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Self and emotions are key motivational factors of a person strivings for health and well-being. Understanding neural mechanisms supporting the relationship between these factors bear far-reaching implications for mental health disorders. Recent work indicates a substantial overlap between self-relevant and emotion information processing and has proposed the medial prefrontal cortex (MPFC) as one shared neural signature. However, the precise cognitive and neural mechanisms represented by the MPFC in investigations of self- and emotion-related processing are largely unknown. Here we examined whether the neural underpinnings of self-related processing in the MPFC link to positive or negative emotions. We collected fMRI data to test the distinct and shared neural circuits of self- and emotion-related processing while participants performed personal (self, friend, or stranger) and emotion (happy, sad, or neutral) associative matching tasks. By exploiting tight control over the factors that determine the effects of self-relevance and emotions (positive: Happy vs. neutral; negative: Sad vs. neutral), our univariate analysis revealed that the ventral part of the MPFC (vmPFC), which has established involvement in self-prioritisation effects, was not recruited in the negative emotion prioritisation effect. In contrast, there were no differences in brain activity between the effects of positive emotion- and self-prioritisation. These results were replicated by both region of interest (ROI)-based analysis in the vmPFC and the seed- to voxel functional connectivity analysis between the MPFC and the rest of the brain. The results suggest that the prioritisation effects for self and positive emotions are tightly linked together, and the MPFC plays a large role in discriminating between positive and negative emotions in relation to self-relevance.

Prioritising sustainable development goals (SDGs) is one of the fundamental approaches to achieving global sustainability objectives, as it helps efficient resource allocation, addresses urgent needs, enhances policy coherence, and measures impact. Despite existing efforts, there remains an unclear understanding of the key factors needed for effective SDG prioritisation, presenting challenges for strategic planning and decision-making. This study provides an evidence-based analysis of these critical factors by examining relevant literature, conducting surveys, and employing Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Analysis (MCDA). The study identifies four primary factors for SDG prioritisation: SDG interrelations, performance, scope, and alignment. The findings confirm that national prioritisation have more priority compared to global, regional, and sub-national systems, and that prioritisation is more valuable at the indicator level rather than at the goal or target levels. Additionally, prioritisation should initially focus on off-track SDGs. Notably, academia ranks SDG prioritisation based on relationships and performance highly, while government officials emphasise alignment and relevance. Moreover, the results indicate that academia prefers target-level prioritisation, while government officials lean towards indicator level. However, both groups favour national scale over global and regional scales.

1. Preventing the arrival of invasive alien species (IAS) is a major priority in managing biological invasions. However, information on introduction pathways is currently scattered across many data bases that often use different categorisations to describe similar pathways. This hampers the identification and prioritisation of pathways to meet the main targets of recent environmental policies. 2. Therefore, we integrate pathway information from two major IAS data bases, IUCN's Global Invasive Species Database (GISD) and the DAISIE European Invasive Alien Species Gateway, applying the new standard categorisation scheme recently adopted by the Convention on Biological Diversity (CBD). We describe the process of mapping pathways from the individual data bases to the CBD scheme and provide, for the first time, detailed descriptions of the standard pathway categories. The combined data set includes pathway information for 8323 species across major taxonomic groups (plants, vertebrates, invertebrates, algae, fungi, other) and environments (terrestrial, freshwater, marine). 3. We analyse the data for major patterns in the introduction pathways, highlighting that the specific research question and context determines whether the combined or an individual data set is the better information source for such analyses. While the combined data set provides an improved basis for direction-setting in invasion management policies on the global level, individual data sets often better reflect regional idiosyncrasies. The combined data set should thus be considered in addition to, rather than replacing, existing individual data sets. 4. Pathway patterns derived from the combined and individual data sets show that the intentional pathways ' Escape' and ' Release' are most important for plants and vertebrates, while for invertebrates, algae, fungi and micro-organisms unintentional transport pathways prevail. Differences in pathway proportions among marine, freshwater and terrestrial environments are much less pronounced. The results also show that IAS with highest impacts in Europe are on average associated with a greater number of pathways than other alien species and are more frequently introduced both intentionally and unintentionally. 5. Synthesis and applications. Linking data bases on invasive alien species by harmonising and consolidating their pathway information is essential to turn dispersed data into useful knowledge. The standard pathway categorisation scheme recently adopted by the Convention on Biological Diversity may be crucial to facilitate this process. Our study demonstrates the value of integrating major invasion data bases to help managers and policymakers reach robust conclusions about patterns in introduction pathways and thus aid effective prevention and prioritisation in invasion management.

