Estrategia de lectura para desarrollar la comprensión lectora en niños de 5to grado “B” de primaria en la Institución Nº 20402 - Huaral - 2013

El presente trabajo de investigación tuvo como problema general: ¿Qué influencia genera la estrategia de lectura en el desarrollo de la comprensión lectora en niños de 5to grado “B” de primaria en la institución Nº 20402 - Huaral -2013?, y el objetivo general fue: Determinar la influencia que genera la estrategia de lectura en el desarrollo de la comprensión lectora en niños de 5to grado “B” de primaria en la institución Nº 20402 - Huaral -2013. El tipo de investigación fue aplicada, según Sánchez Carlesi, ya que está orientado a resolver problemas de la vida cotidiana. Según su profundidad, explicativa, porque se busca las razones o causas que ocasionan cierto fenómeno, en este caso el desarrollo de la comprensión lectora. Según su enfoque, cuantitativo, porque utiliza la recolección y el análisis de datos para contestar preguntas de investigación y probar hipótesis establecidas previamente. El diseño fue cuasi experimental con Pre test y Post test aplicado a los grupos intactos experimental y de control. La población estuvo conformada por 60 niños de 5to grado de primaria y a su vez la misma población pasó a ser la muestra dividida en dos grupos, siendo 30 niños para el grupo experimental y 30 niños para el grupo de control que fueron seleccionados alfabéticamente. Se aplicó la técnica de fichaje En la investigación se ha encontrado que existe una correlación entre estrategias de lectura y comprensión lectora. El plan de acción respondió al problema planteado; se elevó el nivel de comprensión lectora en los niños de 5to grado en la Institución N° 20402 Huaral – 2013

Nonparametric analysis of casein complex genes' epistasis and their effects on phenotypic expression of milk yield and composition in Murciano-Granadina goats

Improving knowledge on the causative polymorphisms or genes regulating the expression of milk quantitative and qualitative traits and their interconnections plays a major role in dairy goat breeding programs and genomic research. This information enables optimization of predictive and selective tools, to obtain better-performing animals to help satisfy market demands more efficiently. Goat milk casein proteins (αS1, αS2, β, and κ) are encoded by 4 loci (CSN1S1, CSN1S2, CSN2, and CSN3) clustered within 250 kb on chromosome 6. Among the statistical methods used to identify epistatic interactions in genome-wide qualitative association studies (GWAS), gene-based methods have recently grown in popularity due to their better statistical power and biological interpretability. However, most of these methods make strong assumptions about the magnitude of the relationships between SNP and phenotype, limiting statistical power. Thus, the aims of this study were to quantify the epistatic relationships among 48 SNP in the casein complex on the expression of milk yield and components (fat, protein, dry matter, lactose, and somatic cells) in MurcianoGranadina goats, to explain the qualitative nature of the SNP used to quantify the genotypes produced as a result. Categorical principal component analysis (CATPCA) was used to delimit and group the number of SNP studied depending on their implications in the explanation of milk yield and components variability. Afterward, nonlinear canonical correlation analysis was used to identify relationships among and within the SNP groups detected by CATPCA. Our results suggest that 79.65% of variability in the traits evaluated may be ascribed to the epistatic relationships across and within 7 SNP groups. Two partially overlapping groups of epistatically interrelated SNP were detected: one group of 21 SNP, explaining 57.56% of variability, and another group of 20 SNP, explaining 42.43% (multiple fit ≥ 0.1). Additionally, SNP18, 32, and 36 (CSN1S2, CSN1S1, and CSN2 loci, respectively) were the most significant SNP to explain intragroup epistatic variability (component loading > |0.5|). Conclusively, milk yield and quality may not only depend on the specific casein gene pool of individuals, but may also be relevantly conditioned by the relationships set across and within such genes. Hence, studying epistasis in isolation may be crucial to optimize selective practices for economically important dairy traits

El tuti li mondi y la cosa bonita : obra utilísima para conocer á los pícaros que hacen la guerra en España á las instituciones liberales

Palau, XXIV, 342566 menciona que esta obra se ha atribuido a José García de León y PizarroEn la cub.: "Esta obra se publicará por cuadernos... y en cada uno irá anotado su precio

Planeamiento financiero y su incidencia en el crecimiento sostenible de la empresa EDWOLIV SAC en el año 2018

