Digestate is a byproduct of anaerobic digestion, which can be considered waste or a product of potential use for the chemical industry or agriculture. In either case, the digestate must usually be ...treated prior to being disposed of or valorized. This review describes digestate processing technologies and their specific characteristics. Nutrient recovery and removal from digestate can be achieved through mechanical, physicochemical or biological processes. Available and potential digestate treatment techniques are presented. The complexities of the technologies available, legislation, the agronomical value of the digestate and the economic value of the process mean a decision support tool is required to help managers choose the best digestate processing technology. To ensure adequate analysis, the whole biomethanization project should be integrated in the use of these decision support tools. The objectives and limits of some of the currently available tools are analyzed at the end of this review.
Over 10% of children with Wilms tumor (WT) have an underlying cancer predisposition syndrome (CPS). Cognizant of increasing demand for genetic evaluation and limited resources across health care ...settings, there is an urgent need to rationalize genetic referrals for this population. The McGill Interactive Pediatric OncoGenetic Guidelines study, a Canadian multi‐institutional initiative, aims to develop an eHealth tool to assist physicians in identifying children at elevated risk of having a CPS. As part of this project, a decisional algorithm specific to WT consisting of five tumor‐specific criteria (age <2 years, bilaterality/multifocality, stromal‐predominant histology, nephrogenic rests, and overgrowth features) and universal criteria including features of family history suspicious for CPS and congenital anomalies, was developed. Application of the algorithm generates a binary recommendation—for or against genetic referral for CPS evaluation. To evaluate the algorithm's sensitivity for CPS identification, we retrospectively applied the tool in consecutive pediatric patients (n = 180) with WT, diagnosed and/or treated at The Hospital for Sick Children (1997–2016). Odds ratios were calculated to evaluate the strengths of associations between each criterion and specific CPS subtypes. Application of the algorithm identified 100% of children with WT and a confirmed CPS (n = 27). Age <2 years, bilaterality/multifocality, and congenital anomalies were strongly associated with pathogenic variants in WT1. Presence of >1 overgrowth feature was strongly associated with Beckwith‐Wiedemann syndrome. Stromal‐predominant histology did not contribute to CPS identification. We recommend the incorporation of the WT algorithm in the routine assessment of children with WT to facilitate prioritization of genetic referrals in a sustainable manner.
What's new?
Over 10% of children with Wilms tumor (WT) have an underlying cancer predisposition syndrome (CPS). CPS recognition can lead to immediate changes in first line cancer therapy, implementation of surveillance strategies, and genetic counselling of family members. Here, as part of the MIPOGG (McGill Interactive Pediatric OncoGenetic Guidelines) study, the authors report the effectiveness of a novel, easy‐to‐use eHealth decision‐support tool in identifying those patients diagnosed with WT who are at highest likelihood of having an underlying CPS. These findings provide a systematic approach for pediatric oncologists worldwide to rationalize genetic referral practices and genetic testing for children with Wilms tumor.
•Challenges for smart intensification of marginal land are manifold•Tools for precise agriculture will aid to detect pollutant hotspots and poor soils•Crop rotation and adapted crop choice will yield ...biomass•Amendments will sequester carbon and release fertilizer when needed•Potentials of marginal soils can be unlocked and lead to ecological and economical success
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The rapid increase of the world population constantly demands more food production from agricultural soils. This causes conflicts, since at the same time strong interest arises on novel bio-based products from agriculture, and new perspectives for rural landscapes with their valuable ecosystem services. Agriculture is in transition to fulfill these demands. In many countries, conventional farming, influenced by post-war food requirements, has largely been transformed into integrated and sustainable farming. However, since it is estimated that agricultural production systems will have to produce food for a global population that might amount to 9.1 billion by 2050 and over 10 billion by the end of the century, we will require an even smarter use of the available land, including fallow and derelict sites. One of the biggest challenges is to reverse non-sustainable management and land degradation. Innovative technologies and principles have to be applied to characterize marginal lands, explore options for remediation and re-establish productivity. With view to the heterogeneity of agricultural lands, it is more than logical to apply specific crop management and production practices according to soil conditions. Cross-fertilizing with conservation agriculture, such a novel approach will provide (1) increased resource use efficiency by producing more with less (ensuring food security), (2) improved product quality, (3) ameliorated nutritional status in food and feed products, (4) increased sustainability, (5) product traceability and (6) minimized negative environmental impacts notably on biodiversity and ecological functions. A sustainable strategy for future agriculture should concentrate on production of food and fodder, before utilizing bulk fractions for emerging bio-based products and convert residual stage products to compost, biochar and bioenergy. The present position paper discusses recent developments to indicate how to unlock the potentials of marginal land.
