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Structural variation (SV) typically refers to alterations in DNA fragments at least 50 base pairs long in the human genome. It can alter thousands of DNA nucleotides and thus significantly influence human health, disease, and clinical phenotypes. There is a shared and growing recognition that the emergence of effective computational tools and high-throughput technologies such as short-read sequencing and long-read sequencing offers novel insight into SV and, by extension, diseases affecting planetary health. However, numerous available SV tools exist with varying strengths and weaknesses. This is currently hampering the abilities of scholars to select the optimal tools to study SVs. Here, we reviewed 175 tools developed in the past two decades for SV detection, annotation, visualization, and downstream analysis of human genomics. In this expert review, we provide a comprehensive catalog of SV-related tools across different technology platforms and summarize their features, strengths, and limitations with an eye to accelerate systems science and planetary health innovations. [ABSTRACT FROM AUTHOR]

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image‐based techniques to contemporary sequence‐based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell–cell interaction analysis, and 3D multi‐slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research. [ABSTRACT FROM AUTHOR]

Periodontitis is a chronic inflammatory disease affecting teeth' supporting tissues. It is caused by specific bacterial species, including Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), and Treponema denticola, known as the "red complex" group. These bacteria manipulate the immune response and promote tissue destruction, making them key players in periodontal pathogenesis. The present study aims to identify the potential molecular targets of Emodin against the red complex pathogens. Method: The interaction between the phytocompound Emodin and red complex pathogens was identified using the STITCH tool. The proteins identified were then classified into functional categories using the VICMPred. The virulent proteins identified were then subjected to Bepired prediction, which provided information about the epitopes in the virulent proteins. Finally, the subcellular location of the proteins was demonstrated with the pSORTb tool. Results: Carbamoyl-phosphate synthase is a large subunit identified as a virulence protein in Pg and Tf. DNA topoisomerase IV subunit A was found to be the common virulence protein for Pg and Td. The DNA gyrase subunit A and ATPase/histidine kinase/DNA gyrase B/HSP90 domain-containing protein were found to be identified in Td and Tf. It was the only protein predicted to be in the cytoplasmic membrane, while others were found in the cytoplasm. The four virulent proteins targeted by Emodin were found to harbor multiple epitopes. Conclusion: Emodin was found to interact with all three pathogens of the red complex group. However, further experimental validation is warranted to prove the antimicrobial effect of Emodin against periodontal pathogens. [ABSTRACT FROM AUTHOR]

This research focuses on an innovative approach utilizing in silico methods to design small interfering RNA (siRNA) targeting the HER2 splice variant mRNA in Homo sapiens. HER2 is known to be overexpressed in certain types of breast cancer, contributing to tumor progression and poor prognosis. By designing siRNA molecules that can specifically bind to and degrade HER2 mRNA, this study aims to reduce HER2 protein levels, thereby hindering the growth and spread of breast cancer cells. The in-silico design process involves identifying optimal siRNA sequences that maximize target specificity and minimize off-target effects, which is crucial for potential therapeutic applications. This approach represents a promising step towards personalized medicine in the treatment of breast cancer, offering a targeted strategy to combat this variant associated with aggressive disease. The methodology comprises the RNA computational tools used for the design, the selection criteria for siRNA candidates, and the potential implications of this research in a clinical setting. The resulting outcomes are 2D and 3D siRNA designs that could potentially silence HER2 mRNA through an in-silico approach. The leads were generated using a de novo modeling approach, with no existing template available in GenBank. Moreover, it is concluded that computational tools can generate sufficiently stable 2D and 3D RNA models that could be advanced for further molecular simulation studies. The benefit of this outcome is that it facilitates better preparation for wet laboratory experiments in siRNA assays, with future implementation in vivo and clinical trial settings. [ABSTRACT FROM AUTHOR]

The 15-minute city concept, designed to ensure that all essential services and amenities are accessible within a 15 min walk or bike ride from home, presents a transformative vision for urban living. This paper explores the concept of a 15-minute city and its implications, along with its main features and pillars. Furthermore, it elaborates on how the integration of artificial intelligence (AI) and computational tools can be utilized in optimizing the 15-minute city model. We reveal how AI-driven algorithms, machine learning techniques, and advanced data analytics can enhance urban planning, improve accessibility, and foster social integration. Our paper focuses on the practical applications of these technologies in creating pedestrian-friendly neighborhoods, optimizing public transport coordination, and enhancing the quality of life for urban residents. By executing some of these computational models, we demonstrate the potential of AI and computational tools to realize the vision of the 15-minute city, making urban spaces more inclusive, resilient, and adaptive to the evolving needs of their inhabitants. [ABSTRACT FROM AUTHOR]

