Search Results (1,355)

Structural maintenance of chromosomes (SMC) complexes play a crucial role in organizing the three-dimensional structure of chromatin, facilitating key processes such as gene regulation, DNA repair, and chromosome segregation. This review explores the molecular mechanisms and biological significance of SMC-mediated loop extrusion complexes, including cohesin, condensins, and SMC5/6, focusing on their structure, their dynamic function during the cell cycle, and their impact on chromatin architecture. We discuss the implications of impairments in loop extrusion machinery as observed in experimental models and human diseases. Mutations affecting these complexes are linked to various developmental disorders and cancer, highlighting their importance in genome stability and transcriptional regulation. Advances in model systems and genomic techniques have provided deeper insights into the pathological roles of SMC complex dysfunction, offering potential therapeutic avenues for associated diseases. Full article

Simultaneous Localization And Mapping (SLAM) algorithms play a critical role in autonomous exploration tasks requiring mobile robots to autonomously explore and gather information in unknown or hazardous environments where human access may be difficult or dangerous. However, due to the resource-constrained nature of mobile robots, they are hindered from performing long-term and large-scale tasks. In this paper, we propose an efficient multi-robot dense SLAM system that utilizes a centralized structure to alleviate the computational and memory burdens on the agents (i.e. mobile robots). To enable real-time dense mapping of the agent, we design a lightweight and accurate dense mapping method. On the server, to find correct loop closure inliers, we design a novel loop closure detection method based on both visual and dense geometric information. To correct the drifted poses of the agents, we integrate the dense geometric information along with the trajectory information into a multi-robot pose graph optimization problem. Experiments based on pre-recorded datasets have demonstrated our system’s efficiency and accuracy. Real-world online deployment of our system on the mobile vehicles achieved a dense mapping update rate of ∼14 frames per second (fps), a onboard mapping RAM usage of ∼3.4%, and a bandwidth usage of ∼302 KB/s with a Jetson Xavier NX. Full article

This study presents a novel AI method for extracting polygon and point features from historical geologic maps, representing a pivotal step for assessing the mineral resources needed for energy transition. Our innovative method involves using map units in the legends as prompts for one-shot segmentation and detection in geological feature extraction. The model, integrated with a human-in-the-loop system, enables geologists to refine results efficiently, combining the power of AI with expert oversight. Tested on geologic maps annotated by USGS and DARPA for the AI4CMA DARPA Challenge, our approach achieved a median F1 score of 0.91 for polygon feature segmentation and 0.73 for point feature detection when such features had abundant annotated data, outperforming current benchmarks. By efficiently and accurately digitizing historical geologic map, our method promises to provide crucial insights for responsible policymaking and effective resource management in the global energy transition. Full article

The fragmented forests of the Kenya highlands, known for their exceptional species richness and endemism, are among the world’s most important biodiversity hotspots. However, detailed studies on the fauna of these ecosystems—especially specialist species that depend on moist forests, which are particularly threatened by habitat fragmentation—are still limited. In this study, we used mitochondrial genes (cytochrome b and the displacement loop) and a nuclear marker (retinol-binding protein 3) to investigate genetic and morphological diversity, phylogenetic associations, historical divergence, population dynamics, and phylogeographic patterns in two rodent species—the soft-furred mouse (Praomys jacksoni) and the African wood mouse (Hylomyscus endorobae)—across Kenya’s forest landscapes. We found a complex genetic structure, with P. jacksoni exhibiting greater genetic diversity than H. endorobae. The Mt. Kenya P. jacksoni populations are significantly genetically different from those in southwestern forests (Mau Forest, Kakamega Forest, and Loita Hills). In contrast, H. endorobae presented no observable biogeographic structuring across its range. The genetic diversity and geographic structuring patterns highlighted selectively strong effects of forest fragmentation and differing species’ ecological and evolutionary responses to these landscape changes. Our findings further underscore the need for expanded sampling across Kenya’s highland forests to better understand species’ changing diversity and distribution patterns in response to the impacts of human-mediated habitat changes. These insights are critical for informing conservation strategies to preserve biodiversity better in this globally important region. Full article

