Search

A good understanding of ternary phase diagrams is required to advance and/or to reproduce experimental research in solid-state and materials chemistry. The aim of this paper is to describe the solutions to problems that appear when studying or determining ternary phase diagrams. A brief description of the principal features shown in phase diagrams of ternary systems in the subsolidus region is included. We present a systematic procedure to obtain specific compositions of particular interest as well as to calculate binary and ternary ratios of compounds inside the Gibbs triangle and a step-by-step methodology to formulate solid solution equations in binary, ternary, or higher-composition compounds. Specific problems and their solutions are presented as a practical guide, linking the learned concepts with their applications in ceramics and solid-state chemistry research. In addition, ready-to-go exercises with worked-out solutions are included for practice. The procedures described herein may be helpful to those interested in phase diagram interpretation, preparation, and analysis.

Biomarkers play a pivotal role in the screening, diagnosis, prevention, and post-treatment follow-up of various malignant tumors. In certain instances, identifying these markers necessitates prior treatment due to the complex nature of the tumor microenvironment. Consequently, advancing techniques that exhibit selectivity, specificity, and enable streamlined analysis hold significant importance. Molecularly imprinted polymers (MIPs) are considered synthetic antibodies because they possess the property of molecular recognition with high selectivity and sensitivity. In recent years, there has been a notable surge in the investigation of these materials, primarily driven by their remarkable adaptability in terms of tailoring them for specific target molecules and integrating them into diverse analytical technologies. This review presents a comprehensive analysis of molecular imprinting techniques, highlighting their application in developing sensors and analytical methods for cancer detection, diagnosis, and monitoring. Therefore, MIPs offer great potential in oncology and show promise for improving the accuracy of cancer screening and diagnosis procedures.

The service life assessment of previous fatigue stress has a fundamental role in estimating the reliability of products like fiber optical connectors, and should replicate use in severe environments in telecommunications systems. However, the vibration prediction models used in the literature present limitations in vibration life prediction. In this paper, we propose using the insertion loss fatigue life and including it in the Weibull distribution to determine the Weibull parameters ƞ and β to evaluate fiber optic connector reliability R(t) under environmental and mechanical testing stress. We analyzed the failure data of a standard telecommunication fiber optical connector under a program of service life stress testing against that of a fiber optical connector under only mechanical vibration stress testing. The fiber connectors were monitored during the vibration testing to review the transient change of the optical signal. Their results showed that the reliability of the fiber connectors submitted to the service life program was Rt=0.694, while that of the fiber connectors submitted to mechanical vibration only was Rt=0.970. In addition, the analysis showed that the service life testing consumed 70.2% of the product’s lifetime. We present in Numerical Validation the steps to determine the acceleration factors of the vibration test developed.

The relationship between sleep, glial cells, and the endocannabinoid system represents a multifaceted regulatory network with profound implications for neuroinflammation and cognitive function. The molecular underpinnings of sleep modulation by the endocannabinoid system and its influence on glial cell activity are discussed, shedding light on the reciprocal relationships that govern these processes. Emphasis is placed on understanding the role of glial cells in mediating neuroinflammatory responses and their modulation by sleep patterns. Additionally, this review examines how the endocannabinoid system interfaces with glia-immune signaling to regulate inflammatory cascades within the central nervous system. Notably, the cognitive consequences of disrupted sleep, neuroinflammation, and glial dysfunction are addressed, encompassing implications for neurodegenerative disorders, mood disturbances, and cognitive decline. Insights into the bidirectional modulation of cognitive function by the endocannabinoid system in the context of sleep and glial activity are explored, providing a comprehensive perspective on the potential mechanisms underlying cognitive impairments associated with sleep disturbances. Furthermore, this review examines potential therapeutic avenues targeting the endocannabinoid system to mitigate neuroinflammation, restore glial homeostasis, and normalize sleep patterns. The identification of novel therapeutic targets within this intricate regulatory network holds promise for addressing conditions characterized by disrupted sleep, neuroinflammation, and cognitive dysfunction. This work aims to examine the complexities of neural regulation and identify potential avenues for therapeutic intervention.

This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the parameters of the membership functions are adapted according to the values of the input data. In the approach, two fuzzy inference systems, Sugeno and Mamdani, are proposed. Additionally, variations of these models are presented using three types of membership functions, including Trapezoidal, Triangular, and Gaussian functions. The proposed models are applied to three classification datasets: Wine, Iris, and Wisconsin Breast Cancer (WDBC). The simulations and results present higher classification accuracy when implementing the Sugeno fuzzy inference system compared to the Mamdani system, and in both models (Mamdani and Sugeno), better results are obtained when the Gaussian membership function is used.

