Geospatial analysis and prevalence of Schistosoma mansoni and soil-transmitted helminth infections in an endemic area in Eastern Brazilian Amazon

OBJECTIVES

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.

Utilizing local likelihood in regression discontinuity design: Investigating the impact of antiretroviral therapy eligibility on retention in clinical HIV care in South Africa

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.

ZmMAPK6, a mitogenactivated protein kinase, regulates maize kernel weight

Kernel weight is a critical agronomic trait in maize production. Many genes are related to it, but only a few of them have been applied to maize breeding and cultivation. Here, we identify a novel function of maize mitogenactivated protein kinase 6 (ZmMPK6) in the regulation of maize kernel weight. The kernel weight reduced in zmmpk6 mutants, while enhanced in ZmMPK6 overexpression lines. In addition, starch granules, starch content, protein content, and grainfilling characteristics are also affected. ZmMAPK6 is mainly localized in the nucleus and cytoplasm, widely distributed across various tissues, and expresses during kernel development, which is consistent with its role in kernel weight. Thus, these results denote new insights into the role of ZmMAPK6, a MAPK, in maize kernel weight, and could be applied to further molecular breeding for kernel quality and yield in maize.

Cognizance detection during mental arithmetic task using statistical approach

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.

Bayesian adaptive design for covariate-adaptive historical control information borrowing

Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study-level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate-adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate-dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo-controlled randomized trials on vertebral fracture risk in post-menopausal women.

Phenotypic and Proteomic Insights into Differential Cadmium Accumulation in Maize Kernels

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.

Responses of Persian walnut on foliar applications of different biostimulants

Biostimulants have different effects on plants. The aim of this paper is to determine responses of the 'Alsószentiváni 117' walnut cultivar on foliar applications of different biostimulants (Wuxal Ascofol, Kondisol, Alga K Plus). The nut traits (nut length, nut diameter, nut weight, kernel weight) and some phenolic compounds of the kernel were measured and detected. In 2020, during warmer early spring weather conditions under pistillate flowering receptivity, chlorogenic acid and quercetin content of kernels treated with Kondisol were higher than in control. All biostimulants influenced positive effects on catechin and rutin content, as well as treatments made with Wuxal Ascofol and Kondisol increased the juglon content of the kernel. In 2021, when the spring weather was typical for that period, only the Kondisol treatments had increasing effects on the catechin and chlorogenic acid content, than the control. The rutin and quercetin concentrations reached the highest value in this trial by Alga K Plus applications. The juglon content decreased in this year compared to the control. The pirocathecin, cinnamic acid, and gallic acid (except Wuxal Ascofol treatment in 2021) content decreased in all treatments in both observed years. Responses of woody fruit species on biostimulants applications depend on the weather conditions. Biostimulants had positive effects on the nut size characteristics in both observed years.

Lipidomic analyses of five Carya illinoinensis cultivars

Carya illinoinensis (Wangenh.) K. Koch, nuts are a renowned health food. However, there are many cultivars of this nut tree, and their mature kernel lipid composition has not been thoroughly studied. Therefore, we used liquid chromatography-mass spectrometry (LC-MS) to analyze the lipid composition of mature nuts of five C. illinoinensis cultivars. In the mature kernels of all cultivars, there were 58 lipid types which were mainly composed of glycerolipids (c. 65%) and phospholipids (>30%). Triacylglycerol (TG) accounted for the largest proportion of mature nuts of all cultivars, exceeding 50%; and diacylglycerol (DG), ceramide (Cer), phosphatidylcholine (PC), and phosphatidylethanolamine (PE) were also relatively high. Additionally, nuts contain fatty acids, mainly oleic, linoleic, and linolenic acids. Our research provides a new perspective for the processing and utilization of plant and edible oils, and for the use of C. illinoinensis kernels in the development of medicine and food science.

