Water Footprint Assessment of Carbon in Pulp Gold Processing in Turkey

This paper presents water the footprint assessment (WFA) of carbon in pulp (CIP) gold processing. The main objectives of the study are determining grey and blue water footprints and identifying the hotspots of the process. Results revealed that the total blue water footprint, including the extraction and processing of the gold, was found to be 452.40 m(3)/kg Au, and the grey WF to be 2300.69 m(3)/kg Au. According to the results, the lost return flow on the direct blue WF side has the largest contribution, with a value of 260.61 m(3)/kg Au, and the only source of the lost return flow is the tailing pond. On the indirect side, it is seen that the oxygen consumption used for the leaching process has the highest value, with 37.38 m(3)/kg. Among the nine contaminants in the mine tailings, the critical component responsible for the grey water footprint is by far arsenic, with a value of 1777 m(3)/kg Au. The results will be used to make recommendations for reducing water consumption in mining operations, for a better design for the environment. The study is a pioneering study, being the first implementation of water footprint assessment in a gold mine in Turkey

Biana: a software framework for compiling biological interactions and analyzing networks

<p>Abstract</p> <p>Background</p> <p>The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.</p> <p>Results</p> <p>We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from <url>http://sbi.imim.es/web/BIANA.php</url>.</p> <p>Conclusions</p> <p>BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.</p

Highlights from the ISCB Student Council Symposium 2013

This report summarizes the scientific content and activities of the annual symposium organized by the Student Council of the International Society for Computational Biology (ISCB), held in conjunction with the Intelligent Systems for Molecular Biology (ISMB) / European Conference on Computational Biology (ECCB) conference in Berlin, Germany, on July 19, 2013

GUILDify v2.0:A Tool to Identify Molecular Networks Underlying Human Diseases, Their Comorbidities and Their Druggable Targets

The genetic basis of complex diseases involves alterations on multiple genes. Unraveling the interplay between these genetic factors is key to the discovery of new biomarkers and treatments. In 2014, we introduced GUILDify, a web server that searches for genes associated to diseases, finds novel disease genes applying various network-based prioritization algorithms and proposes candidate drugs. Here, we present GUILDify v2.0, a major update and improvement of the original method, where we have included protein interaction data for seven species and 22 human tissues and incorporated the disease-gene associations from DisGeNET. To infer potential disease relationships associated with multi-morbidities, we introduced a novel feature for estimating the genetic and functional overlap of two diseases using the top-ranking genes and the associated enrichment of biological functions and pathways (as defined by GO and Reactome). The analysis of this overlap helps to identify the mechanistic role of genes and protein-protein interactions in comorbidities. Finally, we provided an R package, guildifyR, to facilitate programmatic access to GUILDify v2.0 (http://sbi.upf.edu/guildify2).The authors received support from: ISCIII-FEDER (PI13/00082, CP10/00524, CPII16/00026); IMI-JU under grants agreements no. 116030 (TransQST) and no. 777365 (eTRANSAFE), resources of which are composed of financial contribution from the EU-FP7 (FP7/2007- 2013) and EFPIA companies in kind contribution; the EU H2020 Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics) and no. 676559 (Elixir-Excelerate); the Spanish Ministry of Economy (MINECO) [BIO2017-85329-R] [RYC-2015-17519]; "Unidad de Excelencia María de Maeztu", funded by the Spanish Ministry of Economy [ref: MDM-2014-0370]. The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I+D+i 2013-2016, funded by ISCIII and FEDER

