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Abstract Background Genotype imputation is a cost-effective method for obtaining sequence genotypes for downstream analyses such as genome-wide association studies (GWAS). However, low imputation accuracy can increase the risk of false positives, so it is important to pre-filter data or at least assess the potential limitations due to imputation accuracy. In this study, we benchmarked three different imputation programs (Beagle 5.2, Minimac4 and IMPUTE5) and compared the empirical accuracy of imputation with the software estimated accuracy of imputation (Rsqsoft). We also tested the accuracy of imputation in cattle for autosomal and X chromosomes, SNP and INDEL, when imputing from either low-density or high-density genotypes. Results The accuracy of imputing sequence variants from real high-density genotypes was higher than from low-density genotypes. In our software benchmark, all programs performed well with only minor differences in accuracy. While there was a close relationship between empirical imputation accuracy and the imputation Rsqsoft, this differed considerably for Minimac4 compared to Beagle 5.2 and IMPUTE5. We found that the Rsqsoft threshold for removing poorly imputed variants must be customised according to the software and this should be accounted for when merging data from multiple studies, such as in meta-GWAS studies. We also found that imposing an Rsqsoft filter has a positive impact on genomic regions with poor imputation accuracy due to large segmental duplications that are susceptible to error-prone alignment. Overall, our results showed that on average the imputation accuracy for INDEL was approximately 6% lower than SNP for all software programs. Importantly, the imputation accuracy for the non-PAR (non-Pseudo-Autosomal Region) of the X chromosome was comparable to autosomal imputation accuracy, while for the PAR it was substantially lower, particularly when starting from low-density genotypes. Conclusions This study provides an empirically derived approach to apply customised software-specific Rsqsoft thresholds for downstream analyses of imputed variants, such as needed for a meta-GWAS. The very poor empirical imputation accuracy for variants on the PAR when starting from low density genotypes demonstrates that this region should be imputed starting from a higher density of real genotypes.

Abstract Neuromuscular blocking agents are used commonly to induce skeletal muscle relaxation during surgery. While muscle relaxation facilitates surgical procedures and tracheal intubation, adequate recovery of muscle function after surgery is required to support pulmonary function, and even mild residual neuromuscular block increases the risk of severe postoperative pulmonary complications. While recovery of muscle function after surgery involving neuromuscular blocking agents can be monitored and, in addition, be accelerated by use of current antagonists (reversal agents), there is a clear clinical need for a safe drug to antagonize all types of neuromuscular blocking agents. Here, we show that inhibition of the skeletal muscle-specific chloride ion (Cl-) channel, the ClC-1 channel, markedly accelerates recovery of both single contraction (twitch) and, important physiologically, sustained (tetanic) contractions in a rat model mimicking neuromuscular blocking agent-induced muscle block used during surgery. This suggests ClC-1 inhibition as a mechanism for fast and efficacious recovery of neuromuscular function induced by any neuromuscular blocking agents.

Abstract Background Mitochondrial genomes differ from the nuclear genome and in humans it is known that mitochondrial variants contribute to genetic disorders. Prior to genomics, some livestock studies assessed the role of the mitochondrial genome but these were limited and inconclusive. Modern genome sequencing provides an opportunity to re-evaluate the potential impact of mitochondrial variation on livestock traits. This study first evaluated the empirical accuracy of mitochondrial sequence imputation and then used real and imputed mitochondrial sequence genotypes to study the role of mitochondrial variants on milk production traits of dairy cattle. Results The empirical accuracy of imputation from Single Nucleotide Polymorphism (SNP) panels to mitochondrial sequence genotypes was assessed in 516 test animals of Holstein, Jersey and Red breeds using Beagle software and a sequence reference of 1883 animals. The overall accuracy estimated as the Pearson’s correlation squared (R2) between all imputed and real genotypes across all animals was 0.454. The low accuracy was attributed partly to the majority of variants having low minor allele frequency (MAF 0.9 (of 954 variants segregating in the test animals) and 138 of these overlapped the sites with DR2 > 0.9. This suggests that the DR2 statistic is a reasonable proxy to select sites that are imputed with higher accuracy for downstream analyses. Accordingly, in the second part of the study mitochondrial sequence variants were imputed from real mitochondrial SNP panel genotypes of 9515 Australian Holstein, Jersey and Red dairy cattle. Then, using only sites with DR2 > 0.900 and real genotypes, we undertook a genome-wide association study (GWAS) for milk, fat and protein yields. The GWAS mitochondrial SNP effects were not significant. Conclusion The accuracy of imputation of mitochondrial genotypes from the SNP panel to sequence was generally low. The Beagle DR2 statistic enabled selection of sites imputed with higher empirical accuracy. We recommend building larger reference populations with mitochondrial sequence to improve the accuracy of imputing less common variants and ensuring that SNP panels include common variants in the D-loop region.

Abstract This work presents a novel and versatile approach to employ textile capacitive sensing as an effective solution for capturing human body movement through fashionable and everyday-life garments. Conductive textile patches are utilized for sensing the movement, working without the need for strain or direct body contact, wherefore the patches can sense only from their deformation within the garment. This principle allows the sensing area to be decoupled from the wearer’s body for improved wearing comfort and more pleasant integration. We demonstrate our technology based on multiple prototypes which have been developed by an interdisciplinary team of electrical engineers, computer scientists, digital artists, and smart fashion designers through several iterations to seamlessly incorporate the technology of capacitive sensing with corresponding design considerations into textile materials. The resulting accumulation of textile capacitive sensing wearables showcases the versatile application possibilities of our technology from single-joint angle measurements towards multi-joint body part tracking.

