Rapid Evolution Research Articles

"Insights into ALD & AUD diagnosis and prognosis: Exploring AI and multi-modal data streams".

The rapid evolution of artificial intelligence (AI) and the widespread embrace of digital technologies have ushered in a new era of clinical research and practice in hepatology. Although its potential is far from realization, these significant strides have generated new opportunities to address existing gaps in the delivery of care for patients with liver disease. In this review, we discuss how AI and opportunities for multimodal data integration can improve the diagnosis, prognosis and management of alcohol-associated liver disease (ALD). An emphasis is made on how these approaches will also benefit the detection and management of alcohol use disorder (AUD). Our discussion encompasses challenges and limitations, concluding with a glimpse into the promising future of these advancements.

Multiscale engineering of molecular electrocatalysts for the rapid hydrogen evolution reaction

Multiscale engineering of molecular electrocatalysts for the rapid hydrogen evolution reaction

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

Assessing the Copyright Infringement Risk of Generative AI Created Works

The rapid evolution of the Internet and big data technology has ushered in significant advancements in data production and utilization. However, this progress has brought to the forefront the intricate issue of copyright protection. This is particularly evident with the widespread adoption of generative artificial intelligence (AI), where concerns regarding data source protection, data processing standards, and the boundaries of data application have garnered considerable attention from both academia and industry. Big data mining techniques coupled with generative AI algorithms offer robust support for data collection and processing, but they also introduce risks of data infringement and pose challenges regarding algorithmic compliance and ownership rights over generated works. Addressing these issues is imperative. This paper recommends enhancing legal compliance standards throughout each stage of the generative AI process, recalibrating the scope of acceptable copyright use, and establishing a regulatory framework for generative AI works. These measures are crucial for fostering the sustainable and orderly advancement of the generative AI industry.

Voice assistants and U.S. consumer behavior: A comprehensive review: investigating the role and influence of voice-activated technologies on shopping habits and brand loyalty

This study provides a comprehensive review of the influence of voice-activated technologies, particularly voice assistants, on consumer behavior and brand loyalty in the United States. The primary objective is to understand how these emerging technologies are reshaping shopping habits and the dynamics of brand-consumer interactions. The methodology encompasses a thorough analysis of peer-reviewed academic journals, industry reports, and case studies, supplemented by secondary sources such as market analyses and news articles. Key findings reveal that voice assistants have induced a significant paradigm shift in consumer behavior, characterized by changes in purchasing patterns, decision-making processes, and brand interactions. The convenience and personalized experience offered by these technologies have led to increased impulse buying and brand experimentation. However, this shift is accompanied by challenges in data privacy and security. The study also highlights the rapid evolution of voice-activated consumer engagement, with advancements in AI and machine learning enhancing personalization and efficiency. Strategically, the study recommends that brands and retailers invest in voice search optimization, develop secure data practices, and collaborate with technology developers to create engaging consumer experiences. The study concludes that while voice assistants present substantial opportunities for enhanced consumer engagement, they also pose challenges that require careful navigation, particularly in maintaining consumer trust and ethical standards. Future research directions include exploring the long-term effects of voice-activated shopping on consumer loyalty and the impact of demographic factors on the adoption of voice assistants. Keywords: Voice Assistants, Consumer Behavior, Brand Loyalty, Voice-Activated Technologies.

Different Evolutionary Trends of Galloanseres: Mitogenomics Analysis

The two existing clades of Galloanseres, orders Galliformes (landfowl) and Anseriformes (waterfowl), exhibit dramatically different evolutionary trends. Mitochondria serve as primary sites for energy production in organisms, and numerous studies have revealed their role in biological evolution and ecological adaptation. We assembled the complete mitogenome sequences of two species of the genus Aythya within Anseriformes: Aythya baeri and Aythya marila. A phylogenetic tree was constructed for 142 species within Galloanseres, and their divergence times were inferred. The divergence between Galliformes and Anseriformes occurred ~79.62 million years ago (Mya), followed by rapid evolution and diversification after the Middle Miocene (~13.82 Mya). The analysis of selective pressure indicated that the mitochondrial protein-coding genes (PCGs) of Galloanseres species have predominantly undergone purifying selection. The free-ratio model revealed that the evolutionary rates of COX1 and COX3 were lower than those of the other PCGs, whereas ND2 and ND6 had faster evolutionary rates. The CmC model also indicated that most PCGs in Anseriformes exhibited stronger selective constraints. Our study suggests that the distinct evolutionary trends and energy requirements of Galliformes and Anseriformes drive different evolutionary patterns in the mitogenome.