Microsatellites, also known as SSR or STR, are essential molecular markers in genomic research, playing crucial roles in genetic mapping, population genetics, and evolutionary studies. Their applications range from plant breeding to forensics, highlighting their diverse utility across disciplines. Despite their widespread use, traditional methods for SSR analysis are often laborious and time-consuming, requiring significant resources and expertise. To address these challenges, a variety of computational tools for SSR analysis have been developed, offering faster and more efficient alternatives to traditional methods. However, selecting the most appropriate tool can be daunting due to rapid technological advancements and the sheer number of options available. This study presents a comprehensive review and analysis of 74 SSR tools, aiming to provide researchers with a valuable resource for SSR analysis tool selection. The methodology employed includes thorough literature reviews, detailed tool comparisons, and in-depth analyses of tool functionality. By compiling and analyzing these tools, this study not only advances the field of genomic research but also contributes to the broader scientific community by facilitating informed decision-making in the selection of SSR analysis tools. Researchers seeking to understand SSRs and select the most appropriate tools for their projects will benefit from this comprehensive guide. Overall, this study enhances our understanding of SSR analysis tools, paving the way for more efficient and effective SSR research in various fields of study. [ABSTRACT FROM AUTHOR]

Antifreeze proteins (AFPs) have unique features to sustain life in sub-zero environments due to ice recrystallization inhibition (IRI) and thermal hysteresis (TH). AFPs are in demand as agents in cryopreservation, but some antifreeze proteins have low levels of activity. This research aims to improve the cryopreservation activity of an AFPIV. In this in silico study, the helical peptide afp1m from an Antarctic yeast AFP was modeled into a sculpin AFPIV, to replace each of its four α-helices in turn, using various computational tools. Additionally, a new linker between the first two helices of AFPIV was designed, based on a flounder AFPI, to boost the ice interaction activity of the mutants. Bioinformatics tools such as ExPASy Prot-Param, Pep-Wheel, SOPMA, GOR IV, Swiss-Model, Phyre2, MODFOLD, MolPropity, and ProQ were used to validate and analyze the structural and functional properties of the model proteins. Furthermore, to evaluate the AFP/ice interaction, molecular dynamics (MD) simulations were executed for 20, 100, and 500 ns at various temperatures using GROMACS software. The primary, secondary, and 3D modeling analysis showed the best model for a redesigned antifreeze protein (AFP1mb, with afp1m in place of the fourth AFPIV helix) with a QMEAN (Swiss-Model) Z score value of 0.36, a confidence of 99.5%, a coverage score of 22%, and a p value of 0.01. The results of the MD simulations illustrated that AFP1mb had more rigidity and better ice interactions as a potential cryoprotectant than the other models; it also displayed enhanced activity in limiting ice growth at different temperatures. [ABSTRACT FROM AUTHOR]

Introduction: Neuroblastoma (NB) remains a challenging pediatric malignancy with limited treatment options, particularly for high-risk cases. Drug repurposing offers a convenient and cost-effective strategy for treating rare diseases like NB. Using existing drugs with known safety profiles accelerates the availability of new treatments, reduces development costs, and mitigates risks, offering hope for improved patient outcomes in challenging conditions. Areas covered: This review provides an overview of the advances in approaches used to repurpose drugs for NB therapy. The authors discuss strategies employed in drug repurposing, including computational and experimental methods, and rational drug design, highlighting key examples of repurposed drugs with promising clinical results. Additionally, the authors examine the challenges and opportunities associated with drug repurposing in NB and discuss future directions and potential areas for further research. Expert opinion: The fact that only one new drug has been approved in the last 30 years for the treatment of neuroblastoma plus a significant proportion of high-risk NB patients that remain uncurable, evidences the need for new fast and cost-effective alternatives. Drug repurposing may accelerate the treatment development process while reducing expenses and risks. This approach can swiftly bring effective NB therapies to market, enhancing survival rates and patient quality of life. [ABSTRACT FROM AUTHOR]

Private and government agencies have identified an increase in water consumption for agricultural, commercial, industrial, and domestic activities. Agriculture worldwide accounts for 70% of water consumption. However, industrial activities report more than half of the available water for human consumption. Therefore, sustainable nanomaterials have been investigated to improve atmospheric water harvesting technologies and enhance renewable sources of potable water even in remote regions with limited access to potable water and electricity. This research describes a novel methodology to produce biodegradable and hygroscopic materials studying the water harvesting phenomenon. Two hygroscopic compositions of materials are considered: polydimethylsiloxane with modified silica nanoparticles (0.2g silica, 10g toluene, 2g octadecyl trichlorosilane) and polydimethylsiloxane with modified halloysite nanoparticles (0.2g halloysite nanotubes, 10g toluene, 2g octadecyl trichlorosilane). The hygroscopic performance is studied with a three-dimensional printed polymethyl methacrylate structure to compare the experimental and numerical results of the Python-based model. A surface area of 0.018 m2 with an angle less than 44.9° concerning the airflow direction is configured to produce water in terms of 24 hours of water harvesting testing. The error rate less than 5% between experimental and numerical results of polydimethylsiloxane with halloysite nanotubes demonstrates the possibility to study the water harvesting on sustainable materials and printed structures in a virtual environment. [ABSTRACT FROM AUTHOR]

Protein–glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein–glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein–ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein–glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

Computational tools, particularly AI, are becoming more ubiquitous in scientific research; but what impact do they have on the environment?[Graphic: see text]

The research landscape on cognitive computing algorithms, such as Genetic Algorithms (GA), in Cancer/Tumor and Oncological (CTO) research from 2003 to 2022 was examined using Scopus-indexed publications. Bibliometric analysis was employed to assess social networks and thematic areas of GACTO research. The analysis revealed that researchers published 114 articles and 92 conference papers, representing 55.34% and 44.66% of the total publications (TP=206), respectively. Of these, 129 publications were open access, distributed across Gold, Hybrid Gold, Bronze, and Green mediums. Researchers showed a preference for articles over conference papers. Stakeholder analysis highlighted a robust number of active authors, affiliations, and countries involved in GACTO research. Top performers included Zuherman Rustam (TP=5), Universitas Indonesia (TP=6), and India. Productivity was attributed to the availability of resources such as financial support, with top funders being Universitas Indonesia, the National Natural Science Foundation of China, and Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico. Social network analysis indicated a low rate of co-authorship at 18.18%, suggesting limited collaboration at the author level. However, at the national level, collaborative links were stronger, with the largest cluster comprising India, Iran, and the United States, and the smallest including Turkey and the United Kingdom. This reflects better access to resources, funding, and infrastructure at the national level. Hotspot analysis identified three major keywords: genetic algorithms, diseases, and feature extraction. Cluster analysis revealed three focus areas: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In conclusion, the GACTO research landscape actively engages in socially impactful and scientific themes, utilizing computational tools to address challenges posed by cancer and other oncological diseases. [ABSTRACT FROM AUTHOR]

The richness of Brazilian biodiversity translates into a valuable collection of molecules with biological properties that range from ecological functions to pharmacological properties. For over 25 years, the Nucleus of Bioassays, Biosynthesis, and Ecophysiology of Natural Products (NuBBE) has conducted extensive investigations into the chemical entities of numerous plant and microorganism species, resulting in the discovery of over a thousand natural compounds spanning various chemical classes (such as shikimate derivatives, phenylpropanoids, terpenoids, alkaloids, and peptides). The research goals within the natural products field encompass phytochemical studies, investigations of endophytic fungi and marine organisms, biosynthetic studies, medicinal chemistry, and the development of innovative methodologies. This comprehensive review article aims to offer valuable insights into the multifaceted research endeavors conducted in NuBBE. In this way, accomplishments, perspectives, and opportunities for advancing natural products research in Brazil are highlighted, seeking to inspire and motivate other research groups in the field of natural products-especially those located in emerging countries with rich biodiversity. [ABSTRACT FROM AUTHOR]