Wireless Body Area Networks (WBANs), low power, and short-range wireless communication in a near-body area provide advantages, particularly in the medical and healthcare sector: (i) they enable continuous monitoring of patients and (ii) the recording and correlation of physical and biological information. Along with the utilization and integration of these (sensitive) private and personal data, there are substantial requirements concerning security and privacy, as well as protection during processing and transmission. Contrary to the star topology frequently used in various standards, the overall concept of a novel low-data rate token-based WBAN framework is proposed. This work further comprises the evaluation of strategies for handling medical data with WBANs and emphasizes the importance and necessity of encryption and security strategies in the context of sensitive information. Furthermore, this work considers the recent advancements in Artificial Intelligence (AI), which are opening up opportunities for enhancing cyber resilience, but on the other hand, also new attack vectors. Moreover, the implications of targeted regulatory measures, such as the European AI Act, are considered. In contrast to, for instance, the proposed star network topologies of the IEEE 802.15.6 WBAN standard or the Technical Committee (TC) SmartBAN of the European Telecommunication Standards Institute (ETSI), the concept of a ring topology is proposed which concatenates information in the form of a ‘data train’ and thus results in faster and more efficient communication. Beyond that, the conductivity of human skin is included in the approach presented to incorporate a supplementary channel. This direct contact requirement not only fortifies the security of the system but also facilitates a reliable means of secure communication, pivotal in maintaining the integrity of sensitive health data. The work identifies different threat models associated with the WBAN system and evaluates potential data vulnerabilities and risks to maximize security. It highlights the crucial balance between security and efficiency in WBANs, using the token-based approach as a case study. Further, it sets a foundation for future healthcare technology advancements, aiming to ensure the secure and efficient integration of patient data.

The craft box recovered from a 13th­century hillfort at Lõhavere, Estonia, contained a well-preserved selection of copper­alloy objects. The limited corrosion on these objects allowed a more accurate estimation of their compositions through non­destructive surface measurements. Using the resulting pXRF database, this study explored a possible correlation between the types of objects and their material composition. By comparing the most common contaminants with the alloying metals, a qualitative estimation of the material quality was conducted. The results showed that despite a considerable overlap between object types, the material composition dif fered based on the itemsâ appearance and function. A shift from brass to bronze and tinned objects coincided with a greater availability of tin at the hillfort, as well as a rise in silver smith ing. Moreover, objects with a silver appearance seemed to be more desirable in this period, apart from some high­status personal ornaments and traditional spiral tube decorations. The results possibly reflect different metalworking practices compared to earlier periods and contemporary sites. Some evidence of recycling is present among the spiral tubes, suggesting that they originated from different raw material sources, possibly removed from obsolete clothing. Alternatively, this could indicate a varying quality of imported copper­alloy, including wire, along the Hansa trade routes.

Heat stress is a critical environmental factor that adversely affects crop productivity. With the increasing frequency and intensity of heat waves and extreme weather events, heat stress has become a challenge for wheat production, which is one of the most important cereal crops. To sustain wheat production under heat stress conditions, there is an urgent need to develop high-yielding, heat-tolerant wheat varieties. This requires characterizing the genetic and physiological mechanisms underlying heat tolerance, as well as developing efficient phenotyping methods to evaluate a large number of wheat genotypes under heat stress field conditions. In this study, we used 184 wheat genotypes that were sown at two times of sowing (TOS), i.e., optimal sowing as TOS1 and late sowing as TOS2, with higher temperatures faced by plants during heading and grain filling in TOS2. We used a combination of physiological traits, multispectral vegetative indices (VIs) derived from aerial imagery and machine learning approaches to effectively differentiate wheat genotypes for heat tolerance and susceptibility. The response of wheat genotypes to heat stress was delineated as being susceptible, moderate, and tolerant using the stress susceptibility index, percentage loss, and tolerance index. Different VIs varied significantly between the two TOS. The decline in VIs during anthesis and post-anthesis was minimal in heat tolerant genotypes compared to susceptible genotypes under TOS2. We classified the stress severity and yield using VIs with a machine learning approach. A model was created with a random forest classifier (RFC) trained to categorize genotypes based on the stress susceptibility index using Python libraries. The PCA was utilized to reduce dimensionality, and five principal components explaining 99 % of the variability were employed as input for developing the model. The RFC model achieved an accuracy of 64 % and excelled in recognizing crops under extreme stress, with a recall rate of 0.87 and an F1 score of 0.77 for the susceptible class. The model had high precision metrics, with values of 0.69, 0.42, and 0.80 for the susceptible, moderate, and tolerant classes, respectively. Our results suggest that multispectral-driven phenotypic traits can be used by breeders to select and develop wheat varieties tolerant to heat stress.

