Rapid Evolution Research Articles

Persistence of SARS-CoV-2 infection and viral intra- and inter-host evolution in COVID-19 hospitalized patients.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) persistence in COVID-19 patients could play a key role in the emergence of variants of concern. The rapid intra-host evolution of SARS-CoV-2 may result in an increased transmissibility, immune and therapeutic escape which could be a direct consequence of COVID-19 epidemic currents. In this context, a longitudinal retrospective study on eight consecutive COVID-19 patients with persistent SARS-CoV-2 infection, from January 2022 to March 2023, was conducted. To characterize the intra- and inter-host viral evolution, whole genome sequencing and phylogenetic analysis were performed on nasopharyngeal samples collected at different time points. Phylogenetic reconstruction revealed an accelerated SARS-CoV-2 intra-host evolution and emergence of antigenically divergent variants. The Bayesian inference and principal coordinate analysis analysis showed a host-based genomic structuring among antigenically divergent variants, that might reflect the positive effect of containment practices, within the critical hospital area. All longitudinal antigenically divergent isolates shared a wide range of amino acidic (aa) changes, particularly in the Spike (S) glycoprotein, that increased viral transmissibility (K417N, S477N, N501Y and Q498R), enhanced infectivity (R346T, S373P, R408S, T478K, Q498R, Y505H, D614G, H655Y, N679K and P681H), caused host immune escape (S371L, S375F, T376A, K417N, and K444T/R) and displayed partial or complete resistance to treatments (G339D, R346K/T, S371F/L, S375F, T376A, D405N, N440K, G446S, N460K, E484A, F486V, Q493R, G496S and Q498R). These results suggest that multiple novel variants which emerge in the patient during persistent infection, might spread to another individual and continue to evolve. A pro-active genomic surveillance of persistent SARS-CoV-2 infected patients is recommended to identify genetically divergent lineages before their diffusion.

A journey into the innovations and expertise of Dubai police and the general department of forensic science and criminology

This manuscript offers an in-depth exploration of the Dubai Police and its General Department of Forensic Science and Criminology, emphasizing their critical role in law enforcement through advanced forensic science and innovative practices. Beginning with a historical overview, it traces the evolution of forensic disciplines from Edmond Locard's foundational principles to Sir Alec Jeffreys' groundbreaking DNA profiling techniques. The manuscript details the structure and functions of specialized sections within the department, including Biology and DNA, Forensic Chemistry, Forensic Toxicology, Firearms and Tool Marks, Questioned Documents, Forensic Explosive, Forensic Fire Investigation, Forensic and Mechanical Engineering, and Nuclear Physics. Key achievements, such as the Biology and DNA Section's processing of thousands of cases and the generation of highly discriminating DNA profiles, are highlighted. The manuscript also addresses the department's challenges, including the rapid evolution of technology and the increasing complexity of crimes. Future plans to enhance capabilities through investment in new technologies, international collaboration, and continuous professional development are outlined. By showcasing the department's commitment to innovation, excellence, and public safety, this manuscript underscores the pivotal contributions of the Dubai Police and its forensic department to crime resolution and community safety. The insights provided aim to foster a deeper understanding of the critical role of forensic science in the criminal justice system.

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

ZTF J185259.31+124955.2: A new evolved disc-eclipsing binary system

Discs in long-period eclipsing binary systems are rare and can lead to extraordinary eclipsing events. ZTF J185259.31+124955.2 was identified as a candidate disc-eclipsing system through a continuing search programme of ZTF variables with a near-IR excess in the WISE data. Examination of the combined ZTF and ATLAS photometry shows seven eclipses since 2017 with depths of 0 in all bands on a period of $289.57 The eclipse width is $ 40$\,d but this and the profile evolve over time. Comparison with library spectra shows that the spectral energy distribution from the available photometry is consistent with an early K-type giant, and fitting black-body profiles suggests $ 4000$\,K for the stellar component, with a cool component having $ < 500$\,K. The reddening and distance, and hence the luminosity place the star within the giant branch. The most likely scenario is that the system is in a state of rapid evolution following Case B/C mass transfer into an extended disc around an unseen companion.

Rapid evolution of consumptive and non-consumptive predator effects on prey population densities, bioenergetics and stoichiometry.

