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Mas, J.F. et al. have submitted a paper [1] for publication, which aims to respond to a paper published by Gebhardt et al. [2]. Mas, J.F. et al. had received a consultancy in 2013 to assess the quality of the early prototype products partly described in Gebhardt et al. in 2014. This consultancy, although a formal non-disclosure agreement had not been demanded, was awarded under the mutual understanding that the data handed over to Mas et al. constitute the early development phase of the program. Therefore, Mas et al. had been asked to give an assessment on the quality of the prototypes to obtain a proof of concept for the proposed workflow of MAD-Mex. It was clear that this assessment would suffer from limited availability of high quality training and validation data available in 2013. Mas et al. finally did not execute the consultancy due to the limited vector processing capacities in their lab. In October 2014, we sent the latest products, version 4.2 of the MAD-Mex products, including the more than 200,000 validation points gathered from independent expert interpreters of all Mexican ecosystems. Mas et al. did not respond to this transfer or to our request to collaborate in the quality control and assessment of MAD-Mex. [ABSTRACT FROM AUTHOR]

Bioscientists reading papers or patents strive to discern the key relationships reported within a document “D“ where a bioactivity “A” with a quantitative result “R” (e.g., an IC50) is reported for chemical structure “C” that modulates (e.g., inhibits) a protein target “P”. A useful shorthand for this connectivity thus becomes DARCP. The problem at the core of this article is that the community has spent millions effectively burying these relationships in PDFs over many decades but must now spend millions more trying to get them back out. The key imperative for this is to increase the flow into structured open databases. The positive impacts will include expanded data mining opportunities for drug discovery and chemical biology. Over the last decade commercial sources have manually extracted DARCP from ≈300,000 documents encompassing ≈7 million compounds interacting with ≈10,000 targets. Over a similar time, the Guide to Pharmacology, BindingDB and ChEMBL have carried out analogues DARCP extractions. Although their expert-curated numbers are lower (i.e., ≈2 million compounds against ≈3700 human proteins), these open sources have the great advantage of being merged within PubChem. Parallel efforts have focused on the extraction of document-to-compound (D-C-only) connectivity. In the absence of molecular mechanism of action (mmoa) annotation, this is of less value but can be automatically extracted. This has been significantly accomplished for patents, (e.g., by IBM, SureChEMBL and WIPO) for over 30 million compounds in PubChem. These have recently been joined by 1.4 million D-C submissions from three major chemistry publishers. In addition, both the European and US PubMed Central portals now add chemistry look-ups from abstracts and full-text papers. However, the fully automated extraction of DARCLP has not yet been achieved. This stands in contrast to the ability of biocurators to discern these relationships in minutes. Unfortunately, no journals have yet instigated a flow of author-specified DARCP directly into open databases. Progress may come from trends such as open science, open access (OA), findable, accessible, interoperable and reusable (FAIR), resource description framework (RDF) and WikiData. However, we will need to await the technical applicability in respect to DARCP capture to see if this opens up connectivity.

Simple Summary: A cancer diagnosis during adolescence or young adulthood presents unique medical and psychosocial challenges which must be addressed in the provision of quality, comprehensive cancer care. Tailoring services to the needs of this population requires careful work to identify, monitor and evaluate areas of care; however, published work in this area to guide service priorities is limited. This paper presents work done by the Australian Youth Cancer Services to operationalise and deliver quality care to adolescents and young adults with cancer, focusing on nationally coordinated service improvement initiatives and activity data collection in four areas that are of particular concern to young people diagnosed with cancer: clinical trial enrolment, oncofertility, psychosocial care and survivorship. This account may be instructive for health services seeking to improve the delivery and monitoring of cancer care provided to adolescents and young adults. Adolescents and young adults (aged 15–25 years) diagnosed with cancer have unique medical and psychosocial experiences and care needs, distinct from those of paediatric and older adult patients. Since 2011, the Australian Youth Cancer Services have provided developmentally appropriate, multidisciplinary and comprehensive care to these young patients, facilitated by national service coordination and activity data collection and monitoring. This paper reports on how the Youth Cancer Services have conceptualised and delivered quality youth cancer care in four priority areas: clinical trial participation, oncofertility, psychosocial care and survivorship. National activity data collected by the Youth Cancer Services between 2016–17 and 2019–20 are used to illustrate how service monitoring processes have facilitated improvements in coordination and accountability across multiple indicators of quality youth cancer care, including clinical trial participation, access to fertility information and preservation, psychosocial screening and care and the transition from active treatment to survivorship. Accounts of both service delivery and monitoring and evaluation processes within the Australian Youth Cancer Services provide an exemplar of how coordinated initiatives may be employed to deliver, monitor and improve quality cancer care for adolescents and young adults. [ABSTRACT FROM AUTHOR]