Abstract The consequences of short‐term disuse are well known, but effective countermeasures remain elusive. This study investigated the effects of neuromuscular electrical stimulation (NMES) during 5 days of bed rest on retaining lower limb muscle mass and muscle function in healthy young and old participants. One leg received NMES of the quadriceps muscle (3 × 30min/day) (NMES), and the other served as a control (CON). Isometric quadriceps strength (MVC), rate of force development (RFD), lower limb lean mass, and muscle thickness were assessed pre‐and post‐intervention. Muscle thickness remained unaltered with NMES in young and increased in old following bed rest, while it decreased in CON legs. In old participants, mid‐thigh lean mass (MTLM) was preserved with NMES while decreased in CON legs. In the young, only a tendency to change with bed rest was detected for MTLM. MVC and early‐phase RFD decreased in young and old, irrespective of NMES. In contrast, late‐phase RFD was retained in young participants with NMES, while it decreased in young CON legs, and in the old, irrespective of NMES. NMES during short‐term bed rest preserved muscle thickness but not maximal muscle strength. While young and old adults demonstrated similar adaptive responses in preventing the loss of skeletal muscle thickness, RFD was retained in the young only.

The service management and orchestration (SMO) framework plays a major role in the management and orchestration (M&O) of applications, services, and components within the open radio access network (O-RAN) architecture, as defined by the O-RAN Alliance. It comprises the non-real-time RAN intelligence controller (Non-RT RIC) and possesses the capability to incorporate management systems and components from multiple standards development organizations (SDOs), notably the Third Generation Partnership Project (3GPP) and the European Telecommunications Standards Institute (ETSI). This multi-SDO-based SMO framework aims to provide a rich set of M&O services in a coherent and unified manner. Leveraging management data analytics (MDA) to deliver M&O services can enhance SMO capabilities. This article proposes an intelligence-driven approach by integrating MDA into the 3GPP and ETSI management systems within the context of SMO. Furthermore, it introduces the artificial intelligence (AI)/machine learning (ML) Function into the Non-RT RIC, consolidating existing intelligent components and introducing novel ones to enhance the intelligence capabilities of the Non-RT RIC. The article further proposes an architectural solution that unifies and facilitates interoperability among the intelligent systems of the 3GPP, ETSI, and Non-RT RIC – collectively referred to as “three modules” – within SMO. It also presents the end-to-end lifecycle workflow of the AI/ML model across the three modules. Finally, the article outlines key research challenges related to integrating MDA within the SMO framework.

Joint Radar and Communications (JRC) can satisfy the apparent demand for applications based on object detection, tracking, ranging, and positioning. JRC is, therefore, often seen as candidate technology for 6G mobile systems. Implementing JRC will require novel approaches in many research and engineering fields, including protocol design, digital and analog signal processing, and hardware development. The ongoing debates on JRC already include many white papers and research articles ranging in content from very specific technical problems to comprehensive bird’s eye-level reviews. This paper represents the work within the Open6GHub research project in Germany, which aims to investigate and implement potential end-to-end solutions for 6G. In this framework, we propose a consolidated vision for potential JRC architectural approaches. The subsequent discussion on integrating radar sensing with communications highlights this technology’s state-of-the-art and presents relevant opportunities and challenges.

IntroductionDengue virus infection is a global health problem lacking specific therapy, requiring an improved understanding of DENV immunity and vaccine responses. Considering the recent emerging of new dengue vaccines, here we performed an integrative systems vaccinology characterization of molecular signatures triggered by the natural DENV infection (NDI) and attenuated dengue virus infection models (DVTs).Methods and resultsWe analyzed 955 samples of transcriptomic datasets of patients with NDI and attenuated dengue virus infection trials (DVT1, DVT2, and DVT3) using a systems vaccinology approach. Differential expression analysis identified 237 common differentially expressed genes (DEGs) between DVTs and NDI. Among them, 28 and 60 DEGs were up or downregulated by dengue vaccination during DVT2 and DVT3, respectively, with 20 DEGs intersecting across all three DVTs. Enriched biological processes of these genes included type I/II interferon signaling, cytokine regulation, apoptosis, and T-cell differentiation. Principal component analysis based on 20 common DEGs (overlapping between DVTs and our NDI validation dataset) distinguished dengue patients by disease severity, particularly in the late acute phase. Machine learning analysis ranked the ten most critical predictors of disease severity in NDI, crucial for the anti-viral immune response. ConclusionThis work provides insights into the NDI and vaccine-induced overlapping immune response and suggests molecular markers (e.g., IFIT5, ISG15, and HERC5) for anti-dengue-specific therapies and effective vaccination development.

Vessel Monitoring Systems (VMS) and other vessel tracking data have been used for many years to map the distribution of fishing activities. Mapping areas with low levels of fishing activity can be of particular interest; for example to avoid conflicts between fishing and other ocean uses like offshore renewable energy or to protect relatively pristine ecosystems from increasing fishing pressure. A particular problem when trying to delineate areas that are lightly fished, is the relative sparsity of vessel monitoring data in these areas. This paper explores three novel methods for estimating the distribution of fishing activity from VMS data, with particular focus on lightly impacted areas. The first new method divides the area of interest into a nested grid with varying cell sizes (depending on the density of data at each location); the second new method uses Voronoi diagrams to define polygons around observations and the third method applies a local regression to generate a smooth map of fishing intensity. The new methods are compared with two established methods: applying spatial grids and interpolating fishing tracks. The track interpolation method generally performs better than any of the new methods, however it is not always possible or appropriate to apply track interpolation; in those cases the local regression method is the best alternative.