Predators can strongly influence prey populations not only through consumptive effects (CE) but also through non-consumptive effects (NCE) imposed by predation risk. Yet, the impact of NCE on bioenergetic and stoichiometric body contents of prey, traits that are shaping life histories, population and food web dynamics, is largely unknown. Moreover, the degree to which NCE can evolve and can drive evolution in prey populations is rarely studied. A 6-week outdoor mesocosm experiment with Caged-Fish (NCE) and Free-Ranging-Fish (CE and NCE) treatments was conducted to quantify and compare the effects of CE and NCE on population densities, bioenergetic and stoichiometric body contents of Daphnia magna, a keystone species in freshwater ecosystems. We tested for evolution of CE and NCE by using experimental populations consisting of D. magna clones from two periods of a resurrected natural pond population: a pre-fish period without fish and a high-fish period with high predation pressure. Both Caged-Fish and Free-Ranging-Fish treatments decreased the body size and population densities, especially in Daphnia from the high-fish period. Only the Free-Ranging-Fish treatment affected bioenergetic variables, while both the Caged-Fish and Free-Ranging-Fish treatments shaped body stoichiometry. The effects of CE and NCE were different between both periods indicating their rapid evolution in the natural resurrected population. Both the Caged-Fish and Free-Ranging-Fish treatments changed the clonal frequencies of the experimental Daphnia populations of the pre-fish as well as the high-fish period, indicating that not only CE but also NCE induced clonal sorting, hence rapid evolution during the mesocosm experiment in both periods. Our results demonstrate that CE as well as NCE have the potential to change not only the body size and population density but also the bioenergetic and stoichiometric characteristics of prey populations. Moreover, we show that these responses not only evolved in the studied resurrected population, but that CE and NCE also caused differential rapid evolution in a time frame of 6 weeks (ca. four to six generations). As NCE can evolve as well as can drive evolution, they may play an important role in shaping eco-evolutionary dynamics in predator-prey interactions.

The antigenic landscape of human influenza N2 neuraminidases from 2009 until 2017.

Human H3N2 influenza viruses are subject to rapid antigenic evolution which translates into frequent updates of the composition of seasonal influenza vaccines. Despite these updates, the effectiveness of influenza vaccines against H3N2-associated disease is suboptimal. Seasonal influenza vaccines primarily induce hemagglutinin-specific antibody responses. However, antibodies directed against influenza neuraminidase (NA) also contribute to protection. Here, we analysed the antigenic diversity of a panel of N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. The antigenic breadth of these NAs was determined based on the NA inhibition (NAI) of a broad panel of ferret and mouse immune sera that were raised by infection and recombinant N2 NA immunisation. This assessment allowed us to distinguish at least four antigenic groups in the N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. Computational analysis further revealed that the amino acid residues in N2 NA that have a major impact on susceptibility to NAI by immune sera are in proximity of the catalytic site. Finally, a machine learning method was developed that allowed to accurately predict the impact of mutations that are present in our N2 NA panel on NAI. These findings have important implications for the renewed interest to develop improved influenza vaccines based on the inclusion of a protective NA antigen formulation.

Biological Flora of Britain and Ireland: Silene uniflora‡

Abstract This account presents information on all aspects of the biology of Silene uniflora Roth (S. maritima With., S. vulgaris Garcke subsp. maritima (With.) Á. & D. Löve), Sea Campion, that are relevant to understanding its ecological characteristics and behaviour. The main topics are presented within the standard framework of the Biological Flora of Britain and Ireland: distribution, habitat, communities, responses to biotic factors, responses to environment, structure and physiology, phenology, floral and seed characters, herbivores and disease, history and conservation. Silene uniflora is a mat‐like perennial herb that in Britain and Ireland is primarily distributed in coastal habitats, notably on sea cliffs and shingle banks but also at the drift line and rarely on sand; it also has populations on mountains and heavy metal mine waste tips. All of these substrates tend to be oligotrophic, loose‐textured and freely draining, and often with adverse ion concentrations. It is found in suitable habitats around much of the Atlantic and Baltic coasts of Europe. Its breeding system is gynodioecious and gynomonoecious (some individuals bearing female flowers only, some bearing hermaphrodite flowers only and others with both types of flower). The proportions of the three types of individual vary considerably between populations, but hermaphrodite flowers tend to predominate. The showy, white, nectar‐producing flowers are produced continuously over the entire summer and are pollinated mainly by long‐tongued Hymenoptera and night‐flying moths but autogamous selfing is also frequent. Silene uniflora and its close relative S. vulgaris were the subject of a classic, decadal study involving genetical and transplant experiments that was seminal in the development of ‘experimental taxonomy’. More recently, attention has focused on the rapid evolution of Zn‐tolerant populations of S. uniflora growing on mine spoil and contaminated river gravels. It has also become a model organism for the study of sexually transmitted, castrating fungal diseases in plants (notably of the genus Microbotryum).

