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Applications for Extended Reality (XR) are rapidly expanding across a wide range of industries, including gaming, entertainment, and healthcare. Accompanying this trend is the field of digital manufacturing, which encompasses a broad range of applications such as quick product design, remote maintenance, production process simulation, employee safety, and training. Industrial personnel may benefit from ergonomic and user-friendly cyber-representations of production processes using XR applications, particularly in the industrial arena. These cyber-representations are utilized to create augmented environments for training, simulation, and testing. The wave of Industry 5.0 (I5.0) applications that are certain to be human-centric and emphasize trustworthy human-machine collaboration cannot be supported by current systems because of several issues, such as the requirement for individualized XR visualizations and new methods for world-building, content production, and flow control aspects of XR systems. The XR5.0 project is presented in this publication with the goal of addressing these issues and offering a revolutionary Person-Centric and AI-based XR paradigm that will be customized to the needs and characteristics of I5.0 applications. The project outlines the organizing concepts and guidelines for utilizing XR in I5.0 applications, with a focus on the creation of cutting-edge “XR-made-in-Europe” technology that complements human-centered manufacturing techniques and upholds European ideals. The associated applications consider the workers’ environment through the incorporation of human-centered digital twins (DTs), which make up the “digital image” of the workers. This allows for the simultaneous design and implementation of an innovative fusion of cutting-edge AI paradigms and XR technology. The added value of the XR5.0 project is discussed, and potential use cases and user journeys are analyzed, leading to a discussion of the project’s revolutionary benefits and additional steps that should be taken.

Abstract More than 10% of adults in the United States have type 2 diabetes mellitus (DM) with a 2–4 times higher prevalence of ischemic heart disease than the non-diabetics. Despite extensive research approaches to limit this life-threatening condition have proven unsuccessful, highlighting the need for understanding underlying molecular mechanisms. Long noncoding RNAs (lncRNAs), which regulate gene expression by acting as signals, decoys, guides, or scaffolds have been implicated in diverse cardiovascular conditions. However, their role in ischemic heart disease in DM remains poorly understood. We provide new insights into the lncRNA expression profile after ischemic heart disease in DM mice. We performed unbiased RNA sequencing of well-characterized type 2 DM model db/db mice or its control db/+ subjected to sham or MI surgery. Computational analysis of the RNA sequencing of these LV tissues identified several differentially expressed lncRNAs between (db/db sham vs. db/db MI) including Gm19522 and Gm8075. lncRNA Gm-19522 may regulate DNA replication via DNA protein kinases, while lncRNA Gm-8075 is associated with cancer gene dysregulation and PI3K/Akt pathways. Thus, the downregulation of lncRNAs Gm19522 and Gm8075 post-MI may serve as potential biomarkers or novel therapeutic targets to improve cardiac repair/recovery in diabetic ischemic heart disease.

Design and production of highly demanding structural systems, such as the chassis, still rely on conventional forming and welding approaches, both because of their proven performance and the economies of scale achieved. Nevertheless, manufacturing of several chassis’ segments is also expected to soon gradually switch towards AM, for increased design freedom and optimized performance. This paper proposes an alternative design approach for the A-pillar, a typical passenger car chassis segment; a design suitable in form for AM and equally capable in terms of its dynamic behavior, without undermining the chassis’ safety. Prior A-pillar designs along with already published innovative AM-suited design approaches are reviewed. Moreover, these serve as a starting point for an inverse design towards the intended new AM-suited A-pillar alternative. Emphasis is given in the dynamic characteristics of the new structure, through proper modal analysis performed. Finally, the presented research concludes with a scaled-down assessment and verification prototype of the new design, planned to be built via FDM 3D Printing. The prototype is expected to demonstrate primary, as well as secondary/latent benefits from the use of AM in A-pillars, such as the increased diagonal visibility for drivers and passengers, arising from the redesigned, mesh-like form of the segment.