The intricate mechanisms governing brain health and function have long been subjects of extensive investigation. Recent research has shed light on two pivotal systems, the glymphatic system and the endocannabinoid system, and their profound role within the central nervous system. The glymphatic system is a recently discovered waste clearance system within the brain that facilitates the efficient removal of toxic waste products and metabolites from the central nervous system. It relies on the unique properties of the brain’s extracellular space and is primarily driven by cerebrospinal fluid and glial cells. Conversely, the endocannabinoid system, a multifaceted signaling network, is intricately involved in diverse physiological processes and has been associated with modulating synaptic plasticity, nociception, affective states, appetite regulation, and immune responses. This scientific review delves into the intricate interconnections between these two systems, exploring their combined influence on brain health and disease. By elucidating the synergistic effects of glymphatic function and endocannabinoid signaling, this review aims to deepen our understanding of their implications for neurological disorders, immune responses, and cognitive well-being.

In this paper, a Weibull probabilistic methodology is proposed with an approach to model vibration fatigue damage accumulation using two parameters: Weibull distribution and a nonlinear fatigue damage accumulation model. The damage is cumulated based on the application of a vibration stress profile and is used to determine both the Weibull β and η parameters, and the corresponding component reliability R(t). The vibration fatigue damage is analyzed to accumulate the damage as a stress function for a fatigue life exponent derived with the assistance of the acceleration’s force response. The steps to determine the Weibull β and η parameters are estimated based only on the principal vibration stresses σ1 and σ2 that allow the reproduction of the vibration fatigue damage. The method’s efficiency is based on the probabilistic approach by using the vibration fatigue damage as the Yi vector that covers the arithmetic mean as well as the β parameter. Finally, the procedure proposed is applied in a practical case where a mechanical component is used as a support for telecommunication connections and is submitted to vibration stress. The results show that using the damage accumulated as the Yi vector to estimate the parameters allows for the analysis of dynamic and individual applications.

The Aedes aegypti L. mosquito is considered the most important vector of arboviruses in the world. Insecticide-resistant phenomenon is a difficult barrier to overcome for government health entities around the planet. This problem forces to increase the concentrations of insecticides in the environment causing environmental pollution and health threat to human beings. Plants have been used to combat pests for centuries and are an eco-friendly source for the search for molecules with larvicidal activity. In this work, 65 ethanol-soluble extracts from 56 plants of the Colombian Caribbean region were evaluated as potential larvicides against Aedes aegypti mosquito, as well as, for their toxic effects on non-target organism. A high larvicidal activity was found for 16 ethanol extracts, however, the most potent activity against the larvae was obtained for the five plant extracts corresponding to the seeds of Annona squamosa L., Annona cherimolia L., Annona muricata L., Tabernaemontana cymosa Jacq. and Mammea americana L., with LC50 value of 58.44, 65.10, 84.92, 25.02 and 38.58 ppm, respectively. T. cymosa seeds extract was selected for bio-guided fractionation due to the high larvicidal activity showed. Through chromatographic techniques and Nuclear Magnetic Resonance (NMR), five indole alkaloids were isolated and characterized from T. cymosa active fraction; among them, voacangine showed activity with a LC50 of 5.13 ppm indicating a high larvicidal potency besides low toxicity against model organism Caenorhabditis elegans. We also report a new indole alkaloid from T. cymosa. Our study demonstrated the potential of the Colombian Caribbean flora as a host of bioactive plants against important vectors such as the A. aegypti mosquito with potential use in controlled environments.

One of the main concerns in precision agriculture (PA) is the growth of weeds within a crop field. Currently, to prevent the spread of weeds, automatic techniques and computational tools are used to help to identify, classify, and detect the different types of weeds found in agricultural fields. One of the technologies that can help us to process digital information gathered from the agricultural fields is high-performance computing (HPC); this technology has been adopted to carry out projects requiring extra processing and storage in order to execute tasks with a large computational cost. This paper shows the implementation of an HPC cluster (HPCC), in which image processing (IP) and analysis are executed using deep learning (DL) techniques, specifically, convolutional neural networks (CNNs) with the VGG16 and InceptionV3 models, to classify different weed species. The results show the great benefits of using high-performance computing clusters in PA, specifically for classifying images. To apply distributed computing within the HPCC, the Keras and Horovod frameworks were used to train the CNN models, obtaining the best time with the InceptionV3 model with a value of 37 min 55.193 s using six HPCC cores, obtaining an accuracy of 0.65 as a result.