Correlation between Genomic Variants and Worldwide COVID-19 Epidemiology

COVID-19 is a systemic disease caused by the etiologic agent SARS-CoV-2, first reported in Hubei Province in Wuhan, China, in late 2019. The SARS-CoV-2 virus has evolved over time with distinct transmissibility subvariants from ancestral lineages. The clinical manifestations of the disease vary according to their severity and can range from asymptomatic to severe. Due to the rapid evolution to a pandemic, epidemiological studies have become essential to understand and effectively combat COVID-19, as the incidence and mortality of this disease vary between territories and populations. This study correlated epidemiological data on the incidence and mortality of COVID-19 with frequencies of important SNPs in GWAS studies associated with the susceptibility and mortality of this disease in different populations. Our results indicated significant correlations for 11 genetic variants (rs117169628, rs2547438, rs2271616, rs12610495, rs12046291, rs35705950, rs2176724, rs10774671, rs1073165, rs4804803 and rs7528026). Of these 11 variants, 7 (rs12046291, rs117169628, rs1073165, rs2547438, rs2271616, rs12610495 and rs35705950) were positively correlated with the incidence rate, these variants were more frequent in EUR populations, suggesting that this population is more susceptible to COVID-19. The rs2176724 variant was inversely related to incidence rates; therefore, the higher the frequency of the allele is, the lower the incidence rate. This variant was more frequent in the AFR population, which suggests a protective factor against SARS-CoV-2 infection in this population. The variants rs10774671, rs4804803, and rs7528026 showed a significant relationship with mortality rates. SNPs rs10774671 and rs4804803 were inversely related to mortality rates and are more frequently present in the AFR population. The rs7528026 variant, which is more frequent in the AMR population, was positively related to mortality rates. The study has the potential to identify and correlate the genetic profile with epidemiological data, identify populations that are more susceptible to severe forms of COVID-19, and relate them to incidence and mortality.

Phylogenetic diversity of the Pristimantis anaiae species group, with a description of a new species of Pristimantis (Anura, Strabomantidae) from Cerro Candelaria, Tungurahua, Ecuador

Continuous field work on mountains in the upper Pastaza watershed, showed an unexpected cryptic diversity in the recently described Pristimantis anaiae species group. Based on molecular and morphological characters we describe a new frog species from the central region of the eastern Ecuadorian Andes. We present an updated phylogeny for the Pristimantis anaiae species group, based on widely used, ubiquitous, and available genes, three mitochondrial genes (12S rRNA, 16S rRNA, ND1) and RAG1 nuclear gene. Our phylogenetic hypothesis increases genetic diversity of the species group and shows two well-supported subclades. We show the relationships of the new species and include for the first time Pristimantis maryanneae, belonging to a clade related to P. resistencia + P. glendae. Our phylogeny shows that new species, from Cerro Candelaria, is more closely related to P. kunam from Sangay National Park and differs from it by having a less conspicuous dorsolateral row of enlarged tubercles, and sacral oval marks surrounded by thin white pale lines. The new species and two additional undescribed species occur on the south side of the Rio Pastaza Valley, at Cerro Candelaria within the Llanganates – Sangay Ecological Corridor, reflecting the high importance of this area for conservation and understanding of evolution processes in the area, suggesting a probably adaptive radiation zone for the P. anaiae species group.

