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•Synthesis of Bi1-xMxFeO3 porous networks by autocombustion.•Micrometer sized catalysts with nanometer sized optical absorption properties.•Complete degradation of Rhodamine B in 1 h ...under visible light irradiation.•Systematic study of doping effects of rare earth metals.•Stable and reusable photocatalyst with enhanced visible light photocatalytic activity.
Doped and undoped Bi1-xMxFeO3 porous networks (M = La, Gd, Nd; x = 0, 0.03, 0.05, 0.10) were synthesized by a facile glycine-nitrate combustion technique. The samples were analyzed by X-ray powder diffraction, scanning electron microscopy and UV–vis diffuse reflectance spectroscopy. For the first time, these Bi1-xMxFeO3 porous networks were applied in photocatalytic degradation reactions in aqueous solution under visible light irradiation using the dye Rhodamine B as a model. Our studies reveal that the type of rare-earth dopant as well as its concentration has no impact on phase purity and particle morphology and that the dopant significantly affects the optical absorption properties: the micrometer sized porous networks exhibited band gap values and optical absorption properties similar to those reported for nanometer sized samples. Furthermore, all samples showed enhanced photocatalytic activities. Based on its highest overall photocatalytic activity, Bi0.95Gd0.05FeO3 was chosen for stability, optimization and mechanistic experiments. Notably, the Bi0.95Gd0.05FeO3 photocatalyst not only demonstrated very high efficiencies but also a remarkable stability under visible light irradiation, much superior than those of doped Bi1-xMxFeO3 nanoparticles reported previously. This constitutes an important step towards industrial wastewater treatment applications. Under optimal reaction conditions, complete degradation of Rhodamine B was achieved in 1 h. The possible mechanism in the photodegradation process has been discussed in detail on the basis of radical trapping experiments.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Magnetic resonance imaging is an essential tool for the identification of neurological problems since it provides relevant information on brain development. The aim of the present work was the ...detection of neurological alterations in newborns from 4 to 12 months of age by segmentation and analysis of lateral ventricles in magnetic resonance images. For this purpose, an automated deep approach based on U‐net is proposed to segment the cerebral ventricles of the newborn. Subsequently, for these regions, features were extracted based on the patient's clinical history and on the shape (area, roundness, normalized central moment, among others) and pixel intensity (mean gray value, contrast level, among others). Once the features were extracted, different types of intelligent models (Logistic Regression, k‐Nearest Neighbors (kNN), and a Convolutional Neural Network) were assessed to detect the presence of neurological alterations. The segmentation phase of the system was tested on 50 patients and the classification phase on 28 patients (11 healthy, 17 with neurological changes). The results show a DICE similarity coefficient of 0.89 and a volume ratio of 1.05 for the segmentation stage and an accuracy of 98%, precision of 100%, sensitivity of 92%, and specificity of 100% for the classification stage using kNN. The last one proved to be the most computationally feasible model, due to the time required for training and inference (0.36 s and 35.2e‐4 s, respectively), as well as the consumption of computational resources (0.1 GB RAM CPU). In conclusion, it is possible to detect neurological alterations in newborns aged 4 to 12 months by segmenting and classifying the lateral ventricles in magnetic resonance images, using image processing techniques, the U‐net, as well as the kNN algorithm. This proposed methodology could play an important role in the early diagnosis and treatment of neurological disorders.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Magnetic resonance imaging is an essential tool for the identification of neurological problems since it provides relevant information on brain development. The aim of the present work was the ...detection of neurological alterations in newborns from 4 to 12 months of age by segmentation and analysis of lateral ventricles in magnetic resonance images. For this purpose, an automated deep approach based on U‐net is proposed to segment the cerebral ventricles of the newborn. Subsequently, for these regions, features were extracted based on the patient's clinical history and on the shape (area, roundness, normalized central moment, among others) and pixel intensity (mean gray value, contrast level, among others). Once the features were extracted, different types of intelligent models (Logistic Regression, k‐Nearest Neighbors (kNN), and a Convolutional Neural Network) were assessed to detect the presence of neurological alterations. The segmentation phase of the system was tested on 50 patients and the classification phase on 28 patients (11 healthy, 17 with neurological changes). The results show a DICE similarity coefficient of 0.89 and a volume ratio of 1.05 for the segmentation stage and an accuracy of 98%, precision of 100%, sensitivity of 92%, and specificity of 100% for the classification stage using kNN. The last one proved to be the most computationally feasible model, due to the time required for training and inference (0.36 s and 35.2e‐4 s, respectively), as well as the consumption of computational resources (0.1 GB RAM CPU). In conclusion, it is possible to detect neurological alterations in newborns aged 4 to 12 months by segmenting and classifying the lateral ventricles in magnetic resonance images, using image processing techniques, the U‐net, as well as the kNN algorithm. This proposed methodology could play an important role in the early diagnosis and treatment of neurological disorders.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
La enfermedad de Huntington (EH) es un trastorno neurodegenerativo y hereditario. A partir del diagnóstico predictivo se han descrito características clínicas incipientes en la fase prodrómica, y ...varios estudios han reportado aumento de síntomas psiquiátricos en portadores de la mutación causante de la EH, sin síntomas motores, en esta fase.
Objetivo Identificar malestar psicológico en portadores de la mutación causante de la EH sin síntomas motores, mediante el Symptom Checklist 90 (SCL-90), y correlacionar con la carga y cercanía de la enfermedad.
Una muestra de 175 participantes de un Programa de Diagnóstico Predictivo de EH (PDP-EH) se dividió en portadores PEH (39,4%) y no portadores NPEH (61,6%) de la mutación causante de EH. Mediante fórmulas matemáticas se obtuvo la carga de enfermedad y cercanía a la etapa manifiesta en el grupo PEH y se correlacionó con los resultados del inventario SCL-90-R.
