Evaluating the cascading-use of wood furniture: How value-retention processes can contribute to material efficiency and circularity

As utilized technical products, durable wood furniture plays an important role in a future circular economy (CE). However, contemporary CE literature predominantly focuses on wood's biochemical properties and its potential as a consumable material within the bio-cycle. This perspective prevents meaningful consideration of CE strategies for the wood products sector, particularly for value-retention processes (VRPs), including reuse, repair, and refurbishment. We adapt and apply the VRP model introduced by the UN International Resource Panel (IRP) to wood furniture products to quantify select environmental benefits made possible through cascading-use, via VRPs (vs. new manufacturing). Unlike traditional life cycle assessment (LCA), this model accounts for impacts incurred and avoided through product life-extension and VRPs, relative to conventional systems of new manufacturing, disposal, and replacement. Three case studies of wood-based chairs are conducted to demonstrate this new application of the VRP model to compare the relative environmental impacts associated with wood furniture that is diverted to cascading-use, prior to recycling. In collaboration with industry partners, new material requirements (kg/unit), energy requirements (kWh/unit), emissions (kg CO2-e./unit), and waste generation (kg/unit) were calculated for newly manufactured chairs (OEM new) and subsequent cascading-use via reuse, repair, and refurbishment. The differing degrees of environmental impact avoidance and material efficiency are presented for each case study product and VRP, to provoke discussion and future research regarding the effective and optimal utilization of technical, durable wooden furniture within a CE.     

A Comparison of Screening and Refined Exposure Models for Evaluating Toluene Air Emissions from a Washing Machine

Residential clothes washing machines are potential air emission sources for volatile organic compounds (VOCs). The U.S. Environmental Protection Agency has a screening model to predict the potential inhalation exposure to VOC emissions from a washing machine into room air. They also have a refined exposure model that can be extrapolated to evaluate a washing machine. The objectives of this study were to evaluate the screening and refined models and to compare their predictions to experimental results for toluene-contaminated tap water. This study identified a number of important results and it explained why the models over predicted as well as the models’ limitations. The Exposure and Fate Screening Tool (EFAST) Consumer Exposure Module (CEM) screening model predicted the peak concentration was 340% of the experimental result and that 90% of the toluene mass was emitted. The refined Simulation Tool Kit for Indoor Air Quality and Inhalation Exposure (IAQX) model predicted the peak concentration was 35% higher than the experimental value, the Time Weighted Average (TWA)-30 min concentration was 45% higher, and about 30% of the toluene mass was emitted. The screening model provided a quick, conservative exposure result with several limitations. The refined model provided a better estimate of the potential exposure but it required more input data and effort.     

NetiNeti: discovery of scientific names from text using machine learning methods

ABSTRACT: BACKGROUND: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. RESULTS: We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9 % and recall = 70.5 %) compared to a popular dictionary based approach (precision = 97.5 % and recall = 54.3 %) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central's full text articles annotated with scientific names, the precision and recall values are 98.5 % and 96.2 % respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Additionally, we present the comparison results of various machine learning algorithms on our annotated corpus. Naive Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms. CONCLUSIONS: We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org.     

Patient Preferences Regarding Device Reuse and Potential of Devices for Reuse - A Study in a Veteran Population

BackgroundMany cardiac patients need and undergo device implants. Veterans’ preferences regarding post-mortem handling of devices are not known. Cardiac patients in low- and middle-income countries who need but cannot afford devices rely on donations. Charitable organizations have successfully provided devices for reuse to such patients.ObjectiveWe estimated the number of devices with potential for possible reuse in a veteran population.MethodsBetween January and December 2008, at a tertiary medical center, veterans with implanted cardiac devices were surveyed using a questionnaire for their preferences regarding post-mortem handling. One choice was donation to charity for reuse. Although altruistic, it is unclear what percent of such devices have reuse potential. Retrospective chart review of veterans who underwent device implants between 1992 and 2007 identified a cohort of patients with Implantable Cardiac Defibrillators (ICDs) who had died by April 31st 2009. In this cohort, ICDs implanted in the year preceding the patient’s death were counted as having reuse potential.Results94 of 97 veterans completed the survey. 56% were unaware of how devices are handled after death. The top three preferences for postmortem handling were: return to manufacturer, return to hospital and donation for reuse. 88% were willing to sign an advance device directive. Retrospective review identified 161 veterans who had received 301 ICDs. Of these, 77 ICDs (25%) had median reuse potential of 3.1 years.ConclusionIn a VA cohort of deceased patients a substantial proportion of devices had reuse potential. Further research is needed to direct health policy.     

