Unravelling co-catalyst integration methods in Ti-based metal-organic gels for photocatalytic H2 production.

The synthesis, characterization and photocatalytic hydrogen evolution reaction (HER) performance of a series of metal-organic gels (MOGs) constructed from titanium(IV)-oxo clusters and dicarboxylato linkers (benzene-1,4-dicarboxylato and 2-aminobenzene-1,4-dicarboxylato) are described. All the MOGs exhibit a microstructure comprised of metal-organic nanoparticles intertwined into a highly meso-/macroporous structure, as demonstrated by cryogenic transmission electron microscopy and gas adsorption isotherms. Comprehensive chemical characterization enabled the estimation of the complex formula for these defective materials, which exhibit low crystallinity and linker vacancies. To gain deeper insights into the local structure, X-ray absorption fine structure (XAFS) spectroscopy experiments were performed and compared to that of the analogous crystalline metal-organic framework. Additionally, the ultraviolet-visible absorption properties and optical band gaps were determined from diffuse reflectance spectroscopy data. The MOGs were studied as light absorbers for the sacrificial photocatalytic HER under simulated solar light irradiation using a platinum co-catalyst by either (1) in situ photodeposition or (2) ex situ doping process, through a post-synthetic metalation of the MOG structure. The chemical analysis of the metalation, along with high-angle annular dark-field scanning transmission electron microscopy, revealed that although the in situ addition of the co-catalyst led to greater HER rates (227 vs. 110 µmolH2 gMOG-1 h-1 for in situ and ex situ, respectively), the ex situ modification provided a finer distribution of platinum nanoparticles along the porous microstructure and, as a result, it led to a more efficient utilization of the co-catalyst (45 vs. 110 mmolH2 gPt-1 h-1).

A 3D bioprinted hydrogel gut-on-chip with integrated electrodes for transepithelial electrical resistance (TEER) measurements.

Conventional gut-on-chip (GOC) models typically represent the epithelial layer of the gut tissue, neglecting other important components such as the stromal compartment and the extracellular matrix (ECM) that play crucial roles in maintaining intestinal barrier integrity and function. These models often employ hard, flat porous membranes for cell culture, thus failing to recapitulate the soft environment and complex 3D architecture of the intestinal mucosa. Alternatively, hydrogels have been recently introduced in GOCs as ECM analogs to support the co-culture of intestinal cells inin vivo-like configurations, and thus opening new opportunities in the organ-on-chip field. In this work, we present an innovative GOC device that includes a 3D bioprinted hydrogel channel replicating the intestinal villi architecture containing both the epithelial and stromal compartments of the gut mucosa. The bioprinted hydrogels successfully support both the encapsulation of fibroblasts and their co-culture with intestinal epithelial cells under physiological flow conditions. Moreover, we successfully integrated electrodes into the microfluidic system to monitor the barrier formation in real time via transepithelial electrical resistance measurements.

Determining Factors to Understand the External Quantum Efficiency Values: Study Carried Out with Copper(I)-I and 1,2-Bis(4-pyridyl)ethane Coordination Polymers as Downshifters in Photovoltaic Modules.

Downshifters refer to compounds with the capacity to absorb UV photons and transform them into visible light. The integration of such downshifters has the potential to improve the efficiency of commercial photovoltaic modules. Initially, costly lanthanide derivatives and organic fluorescent dyes were introduced, resulting in a heightened module efficiency. In a novel research direction guided by the same physicochemical principles, the utilization of copper(I) coordination compounds is proposed. This choice is motivated by its simpler and more economical synthesis, primarily due to copper being a more abundant and less toxic element. Our proposal involves employing 1,2-bis(4-pyridyl) ethane (bpe), an economically viable commercial ligand, in conjunction with CuI to synthesize coordination polymers: [CuI(bpe)]n(1), [Cu3I3(bpe)3]n(2), and [CuI(bpe)0.5]n(3). These polymers exhibit the ability to absorb UV photons and emit light within the green and orange spectra. To conduct external quantum efficiency studies, the compounds are dispersed on glass and then encapsulated with ethylene vinyl acetate through heating to 150 °C. Interestingly, during these procedural steps, the solvents and temperatures employed induce a phase transformation, which has been thoroughly examined through both experimental analysis and theoretical calculations. The outcomes of these studies reveal an enhancement in external quantum efficiency with [Cu3I3(bpe)3]n(2), at a cost significantly lower (between 340 and 350 times) than that associated with lanthanide DS complexes.