Abstract Introduction The management of postoperative pain in anaesthesia is evolving with a deeper understanding of associating multiple modalities and analgesic medications. However, the motivations and barriers regarding the adoption of opioid-sparing analgesia are not well known. Methods We designed a modified Delphi survey to explore the perspectives and opinions of expert panellists with regard to opioid-sparing multimodal analgesia. 29 anaesthetists underwent an evolving three-round questionnaire to determine the level of agreement on certain aspects of multimodal analgesia, with the last round deciding if each statement was a priority. Results The results were aggregated and a consensus, defined as achievement of over 75% on the Likert scale, was reached for five out of eight statements. The panellists agreed there was a strong body of evidence supporting opioid-sparing multimodal analgesia. However, there existed multiple barriers to widespread adoption, foremost the lack of training and education, as well as the reluctance to change existing practices. Practical issues such as cost effectiveness, increased workload, or the lack of supply of anaesthetic agents were not perceived to be as critical in preventing adoption. Conclusion Thus, a focus on developing specific guidelines for multimodal analgesia and addressing gaps in education may improve the adoption of opioid-sparing analgesia.

Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. This article provides a survey in the research area of 6G-empowered industrial DT system. With a novel vision of 6G industrial DT ecosystem, this survey discusses the ambitions and potential applications of industrial DT in the 6G era, identifying the emerging challenges as well as the key enabling technologies. The introduced ecosystem is supposed to bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios.

As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or highdefinition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.

The definition of the fundamental concepts and the design of the architectural framework for network slicing in fifth-generation communication systems have been successfully concluded; the standardization activities are almost over; and the commercial deployment has already commenced worldwide. To compete for digital supremacy and to be seen as front-runners in the international technological race, researchers from various regions and countries have begun exploring the technical requirements, envisioning potential applications, identifying innovative enablers, developing testbeds for the preliminary validation of several terrestrial and non-terrestrial technologies, and conceptualizing the architectural design for the next generation of mobile communication systems – the sixth-generation (6G) – aiming to connect the human, physical, and digital worlds with a high level of intelligence and openness for the 2030s. In support of such an ambitious vision, this article extends the end-to-end network slicing concepts, methods, solutions, and functioning architectures towards 6G. To this intent, the study first presents several decisive motivating trends behind such an extension of network slicing in order to make forthcoming mobile networks fully slicing-aware. Following that, the paper attempts to highlight the intelligentization of a number of key enabling technologies that will bring a renaissance to network slicing in the next decade. It then proposes a unified architectural framework and its principal building blocks in several layers, paving the way for the implementation of an open and intelligent network and network slicing in 6G. The proposed architectural solution harmonizes the most recent specifications of the relevant de jure and de facto standards development organizations in their applicable layers with the aim of architecting a pre-standard-compliant and preliminary framework for slicing the 6G network. Finally, the article is intended to spur interest and lay the groundwork for further investigations and subsequent research and development by highlighting a number of open research challenges and directions in this flourishing field.

Building upon the significant number of already published 6G position papers, we are concentrating on the immediate next steps toward turning the research vision of software-centric networks into reality. This is accomplished, by summarizing and assessing the various requirements documents and providing a significant number of specific research directions and approaches in order to fulfill them. This article complements the existing body of work, by focusing on future core networks and their infrastructures, yet maintaining a system-level perspective and progressing in the direction of scoping key technology elements and providing high-potential research approaches for them. Additionally, we rigorously discuss the impact that different technological advancements have on the other parts of the system, to provide a coherent, end-to-end network understanding. This is in strong contrast to current approaches, where from the challenges, each research direction becomes independent and, thus, its advances are potentially cancelled out by the next technology in the chain. By maintaining this system perspective, the adoption of the different technologies becomes easier, as they are developed in unison. To address the requirements in a coherent, holistic, and unified way, we extend our high-level architecture concept named “Organic 6G Networks” towards a comprehensive end-to-end system. A holistic software-centric system, adapting the latest software development advancements from the IT industry. The Organic 6G network provides support for building a streamlined software network architecture and offers the next step on the path towards the development and specification of future mobile networks.

While 5G networks are driving a growing number of use cases in the fields of iot and industrial applications, the vision of the next generation of mobile communications systems already includes concepts massively transforming the way people will interact with the digital world through the network, as humans are shifting into the center of diverse network driven applications. Envisaged use cases and possibilities to provide services and resources in a distributed manner render an architectural solution for trust establishment a critical component of 6G networks. This survey provides an overview of terms and visions related to the topic of trust in general and in mobile communications systems. Requirements for an end-to-end trust building framework are derived, in order to give a starting point for the design process of a trust anchor service as a component of 6G networks.

Abstract Studies have demonstrated that structural variants (SV) play a substantial role in the evolution of species and have an impact on Mendelian traits in the genome. However, unlike small variants (

BayesR3 samples the polymorphisms affecting complex traits at reduced computational cost to predict the genetic value, breeding value, or individual risk of genotypes.

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.

