Site-directed mutagenesis by biolistic transformation efficiently generates inheritable mutations in a targeted locus in soybean somatic embryos and transgene-free descendants in the T1 generation.

The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated endonuclease 9 (Cas9) system is being rapidly developed for mutagenesis in higher plants. Ideally, foreign DNA introduced by this system is removed in the breeding of edible crops and vegetables. Here, we report an efficient generation of Cas9-free mutants lacking an allergenic gene, Gly m Bd 30K, using biolistic transformation and the CRISPR/Cas9 system. Five transgenic embryo lines were selected on the basis of hygromycin resistance. Cleaved amplified polymorphic sequence analysis detected only two different mutations in e all of the lines. These results indicate that mutations were induced in the target gene immediately after the delivery of the exogenous gene into the embryo cells. Soybean plantlets (T0 plants) were regenerated from two of the transgenic embryo lines. The segregation pattern of the Cas9 gene in the T1 generation, which included Cas9-free plants, revealed that a single copy number of transgene was integrated in both lines. Immunoblot analysis demonstrated that no Gly m Bd 30K protein accumulated in the Cas9-free plants. Gene expression analysis indicated that nonsense mRNA decay might have occurred in mature mutant seeds. Due to the efficient induction of inheritable mutations and the low integrated transgene copy number in the T0 plants, we could remove foreign DNA easily by genetic segregation in the T1 generation. Our results demonstrate that biolistic transformation of soybean embryos is useful for CRISPR/Cas9-mediated site-directed mutagenesis of soybean for human consumption.

Developmental outcome after corpus callosotomy for infants and young children with drug-resistant epilepsy.

AIM: To examine the developmental and seizure outcomes after corpus callosotomy (CC) in early childhood. METHODS: We retrospectively identified 106 patients who underwent CC for drug-resistant epilepsy before the age of 6 years, at the Nagasaki Medical Center, between July 2002 and July 2016. Patients' developmental outcomes were evaluated one year after CC using the Kinder Infant Development Scale. RESULTS: The mean preoperative developmental quotient (DQ) was 25.0 (standard deviation [SD], 20.8), and the mean difference between preoperative DQ and one-year postoperative DQ was -1.6 points (SD, 11.6). However, 42.5% of patients had a mean DQ increase of 6.5 points (SD, 6.4), one year after CC from that before surgery. Factors related to the improvement in postoperative DQ were 'low preoperative DQ', 'developmental gain 1 month postoperatively', and 'postoperative seizure-free state'. Approximately 21.7% of patients were seizure-free 1 year after CC. INTERPRETATION: Performing CC, in infancy and early childhood for patients with drug-resistant epilepsy and severe developmental impairment, was associated with improved development in 42.5% of patients. Remission of seizures, even if only for a short period, contributed to developmental improvement. From a developmental perspective, CC for drug-resistant epilepsy in early childhood is an effective treatment.

Simultaneous induction of mutant alleles of two allergenic genes in soybean by using site-directed mutagenesis.

BACKGROUND: Soybean (Glycine max) is a major protein crop, because soybean protein has an amino acid score comparable to that of beef and egg white. However, many allergens have been identified among soybean proteins. A decrease in allergenic protein levels would be useful for expanding the market for soybean proteins and processed foods. Recently, the CRISPR/Cas9 system has been adopted as a powerful tool for the site-directed mutagenesis in higher plants. This system is expected to generate hypoallergenic soybean varieties. RESULTS: We used two guide RNAs (gRNAs) and Agrobacterium-mediated transformation for simultaneous site-directed mutagenesis of two genes encoding the major allergens Gly m Bd 28 K and Gly m Bd 30 K in two Japanese soybean varieties, Enrei and Kariyutaka. We obtained two independent T0 Enrei plants and nine T0 Kariyutaka plants. Cleaved amplified polymorphic sequence (CAPS) analysis revealed that mutations were induced in both targeted loci of both soybean varieties. Sequencing analysis showed that deletions were the predominant mutation type in the targeted loci. The Cas9-free plants carrying the mutant alleles of the targeted loci with the transgenes excluded by genetic segregation were obtained in the T2 and T3 generations. Variable mutational spectra were observed in the targeted loci even in T2 and T3 progenies of the same T0 plant. Induction of multiple mutant alleles resulted in six haplotypes in the Cas9-free mutants derived from one T0 plant. Immunoblot analysis revealed that no Gly m Bd 28 K or Gly m Bd 30 K protein accumulated in the seeds of the Cas9-free plants. Whole-genome sequencing confirmed that a Cas9-free mutant had also no the other foreign DNA from the binary vector. Our results demonstrate the applicability of the CRISPR/Cas9 system for the production of hypoallergenic soybean plants. CONCLUSIONS: Simultaneous site-directed mutagenesis by the CRISPR/Cas9 system removed two major allergenic proteins from mature soybean seeds. This system enables rapid and efficient modification of seed components in soybean varieties.

Permutimin: Factor Rotation to Simple Structure with Permutation of Variables.