Synthesis and evaluation of core/shell structured NMC cathode material

Cathode materials are the main elements of the Li-ion batteries and determine their key performing characteristics, including power and energy density values, cell voltage, capacity and cycle life. Up to date, Lithium-Nickel-Manganese-Cobalt-Oxide (so-called NMC) remains the most successful formulas for cathode powder, delivering strong overall performance and excellent specific energy. Within these compositions, Nickel provides high energy density and increased storage capacity at lower cost and contributes to the circular economy due to the durability, recyclability and possible second life. Therefore, Ni-rich composition, NMC 811, is more preferentially used in high performance batteries. However, the formation of the Ni2+ phase during the successive charge/discharge cycles resulting in Ni-oxidation causes chemical and structural degradation and thus, deteriorates the cyclic performance. On the other hand, Manganese in NMC composition acts not only as a stabilizer, but also, prevents Nickel-oxidation and thus, reduces the risk of capacity fading. Regarding these facts, development of core/shell structured NMC morphologies deserved a special attention. This morphology provides surface stabilization of Ni-rich NMC by keeping the energy storage capabilities at higher level and prevents degradation of cathode. In this work, we describe an oxalate-assisted co-precipitation route for synthesis of core/shell structured NMC cathode particles. For achievement of core and shell in different compositions, two-staged wet-chemical synthesis approach was applied. It is well known that morphology is affected strongly by synthesis parameters, therefore the influence of solvent type, co-precipitation temperature, stirring speed, reaction time and sintering parameters was studied in accordance with electrochemical performance testing. Li incorporation was carried by two approaches; top-down and bottom-up methods. Identification of the compositional and structural relations within the core/shell particles, SEM, TEM, FIB and XRD techniques were used. Electrochemical characterization indicated that Li infiltration process and parameters of heat treatment play a significant role in achievement of good cathode performance

Genetic and functional characterization of disease associations explains comorbidity

Understanding relationships between diseases, such as comorbidities, has important socio-economic implications, ranging from clinical study design to health care planning. Most studies characterize disease comorbidity using shared genetic origins, ignoring pathway-based commonalities between diseases. In this study, we define the disease pathways using an interactome-based extension of known disease-genes and introduce several measures of functional overlap. The analysis reveals 206 significant links among 94 diseases, giving rise to a highly clustered disease association network. We observe that around 95% of the links in the disease network, though not identified by genetic overlap, are discovered by functional overlap. This disease network portraits rheumatoid arthritis, asthma, atherosclerosis, pulmonary diseases and Crohn's disease as hubs and thus pointing to common inflammatory processes underlying disease pathophysiology. We identify several described associations such as the inverse comorbidity relationship between Alzheimer's disease and neoplasms. Furthermore, we investigate the disruptions in protein interactions by mapping mutations onto the domains involved in the interaction, suggesting hypotheses on the causal link between diseases. Finally, we provide several proof-of-principle examples in which we model the effect of the mutation and the change of the association strength, which could explain the observed comorbidity between diseases caused by the same genetic alterations

An ensemble learning approach for modeling the systems biology of drug-induced injury

Background: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction. Results: We searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test. Conclusions: When using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.The authors received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreements TransQST and eTRANSAFE (refs: 116030, 777365). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA companies in kind contribution. The authors also received support from Spanish Ministry of Economy (MINECO, refs: BIO2017–85329-R (FEDER, EU), RYC-2015-17519) as well as EU H2020 Programme 2014–2020 under grant agreement No. 676559 (Elixir-Excelerate) and from Agència de Gestió D’ajuts Universitaris i de Recerca Generalitat de Catalunya (AGAUR, ref.: 2017SGR01020). L.I.F. received support from ISCIII-FEDER (ref: CPII16/00026). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D + i 2013–2016, funded by ISCIII and FEDER. The DCEXS is a “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370). J.A.P. received support from the CAMDA Travel Fellowship

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.This work is written on behalf of the Women’s Brain Project (WBP) (www.womensbrainproject.com/), an international organization advocating for women’s brain and mental health through scientific research, debate and public engagement. The authors would like to gratefully acknowledge Maria Teresa Ferretti and Nicoletta Iacobacci (WBP) for the scientific advice and insightful discussions; Roberto Confalonieri (Alpha Health) for reviewing the manuscript; the Bioinfo4Women programme of Barcelona Supercomputing Center (BSC) for the support. This work has been supported by the Spanish Government (SEV 2015–0493) and grant PT17/0009/0001, of the Acción Estratégica en Salud 2013–2016 of the Programa Estatal de Investigación Orientada a los Retos de la Sociedad, funded by the Instituto de Salud Carlos III (ISCIII) and European Regional Development Fund (ERDF). EG has received funding from the Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under grant agreement No 116030 (TransQST), which is supported by the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA).Peer ReviewedPostprint (published version