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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.

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 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.

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.

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.

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.

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.

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.