La presente tesina realizó un análisis y evaluación de la rentabilidad de una empresa aplicando el Planeamiento Financiero como solución al problema que tiene la empresa en cuanto a la rentabilidad. Así mismo hizo énfasis en la Política de Dividendos como una estrategia para mejorar la rentabilidad. Este Planeamiento Financiero giró en base a tres ejes que son: Rentabilidad de la Inversión, Utilidad Neta sobre el Patrimonio y la Política de Dividendos. Estos tres puntos permitieron a la gerencia observar y tomar decisiones para mejorar la rentabilidad de la empresa. La investigación fue de tipo mixta, en este caso usó una metodología que implica recopilar, analizar e integrar cuantitativamente (encuestas). La técnica de la encuesta de 18 preguntas y el análisis financiero teórico-práctico con un caso de investigación sobre la proyección de la planificación financiera. Concluyéndose que la incidencia del Planeamiento Financiero en el crecimiento sostenible de la empresa EDWOLIV S.A.C. es importante porque ha permitido a la empresa visualizar que es posible un crecimiento sostenido de los ingresos por ventas con el autofinanciamiento por medio de la retención de utilidades, es decir, hacer menos vulnerable a la empresa de cambios en el mercado externo financiero.Campus Lima Centr

Software-Automatized Individual Lactation Model Fitting, Peak and Persistence and Bayesian Criteria Comparison for Milk Yield Genetic Studies in Murciano-Granadina Goats

SPSS model syntax was defined and used to evaluate the individual performance of 49 linear and non-linear models to fit the lactation curve of 159 Murciano-Granadina goats selected for genotyping analyses. Lactation curve shape, peak and persistence were evaluated for each model using 3107 milk yield controls with an average of 3.78 ± 2.05 lactations per goat. Best fit (Adjusted R2) values (0.47) were reached by the five-parameter logarithmic model of Ali and Schaeffer. Three main possibilities were detected: non-fitting (did not converge), standard (Adjusted R2 over 75%) and atypical curves (Adjusted R2 below 75%). All the goats fitted for 38 models. The ability to fit different possible functional forms for each goat, which progressively increased with the number of parameters comprised in each model, translated into a higher sensitivity to explaining curve shape individual variability. However, for models for which all goats fitted, only moderate increases in explanatory and predictive potential (AIC, AICc or BIC) were found. The Ali and Schaeffer model reported the best fitting results to study the genetic variability behind goat milk yield and perhaps enhance the evaluation of curve parameters as trustable future selection criteria to face the future challenges offered by the goat dairy industry

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats

Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison

SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R2) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R2 SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p > 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry

Does the Acknowledgement of αS1-Casein Genotype Affect the Estimation of Genetic Parameters and Prediction of Breeding Values for Milk Yield and Composition Quality-Related Traits in Murciano-Granadina?

A total of 2090 lactation records for 710 Murciano-Granadina goats were collected during the years 2005–2016 and analyzed to investigate the influence of the αS1-CN genotype on milk yield and components (protein, fat, and dry matter). Goats were genetically evaluated, including and excluding the αS1-CN genotype, in order to assess its repercussion on the efficiency of breeding models. Despite no significant differences being found for milk yield, fat and dry matter heritabilities, protein production heritability considerably increased after aS1-CN genotype was included in the breeding model (+0.23). Standard errors suggest that the consideration of genotype may improve the model’s efficiency, translating into more accurate genetic parameters and breeding values (PBV). Genetic correlations ranged from −0.15 to −0.01 between protein/dry matter and milk yield/protein and fat content, while phenotypic correlations were −0.02 for milk/protein and −0.01 for milk/fat or protein content. For males, the broadest range for reliability (RAP) (0.45–0.71) was similar to that of females (0.37–0.86) when the genotype was included. PBV ranges broadened while the maximum remained similar (0.61–0.77) for males and females (0.62–0.81) when the genotype was excluded, respectively. Including the αS1-CN genotype can increase production efficiency, milk profitability, milk yield, fat, protein and dry matter contents in Murciano-Granadina dairy breeding programs

Sexual Dimorphism for Coping Styles Complements Traditional Methods for Sex Determination in a Multivariety Endangered Hen Breed

Sex determination is key to designing endangered poultry population conservation and breeding programs when sex distribution departs from Hardy–Weinberg equilibrium. A total of 112 Utrerana chickens (28 per variety, partridge, black, white, and franciscan) were selected for hatching day sexing. Sex assignation was performed through 10 methods. Three sex assignment criteria comprised criteria found in literature, opposite criteria to that in the literature, and composite criteria combining methods reporting the highest predictive success from the previous ones. This study aims to determine which method combinations may more successfully determine sex across the four varieties of Utrerana endangered hen breed to tailor noninvasive early specific models to determine sex in local chicken populations. Although the explanatory power of the three assignation criteria is equal (75%), assignation criteria 2 resulted to be the most efficient as it correctly assigns males more frequently. Only methods 3 (English method), 5 (general down feathers coloration), 7 (wing fan), and 10 (behavior/coping styles) reported significant differences regardless of the variety, hence, are appropriate for early sexing. Sex confirmation was performed at 1.5 months old. Identifying sex proportions enhances genetic management tasks in endangered populations, complementing more standardized techniques, which may result inefficient given the implicit diversity found in local populations

Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison

SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R 2 ) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R 2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R 2 SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p &gt; 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry