator, incorporates effectively information from the samples of both survey times for enhanced estimation efficiency. Unlike other composite regression estimators, the proposed estimator does not require micro-matching of data in the overlap sample, and, therefore, is free of potential issues associated with it. It is also considerably more practical than existing composite regression estimators and the traditional AK-composite estimator.

This paper investigates the spectral efficiency (SE) of an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS) aided cell-free massive multiple-input multiple-output (CF-mMIMO) system under correlated Rayleigh fading. In order to improve the system quality-of-service (QoS) and suppress the pilot contamination, fractional power control and an appropriate pilot assignment are adopted. Furthermore, a large-scale fading decoding (LSFD) method is introduced, based on which the analytical SE expression for the considered system is derived. Numerical and simulation results show that the adopted LSFD detection leads to higher SE than maximal ratio combining does, and as compared to an equivalent active RIS scheme, aSTAR-RIS significantly improves the SE of the CF-mMIMO system.

It is very likely that the terahertz (THz) spectrum will be used for the operation of the upcoming 6th generation wireless communication systems. This article presents a study for the application of ray tracing (RT) for THz channel modeling. Because of the extremely short wave-lengths of THz signals, we argue that signal propagation in the THz spectrum can be precisely modeled by employing RT techniques, and thus accurate channel models can be obtained in an efficient manner. We first review recent research and standardization activities related to RT-assisted channel modeling that mainly deal with millimeter-wave applications and, to a much lesser extent, have been applied the THz spectrum. Then we present the basic THz wave propagation characteristics, including reflection, scattering, penetration, and shadowing as well as signal absorption by atmospheric molecules and tiny particles under different weather conditions. The methodologies for the appropriate construction of target scenarios are presented as well as efficient computational methods for the effective RT operation. Finally, various research and development challenges related to RT-based channels for future THz communication systems are identified, and possible solutions are highlighted.

Fecal microbiota transplantation (FMT) represents a potential strategy to overcome resistance to immune checkpoint inhibitors in patients with refractory melanoma; however, the role of FMT in first-line treatment settings has not been evaluated. We conducted a multicenter phase I trial combining healthy donor FMT with the PD-1 inhibitors nivolumab or pembrolizumab in 20 previously untreated patients with advanced melanoma. The primary end point was safety. No grade 3 adverse events were reported from FMT alone. Five patients (25%) experienced grade 3 immune-related adverse events from combination therapy. Key secondary end points were objective response rate, changes in gut microbiome composition and systemic immune and metabolomics analyses. The objective response rate was 65% (13 of 20), including four (20%) complete responses. Longitudinal microbiome profiling revealed that all patients engrafted strains from their respective donors; however, the acquired similarity between donor and patient microbiomes only increased over time in responders. Responders experienced an enrichment of immunogenic and a loss of deleterious bacteria following FMT. Avatar mouse models confirmed the role of healthy donor feces in increasing anti-PD-1 efficacy. Our results show that FMT from healthy donors is safe in the first-line setting and warrants further investigation in combination with immune checkpoint inhibitors. ClinicalTrials.gov identifier NCT03772899.

AbstractWe explore the similarities and differences between the governing Risk Evaluation and Assessment (REA) principles for rail and road infrastructure. We frame our analysis with infrastructure managers in mind. In England, Network Rail and National Highways are the key rail and road infrastructure managers, respectively. The intention is to demonstrate that seemingly unrelated rail and road infrastructure upgrade projects are not that different – at least from the REA point of view. By breaking the potential REA barriers between the two modes, a number of benefits can be realised across the project lifecycle and beyond. Safely allowing the space for future change and innovation is a key guiding principle. Our findings point towards ‘systems thinking’ where rail and road can be approached as a single system – at least from the REA activities point of view. At a project level, this means that there are potentially many unexplored synergies between the two sectors. This may not only be in terms of safety, but also in terms of managing capital expenditure and operational expenditure and efficiencies. Future work would concentrate on drawing parallels between rail and road REA approaches for operating companies (and vehicles) to support our ‘Systems Thinking’ vision.