Culicoides (Diptera: Ceratopogonidae) Abundance Is Influenced by Livestock Host Species and Distance to Hosts at the Micro Landscape Scale

The vector/host ratio and host preference are important parameters for the modelling of vector-borne livestock diseases. It can be anticipated that Culicoides abundance is not homogeneously distributed in the landscape. We investigated the influence of host species (dairy cow, sheep, and a light-trap (LT) as a surrogate host) and distance of measurement to hosts on Culicoides abundance using a randomized block-design with 12 measuring days and seven 3-min aerial sweep-netting sessions per whole hour at three distances to the host (0, 10, and 25 m), from five hours before to and including one hour after sunset. Dairy cows were found to be a far stronger attractor of Culicoides midges than sheep, while both hosts were far stronger attractors of midges than the LT. Culicoides abundance declined significantly with increasing distance from the livestock hosts; this phenomenon was much stronger for dairy cows than for ewes. In contrast, Culicoides abundance increased with increasing distance from the LT, pin-pointing the apparent shortcomings of the LT as a surrogate host to lure midges. Our data indicate that livestock host species and the distance from these hosts have a profound effect on Culicoides abundance in the landscape.

Optimising Time-Frequency Distributions: A Surface Metrology Approach

Time-frequency signal processing offers a significant advantage over temporal or frequency-only methods, but representations require optimisation for a given signal. Standard practice includes choosing the appropriate time-frequency distribution and fine-tuning its parameters, usually via visual inspection and various measures-the most commonly used ones are based on the Rényi entropies or energy concentration by Stanković. However, a discrepancy between the observed representation quality and reported numerical value may arise when the filter kernel has greater adaptability. Herein, a performance measure derived from the Abbot-Firestone curve similar to the volume parameters in surface metrology is proposed as the objective function to be minimised by the proposed minimalistic differential evolution variant that is parameter-free and uses a population of five members. Tests were conducted on two synthetic signals of different frequency modulations and one real-life signal. The multiform tiltable exponential kernel was optimised according to the Rényi entropy, Stanković's energy concentration and the proposed measure. The resulting distributions were mutually evaluated using the same measures and visual inspection. The optimiser demonstrated a reliable convergence for all considered measures and signals, while the proposed measure showed consistent alignment of reported numerical values and visual assessments.

Ultrahigh-Resolution Photon-Counting Detector CT of the Lungs: Association of Reconstruction Kernel and Slice Thickness With Image Quality

BACKGROUND. Prior work has shown improved image quality for photon-counting detector (PCD) CT of the lungs compared with energy-integrating detector CT. A paucity of the literature has compared PCD CT of the lungs using different reconstruction parameters. OBJECTIVE. The purpose of this study is to the compare the image quality of ultra-high-resolution (UHR) PCD CT image sets of the lungs that were reconstructed using different kernels and slice thicknesses. METHODS. This retrospective study included 29 patients (17 women and 12 men; median age, 56 years) who underwent noncontrast chest CT from February 15, 2022, to March 15, 2022, by use of a commercially available PCD CT scanner. All acquisitions used UHR mode (1024 × 1024 matrix). Nine image sets were reconstructed for all combinations of three sharp kernels (BI56, BI60, and BI64) and three slice thicknesses (0.2, 0.4, and 1.0 mm). Three radiologists independently reviewed reconstructions for measures of visualization of pulmonary anatomic structures and pathologies; reader assessments were pooled. Reconstructions were compared with the clinical reference reconstruction (obtained using the BI64 kernel and a 1.0-mm slice thickness [BI64_1.0-mm]). RESULTS. The median difference in the number of bronchial divisions identified versus the clinical reference reconstruction was higher for reconstructions with BI64_0.4-mm (0.5), BI60_0.4-mm (0.3), BI64_0.2-mm (0.5), and BI60_0.2-mm (0.2) (all p < .05). The median bronchial wall sharpness versus the clinical reference reconstruction was higher for reconstructions with BI64_0.4-mm (0.3) and BI64_0.2-mm (0.3) and was lower for BI56_1.0-mm (-0.7) and BI56_0.4-mm (-0.3) (all p < .05). Median pulmonary fissure sharpness versus the clinical reference reconstruction was higher for reconstructions with BI64_0.4-mm (0.3), BI60_0.4-mm (0.3), BI56_0.4-mm (0.5), BI64_0.2-mm (0.5), BI60_0.2-mm (0.5), and BI56_0.2-mm (0.3) (all p < .05). Median pulmonary vessel sharpness versus the clinical reference reconstruction was lower for reconstructions with BI56_1.0-mm (-0.3), BI60 0.4-mm (-0.3), BI56_0.4-mm (-0.7), BI64_0.2-mm (-0.7), BI60_0.2-mm (-0.7), and BI56_0.2-mm (-0.7). Median lung nodule conspicuity versus the clinical reference reconstruction was lower for reconstructions with BI56_1.0-mm (-0.3) and BI56_0.4-mm (-0.3) (both p < .05). Median conspicuity of all other pathologies versus the clinical reference reconstruction was lower for reconstructions with BI561.0 mm (-0.3), BI56_0.4-mm (-0.3), BI64_0.2-mm (-0.3), BI60_0.2-mm (-0.3), and BI56_0.2-mm (-0.3). Other comparisons among reconstructions were not significant (all p > .05). CONCLUSION. Only the reconstruction using BI64_0.4-mm yielded improved bronchial division identification and bronchial wall and pulmonary fissure sharpness without a loss in pulmonary vessel sharpness or conspicuity of nodules or other pathologies. CLINICAL IMPACT. The findings of this study may guide protocol optimization for UHR PCD CT of the lungs.