Histone proteins form the building blocks of chromatin—nucleosomes. Incorporation of alternative histone variants instead of the major (canonical) histones into nucleosomes is a key mechanism enabling epigenetic regulation of genome functioning. In humans, H2A.J is a constitutively expressed histone variant whose accumulation is associated with cell senescence, inflammatory gene expression, and certain cancers. It is sequence-wise very similar to the canonical H2A histones, and its effects on the nucleosome structure and dynamics remain elusive. This study employed all-atom molecular dynamics simulations to reveal atomistic mechanisms of structural and dynamical effects conferred by the incorporation of H2A.J into nucleosomes. We showed that the H2A.J C-terminal tail and its phosphorylated form have unique dynamics and interaction patterns with the DNA, which should affect DNA unwrapping and the availability of nucleosomes for interactions with other chromatin effectors. The dynamics of the L1-loop and the hydrogen bonding patterns inside the histone octamer were shown to be sensitive to single amino acid substitutions, potentially explaining the higher thermal stability of H2A.J nucleosomes. Taken together, our study demonstrated unique dynamical features of H2A.J-containing nucleosomes, which contribute to further understanding of the molecular mechanisms employed by H2A.J in regulating genome functioning. Full article

Taking advantage of its high-temperature resistance and elongation properties, conductive-coated polyetheretherketone (PEEK) filament yarn can be used as a textile-based electroconductive functional element, in particular as a strain sensor. This study describes the development of electrical conductivity on an inert PEEK filament surface by the deposition of metallic nickel (Ni) layers via an electroless galvanic plating process. To enhance the adhesion properties of the nickel layer, both PEEK multifilament and monofilament yarn surfaces were metalized by plasma torch pretreatment, followed by nickel plating. Electrical characterizations indicate the potential of nickel-coated PEEK for structural monitoring in textile-reinforced composites. In addition, surface energy measurements before and after plasma torch pretreatment, surface morphology, nickel layer thickness, chemical structure changes, and mechanical properties were analyzed and compared with untreated PEEK. The thickness of the Ni layer was measured and showed an average thickness of 1.25 µm for the multifilament yarn and 3.36 µm for the monofilament yarn. FTIR analysis confirmed the presence of new functional groups on the PEEK surface after plasma torch pretreatment, indicating a successful modification of the surface chemistry. Mechanical testing showed an increase in tensile strength after plasma torch pretreatment but a decrease after nickel plating. In conclusion, this study successfully developed conductive PEEK yarns through plasma torch pretreatment and nickel plating. Full article

It is widely recognized that airspace capacity must increase over the coming years. It is also commonly accepted that meeting this challenge while balancing concerns around safety, efficiency, and workforce issues will drive greater reliance on automation. However, if automation is not properly developed and deployed, it represents something of a double-edged sword, and has been linked to several human–machine system performance issues. In this article, we argue that human–automation function and task allocation may not be the way forward, as it invokes serialized interactions that ultimately push the human into a problematic supervisory role. In contrast, we propose a flight-based allocation strategy in which a human controller and digital colleague each have full control authority over different flights in the airspace, thereby creating a parallel system. In an exploratory human-in-the-loop simulation exercise involving six operational en route controllers, it was found that the proposed system was considered acceptable after the users gained experience with it during simulation trials. However, almost all controllers did not follow the initial flight allocations, suggesting that allocation schemes need to remain flexible and/or be based on criteria capturing interactions between flights. In addition, the limited capability of and feedback from the automation contributed to this result. To advance this concept, future work should focus on substantiating flight-centric complexity in driving flight allocation schemes, increasing automation capabilities, and facilitating common ground between humans and automation. Full article

Understanding the structures of noncoding RNAs (ncRNAs) is important for the development of RNA-based therapeutics. There are inherent challenges in employing current experimental techniques to determine the tertiary (3D) structures of RNAs with high complexity and flexibility in folding, which makes computational methods indispensable. In this study, we compared the utilities of three advanced computational tools, namely RNAComposer, Rosetta FARFAR2, and the latest AlphaFold 3, to predict the 3D structures of various forms of RNAs, including the small interfering RNA drug, nedosiran, and the novel bioengineered RNA (BioRNA) molecule showing therapeutic potential. Our results showed that, while RNAComposer offered a malachite green aptamer 3D structure closer to its crystal structure, the performances of RNAComposer and Rosetta FARFAR2 largely depend upon the secondary structures inputted, and Rosetta FARFAR2 predictions might not even recapitulate the typical, inverted “L” shape tRNA 3D structure. Overall, AlphaFold 3, integrating molecular dynamics principles into its deep learning framework, directly predicted RNA 3D structures from RNA primary sequence inputs, even accepting several common post-transcriptional modifications, which closely aligned with the experimentally determined structures. However, there were significant discrepancies among three computational tools in predicting the distal loop of human pre-microRNA and larger BioRNA (tRNA fused pre-miRNA) molecules whose 3D structures have not been characterized experimentally. While computational predictions show considerable promise, their notable strengths and limitations emphasize the needs for experimental validation of predictions besides characterization of more RNA 3D structures. Full article