Abstract Maternal diversity based on a sub-region of mitochondrial genome or variants were commonly used to understand past demographic events in livestock. Additionally, there is growing evidence of direct association of mitochondrial genetic variants with a range of phenotypes. Therefore, this study used complete bovine mitogenomes from a large sequence database to explore the full spectrum of maternal diversity. Mitogenome diversity was evaluated among 1883 animals representing 156 globally important cattle breeds. Overall, the mitogenomes were diverse: presenting 11 major haplogroups, expanding to 1309 unique haplotypes, with nucleotide diversity 0.011 and haplotype diversity 0.999. A small proportion of African taurine (3.5%) and indicine (1.3%) haplogroups were found among the European taurine breeds and composites. The haplogrouping was largely consistent with the population structure derived from alternate clustering methods (e.g. PCA and hierarchical clustering). Further, we present evidence confirming a new indicine subgroup (I1a, 64 animals) mainly consisting of breeds originating from China and characterised by two private mutations within the I1 haplogroup. The total genetic variation was attributed mainly to within-breed variance (96.9%). The accuracy of the imputation of missing genotypes was high (99.8%) except for the relatively rare heteroplasmic genotypes, suggesting the potential for trait association studies within a breed.

Abstract Background This consensus statement was developed because there are concerns about the appropriate use of opioids for acute pain management, with opposing views in the literature. Consensus statement on policies for system-level interventions may help inform organisations such as management structures, government agencies and funding bodies. Methods We conducted a multi-stakeholder survey using a modified Delphi methodology focusing on policies, at the system level, rather than at the prescriber or patient level. We aimed to provide consensus statements for current developments and priorities for future developments. Results Twenty-five experts from a variety of fields with experience in acute pain management were invited to join a review panel, of whom 23 completed a modified Delphi survey of policies designed to improve the safety and quality of opioids prescribing for acute pain in the secondary care setting. Strong agreement, defined as consistent among> 75% of panellists, was observed for ten statements. Conclusions Using a modified Delphi study, we found agreement among a multidisciplinary panel, including patient representation, on prioritisation of policies for system-level interventions, to improve governance, pain management, patient/consumers care, safety and engagement.

COVID-19, similarly to systemic autoimmune diseases, is characterised by the presence of autoantibodies. Authors show here that the abundance and network signature of autoantibodies targeting G protein-coupled receptors and RAS-related proteins are altered in COVID-19 patients, and the level of disruption marks clinical severity.

Abstract Bone morphogenetic protein 1 (BMP1) belongs to the astacin/BMP1/tolloid-like family of zinc metalloproteinases, which play a fundamental role in the development and formation of extracellular matrix (ECM). BMP1 mediates the cleavage of carboxyl terminal (C-term) propeptides from procollagens, a crucial step in fibrillar collagen fiber formation. Blocking BMP1 by small molecule or antibody inhibitors has been linked to anti-fibrotic activity in the preclinical models of skin, kidney and liver fibrosis. Therefore, we reason that BMP1 may be important for the pathogenesis of lung fibrosis and BMP1 could be a potential therapeutic target for progressive fibrotic disease such as idiopathic pulmonary fibrosis (IPF). Here, we observed the increased expression of BMP1 in both human IPF lungs and mouse fibrotic lungs induced by bleomycin. Furthermore, we developed an inducible Bmp1 conditional knockout (cKO) mouse strain. We found that Bmp1 deletion does not protect mice from lung fibrosis triggered by bleomycin. Moreover, we found no significant impact of BMP1 deficiency upon C-term propeptide of type I procollagen (CICP) production in the fibrotic mouse lungs. Based on these results, we propose that BMP1 is not required for lung fibrosis in mice and BMP1 may not be considered a candidate therapeutic target for IPF.

This article reveals an adequate comprehension of basic defense, security challenges, and attack vectors in deploying multi-network slicing. Network slicing is a revolutionary concept of providing mobile network on-demand and expanding mobile networking business and services to a new era. The new business paradigm and service opportunities are encouraging vertical industries to join and develop their own mobile network capabilities for enhanced performances that are coherent with their applications. However, a number of security concerns are also raised in this new era. In this article, we focus on the deployment of multi-network slicing with multi-tenancy. We identify the security concerns and discuss the defense approaches such as network slice isolation and insulation in a multi-layer network slicing security model. Furthermore, we identify the importance to appropriately select the network slice isolation points and propose a generic framework to optimize the isolation policy regarding the implementation cost while guaranteeing the security and performance requirements.

The global roll-out phase of the fifth-generation of mobile communication systems is currently underway. The industry and academia have already begun research on potential sixth-generation (6G) communication systems. The 6G communication system is anticipated to provide network connectivity for an extensive range of use cases in a variety of emerging vertical industries. Consequently, a new set of challenging requirements and more stringent key performance indicators have to be considered, a novel architecture has to be designed, and unique enabling technologies shall be developed in order to fulfill the technical, regulatory, and business demands of the communication service customers. These requirements place enormous pressure on the players in the telecommunications industry, including network operators, service providers, hardware suppliers, standards development organizations (SDOs), and regulatory authorities aimed at developing, standardizing, and regulating an energy-efficient, cost-effective, performing, and sustainable 6G communication ecosystem. One area of focus for 6G communication systems is the digital twin (DT) technology, which is a well-defined set of tools designed to create virtual representations of physical objects that serve as their digital counterparts. This article explores the applicability of the DT technology in the context of 6G communication systems by viewing it as a promising tool to make research, development, operation, and optimization of the next-generation communication systems highly efficient. The major contribution of this article is fivefold. Firstly, we provide critical analysis of the state-of-the-art literature in the field of DT technology in order to capture its essence in several application areas since its inception. Secondly, we conduct a comprehensive survey of the research concerning the deployment of DT technology in 6G communication systems. Thirdly, we discuss potential use cases and key areas of applications (along with detailed examples) of 6G communication systems that can benefit from DT technology. Fourthly, we present an overview of the activities of several SDOs that are active in the field of DT technology. Finally, we identify several open research challenges and future directions that need to be addressed before the end-to-end deployment of DT technology in 6G communication systems.