As shared micromobility (bikes and scooters) has proliferated throughout urban areas, there has been growing interest in how it facilitates connections with rail transit systems. This study explores the magnitude of interactions between shared micromobility and rail public transit systems using shared micromobility trip data and rail transit schedule data. We evaluate over one million trips from October 2019 to February 2020 in four California cities (San Francisco, Los Angeles, Sacramento, and San Jose) and develop criteria to identify trips connecting to rail transit. These include spatial and temporal rules, such as whether a trip starts/terminates close to public transit stations and whether a trip takes place when transit systems are operating. The criteria are examined via sensitivity analyses. The results indicate the degree of interaction between rail public transit and shared micromobility varies across cities and systems (i.e., docked/dockless). Most connections take place in the downtown or around public transit hubs. About 5–20% of all shared micromobility trips are identified as accessing or egressing from rail transit. These connecting trips exhibit commute-driven patterns and greater measured velocities. We conclude by examining the applicability of incorporating schedule information into the identification process of shared micromobility trips connecting to rail transit systems. [ABSTRACT FROM AUTHOR]

Manure management in livestock makes a significant contribution towards greenhouse gas emissions in the Agriculture; Forestry and Other Land Use category in South Africa. Methane and nitrous oxide emissions are prevalent in contrasting manure management systems; promoting anaerobic and aerobic conditions respectively. In this paper; both Tier 1 and modified Tier 2 approaches of the IPCC guidelines are utilized to estimate the emissions from South African livestock manure management. Activity data (animal population, animal weights, manure management systems, etc.) were sourced from various resources for estimation of both emissions factors and emissions of methane and nitrous oxide. The results show relatively high methane emissions factors from manure management for mature female dairy cattle (40.98 kg/year/animal), sows (25.23 kg/year/animal) and boars (25.23 kg/year/animal). Hence, contributions for pig farming and dairy cattle are the highest at 54.50 Gg and 32.01 Gg respectively, with total emissions of 134.97 Gg (3104 Gg CO2 Equivalent). Total nitrous oxide emissions are estimated at 7.10 Gg (2272 Gg CO2 Equivalent) and the three main contributors are commercial beef cattle; poultry and small-scale beef farming at 1.80 Gg; 1.72 Gg and 1.69 Gg respectively. Mitigation options from manure management must be taken with care due to divergent conducive requirements of methane and nitrous oxide emissions requirements. [ABSTRACT FROM AUTHOR]

Estimating forest area at a national scale within the United Nations program of Reducing Emissions from Deforestation and Forest Degradation (REDD) is primarily based on land cover information using remote sensing technologies. Timely delivery for a country of a size like Mexico can only be achieved in a standardized and cost-effective manner by automatic image classification. This paper describes the operational land cover monitoring system for Mexico. It utilizes national-scale cartographic reference data, all available Landsat satellite imagery, and field inventory data for validation. Seven annual national land cover maps between 1993 and 2008 were produced. The classification scheme defined 9 and 12 classes at two hierarchical levels. Overall accuracies achieved were up to 76%. Tropical and temperate forest was classified with accuracy up to 78% and 82%, respectively. Although specifically designed for the needs of Mexico, the general process is suitable for other participating countries in the REDD+ program to comply with guidelines on standardization and transparency of methods and to assure comparability. However, reporting of change is ill-advised based on the annual land cover products and a combination of annual land cover and change detection algorithms is suggested. [ABSTRACT FROM AUTHOR]

This paper presents a new dataset, including behavioral, biometric, and environmental data, obtained from 23 subjects each spending 1 week to 2 months in smart rooms in Tokyo, Japan. The approximate duration of the experiment is 2 years. This dataset includes personal data, such as the use of home appliances, heartbeat rate, sleep status, temperature, and illumination. Although there are many datasets that publish these data individually, datasets that publish them all at once, tied to individual pseudo IDs, are valuable. The number of days for which data were obtained was 488, the number of records was 18,418,359, and the total size of the obtained data was 2.76 GB. This dataset can be used for machine learning and analysis for tips on getting a good night's sleep, for example.