A Review and Typology of Circular Economy Business Model Patterns

The circular economy (CE) requires companies to rethink their supply chains and business models. Several frameworks found in the academic and practitioner literature propose circular economy business models (CEBMs) to redefine how companies create value while adhering to CE principles. A review of these frameworks shows that some models are frequently discussed, some are framework specific, and some use a different wording to refer to similar CEBMs, pointing to the need to consolidate the current state of the art. We conduct a morphological analysis of 26 current CEBMs from the literature, which includes defining their major business model dimensions and identifying the specific characteristics of these dimensions. Based on this analysis, we identify a broad range of business model design options and propose six major CEBM patterns with the potential to support the closing of resource flows: repair and maintenance; reuse and redistribution; refurbishment and remanufacturing; recycling; cascading and repurposing; and organic feedstock business model patterns. We also discuss different design strategies to support the development of these CEBMs.     

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

Computational techniques have been adopted in medi-cal and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from origi- nal data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.     

Steel’s recyclability: demonstrating the benefits of recycling steel to achieve a circular economy

Purpose

In a world where the population is expected to peak at around 9 billion people in the next 30 to 40 years, carefully managing our finite natural resources is becoming critical. We must abandon the outdated ‘take, make, consume and dispose’ mentality and move toward a circular economy model for optimal resource efficiency. Products must be designed for reuse and remanufacturing, which would reduce significant costs in terms of energy and natural resources.

Methods

To measure progress in achieving a circular economy, we need a life cycle approach that measures the social, economic and environmental impact of a product throughout its full life cycle—from raw material extraction to end-of-life (EoL) recycling or disposal. Life cycle thinking must become a key requirement for all manufacturing decisions, ensuring that the most appropriate material is chosen for the specific application, considering all aspects of a products’ life. The steel industry has been developing LCI data for 20 years. This is used to assess a product’s environmental performance from steel production to steel recycling at end-of-life. The steel industry has developed a methodology to show the benefits of using recycled steel to make new products. Using recycled materials also carries an embodied burden that should be considered when undertaking a full LCA.

Results and discussion

The recycling methodology is in accordance with ISO 14040/44:2006 and considers the environmental burden of using steel scrap and the benefit of scrap recycling from end-of-life products. It considers the recycling of scrap into new steel as closed material loop recycling, and thus, recycling steel scrap avoids the production of primary steel. The methodology developed shows that for every 1 kg of steel scrap that is recycled at the end of the products life, a saving of 1.5 kg CO₂-e emissions, 13.4 MJ primary energy and 1.4 kg iron ore can be achieved. This equates to 73, 64 and 90 %, respectively, when compared to 100 % primary production.

Conclusions

Incorporating this recycling methodology into a full LCA demonstrates how the steel industry is an integral part of the circular economy model which promotes zero waste; a reduction in the amount of materials used and encourages the reuse and recycling of materials.

     

Repair-FunMap: a functional database of proteins of the DNA repair systems

Repair-FunMap is a functional database of the DNA repair systems. This database contains not only the proteins directly involved in DNA repair, but also the proteins that interact with the DNA repair proteins. A protein interaction network associated with the human DNA repair processes was established according to the functional relationship between proteins in the database. This network represents the current knowledge on the intrinsic signaling pathways related to DNA repair. The Repair-FunMap could become an essential resource center for cancer research, providing clues to understanding the inter-relationship between proteins in the network, and to building scientific models of the DNA repair processes. AVAILABILITY: http://astro.temple.edu/~feng/Servers/BioinformaticServers.htm     

生态效率方法研究进展与应用

生态效率同时考虑经济效益和环境效益,是将可持续发展的宏观目标融入中观（区域）和微观（企业）的发展规划与管理中的有效工具。回顾了生态效率的概念和发展过程,分析了其内涵和指标体系,探讨了几种典型计算方法与模型,并介绍了国内外在企业、行业和区域3个层次上的应用实践,讨论和提出了进一步开展生态效率研究的焦点问题和未来方向。     

