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Abstract Background There is an emerging need to systematically investigate the causes for the increased cesarean section rates in Greece and undertake interventions so as to substantially reduce its rates. To this end, the ability of the participating Greek obstetricians to follow evidence-based guidelines and respond to other educational and behavioral interventions while managing labor will be explored, along with barriers and enablers. Herein discussed is the protocol of a stepped-wedge designed intervention trial in Greek maternity units with the aforementioned goals in mind, named ENGAGE (ENhancinG vAGinal dElivery in Greece). Methods Twenty-two selected maternity units in Greece will participate in a multicenter stepped-wedge randomized prospective trial involving 20,000 to 25,000 births, with two of them entering the intervention period of the study each month (stepped randomization). The maternity care units entering the study will apply the suggested interventions for a period of 8–18 months depending on the time they enter the intervention stage of the study. There will also be an initial phase of the study lasting from 8 to 18 months including observation and recording of the routine practice (cesarean section, vaginal birth, and maternal and perinatal morbidity and mortality) in the participating units. The second phase, the intervention period, will include such interventions as the application of the HSOG (the Hellenic Society of Obstetrics and Gynecology) Guidelines on labor management, training on the correct interpretation of cardiotocography, and dealing with emergencies in vaginal deliveries, while the steering committee members will be available to discuss and implement organizational and behavioral changes, answer questions, clarify relevant issues, and provide practical instructions to the participating healthcare professionals during regular visits or video conferences. Furthermore, during the study, the results will be available for the participating units in order for them to monitor their own performance while also receiving feedback regarding their rates. Τhe final 2-month phase of the study will be devoted to completing follow-up questionnaires with data concerning maternal and neonatal morbidities that occurred after the completion of the intervention period. The total duration of the study is estimated at 28 months. The primary outcome assessed will be the cesarean section rate change and the secondary outcomes will be maternal and neonatal morbidity and mortality. Discussion The study is expected to yield new information on the effects, advantages, possibilities, and challenges of consistent clinical engagement and implementation of behavioral, educational, and organizational interventions described in detail in the protocol on cesarean section practice in Greece. The results may lead to new insights into means of improving the quality of maternal and neonatal care, particularly since this represents a shared effort to reduce the high cesarean section rates in Greece and, moreover, points the way to their reduction in other countries. Trial registration NCT 04504500 (ClinicalTrials.gov). The trial was prospectively registered. Ethics Reference No: 320/23.6.2020, Bioethics and Conduct Committee, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece

Additive manufacturing techniques have gone beyond their reputation for rapid prototype production and are increasingly adopted for the manufacture of functional components comprising high-end materials and intricate lattice structures. Silicon nitride, renowned for its exceptional mechanical properties and thermal stability, has emerged as a promising candidate for lightweight structural applications. Nonetheless, its high refractive index and density have limited the fabrication of highly complex structures using extrusion and photopolymerization based techniques. In this work, a highly reactive silicon nitride-based ink with high solid loading is developed for the fabrication of ultra-lightweight, truss-based structures. By employing a robot UV-assisted direct ink writing process, it is possible to control the printing head orientation, thus overcoming the limited curing depth of silicon nitride-based inks. The failure behavior of the sintered lattice beam structures under 4-point bending loading has been modeled by applying a linear elastic fracture mechanics (LEFM) based approach to the results of finite element (FE) simulations.

Many variations of the algebraic Riccati equation (ARE) have been used to study nonlinear system stability in the control domain in great detail. Taking the quaternion nonsymmetric ARE (QNARE) as a generalized version of ARE, the time-varying QNARE (TQNARE) is introduced. This brings us to the main objective of this work: finding the TQNARE solution. The zeroing neural network (ZNN) technique, which has demonstrated a high degree of effectiveness in handling time-varying problems, is used to do this. Specifically, the TQNARE can be solved using the high order ZNN (HZNN) design, which is a member of the family of ZNN models that correlate to hyperpower iterative techniques. As a result, a novel HZNN model, called HZ-QNARE, is presented for solving the TQNARE. The model functions fairly well, as demonstrated by two simulation tests. Additionally, the results demonstrated that, while both approaches function remarkably well, the HZNN architecture works better than the ZNN architecture.