Mas, J.F. et al. have submitted a paper [1] for publication, which aims to respond to a paper published by Gebhardt et al. [2]. Mas, J.F. et al. had received a consultancy in 2013 to assess the quality of the early prototype products partly described in Gebhardt et al. in 2014. This consultancy, although a formal non-disclosure agreement had not been demanded, was awarded under the mutual understanding that the data handed over to Mas et al. constitute the early development phase of the program. Therefore, Mas et al. had been asked to give an assessment on the quality of the prototypes to obtain a proof of concept for the proposed workflow of MAD-Mex. It was clear that this assessment would suffer from limited availability of high quality training and validation data available in 2013. Mas et al. finally did not execute the consultancy due to the limited vector processing capacities in their lab. In October 2014, we sent the latest products, version 4.2 of the MAD-Mex products, including the more than 200,000 validation points gathered from independent expert interpreters of all Mexican ecosystems. Mas et al. did not respond to this transfer or to our request to collaborate in the quality control and assessment of MAD-Mex.

This paper presents the Danish emission inventory for non-methane volatile organic compounds (NMVOC) from the use of solvents in industries and households. Solvent use constitutes approximately one third of the total NMVOC emissions in Denmark, i.e. 33,000tonnes in 2006 and represents 103,000tonnes CO2-equivalents, which constitutes 0.2% of the total Danish CO2 emissions. NMVOC emissions may be of small concern in relation to CO2 emissions but they are fundamental in relation to many human and environmental health issues and to long range transport of chemical active species. Use and emission pattern of NMVOCs is diverse and complex, as many chemicals are categorized as NMVOCs and are present in many different industrial activities and products. This new approach utilizes detailed information on chemical use, available from the Nordic database substances in preparations in the Nordic countries (SPIN). The database information and methodology can be adopted by other countries in order to reach more precise CO2 conversion factors and emission inventories. [Copyright &y& Elsevier]

Abstract Background Home care providers struggle to manage their day-to-day work, which is increasing in volume and complexity. In general, they are expected to achieve more with the same planning methods, resources, and capacity. To meet emerging needs and use the available resources more effectively and efficiently, evidence and strategies are needed to inform planning methods for home care services. However, limited data are available to inform this change. This paper investigated the amount of time used to carry out direct activities and six indirect activities across three rural Norwegian municipalities (M1, M2 and M3). Methods Home care staff recorded data over 8 weeks in 2016; the majority of the staff used a smartphone application and some staff used a manual form to report the durations of the activities. Results The median time spent on direct activities was 11–13 min, and this work constituted less than 50% of the total work in the three municipalities. The median driving time was 5–7 min, which accounted for 43–54% of the total indirect work. Administration, particularly reporting and documentation, displayed greater differences across the municipalities, together accounting for 38–50% of the total indirect time. M2 and M3 used substantially more time for documentation, including 20 min in M2 and M3, in contrast to only 1 min in M1. Similarly, the median reporting times were 30 min (M2) and 28 min (M3), compared with only 17 min in M1. Conclusions Home care staff spent less time on direct activities than on indirect activities, of which several activities have the potential for change. These results may help managers utilize resources effectively and plan appropriately, and they may also serve as a basis for future research to identify areas with improvement opportunities and, in turn, make more time available for direct patient care.

