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Diseases such as anthracnose (Colletotrichum lindemuthianum) and angular leaf spot (Pseudocercospora griseola) can occur in common bean and compromise crop production, especially if susceptible cultivars are grown under conditions favorable to these diseases. The most efficient and sustainable alternative for controlling these diseases is the use of resistant cultivars. The identification of sources of resistance and the use of tools that assist in accurate selection of resistant cultivars are crucial to the success of breeding programs. The aim of this study was to characterize 67 carioca common bean cultivars recommended in Brazil regarding reaction to races 65, 73, 81, and 89 of C. lindemuthianumand to races 63‐23 and 63‐63 of P. griseolaand to verify the application of factor analysis in the selection of cultivars resistant to both pathogens. The cultivars and resistant controls BRS Esplendor and MAIII.16‐159 were inoculated in a greenhouse in two experiments for each race. Cultivars were assessed for resistance to multiple races of anthracnose and angular leaf spot using artificial inoculation of plants at V2 stage. Factor analysis proved to be a good alternative for accurate selection of resistant cultivars. The percentage of cultivars simultaneously resistant to all races was low (8.95%). The cultivars Rudá R, VC 15, IAC‐Apuã, IAC‐Carioca Pyatã, BRSMG Amuleto, and BRS FC104 selected by factor analysis showed resistance to anthracnose and angular leaf spot. Therefore, these cultivars are potential sources of resistance for incorporation in bean breeding programs. Tools for selecting resistant lines are crucial for the success in breeding programs aiming resistance.Factor analysis proved to be an alternative for accurate selection of resistant cultivars.Six cultivars were selected by factor analysis as resistant to anthracnose and angular leaf spot.

In the US, sexual and gender minority populations are disproportionately affected by HIV. Pre-exposure prophylaxis (PrEP) is a key prevention method, but its effectiveness relies on consistent usage. Our four-year national cohort study explored PrEP discontinuation among sexual and gender minority people who initiated PrEP. We found a high annual rate of discontinuation (35–40 percent) after PrEP initiation. Multivariable analysis with 6,410 person-years identified housing instability and prior history of PrEP discontinuation as predictors of discontinuation. Conversely, older age, clinical indication for PrEP, and having health insurance were associated with ongoing PrEP use. To promote sustained PrEP use, strategies should focus on supporting those at high risk for discontinuation, such as younger people, those without stable housing or health insurance, and prior PrEP discontinuers.

The performance of inbred lines in advanced endogamous generation is commonly evaluated in successive generations of testing and selection, which we defined as “multistage field trials” (MSFT). MSFT data routinely exhibit heterogeneity of (co)variances at several levels due to genetic and/or statistical imbalance. Nowadays, mixed models have been widely used to deal with unbalanced data. However, few studies on common bean have addressed the use of a mixed model approach with modeling of (co)variance structures for random effects in MSFT. Furthermore, factor analysis and genotype‐ideotype distance (FAI‐BLUP) selection index was originally proposed using best linear unbiased predictions from individual analysis. In this regard, we aimed to study the implications of modeling (co)variance structures for random effects in the estimation of genetic parameters and evaluate the accuracy and efficiency of inbred line selection by the modeled FAI‐BLUP approach. A total of five trials were evaluated from 2018 to 2020. The results revealed that the unstructured covariance matrix fitted better for grain yield, whereas the matrix with uniform correlation and heterogeneity of variances fitted better for grain aspect and plant architecture. The modeled FAI‐BLUP approach increased the values of selection accuracy and selection efficiency. Our results suggest that modeling the different structures of (co)variances and selecting the best‐performing genotypes by modeled FAI‐BLUP approach should be used in common bean assays involving unbalanced data. Genetic and/or statistical imbalance is a reality of plant breeding programs.Selecting the best fitted (co)variance structure for unbalanced data increased the selection accuracy.Low magnitude estimates of genetic correlation indicated the predominance of the complex part of the G × E interaction.The modeled FAI‐BLUP approach increased the selection efficiency by an average of 13.95%.