Sustainable socioeconomic development should provide humans with a suitable environment for safe living. It can be debated whether the term “environment” should be used due to the significant anthropogenic transformation of the environment. Therefore, an essential part of solving environmental problems is innovation. Climate, resource conservation, and environmental protection are recognized worldwide as common challenges. Thus, it is necessary to implement solutions that simultaneously protect the environment and the climate with sustainable and rational use of resources. Related to this issue is the principle of a loop/closed-loop economy. Among other things, it refers to using waste to prepare materials that can be used for other purposes. The use of tools such as LCA (life cycle analysis) contributes to supporting environmental protection. With the LCA method, it is possible to analyze environmental risks and compare new technological alternatives. LCA is a methodology that has been used around the world with great success, especially for studying individual stages of the entire product life cycle. The results of studies that have been conducted in various research centers confirm the possibility of also using the LCA technique for the environmental assessment of new technologies or existing modernized technological processes. The purpose of this study was to assess the feasibility of using the LCA method to determine the environmental impact that the potential production and use of new materials will have. Full article

Intelligent Robotic Process Automation (IRPA) combines Artificial Intelligence (AI) and Robotic Process Automation (RPA) to automate complex unstructured tasks, improve decision-making, and cope with changing scenarios. A process analysis framework for IRPA adoption was developed by identifying key factors through a literature review and semi-structured expert opinion survey. The employed experts in the survey comprised RPA/IRPA consultants, RPA/IRPA initiative team leaders, and RPA/IRPA developers with three years or more experience. For the initial factor collection phase, there were a total of eighteen (18) responses, and for the factor evaluation phase, a total of twenty-six (26) experts were used to collect responses. Identified factors were shortlisted and evaluated using a Relative Importance Index (RII) analysis. The study’s findings are presented through a Causal-Loop Diagram (CLD) to illustrate the relationships between factors. The framework provides practical guidance for organizations planning to adopt IRPA, informing decision-making, resource allocation, and strategy development. The final process analysis framework highlights the importance of accuracy, level of human involvement in a task, and standardization as the main three primary factors for successful IRPA adoption. Three major secondary factors were identified: digital data input, integration with existing systems, and the cost of adopting new technologies. This research contributes to the added value to existing knowledge and serves as a foundation for future research in IRPA adoption. Full article

This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term and long-term intake from anthropogenic sources. Direct assessment of metal retention in the human body is virtually impossible due to the many internal mechanisms that ensure the kinetics of metals and the wide variety of organs, tissues, cellular structures, and secretions that ensure their functional redistribution, transport, and cumulation. We have developed an intelligent multi-neural network model capable of calculating the content of metals in the human body based on data on their environmental content. The model is two interconnected neural networks trained on actual measurement data. Since metals enter the body from the environment, the predictors of the model are metal content in drinking water and soil. In this case, water characterizes the short-term impact on the organism, and drinking water, combined with metal contents in soil, is a depository medium that accumulates metals from anthropogenic sources—the long-term impact. In addition, human physiological characteristics are taken into account in the calculations. Each period of exposure is taken into account by its neural network. Two variants of the model are proposed: open loop, where the calculation is performed by each neural network separately, and closed loop, where neural networks work together. The model built in this way was trained and tested on the data of real laboratory studies of 242 people living in different districts of Kazan. As a result, the accuracy of the neural network block for calculating long-term impact was 90% and higher, and the accuracy of the block for calculating short-term impact was 92% and higher. The closed double-loop model showed an accuracy of at least 96%. Conclusions: Our proposed method of assessing and quantifying metal accumulation in the body has high accuracy and reliability. It does not require expensive laboratory tests and allows quantifying the body’s metal accumulation content based on readily available information. The calculation results can be used as a tool for clinical diagnostics and operational and planned management to reduce the levels of polymetallic contamination in urban areas. Full article

