A Data Mining Approach to Building a Predictive Model of Low-Cost Carriers\u27 Presence in the U.S. Domestic Routes

The purpose of the study was to build the predictive model of the presence of U.S. low-cost carriers (LCCs) in the domestic network structure. SEMMA (Sample, Explore, Modify, Model, and Assess) schematic in data mining was followed and employed as the primary methodological procedure. Data in the period of 1Q2016-1Q2018 were extracted from the Bureau of Transportation Statistics (DB1B database) and reconstructed to form predictors. Stepwise logistic regression showed a significant predictive performance compared to decision tree technique in terms of fitting measures, which was then used as the concluding model. Significant predictors included: (1) Market concentration positively related with the presence of LCCs, (2) nonstop route associated with the presence of LCCs, (3) market airfare factors negatively related with the presence of LCCs, and (4) origin and destination (O&D) airports being hubs, especially medium hubs, associated with the presence of LCCs. The findings may practically aid network planners in airlines and airports in decision making associated with the presence of LCCs, which ultimately leads to building their more robust and efficient route map

Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests

Prediction of stable peptide binding to Class I HLAs is an important component for designing immunotherapies. While the best performing predictors are based on machine learning algorithms trained on peptide-HLA (pHLA) sequences, the use of structure for training predictors deserves further exploration. Given enough pHLA structures, a predictor based on the residue-residue interactions found in these structures has the potential to generalize for alleles with little or no experimental data. We have previously developed APE-Gen, a modeling approach able to produce pHLA structures in a scalable manner. In this work we use APE-Gen to model over 150,000 pHLA structures, the largest dataset of its kind, which were used to train a structure-based pan-allele model. We extract simple, homogenous features based on residue-residue distances between peptide and HLA, and build a random forest model for predicting stable pHLA binding. Our model achieves competitive AUROC values on leave-one-allele-out validation tests using significantly less data when compared to popular sequence-based methods. Additionally, our model offers an interpretation analysis that can reveal how the model composes the features to arrive at any given prediction. This interpretation analysis can be used to check if the model is in line with chemical intuition, and we showcase particular examples. Our work is a significant step toward using structure to achieve generalizable and more interpretable prediction for stable pHLA binding

Biodesulphurized subbituminous coal by different fungi and bacteria studied by reductive pyrolysis. Part 1: Initial coal

One of the perspective methods for clean solid fuels production is biodesulphurization. In order to increase the effect of this approach it is necessary to apply the advantages of more informative analytical techniques. Atmospheric pressure temperature programming reduction (AP-TPR) coupled with different detection systems gave us ground to attain more satisfactory explanation of the effects of biodesulphurization on the treated solid products. Subbituminous high sulphur coal from ‘‘Pirin” basin (Bulgaria) was selected as a high sulphur containing sample. Different types of microorganisms were chosen and maximal desulphurization of 26% was registered. Biodesulphurization treatments were performed with three types of fungi: ‘‘Trametes Versicolor” – ATCC No. 200801, ‘‘Phanerochaeta Chrysosporium” – ME446, Pleurotus Sajor-Caju and one Mixed Culture of bacteria – ATCC No. 39327. A high degree of inorganic sulphur removal (79%) with Mixed Culture of bacteria and consecutive reduction by 13% for organic sulphur (Sorg) decrease with ‘‘Phanerochaeta Chrysosporium” and ‘‘Trametes Versicolor” were achieved. To follow the Sorg changes a set of different detection systems i.e. AP-TPR coupled ‘‘on-line” with mass spectrometry (AP-TPR/MS), on-line with potentiometry (AP-TPR/pot) and by the ‘‘off-line” AP-TPR/GC/MS analysis was used. The need of applying different atmospheres in pyrolysis experiments was proved and their effects were discussed. In order to reach more precise total sulphur balance, oxygen bomb combustion followed by ion chromatography was used

3pHLA-score improves structure-based peptide-HLA binding affinity prediction

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta’s ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein–ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient\u27s own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide-ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide-MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC hot-spots for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient’s own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide–ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide–MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC “hot-spots” for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made

Market, morality and (just) price: the case of recycling economy in Turkey

By drawing on ethnographic fieldwork conducted amongst waste-pickers and recycling traders in the waste paper, plastic and scrap metal sectors, and engaging with literature from economic anthropology and history, as well as archival sources, this paper documents changing perceptions of just price, morality and fairness in the Turkish recycling market. The paper suggests that multiple markets imply multiple prices, which are contingent and contested. When dealing with price mechanisms largely outside their control, actors tend to associate a fair price with the going market price, rather than factors such as state regulation. Approaches to morality and assessments of fairness become more ambiguous when prices are mediated by actors? own practices. These range from gift relations to paternalism, envy and deception

A resources ecosystem for digital and heritage-led holistic knowledge in rural regeneration

This paper presents a digital resources ecosystem prototype of integrated tools and resources to support heritage-led regeneration of rural regions, thanks to a deeper understanding of the complexity of cultural natural landscapes throughout their historical and current development. The ecosystem is conceived as a distributed software platform establishing data ecosystem and open standards for the management of information, aimed at providing different services and applications to address the needs of the various end-users identified. The platform has been conceived and realised in the framework of a Horizon 2020 research project, with a view to building a set of holistic knowledge about rural regions and their cultural and natural heritage and making it available for long-lasting heritage-led territorial processes of change. It is the product of a multidisciplinary collaboration amongst heritage, digital humanities and ICTs experts, and combines data and methodologies from a range of approaches to humanities together with the customisation of effective digital tools. It has been designed for deployment also in cloud systems compliant with the Infrastructure-as-a-Service paradigm. All data is Findable, Accessible, Interoperable, Reusable (FAIR data). It hosts and integrates different tools, making the data gathered with/for local stakeholders usable and making the same data re-usable within the tools’ functions, generating integrated heritage knowledge. It comprises data on 19 rural pilot territories, where the tools and their integration have been developed and tested, while 62 more are partially included as additional territories which participate in certain activities within the project. The main testers for this platform and its functions are the local stakeholders of these territories. The paper describes and analyses the platform and its impact, discussing the integration of tools as an innovative approach that goes beyond the use of individual tools in shaping a multidimensional vision. It also offers an analysis of the potential of an integrated digital ecosystem in evidence-based and place-based regeneration strategies. Some reflections for developments and cooperation during the pandemic are also presented

Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020

We show the distribution of SARS-CoV-2 genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three available genomic nomenclature systems for SARS-CoV-2 to all sequence data from the WHO European Region available during the COVID-19 pandemic until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation. We provide a comparison of the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2.Peer reviewe