Carioca common bean (Phaseolus vulgarisL.) is the most produced and consumed in Brazil. The production of carioca bean is affected by fungal diseases such as anthracnose, caused by the fungus Colletotrichum lindemuthianum. Most sources resistant to anthracnose, used in crosses by breeding programs are not derived from the commercial group or are poorly productive and less adapted. Thus, the objective of this study was to characterize 40 carioca bean cultivars already recommended in Brazil for their reaction to C. lindemuthianumraces 65, 73, 81, and 89. The cultivars and the resistant control, BRS Esplendor, were inoculated in a greenhouse with two different experiments for each race. Seedlings with fully expanded primary leaves were inoculated with a suspension of each pathogen race and assessed after 12 d. Among the 40 cultivars assessed, 12.5, 2.5, 12.5, and 15% resistant to one, two, three, and four races of C. lindemuthianum, respectively. The cultivars IAC Formoso, IAC‐Carioca Pyatã, IAC‐Apuã, VC 15, BRS Notável, and IPR Campos Gerais were resistant to the four races, constituting parental sources of resistance to the pathogen. In regions that favor the occurrence of anthracnose in common bean, cultivars with certain levels of resistance should be selected. Therefore, the important information obtained will be made available to growers and those offering technical assistance to growers. Forty carioca bean cultivars were inoculated with four races of Colletotrichum lindemuthianumin Brazil.Twenty‐seven cultivars were susceptible to the four inoculated C. lindemuthianumraces.There were 12.5, 2.5, 12.5, and 15% resistant to 1, 2, 3, and 4 races of C. lindemuthianum.

Identifying agronomic traits correlated to grain yield can be very useful for soybean [Glycine max(L.) Merr.] breeding, especially if these traits can be measured through unmanned aerial vehicle high‐throughput phenotyping rather than through manual measurements. The objective of the present study was to assess the association between canopy coverage and soybean grain yield through different statistical methodologies. A panel with 97 soybean genotypes was evaluated in two field experiments conducted in Paraná State, Brazil. Canopy coverage was determined by using an RGB camera coupled to a drone. Images taken during flights at phenological stages V3‐V4, V5‐V6, V7‐V8, and V9‐R1 were used to calculate canopy coverage based on the green pixel ratio in each experimental unit. There were significant genotype × environment interactions in all evaluated traits. Selective accuracy values (0.73–0.96) revealed indirect yield selection efficiency based on canopy coverage. High genetic correlation estimates (0.76) were observed between grain yield and canopy coverage at flowering in one of the assessed environments. These results were confirmed through genetic correlation coefficient decomposition in direct and indirect effects and of gain estimates presenting indirect selection. Thus, canopy coverage data remotely collected using drones to soybean indirect selection for grain yield can be a promising strategy to accelerate genetic gains in soybean breeding programs. Use of high‐throughput phenotyping in soybean breeding programs for yield prediction.High efficiency in the response correlated by indirect selection for grain yield using drones.High estimate of genotypic correlation is observed between canopy coverage and grain yield in soybean breeding.

In common bean (Phaseolus vulgarisL.) breeding, several trials are carried out in field conditions to predict the genotypic values, but experimental designs may not be sufficient to capture the field heterogeneity in the experimental area. The objective of this work was to evaluate the potential of spatial models to correct data from a common bean breeding program for spatial trends and improve the prediction of genotypic values. We used real data from 19 field trials from a common bean breeding program and three experimental designs. The traditional statistical model with design effects and independent errors was fitted and used as the basic model. Later, we fitted a sequence of spatial models to include different residual (co)variance structures for local trends and fixed and random effects based on plot position information to capture global and extraneous trends. The basic model and the best‐fit spatial model were compared regarding the estimates of heritability, accuracy, prediction error variance, and discordance in the top‐ranking genotypes. In most cases, the use of spatial models improved the estimates of heritability and accuracy or, at least, reduced the estimates of prediction error variance. Also, changes in the genotypic values classification were observed. Because no single model presented the best fit for all trials, some of the tested models were recommended for future trials based on the patterns of spatial trends observed. Thus, the use of spatial models helped to improve the data analysis and the prediction of genotypic values by capturing the field heterogeneity in our common bean field trials. Common bean breeding is based on field evaluation of candidate genotypes.Experimental designs are not completely efficient to capture field heterogeneity.Spatial models were used to further improve the control of spatial heterogeneity.Accuracy, heritability, and genotype classification were positively affected.

