José M. Sempere
Bio: José M. Sempere is an academic researcher from Polytechnic University of Valencia. The author has contributed to research in topics: Formal language & Grammar induction. The author has an hindex of 16, co-authored 76 publications receiving 1149 citations. Previous affiliations of José M. Sempere include University of Alicante & University of Valencia.
Papers published on a yearly basis
TL;DR: The second release of the Gypsy Database of Mobile Genetic Elements (GyDB 2.0) is introduced, an update based on the analysis of Ty3/Gypsy, Retroviridae, Ty1/Copia and Bel/Pao LTR retroelements and the Caulimoviridae pararetroviruses of plants.
Abstract: This article introduces the second release of the Gypsy Database of Mobile Genetic Elements (GyDB 2.0): a research project devoted to the evolutionary dynamics of viruses and transposable elements based on their phylogenetic classification (per lineage and protein domain). The Gypsy Database (GyDB) is a long-term project that is continuously progressing, and that owing to the high molecular diversity of mobile elements requires to be completed in several stages. GyDB 2.0 has been powered with a wiki to allow other researchers participate in the project. The current database stage and scope are long terminal repeats (LTR) retroelements and relatives. GyDB 2.0 is an update based on the analysis of Ty3/Gypsy, Retroviridae, Ty1/Copia and Bel/Pao LTR retroelements and the Caulimoviridae pararetroviruses of plants. Among other features, in terms of the aforementioned topics, this update adds: (i) a variety of descriptions and reviews distributed in multiple web pages; (ii) protein-based phylogenies, where phylogenetic levels are assigned to distinct classified elements; (iii) a collection of multiple alignments, lineage-specific hidden Markov models and consensus sequences, called GyDB collection; (iv) updated RefSeq databases and BLAST and HMM servers to facilitate sequence characterization of new LTR retroelement and caulimovirus queries; and (v) a bibliographic server. GyDB 2.0 is available at http://gydb.org.
TL;DR: Despite their simplicity, it is shown how the latter networks might be used for solving an NP-complete problem, namely the “3-colorability problem”, in linear time and linear resources (nodes, symbols, rules).
Abstract: In this paper we consider networks of evolutionary processors as language generating and computational devices. When the filters are regular languages one gets the computational power of Turing machines with networks of size at most six, depending on the underlying graph. When the filters are defined by random context conditions, we obtain an incomparability result with the families of regular and context-free languages. Despite their simplicity, we show how the latter networks might be used for solving an NP-complete problem, namely the “3-colorability problem”, in linear time and linear resources (nodes, symbols, rules).
TL;DR: Treatment with intravenous administration of AT-MSC in 13 severe COVID-19 pneumonia under mechanical ventilation in a small case series did not induce significant adverse events and was followed by clinical and biological improvement in most subjects.
Abstract: Background Identification of effective treatments in severe cases of COVID-19 requiring mechanical ventilation represents an unmet medical need. Our aim was to determine whether the administration of adipose-tissue derived mesenchymal stromal cells (AT-MSC) is safe and potentially useful in these patients. Methods Thirteen COVID-19 adult patients under invasive mechanical ventilation who had received previous antiviral and/or anti-inflammatory treatments (including steroids, lopinavir/ritonavir, hydroxychloroquine and/or tocilizumab, among others) were treated with allogeneic AT-MSC. Ten patients received two doses, with the second dose administered a median of 3 days (interquartile range-IQR- 1 day) after the first one. Two patients received a single dose and another patient received 3 doses. Median number of cells per dose was 0.98 × 106 (IQR 0.50 × 106) AT-MSC/kg of recipient's body weight. Potential adverse effects related to cell infusion and clinical outcome were assessed. Additional parameters analyzed included changes in imaging, analytical and inflammatory parameters. Findings First dose of AT-MSC was administered at a median of 7 days (IQR 12 days) after mechanical ventilation. No adverse events were related to cell therapy. With a median follow-up of 16 days (IQR 9 days) after the first dose, clinical improvement was observed in nine patients (70%). Seven patients were extubated and discharged from ICU while four patients remained intubated (two with an improvement in their ventilatory and radiological parameters and two in stable condition). Two patients died (one due to massive gastrointestinal bleeding unrelated to MSC therapy). Treatment with AT-MSC was followed by a decrease in inflammatory parameters (reduction in C-reactive protein, IL-6, ferritin, LDH and d -dimer) as well as an increase in lymphocytes, particularly in those patients with clinical improvement. Interpretation Treatment with intravenous administration of AT-MSC in 13 severe COVID-19 pneumonia under mechanical ventilation in a small case series did not induce significant adverse events and was followed by clinical and biological improvement in most subjects. Funding None.