Application of artificial intelligence to hepatobiliary cancer clinical outcomes research

The rapid evolution of modern technology has made artificial intelligence (AI) an important emerging tool in healthcare. AI, which is a broad field of computer science, can be used to develop systems or machines equipped with the ability to tackle tasks that traditionally necessitate human intelligence. AI can be used to perform multifaceted tasks that involve the synthesis of large amounts of data with the generation of solutions, algorithms, and decision support tools. Various AI approaches, including machine learning (ML) and natural language processing (NLP), are increasingly being used to analyze vast healthcare datasets. In addition, visual AI has the potential to revolutionize surgery and the intraoperative experience for surgeons through augmented reality enhancing surgical navigation in real-time. Specific applications of AI in hepatobiliary tumors such as hepatocellular carcinoma and biliary tract cancer can improve patient diagnosis, prognostic risk stratification, as well as treatment allocation based on ML-based models. The integration of radiomics data and AI models can also improve clinical decision making. We herein review how AI may be of particular interest in the care of patients with complex cancers, such as hepatobiliary tumors, as these patients often require a multimodal treatment approach.

Deepfake Detection using Deep Learning

The detection of deep fakes, which are synthetic media generated using deep learning techniques, has become increasingly important due to their potential to deceive and manipulate individuals, organizations, and society at large. This abstract explores recent advancements in deep fake detection methodologies, including machine learning algorithms, neural network architectures, and forensic techniques. Key challenges such as the rapid evolution of deep fake generation methods and the emergence of highly realistic forgeries are discussed. Additionally, the abstract examines the ethical implications of deep fake technology and the need for robust detection methods to mitigate its harmful effects. Finally, future directions in deep fake detection research are outlined, emphasizing the importance of interdisciplinary collaboration and the development of innovative approaches to combat this growing threat to information integrity and trust

The hidden glacial landscape of the Monti della Laga (central Apennines, Italy)

AbstractThe geological and geomorphological record of the Last Glaciation in the Apennines is extensively documented, except for the Monti della Laga sector, where limited indicators of glacier extension have been reported. This study presents field evidence of glacial deposits and landforms from two specific sites within the Monti della Laga Range: Monte Pelone and Tordino Valley head. Although limited, this evidence suggests glaciation within a structural and lithological setting that facilitated rapid slope evolution. The post-glaciation paraglacial processes in the area erased most glacial traces, but our geomorphological and stratigraphical investigations identified specific landforms of glacial origin. Notably, at Monte Pelone, a semi-circular depositional landform exhibiting a significant presence of coarse clasts and distinctive rotational features has been identified as a moraine. Additionally, a smaller moraine ridge with evidence of diamicton deposition was observed in the Tordino Valley head. These observations align with an Equilibrium Line Altitude estimated at 1750 m a.s.l. in the central Apennines in the Last Glacial Maximum and, considering the favourable topographic conditions of the area, significantly contribute to the understanding of glaciations within the central Apennines.

An evolutionary view of CC-type glutaredoxins functions in land plants development and adaptation to environment.

Land plants have to face an oxidizing, heterogeneous and fast changing environment. Redox-dependent post-translational modifications emerge as a critical component of plant responses to stresses. Among thiols oxidoreductases superfamily, class III CC-type glutaredoxins (called ROXYs) are land plant specific, and their evolutionary history is highly dynamic. Angiosperms encode many isoforms classified into five subgroups (Aα, Aβ, Bα, Bβ, Bγ) that probably evolved from five common ancestral ROXYs, with higher evolutionary dynamics in Bγ compared to other subgroups. ROXYs can modulate the transcriptional activity of TGAs transcription factors target genes, although their biochemical function is still debated. ROXYs participate in the control of proper plant development and reproduction, and are mainly negative regulators of plant responses to biotic and abiotic stresses. This suggests that most ROXYs could play essential and conserved functions in resetting redox-dependent changes in transcriptional activity upon stress signaling to ensure the responsiveness of the system and/or avoid exaggerated responses that could lead to major defects in plant growth and reproduction. Bγ members in Arabidopsis acquired important functions in responses to nitrogen availability and endogenous status, but the rapid and independent evolution of this subclass could suggest that this function results from neofunctionalization, specifically observed in core Eudicots.

Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.

Unveiling the distinctive traits of functional rye centromeres: minisatellites, retrotransposons, and R-loop formation.

