Identification and validation of mutation points associated with waxy phenotype in cassava.

BACKGROUND: The granule-bound starch synthase I (GBSSI) enzyme is responsible for the synthesis of amylose, and therefore, its absence results in individuals with a waxy starch phenotype in various amylaceous crops. The validation of mutation points previously associated with the waxy starch phenotype in cassava, as well as the identification of alternative mutant alleles in the GBSSI gene, can allow the development of molecular-assisted selection to introgress the waxy starch mutation into cassava breeding populations. RESULTS: A waxy cassava allele has been identified previously, associated with several SNPs. A particular SNP (intron 11) was used to develop SNAP markers for screening heterozygote types in cassava germplasm. Although the molecular segregation corresponds to the expected segregation at 3:1 ratio (dominant gene for the presence of amylose), the homozygotes containing the SNP associated with the waxy mutation did not show waxy phenotypes. To identify more markers, we sequenced the GBSS gene from 89 genotypes, including some that were segregated from a cross with a line carrying the known waxy allele. As a result, 17 mutations in the GBSSI gene were identified, in which only the deletion in exon 6 (MeWxEx6-del-C) was correlated with the waxy phenotype. The evaluation of mutation points by discriminant analysis of principal component analysis (DAPC) also did not completely discriminate the waxy individuals. Therefore, we developed Kompetitive Allele Specific PCR (KASP) markers that allowed discrimination between WX and wx alleles. The results demonstrated the non-existence of heterozygous individuals of the MeWxEx6-del-C deletion in the analyzed germplasm. Therefore, the deletion MeWxEx6-del-C should not be used for assisted selection in genetic backgrounds different from the original source of waxy starch. Also, the alternative SNPs identified in this study were not associated with the waxy phenotype when compared to a panel of accessions with high genetic diversity. CONCLUSION: Although the GBSSI gene can exhibit several mutations in cassava, only the deletion in exon 6 (MeWxEx6-del-C) was correlated with the waxy phenotype in the original AM206-5 source.

Modeling copy number variation in the genomic prediction of maize hybrids.

KEY MESSAGE: Our study indicates that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids. Moreover, predicting hybrid phenotypes by combining additive-dominance effects with copy variants has the potential to be a viable predictive model. Non-additive effects resulting from the actions of multiple loci may influence trait variation in single-cross hybrids. In addition, complementation of allelic variation could be a valuable contributor to hybrid genetic variation, especially when crossing inbred lines with higher contents of copy gains. With this in mind, we aimed (1) to study the association between copy number variation (CNV) and hybrid phenotype, and (2) to compare the predictive ability (PA) of additive and additive-dominance genomic best linear unbiased prediction model when combined with the effects of CNV in two datasets of maize hybrids (USP and HELIX). In the USP dataset, we observed a significant negative phenotypic correlation of low magnitude between copy number loss and plant height, revealing a tendency that more copy losses lead to lower plants. In the same set, when CNV was combined with the additive plus dominance effects, the PA significantly increased only for plant height under low nitrogen. In this case, CNV effects explicitly capture relatedness between individuals and add extra information to the model. In the HELIX dataset, we observed a pronounced difference in PA between additive (0.50) and additive-dominance (0.71) models for predicting grain yield, suggesting a significant contribution of dominance. We conclude that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids, although the inclusion of CNVs into datasets does not return significant gains concerning PA.

Variability of amylose content and its correlation with the paste properties of cassava starch.