Genetic dissection of QTLs for oil content in four maize DH populations

Oil is one of the main components in maize kernels. Increasing the total oil content (TOC) is favorable to optimize feeding requirement by improving maize quality. To better understand the genetic basis of TOC, quantitative trait loci (QTL) in four double haploid (DH) populations were explored. TOC exhibited continuously and approximately normal distribution in the four populations. The moderate to high broad-sense heritability (67.00-86.60%) indicated that the majority of TOC variations are controlled by genetic factors. A total of 16 QTLs were identified across all chromosomes in a range of 3.49-30.84% in term of phenotypic variation explained. Among them, six QTLs were identified as the major QTLs that explained phenotypic variation larger than 10%. Especially, qOC-1-3 and qOC-2-3 on chromosome 9 were recognized as the largest effect QTLs with 30.84% and 21.74% of phenotypic variance, respectively. Seventeen well-known genes involved in fatty acid metabolic pathway located within QTL intervals. These QTLs will enhance our understanding of the genetic basis of TOC in maize and offer prospective routes to clone candidate genes regulating TOC for breeding program to cultivate maize varieties with the better grain quality.

Rearrangement with the nkd2 promoter contributed to allelic diversity of the r1 gene in maize (Zea mays)

The maize red1 (r1) locus regulates anthocyanin accumulation and is a classic model for allelic diversity; changes in regulatory regions are responsible for most of the variation in gene expression patterns. Here, an intrachromosomal rearrangement between the distal upstream region of r1 and the region of naked endosperm 2 (nkd2) upstream to the third exon generated a nkd2 null allele lacking the first three exons, and the R1-st (stippled) allele with a novel r1 5' promoter region homologous to 5' regions from nkd2-B73. R1-sc:124 (an R1-st derivative) shows increased and earlier expression than a standard R1-g allele, as well as ectopic expression in the starchy endosperm compartment. Laser capture microdissection and RNA sequencing indicated that ectopic R1-sc:124 expression impacted expression of genes associated with RNA modification. The expression of R1-sc:124 resembled nkd2-W22 expression, suggesting that nkd2 regulatory sequences may influence the expression of R1-sc:124. The r1-sc:m3 allele is derived from R1-sc:124 by an insertion of a Ds6 transposon in intron 4. This insertion blocks anthocyanin regulation by causing mis-splicing that eliminates exon 5 from the mRNA. This allele serves as an important launch site for Ac/Ds mutagenesis studies, and two Ds6 insertions believed to be associated with nkd2 mutant alleles were actually located in the r1 5' region. Among annotated genomes of teosinte and maize varieties, the nkd2 and r1 loci showed conserved overall gene structures, similar to the B73 reference genome, suggesting that the nkd2-r1 rearrangement may be a recent event.

Impact of different levels of zinc and nitrogen on growth, productivity, and quality of aromatic rice cultivated under various irrigation regimes in two districts of Pakistan