Life cycle assessment of commercial furniture: a case study of Formway LIFE chair

Background, aims and scope  The environmental aspects of companies and their products are becoming more significant in delivering competitive advantage. Formway Furniture, a designer and manufacturer of office furniture products, is a New Zealand-based company that is committed to sustainable development. It manufactures two models of the light, intuitive, flexible and environmental (LIFE) office chair: one with an aluminium base and one with a glass-filled nylon (GFN) base. It was decided to undertake a life cycle assessment (LCA) study of these two models in order to: (1) determine environmental hotspots in the life cycle of the two chairs (goal 1); (2) compare the life cycle impacts of the two chairs (goal 2); and (3) compare alternative potential waste-management scenarios (goal 3). The study also included sensitivity analysis with respect to recycled content of aluminium in the product. Materials and methods  The LIFE chair models consist of a mix of metal and plastic components manufactured by selected Formway suppliers according to design criteria. Hence, the research methodology included determining the specific material composition of the two chair models and acquisition of manufacturing data from individual suppliers. These data were compiled and used in conjunction with pre-existing data, specifically from the ecoinvent database purchased in conjunction with the SimaPro7 LCA software, to develop the life cycle inventory of the two chair models. The life cycle stages included in the study extended from raw-material extraction through to waste management. Impact assessment was carried out using CML 2 baseline 2000, the methodology developed by Leiden University’s Institute for Environmental Sciences. Results  This paper presents results for global warming potential (GWP100). The study showed a significant impact contribution from the raw-material extraction/refinement stage for both chair models; aluminium extraction and refining made the greatest contribution to GWP100. The comparison of the two LIFE chair models showed that the model with the aluminium base had a higher GWP100 impact than the model with the GFN base. The waste-management scenario compared the GWP100 result when (1) both chair models were sent to landfill and (2) steel and aluminium components were recycled with the remainder of the chair sent to landfill. The results showed that the recycling scenario contributed to a reduced GWP100 result. Since production and processing of aluminium was found to be significant, a sensitivity analysis was carried out to determine the impact of using aluminium with different recycled contents (0%, 34% and 100%) in both waste-management scenarios; this showed that increased use of recycled aluminium was beneficial. The recycling at end-of-life scenarios was modelled using two different end-of-life allocation approaches, i.e. consequential and attributional, in order to illustrate the variation in results caused by choice of allocation approach. The results using the consequential approach showed that recycling at end-of-life was beneficial, while use of the attributional method led to a similar GWP100 as that seen for the landfill scenario. Discussion  The results show that the main hotspot in the life cycle is the raw-material extraction/refinement stage. This can be attributed to the extraction and processing of aluminium, a material that is energy intensive. The LIFE chair model with the aluminium base has a higher GWP100 as it contains more aluminium. Sensitivity analysis pertaining to the recycled content of aluminium showed that use of aluminium with high recycled content was beneficial; this is because production of recycled aluminium is less energy intensive than production of primary aluminium. The waste-management scenario showed that recycling at end-of-life resulted in a significantly lower GWP100 than landfilling at end-of-life. However, this result is dependent upon the modelling approach used for recycling. Conclusions  With respect to goal 1, the study found that the raw-material extraction/refinement stage of the life cycle was a significant factor for both LIFE chair models. This was largely due to the use of aluminium in the product. For goal 2, it was found that the LIFE chair model with the aluminium base had a higher GWP100 than the GFN model, again due to the material content of the two models. Results for goal 3 illustrated that recycling at end-of-life is beneficial when using a system expansion (consequential) approach to model recycling; if an attributional ‘cut-off’ approach is used to model recycling at end-of-life, there is virtually no difference in the results between landfilling and recycling. Sensitivity analysis pertaining to the recycled content of aluminium showed that use of higher recycled contents leads to a lower GWP100 impact. Recommendation and perspectives  Most of the GWP100 impact was contributed during the raw-material extraction/refinement stage of the life cycle; thus, the overall impact of both LIFE chair models may be reduced through engaging in material choice and supply chain environmental management with respect to environmental requirements. The study identified aluminium components as a major contributor to GWP100 for both LIFE chair models and also highlighted the sensitivity of the results to its recycled content. Thus, it is recommended that the use of aluminium in future product designs be limited unless it is possible to use aluminium with a high recycled content. With respect to waste management, it was found that a substantial reduction in the GWP100 impact would occur if the chairs are recycled rather than landfilled, assuming an expanding market for aluminium. Thus, recycling the two LIFE chair models at end-of-life is highly recommended.     