The United Kingdom’s Living Costs and Food (LCF) Survey has a relatively small sample size but produces estimates which are widely used, notably as a key input to the calculation of weights for consumer price indices. There has been a recent call for the use of additional data sources to improve the estimates from the LCF. Since some LCF variables are shared with the much larger Labour Force Survey (LFS), we investigate combining data from these surveys using composite calibration to improve the precision of estimates from the LCF. We undertake model selection to choose a suitable set of common variables for the composite calibration using the effect on the estimated variances for national and regional totals of important LCF variables. The variances of estimates for common variables are reduced to around 5 percent of their original size. Variances of national estimates are reduced (across several quarters) by around 10 percent for expenditure and 25 percent for income; these are the variables of primary interest in the LCF. Reductions in the variances of regional estimates vary more but are mostly large when using common variables at the regional level in the composite calibration. The composite calibration also makes the LCF estimates for employment status almost consistent with the outputs of the LFS, which is an important property for users of the statistics. A novel alternative method for variance estimation, using stored information produced by the composite calibration, is also presented.

To assess their roles in breast cancer diagnostics, we aimed to compare plasma cell-free DNA (cfDNA) levels with the circulating metabolome in a large breast screening cohort of women recalled for mammography, including healthy women and women with mammographically detected breast diseases, ductal carcinoma in situ and invasive breast cancer: the Breast Screening and Monitoring Study (BSMS). In 999 women, plasma was analyzed by nuclear magnetic resonance (NMR) and Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) and then processed to isolate and quantify total cfDNA. NMR and UPLC-MS results were compared with data for 186 healthy women derived from the AIRWAVE cohort. Results showed no significant differences between groups for all metabolites, whereas invasive cancers had significantly higher plasma cfDNA levels than all other groups. When stratified the supervised OPLS-DA analysis and total cfDNA concentration showed high discrimination accuracy between invasive cancers and the disease/medication-free subjects. Furthermore, comparison of OPLS-DA data for invasive breast cancers with the AIRWAVE cohort showed similar discrimination between breast cancers and healthy controls. This is the first report of agreement between metabolomics and plasma cfDNA levels for discriminating breast cancer from healthy subjects in a true screening population. It also emphasizes the importance of sample standardization. Follow on studies will involve analysis of candidate features in a larger validation series as well as comparing results with serial plasma samples taken at the next routine screening mammography appointment. The findings here help establish the role of plasma analysis in the diagnosis of breast cancer in a large real-world cohort.

ABSTRACTAberration of the “gut-liver axis” contributes to the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD). Here, we use multi-omics to analyze the gut microbiota composition and metabolic profile of patients with type-2 diabetes mellitus (T2DM). T2DM patients were screened for liver disease by blood tests, ultrasound, and liver stiffness measurements. Stool microbiota was analyzed by 16S rRNA gene sequencing; metabolomic profiling by Nuclear Magnetic Resonance spectroscopy and Ultra-High Performance-Mass Spectrometry. Microbiome and metabolic signatures were analyzed in the whole cohort and in matched subsets to identify signatures specific for steatosis (MASLD±) or fibrosis (Fibrosis±). Gut permeability was assessed in-vitrousing monolayers of MDCK cells and trans-epithelial electric resistance (TEER). Cytokine profile was assessed in serum and stools.Overall, 285 patients were enrolled: 255 serum, 252 urine and 97 stool samples were analyzed. Anaeroplasmaand Escherichia/ShigellaASVs were higher, while ButyricicoccusASVs were lower in those with normal liver. In MASLD±, ButyricicoccusASV was significantly higher in those with steatosis. In the Fibrosis±, ButyricicoccusASV was significantly lower in those with fibrosis. Glycochenodeoxycholic acid-3-sulfate (G-UDCA-3S) appeared to be higher in MASLD with fibrosis. Fecal water from patients with MASLD and fibrosis caused the greatest drop in the TEER vs those with normal liver; this was reversed with protease inhibitors. Finally, fecal IL-13 was lower in MASLD with fibrosis. We identified microbiome signatures which were specific for steatosis and fibrosis and independent of other metabolic risk factors. Moreover, we conclude that protease-related gut permeability plays a role in those MASLD patients with fibrosis, and that disease progression is linked to a gut-liver axis which is at least partially independent of T2DM.

The diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY) experiment allows the calculation of diffusion coefficient values of metabolites in complex mixtures. However, this experiment has not yet been broadly used for metabolic profiling due to lack of a standardized protocol. Here we propose a pipeline for the DOSY experimental setup and data processing in metabolic phenotyping studies. Due to the complexity of biological samples, three experiments (a standard DOSY, a relaxation-edited DOSY, and a diffusion-edited DOSY) have been optimized to provide DOSY metabolic profiles with peak-picked diffusion coefficients for over 90% of signals visible in the one-dimensional 1H general biofluid profile in as little as 3 min 36 s. The developed parameter sets and tools are straightforward to implement and can facilitate the use of DOSY for metabolic profiling of human blood plasma and urine samples.