In this paper, the formulation to incorporate the used vibration profile, the stress generated by the product’s application, mass, and the resonance frequency is given. After that, based on the vibration output data, the two-parameter Weibull distribution is used to predict the corresponding reliability indices. In the method, the mentioned stress is incorporated as acceleration response (Ares), and by using a dynamic stress factor (σdyn). In addition, the Weibull parameters are determined based on the generated maximum and minimum principal vibration stress values. In the paper we show the efficiency of the fitted Weibull distribution to predict the reliability indices, by using its Weibull shape and scale parameters, it is always possible to reproduce the principal vibration stress values. Additionally, from the numerical application, we show how to use the Weibull analysis to determine the reliability index for a desired stress or desired cycle value. Finally, we also present the guidelines to apply the proposed method to any vibration fatigue analysis where the Ares (used to determine the σ1 and σ2 values), and the σdyn value are both known.

In this work, a new methodology for the dynamic analysis of non-linear systems is developed by applying the Mamdani fuzzy model. With this model, parameters such as settling time, peak time and overshoot will be obtained. The dynamic analysis of non-linear fuzzy systems with triangular membership functions is performed, and linguistic variables describing overly complex or ill-defined phenomena are used to fit the model. Scaling factors will simplify the modification of the variables, making them easier to find the system model. The specifications of second-order characteristics in the time domain, such as overshoot and peak time, will be represented graphically. As a case study, the proposed methods are implemented to analyse the dynamics of a tank and a simple pendulum for first-order and second-order systems, respectively, where it is observed that the proposed methodology offers highly positive results.

Obesity-induced neuroinflammation is a chronic aseptic central nervous system inflammation that presents systemic characteristics associated with increased pro-inflammatory cytokines such as interleukin 1 beta (IL-1β) and interleukin 18 (IL-18) and the presence of microglia and reactive astrogliosis as well as the activation of the NLRP3 inflammasome. The obesity pandemic is associated with lifestyle changes, including an excessive intake of obesogenic foods and decreased physical activity. Brain areas such as the lateral hypothalamus (LH), lateral septum (LS), ventral tegmental area (VTA), and nucleus accumbens (NAcc) have been implicated in the homeostatic and hedonic control of feeding in experimental models of diet-induced obesity. In this context, a chronic lipid intake triggers neuroinflammation in several brain regions such as the hypothalamus, hippocampus, and amygdala. This review aims to present the background defining the significant impact of neuroinflammation and how this, when induced by an obesogenic diet, can affect feeding control, triggering metabolic and neurological alterations.

Potato microtuber (MT) development through in vitro techniques are ideal propagules for producing high quality potato plants. MT formation is influenced by several factors, i.e., photoperiod, sucrose, hormones, and osmotic stress. We have previously developed a protocol of MT induction in medium with sucrose (8% w/v), gelrite (6g/L), and 2iP as cytokinin under darkness. To understand the molecular mechanisms involved, we performed a transcriptome-wide analysis. Here we show that 1715 up- and 1624 down-regulated genes were involved in this biological process. Through the protein–protein interaction (PPI) network analyses performed in the STRING database (v11.5), we found 299 genes tightly associated in 14 clusters. Two major clusters of up-regulated proteins fundamental for life growth and development were found: 29 ribosomal proteins (RPs) interacting with 6 PEBP family members and 117 cell cycle (CC) proteins. The PPI network of up-regulated transcription factors (TFs) revealed that at least six TFs–MYB43, TSF, bZIP27, bZIP43, HAT4 and WOX9–may be involved during MTs development. The PPI network of down-regulated genes revealed a cluster of 83 proteins involved in light and photosynthesis, 110 in response to hormone, 74 in hormone mediate signaling pathway and 22 related to aging.

In Honduras, some coffee farms must comply with strict standards of social, economic, and environmental sustainability, due to their organic, gender and fair-trade certifications. The principal research aim is to evaluate the musculoskeletal risks in occupations in a Honduran coffee farm certified in sustainable environments and to know the status of its workers within the farm. Musculoskeletal symptom perception during the last twelve months was consulted, assessing exposure to risk factors for work-related musculoskeletal disorders using the Quick Exposure Check method. Data regarding 48 workers were analyzed to provide the results. Within the body regions where discomfort is concentrated, the back, shoulders, wrists, knees, and feet stand out, and the highest risk exposures are presented for the coffee cutters at the neck level and in the wrist/hand segment, in the coffee pickers at the back, shoulder–arm segment, and wrist/hand segment, and in the processors in the back area and shoulder–arm segment. It is concluded that, in all the coffee fruit harvesting processes, the people who work in these jobs are exposed to ergonomic risks.

This study focused on recovering alkaline proteases from the viscera of Scomberomorus sierra through hydrophobic interaction chromatography. Three alkaline proteases were partially separated using this chromatographic technique; two of them, with molecular weights of 19 and 31 kDa, were identified as trypsin-like enzymes according to inhibition assays. The 31 kDa alkaline protease, the only isolated enzyme, was purified under following chromatographic conditions: ammonium sulfate 13% (w/v) and ethylene glycol 27% (w/v); this enzyme showed maximum activity at pH 9 – 10 and 50 – 60 °C and was strongly inhibited by soybean trypsin inhibitor (SBTI) and porcine trypsin inhibitor (TPI). A third alkaline protease with molecular weight of 20 kDa was partially separated and inhibited by tosyl phenylalanyl chloromethyl ketone (TPCK), showing optimum activity at pH 9 – 11 and 60 °C. These results show that the viscera of Scomberomorus sierra may be useful as source of proteases.

The paper’s content allowed us to determine the fatigue life of a component that is being subjected to a random vibration environment. Its estimation is performed in the frequency domain with loading frequencies being closer to the system’s natural frequency. From loads’ amplitude and their interaction effect, we derive a nonlinear damage model to cumulate the generated fatigue damage. The exponent value of 0.4 from the Manson–Halford curve damage model was replaced by a vibration bending stress relation that considers the effect and interaction of loads. The analysis is performed from a progressive accelerated vibration spectrum to predict the fatigue life estimation. From this accelerated scenario, the accelerated coefficients and cumulated damage are both determined. The proposed nonlinear model is based on the following facts: (1) vibration and bending stress σvb values are obtained from the response acceleration of power spectral density (PSD) applied and (2) the model can be applied to any mechanical component analysis where the corresponding acceleration responses Ares and the dynamic load factor σdynamic values are known. The steps to determine the expected fatigue damage accumulation D by using the curve damage are given.

Large volumes of waste are generated in the processing operations of the fishing industry. These effluents contain potentially useful proteins. However, it is necessary to concentrate them for utilization. The stickwater (SW) resulting from this operation was subjected to a protein-fractionation step, pH adjustment (acid + alkaline) and ultrasonic pulsing in order to aid in hydrolysis and evaluate its functional and nutritional properties. The protein fractions, as well as the protein hydrolysates present in the tail water, had a chemical composition of 54.85 ± 4.21 and 74.81 ± 3.89 protein (%), 0.8 ± 0.1 and 0.2 ± 0.015 fat (%), 7.21 ± 0.67% ash (%), respectively. The increase in low-molecular-weight peptides results in an increase in free-radical scavenging activity. However, the increase in ferric-reducing antioxidant power may be due to the HCl treatment performed by the company. An increase in the functional properties of the samples treated with ultrasonic pulses was observed. Therefore, the chemical, nutritional and functional characteristics of stickwater suggest its potential use as a food additive.

In this study, a nano-composite material of a nanostructured Al-based matrix reinforced with Fe40Al intermetallic particles was produced by ball milling. During the non-equilibria processing, the powder mixtures with the compositions of Al-XFe40Al (X = 5, 10, and 15 vol. %) were mechanically milled under a low energy regime. The processed Al-XFe40Al powder mixtures were subjected to uniaxial pressing at room temperature. Afterward, the specimens were subjected to a sintering process under an inert atmosphere. In this thermal treatment, the specimens were annealed at 500 °C for 2 h. The sintering process was performed under an argon atmosphere. The crystallite size of the Al decreased as the milling time advanced. This behavior was observed in the three specimens. During the ball milling stage, the powder mixtures composed of Al-XFe40Al did not experience a mechanochemical reaction that could lead to the generation of secondary phases. The crystallite size of the Al displayed a predominant tendency to decrease during the ball milling process. The microstructure of the consolidated specimens indicated a uniform dispersion of the intermetallic reinforcement phases in the Al matrix. Moreover, according to the Vickers microhardness tests, the hardness varied linearly with the increase in the concentration of the Fe40Al intermetallic phase present in the composite material. The presented graphs indicate that the hardness increased almost linearly with the increasing dislocation density and with the reduction in grain sizes (both occurring during the non-equilibria processing). The microstructural and mechanical properties reported in this paper provide the aluminum matrix composite materials with the ideal conditions to be considered candidates for applications in the automotive and aeronautical industries.