Meta-analysis contrasting freshwater biodiversity in forests and oil palm plantations with and without riparian buffers.

The expansion of oil palm plantations has led to land-use change and deforestation in the tropics, which has affected biodiversity. Although the impacts of the crop on terrestrial biodiversity have been extensively reviewed, its effects on freshwater biodiversity remain relatively unexplored. We reviewed the research assessing the impacts of forest-to-oil palm conversion on freshwater biota and the mitigating effect of riparian buffers on these impacts. We searched for studies comparing taxa richness, species abundance, and community composition of macroinvertebrates, amphibians, and fish in streams in forests (primary and disturbed) and oil palm plantations with and without riparian buffers. Then, we conducted a meta-analysis to quantify the overall effect of the land-use change on the 3 taxonomic groups. Twenty-nine studies fulfilled the inclusion criteria. On average, plantations lacking buffers hosted 44% and 19% fewer stream taxa than primary and disturbed forests, respectively. Stream taxa on plantations with buffers were 24% lower than in primary forest and did not differ significantly from disturbed forest. In contrast, stream community composition differed between forests and plantations regardless of the presence of riparian buffers. These differences were attributed to agrochemical use and altered environmental conditions in the plantations, including temperature changes, worsened water conditions, microhabitat loss, and food and shelter depletion. On aggregate, abundance did not differ significantly among land uses because increases in generalist species offset the population decline of vulnerable forest specialists in the plantation. Our results reveal significant impacts of forest-to-oil palm conversion on freshwater biota, particularly taxa richness and composition (but not aggregate abundance). Although preserving riparian buffers in the plantations can mitigate the loss of various aquatic species, it cannot conserve primary forest communities. Therefore, safeguarding primary forests from the oil palm expansion is crucial, and further research is needed to address riparian buffers as a promising mitigation strategy in agricultural areas.

Metaanálisis contrastando la biodiversidad de agua dulce en los bosques y las plantaciones de palma de aceite con o sin bosques ribereños Resumen La expansión de las plantaciones de palma de aceite ha derivado en cambios en el uso de suelo y deforestación en los trópicos, afectando a la biodiversidad. Existe una revisión extensa del impacto de este cultivo sobre la biodiversidad terrestre, pero sus efectos sobre la biodiversidad de agua dulce todavía no están muy documentados. Revisamos las investigaciones que han evaluado el impacto de la conversión de bosque a plantación de palma de aceite sobre la biota de agua dulce y el efecto mitigante que tienen los bosques ribereños sobre este impacto. Buscamos estudios que compararan la riqueza de taxones, abundancia de especies y composición comunitaria de los macroinvertebrados, anfibios y peces en los riachuelos de los bosques (primarios y perturbados) y los sembradíos de palma de aceite con y sin bosques ribereños. Después realizamos un metaanálisis para cuantificar el efecto del cambio de uso de suelo en los tres grupos taxonómicos. Veintinueve estudios cumplieron con el criterio de inclusión. En promedio, las plantaciones carentes de bosques ribereños albergaron 44% y 19% menos taxones que los bosques primarios y perturbados. Los taxones en los sembradíos con bosques ribereños fueron 24% menos que en el bosque primario y no difirieron significativamente del bosque perturbado. Como contraste, la composición comunitaria del riachuelo difirió entre los bosques y los sembradíos sin importar la presencia de los bosques ribereños. Atribuimos estas diferencias al uso de agroquímicos y las condiciones ambientales alteradas en las plantaciones, incluidas los cambios térmicos, condiciones hidrológicas alteradas, pérdida de microhábitats y reducción de alimentos y refugios. En general, la abundancia no difirió significativamente entre los usos de suelo porque el incremento de especies generalistas en las plantaciones contrarresta la declinación poblacional de los especialistas de bosque vulnerables. Nuestros resultados revelan un impacto significativo de la conversión de bosque a plantación sobre la biota de agua dulce, particularmente la riqueza de taxones y la composición (pero no la abundancia agregada). Aunque mantener los bosques ribereños en las plantaciones puede mitigar la pérdida de varias especies acuáticas, no puede conservar las comunidades del bosque primario. Por lo tanto, es crucial salvaguardar los bosques primarios de la expansión del aceite de palma, además de que se necesitan más investigaciones para abordar los bosques ribereños como una estrategia prometedora de mitigación en las áreas agrícolas.