The mapping of a virtual network service onto a physical network infrastructure is a challenging task due to the joint allocation of virtual resources across nodes and links, the diverse technical requirements of end-users, the coordination between multiple host domains, and others. This issue is exacerbated further by the extension of virtualization to the next-generation radio access network (NG-RAN) architecture and the provisioning of radio access network (RAN) slicing. To that end, this article focuses on the mapping problem of the virtual network functions (VNFs), as well as their internal and external virtual links (VLs), of a RAN slice subnet onto intelligent points of presence (I-PoPs) and transport networks in the NG-RAN architecture. In this context, in contrast to the majority of the state-of-the-art proposals, which frequently fail to achieve performance objectives and neglect resource allocation constraints, this article introduces automation and intelligence at an architectural level to map VNFs and VLs onto their corresponding physical nodes and links, with the goal of achieving superior efficiency in virtual resource utilization while granting the performance of a RAN slice subnet. Benefiting from a top-down approach, the key contributions of this article are: (i) to extend the architectural framework of network slicing towards the NG-RAN architecture and provide a comprehensive overview and critical analysis of the components and functionalities of a RAN slice subnet; (ii) to integrate the Experiential Network Intelligence (ENI) framework into a joint architecture of the network functions virtualization–management and orchestration (NFV–MANO), Third Generation Partnership Project-network slicing management system (3GPP-NSMS), and I-PoPs in order to render automation and intelligence to the management and orchestration aspects of a RAN slice subnet in the NG-RAN architecture; and (iii) to propose a learning-assisted architectural solution for mapping the VNFs, as well as their internal and external VLs, of a RAN slice subnet onto the underlying I-PoPs and transport networks.

This article describes a dataset of high-resolution visible-spectrum images of safflower (Carthamus tinctorius L.) plants obtained from a LemnaTec Scanalyser automated phenomics platform along with the associated image analysis output and manually acquired biomass data. This series contains 1832 images of 200 diverse safflower genotypes, acquired at the Plant Phenomics Victoria, Horsham, Victoria, Australia. Two Prosilica GT RGB (red-green-blue) cameras were used to generate 6576 × 4384 pixel portable network graphic (PNG) images. Safflower genotypes were either subjected to a salt treatment (250 mM NaCl) or grown as a control (0 mM NaCl) and imaged daily from 15 to 36 days after sowing. Each snapshot consists of four images collected at a point in time; one of which is taken from above (top-view) and the remainder from the side at either 0°, 120° or 240°. The dataset also includes analysis output quantifying traits and describing phenotypes, as well as manually collected biomass and leaf ion content data. The usage of the dataset is already demonstrated in Thoday-Kennedy et al. (2021) [1]. This dataset describes the early growth differences of diverse safflower genotypes and identified genotypes tolerant or susceptible to salinity stress. This dataset provides detailed image analysis parameters for phenotyping a large population of safflower that can be used for the training of image-based trait identification pipelines for a wide range of crop species.

Abstract Fungal infections represent a major global health problem affecting over a billion people that kills more than 1.5 million annually. In this study, we employed an integrative approach to reveal the landscape of the human immune responses to Candida spp. through meta-analysis of microarray, bulk, and single-cell RNA sequencing (scRNA-seq) data for the blood transcriptome. We identified across these different studies a consistent interconnected network interplay of signaling molecules involved in both Toll-like receptor (TLR) and interferon (IFN) signaling cascades that is activated in response to different Candida species (C. albicans, C. auris, C. glabrata, C. parapsilosis, and C. tropicalis). Among these molecules are several types I IFN, indicating an overlap with antiviral immune responses. scRNA-seq data confirmed that genes commonly identified by the three transcriptomic methods show cell type-specific expression patterns in various innate and adaptive immune cells. These findings shed new light on the anti-Candida immune response, providing putative molecular pathways for therapeutic intervention.

Abstract Understanding population connectivity within a species as well as potential interactions with its close relatives is crucial to define management units and to derive efficient management actions. However, although genetics can reveal mismatches between biological and management units and other relevant but hidden information such as species misidentification or hybridization, the uptake of genetic methods by the fisheries management process is far from having been consolidated. Here, we have assessed the power of genetics to better understand the population connectivity of white (Lophius piscatorius) and its interaction with its sister species, the black anglerfish (Lophius budegassa). Our analyses, based on thousands of genome‐wide single nucleotide polymorphisms, show three findings that are crucial for white anglerfish management. We found (i) that white anglerfish is likely composed of a single panmictic population throughout the Northeast Atlantic, challenging the three‐stock based management, (ii) that a fraction of specimens classified as white anglerfish using morphological characteristics are genetically identified as black anglerfish (L. budegassa), and iii) that the two Lophius species naturally hybridize leading to a population of hybrids of up to 20% in certain areas. Our results set the basics for a genetics‐informed white anglerfish assessment framework that accounts for stock connectivity, revises and establishes new diagnostic characters for Lophius species identification, and evaluates the effect of hybrids in the current and future assessments of the white anglerfish. Furthermore, our study contributes to provide additional evidence of the potentially negative consequences of ignoring genetic data for assessing fisheries resources.