Purpose: COVID-19 causes lung inflammation and lesions, and chest X-ray and computed tomography images are remarkably suitable for differentiating the new disease from patients with other lung diseases. In this paper, we propose a computer model to classify X-ray images of patients diagnosed with COVID-19. Chest X-ray exams were chosen over computed tomography scans because they are low cost, results are quickly obtained, and X-ray equipment is readily available. Methods: A new CNN network, called CNN-COVID, has been developed to classify X-ray patient’s images. Images from two different datasets were used. The images of Dataset I is originated from the COVID-19 image data collection and the ChestXray14 repository, and the images of Dataset II belong to the BIMCV COVID-19+ repository. To assess the accuracy of the network, 10 training and testing sessions were performed in both datasets. A confusion matrix was generated to evaluate the model’s performance and calculate the following metrics: accuracy (ACC), sensitivity (SE), and specificity (SP). In addition, Receiver Operating Characteristic (ROC) curves and Areas Under the Curve (AUCs) were also considered. Results: After running 10 tests, the average accuracy for Dataset I and Dataset II was 0.9787 and 0.9839, respectively. Since the weights of the best test results were applied in the validation, it was obtained the accuracy of 0.9722 for Dataset I and 0.9884 for Dataset II. Conclusions: The results showed that the CNN-COVID is a promising tool to help physicians classify chest images with pneumonia, considering pneumonia caused by COVID-19 and pneumonia due to other causes.

In breeding of autogamous plants, breeders often deal with the evaluation of progenies derived from multiple populations. We hypothesize that selection strategies that account for the heterogeneous genotypic variability of progenies within these populations associated with the population means may improve progeny selection. Thus, the objective of this work was to use two mixed models with progenies nested to population effects to obtain and evaluate indexes for the genotypic values of progenies. We used simulations and a real dataset from a multipopulation recurrent selection program of common bean (Phaseolus vulgarisL.). Progenies from 20 populations were evaluated for grain yield in two different generations. The three studied indexes consider the merit of populations, but differently account for the genotypic variability of progenies within populations: index gMuses a mean estimate, index gHis based on heterogeneous estimates, and the index gWalso uses a mean estimate but is weighted by the selection accuracy within populations. The studied populations were highly diverse in both generations, justifying the implementation of the two models to obtain the population means and specific estimates of genotypic variability of progenies for each population. The comparison among indexes suggested that index gWmore appropriately explored the information from the genotypic profiles of the common bean studied populations in the genotypic values of progenies. Thus, the index gWhas the potential to help breeders of self‐pollinated plant species to improve progeny selection from multiple populations. Breeding populations of self‐pollinated crops may show diverse genotypic means and variance.Heterogenous genotypic variance within populations needs to be considered in progeny selection.Indexes for progeny genotypic values are the combination of progeny and population effects.The index gWponders the variability within populations by the selection accuracy estimates.Multipopulation recurrent selection programs can be favored during progeny selection stages.