AI-Driven Cybersecurity: Balancing Advancements and Safeguards

As Artificial Intelligence (AI) continues its rapid evolution, its profound influence on cybersecurity becomes increasingly evident. This study delves into the pivotal role of AI in fortifying cybersecurity measures, emphasizing its capacity for enhanced threat detection, automated response mechanisms, and the development of resilient security frameworks. However, alongside its promise, recognition of AI's susceptibility to exploitation in sophisticated cyber-attacks exists, underscoring the imperative for continual advancements in AI-driven security solutions. This research offers a nuanced perspective on AI's impact on cybersecurity, advocating for the proactive integration of AI strategies, sustained research efforts, and formulating ethical guidelines. Adopting supervised machine learning (ML) algorithms like decision trees, support vector machines, and neural networks aims to harness AI's potential to bolster cybersecurity while concurrently addressing associated risks, paving the way for a secure digital landscape. Regarding accuracy, the neural network outperforms other models by 98%.

THE USE OF DIGITAL TECHNOLOGY IN UNIVERSITY LEARNING THROUGH A BIBLIOMETRIC APPROACH

The article proposes a bibliometric analysis of digitalization in higher education. Based on research conducted in October 2023, the study explored 967 articles in the Web of Science (WOS) database. It highlights the growing importance of digitalization in higher education institutions and explores emerging trends in teaching practices and learning patterns. The study reveals a spike in publications in 2021-2022, likely in response to the COVID-19 pandemic and the transition to online learning. The most active countries in publications on the subject are Russia and Germany accounting for more than 45% of articles. In addition, the analysis of bibliographic networks identified collaborations between authors and the most cited articles, while the analysis of key terms highlighted important research topics such as e-learning and digitalization. This bibliometric study sheds valuable light on an abundant field of research at the heart of the current transformations in higher education. However, additional analyses are needed to follow the rapid evolution of digital education in all its richness and diversity.

A survey on imbalanced learning: latest research, applications and future directions

Imbalanced learning constitutes one of the most formidable challenges within data mining and machine learning. Despite continuous research advancement over the past decades, learning from data with an imbalanced class distribution remains a compelling research area. Imbalanced class distributions commonly constrain the practical utility of machine learning and even deep learning models in tangible applications. Numerous recent studies have made substantial progress in the field of imbalanced learning, deepening our understanding of its nature while concurrently unearthing new challenges. Given the field’s rapid evolution, this paper aims to encapsulate the recent breakthroughs in imbalanced learning by providing an in-depth review of extant strategies to confront this issue. Unlike most surveys that primarily address classification tasks in machine learning, we also delve into techniques addressing regression tasks and facets of deep long-tail learning. Furthermore, we explore real-world applications of imbalanced learning, devising a broad spectrum of research applications from management science to engineering, and lastly, discuss newly-emerging issues and challenges necessitating further exploration in the realm of imbalanced learning.

COVID-19 Vaccination Strategies in the Endemic Period: Lessons from Influenza

Coronavirus disease 2019 (COVID-19) is a highly contagious zoonotic respiratory disease with many similarities to influenza. Effective vaccines are available for both; however, rapid viral evolution and waning immunity make them virtually impossible to eradicate with vaccines. Thus, the practical goal of vaccination is to reduce the incidence of serious illnesses and death. Three years after the introduction of COVID-19 vaccines, the optimal vaccination strategy in the endemic period remains elusive, and health authorities worldwide have begun to adopt various approaches. Herein, we propose a COVID-19 vaccination strategy based on the data available until early 2024 and discuss aspects that require further clarification for better decision making. Drawing from comparisons between COVID-19 and influenza vaccination strategies, our proposed COVID-19 vaccination strategy prioritizes high-risk groups, emphasizes seasonal administration aligned with influenza vaccination campaigns, and advocates the co-administration with influenza vaccines to increase coverage.