Undoubtedly, one of the most common machine learning challenges is multiclass classification. In light of this, a novel bio-inspired neural network (NN) has been developed to address multiclass classification-related issues. Given that weights and structure determination (WASD) NNs have been acknowledged to alleviate the disadvantages of conventional back-propagation NNs, such as slow training pace and trapping in a local minimum, we developed a bio-inspired WASD algorithm for multiclass classification problems (BWASDC) by using the metaheuristic beetle antennae search (BAS) algorithm to enhance the WASD algorithm's learning process. The BWASDC's effectiveness is then evaluated through applications in occupational classification systems. It is important to mention that systems of occupational classification serve as a fundamental indicator of occupational exposure. For this reason, they are highly significant in social science research. According to the findings of four occupational classification experiments, the BWASDC model outperformed some of the most modern classification models obtainable through MATLAB's classification learner app on all fronts.

Dynamic Sylvester equation (DSE) problems have drawn a lot of interest from academics due to its importance in science and engineering. Due to this, the quest for the quaternion DSE (QDSE) solution is the subject of this work. This is accomplished using the zeroing neural network (ZNN) technique, which has achieved considerable success in tackling time-varying issues. Keeping in mind that the original ZNN can handle QDSE successfully in a noise-free environment, but it might not work in a noisy one, and the noise-resilient ZNN (NZNN) technique is also utilized. In light of that, one new ZNN model is introduced to solve the QDSE problem and one new NZNN model is introduced to solve the QDSE problem under different types of noises. Two simulation experiments and one application to control of the sine function memristor (SFM) chaotic system show that the models function superbly.

Due to its significance in science and engineering, time-varying linear matrix equation (LME) problems have received a lot of attention from scholars. It is for this reason that the issue of finding the minimum-norm least-squares solution of the time-varying quaternion LME (ML-TQ-LME) is addressed in this study. This is accomplished using the zeroing neural network (ZNN) technique, which has achieved considerable success in tackling time-varying issues. In light of that, two new ZNN models are introduced to solve the ML-TQ-LME problem for time-varying quaternion matrices of arbitrary dimension. Two simulation experiments and two practical acoustic source tracking applications show that the models function superbly.

Abstract Cytotoxic CD8 + T cell (CTL) exhaustion is driven by chronic antigen stimulation. Reversing CTL exhaustion with immune checkpoint blockade (ICB) has provided clinical benefits in different types of cancer. We, therefore, investigated whether modulating chronic antigen stimulation and T-cell receptor (TCR) signaling with an IL2-inducible T-cell kinase (ITK) inhibitor, could confer ICB responsiveness to ICB resistant solid tumors. In vivo intermittent treatment of 3 ICB-resistant solid tumor (melanoma, mesothelioma or pancreatic cancer) with ITK inhibitor significantly improved ICB therapy. ITK inhibition directly reinvigorate exhausted CTL in vitro as it enhanced cytokine production, decreased inhibitory receptor expression, and downregulated the transcription factor TOX. Our study demonstrates that intermittent ITK inhibition can be used to directly ameliorate CTL exhaustion and enhance immunotherapies even in solid tumors that are ICB resistant.

In recent years, it has become essential to compute the time-varying quaternion (TVQ) matrix Moore-Penrose inverse (MP-inverse or pseudoinverse) to solve time-varying issues in a range of disciplines, including engineering, physics and computer science. This study examines the problem of computing the TVQ matrix MP-inverse using the zeroing neural network (ZNN) approach, which is nowadays considered a cutting edge technique. As a consequence, three new ZNN models are introduced for computing the TVQ matrix MP-inverse in the literature for the first time. Particularly, one model directly employs the TVQ input matrix in the quaternion domain, while the other two models, respectively, use its complex and real representations. In four numerical simulations and a real-world application involving robotic motion tracking, the models exhibit excellent performance.

