Universitat de Girona

Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTDioxygen Activation at Mononuclear Nonheme Iron Active Sites: Enzymes, Models, and IntermediatesMiquel Costas, Mark P. Mehn, Michael P. Jensen, and Lawrence QueView Author Information Departament de Quimica, Universitat de Girona, 17071, Girona, Spain, and Department of Chemistry and Center for Metals in Biocatalysis, University of Minnesota, Minneapolis, Minnesota 55455 Cite this: Chem. Rev. 2004, 104, 2, 939–986Publication Date (Web):January 28, 2004Publication History Received15 August 2003Published online28 January 2004Published inissue 1 February 2004https://pubs.acs.org/doi/10.1021/cr020628nhttps://doi.org/10.1021/cr020628nresearch-articleACS PublicationsCopyright © 2004 American Chemical SocietyRequest reuse permissionsArticle Views20000Altmetric-Citations2225LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Hydrocarbons,Iron,Ligands,Metals,Peptides and proteins Get e-Alerts

BACKGROUND: mutations. The efficacy of niraparib in patients with newly diagnosed advanced ovarian cancer after a response to first-line platinum-based chemotherapy is unknown. METHODS: In this randomized, double-blind, phase 3 trial, we randomly assigned patients with newly diagnosed advanced ovarian cancer in a 2:1 ratio to receive niraparib or placebo once daily after a response to platinum-based chemotherapy. The primary end point was progression-free survival in patients who had tumors with homologous-recombination deficiency and in those in the overall population, as determined on hierarchical testing. A prespecified interim analysis for overall survival was conducted at the time of the primary analysis of progression-free survival. RESULTS: Of the 733 patients who underwent randomization, 373 (50.9%) had tumors with homologous-recombination deficiency. Among the patients in this category, the median progression-free survival was significantly longer in the niraparib group than in the placebo group (21.9 months vs. 10.4 months; hazard ratio for disease progression or death, 0.43; 95% confidence interval [CI], 0.31 to 0.59; P<0.001). In the overall population, the corresponding progression-free survival was 13.8 months and 8.2 months (hazard ratio, 0.62; 95% CI, 0.50 to 0.76; P<0.001). At the 24-month interim analysis, the rate of overall survival was 84% in the niraparib group and 77% in the placebo group (hazard ratio, 0.70; 95% CI, 0.44 to 1.11). The most common adverse events of grade 3 or higher were anemia (in 31.0% of the patients), thrombocytopenia (in 28.7%), and neutropenia (in 12.8%). No treatment-related deaths occurred. CONCLUSIONS: Among patients with newly diagnosed advanced ovarian cancer who had a response to platinum-based chemotherapy, those who received niraparib had significantly longer progression-free survival than those who received placebo, regardless of the presence or absence of homologous-recombination deficiency. (Funded by GlaxoSmithKline; PRIMA/ENGOT-OV26/GOG-3012 ClinicalTrials.gov number, NCT02655016.).

We describe a simple method to automate the geometric optimization of molecular orbital calculations of supermolecules on potential surfaces that are corrected for basis set superposition error using the counterpoise (CP) method. This method is applied to the H-bonding complexes HF/HCN, HF/H2O, and HCCH/H2O using the 6-31G(d,p) and D95++(d,p) basis sets at both the Hartree–Fock and second-order Mo/ller–Plesset levels. We report the interaction energies, geometries, and vibrational frequencies of these complexes on the CP-optimized surfaces; and compare them with similar values calculated using traditional methods, including the (more traditional) single point CP correction. Upon optimization on the CP-corrected surface, the interaction energies become more negative (before vibrational corrections) and the H-bonding stretching vibrations decrease in all cases. The extent of the effects vary from extremely small to quite large depending on the complex and the calculational method. The relative magnitudes of the vibrational corrections cannot be predicted from the H-bond stretching frequencies alone.

The objective of this paper is classifying images by the object categories they contain, for example motorbikes or dolphins. There are three areas of novelty. First, we introduce a descriptor that represents local image shape and its spatial layout, together with a spatial pyramid kernel. These are designed so that the shape correspondence between two images can be measured by the distance between their descriptors using the kernel. Second, we generalize the spatial pyramid kernel, and learn its level weighting parameters (on a validation set). This significantly improves classification performance. Third, we show that shape and appearance kernels may be combined (again by learning parameters on a validation set).

We explore the problem of classifying images by the object categories they contain in the case of a large number of object categories. To this end we combine three ingredients: (i) shape and appearance representations that support spatial pyramid matching over a region of interest. This generalizes the representation of Lazebnik et al., (2006) from an image to a region of interest (ROI), and from appearance (visual words) alone to appearance and local shape (edge distributions); (ii) automatic selection of the regions of interest in training. This provides a method of inhibiting background clutter and adding invariance to the object instance 's position; and (iii) the use of random forests (and random ferns) as a multi-way classifier. The advantage of such classifiers (over multi-way SVM for example) is the ease of training and testing. Results are reported for classification of the Caltech-101 and Caltech-256 data sets. We compare the performance of the random forest/ferns classifier with a benchmark multi-way SVM classifier. It is shown that selecting the ROI adds about 5% to the performance and, together with the other improvements, the result is about a 10% improvement over the state of the art for Caltech-256.

