Yield in many crops is affected by abscission during the early stages of fruitlet development. The reasons for fruitlet abscission are often unclear but they may include genetic factors because, in some crops, self-pollinated fruitlets are more likely to abscise than cross-pollinated fruitlets. Pollen parentage can also affect final fruit size and fruit quality. Here, we aimed to understand the effects of pollen parentage on fruitlet retention and nut quality in orchards of macadamia (Macadamia integrifolia Maiden & Betche). We identified the pollen parent of macadamia \'cultivar \'816\' embryos by analysing single nucleotide polymorphisms (SNPs) in their DNA using customised MassARRAY and Single Allele Base Extension Reaction (SABER) methods. This allowed us to determine the proportions of self-fertilised and cross-fertilised progeny during premature fruit drop at 6 weeks and 10 weeks after peak anthesis, as well as at nut maturity. We determined how pollen parentage affected nut-in-shell (NIS) mass, kernel mass, kernel recovery, and oil concentration. Macadamia trees retained cross-fertilised fruitlets rather than self-fertilised fruitlets. The percentage of progeny that were cross-fertilised increased from 6% at 6 weeks after peak anthesis to 97% at nut maturity, with each tree producing on average 22 self-fertilised nuts and 881 cross-fertilised nuts. Three of the four cross-pollen parents provided fruit with significantly higher NIS mass, kernel mass, or kernel recovery than the few remaining self-fertilised fruit. Fruit that were cross-fertilised by \'842\', \'A4\', or \'A203\' had 16-29% higher NIS mass and 24-44% higher kernel mass than self-fertilised fruit. Nuts that were cross-fertilised by \'A4\' or \'A203\' also had 5% or 6% higher kernel recovery, worth approximately $US460-540 more per ton for growers than self-fertilised nuts. The highly selective abscission of self-fertilised fruitlets and the lower nut quality of self-fertilised fruit highlight the critical importance of cross-pollination for macadamia productivity.

Quantitative analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW-MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel-wise fitting are often unreliable. In this study, we propose a kernelized total difference-based curve-fitting method to estimate the IVIM parameters. Simulated DW-MRI data at five signal-to-noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW-MRI data acquired on a 1.5-T MRI scanner with nine b-values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion-encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust-region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root-mean-square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference-based curve-fitting method has the potential to improve the reliability of IVIM parametric imaging.

OBJECTIVE: This study evaluated the occurrence of Schistosoma mansoni and soil-transmitted helminths in an endemic area in the Eastern Brazilian Amazon, analysing prevalence and spatial distribution.
METHODS: The study was conducted in four localities of Primavera Municipality, in Pará state. Data was obtained from the Decit 40/2012 project and the participants were divided into five age range categories for evaluation: children, adolescents, young adults, adults and elderly individuals. For the diagnostic tests, Kato-Katz slides were prepared to detect S. mansoni and soil-transmitted helminths eggs. The spatial distribution map and the Kernel Density Estimation were performed to assess the presence and location of infections.
RESULTS: Stool samples revealed the presence of hookworms, S. mansoni, Ascaris lumbricoides and Trichuris trichiura eggs. Mono-, bi- and poly-parasitic infections were observed, with a significant prevalence of hookworm monoparasitism.
CONCLUSIONS: The high frequency of children infected with soil-transmitted helminths confirms their significance as an ongoing public health problem in the poorest municipalities of Brazil. The Geographic Information System plays a crucial role in environmental surveillance and in the control of epidemics and endemic diseases, enabling accurate assessment and informed decision-making for their control.