Towards classifying species in systems biology papers using text mining

Background

In recent years high throughput methods have led to a massive expansion in the free text literature on molecular biology. Automated text mining has developed as an application technology for formalizing this wealth of published results into structured database entries. However, database curation as a task is still largely done by hand, and although there have been many studies on automated approaches, problems remain in how to classify documents into top-level categories based on the type of organism being investigated. Here we present a comparative analysis of state of the art supervised models that are used to classify both abstracts and full text articles for three model organisms.

Results

Ablation experiments were conducted on a large gold standard corpus of 10,000 abstracts and full papers containing data on three model organisms (fly, mouse and yeast). Among the eight learner models tested, the best model achieved an F-score of 97.1% for fly, 88.6% for mouse and 85.5% for yeast using a variety of features that included gene name, organism frequency, MeSH headings and term-species associations. We noted that term-species associations were particularly effective in improving classification performance. The benefit of using full text articles over abstracts was consistently observed across all three organisms.

Conclusions

By comparing various learner algorithms and features we presented an optimized system that automatically detects the major focus organism in full text articles for fly, mouse and yeast. We believe the method will be extensible to other organism types.

     

Embracing imperfection: Machine-assisted invertebrate classification in real-world datasets

Despite growing concerns over the health of global invertebrate diversity, terrestrial invertebrate monitoring efforts remain poorly geographically distributed. Machine-assisted classification has been proposed as a potential solution to quickly gather large amounts of data; however, previous studies have often used unrealistic or idealized datasets to train and test their models.In this study, we describe a practical methodology for including machine learning in ecological data acquisition pipelines. Here we train and test machine learning algorithms to classify over 72,000 terrestrial invertebrate specimens from morphometric data and contextual metadata. All vouchered specimens were collected in pitfall traps by the National Ecological Observatory Network (NEON) at 45 locations across the United States from 2016 to 2019. Specimens were photographed, and two separate machine learning paradigms were used to classify them. In the first, we used a convolutional neural network (ResNet-50), and in the second, we extracted morphometric data as feature vectors using ImageJ and used traditional machine learning methods to classify specimens. Issues stemming from inconsistent taxonomic label specificity were resolved by making classifications at the lowest identified taxonomic level (LITL). Taxa with too few specimens to be included in the training dataset were classified by the model using zero-shot classification.When classifying specimens that were known and seen by our models, we reached a maximum accuracy of 72.7% using eXtreme Gradient Boosting (XGBoost) at the LITL. This nearly matched the maximum accuracy achieved by the CNN of 72.8% at the LITL. Models that were trained without contextual metadata underperformed models with contextual metadata. We also classified invertebrate taxa that were unknown to the model using zero-shot classification, reaching a maximum accuracy of 65.5% when using the ResNet-50, compared to 39.4% when using XGBoost.The general methodology outlined here represents a realistic application of machine learning as a tool for ecological studies. We found that more advanced and complex machine learning methods such as convolutional neural networks are not necessarily more accurate than traditional machine learning methods. Hierarchical and LITL classifications allow for flexible taxonomic specificity at the input and output layers. These methods also help address the ‘long tail’ problem of underrepresented taxa missed by machine learning models. Finally, we encourage researchers to consider more than just morphometric data when training their models, as we have shown that the inclusion of contextual metadata can provide significant improvements to accuracy.     