Electrocardiogram (ECG) is commonly considered as the reference standard signal to measure the biopotential by the electrical signals that control the expansion and contraction of the heart chambers. However, for long-term heart action monitoring, devices used to collect continuous ECG signals under free-living conditions typically have several operational difficulties. As an alternative, photoplethysmography (PPG) signals can be collected by pulse sensors to sense the rate of blood flow as controlled by the heart’s pumping action. Consequently, ECG signals and PPG signals are intrinsically correlated to reflect the status of the heart. In this article, we study the relation between ECG and PPG by proposing a novel growing multilayer network (GMLN). The approach can design the number and topology of layers automatically. Relying on a growth mapping of input data, with the representation from the lower level to the higher level, the network is to fulfill a reconstruction implicitly to the target signal. Experimental results obtained from benchmark datasets show that the proposed method can achieve higher similarity for the waveform and more accurate heart rate (HR) detection with the target signal.

This article presents a comprehensive study for analyzing and modeling the scattering phenomenon of terahertz (THz) waves bouncing on rough surfaces. More specifically, a generic parametric methodology to accurately model the rough characteristics focusing on the root-mean-square (RMS) height and correlation length of a surface is proposed. Firstly, a Monte Carlo-based approach is developed for the efficient and accurate software realization of such rough surfaces. Then, the full-wave simulator FEKO is used to simulate at 300 GHz the far-field behavior of scattering waves on 30 distinct surfaces having 6 RMS heights and 5 correlation lengths. By employing the directive scattering (DS) model, the variation of the scattering coefficient, $S$, and its equivalent roughness, $\alpha _{R}$, are studied for the scattering amplitude aspect. Furthermore, the statistical characteristics of the phase and polarization of the scattered signals are obtained and compared with those available in existing 3GPP channel model standards which are valid for lower frequency bands. These comparisons show that although the phases follow the normal distribution, as is the case with equivalent 3GPP channel models, the cross-polarization ratios (XPRs) follow the logistic distribution. This is an important difference which could have a great impact on the standardization activities of the current 3GPP-related working groups. Finally, the proposed scattering model at the THz band is realized through an algorithmic implementation. Compared with the classical two-dimensional DS model which characterizes only the amplitude of the scattered signal, the proposed three-dimensional scattering model can be efficiently used to accurately characterize all the important parameters of the scattered signals, namely, amplitude, phase, and polarization at the THz band. Additionally, the proposed scattering model can effectively work in conjunction with ray-tracing (RT) schemes leading to precise scattering and channel modeling for new applications, such as holographic radios and multi-user multiple-input multiple-output (MU-MIMO) systems.

We investigate the performance of an intelligent reflecting surface (IRS) aided secure communication system which operates in a complete imperfect channel state information (CSI) regime. By jointly considering the imperfect CSI of both IRS-Bob and IRS-Eve channels, we propose a robust transmission scheme which is based upon a joint optimization of the continuous transmit beamforming at Alice and the discrete phase shifts at the IRS. To deal with this mixed integer non-convex optimization problem, novel lower- and upper-bounding techniques are used to transform it in a more tractable form for which a solution based upon the derivation of a quantization-based alternative optimization (AO) algorithm is presented. More specifically, on the one hand, a successive convex approximation (SCA) algorithm is proposed to optimize the continuous transmit beamforming. On the other hand, by applying a SCA, penalty convex-concave procedure (PCCP), and quantization-based algorithm, the discrete phase shifts are obtained. Various performance evaluation results obtained by means of computer simulations have shown that the proposed robust transmission scheme guarantees a minimum quality of service (QoS) while simultaneously limits Eve's maximum eavesdropping rate.

This paper presents the first performance analysis in scalable cell-free massive MIMO (SCF-mMIMO) systems by proposing a novel mathematical framework which accommodates for the first time 1-bit quantization (1 b-Q) and multi-bits quantization (Mbits-Q) models. Assuming that each user equipment (UE) and access point (AP) employs finite resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) respectively, and by considering communication over spatially correlated Rician fading for the UE-AP channel, a novel Bussgang MMSE (B-MMSE) channel estimator for the 1 b-Q model is proposed. This approach leads to the derivation of generic spectral efficiency (SE) expressions using maximal ratio combining (MRC) and MMSE detections which are applicable also for the Mbits-Q case. In order to further improve the energy efficiency (EE) by using MMSE detection, two novel MMSE detectors, termed as low complexity local partial MMSE (LC-LP-MMSE) and LC-P-MMSE detectors, are proposed. Simulation results have shown that their SE performance is near to that of the optimal L-MMSE and MMSE detectors, while at the same time their EE performance is significantly improved as compared to the conventional LP-MMSE and P-MMSE detectors. Finally, a low complexity accessing scheme which jointly considers the competition-free cluster formation, pilot assignment, and power control, is proposed. Simulation results have shown that it outperforms the conventional random pilot assignment and user-group based pilot assignment schemes. Furthermore, in contrast to the equal power transmit strategy, it guarantees quality of service (QoS) fairness for all UEs.