The amylose content can significantly impact the diverse industrial applications of cassava starch. This study aimed to assess the variability of cassava germplasm concerning amylose content and other attributes pertinent to root quality, alongside its correlation with paste properties. Starch extracted from 281 genotypes, obtained in germplasm evaluation trials, was evaluated for amylose content, with additional analysis of parameters such as pasting temperature, time to peak viscosity (TPV), viscosity breakdown (BrD), retrogradation tendency, and maximum, minimum, and final viscosities. The genotypes exhibited considerable variation in dry matter content (ranging from 27.06% to 41.02%), starch content (from 14.61% to 25.67%), cyanogenic compounds (1.77 to 7.81), and amylose content (0.05% to 33.23%). High phenotypic variability in paste properties was observed, alongside a low residual effect for most traits, resulting in high broad-sense heritabilities (>0.95). Strong correlations of significant magnitude (>0.80) were found between parameters such as peak viscosity × viscosity breakdown, minimum viscosity × final viscosity, and final viscosity × retrogradation tendency. Moderate correlations were also identified, such as between dry matter content × starch content (0.56). While positive, correlations between amylose content and paste properties were of low magnitude (ranging from 0.13 to 0.35), except for TPV and BrD. Principal component discriminant analysis clustered the germplasm into six distinct groups based on root quality and paste properties, with most improved genotypes falling into two clusters characterized by high starch and dry matter contents. This study underscores the necessity of simultaneous evaluation of amylose content and paste properties in the breeding pipeline. Additionally, clustering cassava genotypes proves beneficial in identifying those that fulfill specific requirements in industrial and breeding applications.

Near infrared spectroscopy for cooking time classification of cassava genotypes.

Cooking time is a crucial determinant of culinary quality of cassava roots and incorporating it into the early stages of breeding selection is vital for breeders. This study aimed to assess the potential of near-infrared spectroscopy (NIRS) in classifying cassava genotypes based on their cooking times. Five cooking times (15, 20, 25, 30, and 40 minutes) were assessed and 888 genotypes evaluated over three crop seasons (2019/2020, 2020/2021, and 2021/2022). Fifteen roots from five plants per plot, featuring diameters ranging from 4 to 7 cm, were randomly chosen for cooking analysis and spectral data collection. Two root samples (15 slices each) per genotype were collected, with the first set aside for spectral data collection, processed, and placed in two petri dishes, while the second set was utilized for cooking assessment. Cooking data were classified into binary and multiclass variables (CT4C and CT6C). Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. The spectral data were split into a training set (80%) and an external validation set (20%). For binary variables, the classification accuracy for cassava cooking time was notably high ( R C a l 2 ranging from 0.72 to 0.99). Regarding multiclass variables, accuracy remained consistent across classes, models, and NIR instruments (~0.63). However, the KNN model demonstrated slightly superior accuracy in classifying all cooking time classes, except for the CT4C variable (QST) in the NoCook and 25 min classes. Despite the increased complexity associated with binary classification, it remained more efficient, offering higher classification accuracy for samples and facilitating the selection of the most relevant time or variables, such as cooking time ≤ 30 minutes. The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached R C a l 2   = 0.86 and R V a l 2 = 0.84, with a Kappa value of 0.53. Overall, the models exhibited a robust fit for all cooking times, showcasing the significant potential of NIRs as a high-throughput phenotyping tool for classifying cassava genotypes based on cooking time.

The relationship between the level of vitamin D and ruptured intracranial aneurysms among patients with high sun exposure.

Non-traumatic subarachnoid hemorrhage (SAH) accounts for 3-5% of acute strokes. Intracranial aneurysm is the most common cause of non-traumatic SAH. Vitamin D influences the cardiovascular system, including the formation and rupture of cerebral aneurysms. To evaluate the serum vitamin D level in patients living in the tropical zone who suffered aneurysmal subarachnoid hemorrhage and its correlation with demographic and neurological characteristics. This is an analytical cross-sectional study to assess the serum level of vitamin D in a study population of 99 patients treated and diagnosed with aSAH in a public hospital in Recife-PE over a period of 12 months. In the study sample, composed of individuals with high sun exposure due to the lifestyle they lead in a tropical region, we observed hypovitaminosis D (85.9%), with a median of 19.9 ng/ml, although the majority of individuals are skin with high concentration of melanin (Fitzpatrick skin type IV and V). In addition, rates of sun exposure are high to all patients (Solar Index 9.03 P50). Most individuals were female (79.8%); there was no statistical difference in solar exposure/solar index between genders. As for the neurological repercussions, there was no statistical relevance in the clinical prognostic scales evaluated. As the sample was composed mainly of individuals whose economic activity is agriculture, the values of solar index found are vastly higher than those of other studies conducted in high latitude regions. In line with the literature review, some aspects were raised with the objective of justifying such findings that go from the base of the poor diet of these individuals, the increase of melanin in the skin and genetic alterations that directs us to possible mechanisms of natural photoprotection to high sun exposure. Thus, we had a vast majority (85%) of hypovitaminosis D, which in fact makes us wonder if there is any influence of calcitriol on vitamin D receptors in vascular walls and in the cardiovascular system as a whole, which influence bleeding events of this nature. As for the neurological repercussions, measured using assessment scales (Glasgow coma scale, WFNS scale, Hunt-Hess and Fisher's tomographic scale) there was no significant difference in the results. As it is only a descriptive study, the causal relationship of the facts cannot be established. However, in a population exposed to high sun exposure and affected by aneurysmal SAH, there is a significant rate of hypovitaminosis D, which supports the hypothesis that vitamin D plays a role in vascular pathologies, such as cerebral aneurysms and SAH.