Statistical estimation of parameters in large models of evolutionary processes is often too computationally inefficient to pursue using exact model likelihoods, even with single-nucleotide polymorphism (SNP) data, which offers a way to reduce the size of genetic data while retaining relevant information. Approximate Bayesian Computation (ABC) to perform statistical inference about parameters of large models takes the advantage of simulations to bypass direct evaluation of model likelihoods. We develop a mechanistic model to simulate forward-in-time divergent selection with variable migration rates, modes of reproduction (sexual, asexual), length and number of migration-selection cycles. We investigate the computational feasibility of ABC to perform statistical inference and study the quality of estimates on the position of loci under selection and the strength of selection. To expand the parameter space of positions under selection, we enhance the model by implementing an outlier scan on summarized observed data. We evaluate the usefulness of summary statistics well-known to capture the strength of selection, and assess their informativeness under divergent selection. We also evaluate the effect of genetic drift with respect to an idealized deterministic model with single-locus selection. We discuss the role of the recombination rate as a confounding factor in estimating the strength of divergent selection, and emphasize its importance in break down of linkage disequilibrium (LD). We answer the question for which part of the parameter space of the model we recover strong signal for estimating the selection, and determine whether population differentiation-based summary statistics or LD-based summary statistics perform well in estimating selection.

Kernel weight is a critical agronomic trait in maize production. Many genes are related to kernel weight but only a few of them have been applied to maize breeding and cultivation. Here, we identify a novel function of maize mitogen-activated protein kinase 6 (ZmMPK6) in the regulation of maize kernel weight. Kernel weight was reduced in zmmpk6 mutants and increased in ZmMPK6-overexpressing lines. In addition, starch granules, starch content, protein content, and grain-filling characteristics were also affected by the ZmMPK6 expression level. ZmMPK6 is mainly localized in the nucleus and cytoplasm, widely distributed across various tissues, and is expressed during kernel development, which is consistent with its role in kernel weight. Thus, these results provide new insights into the role of ZmMPK6, a mitogen-activated protein kinase, in maize kernel weight, and could be applied to further molecular breeding for kernel quality and yield in maize.

The potential of hyperspectral imaging technology (HIT) for the determination of physicochemical and nutritional components, evaluation of fungal/mycotoxins contamination, wheat varieties classification, identification of non-mildew-damaged wheat kernels, as well as detection of flour adulteration is comprehensively illustrated and reviewed. The latest findings (2018-2023) of HIT in wheat quality evaluation through internal and external attributes are compared and summarized in detail. The limitations and challenges of HIT to improve assessment accuracy are clearly described. Additionally, various practical recommendations and strategies for the potential application of HIT are highlighted. The future trends and prospects of HIT in evaluating wheat quality are also mentioned. In conclusion, HIT stands as a cutting-edge technology with immense potential for revolutionizing wheat quality evaluation. As advancements in HIT continue, it will play a pivotal role in shaping the future of wheat quality assessment and contributing to a more sustainable and efficient food supply chain.

The regression discontinuity (RD) design is a widely utilized approach for assessing treatment effects. It involves assigning treatment based on the value of an observed covariate in relation to a fixed threshold. Although the RD design has been widely employed across various problems, its application to specific data types has received limited attention. For instance, there has been little research on utilizing the RD design when the outcome variable exhibits zero-inflation. This study introduces a novel RD estimator using local likelihood, which overcomes the limitations of the local linear regression model, a popular approach for estimating treatment effects in RD design, by considering the data type of the outcome variable. To determine the optimal bandwidth, we propose a modified Ludwig-Miller cross validation method. A set of simulations is carried out, involving binary, count, and zero-inflated outcome variables, to showcase the superior performance of the suggested method over local linear regression models. Subsequently, the proposed local likelihood model is employed on HIV care data, where antiretroviral therapy eligibility is determined by a CD4 count threshold. A comparison is made between the results obtained using the local likelihood model and those obtained using local linear regression.

The handheld diagnosis and analysis are highly dependent on the physiological data in the clinical sector. Detection of the defect in the neuronal-assisted activity raises the challenge to the prevailing treatment that benefits from machine learning approaches. The congregated EEG data is then utilized in design of learning applications to develop a model that classifies intricate EEG patterns into active and inactive segments. During arithmetic problem-solving EEG signal acquired from frontal lobe contributes for intelligence detection. The low intricate statistical parameters help in understanding the objective. The mean of the segmented samples and standard deviation are the features extracted for model building. The feature selection is handled using correlation and Fisher score between {Fp1 and F8} and priority ranking of the regions with enhanced activity are selected for the classifier models to the training net. The R-studio platform is used to classify the data based on active and inactive liability. The radial basis function kernel for support vector machine (SVM) is deployed to substantiate the proposed methodology. The vulnerable regions F1 and F8 for arithmetic activity can be visualized from the correlation fit performed between regions. Using SVM classifier sensitivity of 92.5% is obtained for the selected features. A wide range of clinical problems can be diagnosed using this model and used for brain-computer interface.