At initial breeding stages, using a replicated check design is a viable alternative to reduce experimental field area as well as financial and operational costs. In this situation, spatial modeling could act to increase prediction accuracy of plant genotypic values. The objectives of this study were to demonstrate how spatially adjusted models can be used to reduce experimental error and how to compare statistical models in order to identify the best model for accurate genotype selection. For this purpose, we assessed 200 F3:4tomato families for their resistance to Phytophthora infestansisolates. NC1CELBR, NC25P, and the cultivar Santa Clara were used as checks. Under field conditions, plants were inoculated with P. infestansisolates and scored according to their level of disease severity. Nine statistical models were adjusted to estimate family genotypic values. The selection of the fittest model was based on residual variance values, accuracy, Akaike and Bayesian information criteria, and the maximum likelihood ratio test. We observed spatial patterns within the experimental field area. Spatial modeling decreased error, which is indicated by the better experimental variation distribution. Residual variance decreased, while genotypic variance increased ∼10% when spatial analysis was used. Spatial analysis improved selection accuracy by 19% compared with the traditional analysis. Therefore, we recommend incorporating spatial modeling into data analysis in breeding trials for disease resistance because it can provide higher gains from selection compared with traditional modeling approaches, depending on the experimental condition. Spatial analysis provides a more accurate selection of late blight‐resistant tomato families.The AR1 ⊗ AR1 spatial model is efficient for the selection of resistant tomato families.Spatial analysis minimizes variation within experimental fields caused by external factors.

Pumpkin (Cucurbita moschataDuchesne) has high food importance due to the high nutritional value of its fruit and seeds. Its seed oil is composed of ∼70% unsaturated fatty acids, and its production can be improved by increasing the number of plants per area. Pumpkin plants with reduced growth habits can be grown in shorter spacing and produce smaller fruits, but have a greater quantity of fruits per area. This study aimed to select from F2, BC1:1, and BC1:2populations pumpkin genotypes with shorter internodes and higher seed productivity for oil production using the factor analysis and ideotype design (FAI‐BLUP) selection index. An augmented block design (ABD) was used with four replications. The traditional model of ABD analysis was compared with the spatial model to verify the presence of errors in the local and global trends in the experimental area. There was a significant difference for all traits evaluated in both the ABD and spatial models. Based on the Akaike information criterion, the model with the best fit for all characteristics was the ABD, which was efficient in guaranteeing the independence of the residues and the absence of errors in local and global trends. In general, the characteristics expressed high values of individual heritability and accuracy. Three common factors explained the seven characteristics studied, presenting a variability of 70.98%. Six of the seven characteristics showed desirable gains of up to 63.85%. The FAI‐BLUP index allowed the selection of superior pumpkin genotypes with a smaller size and higher seed productivity for oil production.

Finding effective ways to evaluate the process and a team in software engineering is not a trivial task. Therefore, it is pertinent to study metrics that can be used in Scrum environments to monitor and evaluate the team's progress and support the implementation of improvements. This study explores the relevance of 12 specific metrics applied in a Scrum environment and explores their importance considering multiple dimensions through the adoption of a quantitative methodology based on a survey that received 137 valid answers from Portuguese Scrum professionals. The results allowed us to conclude that the metrics related to the business value delivered and sprint goal success are the most relevant. Furthermore, factors like the number of years of experience of individuals, their role in the Scrum team, and the size of the organisation are factors that influence the perception of the importance of these metrics.

Correlated information from different genetic sources is absent in most of annual self‐pollinated crops using the recurrent selection strategy, which is a breeding strategy that improves crop traits consistently over years. In common bean (Phaseolus vulgarisL.) breeding programs, progenies coming from multiple biparental populations are evaluated across generations of inbred plants with the assumption that the data are not correlated. In this paper, in addition to the effects of progeny, we evaluate the effects of populations and generations and provide information for the selection process in a self‐pollinated recurrent selection breeding program. Nineteen progenies were extracted from 20 breeding populations and evaluated at different sowing times across F3:4and F3:5generations. The evaluated traits were plant architecture, angular leaf spot resistance, grain appearance, and grain yield. Progenies were selected using three methods: means of progenies regardless of generation and population effects; the multigeneration index (MI), which considered the generation effect; and the selection index with parents, populations, progenies, and generations (SIPPPG). We showed that adding variation among progenies correctly weighted for different generations as well as variation among populations yield for an increase in genetic gain. Therefore, selection accuracies of the SIPPPG were the highest for all traits compared with those of MI and when generation and population effects were not considered.