Brazil experienced one of the most prolonged periods of school closures, and reopening could have exposed students to high rates of SARS-CoV-2 infection. However, the infection status of students and school workers at the time of the reopening of schools located in Brazilian cities is unknown. Here we evaluated viral carriage by RT-PCR and seroprevalence of anti-SARS-CoV-2 antibodies (IgM and IgG) by immunochromatography in 2259 individuals (1139 students and 1120 school workers) from 28 schools in 28 Brazilian cities. We collected the samples within 30 days after public schools reopened and before the start of vaccination campaigns. Most students (n = 421) and school workers (n = 446) had active (qRT-PCR + IgM− IgG− or qRT-PCR + IgM + IgG−/+) SARS-CoV-2 infection. Regression analysis indicated a strong association between the infection status of students and school workers. Furthermore, while 45% (n = 515) of the students and 37% (n = 415) of the school workers were neither antigen nor antibody positive in laboratory tests, 16% of the participants (169 students and 193 school workers) were oligosymptomatic, including those reinfected. These individuals presented mild symptoms such as headache, sore throat, and cough. Notably, most of the individuals were asymptomatic (83.9%). These results indicate that many SARS-CoV-2 infections in Brazilian cities during school reopening were asymptomatic. Thus, our study highlights the need to promote a coordinated public health effort to guarantee a safe educational environment while avoiding exacerbating pre-existent social inequalities in Brazil, reducing social, mental, and economic losses for students, school workers, and their families.

Recent insights and developments on health and society urge a critical look at the positive relationship between socioeconomic status (SES) and health. We challenge the notions that it is sufficient to distinguish only between two groups of SES (low and high) and that only overall health is taken into account. A new grouping of SES was developed based on both income and education, resulting in six SES groups. Health was defined in terms of a new positive health concept, operationalized into six health dimensions generating a measure of total general health (TGH). Next, six socioeconomic and demographic determinants of health were included. Linear regression, T-tests and one-way ANOVA were applied to investigate the relationships in a Dutch sample. A subjective way to measure health was applied: self-rated health (SRH). As a result, four out of six dimensions of health determined TGH: bodily functions, daily functioning, quality of life, and social and societal participation. Three out of six socioeconomic and demographic determinants impacted TGH: housing situation, age, and difficulties meeting financial obligations. While this is the general picture for the entire sample, there were interesting similarities and differences between the six SES groups. The similarities lie in the positive impact of the evaluation of bodily functions and daily functioning on TGH in all SES groups. The other dimensions affected TGH in some groups, and some dimensions only in one SES group. None of the socioeconomic and demographic determinants affected TGH in all SES groups. New insights on health inequalities are provided. It is concluded, first that the well-known positive relationship between SES and health is confirmed in this study. Second, further refining the health concept into six dimensions provides more detailed insights on which dimensions impact health the most. The subjective approach applied offers more refined information to better understand which health issues really matter to people. This yields new insights to develop tailor-made interventions aimed at increasing healthy behaviour in specific societal groups.

Abstract Background Imputation to whole-genome sequence is now possible in large sheep populations. It is therefore of interest to use this data in genome-wide association studies (GWAS) to investigate putative causal variants and genes that underpin economically important traits. Merino wool is globally sought after for luxury fabrics, but some key wool quality attributes are unfavourably correlated with the characteristic skin wrinkle of Merinos. In turn, skin wrinkle is strongly linked to susceptibility to “fly strike” (Cutaneous myiasis), which is a major welfare issue. Here, we use whole-genome sequence data in a multi-trait GWAS to identify pleiotropic putative causal variants and genes associated with changes in key wool traits and skin wrinkle. Results A stepwise conditional multi-trait GWAS (CM-GWAS) identified putative causal variants and related genes from 178 independent quantitative trait loci (QTL) of 16 wool and skin wrinkle traits, measured on up to 7218 Merino sheep with 31 million imputed whole-genome sequence (WGS) genotypes. Novel candidate gene findings included the MAT1A gene that encodes an enzyme involved in the sulphur metabolism pathway critical to production of wool proteins, and the ESRP1 gene. We also discovered a significant wrinkle variant upstream of the HAS2 gene, which in dogs is associated with the exaggerated skin folds in the Shar-Pei breed. Conclusions The wool and skin wrinkle traits studied here appear to be highly polygenic with many putative candidate variants showing considerable pleiotropy. Our CM-GWAS identified many highly plausible candidate genes for wool traits as well as breech wrinkle and breech area wool cover.

Across-season biomass assessment is crucial in the cultivar selection process to accurately evaluate the yield performance of lines under different growing conditions. However, it has been difficult to have an accurate, reliable, and repeated fresh biomass (FM) estimation of large populations of plants in the field without destructive harvesting, which incurs significant labor and operation costs. Sensor-based phenotyping platforms have advanced in the data collection of structural and vegetative information of plants, but the developed prediction models are still limited by low correlations at different growth stages and seasons. In this study, our objective was to develop and validate the global prediction models for across-season harvested fresh biomass (FM) yield based on the ground- and air-based sensor data including ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI). The study was conducted in a row-plot field trial with 480 rows (3 rows in a plot per cultivar) throughout the whole 2020 growing season up to the reproductive stage. We evaluated the performance of each plant parameter, their relationship, and the best subset prediction models using statistical stepwise selection at the row and plot levels through the seasonal and combined seasonal datasets. The best performing model: FM~LV∗LV_Den∗NDVI had a determination of coefficient R2 of at least 0.9 in vegetative stages and 0.8 in the reproductive stage. Similar results can be achieved in a simpler model with just two LiDAR variables—FM~LV∗LV_Den. In addition, LV and LV_Den showed a robust correlation with FM on their own over seasons and growth stages, while NDVI only performed well in some seasons. The simpler model based on only LiDAR data can be widely applied over season without the need of additional sensor data and may thus make the in-field across-season biomass assessment more feasible and practical for fast and cost-effective development of higher biomass yield cultivars.

