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This Policy Brief summarizes the main outputs and preliminary findings of the Arctic PASSION project’s work related to enhancing evidence-based decision-making at the local and regional level in the Arctic, with respect to two themes: 1) the state of inclusion of Indigenous knowledge in sub-national decision-making (including the patterns of interactions between Indigenous Peoples and local/regional policymakers); and 2) data availability, needs and gaps with respect to managing and planning the green transition. The two topics are part of a selection of themes that interest local and regional governments and Arctic PASSION researchers. A complete overview of the findings is provided in the related Policy Paper., This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101003472

Guyana's capacity to address the impacts of climate change on its coastal environment requires the ability to monitor, quantify and understand coastal change over short-, medium- and long- term. Understanding the drivers of change in coastal and marine environment can be achieved through the accurate measurement and critical analyses of morphologies, flows, processes and responses. This manuscript presents a strategy developed to create a central resource, database and web-based platform to integrate data and information on the drivers and the changes within Guyana coastal and marine environment. The strategy involves four complimentary work packages including data collection, development of a platform for data integration, application of the data for coastal change analyses and consultation with stakeholders. The last aims to assess the role of the integrated data systems to support strategic governance and sustainable decision-making. It is hoped that the output of this strategy would support the country's climate-focused agencies, organisations, decision-makers, and researchers in their tasks and endeavours.

Inclusion of unmanned aircraft systems (UAS) into the weather surveillance network has the potential to improve short-term (

Digital health promises to improve healthcare, health, and wellness through the use of digital technologies. The purpose of this commentary is to review and discuss the field of digital health for Parkinson’s disease (PD) focusing on the needs, expectations, and wishes of people with PD (PwP). Our analysis shows that PwP want to use digital technologies to actively manage the full complexity of living with PD on an individual level, including the unpredictability and variability of the condition. Current digital health projects focusing on PD, however, does not live up to the expectations of PwP. We conclude that for digital health to reach its full potential, the right of PwP to access their own data needs to be recognised, PwP should routinely receive personalised feedback based on their data, and active involvement of PwP as an equal partner in digital health development needs to be the norm.

Student's two-sample t-test is often used in medical research like randomized controlled trials. To control type I errors, normality of the observed data needs to be assessed. In practice, a two-st...

Issue addressed Physical activity tracking devices have potential to improve public health, but their data needs to be reliable. No study has compared movement data between the Fitbit Zip, Apple iPhone Health app and physical activity records in a community setting over 10 days. Methods University students aged 18+ years wore both a Fitbit Zip and an iPhone at/near their right waist and completed a modified Bouchard Activity Record (BAR) for 10 days in a free-living setting. Comparisons were made between the Fitbit Zip and iPhone for the number of steps and the distance travelled and between the Fitbit Zip and BAR for the minutes of activity in three different intensities. Results Eighteen students provided sufficient data for inclusion. There were strong correlations between steps per day (r = .87) and distance travelled (r = .88) between the Fitbit Zips and iPhones. However, the Fitbit Zip measured significantly more steps per day (mean 8437 vs 7303; P ≤ .001) and greater distances (mean 5.9 vs 4.9; P ≤ .001) than the iPhone. Correlations between the Fitbit Zips and the BARs were moderate for minutes of total (r = .51) and light (r = .40) activity and weak for moderate/fairly active (r = .20) and vigorous/very active (r = .25). Conclusions There were strong correlations between the physical activity data measured by Fitbit Zips and iPhones, but the iPhone Health app significantly underestimated the number of steps per day taken and the distance travelled when compared to the Fitbit Zip. SO WHAT?: Understanding the comparability of accelerometer devices provides useful information for future pragmatic physical activity measurement.

Place-based philanthropic organizations have long defined their value in terms of ability to improve well-being in the communities they serve. Desire to quantify and prove this impact has led such charities to be interested in and even invest in measures of community well-being. In this paper I explore how the onset of the COVID-19 pandemic has affected local philanthropy's relationship with data and information by increasing public awareness of community data as a tool for describing rapidly changing community needs, raising expectations for an expedited connection between data analysis and action, and compelling civic leaders to engage in scenario planning. I draw on the case of Syracuse, NY to illustrate how the presence of a real time collaborative data infrastructure presents promising opportunities to address the data needs of place-based philanthropy when it comes to monitoring and acting to improve community well-being in the COVID era.

The years 1997/1998 and 2015/2016 saw the worst El Niño occurrence in human history. The occurrence of El Niño causes extreme temperature events which are higher than usual, drought and prolonged drought. The incident caused a decline in the ability of plants in carrying out the process of photosynthesis. This causes the carbon dioxide content to be higher than normal. Studies on the effects of El Niño and its degree of strength are still under-studied especially by researchers in the tropics. This study uses remote sensing technology that can provide spatial information. The first step of remote sensing data needs to go through the pre-process before building the NDVI (Normalized Difference Vegetation Index) and Normalized Difference Water Index (NDWI) maps. Next this study will identify the relationship between Oceanic Nino Index (ONI) with Application Remote Sensing in The Study Of El Niño Extreme Effect 1997/1998 and 2015/2016 On Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)NDWI and NDWI landscape indices. Next will make a comparison, statistical and spatial information space between NDWI and NDVI for each year 1997/1998 and 2015/2016. This study is very important in providing spatial information to those responsible in preparing measures in reducing the impact of El Niño.

Introduction The opioid crisis is a pervasive public health threat in the U.S. Simulation modeling approaches that integrate a systems perspective are used to understand the complexity of this crisis and analyze what policy interventions can best address it. However, limitations in currently available data sources can hamper the quantification of these models. Methods To understand and discuss data needs and challenges for opioid systems modeling, a meeting of federal partners, modeling teams, and data experts was held at the U.S. Food and Drug Administration in April 2019. This paper synthesizes the meeting discussions and interprets them in the context of ongoing simulation modeling work. Results The current landscape of national-level quantitative data sources of potential use in opioid systems modeling is identified, and significant issues within data sources are discussed. Major recommendations on how to improve data sources are to: maintain close collaboration among modeling teams, enhance data collection to better fit modeling needs, focus on bridging the most crucial information gaps, engage in direct and regular interaction between modelers and data experts, and gain a clearer definition of policymakers’ research questions and policy goals. Conclusions This article provides an important step in identifying and discussing data challenges in opioid research generally and opioid systems modeling specifically. It also identifies opportunities for systems modelers and government agencies to improve opioid systems models.

Numerous technologies exist for promoting a healthier lifestyle. These technologies collectively referred to as “Behavior Change Support Systems”. However, the majority of existing apps use quantitative data representation. Since it is difficult to understand the meaning behind quantitative data, this approach has been suggested to lower users’ motivation and fail to promote behavior change. Therefore, an interpretation of quantitative data needs to be provided as a supplement. However, different descriptions of the same data may lead to different outcomes. In this paper, we explore the impact of different communication styles for interpretations of quantitative data on behavior change by developing and evaluating Walkeeper – a web-based app that provides interpretations of the users’ daily step counts using different levels of elaborateness and indirectness with the aim of promoting walking. Through the quantitative analysis and results of a user study, we contribute new knowledge on designing such interpretations for quantitative data.

After an initial delay, Sub-Saharan Africa (SSA) is being hit by the pandemic Demand for exports is falling and caseloads are rising Governments have approached this crisis with a range of policy options Optimal policy balances reduced infection rates with lost economic output This paper discusses how an economic-epidemiological model used to analyze policy in high-income countries could be adapted to a context where poverty considerations are paramount Differences in country characteristics across the continent affect benefits and costs of alternative policy designs We conclude by highlighting data needs and model calibration challenges for COVID-19 policy research in SSA

The delayed delivery, poor fitting and discomfort of customised orthoses are reported in rehabilitation clinics as resulting in more invasive interventions. The current practice of orthosis customisation relies heavily upon the experience and fabrication processes of therapists. In order to better understand the current practice, and thus identify data that is required for better comfort moving towards a data-driven customisation, this article describes a study generating working models of therapists. Customisations of hand and wrist orthoses for 18 patients were observed. Verbal protocol analysis was employed to extend the current understanding of fabrication processes. Working models of four therapists were established with quantitative evaluation on major phases, interactive activities and iterations of performing tasks during fabrication, revealing different working models between in- and out-patient departments (e.g. fabrication for in-patients was more complex and focussed on ergonomic fitting whereas fabrication for out-patients paid attention to durability) which were qualitatively explained. Practitioner summary: Fit and comfort are imperative for orthosis design and fabrication, however the current practice of customisation of an orthosis relies upon the experience of individual hand therapist. The article presents working models of hand therapists, and relevant data that would enable customisation of orthosis for better fit. Abbreviations: VPA: verbal protocol analysis; hw LTT: low temperature thermoplastic; ANOVA: analysis of variance.

A variety of data needs challenge the successful restoration and management of alosine populations, including information on the migration, mortality, behavior, demographic rates, and distribution of fish, both in riverine and marine environments. Radiotelemetry with gastric‐implanted transmitters has typically been used to answer some of these questions; however, observing alosines over extended periods and in the marine environment has remained beyond the limitations of this technology and implantation technique. To address these issues, we conducted an acoustic telemetry study on American Shad Alosa sapidissima by using surgical implantation methods. We tagged fish during 2015 (n = 46) and 2016 (n = 52) in the Charles River, Massachusetts, an urbanized watershed where American Shad were believed to be extirpated prior to restoration efforts beginning in 2006. Surgical implantation produced rates of in‐river mortality (40% overall) and posttagging fallback (39% overall) that were comparable to those from traditionally used gastric implantation methods. Data from American Shad that were retained for statistical analyses (n = 59) demonstrated that Watertown Dam (at river kilometer 14.3) impeded upstream migration and that New Boston Dam and Locks (at the mouth of the river) delayed postspawn emigration from the river. In total, 49 American Shad were detected outside of the Charles River. The distribution and low number of total detections, despite a large number of nearshore arrays, suggest that American Shad occupy waters farther offshore during their marine phase. American Shad were detected as overwintering on the Scotian Shelf (n = 5) and the Mid‐Atlantic Bight (n = 1). In 2017, 10 of the individuals that were tagged in 2016 returned to spawn, providing the first reported data on total migration timing and migratory behavior free of handling effects. Surgical implantation of acoustic telemetry tags is an effective method that can provide necessary and previously unattainable data on a species of conservation need.

Considerable amount of electromechanical admittance data needs to be collected, transmitted and stored during in-situ and long-term structural health monitoring applications, and data loss could be inevitably met when processing the monitoring electromechanical admittance signals. In this article, an innovative compressed sensing–based approach is proposed to implement data recovery for electromechanical admittance technique–based concrete structural health monitoring. The basis of this approach is to first project the original conductance signature onto an observation vector as sampled data, and then transmit the observation vector with data loss to storage station, and finally recover the missing data via a compressed sensing process. For comparison, both convex optimization theory and orthogonal matching pursuit algorithm are introduced to accomplish the compressed sensing–based electromechanical admittance data loss recovery. Prior detection test of a concrete cube subjected to varied temperatures and practical monitoring experiment of full-scale concrete shield tunnel segment undergone bolt-loosened defects are utilized to validate the feasibility of the proposed approach. In lost electromechanical admittance data recovery process, two types of data loss, namely, single-consecutive-segment loss and multiple-consecutive-segment losses, in sampled data are taken into consideration for sufficiently interpreting the effectiveness and accuracy of the convex optimization and orthogonal matching pursuit approaches. In the temperature recognition and damage identification stage, amplitude and frequency shifts in resonance peaks, cooperated with a common statistical index called root-mean-squared-deviation, are harnessed to achieve the goal after the lossy conductance signatures are recovered. The results show that the orthogonal matching pursuit–based data recovery approach is superior to the convex optimization approach because of its smaller calculation of consumption as well as lower recovered errors.

Data only generates value for a few organizations with expertise and resources to make data shareable, discoverable, and easy to integrate. Sharing data that is easy to discover and integrate is hard because data owners lack information (who needs what data) and they do not have incentives to prepare the data in a way that is easy to consume by others. In this paper, we propose data market platforms to address the lack of information and incentives and tackle the problems of data sharing, discovery, and integration. In a data market platform, data owners want to share data because they will be rewarded if they do so. Consumers are encouraged to share their data needs because the market will solve the discovery and integration problem for them in exchange for some form of currency. We consider internal markets that operate within organizations to bring down data silos, as well as external markets that operate across organizations to increase the value of data for everybody. We outline a research agenda that revolves around two problems. The problem of market design, or how to design rules that lead to desired outcomes, and the systems problem, how to implement the market and enforce the rules. Treating data as a first-class asset is sorely needed to extend the value of data to more organizations, and we propose data market platforms as one mechanism to achieve this goal.

Synchrotron beamlines acquiring data for tomographic imaging routinely produce a few TB of data per day, and significantly more in the case of time-resolved studies. This work investigates the impact of lossy compression of raw acquisitions onto tomographic reconstructions, aiming for production use. It is shown that a compression factor of at least three to four times does generally not affect the reconstruction quality and that higher factors (six to eight times) can be achieved for tomographic volumes with a high signal-to-noise ratio as it is the case for phase-retrieved datasets., Modern detectors used at synchrotron tomographic microscopy beamlines typically have sensors with more than 4–5 mega-pixels and are capable of acquiring 100–1000 frames per second at full frame. As a consequence, a data rate of a few TB per day can easily be exceeded, reaching peaks of a few tens of TB per day for time-resolved tomographic experiments. This data needs to be post-processed, analysed, stored and possibly transferred, imposing a significant burden onto the IT infrastructure. Compression of tomographic data, as routinely done for diffraction experiments, is therefore highly desirable. This study considers a set of representative datasets and investigates the effect of lossy compression of the original X-ray projections onto the final tomographic reconstructions. It demonstrates that a compression factor of at least three to four times does not generally impact the reconstruction quality. Potentially, compression with this factor could therefore be used in a transparent way to the user community, for instance, prior to data archiving. Higher factors (six to eight times) can be achieved for tomographic volumes with a high signal-to-noise ratio as it is the case for phase-retrieved datasets. Although a relationship between the dataset signal-to-noise ratio and a safe compression factor exists, this is not simple and, even considering additional dataset characteristics such as image entropy and high-frequency content variation, the automatic optimization of the compression factor for each single dataset, beyond the conservative factor of three to four, is not straightforward.

UMKM is recognized as having an important role as an economic buffer through its contribution to national GDP and unemployment reduction. However, the acknowledgment, the reality is not linear with improving business performance and the development of the UMKM business itself. Even though it has contributed greatly, its performance and development still cannot compete with MSMEs in other Asian countries. This phenomenon encourages a study to empirically model determinant factors that influence the development of MSME businesses. The result is an empirical model of business development that is explained by business performance, entrepreneurial competence, personal factors, organizational factors and environmental factors. To meet the data needs of these variables, interviews were conducted using a questionnaire. Testing empirical models and hypotheses through the research data obtained was carried out using the Structural Equation Modeling (SEM) approach. The findings from the test results are that business development is explained by business performance. While business performance is determined by entrepreneurial competence, personal factors and environmental factors. Organizational factors cannot be proven in this study.

This paper presents a dataset produced from the largest known survey examining how researchers and support professionals discover, make sense of and reuse secondary research data. 1677 respondents in 105 countries representing a variety of disciplinary domains, professional roles and stages in their academic careers completed the survey. The results represent the data needs, sources and strategies used to locate data, and the criteria employed in data evaluation of these respondents. The data detailed in this paper have the potential to be reused to inform the development of data discovery systems, data repositories, training activities and policies for a variety of general and specific user communities., Measurement(s) data discovery behaviours • data reuse behaviours • data management Technology Type(s) Survey • Self-Report Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12445034

The emerging area of the internet of things (IoT) generates a large amount of data from IoT applications such as health care, smart cities, etc. This data needs to be analyzed in order to derive useful inferences. Machine learning (ML) plays a significant role in analyzing such data. It becomes difficult to select optimal algorithm from the available set of algorithms/classifiers to obtain best results. The performance of algorithms differs when applied to datasets from different application domains. In learning, it is difficult to understand if the difference in performance is real or due to random variation in test data, training data, or internal randomness of the learning algorithms. This study takes into account these issues during a comparison of ML algorithms for binary and multivariate classification. It helps in providing guidelines for statistical validation of results. The results obtained show that the performance measure of accuracy for one algorithm differs by critical difference (CD) than others over binary and multivariate datasets obtained from different application domains.

Data plays a major role in helping to understand clearly the changing needs of academic library users, and in helping libraries to innovate their services and procedures accordingly. Data needs to be transformed into information for decision-making and strategic planning. Business intelligence offers powerful analytical tools, such as visualization and data-mining tools, which lead to informed decisions and hence transform the user’s experience, bringing it to a more advanced level. This research investigates the concept of business intelligence from the perceptions of information department staff at academic libraries in Jordan. The opportunities and challenges associated with it are also discussed and explored. As indicated by the results, information department staff agree that business intelligence improves decision-making, helping decision-makers to make the most accurate and timely decisions for the library. The results also indicate that an appropriate infrastructure is important for the successful implementation of business intelligence in academic libraries in Jordan.

Policy document mention is considered to indicate the significance and societal impact of scientific product. However, the accuracy of policy document altmetrics data needs to be evaluated to fully understand its strength and limitation. An in-depth coding analysis was conducted on sample policy documents records of Altmetric.com database. The sample consists of 2079 records from all 79 distinct policy document source platforms tracked by the database. Errors about mentioned publications in the policy documents (type A error) are found in 8% of the records, while errors about either the recorded policy documents or the mentioned publications in the altmetrics database (type B error) are found in 70% of the records. In type B error, policy document link error (5% of the records) could be attributable to the policy document website, transcription error (52% of the records) could be attributable to the third-party bibliographic data provider. These two categories of error are relatively minor and may have limited influence on altmetrics research and practices. False positive policy document mention (13% of the records), however, could be attributable to the Altmetric database and may diminish the validity of research based on the policy document altmetrics data. The underlying reasons remain to be further investigated. Considering the high complexity of extracting mentions of publications from various sources and formats of policy documents as well as its short history, Altmetric database has achieved excellent performance.

Demographic maps can present statistical data on demographic education levels, population stages, family stages, education levels, age, and number of lives in families based on participation in the National Health Insurance (JKN). Where every Regional Apparatus Organization (OPD) in West Bandung Regency that requires population control data needs a data integration and performance optimization, so that in each Regional Apparatus Organization (OPD) in work there is no more duplication of work. In this study, it is expected to be able to map all information about population control data to help the community and Regional Apparatus Organizations (OPD) in West Bandung Regency in order to obtain information clearly from population control data based on the results of family data collection which will make it easier for Regional Apparatus Organizations (OPD) related in order to help the entire community in West Bandung Regency. In this case a solution will be made by utilizing the QGIS application in solving problems where for population control maps can be integrated in the Regional Apparatus Organization (OPD) in West Bandung Regency.

The level of traffic stress (LTS) methodology was developed to measure, track, and improve the suitability of bicycle networks. Thanks to the simplicity of its data needs and interpretation, LTS has been implemented by several states, regions, cities, non-profits, and researchers. However, relatively few validations of the methodology exist. There is a specific gap in relation to safety perceptions for children, an important group since it serves as the critical population for LTS 1. This study validates LTS using a survey of parents in Denver, Colorado, in which they are asked about perceived safety and biking allowance relative to roadway design characteristics. After the LTS score and biking allowance rates for 612 roadway scenarios are determined, a one-way analysis of variance (ANOVA) is used to determine the suitability of LTS for children. Findings suggest that while LTS 1 and LTS 4 align well with stated preferences, parents told that their children would be able to tolerate some roadway conditions—when allowing for adult supervision—that are currently considered LTS 2 or even LTS 3. These scenarios are primarily on low-volume roadways that have bike lanes. By further refining LTS, it is hoped to ensure that all populations have access to safe and comfortable bicycle facilities.

A group of 38 experts specializing in hyperspectral remote-sensing methods for aquatic ecosystems attended an interactive Euromarine Foresight Workshop at the Flanders Marine Institute (VLIZ) in Ostend, Belgium, June 4–6, 2019. The objective of this workshop was to develop recommendations for comprehensive, efficient, and effective laboratory and field programs to supply data for development of algorithms and validation of hyperspectral satellite imagery for micro-, macro- and endosymbiotic algal characterization across the globe. The international group of researchers from Europe, Asia, Australia, and North and South America (see online Supplementary Materials) tackled how to develop global databases that merge hyperspectral optics and phytoplankton group composition to support the next generation of hyperspectral satellites for assessing biodiversity in the ocean and in food webs and for detecting water quality issues such as harmful algal blooms. Through stimulating discussions in breakout groups, the team formulated a host of diverse programmatic recommendations on topics such as how to better integrate optics into phytoplankton monitoring programs; approaches to validating phytoplankton composition with ocean color measurements and satellite imagery; new database specifications that match optical data with phytoplankton composition data; requirements for new instrumentation that can be implemented on floats, moorings, drones, and other platforms; and the development of international task forces. Because in situ observations of phytoplankton biogeography and abundance are scarce, and many vast oceanic regions are too remote to be routinely monitored, satellite observations are required to fully comprehend the diversity of micro-, macro-, and endosymbiotic algae and any variability due to climate change. Ocean color remote sensing that provides regular synoptic monitoring of aquatic ecosystems is an excellent tool for assessing biodiversity and abundance of phytoplankton and algae in aquatic ecosystems. However, neither the spatial, temporal, nor spectral resolution of the current ocean color missions are sufficient to characterize phytoplankton community composition adequately. The near-daily overpasses from ocean color satellites are useful for detecting the presence of blooms, but the spatial resolution is often too coarse to assess the patchy distribution of blooms, and the multiband spectral resolution is generally insufficient to identify different types of phytoplankton from each other, even if progress has undeniably been achieved during the last two decades (e.g., IOCGG, 2014). Moreover, the methods developed for multichannel sensor use are often highly tuned to a region but are inaccurate when applied broadly. New orbital imaging spectrometers are being developed that cover the full visible and near-infrared spectrum with a large number of narrow bands dubbed “hyperspectral” (e.g., TROPOMI, PRISMA, EnMAP, PACE, CHIME, SBG). Hyper-spectral methods have been explored for many years to assess phytoplankton groups and map seafloor habitats. However, the utility of hyperspectral imaging still needs to be demonstrated across diverse aquatic regimes. Aquatic applications of hyperspectral imagery have been limited by both the technology and the ability to validate products. Some of the past hyperspectral space-based sensors have suffered from calibration artifacts, low sensitivity in aquatic ecosystems (e.g., CHRIS, HICO), and very low spatial resolution (e.g., SCIAMACHY), but the next generation of sensors are planned to have high signal-to-noise ratio and improved performance over aquatic targets. Providing data to develop and validate hyperspectral approaches to characterize phytoplankton groups across the globe poses new challenges. Several recent studies have documented gaps that need to be filled in order to assess algal diversity across the globe (IOCCG, 2014; Mouw et al., 2015; Bracher et al., 2017), which promoted/inspired the formation of this workshop.

In its revised bioeconomy strategy, the European Union (EU) has extended the scope of activities to include services. Employing an output-based approach, this study quantifies the contribution of bioeconomy services to gross domestic product and employment in the EU Member States over 2008-2017. Moreover, it also identifies the main sectoral sources of employment and growth within bioeconomy services. The choice of Eurostat statistics ensures data harmonisation across countries and continuity for future updates, although important data needs are identified to enhance the representation of bioeconomy services within European statistical frameworks. In 2015-2017, economic growth was stronger in bioeconomy services than in the total EU economy. Bioeconomy services accounted for between 5.0-8.6% and 10.2-16.9% of EU gross domestic product and the EU labour force, respectively, whilst three service sectors account for more than 60% of bioeconomy services employment and value added. Interestingly, in the decade up to 2017, labour productivity in bioeconomy services improved.

Modern sensor technology and decreasing hardware costs enable the collection of a wide range of data. Nonetheless, the collection of data itself does not generate value. The collected data must be processed and analysed. Many small and medium-sized enterprises already collect a number of data. However, there is no definite strategy, which data needs to be collected in order to acquire relevant insights into processes. The enormous potential of data analysis and the current lack of its implementation caused the development of this framework. It will assist enterprises to evaluate their own level of digitalization to assess resource use.

The paper identifies for the first time for the Volgograd region (Russia) the clinical and epidemiological characteristics of children 0-16 years old with the laboratory confirmed diagnosis of COVID-19 hospitalized in a children’s infectious clinic in April-August 2020. The Volgograd Region is one of the biggest territorial entities in Southern Russia; therefore our first published analytical data needs to be compared with relevant Russian/ European data. The diagnosis was verified by the nucleic acid amplification: isolation of SARS-CoV-2 RNA by the polymerase chain reaction (PCR) from the upper respiratory tract mucosa. The study did not include outpatients. Rarity of such symptoms as headache and weakness (7.1%), as well as dysosmia (4.1%) in the complete absence of dysgeusia attracted our attention. In none of the patients we were able to identify an exanthem over the course of infection.

Rapid commercialisation of nano-enabled products (NEPs) elevates the potential environmental release of engineered nanomaterials (ENMs) along the product life cycle. The current review examined the state of the art literature on aquatic environment exposure and ecotoxicity of product released (PR) engineered nanomaterials (PR–ENMs). Additionally, the data obtained were applied to estimate the risk posed by PR–ENMs to various trophic levels of aquatic biota as a means of identifying priority NEPs cases that may require attention with regards to examining environmental implications. Overall, the PR–ENMs are predominantly associated with the matrix of the respective NEPs, a factor that often hinders proper isolation of nano-driven toxicity effects. Nevertheless, some studies have attributed the toxicity basis of observed adverse effects to a combination of the released ions, ENMs and other components of NEPs. Notwithstanding the limitation of current ecotoxicology data limitations, the risk estimated herein points to an elevated risk towards fish arising from fabrics’ PR–nAg, and the considerable potential effects from sunscreens’ PR–nZnO and PR–nTiO2 to algae, echinoderms, and crustaceans (PR–nZnO), whereas PR–nTiO2 poses no significant risk to echinoderms. Considering that the current data limitations will not be overcome immediately, we recommend the careful application of similar risk estimation to isolate/prioritise cases of NEPs for detailed characterisation of ENMs’ release and effects in aquatic environments.

Computational Biodiversity can broadly be understood as the effort of computational approaches for exploring, interpreting, and analyzing biodiversity data. An enormous load of growing biodiversity data needs algorithmic care for accurate data management, and therefore the term computational biodiversity comes. Instead of relying purely on presence data, the probabilistic forecast of member distribution including the regions of not occurrence can neutralize biodiversity loss by restoring potential ecosystems. This paper is aiming at revealing the perspective of computational biodiversity as a counteract for biodiversity loss by correlating the concept of dark diversity. The computation of the dark diversity is accompanied by a data mining algorithm for establishing rules with more nobility to manage the depletion of biodiversity. We generate a dataset for the Indian estuarine ecosystem and show the use of our approach by ending up with rules worthwhile for the ecologists. These would step up reinforcing biological diversity via introducing or rehabilitating specific faunal groups to an estuary under survey.