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Astrotourism has emerged as a powerful cross sectoral tool to promote science education, sustainable economic development, and cultural exchange. Recognising its potential, the International Astronomical Union's Office of Astronomy for Development (IAU OAD) has developed a suite of openly accessible resources to support individuals and institutions interested in implementing astrotourism initiatives globally. These resources also encourage individuals and existing businesses to broaden their offerings to include activities that use the night sky as a backdrop, such as food experiences, wellness practices, and cultural exploration. This paper offers a comprehensive summary of these resources, available on the OAD's Astrotourism Portal, and situates them within the broader context of astronomy for development work. The paper is targeted at educators, policymakers, tourism operators, grassroots organisers, and entrepreneurs, providing guidance on how they can foster inclusive, locally grounded, and sustainable astrotourism efforts, particularly in underresourced or emerging contexts.

The formation mechanism of operons remains controversial despite the proposal of many models. Although acquisition of genes from other species, horizontal gene transfer, is considered to occur, definitive concrete cases have been unavailable. It is desirable to select horizontally transferred genes reliably and examine their relationship to operons. We here developed a method to identify candidates of horizontally transferred genes based on minimization of gene cluster insertions/deletions. To select a benchmark set of positively horizontally transferred genes against which the candidate set can be appraised, we devised another procedure using intergenetic alignments. Comparison with the benchmark set of horizontally transferred genes demonstrated the absence of a significant number of false positives in the candidates, showing that the method identifies horizontally transferred genes with a high degree of confidence. Horizontally transferred genes constitute at least 5.5% of the genes in Escherichia, Shigella, and Salmonella and ~46% of which originate from other gamma-proteobacteria. Not only informational genes, but also operational genes (those involved in housekeeping) are hor

Background: Advanced biological techniques have helped produce more insightful findings on the genetic etiology of infertility that may lead to better management of the condition. This review provides an update on genes predisposing to syndromic and nonsyndromic infertility. Main body: The review identified 65 genes linked with infertility and infertility-related disorders. These genes regulate fertility. However, mutational loss of the functions of the genes predisposes to infertility. Twenty-three (23) genes representing 35% were linked with syndromic infertility, while 42 genes (65%) cause nonsyndromic infertility. Of the 42 nonsyndromic genes, 26 predispose to spermatogenic failure and sperm morphological abnormalities, 11 cause ovarian failures, and 5 cause sex reversal and puberty delay. Overall, 31 genes (48%) predispose to male infertility, 15 genes (23%) cause female infertility, and 19 genes (29%) predispose to both. The common feature of male infertility was spermatogenic failure and sperm morphology abnormalities, while ovarian failure has been the most frequently reported among infertile females. The mechanisms leading to these pathologies are gene-specific, which, if

Spatial transcriptomics (ST) is a novel technology that enables the observation of gene expression at the resolution of individual spots within pathological tissues. ST quantifies the expression of tens of thousands of genes in a tissue section; however, heavy observational noise is often introduced during measurement. In prior studies, to ensure meaningful assessment, both training and evaluation have been restricted to only a small subset of highly variable genes, and genes outside this subset have also been excluded from the training process. However, since there are likely co-expression relationships between genes, low-expression genes may still contribute to the estimation of the evaluation target. In this paper, we propose $Auxiliary \ Gene \ Learning$ (AGL) that utilizes the benefit of the ignored genes by reformulating their expression estimation as auxiliary tasks and training them jointly with the primary tasks. To effectively leverage auxiliary genes, we must select a subset of auxiliary genes that positively influence the prediction of the target genes. However, this is a challenging optimization problem due to the vast number of possible combinations. To overcome this

The goal of this research is to uncover the channels through which research and development (R&D) impacts economic growth in developing countries. The study employed nine variables from three broader categories in the World Economic Forum database, each covering 32 countries from the lower-middle-income group for the year 2019. The theoretical framework is based on the R&D ecosystem, which includes components such as Institutions, Human capital, Capital market, R&D, and Innovation. Each of these components can contribute to the economic development of the country. Using Structural Equation Modelling (SEM), we build a path diagram to visualize and confirm a potential relationship between the components. R&D features had a positive impact on innovation (regression weight estimate: +0.34, p = 0.001), as did capital market institutions (regression weight estimate: +0.12, p = 0.007), but neither had a significant impact on growth. According to the Schumpeterian institutional interpretation, R&D and innovation efforts may not lead to sustained growth in middle-income countries. We find no significant connection between innovation performance and economic growth. This

Understanding how genes interact and relate to each other is a fundamental question in biology. However, current practices for describing these relationships, such as drawing diagrams or graphs in a somewhat arbitrary manner, limit our ability to integrate various aspects of the gene functions and view the genome holistically. To overcome these limitations, we need a more appropriate way to describe the intricate relationships between genes. Interestingly, category theory, an abstract field of mathematics seemingly unrelated to biology, has emerged as a powerful language for describing relations in general. We propose that category theory could provide a framework for unifying our knowledge of genes and their relationships. As a starting point, we construct a category of genes, with its morphisms abstracting various aspects of the relationships betweens genes. These relationships include, but not limited to, the order of genes on the chromosomes, the physical or genetic interactions, the signalling pathways, the gene ontology causal activity models (GO-CAM) and gene groups. Previously, they were encoded by miscellaneous networks or graphs, while our work unifies them in a consisten