13 Jun 2001
TL;DR: A computational device based on evolutionary rules and communication within a network, similar to that introduced in , is proposed, called network of evolutionary processors, which solves the NP-complete problem of linear size in linear time.
Abstract: We propose a computational device based on evolutionary rules and communication within a network, similar to that introduced in , called network of evolutionary processors. An NP-complete problem is solved by networks of evolutionary processors of linear size in linear time. Some furher directions of research are finally discussed.
••21 Sep 1994
TL;DR: This work proposes a characterization of Even Linear Language class using a relation of finite index and proposes a method to learn thisclass using a reduction to the problem of learning regular languages.
Abstract: Even Linear Language class is a subclass of context-free class. In this work we propose a characterization of this class using a relation of finite index. Theorems are provided in order to prove the consistence of the characterization. Finally, we propose a method to learn this class using a reduction to the problem of learning regular languages.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
01 Jan 2010
TL;DR: It is found that women over 50 are more likely to have a family history of diabetes, especially if they are obese, than women under the age of 50.
Abstract: Hypertension 66 (20.3%) 24 (24.2%) 30 (16.3%) NS Diabetes 20 (6.2%) 7 (7.1%) 10 (5.4%) NS Excess weight 78 (24%) 27 (27.3%) 44 (23.9%) NS Smokers 64 (19.7%) 17 (17.2%) 35 (19.0%) NS Age >50 years 137 (42.2%) 54 (54.5%) 67 (36.4%) <0.02 Kidney disease 7 (2.2%) 1 (1%) 5 (2.7%) NS Family history, DM 102 (31.4%) 28 (28.3%) 66 (35.9%) NS
Seoul National University1, Soongsil University2, Agricultural Research Organization, Volcani Center3, Chonnam National University4, Korea Research Institute of Bioscience and Biotechnology5, Myongji University6, New Mexico State University7, University of Arizona8, Rural Development Administration9, University of California, Davis10, Yonsei University11, Andong National University12, Cornell University13, United States Department of Agriculture14
TL;DR: The genome size of the hot pepper was approximately fourfold larger than that of its close relative tomato, and the genome showed an accumulation of Gypsy and Caulimoviridae family elements.
Abstract: Doil Choi and colleagues report the genome sequence of the hot pepper, Capsicum annuum, as well as the resequencing of two cultivated peppers and a wild species, Capsicum chinense. Comparative genomic analysis across Solanaceae provides insights into genome expansion, pungency, ripening and disease resistance in hot peppers.
TL;DR: Genome structure and phylogenomic analyses indicate that the ancestral Angiosperm was a polyploid with a large constellation of both novel and ancient genes that survived to play key roles in angiosperm biology.
Abstract: Amborella trichopoda is strongly supported as the single living species of the sister lineage to all other extant flowering plants, providing a unique reference for inferring the genome content and structure of the most recent common ancestor (MRCA) of living angiosperms. Sequencing the Amborella genome, we identified an ancient genome duplication predating angiosperm diversification, without evidence of subsequent, lineage-specific genome duplications. Comparisons between Amborella and other angiosperms facilitated reconstruction of the ancestral angiosperm gene content and gene order in the MRCA of core eudicots. We identify new gene families, gene duplications, and floral protein-protein interactions that first appeared in the ancestral angiosperm. Transposable elements in Amborella are ancient and highly divergent, with no recent transposon radiations. Population genomic analysis across Amborella's native range in New Caledonia reveals a recent genetic bottleneck and geographic structure with conservation implications.