Small proteins (SPs) are typically characterized as eukaryotic proteins shorter than 100 amino acids and prokaryotic proteins shorter than 50 amino acids. Historically, they were disregarded because of the arbitrary size thresholds to define proteins. However, recent research has revealed the existence of many SPs and their crucial roles. Despite this, the identification of SPs and the elucidation of their functions are still in their infancy. To pave the way for future SP studies, we briefly introduce the limitations and advancements in experimental techniques for SP identification. We then provide an overview of available computational tools for SP identification, their constraints, and their evaluation. Additionally, we highlight existing resources for SP research. This survey aims to initiate further exploration into SPs and encourage the development of more sophisticated computational tools for SP identification in prokaryotes and microbiomes. [ABSTRACT FROM AUTHOR]

The study presents ten different exercises covering various computational tools. These exercises are practical applications presented to improve the understanding and skills of students in important concepts of chemical-aided process synthesis. A few exercises aim to build a foundation in computational techniques for chemical engineering undergraduates. The exercises are based on a spreadsheet that covers the design of regression analysis to find the optimum Antoine constants, array calculation for multicomponent distillation material balance, and the generation of a Gantt chart to plan and study the activities of batch processes. The other exercises included an introduction to process simulation, simulation, and reactor rating, and a simulation of multicomponent shortcut distillation. These exercises provide students with hands-on experience in utilizing process simulation software essential for analysing and optimizing chemical processes in real-world scenarios. The exercises also included the design of a heat exchanger network and solving a linear programming problem. An anonymous survey was collected from the cohort that had undergone the exercises, and the practical grades were compared with the batch that did not study the proposed exercises. Additionally, student feedback on practical exercises was collected. Based on the experience of the course coordinator and the collected feedback from participants, it was clear that the exercises helped students to inculcate critical thinking and self-learning abilities. An article essentially sheds light on the computer-aided practical exercises that enable chemical engineering graduates to engage in lifelong learning. [Display omitted] • Ten exercises cover spreadsheet, regression, array, Gantt charts, and more. • Diverse computer-aided exercises promote lifelong learning. • Coordinator experience and feedback highlight self-learning capability. • An integrative approach that combines engaging practical exercises enhances the application of theory. • Computer-aided practical exercises for process synthesis actively engage students. [ABSTRACT FROM AUTHOR]

Bioremediation is an ingenious and promising method pertinent for recuperating and removing toxic metals in polluted water and lands. Arsenic (As) contamination is a crucial matter worldwide as its supersaturation is gradually intensifying in our environment because of natural and anthropogenic exertion. Microorganisms have a pivotal role in the bioremediation of various arsenic species. Microorganisms can presumably decrease arsenic by employing genetic engineering. By introducing innovative catabolic pathways with the help of different computational tools or by engineering the prevailing metabolic pathways, the limitations likely to crop up in bioremediation can be suppressed. This review accentuates the significance of microbes for the bioremediation of arsenic, and along with that, it scrutinizes the disadvantages and incapability of native and engineered microbes for bioremediation. In addition, arsenic bioremediation employing genetically modified bacteria is more ecologically friendly. It has fewer health hazards than physiochemical-based methods such as coagulation or filtration sedimentation, electrodialysis or osmosis, chemical precipitations, and adsorptions. [ABSTRACT FROM AUTHOR]

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Microsatellites, also known as SSR or STR, are essential molecular markers in genomic research, playing crucial roles in genetic mapping, population genetics, and evolutionary studies. Their applications range from plant breeding to forensics, highlighting their diverse utility across disciplines. Despite their widespread use, traditional methods for SSR analysis are often laborious and time-consuming, requiring significant resources and expertise. To address these challenges, a variety of computational tools for SSR analysis have been developed, offering faster and more efficient alternatives to traditional methods. However, selecting the most appropriate tool can be daunting due to rapid technological advancements and the sheer number of options available. This study presents a comprehensive review and analysis of 74 SSR tools, aiming to provide researchers with a valuable resource for SSR analysis tool selection. The methodology employed includes thorough literature reviews, detailed tool comparisons, and in-depth analyses of tool functionality. By compiling and analyzing these tools, this study not only advances the field of genomic research but also contributes to the broader scientific community by facilitating informed decision-making in the selection of SSR analysis tools. Researchers seeking to understand SSRs and select the most appropriate tools for their projects will benefit from this comprehensive guide. Overall, this study enhances our understanding of SSR analysis tools, paving the way for more efficient and effective SSR research in various fields of study.