Thermodynamic rarity of electrical and electronic waste: Assessment and policy implications for critical materials

The strategic relevance of extracting raw materials from waste from electrical and electronic equipment (WEEE) in the EU is increasing due to value chain risks caused by geopolitical instability, accessibility of specific minerals, and decreasing reserves due to growing extraction rates. This article examines the quantities of so-called critical raw materials (CRMs) originating within WEEE streams from a depletion perspective. Presently, current recycling targets are based solely on mass collection and recycling rates. We examine the potential limitations of this approach using an exergy-based indicator named thermodynamic rarity. This indicator represents the exergy costs needed for producing materials from the bare rock to market. The case of Italy is used to explore the application of the indicator at the macro (national) and micro (company) level for the product categories “small electronics” and “screens and monitors.” Our estimations show significant differences between the mass and rarity of materials within Italian WEEE streams. While iron accounts for more than 70% of the weight of the product categories analyzed, it accounts for less than 15% of the rarity. Similarly, several CRMs with a small mass have a higher rarity value, for example, tungsten with less than 0.1% of the mass and over 6% of the rarity. The policy context is reflected upon, where it is argued that thermodynamic rarity can provide novel insights to support end-of-life WEEE decision-making processes, for example, target development and recycling standards setting to help prioritize material monitoring and recovery options.     

A novel flexible model for lot sizing and scheduling with non-triangular,period overlapping and carryover setups in different machine configurations

This paper develops and tests an efficient mixed integer programming model for capacitated lot sizing and scheduling with non-triangular and sequence-dependent setup times and costs incorporating all necessary features of setup carryover and overlapping on different machine configurations. The model’s formulation is based on the asymmetric travelling salesman problem and allows multiple lots of a product within a period. The model conserves the setup state when no product is being processed over successive periods, allows starting a setup in a period and ending it in the next period, permits ending a setup in a period and starting production in the next period(s), and enforces a minimum lot size over multiple periods. This new comprehensive model thus relaxes all limitations of physical separation between the periods. The model is first developed for a single machine and then extended to other machine configurations, including parallel machines and flexible flow lines. Computational tests demonstrate the flexibility and comprehensiveness of the proposed models.     

An Approach Based on Mutually Informed Neural Networks to Optimize the Generalization Capabilities of Decision Support Systems Developed for Heart Failure Prediction

Available clinical methods for heart failure (HF) diagnosis are expensive and require a high-level of experts intervention. Recently, various machine learning models have been developed for the prediction of HF where most of them have an issue of over-fitting. Over-fitting occurs when machine learning based predictive models show better performance on the training data yet demonstrate a poor performance on the testing data and the other way around. Developing a machine learning model which is able to produce generalization capabilities (such that the model exhibits better performance on both the training and the testing data sets) could overall minimize the prediction errors. Hence, such prediction models could potentially be helpful to cardiologists for the effective diagnose of HF. This paper proposes a two-stage decision support system to overcome the over-fitting issue and to optimize the generalization factor. The first stage uses a mutual information based statistical model while the second stage uses a neural network. We applied our approach to the HF subset of publicly available Cleveland heart disease database. Our experimental results show that the proposed decision support system has optimized the generalization capabilities and has reduced the mean percent error (MPE) to 8.8% which is significantly less than the recently published studies. In addition, our model exhibits a 93.33% accuracy rate which is higher than twenty eight recently developed HF risk prediction models that achieved accuracy in the range of 57.85% to 92.31%. We can hope that our decision support system will be helpful to cardiologists if deployed in clinical setup.     

Life cycle assessment comparison among different reuse intensities for industrial wooden containers