Isoreticular Chemistry and Applications of Supramolecularly Assembled Copper-Adenine Porous Materials.

The useful concepts of reticular chemistry, rigid and predictable metal nodes together with strong and manageable covalent interactions between metal centers and organic linkers, have made the so-called metal-organic frameworks (MOFs) a flourishing area of enormous applicability. In this work, the extension of similar strategies to supramolecularly assembled metal-organic materials has allowed us to obtain a family of isoreticular compounds of the general formula [Cu7(µ-adeninato-κN3:κN9)6(µ3-OH)6(µ-OH2)6](OOC-R-COO)·nH2O (R: ethylene-, acetylene-, naphthalene-, or biphenyl-group) in which the rigid copper-adeninato entities and the organic dicarboxylate anions are held together not by covalent interactions but by a robust and flexible network of synergic hydrogen bonds and π-π stacking interactions based on well-known supramolecular synthons (SMOFs). All compounds are isoreticular, highly insoluble, and water-stable and show a porous crystalline structure with a pcu topology containing a two-dimensional (2D) network of channels, whose dimensions and degree of porosity of the supramolecular network are tailored by the length of the dicarboxylate anion. The partial loss of the crystallization water molecules upon removal from the mother liquor produces a shrinkage of the unit cell and porosity, which leads to a color change of the compounds (from blue to olive green) if complete dehydration is achieved by means of gentle heating or vacuuming. However, the supramolecular network of noncovalent interactions is robust and flexible enough to reverse to the expanded unit cell and color after exposure to a humid atmosphere. This humidity-driven breathing behavior has been used to design a sensor in which the electrical resistance varies reversibly with the degree of humidity, very similar to the water vapor adsorption isotherm of the SMOF. The in-solution adsorption properties were explored for the uptake and release of the widely employed 5-fluorouracil, 4-aminosalycilic acid, 5-aminosalycilic acid, and allopurinol drugs. In addition, cytotoxicity activity assays were completed for the pristine and 5-fluorouracil-loaded samples.

Thermochemical CO2 Reduction Catalyzed by Homometallic and Heterometallic Nanoparticles Generated from the Thermolysis of Supramolecularly Assembled Porous Metal-Adenine Precursors.

A family of unprecedented supramolecularly assembled porous metal-organic compounds (SMOFs), based on [Cu6M(µ-adeninato)6(µ3-OH)6(µ-H2O)6]2+ cations (MII: Cu, Co, Ni, and Zn) and different dicarboxylate anions (fumarate, benzoate, and naphthalene-2,6-dicarboxylate), have been employed as precursors of catalysts for the thermocatalytic reduction of CO2. The selected metal-organic cation allows us to tune the composition of the SMOFs and, therefore, the features and performance of the final homometallic and bimetallic catalysts. These catalysts were obtained by thermolysis at 600 °C under a N2 atmosphere and consist of big metal particles (10-20 µm) placed on the surface of the carbonaceous matrix and very tiny metal aggregates (<10 nm) within this carbonaceous matrix. The latter are the most active catalytic sites for the CO2 thermocatalytic reduction. The amount of this carbonaceous matrix correlates with the organic content present in the metal-organic precursor. In this sense, CO2 thermocatalytic reduction experiments performed over the homometallic, copper only, catalysts with different carbon contents indicate that above a certain value, the increase of the carbonaceous matrix reduces the overall performance by encapsulating the nanoparticles within this matrix and isolating them from interacting with CO2. In fact, the best performing homometallic catalyst is that obtained from the precursor containing a small fumarate counterion. On the other hand, the structural features of these precursors also provide a facile route to work with a solid solution of nanoparticles as many of these metal-organic compounds can replace up to 1/7 of the copper atoms by zinc, cobalt, or nickel. Among these heterometallic catalysts, the best performing one is that of copper and zinc, which provides the higher conversion and selectivity toward CO. XPS spectroscopy and EDX mappings of the latter catalyst clearly indicate the presence of Cu1-xZnx nanoparticles covered by small ZnO aggregates that provide a better CO2 adsorption and easier CO release sites.

