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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 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.

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.

“One hundred worst” lists of alien species of the greatest concern proved useful for raising awareness of the risks and impacts of biological invasions amongst the general public, politicians and stakeholders. All lists so far have been based on expert opinion and primarily aimed at representativeness of the taxonomic and habitat diversity rather than at quantifying the harm the alien species cause. We used the generic impact scoring system (GISS) to rank 486 alien species established in Europe from a wide range of taxonomic groups to identify those with the highest environmental and socioeconomic impact. GISS assigns 12 categories of impact, each quantified on a scale from 0 (no impact detectable) to 5 (the highest impact possible). We ranked species by their total sum of scores and by the number of the highest impact scores. We also compared the listing based on GISS with other expert-based lists of the “worst” invaders. We propose a list of 149 alien species, comprising 54 plants, 49 invertebrates, 40 vertebrates and 6 fungi. Among the highest ranking species are one bird (Branta canadensis), four mammals (Rattus norvegicus, Ondatra zibethicus, Cervus nippon, Muntiacus reevesi), one crayfish (Procambarus clarkii), one mite (Varroa destructor), and four plants (Acacia dealbata, Lantana camara, Pueraria lobata, Eichhornia crassipes). In contrast to other existing expert-based “worst” lists, the GISS-based list given here highlights some alien species with high impacts that are not represented on any other list. The GISS provides an objective and transparent method to aid prioritization of alien species for management according to their impacts, applicable across taxa and habitats. Our ranking can also be used for justifying inclusion on lists such as the alien species of Union concern of the European Commission, and to fulfill Aichi target 9.

Aim As a step towards providing support for an ecological approach to strengthening marine protected areas (MPAs) and meeting international commitments, this study combines cumulative impact assessment and conservation planning approach to undertake a large‐scale spatial prioritization. Location Exclusive Economic Zone (EEZ) of Brazil, Southwest Atlantic Ocean. Methods We developed a prioritization approach to protecting different habitat types, threatened species ranges and ecological connectivity, while also mitigating the impacts of multiple threats on biodiversity. When identifying priorities for conservation, we accounted for the co‐occurrence of 24 human threats and the distribution of 161 marine habitats and 143 threatened species, as well as their associated vulnerabilities. Additionally, we compared our conservation priorities with MPAs proposed by local stakeholders. Results We show that impacts to habitats and species are widespread and identify hot spots of cumulative impacts on inshore and offshore areas. Industrial fisheries, climate change and land‐based activities were the most severe threats to biodiversity. The highest priorities were mostly found towards the coast due to the high cumulative impacts found in nearshore areas. As expected, our systematic approach showed a better performance on selecting priority sites when compared to the MPAs proposed by local stakeholders without a typical conservation planning exercise, increasing the existing coverage of MPAs by only 7.9%. However, we found that proposed MPAs still provide some opportunities to protect areas facing high levels of threats. Main conclusions The study presents a blueprint of how to embrace a comprehensive ecological approach when identifying strategic priorities for conservation. We advocate protecting these crucial areas from degradation in emerging conservation efforts is key to maintain their biodiversity value.

The Murredu watershed in Telangana State was chosen for the morphometric and land use/land cover (LULC) analysis in this current study. Geographical information system (GIS) and remote sensing (RS) techniques can estimate the morphometric features and LULC analysis of a catchment. A total of fourteen sub-watersheds (SWs) were created from the watershed (SW 1 to SW 14), and sub-watersheds were prioritized based on morphometric and LULC features. Evaluation of various morphometric characteristics such as linear aspects, relief aspects, and aerial aspects has been carried out for every sub-watershed to prefer ranking. Four parameters were utilized for the LULC analysis to rank and prioritize sub-watersheds. The sub-watersheds were categorized into three groups as low, medium, and high, for soil and water conservation priority based on morphometric and LULC analysis. Using morphometric analysis, higher priorities have been assigned to SW 12 and SW 1, while using LULC analysis, higher priorities have been assigned to SW 9 and SW 11. SW 10 and SW 13 are the most common sub-watersheds that fall within the same priority while using morphometric and LULC analysis. The coefficient of regression results reveals that stream length and stream order, and also stream number and stream order, have a strong association. The deployment of soil and water conservation measures may be conducted in the high-priority sub-watersheds.