Molecular Mechanism of Interaction between DNA Aptamer and Receptor-Binding Domain of Severe Acute Respiratory Syndrome Coronavirus 2 Variants Revealed by Steered Molecular Dynamics Simulations

The ongoing SARS-CoV-2 pandemic has underscored the urgent need for versatile and rapidly deployable antiviral strategies. While vaccines have been pivotal in controlling the spread of the virus, the emergence of new variants continues to pose significant challenges to global health. Here, our study focuses on a novel approach to antiviral therapy using DNA aptamers, short oligonucleotides with high specificity and affinity for their targets, as potential inhibitors against the spike protein of SARS-CoV-2 variants Omicron and JN.1. Our research utilizes steered molecular dynamics (SMD) simulations to elucidate the binding mechanisms of a specifically designed DNA aptamer, AM032-4, to the receptor-binding domain (RBD) of the aforementioned variants. The simulations reveal detailed molecular insights into the aptamer–RBD interaction, demonstrating the aptamer’s potential to maintain effective binding in the face of rapid viral evolution. Our work not only demonstrates the dynamic interaction between aptamer–RBD for possible antiviral therapy but also introduces a computational method to study aptamer–protein interactions.

The special adaptation to hypoxia facilitated the expansion of the Asian house rat (Rattus tanezumi) into Tibet but not other Rattus species.

Rattus species are thought to live only at altitudes less than 2500 m, but the Asian house rat (R. tanezumi) (RT) has recently expanded to altitudes greater than 3500 m in China. Other Rattus species, especially brown rats (R. norvegicus) (RN), still reach only low altitudes on the Tibetan Plateau. Comparative genomics revealed the positive selection of hypoxia-inducible transcription factors 1 and 2 (HIFs) in RT, with the rapid evolution of HIF pathway genes in RT and Mus musculus (MM) but not RN or R. rattus. Population genomics revealed that genes associated with energy metabolism and oxygen transport were positively selected in RT compared with the other four Rattus species, and two specific substitutions (arginine 31 serine and leucine 33 methionine) were identified in the hemoglobin subunit beta (HBB) in RT. The above results suggested that RT possesses unique genetic adaptations to hypoxia, which was further confirmed by behavioral experiments on RT and RN. Normobaric hypoxia significantly reduced locomotion in RN but not in RT. Moreover, through intraspecific transcriptome analysis, the expression of Hbb and genes related to angiogenesis, oxygen transport, and glycolysis was upregulated, and the expression of genes associated with immunological functions in the liver, lungs, and/or sperm was downregulated in RT compared to those in RN. Interspecific transcriptome analysis further revealed that HIF-1α plays a role in modulating the hypoxic adaptation of RT rather than RN. Our work provides genomic, behavioral, and physiological insights into why RT, but not other Rattus species, could invade the Tibetan Plateau.

Outpatient PCI - early discharge PCI, single center experience

Abstract Funding Acknowledgements None. Background Percutaneous coronary intervention (PCI) has undergone rapid evolution over the last years and is currently a widely generalized therapeutic option. In the past, it was associated with a significant rate of complications, yet, with the improvement of new generation drug eluting stents, and the radial access (RA) development, PCI has become safer and simpler, with a minimum rate of complications. This allows patient’s faster discharge. Besides the pressure from the limited number of beds in wards, the improvement of PCI has given the confidence to urge outpatient PCI programs. However, there are still centers where there is no ambulatory PCI. Purpose This study aims to describe our center experience of ambulatory PCI program, in terms of safety and efficacy. Methods We analyzed our center’s retrospective cohort of 723 PCI, in 2022. Ambulatory PCI was defined as PCI with same day hospital discharge. Our inclusion criteria were patients submitted to PCI. We excluded patients with acute coronary syndromes and patients needing hospital admission for other non-coronary reasons. Results 467 (65%), of the 723 PCI were performed in ambulatory setting. Patients mean age was 67,8±11 years and 23% (n=107) were female. In 12% (n=55) of the cases, the coronary anatomy was previously known, from a differed procedure. 63% (n=298) had a normal left ventricle ejection fraction (LVEF), 6% (n=26) had a moderate depression of LVEF and 3% (n=13) had LVEF severe depression. RA was possible in 82% of the cases (n=384), and 18% (n=83) of PCI was done by femoral access (FA). The access size was 6 Fr in 77% (n=359) of the cases, 7Fr in 22% (n=105) and 8Fr in 1% (n=2). The RA hemostasis was by compression (either manually or by external device) and FA hemostasis was first intended by closing device, however in 17% of the transfemoral PCI (n=14) had to be compressed, due to device hemostasis failure. PCI was successful in 97% of the cases (n=449), with revascularization of the goal lesion. The unsuccessful PCI (3% n=18) were because uncrossable lesion in 2% (n=14), and no reflow after PCI in 1% (n=4). 6 patients had to be admitted, after planned outpatient PCI, 5 due to peripheral access complication with hemorrhage of FA (after closing device and manual compression failure) and 1 due to PCI complication, with small coronary perforation. There was a higher probability of admission in patients submitted to transfemoral PCI (p<0,05), and there was no statistically significant relation between unplanned admission and patient’s LVEF. There were no episodes of death, MACE, non-planned hospital readmission and stent failure in the ambulatory PCI population. PCI has become a predictable and reliable technique, capable to treat outpatients. Patients undergoing successful non-complex PCI, without events, can safely be discharged on the same day. Outpatient PCI helps with the functioning of inpatient care, allowing beds to be freed up for other situations.