A Lamellar Zn-Based Coordination Polymer Showing Increasing Photoluminescence upon Dehydration.

The present study reports on a 2D lamellar coordination polymer (CP) of {[Zn(µ3-pmdc)(H2O)]·H2O}n formula (pmdc = pyrimidine-4,6-dicarboxylate). This CP is synthesized under an appropriate acid-base reaction between the gently mortared reagents in the solid state through a solvent-free procedure that avoids the presence of concomitant byproducts. The X-ray crystal structure reveals the occurrence of Zn2 entities connected through carboxylate groups of pmdc, which behave as triconnected nodes, giving rise to six-membered ring-based layers that are piled up through hydrogen bonding interactions. In addition to a routine physico-chemical characterization, the thermal evolution of the compound has been studied by combining thermogravimetric and thermodiffractometric data. The photoluminescence properties are characterized in the solid state and the processes governing the spectra are described using time-dependent density-functional theory (TD-DFT) with two different approaches employing different program packages. The emissive capacity of the material is further analyzed according to the dehydration and decreasing temperature of the polycrystalline sample.

Effect of a nitrogenous nanocomposite on leaching and N content in lettuce in soil columns.

Nanofertilizers could promote nutrient efficiency with slow release compared to conventional fertilizers (CF). Most of the applied nitrogen is lost on the soil by leaching, due to the rapid release behavior of CF. Clays can function as a nanosized porous structure to retain and slowly release nutrients. The objective of this study was to evaluate a nitrogenous nanocomposite (NCN) and its effect on leaching and N content of lettuce (Lactuca sativa). The treatments applied were: 100% conventional fertilizer, 100% nitrogenous nanocomposite and the mixture in percentage of CF/NCN 25/75, 50/50, 75/25 and 25/0, 50/0 75/0% on columns of soil with lettuce for 45 days. Leachates at the end of the cycle increased in treatments with NCN. Treatments with NCN have higher N content in the leaf. In regard to biomass growth, leaf area, leaf N, drained variables, electrical conductivity and NO3- content, it was possible to show that the doses of 50 and 75% of NCN match the characteristics of the crop compared to the control, which allows us to use lower doses than those recommended with CFs.

Lower Prevalence of Body Fat-Defined Obesity at Higher Altitudes in Peruvian Adults.