Factor rotation is usually performed for a p-variables [Formula: see text]-factors loading matrix so that the resulting rotated matrix has a simple structure. This simple structure was originally defined by Thurstone (1947) by specifying how zero elements are arranged in the loading matrix. In this article, we propose a new rotation technique, which is directly based on Thurstone's definition. It can give a p-variables [Formula: see text]-factors target matrix of zero and nonzero elements, which stands for the properties to be possessed by the rotated loading matrix. However, it is unknown how the rows of the target matrix are associated with those of the loading matrix. In the proposed procedure, a loading matrix is rotated simultaneously with a permutation of the rows of the target matrix, so that the rotated loading matrix is optimally matched to the permuted target matrix in a least squares sense. Its novel feature is the use of permutation, thus we call the technique Permutimin. Its algorithm is presented, with Thurstone's definition of simple structure modified so as to specify the target matrix uniquely. Permutimin is illustrated with real data examples. Finally, we discuss the relationships between Permutimin and Procrustes rotation.

Recent advances in the improvement of soybean seed traits by genome editing.

Genetic improvement of soybean seed traits is important for developing new varieties that meet the demand for soybean as a food, forage crop, and industrial products. A large number of soybean genome sequences are currently publicly available. This genome sequence information provides a significant opportunity to design genomic approaches to improve soybean traits. Genome editing represents a major advancement in biotechnology. The production of soybean mutants through genome editing is commonly achieved with either an Agrobacterium-mediated or biolistic transformation platform, which have been optimized for various soybean genotypes. Currently, the clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated endonuclease 9 (Cas9) system, which represents a major advance in genome editing, is used to improve soybean traits, such as fatty acid composition, protein content and composition, flavor, digestibility, size, and seed-coat color. In this review, we summarize the recent advances in the improvement of soybean seed traits through genome editing. We also discuss the characteristics of genome editing using the CRISPR/Cas9 system with transformation platforms.

Factor Analysis Procedures Revisited from the Comprehensive Model with Unique Factors Decomposed into Specific Factors and Errors.

Factor analysis (FA) procedures can be classified into three types (Adachi in WIREs Comput Stat https://onlinelibrary.wiley.com/doi/abs/10.1002/wics.1458 , 2019): latent variable FA (LVFA), matrix decomposition FA (MDFA), and its variant (Stegeman in Comput Stat Data Anal 99: 189-203, 2016) named completely decomposed FA (CDFA) through the theorems proved in this paper. We revisit those procedures from the Comprehensive FA (CompFA) model, in which a multivariate observation is decomposed into common factor, specific factor, and error parts. These three parts are separated in MDFA and CDFA, while the specific factor and error parts are not separated, but their sum, called a unique factor, is considered in LVFA. We show that the assumptions in the CompFA model are satisfied by the CDFA solution, but not completely by the MDFA one. Then, how the CompFA model parameters are estimated in the FA procedures is examined. The study shows that all parameters can be recovered well in CDFA, while the sum of the parameters for the specific factor and error parts is approximated by the LVFA estimate of the unique factor parameter and by the MDFA estimate of the specific factor parameter. More detailed results are given through our subdivision of the CompFA model according to whether the error part is uncorrelated among variables or not.

Larrealignans A and B, novel lignan glycosides from the aerial parts of Larrea tridentata.

Two new lignan glycosides, named larrealignans A (1) and B (2), and a known lignan (3) were isolated from the aerial parts of Larrea tridentata (Zygophyllaceae). The structures of 1 and 2 were determined on the basis of spectroscopic analysis and the results of hydrolytic cleavage. The isolated compounds (1-3) and aglycones (1a, 2a) of 1 and 2 were evaluated for their cytotoxic activities against HL-60 human leukemia cells.

Clustered Common Factor Exploration in Factor Analysis.

The factor analysis (FA) model does not permit unique estimation of the common and unique factor scores. This weakness is notorious as the factor indeterminacy in FA. Luckily, some part of the factor scores can be uniquely determined. Thus, as a whole, they can be viewed as a sum of determined and undetermined parts. The paper proposes to select the undetermined part, such that the resulting common factor scores have the following feature: the rows (i.e., individuals) of the common factor score matrix are as well classified as possible into few clusters. The clear benefit is that we can easily interpret the factor scores simply by focusing on the clusters. The procedure is called clustered common factor exploration (CCFE). An alternating least squares algorithm is developed for CCFE. It is illustrated with real data examples. The proposed approach can be viewed as a parallel to the rotation techniques in FA. They exploit another FA indeterminacy, the rotation indeterminacy, which is resolved by choosing the rotation that transforms the loading matrix into the 'most' interpretable one according to a pre-specified criterion. In contrast to the rotational indeterminacy, the factor indeterminacy is utilized to achieve well-clustered factor scores by CCFE. To the best of our knowledge, such an approach to the FA interpretation has not been studied yet.

Some Mathematical Properties of the Matrix Decomposition Solution in Factor Analysis.

A new factor analysis (FA) procedure has recently been proposed which can be called matrix decomposition FA (MDFA). All FA model parameters (common and unique factors, loadings, and unique variances) are treated as fixed unknown matrices. Then, the MDFA model simply becomes a specific data matrix decomposition. The MDFA parameters are found by minimizing the discrepancy between the data and the MDFA model. Several algorithms have been developed and some properties have been discussed in the literature (notably by Stegeman in Comput Stat Data Anal 99:189-203, 2016), but, as a whole, MDFA has not been studied fully yet. A number of new properties are discovered in this paper, and some existing ones are derived more explicitly. The properties provided concern the uniqueness of results, covariances among common factors, unique factors, and residuals, and assessment of the degree of indeterminacy of common and unique factor scores. The properties are illustrated using a real data example.

Sparse Exploratory Factor Analysis.

Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods.

Perceived size and perceived distance of targets viewed from between the legs: evidence for proprioceptive theory.

We investigated, using three comparisons, perceived size and perceived distance of targets seen from between the legs. Five targets, varying from 32 to 163 cm in height, were presented at viewing distances of 2.5-45 m, and a total of 90 observers verbally judged the perceived size and perceived distance of each target. In comparison 1, 15 observers inverted their heads upside down and saw the targets between their own legs; another 15 observers viewed them while being erect on the ground. The results showed that inverting the head lowered the degree of size constancy and compressed the scale for distance. To examine whether these results were due to an inversion of retinal-image or body orientation, comparisons 2 and 3 were performed. In comparison 2, 15 observers stood upright and saw the targets with prism goggles that rotated the visual field 180 degrees , while other 15 observers stood upright, but viewed the targets with a hollow frame lacking the prisms. The results showed that, in both goggle conditions, size constancy prevailed and perceived distance was a linear function of physical distance. In comparison 3, 15 observers wore the 180 degrees rotation goggles and viewed the targets by bending their heads forwardly, and the other 15 observers viewed them while wearing hollow goggles and lying on the belly. The results showed a low degree of size constancy and compressed the scale for distance. Therefore, it is suggested that perceived size and perceived distance are affected by an inversion of body orientation, not of retinal image orientation. When path analysis and partial correlation analysis were applied to the whole data, perceived size was found to be independent of perceived distance. These results supported the direct perception model, rather than the apparent distance model.

Sparse Versus Simple Structure Loadings.

The component loadings are interpreted by considering their magnitudes, which indicates how strongly each of the original variables relates to the corresponding principal component. The usual ad hoc practice in the interpretation process is to ignore the variables with small absolute loadings or set to zero loadings smaller than some threshold value. This, in fact, makes the component loadings sparse in an artificial and a subjective way. We propose a new alternative approach, which produces sparse loadings in an optimal way. The introduced approach is illustrated on two well-known data sets and compared to the existing rotation methods.

Factor analysis with EM algorithm never gives improper solutions when sample covariance and initial parameter matrices are proper.

Rubin and Thayer (Psychometrika, 47:69-76, 1982) proposed the EM algorithm for exploratory and confirmatory maximum likelihood factor analysis. In this paper, we prove the following fact: the EM algorithm always gives a proper solution with positive unique variances and factor correlations with absolute values that do not exceed one, when the covariance matrix to be analyzed and the initial matrices including unique variances and inter-factor correlations are positive definite. We further numerically demonstrate that the EM algorithm yields proper solutions for the data which lead the prevailing gradient algorithms for factor analysis to produce improper solutions. The numerical studies also show that, in real computations with limited numerical precision, Rubin and Thayer's (Psychometrika, 47:69-76, 1982) original formulas for confirmatory factor analysis can make factor correlation matrices asymmetric, so that the EM algorithm fails to converge. However, this problem can be overcome by using an EM algorithm in which the original formulas are replaced by those guaranteeing the symmetry of factor correlation matrices, or by formulas used to prove the above fact.

Oblique rotaton in canonical correlation analysis reformulated as maximizing the generalized coefficient of determination.

To facilitate the interpretation of canonical correlation analysis (CCA) solutions, procedures have been proposed in which CCA solutions are orthogonally rotated to a simple structure. In this paper, we consider oblique rotation for CCA to provide solutions that are much easier to interpret, though only orthogonal rotation is allowed in the existing formulations of CCA. Our task is thus to reformulate CCA so that its solutions have the freedom of oblique rotation. Such a task can be achieved using Yanai's (Jpn. J. Behaviormetrics 1:46-54, 1974; J. Jpn. Stat. Soc. 11:43-53, 1981) generalized coefficient of determination for the objective function to be maximized in CCA. The resulting solutions are proved to include the existing orthogonal ones as special cases and to be rotated obliquely without affecting the objective function value, where ten Berge's (Psychometrika 48:519-523, 1983) theorems on suborthonormal matrices are used. A real data example demonstrates that the proposed oblique rotation can provide simple, easily interpreted CCA solutions.