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

Electrochemical processes have been extensively investigated for the removal of a range of organic and inorganic contaminants. The great majority of these studies were conducted using nitrate-, perchlorate-, sulfate-, and chloride-based electrolyte solutions. In actual treatment applications, organic and inorganic constituents may have substantial effects on the performance of electrochemical treatment. In particular, the outcome of electrochemical oxidation will depend on the concentration of chloride and bromide. Formation of chlorate, perchlorate, chlorinated, and brominated organics may compromise the quality of the treated effluent. A critical review of recent research identifies future opportunities and research needed to overcome major challenges that currently limit the application of electrochemical water treatment systems for industrial and municipal water and wastewater treatment. Given the increasing interest in decentralized wastewater treatment, applications of electrolytic systems for treatment of domestic wastewater, greywater, and source-separated urine are also included. To support future adoption of electrochemical treatment, new approaches are needed to minimize the formation of toxic byproducts and the loss of efficiency caused by mass transfer limitations and undesired side reactions. Prior to realizing these improvements, recognition of the situations where these limitations pose potential health risks is a necessary step in the design and operation of electrochemical treatment systems.

In recent years, there has been an increasing interest in applying Augmented Reality (AR) to create unique educational settings. So far, however, there is a lack of review studies with focus on investigating factors such as: the uses, advantages, limitations, effectiveness, challenges and features of augmented reality in educational settings. Personalization for promoting an inclusive learning using AR is also a growing area of interest. This paper reports a systematic review of literature on augmented reality in educational settings considering the factors mentioned before. In total, 32 studies published between 2003 and 2013 in 6 indexed journals were analyzed. The main findings from this review provide the current state of the art on research in AR in education. Furthermore, the paper discusses trends and the vision towards the future and opportunities for further research in augmented reality for educational settings.

Plastics have become a severe transboundary threat to natural ecosystems and human health, with studies predicting a twofold increase in the number of plastic debris (including micro and nano-sized plastics) by 2030. However, such predictions will likely be aggravated by the excessive use and consumption of single-use plastics (including personal protective equipment such as masks and gloves) due to COVID-19 pandemic. This review aimed to provide a comprehensive overview on the effects of COVID-19 on macroplastic pollution and its potential implications on the environment and human health considering short- and long-term scenarios; addressing the main challenges and discussing potential strategies to overcome them. It emphasises that future measures, involved in an emergent health crisis or not, should reflect a balance between public health and environmental safety as they are both undoubtedly connected. Although the use and consumption of plastics significantly improved our quality of life, it is crucial to shift towards sustainable alternatives, such as bio-based plastics. Plastics should remain in the top of the political agenda in Europe and across the world, not only to minimise plastic leakage and pollution, but to promote sustainable growth and to stimulate both green and blue- economies. Discussions on this topic, particularly considering the excessive use of plastic, should start soon with the involvement of the scientific community, plastic producers and politicians in order to be prepared for the near future.

Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.

This international consensus statement provides the state of genetic testing for the channelopathies and cardiomyopathies. It summarizes the opinion of the international writing group members based on their own experience and on a general review of the literature with respect to the use and role of genetic testing for these potentially heritable cardiac conditions. This document focuses primarily on the state of genetic testing for the 13 distinct entities detailed and the relative diagnostic, prognostic, and therapeutic impact of the genetic test result for each entity. It does not focus on the therapeutic management of the various channelopathies and cardiomyopathies. Treatment/ management issues are only discussed for those diseases (i.e., LQTS, HCM, DCM + CCD, RCM) in which the genetic test result could potentially influence treatment considerations.

Developing more efficient catalysts remains one of the primary targets of organometallic chemists. To accelerate reaching this goal, effective molecular descriptors and visualization tools can represent a remarkable aid. Here, we present a Web application for analyzing the catalytic pocket of metal complexes using topographic steric maps as a general and unbiased descriptor that is suitable for every class of catalysts. To show the broad applicability of our approach, we first compared the steric map of a series of transition metal complexes presenting popular mono-, di-, and tetracoordinated ligands and three classic zirconocenes. This comparative analysis highlighted similarities and differences between totally unrelated ligands. Then, we focused on a recently developed Fe(II) catalyst that is active in the asymmetric transfer hydrogenation of ketones and imines. Finally, we expand the scope of these tools to rationalize the inversion of enantioselectivity in enzymatic catalysis, achieved by point mutation of three amino acids of mononuclear p-hydroxymandelate synthase.