Background and Aim: Staphylococcus aureus, with its diverse virulence factors and immune response evasion mechanisms, presents a formidable challenge as an opportunistic pathogen. Developing an effective vaccine against S. aureus has proven elusive despite extensive efforts. Autologous Staphylococcus lysate (ASL) treatment has proven effective in triggering an immune response against bovine mastitis. Peptides that stimulate the immune response can be the subject of further research. The study aimed to use immunoinformatics tools to identify epitopes on S. aureus surface and secretory proteins that can bind to major histocompatibility complex class I (MHC I) and CD8+ T-cells. This method aids in discovering prospective vaccine candidates and elucidating the rationale behind ASL therapy’s efficacy. Materials and Methods: Proteins were identified using both literature search and the National Center for Biotechnology Information search engine Entrez. Self and non-self peptides, allergenicity predictions, epitope locations, and physicochemical characteristics were determined using sequence alignment, AllerTOP, SVMTriP, and Protein-Sol tools. Hex was employed for simulating the docking interactions between S. aureus proteins and the MHC I + CD8+ T-cells complex. The binding sites of S. aureus proteins were assessed using Computer Atlas of Surface Topography of Proteins (CASTp) while docked with MHC I and CD8+ T-cells. Results: Nine potential S. aureus peptides and their corresponding epitopes were identified in this study, stimulating cytotoxic T-cell mediated immunity. The peptides were analyzed for similarity with self-antigens and allergenicity. 1d20, 2noj, 1n67, 1nu7, 1amx, and 2b71, non-self and stable, are potential elicitors of the cytotoxic T-cell response. The energy values from docking simulations of peptide-MHC I complexes with the CD8+ and T-cell receptor (TCR) indicate the stability and strength of the formed complexes. These peptides – 2noj, 1d20, 1n67, 2b71, 1nu7, 1yn3, 1amx, 2gi9, and 1edk – demonstrated robust MHC I binding, as evidenced by their low binding energies. Peptide 2gi9 exhibited the lowest energy value, followed by 2noj, 1nu7, 1n67, and 1d20, when docked with MHC I and CD8 + TCR, suggesting a highly stable complex. CASTp analysis indicated substantial binding pockets in the docked complexes, with peptide 1d20 showing the highest values for area and volume, suggesting its potential as an effective elicitor of immunological responses. These peptides – 2noj, 2gi9, 1d20, and 1n67 – stand out for vaccine development and T-cell activation against S. aureus. Conclusion: This study sheds light on the design and development of S. aureus vaccines, highlighting the significance of employing computational methods in conjunction with experimental verification. The significance of T-cell responses in combating S. aureus infections is emphasized by this study. More experiments are needed to confirm the effectiveness of these vaccine candidates and discover their possible medical uses.

Introduction: The chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant. Objectives: Thus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids. Methods: Mass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC). Results: The results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids and phospholipids at varying distributions across the cultivar plant tissues, where the phenylpropnoid superclass was more abundant in the leaves than in the flowers. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles. Conclusion: These findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use. [ABSTRACT FROM AUTHOR]

Human extrachromosomal circular DNA, or eccDNA, has been the topic of extensive investigation in the last decade due to its prominent regulatory role in the development of disorders including cancer. With the rapid advancement of experimental, sequencing and computational technology, millions of eccDNA records are now accessible. Unfortunately, the literature and databases only provide snippets of this information, preventing us from fully understanding eccDNAs. Researchers frequently struggle with the process of selecting algorithms and tools to examine eccDNAs of interest. To explain the underlying formation mechanisms of the five basic classes of eccDNAs, we categorized their characteristics and functions and summarized eight biogenesis theories. Most significantly, we created a clear procedure to help in the selection of suitable techniques and tools and thoroughly examined the most recent experimental and bioinformatics methodologies and data resources for identifying, measuring and analyzing eccDNA sequences. In conclusion, we highlighted the current obstacles and prospective paths for eccDNA research, specifically discussing their probable uses in molecular diagnostics and clinical prediction, with an emphasis on the potential contribution of novel computational strategies. [ABSTRACT FROM AUTHOR]