Urban functional zones (UFZs) contain abundant landscape information that can be adopted to better understand the surroundings. Various landscape compositions and configurations reflect different human activities, which may affect the particulate matter (PM2.5) concentrations. The very high-resolution (VHR) image features can reflect the physical and spatial structures of the UFZs. However, the existing PM2.5 estimation methods neither have been based on the scale of UFZs, nor have the VHR image features of UFZs as independent variables. Hence, this article proposes a spatiotemporal interpolation graph convolutional network (STI-GCN) model and introduces VHR image features to achieve PM2.5 estimation in UFZs. First, UFZs are split, and VHR image features are extracted by the visual geometry group 16 (VGG16). Subsequently, meteorological factors, aerosol optical depth (AOD), and VHR image features are used to estimate the PM2.5 concentrations at the scale of the UFZs. The two metropolises, Beijing and Shanghai, are chosen to assess the validity of the STI-GCN model. As for Beijing and Shanghai, the overall accuracy ${R^{2}}$ of the STI-GCN model can reach 0.96 and 0.89, the root-mean-square errors (RMSEs) are 8.15 and 6.40 $\mu \text {g}/{\text {m}^{3}}$ , the mean absolute errors (MAEs) are 5.51 and 4.78 $\mu \text {g}/{\text {m}^{3}}$ , and the relative prediction errors (RPEs) are 18.53% and 17.38%, respectively. Experiments show that the STI-GCN consistently outperforms other models. What’s more, the PM2.5 values are relatively high in commercial and official zones (COZs) and relatively low in urban green zones (UGZs).

Spin-triplet supercurrent spin valves are of practical importance for the realization of superconducting spintronic logic circuits. In ferromagnetic Josephson junctions, the magnetic-field-controlled non-collinearity between the spin-mixer and spin-rotator magnetizations switches the spin-polarized triplet supercurrents on and off. Here we report an antiferromagnetic equivalent of such spin-triplet supercurrent spin valves in chiral antiferromagnetic Josephson junctions as well as a direct-current superconducting quantum interference device. We employ the topological chiral antiferromagnet Mn3Ge, in which the Berry curvature of the band structure produces fictitious magnetic fields, and the non-collinear atomic-scale spin arrangement accommodates triplet Cooper pairing over long distances (>150 nm). We theoretically verify the observed supercurrent spin-valve behaviours under a small magnetic field of <2 mT for current-biased junctions and the direct-current superconducting quantum interference device functionality. Our calculations reproduce the observed hysteretic field interference of the Josephson critical current and link these to the magnetic-field-modulated antiferromagnetic texture that alters the Berry curvature. Our work employs band topology to control the pairing amplitude of spin-triplet Cooper pairs in a single chiral antiferromagnet.

As the “smart grid” paradigm becomes more prevalent, fundamental techno-economic challenges prominently arise. The variability of renewables energy sources (RES) requires conventional generators to remain active and operate inefficiently. The grid’s inertia grows weaker, jeopardizing its stability. Ever more frequent network-related issues threaten the grid’s security. The industry experiences extensive degrees of price cannibalization, resulting in negative prices and RES down-regulating their production to avoid severe penalties. To this day, no work has presented a critical industrial view of the issues and each asset’s presence within the grid-market setting. This letter showcases the main industrial criticisms of electricity markets and investigates the “flexicurity” potential of energy assets. We ponder how flexibility and security could be grouped into a unified product, and discuss a shift towards a flexicurity-based market. Considering the desired state of the 2050 European grid, we offer some thoughts on possible asset integration routes.

Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where 1H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r2= 0.423, p< 2.2 × 10–16). This correlation was significantly reduced (r2= 0.163, p< 2.2 × 10–16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.

Dexamethasone was the first intervention proven to reduce mortality in patients with COVID-19 being treated in hospital. We aimed to evaluate the adoption of corticosteroids in the treatment of COVID-19 in the UK after the RECOVERY trial publication on June 16, 2020, and to identify discrepancies in care.

‘Two colour’ pump–probe experiments on yttrium iron garnet discs demonstrate how to harness dissipation of magnetic oscillations. This may have important implications for the use of magnetic materials for information processing.