Jatropha curcas L. is a perennial plant with great potential for biodiesel production. Thus, the aim of this study was to select J. curcas genotypes for bioenergy purpose considering multitrait, multiharvest and effective population size. To this end, a data set with 70 J. curcas families, obtained through controlled crosses, evaluated in a randomized block design, with six replications, three plants per plot, spaced at 4 × 2 m was used. The following traits were evaluated: plant height in the 2014 and 2015 crop seasons; canopy projection in the row (2015 crop season); canopy projection between rows (2015 crop season); and grain yield (2015, 2016, and 2017 harvests). The mixed model methodology was used to estimate the variance components and to predict the genetic values. Superior genotypes were selected through Multitrait index based on factor analysis and genotype‐ideotype distance (FAI-BLUP index), considering the effective population size. According to this index, 98% of all additive genetic variability were summarized in three factors. By adopting the selection intensity that allows maintaining the effective population size equal to 30 (i.e., selection of 214 individuals), the predicted gains were 15.56, 21.75, 4.16, and 5.37% for the traits grain yield in the 2015 and 2016 harvests, canopy projection in the row, and canopy projection between rows in the 2015 crop season, respectively. Thus, the results suggest that the FAI-BLUP index can be successfully used in J. curcas breeding. [ABSTRACT FROM AUTHOR]

Emerging evidence identifies major contributions of platelets to inflammatory amplification in dengue, but the mechanisms of infection-driven platelet activation are not completely understood. Dengue virus nonstructural protein-1 (DENV NS1) is a viral protein secreted by infected cells with recognized roles in dengue pathogenesis, but it remains unknown whether NS1 contributes to the inflammatory phenotype of infected platelets. This study shows that recombinant DENV NS1 activated platelets toward an inflammatory phenotype that partially reproduced DENV infection. NS1 stimulation induced translocation of α-granules and release of stored factors, but not of newly synthesized interleukin-1β (IL-1β). Even though both NS1 and DENV were able to induce pro-IL-1β synthesis, only DENV infection triggered caspase-1 activation and IL-1β release by platelets. A more complete thromboinflammatory phenotype was achieved by synergistic activation of NS1 with classic platelet agonists, enhancing α-granule translocation and inducing thromboxane A2 synthesis (thrombin and platelet-activating factor), or activating caspase-1 for IL-1β processing and secretion (adenosine triphosphate). Also, platelet activation by NS1 partially depended on toll-like receptor-4 (TLR-4), but not TLR-2/6. Finally, the platelets sustained viral genome translation and replication, but did not support the release of viral progeny to the extracellular milieu, characterizing an abortive viral infection. Although DENV infection was not productive, translation of the DENV genome led to NS1 expression and release by platelets, contributing to the activation of infected platelets through an autocrine loop. These data reveal distinct, new mechanisms for platelet activation in dengue, involving DENV genome translation and NS1-induced platelet activation via platelet TLR4.

Emerging evidence identifies major contributions of platelets to inflammatory amplification in dengue, but the mechanisms of infection-driven platelet activation are not completely understood. Dengue virus nonstructural protein-1 (DENV NS1) is a viral protein secreted by infected cells with recognized roles in dengue pathogenesis, but it remains unknown whether NS1 contributes to the inflammatory phenotype of infected platelets. This study shows that recombinant DENV NS1 activated platelets toward an inflammatory phenotype that partially reproduced DENV infection. NS1 stimulation induced translocation of α-granules and release of stored factors, but not of newly synthesized interleukin-1β (IL-1β). Even though both NS1 and DENV were able to induce pro-IL-1β synthesis, only DENV infection triggered caspase-1 activation and IL-1β release by platelets. A more complete thromboinflammatory phenotype was achieved by synergistic activation of NS1 with classic platelet agonists, enhancing α-granule translocation and inducing thromboxane A2synthesis (thrombin and platelet-activating factor), or activating caspase-1 for IL-1β processing and secretion (adenosine triphosphate). Also, platelet activation by NS1 partially depended on toll-like receptor-4 (TLR-4), but not TLR-2/6. Finally, the platelets sustained viral genome translation and replication, but did not support the release of viral progeny to the extracellular milieu, characterizing an abortive viral infection. Although DENV infection was not productive, translation of the DENV genome led to NS1 expression and release by platelets, contributing to the activation of infected platelets through an autocrine loop. These data reveal distinct, new mechanisms for platelet activation in dengue, involving DENV genome translation and NS1-induced platelet activation via platelet TLR4.