Based on the principal stress values generated by the bending beam, the material’s strength required at 106 cycles is determined depending on time. To determine the stress/strength reliability (R(t)), the stress distribution is determined directly from the range of the principal stresses values, and the strength distribution is determined based on the reduced tensile strength (S′e) and fatigue strength (Se) range. Therefore, based on the time-dependent stress and the material’s strength, a step-by-step method to determine the reliability R(t) of the structural element at 106 cycles is provided. The R(t) index is used to select the best among the feasible beam alternatives of the static/elastic and plastic methodologies. The method’s efficiency is based on the time-dependent stress analysis performed by using the elastic modulus, and corresponding strain as time dependence variables. Because the time-dependent stress is related to the changes of the bending deflection through time, it is determined based on the addressed equivalent stress at 106 cycles.

The inhabitants of Santiago, Chile have been exposed to harmful levels of air pollutants for decades. The city's poor air quality is a result of steady economic growth, and stable atmospheric conditions adverse to mixing and ventilation that favor the formation of oxidants and secondary aerosols. Identifying and quantifying the sources that contribute to the ambient levels of pollutants is key for designing adequate mitigation measures. Estimating the evolution of source contributions to ambient pollution levels is also paramount to evaluating the effectiveness of pollution reduction measures that have been implemented in recent decades. Here, we quantify the main sources that have contributed to fine particulate matter (PM2. 5) between April 1998 and August 2012 in downtown Santiago by using two different source-receptor models (PMF 5.0 and UNMIX 6.0) that were applied to elemental measurements of 1243 24 h filter samples of ambient PM2.5. PMF resolved six sources that contributed to ambient PM2. 5, with UNMIX producing similar results: motor vehicles (37.3 ± 1.1 %), industrial sources (18.5 ± 1.3 %), copper smelters (14.4 ± 0.8 %), wood burning (12.3 ± 1.0 %), coastal sources (9.5 ± 0.7 %) and urban dust (3.0 ± 1.2 %). Our results show that over the 15 years analyzed here, four of the resolved sources significantly decreased [95 % confidence interval]: motor vehicles 21.3 % [2.6, 36.5], industrial sources 39.3 % [28.6, 48.4], copper smelters 81.5 % [75.5, 85.9], and coastal sources 58.9 % [38.5, 72.5], while wood burning did not significantly change and urban dust increased by 72 % [48.9, 99.9]. These changes are consistent with emission reduction measures, such as improved vehicle emission standards, cleaner smelting technology, introduction of low-sulfur diesel for vehicles and natural gas for industrial processes, public transport improvements, etc. However, it is also apparent that the mitigation expected from the above regulations has been partially offset by the increasing amount of private vehicle use in the city, with motor vehicles becoming the dominant source of ambient PM2. 5 in recent years. Consequently, Santiago still experiences ambient PM2. 5 levels above the annual and 24 h Chilean and World Health Organization standards, and further regulations are required to reach ambient air quality standards.

Glucocorticoids (GCs) are critical regulators of energy balance. Their deregulation is associated with the development of obesity and metabolic syndrome. However, it is not understood if obesity alters the tissue glucocorticoid receptor (GR) response, and moreover whether a moderate aerobic exercise prevents the alteration in GR response induced by obesity. Methods: To evaluate the GR response in obese mice, we fed C57BL6J mice with a high-fat diet (HFD) for 12 weeks. Before mice were sacrificed, we injected them with dexamethasone. To assess the exercise role in GR response, we fed mice an HFD and subjected them to moderate aerobic exercise three times a week. Results: We found that mice fed a high-fat diet for 12 weeks developed hepatic GC hypersensitivity without changes in the gastrocnemius or epididymal fat GR response. Therefore, moderate aerobic exercise improved glucose tolerance, increased the corticosterone plasma levels, and prevented hepatic GR hypersensitivity with an increase in epididymal fat GR response. Conclusion: Collectively, our results suggest that mice with HFD-induced obesity develop hepatic GR sensitivity, which could enhance the metabolic effects of HFD in the liver. Moreover, exercise was found to be a feasible non-pharmacological strategy to prevent the deregulation of GR response in obesity.

Applying Goodman, Gerber, Soderberg and Elliptical failure theories does not make it possible to determine the span of failure times (cycles to failure-Ni) of a mechanical element, and so in this paper a fatigue-life/Weibull method to predict the span of the Ni values is formulated. The input’s method are: (1) the equivalent stress (σeq) value given by the used failure theory; (2) the expected Neq value determined by the Basquin equation; and (3) the Weibull shape β and scale η parameters that are fitted directly from the applied principal stress σ1 and σ2 values. The efficiency of the proposed method is based on the following facts: (1) the β and η parameters completely reproduce the applied σ1 and σ2 values. (2) The method allows us to determine the reliability index R(t), that corresponds to any applied σ1i value or observed Ni value. (3) The method can be applied to any mechanical element’s analysis where the corresponding σ1 and σ2, σeq and Neq values are known. In the performed application, the σ1 and σ2 values were determined by finite element analysis (FEA) and from the static stress analysis. Results of both approaches are compared. The steps to determine the expected Ni values by using the Weibull distribution are given.

In this paper a Weibull methodology to determine the probabilistic percentiles for the S-N curve of the A572 Gr. 50 steel is formulated. The given Weibull/S-N formulation is based on the true stress and true strain values, which are both determined from the stress-strain analysis. For the analysis, the Weibull β and η parameters are both determined directly from the maximum and minimum addressed stresses values. The S-N curve parameters are determined for 103 and 106 cycles. In the application, published experimental data for the CSA G40.21 Gr. 350W steel is used to derive the true stress and true strain parameters of the A572 Gr. 50 steel. Additionally, the application of the S-N curve, its probabilistic percentiles and the Weibull parameters that represent these percentiles are all determined step by step. Since the proposed method is flexible, then it can be applied to determine the probabilistic percentiles of any other material.

Polycystic ovarian syndrome (PCOS) is the main cause of female infertility. It is a multifactorial disorder with varying clinical manifestations including metabolic/endocrine abnormalities, hyperandrogenism, and ovarian cysts, among other conditions. D-Chiro-inositol (DCI) is the main treatment available for PCOS in humans. To address some of the mechanisms of this complex disorder and its treatment, this study examines the effect of DCI on reproduction during the development of different PCOS-associated phenotypes in aged females and two mouse models of PCOS. Aged females (8 months old) were treated or not (control) with DCI for 2 months. PCOS models were generated by treatment with dihydrotestosterone (DHT) on Days 16, 17, and 18 of gestation, or by testosterone propionate (TP) treatment on the first day of life. At two months of age, PCOS mice were treated with DCI for 2 months and their reproductive parameters analyzed. No effects of DCI treatment were produced on body weight or ovary/body weight ratio. However, treatment reduced the number of follicles with an atretic cyst-like appearance and improved embryo development in the PCOS models, and also increased implantation rates in both aged and PCOS mice. DCI modified the expression of genes related to oocyte quality, oxidative stress, and luteal sufficiency in cumulus-oocyte complexes (COCs) obtained from the aged and PCOS models. Further, the phosphorylation of AKT, a main metabolic sensor activated by insulin in the liver, was enhanced only in the DHT group, which was the only PCOS model showing glucose intolerance and AKT dephosphorylation. The effect of DCI in the TP model seemed mediated by its influence on oxidative stress and follicle insufficiency. Our results indicate that DCI works in preclinical models of PCOS and offer insight into its mechanism of action when used to treat this infertility-associated syndrome.