The objective of science is to understand the natural world; we argue that prediction is the only way to demonstrate scientific understanding, implying that prediction should be a fundamental aspect of all scientific disciplines. Reproducibility is an essential requirement of good science and arises from the ability to develop models that make accurate predictions on new data. Ecology, however, with a few exceptions, has abandoned prediction as a central focus and faces its own crisis of reproducibility. Models are where ecological understanding is stored and they are the source of all predictions – no prediction is possible without a model of the world. Models can be improved in three ways: model variables, functional relationships among dependent and independent variables, and in parameter estimates. Ecologists rarely test to assess whether new models have made advances by identifying new and important variables, elucidating functional relationships, or improving parameter estimates. Without these tests it is difficult to know if we understand more today than we did yesterday. A new commitment to prediction in ecology would lead to, among other things, more mature (i.e. quantitative) hypotheses, prioritization of modeling techniques that are more appropriate for prediction (e.g. using continuous independent variables rather than categorical) and, ultimately, advancement towards a more general understanding of the natural world. Synthesis Ecology, with a few exceptions, has abandoned prediction and therefore the ability to demonstrate understanding. Here we address how this has inhibited progress in ecology and explore how a renewed focus on prediction would benefit ecologists. The lack of emphasis on prediction has resulted in a discipline that tests qualitative, imprecise hypotheses with little concern for whether the results are generalizable beyond where and when the data were collected. A renewed commitment to prediction would allow ecologists to address critical questions about the generalizability of our results and the progress we are making towards understanding the natural world.

Background/Objective: Seasonal influenza vaccines reduce global morbidity and mortality but remain underused in low- and middle-income countries (LMICs). This study was part of the World Health Organization's Full Value of Improved Influenza Vaccines Assessment (FVIVA) and explored LMIC decision-makers' priorities and preferences for improved influenza vaccines (IIVs) to enhance their adoption. Methods: A multi-criteria decision analysis (MCDA) to evaluate IIVs was conducted in Kenya and Thailand. Criteria were defined through a literature review and expert opinion, then refined in an online workshop (WS1) with national stakeholders. Criteria weights and scores were elicited in a second workshop (WS2) using a swing weighting approach and a bisection approach, respectively. Results were validated against one current influenza vaccine and three hypothetical IIVs for which a performance matrix had been developed. WS1 was also conducted in 11 other countries to gain a more generalizable understanding of product preferences regarding IIVs. Results: In Kenya and Thailand, 7 criteria were included in the MCDA, 6 of which were shared: vaccine efficacy, duration of protection, breadth of protection, serious adverse events, temperature requirements, and shelf life. Scores and weights were also similar across criteria in both countries. Conclusions: Results from 13 countries identified a core set of decision-making criteria for IIVs. MCDA was a well-accepted and feasible approach to for evidence-based prioritization and optimisation of vaccine portfolios in national immunisation programmes. Future development efforts should focus on improving IIVs based on country-identified criteria. •We explored preferred attributes of improved influenza vaccines in 13 countries.•A full Multi-Criteria Decision Analysis was conducted in Kenya and Thailand.•An analysis of preferred vaccine characteristics was also done in other 11 countries.•Efficacy, safety, duration and breath of protection were highly ranked criteria.•Vaccine temperature requirements and shelf life were also considered important.

ContextSpatial conservation prioritization (SCP) has most often been applied to the design of reserve network expansion. In addition to occurrences of species and habitats inside protected area candidate sites, one may also be interested about network-level connectivity considerations.ObjectivesWe applied SCP to the identification of ecological networks to inform the development of a new regional plan for the region of Uusimaa (South-Finland, including the Finnish capital district).MethodsInput data were 59 high-quality layers of biotope and species distribution data. We identified ecological networks based on a combination of a Zonation balanced priority ranking map and a weighted range size rarity map, to account for both relative and absolute conservation values in the process. We also identified ecological corridors between protected areas and other ecologically high-priority areas using the corridor retention method of Zonation. Furthermore, we identified candidate sites for habitat restoration.ResultsWe found seven large ecological networks (132–1201 km2) which stand out from their surrounding landscape in terms of ecological value and have clear connectivity bottlenecks between them. Highest restoration needs were found between large high-priority sites that are connected via remnant habitat fragments in comparatively highly modified areas.ConclusionsLand conversion should be avoided in areas of highest ecological priorities and network-level connectivity. Restoration should be considered for connectivity bottlenecks. Methods described here can be applied in any location where relevant spatial data are available. The present results are actively used by the regional council and municipalities in the region of Uusimaa.

Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C-F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization using several PFAS-specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS2), fragment mass differences (MS2), and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub (https://github.com/JonZwe/PFAScreen).