Woolcott, Orison O., Till Seuring, and Oscar A. Castillo. Lower prevalence of body fat-defined obesity at higher altitudes in Peruvian adults. High Alt Med Biol. 24:214-222, 2023. Background: Previous studies have reported a lower prevalence of obesity (defined as a body mass index [BMI] ≥30 kg/m2) in populations from higher altitudes. Since BMI does not distinguish fat mass and fat-free mass, it is unclear whether there is an inverse association between altitude and body fat-defined obesity. Methods: We performed an analysis of cross-sectional data to examine the association between altitude and body fat-defined obesity (as opposed to BMI-defined obesity) using individual-level data from a nationally representative sample of the Peruvian adult population living between 0 and 5,400 m altitude. Body fat-defined obesity was diagnosed using the relative fat mass (RFM), an anthropometric index validated to estimate whole-body fat percentage. RFM cutoffs for obesity diagnosis were ≥40% for women and ≥30% for men. We utilized Poisson regression to estimate the prevalence ratio and confidence intervals (CIs) as the measure of the association, adjusting for age, cigarette use, and diabetes. Results: Analysis comprised 36,727 individuals (median age, 39 years; 50.1% women). In rural areas, for a one-km increase in altitude, the prevalence of body fat-defined obesity decreased by 12% among women (adjusted prevalence ratio: 0.88; 95% CI, 0.86 - 0.90; p < 0.001) and 19% among men (adjusted prevalence ratio: 0.81; 95% CI, 0.77 - 0.86; p < 0.001), on average, when all the other variables were held constant. The inverse association between altitude and obesity was less strong in urban areas than in rural areas but remained significant among women (p = 0.001) and men (p < 0.001). However, the relationship between altitude and obesity in women who live in urban areas appears to be nonlinear. Conclusions: In Peruvian adults, the prevalence of body fat-defined obesity was inversely associated with altitude. Whether this inverse association is explained by altitude per se or confounded by socioeconomic or other environmental factors, or differences in race/ethnicity or lifestyle, warrants further investigation.

Exposure to anandamide on young rats causes deficits in learning, temporal perception and induces changes in NMDA receptor expression.

Human use of marijuana at an early age has been reported to lead to cognitive impairment. However, researchers have not yet clearly determined whether this impairment is due to marijuana-induced alterations in the developing nervous system and whether this deficit persists into adulthood after marijuana use has ceased. We administered anandamide to developing rats to assess the effect of cannabinoids on development. We subsequently evaluated learning and performance on a temporal bisection task in adulthood and assessed the expression of genes encoding principal subunits of NMDA receptors (Grin1, Grin2A, and Grin2B) in the hippocampus and prefrontal cortex. Rats in two age groups, namely, 21-day-old and 150-day-old rats, received intraperitoneal injections of anandamide or the vehicle for 14 days. Both groups performed a temporal bisection test, which included listening to tones of different durations and classifying them as short or long. The expression of the Grin1, Grin2A and Grin2B mRNAs was evaluated using quantitative PCR in both age groups after extracting mRNA from the hippocampus and prefrontal cortex. We observed a learning impairment in the temporal bisection task (p < 0.05) and changes in the response latency (p < 0.05) in rats that received anandamide. Furthermore, these rats exhibited decreased expression of Grin2b (p = 0.001) compared to those that received the vehicle. In human subjects, the use of cannabinoids during development induces a long-term deficit, but this deficit is not observed in subjects who use cannabinoids in adulthood. Rats treated with anandamide earlier in development took longer to learn the task, suggesting that anandamide exerts a harmful effect on cognition in developing rats. Administration of anandamide during early stages of development induced deficits in learning and other cognitive processes that depend on an adequate estimation of time. The cognitive demands of the environment must be considered when evaluating the cognitive effects of cannabinoids on developing or mature brains. High cognitive demands might induce differential expression of NMDA receptors that improves cognitive capacity, overcoming altered glutamatergic function.

Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm.

Recurrent Neural Networks (RNN) are basically used for applications with time series and sequential data and are currently being used in embedded devices. However, one of their drawbacks is that RNNs have a high computational cost and require the use of a significant amount of memory space. Therefore, computer equipment with a large processing capacity and memory is required. In this article, we experiment with Nonlinear Autoregressive Neural Networks (NARNN), which are a type of RNN, and we use the Discrete Mycorrhizal Optimization Algorithm (DMOA) in the optimization of the NARNN architecture. We used the Mackey-Glass chaotic time series (MG) to test the proposed approach, and very good results were obtained. In addition, some comparisons were made with other methods that used the MG and other types of Neural Networks such as Backpropagation and ANFIS, also obtaining good results. The proposed algorithm can be applied to robots, microsystems, sensors, devices, MEMS, microfluidics, piezoelectricity, motors, biosensors, 3D printing, etc.

Magnetic sustentation as an adsorption characterization technique for paramagnetic metal-organic frameworks.

