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  • 1
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    Online Resource
    COVID-19 Survivor Patients Carrying the Rs35705950 Risk Allele in MUC5B Have Higher Plasma Levels of Mucin 5B
    MDPI AG ; 2022
    In:  Current Issues in Molecular Biology Vol. 44, No. 8 ( 2022-07-22), p. 3283-3290
    Details
    In: Current Issues in Molecular Biology, MDPI AG, Vol. 44, No. 8 ( 2022-07-22), p. 3283-3290
    Abstract: Background: Genetic susceptibility to infectious diseases is partly due to the variation in the human genome, and COVID-19 is not the exception. This study aimed to identify whether risk alleles of known genes linked with emphysema (SERPINA1) and pulmonary fibrosis (MUC5B) are associated with severe COVID-19, and whether plasma mucin 5B differs according to patients’ outcomes. Materials and methods: We included 1258 Mexican subjects diagnosed with COVID-19. We genotyped rs2892474 and rs17580 of the SERPINA1 gene and rs35705950 of MUC5B. Based on the rs35705950 genotypes, mucin 5B plasma protein levels were quantified. Results: Homozygous for the risk alleles of the three polymorphisms were found in less than 5% of the study population, but no statistically significant difference in the genotype or allele association analysis. At the protein level, non-survivors carrying one or two copies of the risk allele rs35705950 in MUC5B (GT + TT) had lower levels of mucin 5B compared to the survivors (0.0 vs. 0.17 ng/mL, p = 0.0013). Conclusion: The polymorphisms rs28929474 and rs17580 of SERPINA1 and rs35705950 of MUC5B are not associated with the risk of severe COVID-19 in the Mexican population. COVID-19 survivor patients bearing one or two copies of the rs35705950 risk allele have higher plasma levels of mucin 5B.
    Type of Medium: Online Resource
    ISSN: 1467-3045
    URL: Article
    DOI: 10.3390/cimb44080226
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2090836-2
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  • 2
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    Ontology-Based Nutritional Recommender System
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 12, No. 1 ( 2021-12-24), p. 143-
    Details
    In: Applied Sciences, MDPI AG, Vol. 12, No. 1 ( 2021-12-24), p. 143-
    Abstract: Obesity is considered an epidemic that is continuously growing around the world. Heart diseases, diabetes, and bone and joint diseases are some of the diseases that people who are overweight or obese can develop. One of the vital causes of those disorders is poor nutrition education; there is no raising awareness about eating healthy food and practicing physical activities to burn off the excess energy. Therefore, it is necessary to use new technologies to build methods/tools that help people to overcome these avoidable nutrition disorders. For this reason, we implemented a recommendation engine capable of identifying the different levels of overweight and obesity in users and providing dietary strategies to mitigate them. To do so, we defined the Ontology of Dietary Recommendations (ODR) with axioms to model recipes, ingredients, and a set of diets to assist people who suffer from obesity. We validated the defined model by using a real set of individuals who were anonymized. A panel of advisors evaluated each individual record and suggested the most appropriate diets from those included in the ontology. Then, the proposed system was asked to also provide diet recommendations for each individual, which were compared with those proposed by the advisors (ground truth), reaching a mean accuracy of 87%.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app12010143
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 3
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    Knowledge-Based System for Crop Pests and Diseases Recognition
    MDPI AG ; 2021
    In:  Electronics Vol. 10, No. 8 ( 2021-04-10), p. 905-
    Details
    In: Electronics, MDPI AG, Vol. 10, No. 8 ( 2021-04-10), p. 905-
    Abstract: With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics10080905
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662127-7
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  • 4
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    Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets
    MDPI AG ; 2020
    In:  Mathematics Vol. 8, No. 11 ( 2020-11-20), p. 2075-
    Details
    In: Mathematics, MDPI AG, Vol. 8, No. 11 ( 2020-11-20), p. 2075-
    Abstract: Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math8112075
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704244-3
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  • 5
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    Effect of the Melanocortin 4-Receptor Ile269Asn Mutation on Weight Loss Response to Dietary, Phentermine and Bariatric Surgery Interventions
    MDPI AG ; 2022
    In:  Genes Vol. 13, No. 12 ( 2022-12-01), p. 2267-
    Details
    In: Genes, MDPI AG, Vol. 13, No. 12 ( 2022-12-01), p. 2267-
    Abstract: The loss of function melanocortin 4-receptor (MC4R) Ile269Asn mutation has been proposed as one of the most important genetic contributors to obesity in the Mexican population. However, whether patients bearing this mutation respond differently to weight loss treatments is unknown. We tested the association of this mutation with obesity in 1683 Mexican adults, and compared the response of mutation carriers and non-carriers to three different weight loss interventions: dietary restriction intervention, phentermine 30 mg/day treatment, and Roux-en-Y gastric bypass (RYGB) surgery. The Ile269Asn mutation was associated with obesity [OR = 3.8, 95% CI (1.5–9.7), p = 0.005]. Regarding interventions, in the dietary restriction group only two patients were MC4R Ile269Asn mutation carriers. After 1 month of treatment, both mutation carriers lost weight: −4.0 kg (−2.9%) in patient 1, and −1.8 kg (−1.5%) in patient 2; similar to the mean weight loss observed in six non-carrier subjects (−2.9 kg; −2.8%). Phentermine treatment produced similar weight loss in six carriers (−12.7 kg; 15.5%) and 18 non-carriers (−11.3 kg; 13.6%) after 6 months of pharmacological treatment. RYGB also caused similar weight loss in seven carriers (29.9%) and 24 non-carriers (27.8%), 6 months after surgery. Our findings suggest that while the presence of a single MC4R loss of function Ile269Asn allele significantly increases obesity risk, the presence of at least one functional MC4R allele seems sufficient to allow short-term weight loss in response to dietary restriction, phentermine and RYGB. Thus, these three different interventions may be useful for the short-term treatment of obesity in MC4R Ile269Asn mutation carriers.