1. Earlier declines in marine resources, combined with current fishing pressures and devastating coral mortality in 2015, have resulted in a degraded coral reef ecosystem state at Puakō in West ...Hawai'i. Changes to resource management are needed to facilitate recovery of ecosystem functions and services. 2. We developed a customised ecosystem model to evaluate the performance of alternative management scenarios at Puakō in the provisioning of ecosystem services to human users (marine tourists, recreational fishers) and enhancing the reef's ability to recover from pressures (resilience). 3. Outcomes of the continuation of current management plus five alternative management scenarios were compared under both high and low coral-bleaching related mortality over a 15-year time span. 4. Current management is not adequate to prevent further declines in marine resources. Fishing effort is already above the multispecies sustainable yield, and, at its current level, will likely lead to a shift to algal-dominated reefs and greater abundance of undesirable fish species. Scenarios banning all gears other than line fishing, or prohibiting take of herbivorous fishes, were most effective at enhancing reef structure and resilience, dive tourism, and the recreational fishery. Allowing only line fishing generated the most balanced trade-off between stakeholders, with positive gains in both ecosystem resilience and dive tourism, while only moderately decreasing fishery value within the area. 5. Synthesis and applications. Our customised ecosystem model projects the impacts of multiple, simultaneous pressures on a reef ecosystem. Trade-offs of alternative approaches identified by local managers were quantified based on indicators for different ecosystem services (e.g. ecosystem resilience, recreation, food). This approach informs managers of potential conflicts among stakeholders and provides guidance on approaches that better balance conservation objectives and stakeholders' interests. Our results indicate that a combination of reducing land-based pollution and allowing only line fishing generated the most balanced trade-off between stakeholders and will enhance reef recovery from the detrimental effects of coral bleaching events that are expected over the next 15 years.
Electronic clinical decision support tools (eCDSTs) are interventions designed to facilitate clinical decision-making using targeted medical knowledge and patient information. While eCDSTs have been ...demonstrated to improve quality of care, there is a paucity of research relating to the acceptability of eCDSTs in primary care from the patients’ perspective. This study aims to summarize current evidence relating to primary care patients' perceptions and experiences on the use of eCDSTs by their clinician to provide care.
Four databases (Medline, Embase, CINAHL and Cochrane Library) were searched for qualitative and quantitative studies with outcomes relating to patients' perceptions of the use of clinician-facing or shared-eCDSTs. Data extraction and critical appraisal using the Johanna Briggs Institute Critical Appraisal checklists were carried out independently by reviewers. Qualitative and quantitative outcomes were synthesized independently. We used Richardson et al. ‘Patient Evaluation of Artificial Intelligence (AI) in Healthcare’ framework for qualitative analysis.
20 papers were included for synthesis. eCDSTs were generally well-regarded by patients. The key facilitators for use were promoting informed decision-making, prompting discussions, aiding clinical decision-making, and enabling information sharing. Key barriers for use were lack of holistic care, 'medicalized’ language, and confidentiality concerns.
Our study identified important aspects to consider in the development of future eCDSTs. Patients were generally positive regarding the use of eCDSTs; however, patient’s perspectives
should be included from the conception of new eCDSTs to ensure recommendations align with the needs of patients and clinicians.
The study results contribute to ensuring the acceptability of eCDSTs for patients and their unique needs. Encouragement is given for future development to adopt and build upon these findings. Additional research focusing on patients' perceptions of using eCDSTs for specific health conditions is deemed necessary.
•Patients reported that eCDSTs enhance communication and discussions with their GPs in consultations.•eCDSTs were viewed as a source of education and a useful tool that can enhance GP performance.•eCDSTs can facilitate inter-clinician information sharing.•-eCDST language should be clearer and understandable for lay individuals.•-eCDST recommendations were too ‘medicalised’ and should incorporate more personal factors.