Minor splicing plays an important role in vertebrate development. Zrsr1 and Zrsr2 paralog genes have essential roles in alternative splicing, mainly participating in the recognition of minor (U12) introns. To further explore their roles during early embryo development, we produced Zrsr1mu and Zrsr2mu mutant mice, containing truncating mutations within the second zinc finger domain. Both homozygous mutant mice were viable with a normal lifespan. When we crossed a homozygous Zrsr2mu/mu female with Zrsr1mu/mu male, the double heterozygotes were non-viable, giving rise to embryos that stopped developing mainly between the 2- and 4-cell stages, just after zygotic gene activation. RNA-seq analysis of Zrsr1/2mu 2-cell embryos showed altered gene and isoform expression of thousands of genes enriched in gene ontology terms and biological pathways related to ribosome, RNA transport, spliceosome, and essential zygotic gene activation steps. Alternative splicing was analyzed, showing a significant increase in intron retention in both U2 and U12 intron-containing genes related to cell cycle and mitotic nuclear division. Remarkably, both Zrsr1 and Zrsr2 were required for the conversion of mouse-induced pluripotent stem cells into 2C-like cells. According to our results, Zrsr1 or Zrsr2 are necessary for ZGA and both are indispensable for the conversion of induced pluripotent stem cells into 2C-like cells.

Variations in genes involved in DNA repair systems have been proposed as risk factors for the development of preeclampsia (PE). We conducted a case-control study to investigate the association of Human apurinic/apyrimidinic (AP) endonuclease (APEX1) Asp148Glu (rs1130409), Xeroderma Pigmentosum group D (XPD) Lys751Gln (rs13181), X-ray repair cross-complementing group 1 (XRCC) Arg399Gln (rs25487) and X-ray repair cross-complementing group 3 (XRCC3) Thr241Met (rs861539) polymorphisms with PE in a Mexican population. Samples of 202 cases and 350 controls were genotyped using RTPCR. Association analyses based on a χ2 test and binary logistic regression were performed to determine the odds ratio (OR) and a 95% confidence interval (95% CI) for each polymorphism. The allelic frequencies of APEX1 Asp148Glu polymorphism showed statistical significant differences between preeclamptic and normal women (p = 0.036). Although neither of the polymorphisms proved to be a risk factor for the disease, the APEX1 Asp148Glu polymorphism showed a tendency of association (OR: 1.74, 95% CI = 0.96–3.14) and a significant trend (p for trend = 0.048). A subgroup analyses revealed differences in the allelic frequencies of APEX1 Asp148Glu polymorphism between women with mild preeclampsia and severe preeclampsia (p = 0.035). In conclusion, our results reveal no association between XPD Lys751Gln, XRCC Arg399Gln and XRCC3 Thr241Met polymorphisms and the risk of PE in a Mexican mestizo population; however, the results in the APEX1 Asp148Glu polymorphism suggest the need for future studies using a larger sample size.

A numerical investigation regarding ductility evaluation of steel buildings with moment resisting steel frames is conducted. Bending (µLϕ) and tension (µLδ) local ductilities as well as story (µS) and global ductilities are studied. Global ductility is calculated as the mean values of story ductilities (µGS) and as the ratio of the maximum inelastic to yielding top displacements (µGt). The ductility capacity is associated to drifts of about 5%. Ductility values significantly may vary with the strong motion, ductility definition, structural element, story number, type of analysis, and model. µLϕ is much larger for beams than for columns. Even though the demands of µLδ are considered an important issue they are less relevant than µLϕ. µS is much smaller than µLϕ for beams. µGS for dynamic analysis give reasonable values, but µGt does not. µLϕ, µS and µGS obtained from pushover are larger than those obtained from dynamic analysis and unlike the case of dynamic analysis, µLϕ tend to increase with the story number showing an opposite trend. Considering that: µGt for dynamic analysis results in unreasonable values, pushover analysis does not consider energy dissipation, the strong column–weak beam (SCWB) concept was followed in the model designs, and µLδ is not relevant in framed steel buildings, the ratio (RLG) of global to local ductility capacity is calculated as the ratio of µGS to µLϕ of beams, for dynamic analysis. A value of 1/3 is proposed. Thus, if bending local ductility capacity is stated as the basis for the design, the global ductility capacity can be easily estimated.

The aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF) structures subjected to dynamics wind load using Artificial Neural Networks (ANNs) through the combination of several structural and turbulent wind parameters. The maximum story drift of 1600 MDOF structures under 16 simulated wind conditions are computed with the purpose of generating the data set for the networks training with the Levenberg–Marquardt method. The Shinozuka and Newmark methods are used to simulate the turbulent wind and dynamic response, respectively. In order to optimize the computational time required for the dynamic analyses, an array format based on the Shinozuka method is presented to perform the parallel computing. Finally, it is observed that the already trained ANNs allow for predicting adequately the maximum story drift with a correlation close to 99%.