European and United States' (U.S.) regulatory schemes mandate the use of biofuels based on estimates of life cycle GHG emissions that indicate their environmental superiority compared with conventional fuels. This paper analyzes the quality of the activity data used in calculating these estimates for Brazilian sugarcane and U.S. corn ethanol, in terms of completeness, reliability, and transparency. The findings demonstrate that it is impossible to determine the completeness of the data because the regulation's texts and related documents, and their sources fail to provide information on the representativeness or origins of the data. Moreover, there is no transparency about the sample from which data is extracted. To obtain this information it is necessary to review the literary sources referenced in the regulation's documents. Among the 37 sources of data examined in this analysis, 23 are no longer available, and only 3 sources contain information allowing for the characterization of the adequacy of the sample. In addition, none of the data items analyzed were subject to a verification process. These findings highlight the urgent need to modify biofuel regulation to set minimum standards for data quality and the reporting of data sources, paying particular attention to default activity data. • Data quality is assessed in terms of completeness, reliability, and transparency. • The quality of data on crops and ethanol production in biofuel regulations is low. • The environmental superiority of Brazilian sugarcane ethanol is unclear. • Biofuel regulators should set minimum standards for LCA data quality. [ABSTRACT FROM AUTHOR]

Adolescents and young adults (aged 15–25 years) diagnosed with cancer have unique medical and psychosocial experiences and care needs, distinct from those of paediatric and older adult patients. Since 2011, the Australian Youth Cancer Services have provided developmentally appropriate, multidisciplinary and comprehensive care to these young patients, facilitated by national service coordination and activity data collection and monitoring. This paper reports on how the Youth Cancer Services have conceptualised and delivered quality youth cancer care in four priority areas: clinical trial participation, oncofertility, psychosocial care and survivorship. National activity data collected by the Youth Cancer Services between 2016–17 and 2019–20 are used to illustrate how service monitoring processes have facilitated improvements in coordination and accountability across multiple indicators of quality youth cancer care, including clinical trial participation, access to fertility information and preservation, psychosocial screening and care and the transition from active treatment to survivorship. Accounts of both service delivery and monitoring and evaluation processes within the Australian Youth Cancer Services provide an exemplar of how coordinated initiatives may be employed to deliver, monitor and improve quality cancer care for adolescents and young adults.

GPS recording devices offer a painless way to collect travel data, but are not directly useful to a standard activity survey. This paper documents one method for linking activities with location data. Based on a small but extended pilot survey, a technique has been developed to estimate the most likely activity at a destination, based on the respondents' past responses. If destinations and activities were randomly paired, this information would be irrelevant. But the pilot survey also demonstrated that activities are tightly clustered in space.

Manure management in livestock makes a significant contribution towards greenhouse gas emissions in the Agriculture; Forestry and Other Land Use category in South Africa. Methane and nitrous oxide emissions are prevalent in contrasting manure management systems; promoting anaerobic and aerobic conditions respectively. In this paper; both Tier 1 and modified Tier 2 approaches of the IPCC guidelines are utilized to estimate the emissions from South African livestock manure management. Activity data (animal population, animal weights, manure management systems, etc.) were sourced from various resources for estimation of both emissions factors and emissions of methane and nitrous oxide. The results show relatively high methane emissions factors from manure management for mature female dairy cattle (40.98 kg/year/animal), sows (25.23 kg/year/animal) and boars (25.23 kg/year/animal). Hence, contributions for pig farming and dairy cattle are the highest at 54.50 Gg and 32.01 Gg respectively, with total emissions of 134.97 Gg (3104 Gg CO2 Equivalent). Total nitrous oxide emissions are estimated at 7.10 Gg (2272 Gg CO2 Equivalent) and the three main contributors are commercial beef cattle; poultry and small-scale beef farming at 1.80 Gg; 1.72 Gg and 1.69 Gg respectively. Mitigation options from manure management must be taken with care due to divergent conducive requirements of methane and nitrous oxide emissions requirements.

New models and methods have been designed to predict the influence of the user's environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a self-assessment of the driving style; (ii) the prediction of aggressive driving style based on drivers' activity and environment parameters. Sixty seven h of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users' activity. The driving style was predicted from the user's environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user's environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts. [ABSTRACT FROM AUTHOR]

Estimating forest area at a national scale within the United Nations program of Reducing Emissions from Deforestation and Forest Degradation (REDD) is primarily based on land cover information using remote sensing technologies. Timely delivery for a country of a size like Mexico can only be achieved in a standardized and cost-effective manner by automatic image classification. This paper describes the operational land cover monitoring system for Mexico. It utilizes national-scale cartographic reference data, all available Landsat satellite imagery, and field inventory data for validation. Seven annual national land cover maps between 1993 and 2008 were produced. The classification scheme defined 9 and 12 classes at two hierarchical levels. Overall accuracies achieved were up to 76%. Tropical and temperate forest was classified with accuracy up to 78% and 82%, respectively. Although specifically designed for the needs of Mexico, the general process is suitable for other participating countries in the REDD+ program to comply with guidelines on standardization and transparency of methods and to assure comparability. However, reporting of change is ill-advised based on the annual land cover products and a combination of annual land cover and change detection algorithms is suggested.

Background: Remote sensing products can provide regular and consistent observations of the Earth's surface to monitor and understand the condition and change of forest ecosystems and to inform estimates of terrestrial carbon dynamics. Yet, challenges remain to select the appropriate satellite data source for ecosystem carbon monitoring. In this study we examine the impacts of three attributes of four remote sensing products derived from Landsat, Landsat- SPOT, and MODIS satellite imagery on estimates of greenhouse gas emissions and removals: (1) the spatial resolution (30 vs. 250 m), (2) the temporal resolution (annual vs. multi-year observations), and (3) the attribution of forest cover changes to disturbance types using supplementary data. Results: With a spatially-explicit version of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), we produced annual estimates of carbon fluxes from 2002 to 2010 over a 3.2 million ha forested region in the Yucatan Peninsula, Mexico. The cumulative carbon balance for the 9-year period differed by 30.7 million MgC (112.5 million Mg CO2e) among the four remote sensing products used. The cumulative difference between scenarios with and without attribution of disturbance types was over 5 million Mg C for a single Landsat scene. Conclusions: Uncertainty arising from activity data (rates of land-cover changes) can be reduced by, in order of priority, increasing spatial resolution from 250 to 30 m, obtaining annual observations of forest disturbances, and by attributing land-cover changes by disturbance type. Even missing a single year in the land-cover observations can lead to substantial errors in ecosystems with rapid forest regrowth, such as the Yucatan Peninsula. [ABSTRACT FROM AUTHOR]

Climate change mitigation schemes, such as REDD and biodiversity conservation in tropical rainforests, necessitate remote sensing based forest monitoring capabilities with high spatial resolution and temporal coverage. Regular monitoring has to be capable of detecting rapid changes in forest extent, i.e. deforestation, and subtle changes to the forest cover caused by logging and/or fire, described as forest degradation. Particularly the early detection of illegal logging activities is important for the conservation of tropical forests. In the present study, a forest disturbance monitoring approach was developed and tested, which makes use of high resolution satellite imagery. A time series consisting of three images, acquired between May 2009 and June 2010, was analyzed covering a remote area of tropical peat swamp forest in Central Kalimantan, Indonesia. The forest area was assessed by an object-oriented classification. Logging activities and the impact of fire were detected by a pixel-based spectral mixture analysis. Forest, non-forest and logging trails could be differentiated with an overall classification accuracy of 91.5% (Kappa of 0.87). A high forest disturbance rate of 8.7% was found in the study area. Low impact logging could be detected reliably and the progress was tracked over time. The results show that the timely detection of forest disturbances is necessary because of the fast regrowth of vegetation. The study emphasises the importance of high resolution satellite imagery for tropical forest monitoring and for timely updating forest status assessments, which is important for the implementation of REDD. [ABSTRACT FROM PUBLISHER]

In pharmaceutical research, compounds with multitarget activity receive increasing attention. Such promiscuous chemical entities are prime candidates for polypharmacology, but also prone to causing undesired side effects. In addition, understanding the molecular basis and magnitude of multitarget activity is a stimulating topic for exploratory research. Computationally, compound promiscuity can be estimated through large-scale analysis of activity data. To these ends, it is critically important to take data confidence criteria and data consistency across different sources into consideration. Especially the consistency aspect has thus far only been little investigated. Therefore, we have systematically determined activity annotations and profiles of known multitarget ligands (MTLs) on the basis of activity data from different sources. All MTLs used were confirmed by X-ray crystallography of complexes with multiple targets. One of the key questions underlying our analysis has been how MTLs act in biological screens. The results of our analysis revealed significant variations of MTL activity profiles originating from different data sources. Such variations must be carefully considered in promiscuity analysis. Our study raises awareness of these issues and provides guidance for large-scale activity data analysis. [ABSTRACT FROM AUTHOR]