Despite the severe threats to plant habitats and high levels of extinction risk for plant species in many parts of the world, plant conservation priorities are often poorly represented in national and global frameworks because of a lack of data in an accessible and consistent format to inform conservation decision making. The Important Plant Areas (IPAs) criteria system offers a pragmatic yet scientifically rigorous means of delivering these datasets, enabling informed national- or regional-scale conservation prioritisation, and contributing significantly towards global prioritisation systems including the International Union for Conservation of Nature Key Biodiversity Areas (KBAs) Standard. In this paper, we review the IPA rationale and progress on IPA identification to date, including the perceived limitations of the process and how these may be overcome. We then present a revised set of criteria for use globally, developed through the combined experiences of IPA identification over the past decade and a half and through a recent global consultation process. An overview of how the revised IPA criteria can work alongside the newly published KBA Standard is also provided. IPA criteria are based around a sound, scientific, global framework which acknowledges the practical problems of gathering plant and habitat data in many regions of the world, and recognises the role of peer reviewed expert opinion in the selection process. National and sub-national engagement in IPA identification is essential, providing a primary route towards long term conservation of key sites for plant diversity. The IPA criteria can be applied to the conservation of all organism groups within the plant and fungal kingdoms.

Existing circular RNA (circRNA) databases have become essential for transcriptomics. However, most are unsuitable for mining in-depth information for candidate circRNA prioritization. To address this, we integrate circular transcript collections to develop the circAtlas database based on 1070 RNA-seq samples collected from 19 normal tissues across six vertebrate species. This database contains 1,007,087 highly reliable circRNAs, of which over 81.3% have been assembled into full-length sequences. We profile their expression pattern, conservation, and functional annotation. We describe a novel multiple conservation score, co-expression, and regulatory networks for circRNA annotation and prioritization. CircAtlas can be accessed at http://circatlas.biols.ac.cn/ .

The protein-coding exome of a patient with a monogenic disease contains about 20,000 variants, only one or two of which are disease causing. We found that 58% of rare variants in the protein-coding exome of the general population are located in only 2% of the genes. Prompted by this observation, we aimed to develop a gene-level approach for predicting whether a given human protein-coding gene is likely to harbor disease-causing mutations. To this end, we derived the gene damage index (GDI): a genome-wide, gene-level metric of the mutational damage that has accumulated in the general population. We found that the GDI was correlated with selective evolutionary pressure, protein complexity, coding sequence length, and the number of paralogs. We compared GDI with the leading gene-level approaches, genic intolerance, and de novo excess, and demonstrated that GDI performed best for the detection of false positives (i.e., removing exome variants in genes irrelevant to disease), whereas genic intolerance and de novo excess performed better for the detection of true positives (i.e., assessing de novo mutations in genes likely to be disease causing). The GDI server, data, and software are freely available to noncommercial users from lab.rockefeller.edu/casanova/GDI.