The genome of bacterial species is much more flexible than that of eukaryotes. Moreover, the distributed genome hypothesis for bacteria states that the total number of genes present in a bacterial population is greater than the genome of every single individual. The pangenome, i.e. the set of all genes of a bacterial species (or a sample), comprises the core genes which are present in all living individuals, and accessory genes, which are carried only by some individuals. In order to use accessory genes for adaptation to environmental forces, genes can be transferred horizontally between individuals. Here, we extend the infinitely many genes model from Baumdicker, Hess and Pfaffelhuber (2010) for horizontal gene transfer. We take a genealogical view and give a construction -- called the Ancestral Gene Transfer Graph -- of the joint genealogy of all genes in the pangenome. As application, we compute moments of several statistics (e.g. the number of differences between two individuals and the gene frequency spectrum) under the infinitely many genes model with horizontal gene transfer.

In this paper, to unveil interpretable development-specific gene signatures in human PFC, we propose a novel gene selection method, named Interpretable Causality Gene Selection (ICGS), which adopts a Bayesian Network (BN) to represent causality between multiple gene variables and a development variable. The proposed ICGS method combines the positive instances-based contrastive learning with a Variational AutoEncoder (VAE) to obtain this optimal BN structure and use a Markov Blanket (MB) to identify gene signatures causally related to the development variable. Moreover, the differential expression genes (DEGs) are used to filter redundant genes before gene selection. In order to identify gene signatures, we apply the proposed ICGS to the human PFC single-cell transcriptomics data. The experimental results demonstrate that the proposed method can effectively identify interpretable development-specific gene signatures in human PFC. Gene ontology enrichment analysis and ASD-related gene analysis show that these identified gene signatures reveal the key biological processes and pathways in human PFC and have more potential for neurodevelopment disorder cure. These gene signatures are ex

Astronomy, often perceived as a distant or luxury science, holds immense potential as a driver for sustainable local socio-economic development. This paper explores how astronomy can create tangible benefits for communities through education, tourism, technology transfer, and capacity building. Using case studies from South Africa, Chile, Indonesia, and India, we demonstrate how astronomical facilities and initiatives have stimulated local economies, generated employment, supported small enterprises, and enhanced STEM participation, while simultaneously inspiring a sense of shared global heritage. The analysis identifies both successes and challenges, including unequal benefit distribution, limited local ownership, and sustainability gaps once external funding ends. Building on these lessons, we propose a practical framework/guidelines for designing, implementing, and evaluating astronomy-based community initiatives, rooted in participatory engagement and aligned with the UN Sustainable Development Goals (SDGs). This paper positions astronomy as a catalyst for inclusive growth, demonstrating that investment in the cosmos can translate into grounded, measurable benefits for people a

Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein coding potential is the detection of cellular protein expression through peptide mass spectrometry experiments. Here we map the peptides detected in 7 large-scale proteomics studies to almost 60% of the protein coding genes in the GENCODE annotation the human genome. We find that conservation across vertebrate species and the age of the gene family are key indicators of whether a peptide will be detected in proteomics experiments. We find peptides for most highly conserved genes and for practically all genes that evolved before bilateria. At the same time there is almost no evidence of protein expression for genes that have appeared since primates, or for genes that do not have any protein-like features or cross-species conservation. We identify 19 non-protein-like features such as weak conservation, no protein features or ambiguous annotations in major databases that are indicators of low peptide detection rates. We use these features to describe a set of 2,001 genes that are potentially non-coding, and show that many of these genes behave m

Objectives Lung squamous cell carcinoma (LUSC) often diagnosed as advanced with poor prognosis. The mechanisms of its pathogenesis and prognosis require urgent elucidation. This study was performed to screen potential biomarkers related to the occurrence, development and prognosis of LUSC to reveal unknown physiological and pathological processes. Materials and Methods Using bioinformatics analysis, the lung squamous cell carcinoma microarray datasets from the GEO and TCGA databases were analyzed to identify differentially expressed genes(DEGs). Furthermore, PPI and WGCNA network analysis were integrated to identify the key genes closely related to the process of LUSC development. In addition, survival analysis was performed to achieve a prognostic model that accomplished a high level of prediction accuracy. Results and Conclusion Eighty-five up-regulated and 39 down-regulated genes were identified, on which functional and pathway enrichment analysis was conducted. GO analysis demonstrated that up-regulated genes were principally enriched in epidermal development and DNA unwinding in DNA replication. Down-regulated genes were mainly involved in cell adhesion, signal transduction an

