A mathematical model for the optimization of the non-metallic mining supply chain in the mining district of Calamarí-Sucre (Colombia)

This article presents a mathematical model of the Supply chain of non-metallic mining. The model considers uncertainty scenarios in materials, elements for capacity planning in a multilevel chain and with multiple products. The mathematical model is collaborative and maximizes the profits of the actors in the supply chain. The model is implemented in Calamarí-Sucre mining district (Colombia). The scenario is applied to the extraction, processing, storage, and distribution of limestone. To solve the model, the GAMS software was used through libraries of relaxed mixed nonlinear programming - RMINLP and the DICOPT solver. The results indicate that the greatest benefits occur in a scenario of the high provision of raw materials. The equity in the economic benefits show a dynamics of vertical integration in the sector. The model applied to non-metallic mining complexes helps determine optimal strategies and decisions in different echelons

Model of optimization of mining complex for the planning of flow of quarry production of limestone in multiple products and with elements for the analysis of the capacity

Activities in mining complexes contain multiple decisions that affect the operations of the system for the extraction, transformation, transport and storage of various subsoil components. The purpose of this research is the planning of continuous flow production systems for mixed products, in non-metallic mining extraction processes, considering bottlenecks and capacity planning. This paper presents a model for production, based on mathematical optimization, that facilitates the planning and management of operations in the area of extraction, crushing and transformation of a quarry of aggregates for construction, considering the resources and the constraints that allow to define effective strategies in the increase of the productivity of the lines of low production environment by scenarios. This research develops an analysis of bottlenecks and contrasts the nature of the production system by means of a mathematical model of optimization, which considers the capacities and balances in the flows of the Limestone production line. The mathematical model that maximizes profits can be adapted to systems of continuous flow production in mining complexes where their products are part of a reverse logistics process, analysis of alternatives of extraction, transformation and transport

Exploring the (missed) connections between digital scholarship and faculty development: a conceptual analysis

Abstract The aim of this paper is to explore the relationship between two research topics: digital scholarship and faculty development. The former topic drives attention on academics' new practices in digital, open and networked contexts; the second is focused on the requirements and strategies to promote academics' professional learning and career advancement. The research question addressing this study is: are faculty development strategies hindered by the lack of a cohesive view in the research on digital scholarship? The main assumption guiding this research question is that clear conceptual frameworks and models of professional practice lead to effective faculty development strategies. Through a wide overview of the evolution of both digital scholarship and faculty development, followed by a conceptual analysis of the intersections between fields, the paper attempts to show the extent on which the situation in one area (digital scholarship) might encompass criticalities for the other (faculty development) in terms of research and practices. Furthermore, three scenarios based on the several perspectives of digital scholarship are built in order to explore the research question in depth. We conclude that at the current state of art the relationship between these two topics is weak. Moreover, the dialogue between digital scholarship and faculty development could put the basis to forge effective professional learning contexts and instruments, with the ultimate goal of supporting academics to become digital scholars towards a more open and democratic vision of scholarship

A miRNA-Target Prediction Case Study

Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.authorsversionpublishe

Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms' complexity is a reason for their increased performance, it also leads to the "black box" problem, consequently decreasing trust towards AI. In this regard, "Explainable Artificial Intelligence" (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability

MSH6 and PMS2 mutation positive Australian Lynch syndrome families: novel mutations, cancer risk and age of diagnosis of colorectal cancer

Background: Approximately 10% of Lynch syndrome families have a mutation in MSH6 and fewer families have a mutation in PMS2. It is assumed that the cancer incidence is the same in families with mutations in MSH6 as in families with mutations in MLH1/MSH2 but that the disease tends to occur later in life, little is known about families with PMS2 mutations. This study reports on our findings on mutation type, cancer risk and age of diagnosis in MSH6 and PMS2 families. Methods: A total of 78 participants (from 29 families) with a mutation in MSH6 and 7 participants (from 6 families) with a mutation in PMS2 were included in the current study. A database of de-identified patient information was analysed to extract all relevant information such as mutation type, cancer incidence, age of diagnosis and cancer type in this Lynch syndrome cohort. Cumulative lifetime risk was calculated utilising Kaplan-Meier survival analysis. Results: MSH6 and PMS2 mutations represent 10.3% and 1.9%, respectively, of the pathogenic mutations in our Australian Lynch syndrome families. We identified 26 different MSH6 and 4 different PMS2 mutations in the 35 families studied. We report 15 novel MSH6 and 1 novel PMS2 mutations. The estimated cumulative risk of CRC at age 70 years was 61% (similar in males and females) and 65% for endometrial cancer in MSH6 mutation carriers. The risk of developing CRC is different between males and females at age 50 years, which is 34% for males and 21% for females. Conclusion: Novel MSH6 and PMS2 mutations are being reported and submitted to the current databases for identified Lynch syndrome mutations. Our data provides additional information to add to the genotype-phenotype spectrum for both MSH6 and PMS2 mutations