Abstract Background Studies focusing on sex differences in circulating proteins in patients with heart failure with reduced ejection fraction (HFrEF) are scarce. Insight into sex-specific cardiovascular protein profiles and their associations with the risk of adverse outcomes may contribute to a better understanding of the pathophysiological processes involved in HFrEF. Moreover, it could provide a basis for the use of circulating protein measurements for prognostication in women and men, wherein the most relevant protein measurements are applied in each of the sexes. Methods In 382 patients with HFrEF, we performed tri-monthly blood sampling (median follow-up: 25 [13–31] months). We selected all baseline samples and two samples closest to the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization) or censoring. We then applied an aptamer-based multiplex proteomic assay identifying 1105 proteins previously associated with cardiovascular disease. We used linear regression models and gene-enrichment analysis to study sex-based differences in baseline levels. We used time-dependent Cox models to study differences in the prognostic value of serially measured proteins. All models were adjusted for the MAGGIC HF mortality risk score and p-values for multiple testing. Results In 104 women and 278 men (mean age 62 and 64 years, respectively) cumulative PEP incidence at 30 months was 25% and 35%, respectively. At baseline, 55 (5%) out of the 1105 proteins were significantly different between women and men. The female protein profile was most strongly associated with extracellular matrix organization, while the male profile was dominated by regulation of cell death. The association of endothelin-1 (Pinteraction

Hyperpolarization with the dissolution dynamic nuclear polarization (dDNP) technique yields > 10,000-fold signal increases for NMR-active nuclei (e.g. 13C). Hyperpolarized 13C-labeled metabolic tracer molecules thus allow real-time observations of biochemical pathways in living cellular systems without interfering background. This methodology lends itself to the direct observation of altered intracellular reaction chemistry imparted for instance by drug treatment, infections, or other diseases. A reoccurring challenge for longitudinal cell studies of mammalian cells with NMR and dDNP-NMR is maintaining cell viability in the NMR spectrometer. 3D cell culture methods are increasing in popularity because they provide a physiologically more relevant environment compared to 2D cell cultures. Based on such strategies a mobile 3D culture system was devised. The clinical drug etoposide was used to treat cancer cells (HeLa) and the resulting altered metabolism was measured using hyperpolarized [1–13C]pyruvate. We show that sustaining the cell cultivation in cell incubators and only transferring the cells to the NMR spectrometer for the few minutes required for the dDNP-NMR measurements is an attractive alternative to cell maintenance in the NMR tube. High cell viability is sustained, and experimental throughput is many doubled.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid tumors and is resistant to immunotherapy. B cells play an essential role in PDAC progression and immune responses, both locally and systemically. Moreover, increasing evidence suggests that microbial compositions inside the tumor, as well as in the oral cavity and the gut, are important factors in shaping the PDAC immune landscape. However, the gut-associated lymphoid tissue (GALT) has not previously been explored in PDAC patients. In this study, we analyzed healthy vermiform appendix (VA) from 20 patients with PDAC and 32 patients with colon diseases by gene expression immune profiling, flow cytometry analysis, and microbiome sequencing. We show that the VA GALT of PDAC patients exhibits markers of increased inflammation and cytotoxic cell activity. In contrast, B cell function is decreased in PDAC VA GALT based on gene expression profiling; B cells express significantly fewer MHC class II surface receptors, whereas plasma cells express the immune checkpoint molecule HLA-G. Additionally, the vermiform appendix microbiome of PDAC patients is enriched with Klebsiella pneumoniae, Bifidobacterium animalis, and Adlercreutzia equolifaciens, while certain commensals are depleted. Our findings may suggest impaired B cell function within the GALT of PDAC patients, which could potentially be linked to microbial dysbiosis. Additional investigations are imperative to validate our observations and explore these potential targets of future therapies.

The importance of quaternions in a variety of fields, such as physics, engineering and computer science, renders the effective solution of the time-varying quaternion matrix linear equation (TV-QLME) an equally important and interesting task. Zeroing neural networks (ZNN) have seen great success in solving TV problems in the real and complex domains, while quaternions and matrices of quaternions may be readily represented as either a complex or a real matrix, of magnified size. On that account, three new ZNN models are developed and the TV-QLME is solved directly in the quaternion domain as well as indirectly in the complex and real domains for matrices of arbitrary dimension. The models perform admirably in four simulation experiments and two practical applications concerning color restoration of images.