Exoskeleton robots have become a promising tool in neurorehabilitation, offering effective physical therapy and continuous recovery monitoring. The success of these therapies relies on precise motion control systems. Although computed torque control based on inverse dynamics provides a robust theoretical foundation, its practical application in rehabilitation is limited by its sensitivity to model accuracy, making it less effective when dealing with unpredictable payloads. To overcome these limitations, this study introduces a novel realistic model reference computed torque controller that accounts for parametric uncertainties while optimizing computational efficiency. A dynamic model of a seven-degrees-of-freedom human lower limb exoskeleton is developed, incorporating a realistic joint friction model to accurately reflect the physical behavior of the robot. To reduce computational demands, the control system is split into two loops: a slower loop that predicts joint torque requirements based on reference trajectories and robot dynamics, and a faster PID loop that corrects trajectory tracking errors. Coriolis and centrifugal forces are excluded from the model due to their minimal impact on system dynamics relative to their computational cost. The experimental results show high accuracy in trajectory tracking, and statistical analyses confirm the controller’s robustness and effectiveness in handling parametric uncertainties. This approach presents a promising advancement for improving the stability and performance of exoskeleton-based neurorehabilitation. Full article

Coupled human and natural systems (CHANS) represent dialectic interaction between human and nature subsystems. This dynamic interaction involves a prominent level of complexity stemming from the uncertain interrelation between the systems and the incorporated subsystems. The complexity within CHANS includes reciprocal effects, nonlinearity, uncertainties, and heterogeneity. Although many researchers have highlighted the significance of understanding the nature of the coupling effect, most of the prevailing literature emphasises either human or natural systems separately, while considering the other as exogenous, despite evaluating the reciprocal and complex interrelations. The current review utilises the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). It focuses on synthesising the prevailing literature on the CHANS framework in several disciplines, focusing on the approach, findings, limitations, and implications. The review comprises 56 relevant articles, found through Endnote and Covidence database searches. The findings identify the dominant complexity character as reciprocal effects and feedback loops, confirming the complex interactions between human and natural systems. Furthermore, the review provides evidence surrounding the significance of developing an analytical framework that can better explain the complex connections between humans and nature, as it provides a comprehensive understanding of CHANS and their potential impacts. Full article

This paper examines the influence of emotions on political polarization, looking at online propagation of conspiracy thinking by extreme right movements in Southern Europe. Integrating insights from psychology, political science, media studies, and system theory, we propose the ‘polarization loop’, a causal mechanism explaining the cyclical relationship between extreme messages, emotional engagement, media amplification, and societal polarization. We illustrate the utility of the polarization loop observing the use of the Great Replacement Theory by extreme right movements in Italy, Portugal, and Spain. We suggest possible options to mitigate the negative effects of online polarization in democracy, including public oversight of algorithmic decission-making, involving social science and humanities in algorithmic design, and strengthening resilience of citizenship to prevent emotional overflow. We encourage interdisciplinary research where historical analysis can guide computational methods such as Natural Language Processing (NLP), using Large Language Models fine-tunned consistently with political science research. Provided the intimate nature of emotions, the focus of connected research should remain on structural patterns rather than individual behavior, making it explicit that results derived from this research cannot be applied as the base for decisions, automated or not, that may affect individuals. Full article

Vaccination against human papillomavirus (HPV) significantly reduces the incidence of HPV-related lesions worldwide. Considering the increasingly young age of patients in gynecological offices and earlier sexual initiation and potential contact with the HPV virus, doctors need the tools to verify diagnoses. Currently, women plan to pursue motherhood later, so it is necessary to consider whether sexual treatment in the form of, among others, loop electrosurgical excision procedures (LEEPs) may increase the risk of premature birth or difficulty dilating the cervix during labour. For this reason, to avoid the overtreatment of low-grade squamous intraepithelial lesions (LSILs), methylation testing may be considered. In patients with histopathologically confirmed high-grade squamous intraepithelial lesions (HSILs) during biopsy and, ultimately, a lower diagnosis, i.e., LSIL or no signs of atypia, methylation was found to be a useful tool. We performed a Pap smear, HPV genotyping, a punch biopsy, LEEP-conization (if needed), and methylation tests on 108 women admitted to the District Public Hospital in Poland. Women with a negative methylation test result were significantly more likely to be ultimately diagnosed with LSIL (p = 0.013). This means that in 85.7% of the patients with HSIL, major cervical surgery could be avoided if the methylation test was negative. Methylation testing, as well as dual-staining and diagnostics detecting the mRNA transcripts of highly oncogenic types of HPV, might be used in the future in the diagnosis of pre-cancerous conditions, mainly of the cervix, and in HPV-dependent cervical cancer screening. The methylation test may also be used in the diagnosis and identification of lesions within the cervical canal, including those located deep within the frontal crypts, not visible even during a professional colposcopic evaluation of the cervix. Full article