Flowering induction in cassava using photoperiod extension premature pruning and plant growth regulators.

Cassava (Manihot esculenta Crantz) is a vital crop for food and economic security in many regions of the world. Despite the economic and social importance of cassava, challenges persist in developing superior varieties that meet the needs of farmers in terms of agronomic performance, nutritional quality, and resistance to pests and diseases. One of the main obstacles for genetic improvement is the lack of synchronization in flowering and the abortion of young flowers, making planned crosses and progeny production difficult. Therefore, the aim of this study was to evaluate the effect of photoperiod, premature pruning, and growth regulators on cassava flowering under low-altitude conditions in Brazil. Eight cassava clones with contrasting flowering capacity were assessed in Cruz das Almas, Bahia, using two photoperiods (ambient condition and extended photoperiod with red light for 12 hours), premature pruning at the first and second branching levels (with and without pruning), and the application of growth regulators: 0.5 mM 6-benzyladenine (BA) and 4.0 mM silver thiosulfate (STS) (with and without). Plots were assessed weekly for the number of female (NFF) and male (NMF) flowers, height of the first branching (H1B, in cm), number of days to the first branching (ND1B), and the number of branching events up to 240 days after planting (NOB). The extended photoperiod did not promote an increase in the number of flowers but allowed for precocity in cassava flowering, reducing the onset of flowering by up to 35 days, and significantly increasing the number of branches, which is closely related to flowering. The use of pruning and plant growth regulators (PGR) resulted in an increase in NFF from 2.2 (control) to 4.6 and NMF from 8.1 to 21.1 flowers. Therefore, under hot and humid tropical conditions at low altitudes in the Recôncavo of Bahia, manipulating the photoperiod and using premature pruning and plant growth regulators can accelerate cassava flowering, benefiting genetic improvement programs.

Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis.

Cassava (Manihot esculenta Crantz) starch consists of amylopectin and amylose, with its properties determined by the proportion of these two polymers. Waxy starches contain at least 95% amylopectin. In the food industry, waxy starches are advantageous, with pastes that are more stable towards retrogradation, while high-amylose starches are used as resistant starches. This study aimed to associate near-infrared spectrophotometry (NIRS) spectra with the waxy phenotype in cassava seeds and develop an accurate classification model for indirect selection of plants. A total of 1127 F2 seeds were obtained from controlled crosses performed between 77 F1 genotypes (wild-type, Wx_). Seeds were individually identified, and spectral data were obtained via NIRS using a benchtop NIRFlex N-500 and a portable SCiO device spectrometer. Four classification models were assessed for waxy cassava genotype identification: k-nearest neighbor algorithm (KNN), C5.0 decision tree (CDT), parallel random forest (parRF), and eXtreme Gradient Boosting (XGB). Spectral data were divided between a training set (80%) and a testing set (20%). The accuracy, based on NIRFlex N-500 spectral data, ranged from 0.86 (parRF) to 0.92 (XGB). The Kappa index displayed a similar trend as the accuracy, considering the lowest value for the parRF method (0.39) and the highest value for XGB (0.71). For the SCiO device, the accuracy (0.88-0.89) was similar among the four models evaluated. However, the Kappa index was lower than that of the NIRFlex N-500, and this index ranged from 0 (parRF) to 0.16 (KNN and CDT). Therefore, despite the high accuracy these last models are incapable of correctly classifying waxy and non-waxy clones based on the SCiO device spectra. A confusion matrix was performed to demonstrate the classification model results in the testing set. For both NIRS, the models were efficient in classifying non-waxy clones, with values ranging from 96-100%. However, the NIRS differed in the potential to predict waxy genotype class. For the NIRFlex N-500, the percentage ranged from 30% (parRF) to 70% (XGB). In general, the models tended to classify waxy genotypes as non-waxy, mainly SCiO. Therefore, the use of NIRS can perform early selection of cassava seeds with a waxy phenotype.

Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction.

Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability.

Phenotypic diversity and selection in biofortified cassava germplasm for yield and quality root traits.

Increasing carotenoid content and improving other root quality traits has been the focus of cassava biofortification. This study aimed to (i) evaluate the genetic variability for total carotenoid content (TCC), as well as for root yield and root quality attributes; (ii) estimate potentially useful correlations for selection; and (iii) select parents for breeding and estimate the genetic gain. Data from 2011 to 2020 of 265 cassava genotypes with cream and yellow roots were analyzed for dry matter content (DMC), shoot yield, fresh root yield (FRY), dry root yield (DRY), harvest index, average number of roots per plant, starch content, root pulp color, cyanogenic compounds, and TCC. The best linear unbiased predictions showed great phenotypic variation for all traits. Six distinct groups were formed for productive characteristics of root quality, mainly TCC, DMC and FRY. Only TCC showed high broad-sense heritability ( h 2 = 0.72), while the other traits had low to medium magnitude (0.21 ≤ h 2 ≤ 0.60). TCC was strongly correlated with pulp color (r = 0.70), but null significance for DMC. The network analysis identified a clear separation between the agronomic and quality attributes of cassava roots. The selection of the 30 genotypes for recombination in the breeding program has the potential to raise TCC by 27.05% and reduce the cyanogenic compounds content by 23.03%, in addition to increasing FRY and DRY by 22.72% and 22.95%, respectively. This is the first consolidated study on the potential of germplasm for the development biofortified cassava cultivars in Brazil.

Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones.

Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cπ, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.

Release of extracellular vesicle miR-494-3p by ARPE-19 cells with impaired mitochondria.

BACKGROUND: Mitochondrial function in retinal pigmented epithelial (RPE) cells and extracellular vesicle (EV) formation/release are related through the lysosomal and exocytotic pathways that process and eliminate intracellular material, including mitochondrial fragments. We propose that RPE cells with impaired mitochondria will release EVs containing mitochondrial miRNAs that reflect the diminished capacity of mitochondria within these cells. METHODS: We screened ARPE-19 cells for miRNAs that localize to the mitochondria, exhibit biological activity, and are present in EVs released by both untreated cells and cells treated with rotenone to induce mitochondrial injury. EVs were characterized by vesicle size, size distribution, presence of EV biomarkers: CD81, CD63, and syntenin-1, miRNA cargo, and number concentration of EVs released per cell. RESULTS: We found that miR-494-3p was enriched in ARPE-19 mitochondria. Knockdown of miR-494-3p in ARPE-19 cells decreased ATP production and mitochondrial membrane potential in a dose-dependent manner, and decreased basal oxygen consumption rate and maximal respiratory capacity. Increased number of EVs released per cell and elevated levels of miR-494-3p in EVs released from ARPE-19 cells treated with rotenone were also measured. CONCLUSIONS: ARPE-19 mitochondrial function is regulated by miR-494-3p. Elevated levels of miR-494-3p in EVs released by ARPE-19 cells indicate diminished capacity of the mitochondria within these cells. GENERAL SIGNIFICANCE: EV miR-494-3p is a potential biomarker for RPE mitochondrial dysfunction, which plays a central role in non-neovascular age-related macular degeneration, and may be a diagnostic biomarker for monitoring the spread of degeneration to neighboring RPE cells in the retina.

Reproductive barriers in cassava: Factors and implications for genetic improvement.