Biomass sorghum [Sorghum bicolor(L.) Moench] is considered as a raw material of great potential for bioenergy generation. The objective of this study was to estimate the combining ability of biomass sorghum of A and R lines, carrying the bmr‐6allele, evaluating different crop years and site effect, aiming at the bioenergetic potential for the production of second‐generation ethanol. Four male‐sterile lines (A), and 10 fertility restorative lines (R) were used, obtaining 40 hybrids bmr and sensitive to the photoperiod. From the F1seeds, four experiments were conducted in different crop years and sites. The data were submitted to individual diallel analysis according to IV model proposed by Griffing (1956), adapted for partial diallel design by Geraldi and Miranda Filho (1988). Promising lines were identified to breeding line groups A and R, aiming to obtain new lines breeding to obtain superior hybrids. The crop year and site effects were significant for most traits. The use of partial diallel analysis is promising for the biomass sorghum breeding program and A and R lines with potential bioenergetic were identified for the production of second‐generation ethanol.

Sorghum [Sorghum bicolor(L.) Moench] biomass hybrids with high productivity and enhanced levels of lignin are seen as a promising alternative of feedstock for direct burning in ovens designed for cogeneration of electricity. The objective of this study was to estimate the genetic combing capacity of biomass sorghum lines and conduct multivariable selection of photosensitive biomass sorghum hybrids for use in cogeneration. Thirty‐six photosensitive hybrids, the control BRS716, and 12 parental lines were evaluated in a seven‐by‐seven triple lattice design at two locations, and 12 characters were evaluated. There was superiority of additive effects on the genetic control of all the characteristics studied in both environments, less for female lines in the diallelic analysis of F1hybrids. The inclusion of parents in the estimates of combining capacities indicated predominance of dominance effects involved in the genetic control of the traits analyzed. The results demonstrate the action of epistasis of the dwarf genes present in the female lines for the biomass parameter and the false interpretation when these lines are included in the diallelic analysis. With the use of the index based on factor analysis and genotype–ideotype distance (FAI‐BLUP index), four factors were established, which separated the characteristics of production and quality, as well as the two environments, resulting from the high hybrid × environment interaction. With the index, five hybrids with higher potential for burning (H5‐5, H2‐1, H1‐1, H1‐5, and H5‐1) were selected. However, no hybrids obtained gains for the characteristics of production and quality, simultaneously, which indicates the need for genetic improvement of the parents used in the program.

In diallel analysis, using mixed models, it is possible to estimate the variance components of general combining ability (GCA), specific combining ability (SCA), reciprocal GCA (RGCA), reciprocal SCA (RSCA), and parents, even in unbalanced datasets. These variances can be estimated by likelihood maximization via numerical algorithms (e.g., expectation maximization [EM] and average information [AI]), which have different advantages. Thus, the objective of this study was to describe and implement the EM‐AI algorithm (combination of EM and AI) in R software to estimate the variance components of RGCA, RSCA, and parents effects in diallel analysis. Two real datasets and three diallel models (Griffing's Model 1, Griffing's Model 1 + RGCA, and Model 3, a general diallel model with the effects of GCA, SCA, RGCA, RSCA, and parents) were used to evaluate the efficiency of the algorithms AI, EM, and EM‐AI. The AI algorithm failed to converge for the three diallel models in both datasets. The other algorithms (EM and EM‐AI) converged normally, and the estimated variance components with these algorithms were similar for the three diallel models in both datasets. However, the EM‐AI algorithm was more efficient than the EM algorithm, and the general diallel model (Model 3) provided more accurate estimates of variance parameters. Thus, the EM‐AI algorithm with routine implemented in R has potential for use in diallel analyses in plant breeding.