We investigated the benefit from introgression of external lines into a cereal breeding programme and strategies that accelerated introgression of the favourable alleles while minimising linkage drag using stochastic computer simulation. We simulated genomic selection for disease resistance and grain yield in two environments with a high level of genotype-by-environment interaction (G × E) for the latter trait, using genomic data of a historical barley breeding programme as the base generation. Two populations (existing and external) were created from this base population with different allele frequencies for few (N = 10) major and many (N ~ 990) minor simulated disease quantitative trait loci (QTL). The major disease QTL only existed in the external population and lines from the external population were introgressed into the existing population which had minor disease QTL with low, medium and high allele frequencies. The study revealed that the benefit of introgression depended on the level of genetic variation for the target trait in the existing cereal breeding programme. Introgression of external resources into the existing population was beneficial only when the existing population lacked variation in disease resistance or when minor disease QTL were already at medium or high frequency. When minor disease QTL were at low frequencies, no extra genetic gain was achieved from introgression. More benefit in the disease trait was obtained from the introgression if the major disease QTL had larger effect sizes, more selection emphasis was applied on disease resistance, or more external lines were introgressed. While our strategies to increase introgression of major disease QTL were generally successful, most were not able to completely avoid negative impacts on selection for grain yield with the only exception being when major introgression QTL effects were very large. Breeding programmes are advised to carefully consider the level of genetic variation in a trait available in their breeding programme before deciding to introgress germplasms.

Genomic prediction of phenotype may be improved by using DNA mutations with functional, evolutionary, and pleiotropic consequences. Here the authors describe a method for genome-wide fine-mapping of QTLs and develop a genotyping array for improved prediction of genetic values for cattle traits.

Abstract Background Variants that regulate transcription, such as expression quantitative trait loci (eQTL), have shown enrichment in genome-wide association studies (GWAS) for mammalian complex traits. However, no study has reported eQTL in sheep, although it is an important agricultural species for which many GWAS of complex meat traits have been conducted. Using RNA sequence data produced from liver and muscle from 149 sheep and imputed whole-genome single nucleotide polymorphisms (SNPs), our aim was to dissect the genetic architecture of the transcriptome by associating sheep genotypes with three major molecular phenotypes including gene expression (geQTL), exon expression (eeQTL) and RNA splicing (sQTL). We also examined these three types of eQTL for their enrichment in GWAS of multi-meat traits and fatty acid profiles. Results Whereas a relatively small number of molecular phenotypes were significantly heritable (h2 > 0, P

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the “fourth industrial revolution”, referred to as “Industry 4.0”. Current systems usually rely on wire-line technologies to connect sensors and actuators, but new use cases such as moving robots or drones demand a higher flexibility on communication services. Wireless technologies, especially 5th generation wireless communication systems (5G) are best suited to address these new requirements. Furthermore, this facilitates the renewal of brownfield deployments to enable a smooth migration to Industry 4.0. This paper presents results from the Tactile Internet 4.0 (TACNET 4.0) project and introduces a tailored architecture that is focused on the communication needs given by representative Industry 4.0 use cases while ensuring parallel compliance to latest developments in relevant standardization.

Previous studies have shown reduced enteric methane emissions (ME) and residual feed intake (RFI) through the application of genomic selection in ruminants. The objective of this study was to evaluate feeding behaviour traits as genetic indicators for ME and RFI in Australian Maternal Composite ewes using data from an automated feed intake facility. The feeding behaviour traits evaluated were the amount of time spent eating per day (eating time; ETD; min/day) and per visit (eating time per event; ETE; min/event), daily number of events (DNE), event feed intake (EFI; g/event) and eating rate (ER; g/min). Genotypes and phenotypes of 445 ewes at three different ages (post-weaning, hogget, and adult) were used to estimate the heritability of ME, RFI, and the feeding behaviour traits using univariate genomic best linear unbiased prediction models. Multivariate models were used to estimate the correlations between these traits and within each trait at different ages. The response to selection was evaluated for ME and RFI with direct selection models and indirect models with ETE as an indicator trait, as this behaviour trait was a promising indicator based on heritability and genetic correlations. Heritabilities were between 0.12 and 0.18 for ME and RFI, and between 0.29 and 0.47 for the eating behaviour traits. In our data, selecting for more efficient animals (low RFI) would lead to higher methane emissions per day and per kg of dry matter intake. Selecting for more ETE also improves feed efficiency but results in more methane per day and per kg dry matter intake. Based on our results, ETE could be evaluated as an indicator trait for ME and RFI under an index approach that allows simultaneous selection for improvement in emissions and feed efficiency. Selecting for ETE may have a tremendous impact on the industry, as it may be easier and cheaper to obtain than feed intake and ME data. As the data were collected using individual feeding units, the findings on this research should be validated under grazing conditions.