Cassava breeding is hampered by high flower abortion rates that prevent efficient recombination among promising clones. To better understand the factors causing flower abortion and propose strategies to overcome them, we 1) analyzed the reproductive barriers to intraspecific crossing, 2) evaluated pollen-pistil interactions to maximize hand pollination efficiency, and 3) identified the population structure of elite parental clones. From 2016 to 2018, the abortion and fertilization rates of 5,748 hand crossings involving 91 parents and 157 progenies were estimated. We used 16,300 single nucleotide polymorphism markers to study the parents' population structure via discriminant analysis of principal components, and three clusters were identified. To test for male and female effects, we used a mixed model in which the environment (month and year) was fixed, while female and male (nested to female) were random effects. Regardless of the population structure, significant parental effects were identified for abortion and fertilization rates, suggesting the existence of reproductive barriers among certain cassava clones. Matching ability between cassava parents was significant for pollen grains that adhered to the stigma surface, germinated pollen grains, and the number of fertilized ovules. Non-additive genetic effects were important to the inheritance of these traits. Pollen viability and pollen-pistil interactions in cross- and self-pollination were also investigated to characterize pollen-stigma compatibility. Various events related to pollen tube growth dynamics indicated fertilization abnormalities. These abnormalities included the reticulated deposition of callose in the pollen tube, pollen tube growth cessation in a specific region of the stylet, and low pollen grain germination rate. Generally, pollen viability and stigma receptivity varied depending on the clone and flowering stage and were lost during flowering. This study provides novel insights into cassava reproduction that can assist in practical crossing and maximize the recombination of contrasting clones.

Cassava yield traits predicted by genomic selection methods.

Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions' BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 -RR-BLUP to 0.4756-RKHS) and dry root yield (0.4689 -G-BLUP to 0.4818-RKHS) in comparison with dry matter content (0.5655 -BLASSO to 0.5670 -RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99-1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.

Effect of F1 and F2 generations on genetic variability and working steps of doubled haploid production in maize.

For doubled haploid (DH) production in maize, F1 generation has been the most frequently used for haploid induction due to facility in the process. However, using F2 generation would be a good alternative to increase genetic variability owing to the additional recombination in meiosis. Our goals were to compare the effect of F1 and F2 generations on DH production in tropical germplasm, evaluating the R1-navajo expression in seeds, the working steps of the methodology, and the genetic variability of the DH lines obtained. Sources germplasm in F1 and F2 generations were crossed with the tropicalized haploid inducer LI-ESALQ. After harvest, for both induction crosses were calculated the haploid induction rate (HIR), diploid seed rate (DSR), and inhibition seed rate (ISR) using the total number of seeds obtained. In order to study the effectiveness of the DH working steps in each generation, the percentage per se and the relative percentage were verified. In addition, SNP markers were obtained for genetic variability studies. Results showed that the values for HIR, ISR, and DSR were 1.23%, 23.48%, and 75.21% for F1 and 1.78%, 15.82%, and 82.38% for F2, respectively. The effectiveness of the DH working step showed the same percentage per se value (0.4%) for F1 and F2, while the relative percentage was 27.2% for F1 and 22.4% for F2. Estimates of population parameters in DH lines from F1 were higher than F2. Furthermore, population structure and kinship analyses showed that one additional generation was not sufficient to create new genotype subgroups. Additionally, the relative efficiency of the response to selection in the F1 was 31.88% higher than F2 due to the number of cycles that are used to obtain the DH. Our results showed that in tropical maize, the use of F1 generation is recommended due to a superior balance between time and genetic variability.

Visual Outcomes and Aberrometric Changes With Topography-Guided Photorefractive Keratectomy Treatment of Irregular Astigmatism After Penetrating Keratoplasty.

