# Universidad de Colima

• Colima, Colima, Mexico
Recent publications
Background The two-spotted spider mite, Tetranychus urticae Koch (Trombidiformes: Tetranychidae), is one of the most damaging mites in agriculture. Due to the concern for the intensive use of synthetic acaricides, entomopathogenic fungi represents a feasible alternative to T. urticae management. In the present study, 7 isolates of Metarhizium were characterized physiological and molecularly (based on the ITS1-5.8s-ITS2 rDNA) and evaluated for their acaricidal activity [mortality, mean and 90 lethal concentration (LC 50 : LC 90 ) and mean and 90 lethal time (LT 50 : LT 90 )] against T. urticae under laboratory conditions. Results Sequencing of the ITS1-5.8s-ITS2 rDNA region indicated that the 7 isolates belong to M. anisopliae . The isolates Ma114 (3.7 ± 0.006 mm day ⁻¹ ), Ma109 (3.5 ± 0.009 mm day ⁻¹ ) and Ma106 (3.5 ± 0.006 mm day ⁻¹ ) had the highest radial growth rate and Ma114 (92.2 ± 0.86%) and Ma108 (94.4 ± 1.07%) had the highest germination percentage. All isolates were pathogenic to T. urticae , causing mortality that ranged from 45.3 to 85.3%. The LC 50 and LC 90 were 1.2 and 2.8, 1.1 and 2.5, and 1.2 and 2.8 × 10 ⁸ conidia mL ⁻¹ for isolates Ma110, Ma109 and Ma106, respectively, while the LT 50 and LT 90 were 7.7 and 16.5, and 7.2 and 16.1 days for isolates M110 and Ma109, respectively. Conclusion The isolates Ma110 and Ma109 of M. anisopliae were moderately pathogenic and virulent against T. urticae .
In this work, nanoparticles of the trirutile-type oxide NiSb2O6 were synthesized for its application as a gas sensor using the colloidal method assisted by microwave radiation. The crystalline evolution of the powders was analyzed by X-ray diffraction, finding the phase NiSb2O6 at 600 °C. SEM micrographs revealed the growth of microspheres, microrods, and irregularly shaped particles. Using TEM, the average size of the nanoparticles was calculated at ~ 17.1 nm. For dynamic tests, pellets and thick films were made from the powders calcined at 600 °C. For the thick films, alternating current was used at frequencies of 0.1 and 1 kHz in C3H8 and CO2 atmospheres at 360 °C, where the material’s sensitivity magnitude in CO2 was ~ 2.61% (0.1 kHz) and ~ 2.97% (1 kHz). In contrast, for C3H8, the sensitivity was ~ 6.69% (0.1 kHz) and ~ 5.12% (1 kHz) on average. For the pellets, direct current signals and volumetric flow rates of 100, 150, and 200 cm3/min of CO at 200 °C were applied, where the sensitivities were ~ 24.37, ~ 35.33, and ~ 40.77%, respectively. In each test, the sensitivity visibly increased when the gases were injected. Likewise, the response and recovery times decreased when the frequency and gas concentration increased. The results obtained for the trirutile-type oxide NiSb2O6, which showed good stability, efficiency, and high sensitivity in CO2, C3H8, and CO atmospheres, make it ideal as a toxic gas sensor.