Goal, scope and background  The industrial packages sector has great importance for the transport sector in Europe. These containers, mainly wooden pallets and spools, are subject to European legislation, which promotes their reuse and recycling. This study uses life cycle assessment (LCA) to assess the environmental impact of the current management system in this sector and the benefits and drawbacks of different reuse intensities as a waste prevention strategy as opposed to the recycling option. Materials and methods  In this paper, four case studies located in Spain and representative of the wooden package sector in Europe are analysed: high reuse pallet, low reuse pallet, low reuse spool and null reuse spool. For the LCA study cases, the functional unit is that required to satisfy the transport necessity of 1,000 t by road. The impact and energy consumption assessment methods used are CML 2 Baseline 2000 and Cumulative Energy Demand. Data are mostly provided by the leading enterprises and organisations in this sector. Results  The paper provides, as a first result, a comprehensive inventory of the systems under study. Secondly, our assessment shows that the systems with higher reuse intensity show a reduction in energy and wood consumption and all the environmental impact categories except for the global warming potential from 34.0% to 81.0% in the pallet study cases and from 50.4% to 72.8% in the spool ones. This reduction is at the expense of the maintenance stage, which on the contrary increases its impact, although it is still relatively small—less than 7% in all the impact categories and flow indicators of the study cases. The highest impact stages are transport, raw material extraction and the process chain. The final disposal and maintenance stages are the lowest impact, contributing at most to less than 30% of the impact in the pallet study cases and 10% in the spool cases. Discussion  Wood consumption (WC), directly related to the number of containers needed to satisfy the functional unit, is the main factor in determining the impact of the stages, especially in the raw materials extraction and process chain stages, assuming that these are undertaken with the same technologies in all the case studies. Other variables, such as the management system, the maintenance index and the final disposal scenario, affect the impact of the remaining stages: transport, maintenance and final disposal. The global warming potential results obtained demonstrate the environmental benefits of using containers made of a renewable resource such as wood instead of using other materials, but these results are not expected to prioritise the lower reuse systems because of their better performance in this category. Conclusions  Reuse, a strategy capable of reducing the environmental impacts of the wooden container systems, is preferable to recycling, while the package maintenance tasks are still feasible. Therefore, reuse, combined with recycling as final disposal, should be encouraged to reduce the demand for natural resources and the waste generated. Recommendations  Based on these results, attention should be paid to the maintenance stage, which, being the lowest-impact one, could substantially reduce the impact of the remaining stages.     

Incorporation in Lipid Microparticles of the UVA Filter, Butyl Methoxydibenzoylmethane Combined with the UVB Filter, Octocrylene: Effect on Photostability

The aim of this study was to reduce the photoinstability of butyl methoxydibenzoylmethane (BMDBM), the most widely used UVA filter, by incorporating it in lipid microparticles (LMs) alone or together with the UVB filter octocrylene (OCR), acting also as photostabilizer. Microparticles loaded with BMDBM or with combined BMDBM and OCR were produced by the hot emulsion technique, using glyceryl behenate as lipid material and poloxamer 188 as surfactant. The LMs were characterized by release studies, scanning electron microscopy, and powder X-ray diffractometry. The BMDBM and OCR loading was 15.2% and 10.6%, respectively. In order to reproduce the conditions prevalent in commercial sunscreen products, the photoprotective efficacy of the LMs was evaluated after their introduction in a model cream (oil-in-water emulsion) containing a mixture of UVA and UVB filters. A small but statistically significant decrease in BMDBM photodegradation was obtained when the UVA filter was encapsulated alone into the LMs (the extent of degradation was 28.6% ±2.4 for non-encapsulated BMDBM and 26.0% ±2.5 for BMDBM-loaded microparticles). On the other hand, the co-loading of OCR in the LMs produced a more marked reduction in the light-induced decomposition of microencapsulated BMDBM (the UVA filter loss was 21.5% ±2.2). Therefore, incorporation in lipid microparticles of BMDBM together with the sunscreen OCR is more effective in enhancing the UVA filter photostability than LMs loaded with BMDBM alone.     

ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text

ABNER (A Biomedical Named Entity Recognizer) is an open source software tool for molecular biology text mining. At its core is a machine learning system using conditional random fields with a variety of orthographic and contextual features. The latest version is 1.5, which has an intuitive graphical interface and includes two modules for tagging entities (e.g. protein and cell line) trained on standard corpora, for which performance is roughly state of the art. It also includes a Java application programming interface allowing users to incorporate ABNER into their own systems and train models on new corpora.     

A review on greywater reuse: quality,risks, barriers and global scenarios

Greywater is wastewater collected from household sources without input from toilet or commode streams. Greywater represent ca. 65% of total household wastewaters globally. Different aspects of greywater, including its production sources, its characteristics, the barriers and the global scenarios of its reuse, have been critically reviewed in this paper. Given the current projections on acute water scarcity in many regions of the world, the importance of water management cannot be overlooked and a decentralized approach, segregating wastewater streams of a household with proper sanitation is a potential option for recycling greywater. This paper aims to explore the improvements achieved in the reuse of greywater. Therefore, reutilization rates were estimated, hazards and risks associated with the use of untreated greywater were analyzed were summarized. Guidelines for greywater treatment were established and barriers for successful implementation of reuse strategies were identified. Global scenarios of successful implementations of greywater reuse were presented.