PURPOSE: To evaluate the visual acuity and keratometric and aberrometric changes in patients with corneal transplants (PKP), who underwent topography-guided photorefractive keratectomy (TG-PRK) with mitomycin C (MMC). METHODS: In this case study, 15 patients with spherical equivalents ranging from -11.00 to -0.25 diopters (D) who underwent penetrating corneal transplantation and had irregular astigmatism ranging from -7.5 to -2.0 D underwent TG-PRK with MMC. Corneal topography and wavefront of all patients were measured preoperatively and 12 months postoperatively. RESULTS: Twelve months after TG-PRK with MMC, 46% of eyes achieved a best spectacle-corrected visual acuity (BSCVA) of 20/20 compared with 1 eye preoperatively (P = 0.0221, χ test). The BSCVA did not improve in 1 patient and increased by 1 line or more in all others. Astigmatism decreased significantly (P = 0.003) from 5.10 ± 0.4 D to 3.37 ± 0.06 D, the corneal best-fit sphere increased and keratometry measurements flattened significantly (P = 0.0001 for both comparisons), and the corneal total root mean square aberrations and trefoil decreased significantly (P = 0.0077 and P = 0.0054, respectively) from 9.11 ± 2.56 µm to 7.58 ± 3.15 µm and 2.00 ± 1.2 to 1.38 ± 0.27 µm, respectively, as measured by wavefront aberrometry. CONCLUSIONS: Twelve months postoperatively, the BSCVA improved significantly, the lines of vision increased, and astigmatism, corneal best-fit sphere, mean keratometry, corneal thickness, corneal root mean square total, and corneal spherical aberrations decreased. TG-PRK with MMC is a good alternative for correcting post-PKP cases with irregular astigmatism with elevated higher-order aberrations.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines ([Formula: see text]) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy.

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.

New chromatographic and biochemical strategies for quick preparative isolation of tRNA.

A combination of hydrophobic chromatography on phenyl-Sepharose and reversed phase HPLC was used to purify individual tRNAs with high specific activity. The efficiency of chromatographic separation was enhanced by biochemical manipulations of the tRNA molecule, such as aminoacylation, formylation of the aminoacyl moiety and enzymatic deacylation. Optimal combinations are presented for three different cases. (i) tRNA(Phe) from Escherichia coli. This species was isolated by a combination of low pressure phenyl-Sepharose hydrophobic chromatography with RP-HPLC. (ii) tRNA(Ile) from E. coli: Aminoacylation increases the retention time for this tRNA in RP-HPLC. The recovered acylated intermediate is deacylated by reversion of the aminoacylation reaction and submitted to a second RP-HPLC run, in which deacylated tRNA(Ile) is recovered with high specific activity. (iii) tRNA(i)(Met) from Saccharomyces cerevisiae. The aminoacylated form of this tRNA is unstable. To increase stability, the aminoacylated form was formylated using E.coli: enzymes and, after one RP-HPLC step, the formylated derivative was deacylated using peptidyl-tRNA hydrolase from E.COLI: The tRNA(i)(Met) recovered after a second RP-HPLC run exhibited electrophoretic homogeneity and high specific activity upon aminoacylation. These combinations of chromatographic separation and biochemical modification can be readily adapted to the large-scale isolation of any particular tRNA.

[Transient ischemic attacks in a patient with superior vena cava obstruction: case report]. / Ataques isquêmicos transitórios em paciente com síndrome da veia cava superior. Relato de caso.

The superior vena cava obstruction is a relatively rare condition. We report the case of a 42 year old man suffering of hypertension for about fifteen years. He reported a cervical and thoracic pain for one year, that was related to a 95% of occlusion on the right coronary artery. An angioplasty has been done but the patient still related the thoracic pain. Afterwards the patient had recurrent episodes of right hemiplegia and hypertensive emergencies that have been treated with anti-hypertensive agents. A venous disease was suspected because of cyanosis in the face especially when episodes of transient ischemic attacks occurred. A venography showed obstruction of the right jugular vein near the junction with the superior vena cava. In conclusion, it was not possible to define with certainty the relationship between the two pathologies presented by the patient, even so, we call attention to the improvement of the neurological symptoms after the control of superior vena cava obstruction with the treatment.

Analysis of Usnea fasciata crude extracts with antineoplastic activity.

Different fractions, isolated from the lichen Usnea fasciata, were analyzed by PC, TLC, and RP-HPLC. Analysis of the organic phases, mainly containing phenolics, revealed that usnic acid is the main product from secondary metabolites, whereas the polysaccharides isolichenin and raffinose are the most abundant water-soluble carbohydrates. Fractions containing usnic acid, as well as those containing isolichenin, showed moderate activity against sarcoma 180 and Ehrlich tumor cells. High antitumoral activity, near 90% inhibition, was found associated with the fraction containing raffinose.