Aedes aegypti is a mosquito that transmits viral diseases such as dengue, chikungunya, Zika, and yellow fever. The insect’s microbiota is recognized for regulating several biological processes, including digestion, metabolism, egg production, development, and immune response. However, the role of the bacteria involved in insecticide susceptibility has not been established. Therefore, the objective of this study was to characterize the resident microbiota in a field population of A. aegypti to evaluate its role associated with susceptibility to the insecticides permethrin and deltamethrin. Mosquitoes were fed 10% sucrose mixed with antibiotics and then exposed to insecticides using a diagnostic dose. DNA was extracted, and sequencing of bacterial 16S rRNA was carried out on Illumina® MiSeq™. Proteobacteria (92.4%) and Bacteroidetes (7.6%) were the phyla, which are most abundant in mosquitoes fed with sucrose 10%. After exposure to permethrin, the most abundant bacterial species were Pantoea agglomerans (38.4%) and Pseudomonas azotoformans-fluorescens-synxantha (14.2%). Elizabethkingia meningoseptica (38.4%) and Ps. azotoformans-fluorescens-synxantha (26.1%) were the most abundant after exposure to deltamethrin. Our results showed a decrease in mosquitoes’ survival when exposed to permethrin, while no difference in survival when exposed to deltamethrin when the microbiota was modified. We found that the change in microbiota modifies the response of mosquitoes to permethrin. These results are essential for a better understanding of mosquito physiology in response to insecticides.
A study was carried out under the framework of density functional theory where the adsorption capacity of nine graphene systems with different organic moieties (CO, COOH, OH, O and SH) as glyphosate adsorbents was determined. The systems with the highest adsorption capacities were those with the alcohol, acid, epoxide and thiol substituents, according to energetic parameters such as interaction energy, enthalpy and Gibbs free energy of adsorption. The stability of the interaction is mainly given by hydrogen bonds, which was corroborated by the study of the bond critical points, as well as the index of non-covalent interactions. However, classical molecular dynamics studies suggest the importance of Van der Waals forces in the adsorption phenomenon at 298.15 K. The systems under study show promise for further studies and possible application as glyphosate trapping agents.
A theoretical study of cell evolution is presented here. By using a toolbox containing an intracellular catalytic reaction network model and a mutation-selection process, four distinct phases of self-organization were unveiled. First, the nutrients prevail as the central substrate of the chemical reactions. Second, the cell becomes a small-world. Third, a highly connected core component emerges, concurrently with the nutrient carriers becoming the central product of reactions. Finally, the cell reaches a steady configuration where the concentrations of the core chemical species are described by Zipf’s law.
In patients with head and neck cancer, malnutrition is common. Most cases are treated by chemo-radiotherapy and surgery, with adverse effects on the aerodigestive area. Clinical and biochemical characteristics, health-related quality of life, survival, and risk of death were studied. The selected subjects were divided into normal- and low-phase-angle (PA) groups and followed up for at least two years. Mean ages were 67.2 and 59.3 years for low and normal PA, respectively. Patients with PA < 4.42° had significant differences in age, anthropometric and biochemical indicators of malnutrition, and inflammatory status compared to patients with PA > 4.42°. Statistical differences were found in the functional and symptom scales, with lower functional scores and higher symptom scores in patients with low PA. Median survival was 19.8 months for those with PA < 4.42° versus 34.4 months for those with PA > 4.42° (p < 0.001).The relative risk of death was related to low PA (2.6; p < 0.001). The percentage of living patients (41.7%) is almost the same as the percentage of deceased subjects (43.1%; p = 0.002), with high death rates in patients with PA < 4.42°. Phase angle was the most crucial predictor of survival and a risk factor for death in the studied cases.
Pharmacological synergism is a current strategy for the treatment of pain. However, few studies have been explored to provide evidence of the possible synergism between a non-steroidal anti-inflammatory drug (NSAID) and a cannabinoid agonist, in order to establish which combinations might be effective to manage pain. The aim of this study was to explore the synergism between ibuprofen (IBU) and the synthetic cannabinoid WIN 55,212-2 (WIN) to improve pain relief by analyzing the degree of participation of the CB1 and CB2 cannabinoid receptors in the possible antinociceptive synergism using an experimental model of pain in Wistar rats. First, the effective dose thirty (ED30) of IBU (10, 40, 80, and 160 mg/kg, subcutaneous) and WIN (3, 10, and 30 µg/p, intraplantar) were evaluated in the formalin test. Then, the constant ratio method was used to calculate the doses of IBU and WIN to be administered in combination (COMB) to determine the possible synergism using the isobolographic method. The participation of the CB1 and CB2 receptors was explored in the presence of the antagonists AM281 and AM630, respectively. The combination of these drugs produced a supra-additive response with an interaction index of 0.13. In addition, AM281 and AM630 antagonists reversed the synergistic effect in 45% and 76%, respectively, suggesting that both cannabinoid receptors are involved in this synergism, with peripheral receptors playing a relevant role. In conclusion, the combination of IBU + WIN synergism is mainly mediated by the participation of the CB2 receptor, which can be a good option for the better management of pain relief.