    Type of Medium: Online Resource
    ISSN: 2073-4425
    URL: Article
    DOI: 10.3390/genes13122267
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527218-4
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  • 6
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    Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus
    MDPI AG ; 2024
    In:  Electronics Vol. 13, No. 4 ( 2024-02-09), p. 717-
    Details
    In: Electronics, MDPI AG, Vol. 13, No. 4 ( 2024-02-09), p. 717-
    Abstract: Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics13040717
    Language: English
    Publisher: MDPI AG
    Publication Date: 2024
    detail.hit.zdb_id: 2662127-7
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  • 7
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    Leveraging Zero and Few-Shot Learning for Enhanced Model Generality in Hate Speech Detection in Spanish and English
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 24 ( 2023-12-18), p. 5004-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 24 ( 2023-12-18), p. 5004-
    Abstract: Supervised training has traditionally been the cornerstone of hate speech detection models, but it often falls short when faced with unseen scenarios. Zero and few-shot learning offers an interesting alternative to traditional supervised approaches. In this paper, we explore the advantages of zero and few-shot learning over supervised training, with a particular focus on hate speech detection datasets covering different domains and levels of complexity. We evaluate the generalization capabilities of generative models such as T5, BLOOM, and Llama-2. These models have shown promise in text generation and have demonstrated the ability to learn from limited labeled data. Moreover, by evaluating their performance on both Spanish and English datasets, we gain insight into their cross-lingual applicability and versatility, thus contributing to a broader understanding of generative models in natural language processing. Our results highlight the potential of generative models to bridge the gap between data scarcity and model performance across languages and domains.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11245004
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704244-3
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  • 8
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    Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases
    MDPI AG ; 2019
    In:  Applied Sciences Vol. 9, No. 14 ( 2019-07-18), p. 2858-
    Details
    In: Applied Sciences, MDPI AG, Vol. 9, No. 14 ( 2019-07-18), p. 2858-
    Abstract: Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of algorithms that handle natural language efficiently. In the bibliography, it is possible to find different techniques to carry out these infodemiology studies. However, as far as our knowledge, there are no comprehensive studies that compare the accuracy of these techniques. Consequently, we conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases so that future syndromic surveillance systems can be improved. The contribution of this paper is two-fold. On the one hand, we use Twitter to compile and label a balanced corpus of infectious diseases with 6164 utterances written in Spanish and collected from Central America. On the other hand, we compare two statistical-models: word-grams and char-grams. The experimentation involved the analysis of different gram sizes, different partitions of the corpus, and two machine-learning classifiers: Random-Forest and Sequential Minimal Optimization. The results reach a 90.80% of accuracy applying the char-grams model with five-char-gram sequences. As a final contribution, the compiled corpus is released.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app9142858
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2704225-X
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  • 9
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    AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 3 ( 2020-02-04), p. 1040-
    Details
    In: Applied Sciences, MDPI AG, Vol. 10, No. 3 ( 2020-02-04), p. 1040-
    Abstract: In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app10031040
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704225-X
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  • 10
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    Haplotype in SERPINA1 (AAT) Is Associated with Reduced Risk for COPD in a Mexican Mestizo Population
    MDPI AG ; 2019
    In:  International Journal of Molecular Sciences Vol. 21, No. 1 ( 2019-12-27), p. 195-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 21, No. 1 ( 2019-12-27), p. 195-
    Abstract: Protease inhibitor S (PiS) and protease inhibitor Z (PiZ) variants in the SERPINA1 gene are the main genetics factors associated with COPD; however, investigations about other polymorphisms are scanty. The aim of this study was to evaluate two missense single nucleotide polymorphisms (SNPs) (rs709932 and rs1303) in the SERPINA1 gene in Mexican mestizo patients with chronic obstructive pulmonary disease (COPD) related to tobacco smoking and biomass-burning exposure. 1700 subjects were genotyped and divided into four groups: COPD related to tobacco smoking (COPD-S, n = 297), COPD related to biomass-burning exposure (COPD-BB, n = 178), smokers without COPD (SWOC, n = 674), and biomass-burning exposed subjects (BBES, n = 551) by real-time PCR. Moreover, the patients’ groups were divided according to their exacerbations’ frequency. We carried out a haplotype analysis. We did not find differences in allele and genotype frequencies between groups in unadjusted and adjusted analyses, neither with these SNPs and lung function decline. Exacerbations’ frequency is not associated with these SNPs. However, we found a haplotype with major alleles (CT) associated with reduced risk for COPD (p 〈 0.05). Our analysis reveals that SNPs different from PiS and PiZ (rs709932 and rs1303) in the SERPINA1 gene are not associated with COPD and lung function decline in a Mexican mestizo population. However, a haplotype shaped by both major alleles (CT haplotype) is associated with reduced risk for COPD.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms21010195
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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