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Abstract Background Understanding genetic-metabolite associations has translational implications for informing cardiovascular risk assessment. Interrogating functional genetic variants enhances our understanding of disease pathogenesis and the development and optimization of targeted interventions. Methods In this study, a total of 187 plasma metabolite levels were profiled in 4974 individuals of European ancestry of the GCAT| Genomes for Life cohort. Results of genetic analyses were meta-analysed with additional datasets, resulting in up to approximately 40,000 European individuals. Results of meta-analyses were integrated with reference gene expression panels from 58 tissues and cell types to identify predicted gene expression associated with metabolite levels. This approach was also performed for cardiovascular outcomes in three independent large European studies (N = 700,000) to identify predicted gene expression additionally associated with cardiovascular risk. Finally, genetically informed mediation analysis was performed to infer causal mediation in the relationship between gene expression, metabolite levels and cardiovascular risk. Results A total of 44 genetic loci were associated with 124 metabolites. Lead genetic variants included 11 non-synonymous variants. Predicted expression of 53 fine-mapped genes was associated with 108 metabolite levels; while predicted expression of 6 of these genes was also associated with cardiovascular outcomes, highlighting a new role for regulatory gene HCG27. Additionally, we found that atherogenic metabolite levels mediate the associations between gene expression and cardiovascular risk. Some of these genes showed stronger associations in immune tissues, providing further evidence of the role of immune cells in increasing cardiovascular risk. Conclusions These findings propose new gene targets that could be potential candidates for drug development aimed at lowering the risk of cardiovascular events through the modulation of blood atherogenic metabolite levels.

The experimental approach developed in this research demonstrated how the cloud, the Internet of Things (IoT), edge computing, and Artificial Intelligence (AI), considered key technologies in Industry 4.0, provide the expected horizon for adaptive vision in Continued Process Verification (CPV), the final stage of Process Validation (PV). Pichia pastoris producing Candida rugosa lipase 1 under the regulation of the constitutive GAP promoter was selected as an experimental bioprocess. The bioprocess worked under hypoxic conditions in carbon-limited fed-batch cultures through a physiological control based on the respiratory quotient (RQ). In this novel bioprocess, a digital twin (DT) was built and successfully tested. The implementation of online sensors worked as a bridge between the microorganism and AI models, to provide predictions from the edge and the cloud. AI models emulated the metabolism of Pichia based on critical process parameters and actionable factors to achieve the expected quality attributes. This innovative AI-aided Adaptive-Proportional Control strategy (AI-APC) improved the reproducibility comparing to a Manual-Heuristic Control strategy (MHC), showing better performance than the Boolean-Logic-Controller (BLC) tested. The accuracy, indicated by the Mean Relative Error (MRE), was for the AI-APC lower than 4%, better than the obtained for MHC (10%) and BLC (5%). Moreover, in terms of precision, the same trend was observed when comparing the Root Mean Square Deviation (RMSD) values, becoming lower as the complexity of the controller increases. The successful automatic real time control of the bioprocess orchestrated by AI models proved the 4.0 capabilities brought by the adaptive concept and its validity in biopharmaceutical upstream operations.

Abstract Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

Abstract Genome-wide association studies have identified thousands of loci associated with common diseases and traits. However, a large fraction of heritability remains unexplained. Epigenetic modifications, such as the observed in DNA methylation have been proposed as a mechanism of intergenerational inheritance. To investigate the potential contribution of DNA methylation to the missing heritability, we analysed the methylomes of four healthy trios (two parents and one offspring) using whole genome bisulphite sequencing. Of the 1.5 million CpGs (19%) with over 20% variability between parents in at least one family and compatible with a Mendelian inheritance pattern, only 3488 CpGs (0.2%) lacked correlation with any SNP in the genome, marking them as potential sites for intergenerational epigenetic inheritance. These markers were distributed genome-wide, with some preference to be located in promoters. They displayed a bimodal distribution, being either fully methylated or unmethylated, and were often found at the boundaries of genomic regions with high/low GC content. This analysis provides a starting point for future investigations into the missing heritability of simple and complex traits.

Octave equivalence describes the perception that two notes separated by a doubling in frequency have a similar quality. In humans, octave equivalence is important to both music and language learning and is found cross-culturally. Cross-species studies comparing human and non-human animals can help illuminate the necessary pre-conditions to developing octave equivalence. Here, we tested whether rats (Rattus norvegicus) perceive octave equivalence using a standardized cross-species paradigm. This allowed us to disentangle concurring hypotheses regarding the evolutionary roots of this phenomenon. One hypothesis is that octave equivalence is directly connected to vocal learning, but this hypothesis is only partially supported by data. According to another hypothesis, the harmonic structure of mammalian vocalizations may be more important. If rats perceive octave equivalence, this would support the importance of vocal harmonic structure. If rats do not perceive octave equivalence, this would suggest that octave equivalence evolved independently in several mammalian clades due to a more complex interplay of different factors such as—but not exclusively—the ability to vocally learn. Evidence from our study suggests that rats do perceive octave equivalence, thereby suggesting that the harmonic vocal structure found in mammals may be a key pre-requisite for octave equivalence. Stage 1 approved protocol: the study reported here was originally accepted as a Registered Report and the study design was approved in Stage 1. We hereby confirm that the completed experiment(s) have been executed and analysed in the manner originally approved with any unforeseen changes in those approved methods and analyses clearly noted. The approved Stage 1 protocol can be found at: https://osf.io/gvf7c/?view_only=76dc1840f31c4f9ab59eb93cbadb98b7.