Scorpionism in México is a public health problem caused by stings from the Centruroides scorpion family. The Ct1a and Ct17 toxins from the venom of the Centruroides tecomanus scorpion are the most abundant and toxic for mammals. This study describes the heterologous expression of recombinant proteins from genes encoding these toxins merged with thioredoxin in the vector pET-22b + and expressed in Escherichia coli BL21 (DE3). The yield of Ct1a and Ct17 recombinant toxins was 1.25 mg and 1.737 mg per liter of culture, respectively. These were purified by metal ion affinity chromatography and RP-HPLC and were used for immunization in rabbits, obtaining polyclonal antibodies that confer a positive immune response against the complete venom of Centruroides tecomanus. The serum was tested in vitro and in vivo, obtaining neutralization and protection against the venom. Both toxins were produced recombinantly and fused to the thioredoxin protein; remarkably, the recombinant toxins were excellent immunogens. 300 μl of each immunized rabbit serum was tested in mice, resulting in 50% of the mice protected with each serum, but when the sera were pooled, the protection increased to 83%. This communication reveals the possibility of producing a specific and regional antivenom with polyclonal antibodies that neutralize the complete venom of Centruroides tecomanus. The sequence similarity of Ct1a and Ct17 to Cn2, a toxin that recognizes sodium channels, allowed in silico modeling analysis and a proposal for the different toxicities of Ct1a and Ct17.
Reseña del libro de Virginia García Acosta y Raymundo Padilla Lozoya (coordinadores) (2019). Historia y memoria de los huracanes y otros episodios hidrometeorológicos extremos en México. Cinco siglos: del año 5 pedernal a Janet. México: CIESAS/Universidad de Colima/Universidad Veracruzana.
In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as "leave one environment out," is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accuracy of future seasons or new locations because it is a predictive methodology. However, most statistical machine learning methods used for the task of predicting a new environment or season struggle to produce moderate or high prediction accuracies. For this reason, in this study we explore the use of the partial least squares (PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Genomic Best Linear Unbiased Predictor (GBLUP) method. The benchmarking process was done with 14 real datasets. We found that in all datasets the PLS method outperformed the popular GBLUP method by margins between 0% (in the Indica data) and 228.28% (in the Disease data) across traits, environments, and types of predictors. Our results show great empirical evidence of the power of the PLS methodology for the prediction of future seasons or new environments.
In order to know the capacity of the tetrazolium cation [Ph3CN4]⁺ to act as a stabilizing influence in the presence of bulky anions and facilitator to obtain suitable crystals for single-crystal X-ray diffraction studies, four new compounds [Ph3CN4] [Ph2P(X)NP(Y)Ph2] [X = Y = O (1); X=Y=S (2); X=Y=Se (3); X = S, Y = Se (4)] were synthesized. The compounds were characterized by elemental analysis, IR, FAB⁺ mass spectrometry, ¹H, ¹³C, and ³¹P NMR spectroscopy, and the corresponding single-crystal X-ray structural determinations were acquired. In the four compounds, the neighboring [Ph3CN4]⁺ and [Ph2P(X)NP(Y)Ph2]⁻ ions are linked by electrostatic interactions, and thus supramolecular polymer chains are generated. To explain the energetic implications of the structural deformations, the interactions and the stabilizer hydrogen bonds, a study was carried out through the Density Functional Theory (DFT).