Nowadays, there are many reliable characterization techniques for the study of adsorption properties in gas phase. However, the techniques available for the study of adsorption processes in solution, rely on indirect characterization techniques that measure the adsorbate concentration remaining in solution. In this work, we present a sensing method based on the magnetic properties of metal-organic frameworks (MOFs) containing paramagnetic metal centres, which stands out for the rapidity, low cost and in situ direct measurement of the incorporated adsorbate within the porous material. To illustrate this sensing technique, the adsorption in solution of four MOFs have been characterized: MIL-88A(Fe), MOF-74(Cu, Co) and ZIF-67(Co). Our simple and efficient method allows the direct determination of the adsorbed mass, as well as the measurement of adsorption isotherm curves, which we hope will greatly advance the study of adsorption processes in solution, since this method is independent of the chemical nature of the adsorbate that often makes its quantification difficult.

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm.

The novel coronavirus infection (COVID-19) that was first identified in China in December 2019 has spread across the globe rapidly infecting over ten million people. The World Health Organization (WHO) declared it as a pandemic on March 11, 2020. What makes it even more critical is the lack of vaccines available to control the disease, although many pharmaceutical companies and research institutions all over the world are working toward developing effective solutions to battle this life-threatening disease. X-ray and computed tomography (CT) images scanning is one of the most encouraging exploration zones; it can help in finding and providing early diagnosis to diseases and gives both quick and precise outcomes. In this study, convolution neural networks method is used for binary classification pneumonia-based conversion of VGG-19, Inception_V2 and decision tree model on X-ray and CT scan images dataset, which contains 360 images. It can infer that fine-tuned version VGG-19, Inception_V2 and decision tree model show highly satisfactory performance with a rate of increase in training and validation accuracy (91%) other than Inception_V2 (78%) and decision tree (60%) models.

A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network.

We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case), the modules are designed to be efficient predictors for each country. In this case, an integrator is used to combine the outputs of the modules, in this way achieving the goal of predicting a set of countries. At the level of the ensembles, forming a part of the modular network, these are constituted by a set of modules, which are nonlinear autoregressive neural networks that are designed to be efficient predictors under particular conditions for each country. In each ensemble, the results of the modules are combined with an aggregator to achieve a better and improved result for the ensemble. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained that could be helpful in deciding the best strategies in dealing with this virus for countries in their fight against the coronavirus pandemic. In addition, the proposed approach could be helpful in proposing strategies for similar countries.

Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction.

In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.

Interval Type-3 Fuzzy Adaptation of the Bee Colony Optimization Algorithm for Optimal Fuzzy Control of an Autonomous Mobile Robot.

In this study, the first goal is achieving a hybrid approach composed by an Interval Type-3 Fuzzy Logic System (IT3FLS) for the dynamic adaptation of α and ß parameters of Bee Colony Optimization (BCO) algorithm. The second goal is, based on BCO, to find the best partition of the membership functions (MFs) of a Fuzzy Controller (FC) for trajectory tracking in an Autonomous Mobile Robot (AMR). A comparative with different types of Fuzzy Systems, such as Fuzzy BCO with Type-1 Fuzzy Logic System (FBCO-T1FLS), Fuzzy BCO with Interval Type-2 Fuzzy Logic System (FBCO-IT2FLS) and Fuzzy BCO with Generalized Type-2 Fuzzy Logic System (FBCO-GT2FLS) is analyzed. A disturbance is added to verify if the FBCO-IT3FLS performance is better when the uncertainty is present. Several performance indices are used; RMSE, MSE and some metrics of control such as, ITAE, IAE, ISE and ITSE to measure the controller's performance. The experiments show excellent results using FBCO-IT3FLS and are better than FBCO-GT2FLS, FBCO-IT2FLS and FBCO-T1FLS in the adaptation of α and ß parameters.

Interval type-3 fuzzy aggregators for ensembles of neural networks in COVID-19 time series prediction.