Simultaneous breaking of inversion and time-reversal symmetries in a conductor yields a non-reciprocal electronic transport1–3, known as the diode or rectification effect, that is, low (ideally zero) conductance in one direction and high (ideally infinite) conductance in the other. So far, most of the diode effects observed in non-centrosymmetric polar/superconducting conductors4–7and Josephson junctions8–10require external magnetic fields to break the time-reversal symmetry. Here we report zero-field polarity-switchable Josephson supercurrent diodes, in which a proximity-magnetized Pt layer by ferrimagnetic insulating Y3Fe5O12serves as the Rashba(-type) Josephson barrier. The zero-field diode efficiency of our proximity-engineered device reaches up to ±35% at 2 K, with a clear square-root dependence on temperature. Measuring in-plane field-strength/angle dependences and comparing with Cu-inserted control junctions, we demonstrate that exchange spin-splitting11–13and Rashba(-type) spin-orbit coupling13–15at the Pt/Y3Fe5O12interface are key for the zero-field giant rectification efficiency. Our achievement advances the development of field-free absolute Josephson diodes.

The proximity-coupling of a chiral non-collinear antiferromagnet (AFM)1–5with a singlet superconductor allows spin-unpolarized singlet Cooper pairs to be converted into spin-polarized triplet pairs6–8, thereby enabling non-dissipative, long-range spin correlations9–14. The mechanism of this conversion derives from fictitious magnetic fields that are created by a non-zero Berry phase15in AFMs with non-collinear atomic-scale spin arrangements1–5. Here we report long-ranged lateral Josephson supercurrents through an epitaxial thin film of the triangular chiral AFM Mn3Ge (refs. 3–5). The Josephson supercurrents in this chiral AFM decay by approximately one to two orders of magnitude slower than would be expected for singlet pair correlations9–14and their response to an external magnetic field reflects a clear spatial quantum interference. Given the long-range supercurrents present in both single- and mixed-phase Mn3Ge, but absent in a collinear AFM IrMn16, our results pave a way for the topological generation of spin-polarized triplet pairs6–8via Berry phase engineering15of the chiral AFMs.

We examine the impact of Brexit on London as an international financial centre through the lens of the global financial network (GFN) framework, using quantitative data on selected key financial flows and stocks, as well as qualitative data from interviews and other sources. Our results show very limited impacts on London, and possible gains in New York and the USA rather than in the European Union. The results are compatible with the logic and history of sticky power in the global financial network. Despite some relocations from London, Brexit has not (yet) undermined London’s attractiveness to financial and business services, and the global connectivity they afford to London as an international financial centre. London remains the global conductor of offshore jurisdictions, a role which may be enhanced with more flexible regulation after Brexit. Any forecasts about the future impacts of Brexit on London need to consider the sticky power of the global financial network, and close relationships among its building blocks.

COVID-19 is a multisystem disease and patients who survive might have in-hospital complications. These complications are likely to have important short-term and long-term consequences for patients, health-care utilisation, health-care system preparedness, and society amidst the ongoing COVID-19 pandemic. Our aim was to characterise the extent and effect of COVID-19 complications, particularly in those who survive, using the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK.

Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional 1H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.

Cell and gene therapies have achieved impressive results in the treatment of rare genetic diseases using gene corrected stem cells and haematological cancers using chimeric antigen receptor T cells. However, these two fields face significant challenges such as demonstrating long-term efficacy and safety, and achieving cost-effective, scalable manufacturing processes. The use of small molecules is a key approach to overcome these barriers and can benefit cell and gene therapies at multiple stages of their lifecycle. For example, small molecules can be used to optimise viral vector production during manufacturing or used in the clinic to enhance the resistance of T cell therapies to the immunosuppressive tumour microenvironment. Here, we review current uses of small molecules in cell and gene therapy and highlight opportunities for medicinal chemists to further consolidate the success of cell and gene therapies.

Advances in neonatology enabled significant improvements in neonatal survival, often at the expense of long-term morbidity. End-of-life decisions concern neonatal patients with fatal congenital anomalies, severe neurological deficits, and extreme prematurity at the limits of viability, presenting a complex issue for both healthcare professionals and parents. Since newborns cannot express their wishes or claim their rights, physicians and parents carry the responsibility to decide in their best interests, considering their future quality of life. Harmonization of scientific progress and legislation defining clear rules is necessary so neonatologists can proceed to such agonizing decisions. Greece lacks both specific legislation regarding such decisions and medical training on handling respective ethical dilemmas. Thus, guidelines improving such decisions are closely related to the quality of the healthcare system and should be established along with the legal system's adaptation.

Encouraged by next-generation networks and autonomous vehicle systems, vehicular networks must employ advanced technologies to guarantee personal safety, reduce traffic accidents and ease traffic jams. By leveraging the computing ability at the network edge, multi-access edge computing (MEC) is a promising technique to tackle such challenges. Compared to traditional full offloading, partial offloading offers more flexibility in the perspective of application as well as deployment of such systems. Hence, in this paper, we investigate the application of partial computing offloading in-vehicle networks. In particular, by analyzing the structure of many emerging applications, e.g., AR and online games, we convert the application structure into a sequential multi-component model. Focusing on shortening the application execution delay, we extend the optimization problem from the single-vehicle computing offloading (SVCOP) scenario to the multi-vehicle computing offloading (MVCOP) by taking multiple constraints into account. A deep reinforcement learning (DRL) based algorithm is proposed as a solution to this problem. Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.

Graphene quantum dots (GQDs) have recently attracted considerable attention, with appealing properties for terahertz (THz) technology. This includes the demonstration of large thermal bolometric effects in GQDs when illuminated by THz radiation. However, the interaction of THz photons with GQDs in the Coulomb blockade regime, i.e., single electron transport regime, remains unexplored. Here, we demonstrate the ultrasensitive photoresponse to THz radiation (from <0.1 to 10 THz) of a hBN-encapsulated GQD in the Coulomb blockade regime at low temperature (170 mK). We show that THz radiation of ∼10 pW provides a photocurrent response in the nanoampere range, resulting from a renormalization of the chemical potential of the GQD of ∼0.15 meV. We attribute this photoresponse to an interfacial photogating effect. Furthermore, our analysis reveals the absence of thermal effects, opening new directions in the study of coherent quantum effects at THz frequencies in GQDs.

A quantum system interacts with its environment—if ever so slightly—no matter how much care is put into isolating it1. Therefore, quantum bits undergo errors, putting dauntingly difficult constraints on the hardware suitable for quantum computation2. New strategies are emerging to circumvent this problem by encoding a quantum bit non-locally across the phase space of a physical system. Because most sources of decoherence result from local fluctuations, the foundational promise is to exponentially suppress errors by increasing a measure of this non-locality3,4. Prominent examples are topological quantum bits, which delocalize information over real space and where spatial extent measures non-locality. Here, we encode a quantum bit in the field quadrature space of a superconducting resonator endowed with a special mechanism that dissipates photons in pairs5,6. This process pins down two computational states to separate locations in phase space. By increasing this separation, we measure an exponential decrease of the bit-flip rate while only linearly increasing the phase-flip rate7. Because bit-flips are autonomously corrected, only phase-flips remain to be corrected via a one-dimensional quantum error correction code. This exponential scaling demonstrates that resonators with nonlinear dissipation are promising building blocks for quantum computation with drastically reduced hardware overhead8.

This letter considers an uplink short-packet ultra-reliable low latency communications (URLLC) system with finite blocklength (FBL). A quasi-static Rayleigh fading channel is adopted in this letter, and we employ wireless power transfer (WPT) to guarantee the data packets transmission of user equipment (UE). A novel non-orthogonal multiple access (NOMA) scheme with WPT under FBL is first proposed to improve reliability and reduce latency. Furthermore, we adopt outage probability (OP) as an important URLLC metric, and deduce two closed-form approximate bounds of system OP for finite and infinite battery capacities to characterize the relationship between reliability and latency. Simulation results verify that the derived bounds are tight with theoretical values, and the proposed scheme outperforms traditional OMA methods.

The performance of full-duplex (FD) cooperative non-orthogonal multiple access (C-NOMA) relaying systems in the presence of in-phase and quadrature-phase imbalance (IQI) and imperfect successive interference cancellation (ipSIC) is analyzed and evaluated. In particular, by considering two classes of users located far from and near to the information source, closed-form exact and analytical approximate expressions for the outage probability (OP) and ergodic sum rate (ESR), respectively, are derived. The accuracy of the analytical approach is verified by computer simulations which have shown that for both C-NOMA and equivalent OMA systems IQI has a deleterious effect on their OP and ESR performance in moderate and high signal-to-noise ratio (SNR) regions and the OP of C-NOMA is always slightly lower than that of the traditional OMA. Additionally, comparisons with equivalent half-duplex (HD) C-NOMA schemes have revealed that for the far user the FD C-NOMA scheme is less sensitive to ipSIC.

The field of cell and gene therapy (GT) is expanding rapidly and there is undoubtedly a wave of enthusiasm and anticipation for what these treatments could achieve next. Here we assessed the worldwide landscape of GT assets currently in early clinical development (clinical trial phase 1/2 or about to enter clinical trial). We included all gene therapies, i.e., strategies that modify an individual’s protein make-up by introducing exogenous nucleic acid or nucleic acid modifiers, regardless of delivery. Unmodified cell therapies, oncology therapies (reviewed elsewhere), and vaccine programs (distinct therapeutic strategy) were not included. Using a December 31, 2018 cutoff date, we identified 336 gene therapies being developed for 138 different indications covering 165 genetic targets. In all, we found that the early clinical GT landscape comprises a very disparate group of drug candidates in terms of indications, organizations, and delivery methods. We also highlight interesting trends, revealing the evolution of the field toward in vivotherapies and adeno-associated virus vector-based delivery systems. It will be interesting to witness what proportion of this current list effectively translates into new medicines.

This article shows that an orientation towards shareholder value and corporate indebtedness at non-financial firms have been negatively associated with union density in the EU over the past 21 years. We argue that the financialisation of non-financial firms makes them prioritise their ‘external (economic) balance’ at the expense of a cooperative ‘internal equilibrium’ model. In other words, corporate financialisation pushes non-financial firms to shift to non-participatory, market-based HR systems that directly undermine the role of trade unions. This study examines this corporate financialisation-induced shift within the EU in the wake of deeper economic integration since 1999 and provides panel data econometric evidence that it has significantly undermined union membership.

•We identified 7 new genetic regions for factor VIII levels, 1 for von Willebrand factor levels, and 3 in a combined analysis.•Silencing B3GNT2and CD36reduced factor VIII release in vitro.Silencing B3GNT2, CD36, and PDIA3reduced von Willebrand factor release.

This article in the journal “Zeitschrift für Psychodrama und Soziometrie” discusses the use of ancient Greek myths in the treatment of addiction through the method of psychodrama. The clinical material in the case of Lambros and the discussion that follows derive from a psychodrama group that the author led on a weekly basis, for a year, at the Psychiatric Hospital of Athens; Department of Adolescent and Young Adult Drug Users “18 ANO” Detoxification Unit. The case is analyzed through the prism of psychodramatic role theory and the psychodynamic perspective. Dieser Artikel handelt vom Einsatz altgriechischer Mythen in der Suchtbehandlung mit der Methode des Psychodramas. Das klinische Material im Fall Lambros und die anschließende Diskussion stammen aus einer Psychodrama-Gruppe, die der Autor ein Jahr lang wöchentlich in der Psychiatrischen Klinik von Athen, Abteilung für Jugendliche und junge erwachsene Drogenkonsumenten „18 ANO“ Entgiftungsstation, leitete. Der Fall wird mithilfe der psychodramatischen Rollentheorie und der psychodynamischen Perspektive analysiert.

Introduction We aimed to assess alterations in glucose, blood pressure and temperature in acute ischaemic stroke and investigate their association with early all-cause mortality and functional outcome.Patients and methods We studied all consecutive acute ischaemic stroke patients admitted in 2001–2010 to the Acute Stroke Unit, at Alexandra University Hospital, in Athens. Serial measurements were performed in the first seven days post-stroke and different parameters have been estimated: mean daily values, variability, subject-specific baseline levels and rate of change in serial measurements. Cox-proportional-hazards-model analysis and logistic-regression analysis were applied to investigate the association between these parameters and all-cause mortality and functional outcome after adjustment for known confounders of stroke outcome.Results In 1271 patients (mean age 72.3 ± 11.2 years), after adjusting for confounders, baseline glucose levels (HR: 1.005, 95%CI: 1.001–1.01; p =0.017), variability of systolic BP (SBP) as estimated by standard deviation (HR: 1.028, 95%CI: 1.01–1.048; p =0.005), the baseline temperature (HR: 2.758, 95%CI: 2.067–3.68; p <0.001) and the rate of temperature change (HR: 1.841, 95%CI: 1.616–2.908; p <0.001) were independently associated with all-cause mortality within three months. Poor functional outcome was associated with subject-specific baseline values of temperature (OR: 1.743; 95%CI: 1.076–2.825; p =0.024), the rate of SBP (OR: 1.159; 95% CI: 1.047–1.280; p =0.004) and temperature change (OR: 1.402; 95% CI: 1.061–1.853; p =0.018).Discussion The main strength of our study is that we analysed simultaneously three parameters and we used four different variables for each parameter of interest.Conclusion Baseline glucose levels, variability of SBP and baseline temperature and its rate of change are independent predictors of all-cause mortality. Baseline values of temperature and the rate of changes in SBP and temperature are independent predictors of poor functional outcome.