In this work, we develop a stochastic model of gene gain and loss with the aim of inferring when (if at all) in evolutionary history and association between two genes arises. The data we consider is a species tree along with information on the presence or absence of two genes in each of the species. The biological motivation for our model is that if two genes are involved in the same biochemical pathway, i.e. they are both required for some function, then the rate of gain or loss of one gene in the pathway should depend upon the presence or absence of the other gene in the pathway. However, if the two genes are not functionally linked, then the rate of gain or loss of one gene should be independent of the state of another gene. We simulate data under this model to determine under what conditions a shift from the independent rates class to the dependent rates class can be detected. For example, how large a tree is required and how large a shift in the rates is needed before Akaike information criterion (AIC) supports a model with two rate classes over a simpler model with just one rate class? If a model with two rate classes is preferred, can it correctly detect where on the evoluti

Quantitative criteria are proposed to identify genes (and sets of genes) whose expression marks a specific brain region (or a set of brain regions). Gene-expression energies, obtained for thousands of mouse genes by numerization of in-situ hybridization images in the Allen Gene Expression Atlas, are used to test these methods in the mouse brain. Individual genes are ranked using integrals of their expression energies across brain regions. The ranking is generalized to sets of genes and the problem of optimal markers of a classical region receives a linear-algebraic solution. Moreover, the goodness of the fitting of the expression profile of a gene to the profile of a brain region is closely related to the co-expression of genes. The geometric interpretation of this fact leads to a quantitative criterion to detect markers of pairs of brain regions. Local properties of the gene-expression profiles are also used to detect genes that separate a given grain region from its environment.

This study explores the benefits and challenges of integrating Artificial Intelligence with Agile software development methodologies, focusing on improving continuous integration and delivery. A systematic literature review and longitudinal meta-analysis of the retrieved studies was conducted to analyse the role of Artificial Intelligence and it's future applications within Agile software development. The review helped identify critical challenges, such as the need for specialised socio-technical expertise. While Artificial Intelligence holds promise for improved software development practices, further research is needed to better understand its impact on processes and practitioners, and to address the indirect challenges associated with its implementation.

Context: Social aspects are of high importance for being successful using agile methods in software development. People are influenced by their cultural imprint, as the underlying cultural values are guiding us in how we think and act. Thus, one may assume that in multicultural agile software development teams, cultural characteristics influence the result in terms of quality of the team work and consequently, the product to be delivered. Objective: We aim to identify barriers and potentials that may arise in multicultural agile software development teams to provide valuable strategies for both researchers and practitioners faced with barriers or unrealized potentials of cultural diversity. Method: The study is designed as a single-case study with two units of analysis using a mixed-method design consisting quantitative and qualitative methods. Results: First, our results suggest that the cultural characteristics at the team level need to be analyzed individually in intercultural teams, Second, we identified key potentials regarding cultural characteristics providing key potentials such as a individual team subculture that fits agile values like open communication. Third, we derive

Agile methods have transformed the way software is developed, emphasizing active end-user involvement, tolerance to change, and evolutionary delivery of products. The first special issue on agile development described the methods as focusing on "feedback and change". These methods have led to major changes in how software is developed. Scrum is now the most common framework for development in most countries, and other methods like extreme programming (XP) and elements of lean software development and Kanban are widely used. What started as a bottom-up movement amongst software practitioners and consultants has been taken up by major international consulting companies who prescribe agile development, particularly for contexts where learning and innovation are key. Agile development methods have attracted interest primarily in software engineering, but also in a number of other disciplines including information systems and project management. The agile software development methods were originally targeted towards small, co-located development teams, but are increasingly applied in other contexts. They were initially used to develop Web systems and internal IT systems, but are now use

Background: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. Results: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to

One of the important issues in oncology is finding the genes that perturbation the cell functionality, and result in cancer propagation. The genes, namely driver genes, when they mutate in expression, result in cancer through activation of the mutated proteins. So, many methods have been introduced to predict this group of genes. These are mostly computational methods based on the number of mutations of each gene. Recently, some network-based methods have been proposed to predict Cancer Driver Genes (CDGs). In this study, we use a network-based approach and relative importance of each gene in the propagation and absorption of genes anomalies in the network to recognize CDGs. The experimental results are compared with 19 previous methods that show our proposed algorithm is better than the others in terms of accuracy, precision, and the number of recognized CDGs.

The advent of high--throughput transcription profiling technologies has enabled identification of genes and pathways associated with disease, providing new avenues for precision medicine. A key challenge is to analyze this data in the context of the regulatory networks and pathways that control cellular processes, while still obtaining insights that can be used to design new diagnostic and therapeutic interventions. While classical differential expression analysis provides specific and hence targetable gene-level insights, it does not include any systems-level information. On the other hand, pathway analyses integrate systems-level information with expression data, but are often limited in their ability to indicate specific molecular targets. We introduce GeneSurrounder, an analysis method that takes into account the complex structure of interaction networks to identify specific genes that disrupt pathway activity in a disease-specific manner. GeneSurrounder integrates transcriptomic data and pathway network information in a novel two-step procedure to detect genes that (i) appear to influence the expression of other genes local to it in the network and (ii) are part of a subnetwor