Mutagénesis inducida en plantas de Solanum betaceum Cav. mediante el uso de dietil sulfato (DES) / Induced mutagenesis in Solanum betaceum Cav. plants by the use of diethyl sulphate (DES)

RESUMEN La materia prima del fitomejoramiento es la variabilidad genética, que se presenta baja, en especies en proceso de domesticación, que no han sido sometidas a selección, como en Solanum betaceum. Una de las tecnologías para incrementar la variabilidad genética es la inducción de mutagénesis. El objetivo del estudio fue evaluar, a través de marcadores RAMs, las variaciones moleculares presentes en plántulas de S. betaceum, provenientes de semillas sometidas a diferentes concentraciones del agente mutante dietil sulfato (DES). Los loci polimórficos oscilaron entre 87,5 y 100 % y el número de alelos efectivos (Ne), entre 1,0 y 1,99. Los loci más polimórficos se observaron en TG, AG, ACA y CGA, que mostraron una heterosis media insesgada entre 0,34 y 0,51, que permite establecer que estos marcadores sean útiles para obtener mayor discriminación entre mutantes en S. betaceum. Las distancias genéticas oscilaron entre 0,30 y 1,0. El 81,28 % de estos registros se dieron entre 0,60 y 0,90; esto revela bajo nivel de cambios, debido al DES. Estos pequeños cambios contribuyeron a enriquecer la variabilidad genética de la muestra tratada con DES. Los marcadores RAMs fueron útiles para detectar cambios entre plantas provenientes de semillas tratadas con DES y plantas normales. La variabilidad genética entre tratamientos con DES fueron más altos que tratamientos sin DES. Las similitudes genéticas fueron bajas entre plantas tratadas y no tratadas y fueron altas, entre no tratadas. Los cambios producidos por DES fueron de baja magnitud; sin embargo, produjeron cambios en los niveles de variabilidad genética.

ABSTRACT The raw material for plant breeding is genetic variability, which is low in species in the process of domestication that have not been subjected to selection, as is the case with Solanum betaceum. One of the technologies to increase genetic variability is mutagenesis induction. The objective was to evaluate, through RAMs markers, the molecular variations present in S. betaceum seedlings from seeds previously subjected to different concentrations of the mutant agent diethyl sulfate (DES). The polymorphic loci ranged from 87.5 to 100%, number of effective alleles (Ne) between 1.0 and 1.99. The most polymorphic loci were observed in TG, AG, ACA, and CGA, which showed a mean unbiased heterosis between 0.34 and 0.51 with an average of 0.44, which allows establishing that these markers are useful to obtain greater discrimination between mutants in S. betaceum. Genetic distances ranged from 0.30 to 1.0. The 81.28% of these records were between 0.60 and 0.90. This reveals a low level of changes due to DES. These small changes contribute to enriching the genetic variability of the DES-treated sample. The RAMs markers were useful for detecting changes between plants from DES treated seeds and normal plants. Genetic variability between DES treatments was higher than non-DES treatments. Genetic similarities were low between treated and untreated plants and were high among untreated plants. The changes produced by DES were of low magnitude, however, they produced changes in the levels of genetic variability.

The use of a genetic relationship matrix biases the best linear unbiased prediction

The best linear unbiased prediction (BLUP), derived from the linear mixed model (LMM), has been popularly used to estimate animal and plant breeding values (BVs) for a few decades. Conventional BLUP has a constraint that BVs are estimated from the assumed covariance among unknown BVs, namely conventional BLUP assumes that its covariance matrix is a kK, in which k is a coefficient that leads to the minimum mean square error of the LMM, and K is a genetic relationship matrix. The uncertainty regarding the use of kK in conventional BLUP was recognized by past studies, but it has not been sufficiently investigated. This study was motivated to answer the following question: is it indeed reasonable to use a kK in conventional BLUP? The mathematical investigation concluded: (i) the use of a kK in conventional BLUP biases the estimated BVs, and (ii) the objective BLUP, mathematically derived from the LMM, has the same representation as the least squares.

Parámetros genéticos para crecimiento y reproducción en ganado Simmental mediante parentesco por pedigrí y genómico / Genetic parameters for growth and reproduction in Simmental cattle from pedigree and genomic relationship

RESUMEN Objetivo. Estimar parámetros genéticos para peso a los ocho meses de edad (W8M), edad al primer parto (AFC) y primer intervalo entre partos (FCI) usando parentesco genómico y por pedigrí. Materiales y métodos. Se utilizaron 481, 3063 y 1098 registros fenotípicos para W8M, AFC y FCI, respectivamente. La información genómica estuvo compuesta por una población de 718 animales genotipados con un chip que incluyó 30106 marcadores genéticos tipo polimorfismo de nucleótido simple (SNP). Modelos univariado y bivariado fueron construidos bajo la metodología del mejor predictor lineal insesgado convencional (BLUP) y genómico en una etapa (ssGBLUP). Resultados. Las heredabilidades para W8M, AFC y FCI variaron desde 0.25 a 0.26, 0.20 a 0.22 y 0.04 a 0.08, respectivamente. Los modelos de AFC y FCI con la metodología ssGBLUP disminuyeron ligeramente el error y aumentaron la varianza genética aditiva, respectivamente. Conclusiones. La inclusión de información genómica mejora levemente la precisión de las estimaciones genéticas en esta población. Sin embargo, una población de animales genotipados más grande y con mayor conectividad genética por parentesco permitiría aumentar para los criadores el potencial de la metodología ssGBLUP en ganado Simmental de Colombia.