The contamination of agricultural soil with cadmium (Cd), a heavy metal, poses a significant environmental challenge, affecting crop growth, development, and human health. Previous studies have established the pivotal role of the ZmHMA3 gene, a P-type ATPase heavy metal transporter, in determining variable Cd accumulation in maize grains among 513 inbred lines. To decipher the molecular mechanism underlying mutation-induced phenotypic differences mediated by ZmHMA3, we conducted a quantitative tandem mass tag (TMT)-based proteomic analysis of immature maize kernels. This analysis aimed to identify differentially expressed proteins (DEPs) in wild-type B73 and ZmHMA3 null mutant under Cd stress. The findings demonstrated that ZmHMA3 accumulated higher levels of Cd compared to B73 when exposed to varying Cd concentrations in the soil. In comparison to soil with a low Cd concentration, B73 and ZmHMA3 exhibited 75 and 142 DEPs, respectively, with 24 common DEPs shared between them. ZmHMA3 showed a higher induction of upregulated genes related to Cd stress than B73. Amino sugar and nucleotide sugar metabolism was specifically enriched in B73, while phenylpropanoid biosynthesis, nitrogen metabolism, and glyoxylate and dicarboxylate metabolism appeared to play a more significant role in ZmHMA3. This study provides proteomics insights into unraveling the molecular mechanism underlying the differences in Cd accumulation in maize kernels.

BACKGROUND: Juglans sigillata L. (walnut) has a high economic value for nuts and wood and has been widely grown and eaten around the world. Light plays an important role in regulating the development of the walnut embryo and promoting nucleolus enlargement, which is one of the factors affecting the yield and quality of walnut. However, little is known about the effect of light on the growth and quality of walnuts. Studies have shown that far red prolonged hypocotyl 3 (FHY3) and far red damaged response (FAR1) play important roles in plant growth, light response, and resistance. Therefore, FHY3/FAR1 genes were identified in walnuts on a genome-wide basis during their growth and development to reveal the potential regulation mechanisms involved in walnut kernel growth and development.
RESULTS: In the present study, a total of 61 FHY3/FAR1 gene family members in walnuts have been identified, ranging in length from 117 aa to 895 aa. These gene family members have FHY3 or FAR1 conserved domains, which are unevenly distributed on the 15 chromosomes (Chr) of the walnut (except for the Chr16). All 61 FHY3/FAR1 genes were divided into five subclasses (I, II, III, IV, and V) by phylogenetic tree analysis. The results indicated that FHY3/FAR1 genes in the same subclasses with similar structures might be involved in regulating the growth and development of walnut. The gene expression profiles were analyzed in different walnut kernel varieties (Q, T, and F). The result showed that some FHY3/FAR1 genes might be involved in the regulation of walnut kernel ripening and seed coat color formation. Seven genes (OF07056-RA, OF09665-RA, OF24282-RA, OF26012-RA, OF28029-RA, OF28030-RA, and OF08124-RA) were predicted to be associated with flavonoid biosynthetic gene regulation cis-acting elements in promoter sequences. RT-PCR was used to verify the expression levels of candidate genes during the development and color change of walnut kernels. In addition, light responsiveness and MeJA responsiveness are important promoter regulatory elements in the FHY3/FAR1 gene family, which are potentially involved in the light response, growth, and development of walnut plants.
CONCLUSIONS: The results of this study provide a valuable reference for supplementing the genomic sequencing results of walnut, and pave the way for further research on the FHY3/FAR1 gene function of walnut.