TL;DR: The WAO Diagnosis and Rationale for Action against Cow's Milk Allergy (DRACMA) Guidelines was planned to provide physicians everywhere with a management tool to deal with CMA from suspicion to treatment and to relieve the burden of issues through an ongoing and collective effort of more interactive debate and integrated learning.
Abstract: Alessandro Fiocchi, MD, Pediatric Division, Department of Child and Maternal Medicine, University of Milan Medical School at the Melloni Hospital, Milan 20129, Italy. Holger Schunemann, MD, Department of Clinical Epidemiology & Biostatistics, McMaster University Health Sciences Centre, 1200 Main Street West Hamilton, ON L8N 3Z5, Canada. Sami L. Bahna, MD, Pediatrics & Medicine, Allergy & Immunology, Louisiana State University Health Sciences Center, Shreveport, LA 71130. Andrea Von Berg, MD, Research Institute, Children s department , Marien-Hospital, Wesel, Germany. Kirsten Beyer, MD, Charite Klinik fur Padiatrie m.S. Pneumologie und Immunologie, Augustenburger Platz 1, D-13353 Berlin, Germany. Martin Bozzola, MD, Department of Pediatrics, British Hospital-Perdriel 74-CABA-Buenos Aires, Argentina. Julia Bradsher, PhD, Food Allergy & Anaphylaxis Network, 11781 Lee Jackson Highway, Suite 160, Fairfax, VA 22033. Jan Brozek, MD, Department of Clinical Epidemiology & Biostatistics, McMaster University Health Sciences Centre, 1200 Main Street West Hamilton, ON L8N 3Z5, Canada. Enrico Compalati, MD, Allergy & Respiratory Diseases Clinic, Department of Internal Medicine. University of Genoa, 16132, Genoa, Italy. Motohiro Ebisawa, MD, Department of Allergy, Clinical Research Center for Allergy and Rheumatology, Sagamihara National Hospital, Kanagawa 228-8522, Japan. Maria Antonieta Guzman, MD, Immunology and Allergy Division, Clinical Hospital University of Chile, Santiago, Chile. Santos Dumont 999. Haiqi Li, MD, Professor of Pediatric Division, Department of Primary Child Care, Children’s Hospital, Chongqing Medical University, China, 400014. Ralf G. Heine, MD, FRACP, Department of Allergy & Immunology, Royal Children’s Hospital, University of Melbourne, Murdoch Children’s Research Institute, Melbourne, Australia. Paul Keith, MD, Allergy and Clinical Immunology Division, Department of Medicine, McMaster University, Hamilton, Ontario, Canada. Gideon Lack, MD, King’s College London, Asthma-UK Centre in Allergic Mechanisms of Asthma, Department of Pediatric Allergy, St Thomas’ Hospital, London SE1 7EH, United Kingdom. Massimo Landi, MD, National Pediatric Healthcare System, Italian Federation of Pediatric Medicine, Territorial Pediatric Primary Care Group, Turin, Italy. Alberto Martelli, MD, Pediatric Division, Department of Child and Maternal Medicine, University of Milan Medical School at the Melloni Hospital, Milan 20129, Italy. Fabienne Rance, MD, Allergologie, Hopital des Enfants, Pole Medicochirurgical de Pediatrie, 330 av. de Grande Bretagne, TSA 70034, 31059 Toulouse CEDEX, France. Hugh Sampson, MD, Jaffe Food Allergy Institute, Mount Sinai School of Medicine, One Gustave L. Levy Place, NY 10029-6574. Airton Stein, MD, Conceicao Hospital, Porto Alegre, Brazil. Luigi Terracciano, MD, Pediatric Division, Department of Child and Maternal Medicine, University of Milan Medical School at the Melloni Hospital, Milan 20129, Italy. Stefan Vieths, MD, Division of Allergology, Paul-EhrlichInstitut, Federal Institute for Vaccines and Biomedicines, Paul-Ehrlich-Str. 51-59, D-63225 Langen, Germany.