ABSTRACT Objective. To estimate genetic parameters for weight at eight months of age (W8M), age at first calving (AFC) and first calving interval (FCI) using pedigree and genomic relationship. Materials and methods. Phenotypic data on 481, 3063 and 1098 animals for W8M, AFC and FCI were used, respectively. The genomic information came from a population of 718 genotyped animals with a density chip of 30,106 single nucleotide polymorphism markers (SNP). Univariate and bivariate models were used under the conventional (BLUP) and single step genomic best linear unbiased predictor (ssGBLUP) methodologies. Results. The heritabilities for W8M, AFC and FCI ranged from 0.25 to 0.26, from 0.20 to 0.22 and from 0.04 to 0.08, respectively. The AFC and FCI models under ssGBLUP slightly decreased the error and increased the additive genetic variance, respectively. Conclusions. The inclusion of genomic information slightly increases the accuracy of the genetic estimates in this population. However, a larger amount of genotyped animals and with a higher genetic relationship connectivity would allow breeders to increase the potential of the ssGBLUP methodology in Colombian Simmental cattle.

An Automatic Method for Generating an Unbiased Intensity Normalizing Factor in Positron Emission Tomography Image Analysis After Stroke / 神经科学通报·英文版

Positron emission tomography (PET) imaging of functional metabolism has been widely used to investigate functional recovery and to evaluate therapeutic efficacy after stroke. The voxel intensity of a PET image is the most important indicator of cellular activity, but is affected by other factors such as the basal metabolic ratio of each subject. In order to locate dysfunctional regions accurately, intensity normalization by a scale factor is a prerequisite in the data analysis, for which the global mean value is most widely used. However, this is unsuitable for stroke studies. Alternatively, a specified scale factor calculated from a reference region is also used, comprising neither hyper- nor hypo-metabolic voxels. But there is no such recognized reference region for stroke studies. Therefore, we proposed a totally data-driven automatic method for unbiased scale factor generation. This factor was generated iteratively until the residual deviation of two adjacent scale factors was reduced by < 5%. Moreover, both simulated and real stroke data were used for evaluation, and these suggested that our proposed unbiased scale factor has better sensitivity and accuracy for stroke studies.

Image Denoising for Metal MRI Exploiting Sparsity and Low Rank Priors

PURPOSE: The management of metal-induced field inhomogeneities is one of the major concerns of distortion-free magnetic resonance images near metallic implants. The recently proposed method called “Slice Encoding for Metal Artifact Correction (SEMAC)” is an effective spin echo pulse sequence of magnetic resonance imaging (MRI) near metallic implants. However, as SEMAC uses the noisy resolved data elements, SEMAC images can have a major problem for improving the signal-to-noise ratio (SNR) without compromising the correction of metal artifacts. To address that issue, this paper presents a novel reconstruction technique for providing an improvement of the SNR in SEMAC images without sacrificing the correction of metal artifacts. MATERIALS AND METHODS: Low-rank approximation in each coil image is first performed to suppress the noise in the slice direction, because the signal is highly correlated between SEMAC-encoded slices. Secondly, SEMAC images are reconstructed by the best linear unbiased estimator (BLUE), also known as Gauss-Markov or weighted least squares. Noise levels and correlation in the receiver channels are considered for the sake of SNR optimization. To this end, since distorted excitation profiles are sparse, l1 minimization performs well in recovering the sparse distorted excitation profiles and the sparse modeling of our approach offers excellent correction of metal-induced distortions. RESULTS: Three images reconstructed using SEMAC, SEMAC with the conventional two-step noise reduction, and the proposed image denoising for metal MRI exploiting sparsity and low rank approximation algorithm were compared. The proposed algorithm outperformed two methods and produced 119% SNR better than SEMAC and 89% SNR better than SEMAC with the conventional two-step noise reduction. CONCLUSION: We successfully demonstrated that the proposed, novel algorithm for SEMAC, if compared with conventional de-noising methods, substantially improves SNR and reduces artifacts.

The Usage of an SNP-SNP Relationship Matrix for Best Linear Unbiased Prediction (BLUP) Analysis Using a Community-Based Cohort Study

Best linear unbiased prediction (BLUP) has been used to estimate the fixed effects and random effects of complex traits. Traditionally, genomic relationship matrix-based (GRM) and random marker-based BLUP analyses are prevalent to estimate the genetic values of complex traits. We used three methods: GRM-based prediction (G-BLUP), random marker-based prediction using an identity matrix (so-called single-nucleotide polymorphism [SNP]-BLUP), and SNP-SNP variance-covariance matrix (so-called SNP-GBLUP). We used 35,675 SNPs and R package "rrBLUP" for the BLUP analysis. The SNP-SNP relationship matrix was calculated using the GRM and Sherman-Morrison-Woodbury lemma. The SNP-GBLUP result was very similar to G-BLUP in the prediction of genetic values. However, there were many discrepancies between SNP-BLUP and the other two BLUPs. SNP-GBLUP has the merit to be able to predict genetic values through SNP effects.

Need of medicines information OPD in tertiary health care settings: A cross sectional study .

Background: Population burden, illiteracy, availability of few doctors for larger group of population all these leads to many unanswered questions left in a patient’s mind. Incomplete information results into noncompliance, therapeutic failure, and adverse drug reactions (ADR). It is very important to establish a system which will provide noncommercial, independent, unbiased source of medicine information. Medicines Info OPD is a concept and step towards safe and appropriate use of medicines. Objective: (1) To assess the present status of knowledge about the medicines in the patients and its correlation with education. (2) To assess the medicine information dispensing modalities, their use and sufficiency from the patients view point. (3) To assess the overall need for Medicines Information OPD in present scenario. Materials and Methods: A pre-validated questionnaire based study was conducted amongst 500 patients of tertiary health care hospital. The questionnaire consisted of specific questions regarding understanding of prescription, knowledge about adverse drug reaction, view about self-medication and opinion regarding the need of Medicines Info OPD. Results: Significantly large proportion of patients opined that doctors do not have sufficient time in current Indian healthcare to explain the prescription and they are not aware of adverse drug reactions, expiry date or use the package inserts etc. Conclusion: Clinically relevant, up to date, user specific, independent, objective and unbiased Medicines Info OPD is essential for appropriate drug use and can help in a big way to common public to address many problems faced by them.

Uso de redes neurais artificais na predição de valores genéticos para peso aos 205 dias em bovinos da raça Tabapuã / Use of artificial neural networks in breeding values prediction for weight at 205 days in Tabapuã beef cattle

Dados de 19240 animais Tabapuã, provenientes de 152 fazendas localizadas em diversos estados brasileiros, nascidos entre 1976 e 1995, foram utilizados para predição do valor genético do peso aos 205 dias de idade (VG_P205) por meio de redes neurais artificiais (RNAs) e usando o algoritmo LM - Levenberg Marquardt - para treinamento dos dados de entrada. Por se tratar de rede com aprendizado supervisionado, foram utilizados, como saída desejada, os valores genéticos preditos pelo BLUP para a característica P205. Os valores genéticos do P205 obtidos pela RNA e os preditos pelo BLUP foram altamente correlacionados. A ordenação dos valores genéticos do P205 oriundos das RNAs e os valores preditos pelo BLUP (VG_P205_RNA) sugeriram que houve variação na classificação dos animais, indicando riscos no uso de RNAs para avaliação genética dessa característica. Inserções de novos animais necessitam de novo treinamento dos dados, sempre dependentes do BLUP.

Data from 19,240 Tabapuã animals from 152 farms located in different states of Brazil, born from 1976 to 1995, were used to predict the genetic value of body weight at 205 days of age (BV_P205) of Tabapuã beef cattle using Artificial Neural Networks (ANN) and LM algorithm - Levenberg Marquardt training for data entry. Due to the use of networks with supervised learning, the predicted breeding values for P205 from BLUP were used as desired output. The breeding values for P205 obtained from RNA and those predicted by BLUP were highly correlated. The ranked breeding values for body weight at 205 days through RNA and those predicted by BLUP (VG_P205_RNA) showed a variation in the classification of animals indicating risks in the use of ANNs procedure for genetic evaluation of this trait. Insertions of new animals require new training data always dependent on BLUP.

Gastric Impedance Signal Analysis Based on Wavelet Transform and Stein Unbiased Risk Estimation Rule / 医疗卫生装备

Objecive To discuss a denoise method of gastric impedance signal based on wavelet transform and Stein Unbiased Risk Estimation rule.Methods Firstly,the signal was decomposed by Multi-resolution Analysis(MRA).Secondly,the signal was denoised by Stein Unbiased Risk Estimation rule,whose threshold was adaptive.Results The experimentation results demonstrated that breathing and blood flowing impedance interference could be cancelled effectively by using this method.Conclusion Wavelet analysis provides a new method for further study of gastric motility signal parameter extraction.