Al comparar los resultados obtenidos en el SCL-90-R de los PEH y NPEH, la diferencia se observa en el índice de malestar por síntomas positivos, donde los portadores obtienen mayor puntuación promedio. Las correlaciones entre carga de enfermedad y síntomas psicológicos se dan en los dominios: obsesiones y compulsiones, sensibilidad interpersonal, hostilidad, índice de severidad global e índice de malestar somático positivo. Se observa una correlación baja entre la carga de enfermedad y las puntuaciones obtenidas en el malestar psicológico.
En general encontramos que el grupo PEH obtiene un puntaje mayor en las dimensiones evaluadas con el SCL-90, muestran relación con la carga y diferencias por la cercanía de la enfermedad. Puntajes mayores en las dimensiones del SCL-90-R en portadores del gen para la EH pueden sugerir un hallazgo temprano de la sintomatología psicológica en la enfermedad.
Huntington's disease (HD) is a neurodegenerative and hereditary disorder, due to the predictive diagnosis, incipient clinical characteristics have been described in the prodromal phase. Several studies have reported an increase in psychiatric symptoms in carriers of the HD gene without motor symptoms.
To identify psychological distress in carriers of the mutation that causes HD, without motor symptoms, utilizing the Symptom Checklist 90 (SCL-90), and to correlate with the burden and proximity of the disease.
A sample of 175 participants in a HD Predictive Diagnostic Program (PDP-HD) was divided into HEP carriers (39.4%) and NPEH non-carriers (61.6%) of the HD-causing mutation. By means of mathematical formulas, the disease burden and proximity to the manifest stage in the PEH group were obtained and it was correlated with the results of the SCL-90-R.
Comparing the results obtained in the SCL-90-R of the PEH and NPEH, the difference is observed in the positive somatic male index, where the PEH obtains higher average scores. The correlations between disease burden and psychological distress occur in the domains; obsessions and compulsions, interpersonal sensitivity, hostility, global severity index and positive somatic distress index. A low correlation is observed between the burden of disease and the scores obtained in psychological discomfort.
In general, we found that the PEH group obtained a higher score in the dimensions evaluated with the SCL-90-R, showing a relationship with the burden and differences due to the proximity of the disease. Higher scores on the SCL-90-R dimensions in carriers of the HD gene may suggest an early finding of psychological symptoms in the disease.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Breaking waves are a natural phenomenon that can affect infrastructure located near the coast and cause soil erosion problems as well. For this reason, it has been investigated through experiments ...and numerical simulations for decades. With the advancement of new numerical approaches such as the moving particle semi-implicit (MPS) method that allows a better modeling of the fragmentation process, it is possible now to adequately represent its complex behavior on breaking waves. In this work we apply the MPS method to simulate breaking waves in a slope beach, which is simulated as a two-dimensional numerical tank, where cnoidal waves are generated by using Iwagaki wave theory. To achieve numerical wave generation, we first used an approximation through linear wave theory using piston, flap and a hyperbolic wavemakers with a 1.25-s wave period. Then, a numerical tank was modeled using nonlinear wave theory to simulate a series of different wave periods. Our results show that through the MPS method it is possible to represent the type of wave breaking defined analytically, as well as to determine the predicted wave profile and the momentum for the breaking wave, in the same way wave velocity and pressure under the wave can be approximated to their theoretical values.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This paper presents the estimation and analysis of energy performance indicators based on the equivalent production method to improve the decisions and operational actions to reduce the energy ...consumption of a fertilizer company in Colombia, where the global consumption of natural gas in the plant is 4800 kcf per day for an equivalent production of 1,029 MT of NPK fertilizer 15-15-15, where the 63.4% of the energy is consumed by the complex fertilizers line, the 29.2% by equipment of the energy supply plant, and finally the 7.4% is consumed by the fertigation SoluNKP plant. Based on the standard ISO 50001 and the operational data for 2015, some energy performance indicators such as the baseline and goal line of consumption, the actual consumption rate compared to the theoretical consumption index, the efficiency Index Base 100 and finally the consumption trend accumulated (CUSUM) were calculated, allowing to identify energy saving potentials of 16.1% through good operational practices without technological changes, besides 25.6% for production planning. Finally, the natural gas potential savings identified have been achieved by the organization through the implementation of an Integrated Energy Management System.
Huntington disease (HD) is a hereditary neurodegenerative disorder. Thanks to predictive diagnosis, incipient clinical characteristics have been described in the prodromal phase.
To compare ...performance in cognitive tasks of carriers (HDC) and non-carriers (non-HDC) of the huntingtin gene and to analyse the variability in performance as a function of disease burden and proximity to the manifest stage (age of symptom onset).
A sample of 146 participants in a predictive diagnosis of HD programme were divided into the HDC (41.1%) and non-HDC groups (58.9%). Mathematical formulae were used to calculate disease burden and proximity to the manifest stage in the HDC group; these parameters were correlated with neuropsychological performance.
Significant differences were observed between groups in performance on the Mini-Mental State Examination (MMSE), Stroop-B, Symbol-Digit Modalities Test (SDMT), and phonological fluency. In the HDC group, correlations were observed between disease burden and performance on the MMSE, Stroop-B, and SDMT. The group of patients close to the manifest stage scored lowest on the MMSE, Stroop-B, Stroop-C, SDMT, and semantic verbal fluency. According to the multivariate analysis of covariance, the MMSE effect shows statistically significant differences in disease burden and proximity to onset of symptoms.
Members of the HDC group close to the manifest phase performed more poorly on tests assessing information processing speed and attention. Prefrontal cognitive dysfunction appears early, several years before the motor diagnosis of HD.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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