Rethinking the Role of Biodosimetry to Assess Risks for Acute Radiation Syndrome in Very Large Radiation Events: Reconsidering Legacy Concepts.

The development of effective uses of biodosimetry in large-scale events has been hampered by residual, i.e., "legacy" thinking based on strategies that scale up from biodosimetry in small accidents. Consequently, there remain vestiges of unrealistic assumptions about the likely magnitude of victims in "large" radiation events and incomplete analyses of the logistics for making biodosimetry measurements/assessments in the field for primary triage. Elements remain from an unrealistic focus on developing methods to use biodosimetry in the initial stage of triage for a million or more victims. Based on recent events and concomitant increased awareness of the potential for large-scale events as well as increased sophistication in planning and experience in the development of biodosimetry, a more realistic assessment of the most effective roles of biodosimetry in large-scale events is urgently needed. We argue this leads to a conclusion that the most effective utilization of biodosimetry in very large events would occur in a second stage of triage, after initially winnowing the population by identifying those most in need of acute medical attention, based on calculations of geographic sites where significant exposures could have occurred. Understanding the potential roles and limitations of biodosimetry in large-scale events involving significant radiation exposure should lead to development of the most effective and useful biodosimetric techniques for each stage of triage for acute radiation syndrome injuries, i.e., based on more realistic assumptions about the underlying event and the logistics for carrying out biodosimetry for large populations.

The potentiality of bacteria to drive SARS-CoV-2 mutation.

A recent study published in mBio by Cao et al. revealed the crucial roles of bacteria in benefitting SARS-CoV-2 mutations (B. Cao, X. Wang, W. Yin, Z. Gao, and B. Xia, mBio e3187-23, 2024, https://doi.org/10.1128/mbio.03187-23). Understanding the underlying mechanisms driving the evolution of SARS-CoV-2 is crucial for predicting the future trajectory of the COVID-19 pandemic and developing preventive and treatment strategies. This study provides important insights into the rapid and complex evolution of viruses facilitated by bacterial-virus interactions.

Emerging Technology Integration - Artificial Intelligence (AI) and Machine Learning (ML) for Predictive Analysis for Safety and Toxicity Assessment in Environmental Toxicology

Environmental toxicology is facing unprecedented challenges due to the escalating complexity of environmental issues. Traditional methodologies for safety and toxicity assessment are struggling to keep pace with the rapid evolution of pollutants and their impacts on ecosystems and human health. In response to this pressing need, there has been a paradigm shift in the field towards the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. This research article delves into the transformative potential of AI and ML in environmental toxicology, elucidating how these advanced computational tools offer promising solutions for predictive analysis. By leveraging vast datasets and sophisticated algorithms, AI and ML enable more efficient, accurate, and timely assessment of environmental risks. They empower researchers and policymakers to anticipate and mitigate potential hazards before they escalate into crises. This paper provides a comprehensive overview of the diverse applications of AI and ML in environmental toxicology, ranging from predictive modeling of chemical hazards to early detection of environmental threats. Furthermore, it highlights the potential of AI and ML to revolutionize decision-making processes in environmental management and policy formulation. By synthesizing vast amounts of data and identifying complex patterns and correlations, these technologies offer invaluable insights for crafting evidence-based policies and strategies to safeguard the environment and public health. This research article underscores the pivotal role of AI and ML in addressing the multifaceted challenges of environmental toxicology. Through their integration, we have the opportunity to enhance our understanding of environmental risks, optimize resource allocation, and ultimately, pave the way towards a more sustainable and resilient future.

Macroevolutionary dynamics in micro-organisms: generalists give rise to specialists across biomes in the ubiquitous bacterial phylum Myxococcota.

