On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction

Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that groups chemically equivalent residues combined with super-secondary structure motifs to model protein structures. Interestingly, the simplification produced by grouping amino acids into only two groups (polar and non-polar) is still representative of the physicochemical nature that characterizes the protein structure and it is in line with the role of hydrophobic forces in protein-folding funneling. As a result of a compressed alphabet, the number of sequences required for the multiple sequence alignment is reduced. The number of long-range contacts predicted is limited; therefore, our approach requires the use of neighboring sequence-positions. We use the prediction of secondary structure and motifs of super-secondary structures to predict local contacts. We use RADI and raDIMod, a fragment-based protein structure modelling, achieving near native conformations when the number of super-secondary motifs covers >30–50% of the sequence. Interestingly, although different contacts are predicted with different alphabets, they produce similar structures.Spanish Ministry of Economy MINECO [BIO2014-57518-R, BIO2017-83591-R (FEDER, UE), BIO2017-85329-R (FEDER, UE)]; Generalitat de Catalunya [SGR17-1020].Peer ReviewedPostprint (published version

Processes of proliferation: impact beyond scaling in sharing and collaborative economies

While scalability and growth are key concerns for mainstream, venture-backed digital platforms, local and location-oriented collaborative economies are diverse in their approaches to evolving and achieving social change. Their aims and tactics differ when it comes to broadening their activities across contexts, spreading their concept, or seeking to make a bigger impact by promoting co-operation. This paper draws on three pairs of European, community-centred initiatives which reveal alternative views on scale, growth, and impact. We argue that proliferation – a concept that emphasises how something gets started and then travels in perhaps unexpected ways – offers an alternative to scaling, which we understand as the use of digital networks in a monocultural way to capture an ever-growing number of participants. Considering proliferation is, thus, a way to reorient and enrich discussions on impact, ambitions, modes of organising, and the use of collaborative technologies. In illustrating how these aspects relate in processes of proliferation, we offer CSCW an alternative vision of technology use and development that can help us make sense of the impact of sharing and collaborative economies, and design socio-technical infrastructures to support their flourishing

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Collaborative Database to Track Mass Mortality Events in the Mediterranean Sea

Anthropogenic climate change, and global warming in particular, has strong and increasing impacts on marine ecosystems (Poloczanska et al., 2013; Halpern et al., 2015; Smale et al., 2019). The Mediterranean Sea is considered a marine biodiversity hot-spot contributing to more than 7% of world's marine biodiversity including a high percentage of endemic species (Coll et al., 2010). The Mediterranean region is a climate change hotspot, where the respective impacts of warming are very pronounced and relatively well documented (Cramer et al., 2018). One of the major impacts of sea surface temperature rise in the marine coastal ecosystems is the occurrence of mass mortality events (MMEs). The first evidences of this phenomenon dated from the first half of'80 years affecting the Western Mediterranean and the Aegean Sea (Harmelin, 1984; Bavestrello and Boero, 1986; Gaino and Pronzato, 1989; Voultsiadou et al., 2011). The most impressive phenomenon happened in 1999 when an unprecedented large scale MME impacted populations of more than 30 species from different phyla along the French and Italian coasts (Cerrano et al., 2000; Perez et al., 2000). Following this event, several other large scale MMEs have been reported, along with numerous other minor ones, which are usually more restricted in geographic extend and/or number of affected species (Garrabou et al., 2009; Rivetti et al., 2014; Marbà et al., 2015; Rubio-Portillo et al., 2016, authors' personal observations). These events have generally been associated with strong and recurrent marine heat waves (Crisci et al., 2011; Kersting et al., 2013; Turicchia et al., 2018; Bensoussan et al., 2019) which are becoming more frequent globally (Smale et al., 2019). Both field observations and future projections using Regional Coupled Models (Adloff et al., 2015; Darmaraki et al., 2019) show the increase in Mediterranean sea surface temperature, with more frequent occurrence of extreme ocean warming events. As a result, new MMEs are expected during the coming years. To date, despite the efforts, neither updated nor comprehensive information can support scientific analysis of mortality events at a Mediterranean regional scale. Such information is vital to guide management and conservation strategies that can then inform adaptive management schemes that aim to face the impacts of climate change.MV-L was supported by a postdoctoral contract Juan de la Cierva-Incorporación (IJCI-2016-29329) of Ministerio de Ciencia, Innovación y Universidades. AI was supported by a Technical staff contract (PTA2015-10829-I) Ayudas Personal Técnico de Apoyo of Ministerio de Economía y Competitividad (2015). Interreg Med Programme (grant number Project MPA-Adapt 1MED15_3.2_M2_337) 85% cofunded by the European Regional Development Fund, the MIMOSA project funded by the Foundation Prince Albert II Monaco and the European Union's Horizon 2020 research and innovation programme under grant agreement no 689518 (MERCES). DG-G was supported by an FPU grant (FPU15/05457) from the Spanish Ministry of Education. J-BL was partially supported by the Strategic Funding UID/Multi/04423/2013 through national funds provided by FCT - Foundation for Science and Technology and European Regional Development Fund (ERDF), in the framework of the programme PT2020