Nowadays, guaranteeing the highest product variety in the shortest delivery time represents one of the main challenges for most of industries. The dynamic contexts where they have to compete push ...them to quickly readapt their processes, increasing the need for reactive decision-support tools to identify targeted actions to improve performance. Starting from the analysis of existing decision-support tools separately adopting simulation or data mining techniques, a framework that combines Association Rule Mining (ARM) and simulation has been developed to capitalize on the benefits brought by both techniques. On the one hand, ARM supports companies in identifying the main criticalities that slow down production processes, such as different causes of stoppage, giving a priority ranking of interventions. On the other hand, data-driven simulation is used to validate the ARM results and to conduct scenario analyses to compare the KPIs values resulting from different configurations of the production processes. Once the best-impacting mitigating actions have been implemented, the proposed framework can be iteratively used to define an updated set of intervention areas to enhance, promoting continuous improvement. This data-driven approach represents the key value of the framework, guaranteeing its easy-to-readapt and iteratively application. Theoretical contributions refer to the use of simulation with ARM not only to validate relations but to perform scenario analyses in an iterative way, as well as to the novelty application in a low-tech sector. From a practical point of view, a case study in the fashion industry demonstrates the usability and reliability of the proposed framework.
•Dynamic contexts push companies to quickly readapt their processes, increasing the need of reactive data-driven decision support tools to make the data acquisition and updating faster.•Association Rule Mining (ARM) supports companies to identify the main criticalities that slow down production processes.•Data-driven simulation could be used to validate the results of the ARM and to conduct scenario analyses.•Companies could optimize production performance and planning, combining data-driven ARM and simulation approaches.•A case study in the fashion industry demonstrates the usability of the proposed framework.
Shared decision making (SDM), an integrative patient-provider communication process emphasizing discussion of scientific evidence and patient/family values, may improve quality care delivery, promote ...evidence-based practice, and reduce overuse of surgical care. Little is known, however, regarding SDM in elective surgical practice. The purpose of this systematic review is to synthesize findings of studies evaluating use and outcomes of SDM in elective surgery.
PubMed, Cochrane CENTRAL, EMBASE, CINAHL, and SCOPUS electronic databases.
We searched for English-language studies (January 1, 1990, to August 9, 2015) evaluating use of SDM in elective surgical care where choice for surgery could be ascertained. Identified studies were independently screened by 2 reviewers in stages of title/abstract and full-text review. We abstracted data related to population, study design, clinical dilemma, use of SDM, outcomes, treatment choice, and bias.
Of 10,929 identified articles, 24 met inclusion criteria. The most common area studied was spine (7 of 24), followed by joint (5 of 24) and gynecologic surgery (4 of 24). Twenty studies used decision aids or support tools, including modalities that were multimedia/video (13 of 20), written (3 of 20), or personal coaching (4 of 20). Effect of SDM on preference for surgery was mixed across studies, showing a decrease in surgery (9 of 24), no difference (8 of 24), or an increase (1 of 24). SDM tended to improve decision quality (3 of 3) as well as knowledge or preparation (4 of 6) while decreasing decision conflict (4 of 6).
SDM reduces decision conflict and improves decision quality for patients making choices about elective surgery. While net findings show that SDM may influence patients to choose surgery less often, the impact of SDM on surgical utilization cannot be clearly ascertained.
Hospital length of stay (LoS) varies widely across hip (HA) and knee arthroplasty (KA) patients and depends on multiple factors. Prediction methods are necessary to improve hospital capacity planning ...and identify patients at risk of long LoS. This study aims (1) to compare the performance of previously applied machine learning (ML) as well as regression methods for either LoS classification or regression in a multi-hospital setting for primary HA and KA patients. In addition, the study aims (2a) to assess which variables are the most important predictors for LoS prediction and, specifically, (2b) whether patient-reported outcome measures (PROMs) collected before surgery act as important predictors.
2611 primary HA and 2077 primary KA patients from eight German hospitals were included to train and test extreme gradient boosting (XGBoost), naïve Bayes (NB) and logistic regression (LogReg) for classification, and XGBoost as well as a linear regression (LinReg) for regression. Area under the receiver operating characteristics curve (AUC) and mean absolute error (MAE) were used as primary performance indicators for classification and regression.