In plant breeding, several multicategorical variables are evaluated using score scales that are treated as interval scales. However, statistics that use ratio operations, such as the experimental CV and the selection gain, are only appropriate for ratio scales data. Thus, this work aimed to propose strategies to mitigate the inconveniences faced in the data analysis of score scales, especially those involving CV and selection gain obtained from different scales. This work proposes the following strategies: (i) the use of a standard score scale with the properties of a ratio scale (ascending scale with 0 as the point of origin); (ii) the conversion of the scores from different scales into a common ratio scale; (iii) the adjustment of the data from an interval scale to a ratio scale using the Delta Scale (Scale δui), and (iv) the use of unbiased estimator for CV and selection gain in score scales data. The proposed strategies resulted in unbiased estimates of CV and selection gain from data of different score scales. These strategies have the potential to be used in the meta‐analysis of data within and between plant breeding programs.

Partitioning of the progenies effect within populations has several advantages.FAI‐BLUP index capitalizes the progenies × growth seasons interaction.The selected inbreed progenies showed favorable genotypes for the target traits.Genetic breeding towards the common bean ideotype can accelerate the cultivar release. The goal of breeding programs is selection toward the ideal plant type. In this study, field experiments were performed to select common bean inbred progenies that maximize the probability of extracting superior lines. A total of 124 inbred progenies of three consecutive generations (F2:3, F2:4, and F2:5) were conducted in field experiments over three different environments (one generation in each environment). Seven different traits, related to disease severity, commercial acceptance grain, and yield, were evaluated by best linear unbiased prediction. This work underscored the importance of incorporating population information into the statistical model as a means of comparing progenies from different populations with higher efficacy, even when kinship information between populations is not available. Toward the common bean ideotype, 20 inbred progenies of greater potential were selected using the factor analysis and genotype‐ideotype distance (FAI‐BLUP) index. This index is based on the structural equation models by joining the factor analysis technique (exploratory factor analysis) with the ideotype design (confirmatory factor analysis). The predicted genetic gain was increased for all the traits in all generations. Selection strategies that capture the multitrait information capitalize the progenies × growth season interactions and are based on the ideotype, such as as the FAI‐BLUP index, have the potential for use in genetic breeding toward the common bean ideotype and can accelerate the release of more adapted cultivars.

For the common bean (Phaseolus vulgarisL.) crop, most recurrent selection breeding programs use, at the selection stage, inbred families evaluated across two or three generations. This study applied a multigeneration index to produce a best linear unbiased predictor (BLUP) of family effects, aimed at assessing its efficiency in the selection of common bean families. Data from 380 multigenerational families were evaluated across different sowing times and years, and across generations F2:3and F2:4. The evaluated traits were: plant architecture, yield, and grain appearance. Bivariate BLUP with genotype × environment interaction was fit using a compound symmetry model. We also selected 90 families and evaluated the abovementioned traits in the F2:6generation. Comparing the accuracy of the multigeneration index using all three generations simultaneously with the one using only the F2:6generation, the increases in genetic gain were 21, 28, and 3% in plant architecture, yield, and grain appearance, respectively. Therefore, we concluded that the multigeneration index including data from all available generations is the most efficient method for selecting common bean families. Additionally, genotypic values were predicted to be free from interactions with the sowing times and years of planting.