Aim On‐the‐ground conservation efforts require managers to balance various and sometimes conflicting conservation goals. For instance, areas important for conserving threatened and endangered species may have little spatial agreement with high functional redundancy. Using prioritisation tools can further complicate conservation prioritisations if conflicting diversity metrics identify different high‐priority areas. We compared five community‐level diversity metrics for birds across the conterminous US to identify how much agreement existed between each before and after using a prioritisation framework. Location Contiguous US. Methods We examined spatial agreement among metrics before (a priori) and after (a posteriori) prioritisation using integer linear programming. We compared a posteriori outputs for 10% and 30% conservation goals. We also assessed data layer correlation and agreement (i.e., overlap) a priori and a posteriori. Results As expected, the a priori diversity metrics were poorly to moderately correlated (median = 0.31, range = 0.11–0.71), but all a posteriori solutions had areas of agreement. Accordingly, our a posteriori metrics identified different areas as high priority for conservation, none aligning well with the current protected areas (mean = 13%–15% agreement). However, the a posteriori approach allowed us to include a continuity constraint (identify adjacent important pixels) and easily find areas of high‐priority agreement. Main Conclusions Metric agreement depended on a priori or a posteriori evaluation, highlighting managers' challenges when deciding where and how to enact conservation. Given these challenges, a posteriori solutions best support multiple‐objective, complex and large planning conservation problems. Importantly, all of our a posteriori maps agreed in areas, suggesting aggregates of several metrics could instill certainty in decision‐making if prioritisation solutions were obtained at different times. Overall, our results underscore the critical importance of generating maps and metrics useful for on‐the‐ground management, carefully selecting biodiversity metrics that best reflect conservation goals and employing prioritisation software for generating conservation solutions.

Smart city development is gaining considerable recognition in the systematic literature and international policies throughout the world. The study aims to identify the key barriers of smart cities from a review of existing literature and views of experts in this area. This work further makes an attempt on the prioritisation of barriers to recognise the most important barrier category and ranking of specific barriers within the categories to the development of smart cities in India. Through the existing literature, this work explored 31 barriers of smart cities development and divided them into six categories. This research work employed fuzzy Analytic Hierarchy Process (AHP) technique to prioritise the selected barriers. Findings reveal that ‘Governance’ is documented as the most significant category of barriers for smart city development followed by ‘Economic; ‘Technology’; ‘Social’; ‘Environmental’ and ‘Legal and Ethical’. In this work, authors also performed sensitivity analysis to validate the findings of study. This research is useful to the government and policymakers for eradicating the potential interferences in smart city development initiatives in developing countries like India.

Morphometric analysis and sub‐watersheds prioritisation were carried out for the Wadi Easal Basin, Jordan, which is characterised by a high topographic diversity. The total ranking method was applied to prioritise the sub‐watersheds in terms of susceptibility to flash flood. Results of morphometric analysis revealed that the study area is a fifth order drainage system with a dendritic drainage pattern and elongated shape. Prioritisation results showed that about 71% (15 out of 21 sub‐watersheds) of sub‐watersheds have high‐very high susceptibility to flooding, which forms about 64% of the total area of the basin. The main underlying morphometric parameters behind this are the high drainage density, stream frequency, high basin relief, basin slope, ruggedness number, and circulatory ratio, and the low value of basin shape. Overall, the basin has a rugged topography with steep slopes and high relief. Since the basin is ungauged, and no information about its past hydrological behaviour is present, the results of this study can be used as guidance for competent authorities to initialize flood mitigation or artificial groundwater recharge measures.

The Beas sub basin falling under the Indus basin in Northern India is experiencing notable changes due to human interventions since the rise of civilization in the Indus valley. The incessant anthropogenic pressure, infrastructural development, deforestation and encroachment have made the sub basin more vulnerable to land degradation, erosion and landslides. Thus this study attempts to classify the watersheds based on morphometric characteristics and prioritize the watersheds for sub basin management as a whole so that restoration process can concentrate on the high risk prone watersheds. In this study ALOS PALSAR DEM of 12.5 meters was used to extract the drainage network, watershed, catchment sub basin and basin boundary complemented by topographic and hydrological maps. The study analyses 49 morphometric parameters under categories like linear, areal and relief characteristics. The result classifies the erosion capacity of total 4126 streams with the cumulative length of 12,287.51 km over a sub basin area of 19,338.8 Km2. The morphometric parameters were integrated for each watershed and compound factor was given to rank vulnerability in the GIS environment. The results depicted that sub watershed numbers 2, 6, 12, 16 were high risk prone and underlined as an area which requires immediate attention for soil water conservation measures.