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe

Geometric transformations: Translation, rotation and scaling

Postprint (published version

Exploring Temperature-Modulated Operation Mode of Metal Oxide Gas Sensors for Robust Signal Processing

Metal oxide (MOx) gas sensor signals are mainly governed by adsorption and desorption processes of oxygen and its reaction with surrounding gas molecules. Different target gases exhibit different reaction rates leading to characteristic sensor responses for specific gas species and their concentrations. In this work, we compare temperature-modulated sensor operation (TMO) with sensor operation at a single temperature. Further, we explore if under specific TMO regimes, a simple signal processing allows for quantification of gas concentrations. We specifically investigate, if the relevant information can be captured in selected discrete wavelet coefficients. In addition, we compare the results received from this wavelet features to reaction rate evaluation features

CuO Thin Films Functionalized with Gold Nanoparticles for Conductometric Carbon Dioxide Gas Sensing

Metal oxides (MOx) are a well-established material for gas sensing. MOx-based gas sensors are sensitive to a wide variety of gases. Furthermore, these materials can be applied for the fabrication of low-cost and -power consumption devices in mass production. The market of carbon dioxide (CO 2 ) gas sensors is mainly dominated by infra-red (IR)-based gas sensors. Only a few MOx materials show a sensitivity to CO 2 and so far, none of these materials have been integrated on CMOS platforms suitable for mass production. In this work, we report a cupric oxide (CuO) thin film-based gas sensor functionalized with gold (Au) nanoparticles, which exhibits exceptional sensitivity to CO 2 . The CuO-based gas sensors are fabricated by electron beam lithography, thermal evaporation and lift-off process to form patterned copper (Cu) structures. These structures are thermally oxidized to form a continuous CuO film. Gold nanoparticles are drop-coated on the CuO thin films to enhance their sensitivity towards CO 2 . The CuO thin films fabricated by this method are already sensitive to CO 2 ; however, the functionalization of the CuO film strongly increases the sensitivity of the base material. Compared to the pristine CuO thin film the Au functionalized CuO film shows at equal operation temperatures (300 ∘ C) an increase of sensitivity towards the same gas concentration (e.g., 2000 ppm CO 2 ) by a factor of 13. The process flow used to fabricate Au functionalized CuO gas sensors can be applied on CMOS platforms in specific post processing steps

Inferring causal molecular networks: empirical assessment through a community-based effor

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.This work was supported in part by the US National Institutes of Health (National Cancer Institute (NCI) grants U54 CA 112970 (to J.W.G.) and 5R01CA180778 (to J.M.S.), NCI award U54CA143869 to M.F.C. and National Institute of General Medical Sciences award 1R01GM109031 to J.M.S.), the Susan G. Komen Foundation (SAC110012 to J.W.G.), the Prospect Creek Foundation (grant to J.W.G.), the EuroinvesXgacion program of MICINN (Spanish Ministry of Science and InnovaXon), partners of the ERASysBio+ iniXaXve supported under the EU ERA-NET Plus Scheme in FP7 (SHIPREC), MICINN (FEDER BIO2008-0205, FEDER BIO2011-22568 and EUI2009-04018 to B.O.), the Royal Society (Wolfson Research Merit Award to S.M.), the German Federal Ministry of Education and Research GANI_MED Consortium (grant 03IS2061A to T.K.), and the US National Library of Medicine (grants R00LM010822 (to X.J.) and R01LM011663 (to X.J. and R.E.N.)