There is an increasing interest in Faecalibacterium prausnitzii, one of the most abundant bacterial species found in the gut, given its potentially important role in promoting gut health. Although some studies have phenotypically characterized strains of this species, it remains a challenge to determine which factors have a key role in maintaining the abundance of this bacterium in the gut. Besides, phylogenetic analysis has shown that at least two different F. prausnitzii phylogroups can be found within this species and their distribution is different between healthy subjects and patients with gut disorders. It also remains unknown whether or not there are other phylogroups within this species, and also if other Faecalibacterium species exist. Finally, many studies have shown that F. prausnitzii abundance is reduced in different intestinal disorders. It has been proposed that F. prausnitzii monitoring may therefore serve as biomarker to assist in gut diseases diagnostics. In this mini-review, we aim to serve as an overview of F. prausnitzii phylogeny, ecophysiology and diversity. In addition, strategies to modulate the abundance of F. prausnitzii in the gut as well as its application as a biomarker for diagnostics and prognostics of gut diseases are discussed. This species may be a useful potential biomarker to assist in ulcerative colitis and Crohn's disease discrimination.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTDiazonium Salts as Substrates in Palladium-Catalyzed Cross-Coupling ReactionsAnna Roglans, Anna Pla-Quintana, and Marcial Moreno-MañasView Author Information Department of Chemistry, Universitat de Girona, Campus de Montilivi, 17071 Girona, Spain and Department of Chemistry, Universitat Autònoma de Barcelona, Cerdanyola, 08193 Barcelona, Spain Cite this: Chem. Rev. 2006, 106, 11, 4622–4643Publication Date (Web):October 7, 2006Publication History Received21 April 2006Published online7 October 2006Published inissue 1 November 2006https://pubs.acs.org/doi/10.1021/cr0509861https://doi.org/10.1021/cr0509861research-articleACS PublicationsCopyright © 2006 American Chemical SocietyRequest reuse permissionsArticle Views16435Altmetric-Citations708LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Arylation,Catalysts,Hydrocarbons,Palladium,Salts Get e-Alerts

ADVERTISEMENT RETURN TO ISSUEPREVArticleTheoretical Evaluation of Electron Delocalization in Aromatic Molecules by Means of Atoms in Molecules (AIM) and Electron Localization Function (ELF) Topological ApproachesJordi Poater, Miquel Duran, Miquel Solà, and Bernard SilviView Author Information Institut de Química Computacional and Departament de Química, Universitat de Girona, 17071 Girona, Catalonia, Spain, and Laboratoire de Chimie Théorique, Université Pierre et Marie Curie, UMR-CNRS 7616, 4 place Jussieu, 75252 Paris Cedex 05, France Cite this: Chem. Rev. 2005, 105, 10, 3911–3947Publication Date (Web):July 13, 2005Publication History Received28 December 2004Published online13 July 2005Published inissue 1 October 2005https://pubs.acs.org/doi/10.1021/cr030085xhttps://doi.org/10.1021/cr030085xresearch-articleACS PublicationsCopyright © 2005 American Chemical SocietyRequest reuse permissionsArticle Views4920Altmetric-Citations645LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Aromatic compounds,Aromaticity,Delocalization,Hydrocarbons,Molecules Get e-Alerts

The computational approach to the Hirshfeld [Theor. Chim. Acta 44, 129 (1977)] atom in a molecule is critically investigated, and several difficulties are highlighted. It is shown that these difficulties are mitigated by an alternative, iterative version, of the Hirshfeld partitioning procedure. The iterative scheme ensures that the Hirshfeld definition represents a mathematically proper information entropy, allows the Hirshfeld approach to be used for charged molecules, eliminates arbitrariness in the choice of the promolecule, and increases the magnitudes of the charges. The resulting "Hirshfeld-I charges" correlate well with electrostatic potential derived atomic charges.

Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in Transformer-based architectures has enabled the implementation of language models capable of generating text with human-like capabilities. Here, motivated by this success, we describe ProtGPT2, a language model trained on the protein space that generates de novo protein sequences following the principles of natural ones. The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yields well-folded non-idealized structures with embodiments and large loops and reveals topologies not captured in current structure databases. ProtGPT2 generates sequences in a matter of seconds and is freely available.

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The massive consumption of a wide range plastic products has generated a huge amount of plastic waste. There is a need to provide awareness of their uses and routine management as a part of our lifestyle. Nowadays, plastics are increasingly being used in our daily life activities, including the packaging in different food and brewing companies, cosmetics, pharmaceutical, and other production sectors need to pack their end products for efficient and safer product's delivery to the community. Plastics are produced through the biochemical process of polymerization or polycondensation. The post-use of generated plastic waste has many adverse impacts on the environment if not processed and managed in a proper way. This review aims to discuss the lifecycle of plastic products according to their different categories, including polyvinyl chloride (PVC–U), polystyrene or styrofoam (PS), polypropylene (PP), high-density polyethylene (HDPE), polyethylene terephthalate (PETE), and others. Herein, we have also discussed the problems caused by the inadequate processing of plastic waste and the possible solutions that can be provided to ensure a good atmosphere and to reduce the causes of climate changes, which is challenging to life on this planet.