Lesions and peripheral blood of cutaneous leishmaniasis patients have HLA-DR-expressing neutrophils with antigen presenting cell-like characteristics, which retain morphologic and functional characteristics of neutrophils. The protozoan Leishmania braziliensiscauses cutaneous leishmaniasis (CL) in endemic regions. In murine models, neutrophils (PMNs) are recruited to the site of infection soon after parasite inoculation. However, the roles of neutrophils during chronic infection and in human disease remain undefined. We hypothesized that neutrophils help maintain a systemic inflammatory state in subjects with CL. Lesion biopsies from all patients with CL tested contained neutrophils expressing HLA-DR, a molecule thought to be restricted to professional antigen-presenting cells. Although CL is a localized disease, a subset of patients with CL also had circulating neutrophils expressing HLA-DR and the costimulatory molecules CD80, CD86, and CD40. PMNs isolated from a low-density leukocyte blood fraction (LD-PMNs) contained a higher percentage of HLA-DR+PMNs than did normal-density PMNs. In vitro coculture experiments suggested LD-PMNs do not suppress T cell responses, differentiating them from MDSCs. Flow-sorted HLA-DR+PMNs morphologically resembled conventional PMNs, and they exhibited functional properties of PMNs. Compared with conventional PMNs, HLA-DR+PMNs showed increased activation, degranulation, DHR123 oxidation, and phagocytic capacity. A few HLA-DR+PMNs were observed in healthy subjects, and that proportion could be increased by incubation in either inflammatory cytokines or in plasma from a patient with CL. This was accompanied by an increase in PMN hladrb1mRNA, suggesting a possible connection between neutrophil “priming” and up-regulation of HLA-DR. These data suggest that PMNs that are primed for activation and that also express surface markers of antigen-presenting cells emerge in the circulation and infected tissue lesions of patients with CL.

Autogamous plant breeders obtain numerous populations annually, and in the progeny selection process, the merit of the population is not considered. Thus, it would be important to have a progeny selection index that includes not only the effects of progenies in the different generations but also the effects of populations in all the generations and the data from parents and the F1and F2generations simultaneously. The main objective of this paper was to develop a selection method that encompasses the entire structure of an autogamous plant breeding program including all the data as of the parents, the F1and F2generations, and also both the progeny and population effects in the F3to F6generations. To do so, a selection index (called selection index with parents, populations, progenies, and generations [SIPPPG]), which includes all these effects, was proposed. Estimators were also derived for computation of the contribution (through indirect heritabilities and selective accuracies) of each source of information for predicting the additive genetic value of the lines to be obtained at the end of the selection process. The new approach was assessed through numerical evaluation and a total index was also obtained using field data derived from a breeding program with the common bean crop. This new index yields gains in selection efficiency ranging from 5 to 28% depending on the relative magnitude of the genetic variation among populations.

Whitefly (Bemisia tabaci) causes damage to tomato (Solanum lycopersicumL.) and is controlled by insecticides harmful to man and the environment. Development of resistant cultivars is ideal for whitefly management, with alternate genetic sources being indispensable. Germplasm banks are potentially good sources of resistant cultivars. Agronomic characteristics of the tomato subsamples from the Horticultural Germplasm Bank (HGB) at Universidade Federal de Viçosa (UFV) have been typified but little is known about their insect resistance. Thus, the objective of this study was to assess the resistance of 103 HGB‐UFV tomato subsamples to B. tabacias well evaluate the resistance mechanism. The characteristics of the subsamples evaluated were the number of nymphs and eggs per plant and the resistance index compared with the susceptible cultivar Santa Clara, arbitrarily chosen as the susceptible standard. The trichome number per 0.04 cm2of the leaf blades and 15 leaf hydrocarbon concentrations were also determined. Resistant subsamples were submitted to antibiosis test and the mortality (%) and life cycle of the B. tabaciwere evaluated. The difference in the number of eggs per plant, nymphs per plant, and nymph/egg ratio in the tomato subsamples was evaluated. A positive correlation was observed between the hydrocarbons undecane and tridecane with B. tabacinymphs. Significant and positive differences in the trichome per 0.04‐cm2density were found between the trichome density and the number of eggs per plant. The subsamples HGB‐225, ‐327, ‐630, ‐813, ‐985, ‐2029, ‐2030, ‐2055, ‐2057, ‐2060, ‐2062, and ‐2068 were resistant to B. tabacibiotype B through antixenosis and antibiosis resistance mechanisms.

The article discusses the selection of "One Study One Summary" by John Psathas, "Liquid Bars" by João Pedro Oliveira and "Psappha" by Iannis Xenakis to be presented and performed at the Percussive Arts Society International Convention (PASIC) in Austin, Texas in November 2008. One common element among the pieces is the composers' attempt to extend the possibilities of percussion instruments.