For classification, the highest AUC was reached by XGBoost and LogReg (AUC = 0.81) in the HA sample, whereas NB was statistically significantly outperformed by both other methods. In the KA sample, no statistical difference between any method was found, and AUC was lower for all models compared with HA. For regression, MAE was lowest for XGBoost (1.43 days for HA and 1.21 days for KA). PROMs and hospital indicators were among the most relevant predictors in all cases.
The study demonstrated robust performance of ML in predicting LoS. PROMs reflect relevant features for prediction. They should be routinely collected and used for practical applications. XGBoost may act as a superior prediction tool compared to regression or other ML models in certain circumstances.
•Machine learning and regression were compared for length of stay (LoS) prediction in hip and knee arthroplasty patients.•LoS could be predicted with good to fair performance by both regression and machine learning.•Most relevant predictors were hospital indicators and patient reported outcome measures.•The tools may be used for capacity planning, patient expectation management and patient risk management.
Abstract
STUDY QUESTION
Can a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization ...methods be developed for the purpose of supporting the embryologist's decision on which embryo to transfer back to the patient in assisted reproduction?
SUMMARY ANSWER
The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential.
WHAT IS KNOWN ALREADY
Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported.
STUDY DESIGN, SIZE, DURATION
Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5.
PARTICIPANTS/MATERIALS, SETTING, METHODS
The algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the curve (AUC) to establish the predictive strength of the algorithm.
MAIN RESULTS AND THE ROLE OF CHANCE
By applying the here developed algorithm (KIDScore), which was based on six annotations (the number of pronuclei equals 2 at the 1-cell stage, time from insemination to pronuclei fading at the 1-cell stage, time from insemination to the 2-cell stage, time from insemination to the 3-cell stage, time from insemination to the 5-cell stage and time from insemination to the 8-cell stage) and ranking the embryos in five groups, the implantation potential of the embryos was predicted with an AUC of 0.650. On Day 3 the KIDScore algorithm was capable of predicting blastocyst development with an AUC of 0.745 and blastocyst quality with an AUC of 0.679. In a comparison of blastocyst prediction including six other published algorithms and KIDScore, only KIDScore and one more algorithm surpassed an algorithm constructed on conventional Alpha/ESHRE consensus timings in terms of predictive power.
LIMITATIONS, REASONS FOR CAUTION
Some morphological assessments were not available and consequently three of the algorithms in the comparison were not used in full and may therefore have been put at a disadvantage. Algorithms based on implantation data from Day 3 embryo transfers require adjustments to be capable of predicting the implantation potential of Day 5 embryo transfers. The current study is restricted by its retrospective nature and absence of live birth information. Prospective Randomized Controlled Trials should be used in future studies to establish the value of time-lapse technology and morphokinetic evaluation.
WIDER IMPLICATIONS OF THE FINDINGS
Algorithms applicable to different culture conditions can be developed if based on large data sets of heterogeneous origin.
STUDY FUNDING/COMPETING INTEREST(S)
This study was funded by Vitrolife A/S, Denmark and Vitrolife AB, Sweden. B.M.P.’s company BMP Analytics is performing consultancy for Vitrolife A/S. M.B. is employed at Vitrolife A/S. M.M.’s company ilabcomm GmbH received honorarium for consultancy from Vitrolife AB. D.K.G. received research support from Vitrolife AB.
Cement industry is one of the most energy intensive industrial sub-sectors. It accounts for almost 15% of the total energy consumed by manufacturing. Numerous energy efficiency initiatives and ...measures have been introduced and employed in this industry. To implement the most appropriate solutions for a certain cement plant, both technological and non-technological constraints need to be considered. To date, researchers have focused on outcomes such as energy savings, investment and emission reduction and therefore, both qualitative criteria and current circumstances of the plant have been largely overlooked. In this study, an integrated 3-phase model is presented to address these shortcomings and assist the plant managers to select and invest in the most suitable projects. The proposed tool, which is founded on a multi-criteria decision model, will assist the cement managers in achieving their energy saving targets. The tool is tested for 3 cases showing its applicability with real data resulting in the ranked list of opportunities for each of the plants.
•Cement accounts for 83% of total energy use in the production of non-metallic minerals and 94% of CO2 emissions.•Both financial and technical aspects need to be taken into account to determine the energy conservation opportunities.•A decision making model is designed and developed to assist selection of energy efficiency measures.
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