A microvolumetric method for surface hydrophobicity (H0) determination of proteins using a Nanodrop fluorospectrometer was developed. This method reduces the protein and fluorophore quantities that are necessary for sample preparations and readings by two and three orders of magnitude, respectively, compared to conventional methods. In addition, readings can be obtained in just 2-6 s. Bovine serum albumin (BSA) and 1-anilino 8-naphthalene sulfonic acid (ANS) were used for the first optimization of appropriate fluorophore-protein conditions for H0 determination (20 μM ANS, 0.5-4 μM BSA, pH 5). Based on validation guidelines, the novel method shows linear behavior, good intraday precision, accuracy, and sensitivity. This method was robust against several factors, as determined by a Youden-Steiner test. Additional surface hydrophobicity determinations using several proteins demonstrate suitable method applicability. The present microvolumetric method provides a reliable technique to determine the H0 of proteins for pharmaceutical, biotechnological, and food applications.
DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6 $$\times$$ × 6 $$\times$$ × 6 m $$^3$$ 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019–2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.
Se reflexiona en torno a la relevancia de la identidad nacional como ruta metodológica adecuada para la construcción de sociedades justas, pacíficas e inclusivas propuesta por la Agenda 2030 en su objetivo dieciséis. La paz, justicia e inclusión constituyen valores cardinales que asumen una dimensión más intersubjetiva que jurídica. Su concreción podría lograrse de forma sostenida desde una perspectiva esencialmente intersubjetiva a través de la cual la identidad nacional en cuanto identidad colectiva puede asumirse como canal expedito. Se hace un breve estado de la cuestión del objetivo dieciséis de la Agenda para resaltar su relevancia particular e identificar la contribución de la identidad nacional como ruta metodológica eficiente para su realización. Luego, se establece un acercamiento teórico-conceptual de la identidad nacional para recalcar la fuerza del sentimiento que genera a partir de dos modelos de nación, y enfatizar su valor para la construcción de sociedades justas, pacíficas e inclusivas.
Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.
Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases , and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed.
There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.
Background: The empirical prescription of antibiotics to inpatients with Coronavirus Disease 2019 (COVID-19) is frequent despite uncommon bacterial coinfections. Current knowledge of the effect of antibiotics on the survival of hospitalized children with COVID-19 is limited. Objective: To characterize the survival experience of children with laboratory-positive COVID-19 in whom antibiotics were prescribed at hospital admission. Methods: A retrospective cohort study was conducted in Mexico, with children hospitalized due to COVID-19 from March 2020 to December 2021. Data from 1601 patients were analyzed using the Kaplan-Meier method and the log-rank test. We computed hazard ratios (HR) and 95% confidence intervals (CI) to evaluate the effect of the analyzed exposures on disease outcomes. Results: Antibiotics were prescribed to 13.2% ([Formula: see text] = 211) of enrolled children and a higher mortality rate [14.9 (95% CI 10.1-19.8) vs. 8.3 (95% CI 6.8-9.8)] per 1000 person-days, [Formula: see text] < 0.001) was found among them. At any given cut-off, survival functions were lower in antibiotic-positive inpatients ([Formula: see text] < 0.001). In the multiple model, antibiotic prescription was associated with a 50% increase in the risk of fatal outcome (HR = 1.50, 95% CI 1.01-2.22). A longer interval between illness onset and healthcare-seeking and pneumonia at hospital admission was associated with a poorer prognosis. Conclusions: Our results suggest that antibiotic prescription in children hospitalized due to COVID-19 is associated with decreased survival. If later replicated, these findings highlight the need for rational antibiotics in these patients.
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• School of Medicine; and Cancerology State Institute, Colima State Health Services
• School of Electromechanical Engineering (Manzanillo)
• Laboratorio de Bioingeniería
• Faculty of Economics
• Faculty of Sciences
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