Effects of Single Nucleotide Polymorphisms on Human N-Acetyltransferase 2 Structure and Dynamics by Molecular Dynamics Simulation

BACKGROUND: Arylamine N-acetyltransferase 2 (NAT2) is an important catalytic enzyme that metabolizes the carcinogenic arylamines, hydrazine drugs and chemicals. This enzyme is highly polymorphic in different human populations. Several polymorphisms of NAT2, including the single amino acid substitutions R64Q, I114T, D122N, L137F, Q145P, R197Q, and G286E, are classified as slow acetylators, whereas the wild-type NAT2 is classified as a fast acetylator. The slow acetylators are often associated with drug toxicity and efficacy as well as cancer susceptibility. The biological functions of these 7 mutations have previously been characterized, but the structural basis behind the reduced catalytic activity and reduced protein level is not clear. METHODOLOGY/PRINCIPAL FINDINGS: We performed multiple molecular dynamics simulations of these mutants as well as NAT2 to investigate the structural and dynamical effects throughout the protein structure, specifically the catalytic triad, cofactor binding site, and the substrate binding pocket. None of these mutations induced unfolding; instead, their effects were confined to the inter-domain, domain 3 and 17-residue insert region, where the flexibility was significantly reduced relative to the wild-type. Structural effects of these mutations propagate through space and cause a change in catalytic triad conformation, cofactor binding site, substrate binding pocket size/shape and electrostatic potential. CONCLUSIONS/SIGNIFICANCE: Our results showed that the dynamical properties of all the mutant structures, especially in inter-domain, domain 3 and 17-residue insert region were affected in the same manner. Similarly, the electrostatic potential of all the mutants were altered and also the functionally important regions such as catalytic triad, cofactor binding site, and substrate binding pocket adopted different orientation and/or conformation relative to the wild-type that may affect the functions of the mutants. Overall, our study may provide the structural basis for reduced catalytic activity and protein level, as was experimentally observed for these polymorphisms

COMRADES determines in vivo RNA structures and interactions.

The structural flexibility of RNA underlies fundamental biological processes, but there are no methods for exploring the multiple conformations adopted by RNAs in vivo. We developed cross-linking of matched RNAs and deep sequencing (COMRADES) for in-depth RNA conformation capture, and a pipeline for the retrieval of RNA structural ensembles. Using COMRADES, we determined the architecture of the Zika virus RNA genome inside cells, and identified multiple site-specific interactions with human noncoding RNAs.This work was supported by Cancer Research UK (C13474/A18583, C6946/A14492) and the Wellcome Trust (104640/Z/14/Z, 092096/Z/10/Z) to E.A.M. O.Z. was supported by the Human Frontier Science Program (HFSP, LT000558/2015), the European Molecular Biology Organization (EMBO, ALTF1622-2014), and the Blavatnik Family Foundation postdoctoral fellowship. G.K. and M.G. were supported by Wellcome Trust grant 207507 and UK Medical Research Council. A.T.L.L. and J.C.M. were supported by core funding from Cancer Research UK (award no. 17197 to JCM). J.C.M was also supported by core funding from EMBL. I.G. and L.W.M. were supported by the Wellcome Trust Senior Fellowship in Basic Biomedical Science to I.G. (207498/Z/17/Z). I.J.M., L.F.G. and J.S.-G. were supported by grants R01GM104475 and R01GM115649 from NIGMS. C.K.K was supported by City University of Hong Kong Projects 9610363 and 7200520, Croucher Foundation Project 9500030 and Hong Kong RGC Projects 9048103 and 9054020. C.-F.Q. was supported by the NSFC Excellent Young Scientist Fund 81522025 and the Newton Advanced Fellowship from the Academy of Medical Sciences, UK

Hunting for cultivable Micromonospora strains in soils of the Atacama Desert

Innovative procedures were used to selectively isolate small numbers of Micromonospora strains from extreme hyper-arid and high altitude Atacama Desert soils. Micromonosporae were recognised on isolation plates by their ability to produce filamentous microcolonies that were strongly attached to the agar. Most of the isolates formed characteristic orange colonies that lacked aerial hyphae and turned black on spore formation, whereas those from the high altitude soil were dry, blue-green and covered by white aerial hyphae. The isolates were assigned to seven multi- and eleven single-membered groups based on BOX-PCR profiles. Representatives of the groups were assigned to either multi-membered clades that also contained marker strains or formed distinct phyletic lines in the Micromonospora 16S rRNA gene tree; many of the isolates were considered to be putatively novel species of Micromonospora. Most of the isolates from the high altitude soils showed activity against wild type strains of Bacillus subtilis and Pseudomonas fluorescens while those from the rhizosphere of Parastrephia quadrangulares and from the Lomas Bayas hyper-arid soil showed resistance to UV radiation