Drug repositioning or repurposing has gained worldwide attention as a plausible way to search for novel molecules for the treatment of particular diseases or disorders. Drug repurposing essentially refers to uncovering approved or failed compounds for use in various diseases. Cancer is a deadly disease and leading cause of mortality. The search for approved non-oncologic drugs for cancer treatment involved in silico modeling, databases, and literature searches. In this review, we provide a concise account of the existing non-oncologic drug molecules and their therapeutic potential in chemotherapy. The mechanisms and modes of action of the repurposed drugs using computational techniques are also highlighted. Furthermore, we discuss potential targets, critical pathways, and highlight in detail the different challenges pertaining to drug repositioning for cancer immunotherapy. [ABSTRACT FROM AUTHOR]

The accurate evaluation of the thermal performance of building envelope components (e.g., facade walls) is crucial for the reliable evaluation of their energy efficiency. There are several methods available to quantify their thermal resistance, such as analytical formulations (e.g., ISO 6946 simplified calculation method), numerical simulations (e.g., using finite element method), experimental measurements under lab-controlled conditions or in situ. Regarding measurements, when using the heat flow meter (HFM) method, very often, the measured value is based on surface conditions (e.g., temperature and heat flux), achieving in this way the so-called surface-to-surface or conductive thermal resistance ( R c o n d ). When the building components are made of homogeneous layers, their R c o n d values are constant, regardless of their internal and external surface boundary conditions. However, whenever this element is composed of inhomogeneous layers, such as in lightweight steel-framed (LSF) walls, their R c o n d values are no longer constant, depending on their thermal surface resistance. In the literature, such systematic research into how these R c o n d values vary is not available. In this study, the values of four LSF walls were computed, with different levels of thermal conductivity inhomogeneity, making use of four finite elements' numerical simulation tools. Six external thermal surface resistances ( R s e ) were modelled, ranging from 0.00 up to 0.20 m2·K/W. The average temperature of the partition LSF walls is 15 °C, while for the facade LSF walls it is 10 °C. It was found that the accuracy values of all evaluated numerical software are very high and similar, the R c o n d values being nearly constant for walls with homogeneous layers, as expected. However, the variation in the R c o n d value depends on the level of inhomogeneity in the LSF wall layers, increasing up to 8%, i.e., +0.123 m2·K/W, for the evaluated R s e values. [ABSTRACT FROM AUTHOR]

Analysis of small‐angle scattering (SAS) data requires intensive modeling to infer and characterize the structures present in a sample. This iterative improvement of models is a time‐consuming process. Presented here is Scattering Equation Builder (SEB), a C++ library that derives exact analytic expressions for the form factors of complex composite structures. The user writes a small program that specifies how the sub‐units should be linked to form a composite structure and calls SEB to obtain an expression for the form factor. SEB supports e.g. Gaussian polymer chains and loops, thin rods and circles, solid spheres, spherical shells and cylinders, and many different options for how these can be linked together. The formalism behind SEB is presented and simple case studies are given, such as block copolymers with different types of linkage, as well as more complex examples, such as a random walk model of 100 linked sub‐units, dendrimers, polymers and rods attached to the surfaces of geometric objects, and finally the scattering from a linear chain of five stars, where each star is built up of four diblock copolymers. These examples illustrate how SEB can be used to develop complex models and hence reduce the cost of analyzing SAS data. [ABSTRACT FROM AUTHOR]

The 15-minute city concept, designed to ensure that all essential services and amenities are accessible within a 15 min walk or bike ride from home, presents a transformative vision for urban living. This paper explores the concept of a 15-minute city and its implications, along with its main features and pillars. Furthermore, it elaborates on how the integration of artificial intelligence (AI) and computational tools can be utilized in optimizing the 15-minute city model. We reveal how AI-driven algorithms, machine learning techniques, and advanced data analytics can enhance urban planning, improve accessibility, and foster social integration. Our paper focuses on the practical applications of these technologies in creating pedestrian-friendly neighborhoods, optimizing public transport coordination, and enhancing the quality of life for urban residents. By executing some of these computational models, we demonstrate the potential of AI and computational tools to realize the vision of the 15-minute city, making urban spaces more inclusive, resilient, and adaptive to the evolving needs of their inhabitants.

The expansive field of drug discovery is continually seeking innovative approaches to identify and develop novel peptide-based therapeutics. With the advent of artificial intelligence (AI), there has been a transformative shift in the generation of new peptide drugs. AI offers a range of computational tools and algorithms that enables researchers to accelerate the therapeutic peptide pipeline. This review explores the current landscape of AI applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations. Additionally, it presents case studies and future prospectives that demonstrate the impact of AI on the generation of new peptide drugs.