In this work we are presenting an approach for fuzzy aggregation in ensembles of neural networks for forecasting. The aggregator is used in an ensemble to combine the outputs of the networks forming the ensemble. This is done in such a way that the total output of the ensemble is better than the outputs of the individual modules. In our approach a fuzzy system is used to estimate the weights that will be assigned to the outputs in the process of combining them in a weighted average calculation. The uncertainty in the process of aggregation is modeled with interval type-3 fuzzy, which in theory can outperform type-2 and type-1. Publicly available data sets of COVID-19 cases for several countries in the world were utilized to test the proposed approach. Simulation results of the COVID-19 data show the potential of the approach to outperform other aggregators in the literature.

Polymorphisms of TNF-alpha (- 308), IL-1beta (+ 3954) and IL1-Ra (VNTR) are associated to severe stage of endometriosis in Mexican women: a case control study.

BACKGROUND: Endometriosis is an estrogen-dependent and chronic inflammatory disease affecting up to 10% of women. It is the result of a combined interaction of genetic, epigenetic, environmental, lifestyle, reproductive and local inflammatory factors. In this study, we investigated whether single nucleotide polymorphisms (SNPs) mapping to TNF-alpha (TNF, rs1800629) and IL-1beta (IL1B, rs1143634) and variable number tandem repeat polymorphism mapping to IL1-Ra (IL1RN intron 2, rs2234663) genetic loci are associated with risk for endometriosis in a Mexican mestizo population. METHODS: This study included 183 women with confirmed endometriosis (ENDO) diagnosed after surgical laparoscopy and 186 women with satisfied parity and without endometriosis as controls (CTR). PCR/RFLP technique was used for genotyping SNPs (rs1800629 and rs1143634); PCR for genotyping rs2234663. RESULTS: We found no statistical differences in age between groups nor among stages of endometriosis and the CTR group. We observed no difference in genotype and allele frequencies, nor carriage rate between groups in none of the three studied polymorphisms. The prevalence of TNF*2-allele heterozygotes (p = 0.025; OR 3.8), TNF*2-allele (p = 0.029; OR 3.4), IL1B*2-allele heterozygotes (p = 0.044; OR 2.69) and its carriage rate (p = 0.041; OR 2.64) in endometriosis stage IV was higher than the CTR group. Surprisingly, the carriage rate of IL1RN*2-allele (ENDO: p = 0.0004; OR 0.4; stage I: p = 0.002, OR 0.38; stage II: p = 0.002, OR 0.35; stage III: p = 0.003, OR 0.33), as well as the IL1RN*2-allele frequencies (ENDO: p = 0.0008, OR 0.55; I: p = 0.037, OR 0.60; II: p = 0.002, OR 0.41; III: p = 0.003, OR 0.38) were lower than the CTR group. Women with endometriosis stage IV (severe) had frequencies more alike to the CTR group in the IL1RN*2 allele frequency (31.2% vs. 27.2%) and carriage rate (37.5% vs. 41.9%). CONCLUSION: Although these polymorphisms are not associated with the risk of endometriosis, Mexican mestizo women with severe stage of endometriosis have higher frequencies of TNF*2-, IL1B*2- and IL1RN*2-alleles, which may explain a possible correlation with disease severity rather than predisposition or risk.

Copper(II) invigorated EHU-30 for continuous electroreduction of CO2 into value-added chemicals.

The doping of zirconium based EHU-30 and EHU-30-NH2 metal-organic frameworks with copper(II) yielded a homogeneous distribution of the dopant with a copper/zirconium ratio of 0.04-0.05. The doping mechanism is analysed by chemical analysis, microstructural analysis and pair distribution function (PDF) analysis of synchrotron total scattering data in order to get deeper insight into the local structure. According to these data, the Cu(II) atoms are assembled within the secondary building unit by a transmetalation reaction, contrarily to UiO-66 series in which the post-synthetic metalation of the MOF takes place through chemical anchorage. The resulting materials doubled the overall performance of the parent compounds for the CO2 electroreduction, while retained stable the performance during continuous transformation reaction.