Numerous people are applying for bank loans as a result of the banking industry’s expansion, but because banks only have a certain amount of assets to lend to, they can only do so to a certain number of applicants. Therefore, the banking industry is very interested in finding ways to reduce the risk factor involved in choosing the safe applicant in order to save lots of bank resources. These days, machine learning greatly reduces the amount of work needed to choose the safe applicant. Taking this into account, a novel weights and structure determination (WASD) neural network has been built to meet the aforementioned two challenges of credit approval and loan approval, as well as to handle the unique characteristics of each. Motivated by the observation that WASD neural networks outperform conventional back-propagation neural networks in terms of sluggish training speed and being stuck in local minima, we created a bio-inspired WASD algorithm for binary classification problems (BWASD) for best adapting to the credit or loan approval model by utilizing the metaheuristic beetle antennae search (BAS) algorithm to improve the learning procedure of the WASD algorithm. Theoretical and experimental study demonstrate superior performance and problem adaptability. Furthermore, we provide a complete MATLAB package to support our experiments together with full implementation and extensive installation instructions.

IntroductionCytotoxic CD8+ T cell (CTL) exhaustion is a dysfunctional state of T cells triggered by persistent antigen stimulation, with the characteristics of increased inhibitory receptors, impaired cytokine production and a distinct transcriptional profile. Evidence from immune checkpoint blockade therapy supports that reversing T cell exhaustion is a promising strategy in cancer treatment. Ibrutinib, is a potent inhibitor of BTK, which has been approved for the treatment of chronic lymphocytic leukemia. Previous studies have reported improved function of T cells in ibrutinib long-term treated patients but the mechanism remains unclear. We investigated whether ibrutinib directly acts on CD8+ T cells and reinvigorates exhausted CTLs. MethodsWe used an established in vitro CTL exhaustion system to examine whether ibrutinib can directly ameliorate T cell exhaustion. Changes in inhibitory receptors, transcription factors, cytokine production and killing capacity of ibrutinib-treated exhausted CTLs were detected by flow cytometry. RNA-seq was performed to study transcriptional changes in these cells. Btk deficient mice were used to confirm that the effect of ibrutinib was independent of BTK expression.ResultsWe found that ibrutinib reduced exhaustion-related features of CTLs in an in vitro CTL exhaustion system. These changes included decreased inhibitory receptor expression, enhanced cytokine production, and downregulation of the transcription factor TOX with upregulation of TCF1. RNA-seq further confirmed that ibrutinib directly reduced the exhaustion-related transcriptional profile of these cells. Importantly, using btk deficient mice we showed the effect of ibrutinib was independent of BTK expression, and therefore mediated by one of its other targets. DiscussionOur study demonstrates that ibrutinib directly ameliorates CTL exhaustion, and provides evidence for its synergistic use with cancer immunotherapy.

The core components of both traditional and contemporary control systems are the proportional–integral–derivative (PID) control systems, which have established themselves as standards for technical and industrial applications. Therefore, the tuning of the PID controllers is of high importance. Utilizing optimization algorithms to reduce the mean square error of the controller’s output is one approach of tuning PID controllers. In this paper, an appropriately modified metaheuristic optimization algorithm dubbed beetle antennae search (BAS) is employed for robust tuning of PID controllers. The findings of three simulated experiments on stabilizing feedback control systems show that BAS produces comparable or higher performance than three other well-known optimization algorithms while only consuming a tenth of their time.

Summary: Background: HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment. Methods: In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1–2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated. Findings: We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1–4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76–6.69), and 2.88 (1.37–6.03), respectively). Interpretation: Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT01851538 https://clinicaltrials.gov/ct2/show/NCT01851538. Funding: EU/EFPIA IMI2JU BigData@Heart grant n°116074, Jaap Schouten Foundation and Noordwest Academie.

The stability of nonlinear systems in the control domain has been extensively studied using different versions of the algebraic Riccati equation (ARE). This leads to the focus of this work: the search for the time-varying quaternion ARE (TQARE) Hermitian solution. The zeroing neural network (ZNN) method, which has shown significant success at solving time-varying problems, is used to do this. We present a novel ZNN model called ’ZQ-ARE’ that effectively solves the TQARE by finding only Hermitian solutions. The model works quite effectively, as demonstrated by one application to quadrotor control and three simulation tests. Specifically, in three simulation tests, the ZQ-ARE model finds the TQARE Hermitian solution under various initial conditions, and we also demonstrate that the convergence rate of the solution can be adjusted. Furthermore, we show that adapting the ZQ-ARE solution to the state-dependent Riccati equation (SDRE) technique stabilizes a quadrotor’s flight control system faster than the traditional differential-algebraic Riccati equation solution.