Prokaryotes dominate the Tree of Life, but our understanding of the macroevolutionary processes generating this diversity is still limited. Habitat transitions are thought to be a key driver of prokaryote diversity. However, relatively little is known about how prokaryotes successfully transition and persist across environments, and how these processes might vary between biomes and lineages. Here, we investigate biome transitions and specialisation in natural populations of a focal bacterial phylum, the Myxococcota, sampled across a range of replicated soils and freshwater and marine sediments in Cornwall (UK). By targeted deep sequencing of the protein-coding gene rpoB, we found >2000 unique Myxococcota lineages, with the majority (77%) being biome specialists and with only <5% of lineages distributed across the salt barrier. Discrete character evolution models revealed that specialists in one biome rarely transitioned into specialists in another biome. Instead, evolved generalism mediated transitions between biome specialists. State-dependent diversification models found variation in speciation rates across the tree, but this variation was independent of biome association or specialisation. These results were robust to phylogenetic uncertainty, assumed amounts of unsampled diversity resulting in an incomplete phylogeny, and different levels of species delineation. Overall, our results are consistent with a "jack-of-all-trades" trade-off where generalists suffer a cost in any individual environment, resulting in rapid evolution of niche specialists and shed light on how bacteria could transition between biomes.

Pigs are highly susceptible to but do not transmit mink-derived highly pathogenic avian influenza virus H5N1 clade 2.3.4.4b

Rapid evolution of highly pathogenic avian influenza viruses (HPAIVs) is driven by antigenic drift but also by reassortment, which might result in robust replication in and transmission to mammals. Recently, spillover of clade 2.3.4.4b HPAIV to mammals including humans, and their transmission between mammal species has been reported. This study aimed to evaluate the pathogenicity and transmissibility of a mink-derived clade 2.3.4.4b H5N1 HPAIV isolate from Spain in pigs. Experimental infection caused interstitial pneumonia with necrotizing bronchiolitis with high titers of virus present in the lower respiratory tract and 100% seroconversion. Infected pigs shed limited amount of virus, and importantly, there was no transmission to contact pigs. Notably, critical mammalian-like mutations such as PB2-E627K and HA-Q222L emerged at low frequencies in principal-infected pigs. It is concluded that pigs are highly susceptible to infection with the mink-derived clade 2.3.4.4b H5N1 HPAIV and provide a favorable environment for HPAIV to acquire mammalian-like adaptations.

SiRNA that participates in Drosophila dosage compensation is produced by many 1.688X and 359 bp repeats.

Organisms with differentiated sex chromosomes must accommodate unequal gene dosage in males and females. Male fruit flies increase X-linked gene expression to compensate for hemizygosity of their single X chromosome. Full compensation requires localization of the Male-Specific Lethal (MSL) complex to active genes on the male X, where it modulates chromatin to elevate expression. The mechanisms that identify X chromatin are poorly understood. The euchromatic X is enriched for AT-rich, ∼359 bp satellites termed the 1.688X repeats. Autosomal insertions of 1.688X DNA enable MSL recruitment to nearby genes. Ectopic expression of dsRNA from one of these repeats produces siRNA and partially restores X-localization of MSLs in males with defective X recognition. Surprisingly, expression of double stranded RNA from three other 1.688X repeats failed to rescue males. We reconstructed dsRNA-expressing transgenes with sequence from two of these repeats and identified phasing of repeat DNA, rather than sequence or orientation, as the factor that determines rescue of males with defective X recognition. Small RNA sequencing revealed that siRNA was produced in flies with a transgene that rescues, but not in those carrying a transgene with the same repeat but different phasing. We demonstrate that pericentromeric X heterochromatin promotes X-recognition through a maternal effect, potentially mediated by small RNA from closely related heterochromatic repeats. This suggests that the sources of siRNAs promoting X recognition are highly redundant. We propose that enrichment of satellite repeats on Drosophilid X chromosomes facilitates the rapid evolution of differentiated sex chromosomes by marking the X for compensation.

Regulating the Digital Environment to Protect Users from Harmful Commodity Marketing

Although the creators of the world wide web never intended it to be regulated by state intervention, the rapid evolution of the online environment has necessitated regulation of certain aspects of the digital ecosystem. Harmful commodity marketing (e.g., alcohol, vaping and unhealthy food and beverage product marketing) on social media and in digital spaces has been linked to adverse health outcomes and there have been calls for its regulation. In this commentary we explain why this is important and consider how such regulation could be achieved.