Twentieth century industrial whaling pushed several species to the brink of extinction, with fin whales being the most impacted. However, a small, resident population in the Gulf of California was not targeted by whaling. Here, we analyzed 50 whole-genomes from the Eastern North Pacific (ENP) and Gulf of California (GOC) fin whale populations to investigate their demographic history and the genomic effects of natural and human-induced bottlenecks. We show that the two populations diverged ~16,000 years ago, after which the ENP population expanded and then suffered a 99% reduction in effective size during the whaling period. In contrast, the GOC population remained small and isolated, receiving less than one migrant per generation. However, this low level of migration has been crucial for maintaining its viability. Our study exposes the severity of whaling, emphasizes the importance of migration, and demonstrates the use of genome-based analyses and simulations to inform conservation strategies.

Human group connectome analysis relies on combining individual connectome data to construct a single representative network which can be used to describe brain organisation and identify differences between subject groups. Existing methods adopt different strategies to select the network structural features to be retained or optimised at group level. In the absence of ground truth, however, it is unclear which structural features are the most suitable and how to evaluate the consequences on the group network of applying any given strategy. In this investigation, we consider the impact of defining a connectome as representative if it can recapitulate not just the structure of the individual networks in the cohort tested but also their dynamical behaviour, which we measured using a model of coupled oscillators. We applied the widely used approach of consensus thresholding to a dataset of individual structural connectomes from a healthy adult cohort to construct group networks for a range of thresholds and then identified the most dynamically representative group connectome as that having the least deviation from the individual connectomes given a dynamical measure of the system. We found that our dynamically representative network recaptured aspects of structure for which it did not specifically optimise, with no significant difference to other group connectomes constructed via methods which did optimise for those metrics. Additionally, these other group connectomes were either as dynamically representative as our chosen network or less so. While we suggest that dynamics should be at least one of the criteria for representativeness, given that the brain has evolved under the pressure of carrying out specific functions, our results suggest that the question persists as to which of these criteria are valid and testable.

Abstract The bioavailability of nicotinamide adenine dinucleotide (NAD) is vital for skeletal muscle health, yet the mechanisms or signals regulating NAD homeostasis remain unclear. Here, we uncover a pathway connecting peripheral glucose sensing to the modulation of muscle NAD through TAS1R2, the sugar-sensing G protein-coupled receptor (GPCR) initially identified in taste perception. Muscle TAS1R2 receptor stimulation by glucose and other agonists induces ERK1/2-dependent phosphorylation and activation of poly(ADP-ribose) polymerase1 (PARP1), a major NAD consumer in skeletal muscle. Consequently, muscle-specific deletion of TAS1R2 (mKO) in male mice suppresses PARP1 activity, elevating NAD levels and enhancing mitochondrial capacity and running endurance. Plasma glucose levels negatively correlate with muscle NAD, and TAS1R2 receptor deficiency enhances NAD responses across the glycemic range, implicating TAS1R2 as a peripheral energy surveyor. These findings underscore the role of GPCR signaling in NAD regulation and propose TAS1R2 as a potential therapeutic target for maintaining muscle health.

There has been a growing need for enhancements in healthcare delivery, especially for the improvement of surgical outcomes. Therefore, implementing consistent reporting of complications enables the evaluation of data quality and facilitates its comparison. There are currently many available reporting and grading systems each with its own set of benefits and drawbacks. In this comprehensive review, we tried to present and assess each of them by demonstrating their criteria and their strong and weak points. To sum up, it seems that there is a need for developing a new reporting and categorization system for complications that are specific to urology.

Chiari I Malformation and Intramedullary Hemorrhage in a Female Patient with Klippel Trenaunay Syndrome: A Rare Case Report Study

Island ecosystems provide natural laboratories to assess the impacts of isolation on population persistence. However, most studies of persistence have focused on a single species, without comparisons to other organisms they interact with in the ecosystem. The case study of moose and gray wolves on Isle Royale allows for a direct contrast of genetic variation in isolated populations that have experienced dramatically differing population trajectories over the past decade. Whereas the Isle Royale wolf population recently declined nearly to extinction due to severe inbreeding depression, the moose population has thrived and continues to persist, despite having low genetic diversity and being isolated for ∼120 years. Here, we examine the patterns of genomic variation underlying the continued persistence of the Isle Royale moose population. We document high levels of inbreeding in the population, roughly as high as the wolf population at the time of its decline. However, inbreeding in the moose population manifests in the form of intermediate-length runs of homozygosity suggestive of historical inbreeding and purging, contrasting with the long runs of homozygosity observed in the smaller wolf population. Using simulations, we confirm that substantial purging has likely occurred in the moose population. However, we also document notable increases in genetic load, which could eventually threaten population viability over the long term. Overall, our results demonstrate a complex relationship between inbreeding, genetic diversity, and population viability that highlights the use of genomic datasets and computational simulation tools for understanding the factors enabling persistence in isolated populations.