In this paper, the outage performance of a cognitive relaying network over Nakagami-m fading channels, employing simultaneous wireless information and power transfer (SWIPT) technology is analyzed and evaluated. The operation of this network is considered in conjunction with the conventional decode-and-forward (DF) and incremental DF (IDF) protocols. For the conventional DF protocol, it is assumed that there is no direct link between the secondary transmitter (S) and the secondary destination (D), while (for both protocols) after harvesting energy, the relay node (R) always helps to forward the resulting signal to D. However, for the IDF protocol, R assists in relaying S’s information to D only when the direct communication between S and D has failed. Furthermore, for both DF and IDF protocols, we assume there is no power supply for R, and R harvests energy from the transmitted signal of S. We derive exact analytical expressions for the outage probability at D in DF and IDF protocols, respectively, in terms of the bivariate Meijer’s G-function. Performance evaluation results obtained by means of Monte-Carlo simulations are also provided and have validated the correctness of the proposed analysis.

Gene delivery vectors that do not rely on host cell genome integration offer several advantages for gene transfer, chiefly the avoidance of insertional mutagenesis and position effect variegation. However, unless engineered for replication and segregation, nonintegrating vectors will dilute progressively in proliferating cells, and are not exempt of epigenetic effects. This article provides an overview of the main nonintegrating viral (adenoviral, adeno-associated viral, integration-deficient retro-lentiviral, poxviral), and nonviral (plasmid vectors, artificial chromosomes) vectors used for preclinical and clinical cell and gene therapy applications. Particular emphasis is placed on their use in hematologic disease.

We derive a tight lower bound for symbol error rate (SER) performance of the sparse code multiple access (SCMA) uplink operated over Rayleigh fading channels. In particular, based on the working principle of message passing algorithm receiver and the special structure of the sparse binary matrix, which is used to control the collisions of SCMA system, the system can be simplified to a single user single input multiple output (SU-SIMO) system employing maximal ratio combining (MRC) reception. In this way, the average SER lower bound of SCMA is obtained by calculating the average SER of SU-SIMO system employing MRC reception. Our results show that the derived lower bound is not only an efficient means of analyzing the SER performance, but also is tight, especially in the high signal-to-noise ratio regime.

AbstractThe longest stretch L(n) of consecutive heads in nindependent and identically distributed coin tosses is seen from the prism of large deviations. We first establish precise asymptotics for the moment generating function of L(n) and then show that there are precisely two large deviation principles, one concerning the behavior of the distribution of L(n) near its nominal value log1/pnand one away from it. We discuss applications to inference and to logarithmic asymptotics of functionals of L(n).

This article considers a modular approach to the design of integrated social surveys. The approach consists of grouping variables into ‘modules’, each of which is then allocated to one or more ‘instruments’. Each instrument is then administered to a random sample of population units, and each sample unit responds to all modules of the instrument. This approach offers a way of designing a system of integrated social surveys that balances the need to limit the cost and the need to obtain sufficient information. The allocation of the modules to instruments draws on the methodology of split questionnaire designs. The composition of the instruments, that is, how the modules are allocated to instruments, and the corresponding sample sizes are obtained as a solution to an optimisation problem. This optimisation involves minimisation of respondent burden and data collection cost, while respecting certain design constraints usually encountered in practice. These constraints may include, for example, the level of precision required and dependencies between the variables. We propose using a random search algorithm to find approximate optimal solutions to this problem. The algorithm is proved to fulfil conditions that ensure convergence to the global optimum and can also produce an efficient design for a split questionnaire.

This article considers a modular approach to the design of integrated social surveys. The approach consists of grouping variables into ‘modules’, each of which is then allocated to one or more ‘instruments’. Each instrument is then administered to a random sample of population units, and each sample unit responds to all modules of the instrument. This approach offers a way of designing a system of integrated social surveys that balances the need to limit the cost and the need to obtain sufficient information. The allocation of the modules to instruments draws on the methodology of split questionnaire designs. The composition of the instruments, that is, how the modules are allocated to instruments, and the corresponding sample sizes are obtained as a solution to an optimisation problem. This optimisation involves minimisation of respondent burden and data collection cost, while respecting certain design constraints usually encountered in practice. These constraints may include, for example, the level of precision required and dependencies between the variables. We propose using a random search algorithm to find approximate optimal solutions to this problem. The algorithm is proved to fulfil conditions that ensure convergence to the global optimum and can also produce an efficient design for a split questionnaire.