We propose a reciprocal, self-propelled model swimmer at intermediate Reynolds numbers (Re). Our swimmer consists of two unequal spheres that oscillate in antiphase, generating nonlinear steady streaming (SS) flows. We show computationally that the SS flows enable the swimmer to propel itself, and also switch direction as Re increases. We quantify the transition in the swimming direction by collapsing our data on a critical Re and show that the transition in swimming directions corresponds to the reversal of the SS flows. Based on our findings, we propose that SS can be an important physical mechanism for motility at intermediate Re.

RNA binding proteins (RBP) have a function in T cell activation and differentiation. Here the authors show that in the absence of two RBPs, ZFP36 and ZFP36L1, T cells acquire more rapidly superior effector capabilities, showing a limiting function for RBP in T cell activation and differentiation.

Developing predictive methods to identify patients with high risk of severe COVID-19 disease is of crucial importance. Authors show here that by measuring anti-SARS-CoV-2 antibody and cytokine levels at the time of hospital admission and integrating the data by unsupervised hierarchical clustering/machine learning, it is possible to predict unfavourable outcome.

Objective To combine targeted transcriptomic and proteomic data in an unsupervised hierarchical clustering method to stratify patients with childhood-onset SLE (cSLE) into similar biological phenotypes, and study the immunological cellular landscape that characterises the clusters.Methods Targeted whole blood gene expression and serum cytokines were determined in patients with cSLE, preselected on disease activity state (at diagnosis, Low Lupus Disease Activity State (LLDAS), flare). Unsupervised hierarchical clustering, agnostic to disease characteristics, was used to identify clusters with distinct biological phenotypes. Disease activity was scored by clinical SELENA-SLEDAI (Safety of Estrogens in Systemic Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index). High-dimensional 40-colour flow cytometry was used to identify immune cell subsets.Results Three unique clusters were identified, each characterised by a set of differentially expressed genes and cytokines, and by disease activity state: cluster 1 contained primarily patients in LLDAS, cluster 2 contained mainly treatment-naïve patients at diagnosis and cluster 3 contained a mixed group of patients, namely in LLDAS, at diagnosis and disease flare. The biological phenotypes did not reflect previous organ system involvement and over time, patients could move from one cluster to another. Healthy controls clustered together in cluster 1. Specific immune cell subsets, including CD11c+ B cells, conventional dendritic cells, plasmablasts and early effector CD4+ T cells, differed between the clusters.Conclusion Using a targeted multiomic approach, we clustered patients into distinct biological phenotypes that are related to disease activity state but not to organ system involvement. This supports a new concept where choice of treatment and tapering strategies are not solely based on clinical phenotype but includes measuring novel biological parameters.

Balances for nanoparticles such as resonating fluid-filled cantilevers usually probe only mass through changes in oscillation frequency. Katsikis and Collis et al. tap information from previously ignored rotational motion to simultaneously measure particle mass and volume.