Centromeres play a vital role in cellular division by facilitating kinetochore assembly and spindle attachments. Despite their conserved functionality, centromeric DNA sequences exhibit rapid evolution, presenting diverse sizes and compositions across species. The functional significance of rye centromeric DNA sequences, particularly in centromere identity, remains unclear. In this study, we comprehensively characterized the sequence composition and organization of rye centromeres. Our findings revealed that these centromeres are primarily composed of long terminal repeat retrotransposons (LTR-RTs) and interspersed minisatellites. We systematically classified LTR-RTs into five categories, highlighting the prevalence of younger CRS1, CRS2, and CRS3 of CRSs (centromeric retrotransposons of Secale cereale) were primarily located in the core centromeres and exhibited a higher association with CENH3 nucleosomes. The minisatellites, mainly derived from retrotransposons, along with CRSs, played a pivotal role in establishing functional centromeres in rye. Additionally, we observed the formation of R-loops at specific regions of CRS1, CRS2, and CRS3, with both rye pericentromeres and centromeres exhibiting enrichment in R-loops. Notably, these R-loops selectively formed at binding regions of the CENH3 nucleosome in rye centromeres, suggesting a potential role in mediating the precise loading of CENH3 to centromeres and contributing to centromere specification. Our work provides insights into the DNA sequence composition, distribution, and potential function of R-loops in rye centromeres. This knowledge contributes valuable information to understanding the genetics and epigenetics of rye centromeres, offering implications for the development of synthetic centromeres in future plant modifications and beyond.

Creative Learning for Sustainability in a World of AI: Action, Mindset, Values

In an era marked by unprecedented global challenges, including environmental degradation, social inequalities, and the rapid evolution of technology, the need for innovative educational approaches is critical. This conceptual paper explores the intersection of sustainability, creativity, and technology for education, focusing on artificial intelligence (AI) as an example. We propose a framework that synthesizes sustainability principles and creative pedagogies, detailing its components to guide the integration of AI into sustainability education. The paper illustrates how blending creative pedagogies with the notion of sustainability as a frame of mind offers a framework that allows teachers to support creative learning and problem solving, with and through technology. Using the example of AI technology, we illustrate the potential benefits and inherent challenges of integrating new technologies into education. Generative AI is a cogent example, as it presents unique opportunities for personalizing learning and engaging students in creative problem solving around sustainability issues. However, it also introduces significant environmental and ethical concerns to navigate. Exploring the balance between technological innovation and sustainability imperatives, this paper outlines a framework for incorporating technology into education that promotes environmental care with creative exploration. Through a synthesis of sustainability principles and creative pedagogies, we highlight the benefits and challenges of using AI in education, offering strategic insights to leverage technology for a sustainable and just future.

Severe Bottleneck Impacted the Genomic Structure of Egg-Eating Cichlids in Lake Victoria.

Within 15,000 years, the explosive adaptive radiation of haplochromine cichlids in Lake Victoria, East Africa, generated 500 endemic species. In the 1980s, the upsurge of Nile perch, a carnivorous fish artificially introduced to the lake, drove the extinction of more than 200 endemic cichlids. The Nile perch predation particularly harmed piscivorous cichlids, including paedophages, cichlids eat eggs and fries, which is an example of the unique trophic adaptation seen in African cichlids. Here, aiming to investigate past demographic events possibly triggered by the invasion of Nile perch and the subsequent impacts on the genetic structure of cichlids, we conducted large-scale comparative genomics. We discovered evidence of recent bottleneck events in four species, including two paedophages, which began during the 1970s-1980s, and population size rebounded during the 1990s-2000s. The timing of the bottleneck corresponded to the historical records of endemic haplochromines' disappearance and later resurgence, which is likely associated with the introduction of Nile perch by commercial demand to Lake Victoria in the 1950s. Interestingly, among the four species that likely experienced bottleneck, Haplochromis sp. 'matumbi hunter,' a paedophagous cichlid, showed the most severe bottleneck signatures. The components of shared ancestry inferred by ADMIXTURE suggested a high genetic differentiation between matumbi hunter and other species. In contrast, our phylogenetic analyses highly supported the monophyly of the five paedophages, consistent with the results of previous studies. We conclude that high genetic differentiation of matumbi hunter occurred due to the loss of shared genetic components among haplochromines in Lake Victoria caused by the recent severe bottleneck.