The detection of tumoural cells from whole slide images is an essential task in medical diagnosis and research. In this article, we propose and analyse a novel approach that combines computer vision-based models with graph neural networks to improve the accuracy of automated tumoural cell detection in lung tissue. Our proposal leverages the inherent structure and relationships between cells in the tissue. Experimental results on our own curated dataset show that modelling the problem with graphs gives the model a clear advantage over just working at pixel level. This change in perspective provides extra information that makes it possible to improve the performance. The reduction of dimensionality that comes from working with the graph also allows us to increase the field of view with low computational requirements. Code is available at https://github.com/Jerry-Master/lung-tumour-study, models are uploaded to https://huggingface.co/Jerry-Master/Hovernet-plus-Graphs, and the dataset is published on Zenodo https://zenodo.org/doi/10.5281/zenodo.8368122.

Abstract The application of different types of microwave resonators for sensing cracks in metallic structures has been subject of many studies. While most studies have been focused on improving the sensitivity of planar crack sensors, the theoretical foundation of the topic has not been treated in much detail. The major objective of this study is to perform an exhaustive study of the principles and theoretical foundations for crack sensing based on planar microwave resonators, especially defective ground structures (DGS) including complementary split ring resonators (CSRRs). The analysis is carried out from the equivalent circuit model as well as the electromagnetic (EM) field perspectives, and guidelines for the design of crack sensors with high sensitivity are developed. Numerical and experimental validation of the provided theoretical analysis is another aim of this article. With this aim, the developed guidelines are used to design a crack sensor based on a single-ring CSRR. It is shown that the sensitivity of the proposed sensor is almost three times higher than the sensitivity of a conventional double-ring CSRR. Moreover, it is demonstrated that folded dumbbell-shape DGS resonators can be used to achieve even higher sensitivities. The CSRR-based crack sensors presented in this study and other studies available in the literature are only sensitive to cracks with a specific orientation. To address this limitation, a modified version of the DGS is proposed to sense cracks with arbitrary orientations at the cost of lower sensitivity. The performance of all the presented sensors is validated through EM simulation, equivalent circuit model extraction, and measurement of the fabricated prototypes.

An international survey revealed that 75% PsA and 65% PsO women breastfed and 78% PsA and 44% PsO women felt they had to choose between treatment or breastfeeding. Furthermore, 31% PsO and 40% PsA of women felt they lacked information on the impact of treatment decisions on breastfeeding.10 Our primary objective was describing the impact of PsO in breastfeeding among women with moderate-to-severe PsO included in the BIOBADADERM Registry (Spain). 7 Clowse MEB Comparison of Rheumatoid Arthritis, Ankylosing Spondylitis, and Psoriatic Arthritis Pregnancies: Disease Activity, Treatment, and Outcomes. [Extracted from the article]

ObjectiveReduced diversity at Human Leukocyte Antigen (HLA) loci may adversely affect the host’s ability to recognize tumor neoantigens and subsequently increase disease burden. We hypothesized that increased heterozygosity at HLA loci is associated with a reduced risk of developing colorectal cancer (CRC).MethodsWe imputed HLA class I and II four-digit alleles using genotype data from a population-based study of 5,406 cases and 4,635 controls from the Molecular Epidemiology of Colorectal Cancer Study (MECC). Heterozygosity at each HLA locus and the number of heterozygous genotypes at HLA class -I (A, B, and C) and HLA class -II loci (DQB1, DRB1, and DPB1) were quantified. Logistic regression analysis was used to estimate the risk of CRC associated with HLA heterozygosity. Individuals with homozygous genotypes for all loci served as the reference category, and the analyses were adjusted for sex, age, genotyping platform, and ancestry. Further, we investigated associations between HLA diversity and tumor-associated T cell repertoire features, as measured by tumor infiltrating lymphocytes (TILs; N=2,839) and immunosequencing (N=2,357).ResultsIndividuals with all heterozygous genotypes at all three class I genes had a reduced odds of CRC (OR: 0.74; 95% CI: 0.56-0.97, p= 0.031). A similar association was observed for class II loci, with an OR of 0.75 (95% CI: 0.60-0.95, p= 0.016). For class-I and class-II combined, individuals with all heterozygous genotypes had significantly lower odds of developing CRC (OR: 0.66, 95% CI: 0.49-0.87, p= 0.004) than those with 0 or one heterozygous genotype. HLA class I and/or II diversity was associated with higher T cell receptor (TCR) abundance and lower TCR clonality, but results were not statistically significant.ConclusionOur findings support a heterozygote advantage for the HLA class-I and -II loci, indicating an important role for HLA genetic variability in the etiology of CRC.

This paper provides an overview of balanced (or differential-mode) structures operating at microwaves, including common-mode suppression filters, balanced filters and circuits with intrinsic common-mode suppression, and differential-mode sensors. The increasing interest for balanced structures within the framework of communication circuits and sensors, related to their major robustness against electromagnetic interference (EMI), noise, and environmental factors, is duly justified. Then the paper reports various approaches for the implementation of common-mode suppression filters, balanced filters and circuits (including narrowband and wideband filters, multi-band filters, power dividers, couplers and phase shifters), and differential-mode sensors (mainly devoted to the measurement of permittivity and related variables). Rather than providing a detailed analysis of the different considered implementations/approaches, the main goal of this review paper is to discuss, and compare in some cases, the main proposed strategies (pointed out in the up-to-date state-of-the-art) for optimizing the performance of the various differential-mode devices under study.