BackgroundVaccines can be less immunogenic in people living with HIV (PLWH), but for SARS-CoV-2 vaccinations this is unknown. In this study we set out to investigate, for the vaccines currently approved in the Netherlands, the immunogenicity and reactogenicity of SARS-CoV-2 vaccinations in PLWH.Methods and findingsWe conducted a prospective cohort study to examine the immunogenicity of BNT162b2, mRNA-1273, ChAdOx1-S, and Ad26.COV2.S vaccines in adult PLWH without prior COVID-19, and compared to HIV-negative controls. The primary endpoint was the anti-spike SARS-CoV-2 IgG response after mRNA vaccination. Secondary endpoints included the serological response after vector vaccination, anti-SARS-CoV-2 T-cell response, and reactogenicity. Between 14 February and 7 September 2021, 1,154 PLWH (median age 53 [IQR 44-60] years, 85.5% male) and 440 controls (median age 43 [IQR 33-53] years, 28.6% male) were included in the final analysis. Of the PLWH, 884 received BNT162b2, 100 received mRNA-1273, 150 received ChAdOx1-S, and 20 received Ad26.COV2.S. In the group of PLWH, 99% were on antiretroviral therapy, 97.7% were virally suppressed, and the median CD4+ T-cell count was 710 cells/μL (IQR 520-913). Of the controls, 247 received mRNA-1273, 94 received BNT162b2, 26 received ChAdOx1-S, and 73 received Ad26.COV2.S. After mRNA vaccination, geometric mean antibody concentration was 1,418 BAU/mL in PLWH (95% CI 1322-1523), and after adjustment for age, sex, and vaccine type, HIV status remained associated with a decreased response (0.607, 95% CI 0.508-0.725, p < 0.001). All controls receiving an mRNA vaccine had an adequate response, defined as >300 BAU/mL, whilst in PLWH this response rate was 93.6%. In PLWH vaccinated with mRNA-based vaccines, higher antibody responses were predicted by CD4+ T-cell count 250-500 cells/μL (2.845, 95% CI 1.876-4.314, p < 0.001) or >500 cells/μL (2.936, 95% CI 1.961-4.394, p < 0.001), whilst a viral load > 50 copies/mL was associated with a reduced response (0.454, 95% CI 0.286-0.720, p = 0.001). Increased IFN-γ, CD4+ T-cell, and CD8+ T-cell responses were observed after stimulation with SARS-CoV-2 spike peptides in ELISpot and activation-induced marker assays, comparable to controls. Reactogenicity was generally mild, without vaccine-related serious adverse events. Due to the control of vaccine provision by the Dutch National Institute for Public Health and the Environment, there were some differences between vaccine groups in the age, sex, and CD4+ T-cell counts of recipients.ConclusionsAfter vaccination with BNT162b2 or mRNA-1273, anti-spike SARS-CoV-2 antibody levels were reduced in PLWH compared to HIV-negative controls. To reach and maintain the same serological responses as HIV-negative controls, additional vaccinations are probably required.Trial registrationThe trial was registered in the Netherlands Trial Register (NL9214). https://www.trialregister.nl/trial/9214.

The efficient solution of the time-varying quaternion matrix inverse (TVQ-INV) is a challenging but crucial topic due to the significance of quaternions in many disciplines, including physics, engineering, and computer science. The main goal of this research is to employ the higher-order zeroing neural network (HZNN) strategy to address the TVQ-INV problem. HZNN is a family of zeroing neural network models that correlates to the hyperpower family of iterative methods with adjustable convergence order. Particularly, three novel HZNN models are created in order to solve the TVQ-INV both directly in the quaternion domain and indirectly in the complex and real domains. The noise-handling version of these models is also presented, and the performance of these models under various types of noises is theoretically and numerically tested. The effectiveness and practicality of these models are further supported by their use in robotic motion tracking. According to the principal results, each of these six models can solve the TVQ-INV effectively, and the HZNN strategy offers a faster convergence rate than the conventional zeroing neural network strategy.

There are currently no drugs available to treat SARS-CoV-2 infection. A promising alternative treatment for COVID-19 patients is convalescent plasma. Here, Gharbharan et al. collect covalescent plasma and report no overall clinical benefit for 86 patients hospitalized for COVID-19 and treated with 300 mL convalescent plasma.

Abstract The sixth generation (6G) mobile systems will create new markets, services, and industries making possible a plethora of new opportunities and solutions. Commercially successful rollouts will involve scaling enabling technologies, such as cloud radio access networks, virtualization, and artificial intelligence. This paper addresses the principal technologies in the transition towards next generation mobile networks. The convergence of 6G key-performance indicators along with evaluation methodologies and use cases are also addressed. Free-space optics, Terahertz systems, photonic integrated circuits, softwarization, massive multiple-input multiple-output signaling, and multi-core fibers, are among the technologies identified and discussed. Finally, some of these technologies are showcased in an experimental demonstration of a mobile fronthaul system based on millimeter 5G NR OFDM signaling compliant with 3GPP Rel. 15. The signals are generated by a bespoke 5G baseband unit and transmitted through both a 10 km prototype multi-core fiber and 4 m wireless V-band link using a pair of directional 60 GHz antennas with 10° beamwidth. Results shown that the 5G and beyond fronthaul system can successfully transmit signals with both wide bandwidth (up to 800 MHz) and fully centralized signal processing. As a result, this system can support large capacity and accommodate several simultaneous users as a key candidate for next generation mobile networks. Thus, these technologies will be needed for fully integrated, heterogeneous solutions to benefit from hardware commoditization and softwarization. They will ensure the ultimate user experience, while also anticipating the quality-of-service demands that future applications and services will put on 6G networks.