Deleterious mutations decrease reproductive fitness and are ubiquitous in genomes. Given that many organisms face ongoing threats of extinction, there is interest in elucidating the impact of deleterious variation on extinction risk and optimizing management strategies accounting for such mutations. Quantifying deleterious variation and understanding the effects of population history on deleterious variation are complex endeavors because we do not know the strength of selection acting on each mutation. Further, the effect of demographic history on deleterious mutations depends on the strength of selection against the mutation and the degree of dominance. Here we clarify how deleterious variation can be quantified and studied in natural populations. We then discuss how different demographic factors, such as small population size, nonequilibrium population size changes, inbreeding, and gene flow, affect deleterious variation. Lastly, we provide guidance on studying deleterious variation in nonmodel populations of conservation concern.

Nowadays, the fusion of Artificial Intelligence (AI) comprises a widespread approach for resolving various types of problems in many scientific domains including Protection of Monuments. Non-Destructive Testing (NDT) approaches and Infra-Red Thermography (IRT) specifically, plays a key role for the diagnosis and the assessment of the monuments’ preservation state. Additionally, IRT comprises a powerful tool for continuous monitoring especially when it concerns the physical and/or chemical processes that take place within or on the material and affect the irradiation of the historical surfaces. This study explores the application of Deep Learning (DL) to IRT images of passive approach, focusing on the automated detection of rising damp in historical masonries. The IRT data were acquired from two monuments, the Holy Aedicule of the Holy Sepulchre and the Historical Building ''Msma’a''. Exploiting the capabilities of AI for enhancing the non-intrusive nature of passive IRT, this research seeks to provide a cost-effective and non-destructive approach for the early identification of rising damp, contributing significantly to the long-term preservation, conservation, and protection of the cultural heritage. To achieve this, the study takes advantage of a combination of the PSPNet image segmentation model with the ResNet-50 backbone, the PSP_R50 model. The mmsegment framework, renowned for its versatility and effectiveness, serves as the ideal platform for training, evaluating, and fine-tuning the proposed segmentation model. Despite having a relatively small dataset, a highly effective segmentation model (0.93 accuracy, 0.89 IoU), has been successfully developed.

Hormesis is a biological phenomenon where exposure to a low dose of a stressor or toxin induces a beneficial adaptive response, whereas higher doses may have detrimental effects. The concept of hormesis is being increasingly appreciated not only in toxicology and in pharmacology, but also in nutrition, clinical medicine, and in situations involving everyday life. Hormesis is an adaptive response of cells and organisms to a moderate and intermittent stressful stimulation. Following such stimulation, the organism must respond, and it has to make a choice: either treat it as a positive ‘challenge’, adapting to it and increasing its robustness, or treat it as a negative ‘threat’ with detrimental consequences for physiology and health. In clinical and everyday situations it is usually difficult to advise patients on how to determine the strength of such stimulation, and when to decide that each new stimulation is too low (ineffective), moderate (appropriate for health), or excessive (damaging to health). In this paper we argue that it is possible to rely on the subjective feelings of ‘comfort vs discomfort’, for deciding about the strength of the stimulus: if each exposure to a stimulation is felt by the individual as a ’comfortable’ event, then it is likely that its effects are beneficial (a hormetic challenge). If it is felt as an ‘uncomfortable’ event, then it is likely that it is damaging to health (a threat). These feelings take place in the anterior insula which evaluates the state of resources for responding to an external or internal event, and are a result of the integration of signals from the amygdala, hippocampus, and the prefrontal cortex. Digital cognitive stimulation and nutritional hormesis are mentioned as two detailed examples.

Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase recurrent errors in a computer system, which result in “unfair” outcomes. Bias in the “outside world” and algorithmic bias are interconnected since many types of algorithmic bias originate from external factors. The enormous variety of different types of AI biases that have been identified in diverse domains highlights the need for classifying the said types of AI bias and providing a detailed overview of ways to identify and mitigate them. The different types of algorithmic bias that exist could be divided into categories based on the origin of the bias, since bias can occur during the different stages of the Machine Learning (i.e., ML) lifecycle. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches that have been proposed to identify and mitigate them. This study not only provides ready-to-use algorithms for identifying and mitigating bias, but also enhances the empirical knowledge of ML engineers to identify bias based on the similarity that their use cases have to other approaches that are presented in this manuscript. Based on the findings of this study, it is observed that some types of AI bias are better covered in the literature, both in terms of identification and mitigation, whilst others need to be studied more. The overall contribution of this research work is to provide a useful guideline for the identification and mitigation of bias that can be utilized by ML engineers and everyone who is interested in developing, evaluating and/or utilizing ML models.

In cases of severe wildlife population decline, a key question is whether recovery efforts will be impeded by genetic factors, such as inbreeding depression. Decades of excess mortality from gillnet fishing have driven Mexico's vaquita porpoise (Phocoena sinus) to ~10 remaining individuals. We analyzed whole-genome sequences from 20 vaquitas and integrated genomic and demographic information into stochastic, individual-based simulations to quantify the species' recovery potential. Our analysis suggests that the vaquita's historical rarity has resulted in a low burden of segregating deleterious variation, reducing the risk of inbreeding depression. Similarly, genome-informed simulations suggest that the vaquita can recover if bycatch mortality is immediately halted. This study provides hope for vaquitas and other naturally rare endangered species and highlights the utility of genomics in predicting extinction risk.

Abstract In this paper we examine the possibility of dilute axion stars converting to photons in the weak, large-scale magnetic field of the Milky Way and show that they can resonate with the surrounding plasma and produce a sizable signal. We consider two possibilities for the plasma: free electrons and HII regions. In the former case, we argue that the frequency of the photons will be too small to be observed even by space-based radio telescopes. In the latter case, their frequency is larger, safely above the solar wind cut-off. We provide an estimate of the flux as a function of the decay constant and show that for f a < 2 × 1011 GeV, the signal will be above the radio emission of the solar system’s planets and it could potentially be detected by the NCLE instrument which is on board the Chang’e-4 spacecraft. Finally, we calculate the time scale of decay of the axion star and demonstrate that back-reaction can be neglected for all physically interesting values of the decay constant, while the minimum time scale of decay is in the order of a few hours.

Prostate cancer is the second most common cancer among men, with many treatment modalities available for patients, such as radical prostatectomy, external beam radiotherapy, brachytherapy, high-intensity focused ultrasound, cryotherapy, electroporation and other whole-gland or focal ablative novel techniques. Unfortunately, up to 60% of men with prostate cancer experience recurrence at 5 to 10 years. Salvage radical prostatectomy can be offered as an option in the setting of recurrence after a primary non-surgical treatment. However, the complexity of salvage radical prostatectomy is considered to be greater than that of primary surgery, making it the least popular treatment of choice. With the wide use of robotic platforms in urologic oncologic surgery, salvage radical prostatectomy has attracted attention again because, compared to past data, modern series involving salvage Robot-Assisted Radical Prostatectomy have shown promising results. In this narrative literature review, we comprehensively examined data on salvage radical prostatectomy. We investigated the correlation between the different types of primary prostate cancer therapy and the following salvage radical prostatectomy. Furthermore, we explored the concept of a robotic approach and its beneficial effect in salvage surgery. Lastly, we emphasized several promising avenues for future research in this field.

Abstract Twentieth century industrial whaling pushed several species to the brink of extinction, with fin whales being the most impacted. However, a small, resident population in the Gulf of California was not targeted by whaling. Here, we analyzed 50 whole-genomes from the Eastern North Pacific (ENP) and Gulf of California (GOC) fin whale populations to investigate their demographic history and the genomic effects of natural and human-induced bottlenecks. We show that the two populations diverged ~16,000 years ago, after which the ENP population expanded and then suffered a 99% reduction in effective size during the whaling period. In contrast, the GOC population remained small and isolated, receiving less than one migrant per generation. However, this low level of migration has been crucial for maintaining its viability. Our study exposes the severity of whaling, emphasizes the importance of migration, and demonstrates the use of genome-based analyses and simulations to inform conservation strategies.