Diversification of spider silk properties in an adaptive radiation of Hawaiian orb-weaving spiders

IntroductionThe design of biological structures and the materials composing those structures are intimately connected to performance in biological systems. Spider webs present an excellent example of how design and materials interact during their function in capturing prey. Major shifts in how spider webs capture prey have occurred due to evolutionary changes in both web architecture and silk properties. However, these shifts are mostly described for long timescales deep within the spider’s tree of life. Hawaiian Tetragnatha presents an opportunity to ask if such shifts can occur at much shorter timescales because web design diverges significantly among closely related species on the same island while also converging with more distant relatives on other islands. Here, we provide an initial test of whether or not silk properties diversified during the recent adaptive radiation of Hawaiian Tetragnatha.MethodsWe obtained radial and capture spiral silk from orb webs for spiders on two islands and tested their tensile and adhesive properties. We also used solution-state NMR to compare the composition of low molecular weight compounds in the glue because of their influence on capture spiral stickiness.ResultsResults showed differences in the stiffness of radial silk among four populations of Hawaiian Tetragnatha, while extensibility remained unchanged. Although not statistically different, radial strength and toughness varied twofold among species. Stickiness varied threefold among the four populations of orb weavers. No conspicuous qualitative differences in the low molecular weight compound composition of aggregate glue were found, suggesting that differences in capture spiral stickiness were due to the amount or arrangement of glue droplets on threads.DiscussionWhile our sampling is modest, our data provide the first evidence that silk properties can evolve measurably over the relatively short timescales of the adaptive radiation of Hawaiian Tetragnatha spiders.

Autonomous Full 3D Coverage Using an Aerial Vehicle, Performing Localization, Path Planning, and Navigation Towards Indoors Inventorying for the Logistics Domain

Over the last years, a rapid evolution of unmanned aerial vehicle (UAV) usage in various applications has been observed. Their use in indoor environments requires a precise perception of the surrounding area, immediate response to its changes, and, consequently, a robust position estimation. This paper provides an implementation of navigation algorithms for solving the problem of fast, reliable, and low-cost inventorying in the logistics industry. The drone localization is achieved with a particle filter algorithm that uses an array of distance sensors and an inertial measurement unit (IMU) sensor. Navigation is based on a proportional–integral–derivative (PID) position controller that ensures an obstacle-free path within the known 3D map. As for the full 3D coverage, an extraction of the targets and then their final succession towards optimal coverage is performed. Finally, a series of experiments are carried out to examine the robustness of the positioning system using different motion patterns and velocities. At the same time, various ways of traversing the environment are examined by using different configurations of the sensor that is used to perform the area coverage.

Retrospective Study Regarding the Correlation between Dental Anxiety and Color Preferences in Children with Severe Early Childhood Caries

Background: Severe early childhood caries (S-ECC) is recognized as a significant chronic disease which affects the quality of life starting at very young ages and has a very rapid evolution towards pulp complication and loss of dental tissue. Children with a high caries index DMFT are more likely to develop high levels of dental anxiety, which will influence the degree of cooperation during dental procedures. Emotions play an important role in the psychosomatic development of children, and all the factors that contribute to the modeling of these emotional states must be known and taken into consideration. Aim: The aim of this study was to assess the association between dental anxiety in children with S-ECC and the color preferences for the components of the dental environment to reduce the levels of dental anxiety during medical visits. Material and Method: For this study, 91 children between 3 and 6 years of age diagnosed with S-ECC were selected from the Pediatric Dentistry Department of UMFST from Targu Mures and from a private dental office. The level of dental anxiety was determined by measuring the pulse rate, and a questionnaire was completed to establish the color preferences for the dental office environment and the dentist’s attire. For this purpose, six different colors and their variants with three shades less intensity were chosen by using RGB (Red, Green, and Blue) identification codes for each color. Result: The results showed that there was a statistically significant difference between the age of the subjects and dental anxiety levels, but there was no significant correlation between the DMFT score and dental anxiety. Also, the results showed that there was no significant difference between girls and boys when choosing the colors preferred by them for the dental office, but when comparing the colors that represented happiness, significant statistical difference was found between the girls’ and boys’ groups (p = 0.0039). For all the subjects, the colors associated with happiness were light yellow and pink, while the colors associated with anxiety were red and dark blue. Conclusion: The data obtained showed that dental anxiety is strongly influenced by age, and an important role in inducing positive emotions is played by the dental environment if dressed in specific colors in order to reduce dental anxiety and create familiar conditions, especially for young children with S-ECC.