Rice is a staple food for more than 50% of the global population and it is one of the most valuable cereal crops. To fulfill the dietary requirement of the ever-growing world population, an increase in per-unit production of rice is direly required. In Pakistan, it stands as the 2nd in consumption after wheat, which is a staple food. A huge gap is observed between yield potential and actual yield of the aromatic rice cultivars at a farmer-field level. The significant limitations responsible for this gap are shortage of irrigation water, inappropriate application of fertilizers, less plant population, deficiency of micronutrients, and improper and poor plant protection measures. A field study was planned to assess the yield response and quality attributes of aromatic rice to three levels of zinc (Zn) and nitrogen (N) under three irrigation regimes (8-, 12-, and 16-acre inches) in the Sheikhupura and Sargodha districts of Pakistan. Irrigation treatments significantly influenced the growth, yield, and quality attributes; however, maximum improvement was observed by the application of irrigation at 12-acre inches. Among the Zn treatments, application of Zn at 10 kg ha^-1 was observed to be more responsive to improving the growth and quality parameters of aromatic rice crops. In the case of N treatments, application of N at 140 kg ha^-1 produced the maximum total tillers, as well as productive tillers per hill, spikelets per panicle, leaf area index, leaf area duration, crop growth rate, total dry matter, harvest index, kernel length, kernel width, and 1,000-kernel weight. Application of N at 140 kg ha^-1 not only improved the growth attributes but also increased the net assimilation rate, photosynthetically active radiation, and radiation use efficiency, with respect to total dry matter and kernel yield. The maximum percentage of normal kernels and minimum percentage of opaque, abortive, and chalky kernels were also recorded by application of N at 140 kg ha^-1. The outcomes of current experiments depicted that application of irrigational water, zinc, and nitrogen at 12-acre inches, 10, and 140 kg ha^-1, respectively, are responsible to achieve maximum resource utilization efficiency, along with increased yield and quality of rice.

Influence of CT Image Matrix Size and Kernel Type on the Assessment of HRCT in Patients with SSC-ILD

Background: Interstitial lung disease (ILD) is a frequent complication of systemic sclerosis (SSc), and its early detection and treatment may prevent deterioration of lung function. Different vendors have recently made larger image matrices available as a post-processing option for computed tomography (CT), which could facilitate the diagnosis of SSc-ILD. Therefore, the objective of this study was to assess the effect of matrix size on lung image quality in patients with SSc by comparing a 1024-pixel matrix to a standard 512-pixel matrix and applying different reconstruction kernels. Methods: Lung scans of 50 patients (mean age 54 years, range 23−85 years) with SSc were reconstructed with these two different matrix sizes, after determining the most appropriate kernel in a first step. Four observers scored the images on a five-point Likert scale regarding image quality and detectability of clinically relevant findings. Results: Among the eight tested kernels, the Br59-kernel (sharp) reached the highest score (19.48 ± 3.99), although differences did not reach statistical significance. The 1024-pixel matrix scored higher than the 512-pixel matrix HRCT overall (p = 0.01) and in the subcategories sharpness (p < 0.01), depiction of bronchiole (p < 0.01) and overall image impression (p < 0.01), and lower for the detection of ground-glass opacities (GGO) (p = 0.04). No significant differences were found for detection of extent of reticulations/bronchiectasis/fibrosis (p = 0.50) and image noise (p = 0.09). Conclusions: Our results show that with the use of a sharp kernel, the 1024-pixel matrix HRCT, provides a slightly better subjective image quality in terms of assessing interstitial lung changes, whereby GGO are more visible on the 512-pixel matrix. However, it remains to be answered to what extent this is related to the improved representation of the smallest structures.

Fast heritability estimation based on MINQUE and batch training

Heritability, the proportion of phenotypic variance explained by genome-wide single nucleotide polymorphisms (SNPs) in unrelated individuals, is an important measure of the genetic contribution to human diseases and plays a critical role in studying the genetic architecture of human diseases. Linear mixed model (LMM) has been widely used for SNP heritability estimation, where variance component parameters are commonly estimated by using a restricted maximum likelihood (REML) method. REML is an iterative optimization algorithm, which is computationally intensive when applied to large-scale datasets (e.g. UK Biobank). To facilitate the heritability analysis of large-scale genetic datasets, we develop a fast approach, minimum norm quadratic unbiased estimator (MINQUE) with batch training, to estimate variance components from LMM (LMM.MNQ.BCH). In LMM.MNQ.BCH, the parameters are estimated by MINQUE, which has a closed-form solution for fast computation and has no convergence issue. Batch training has also been adopted in LMM.MNQ.BCH to accelerate the computation for large-scale genetic datasets. Through simulations and real data analysis, we demonstrate that LMM.MNQ.BCH is much faster than two existing approaches, GCTA and BOLT-REML.

Effects of Different Pre-drying and Drying Methods on Volatile Compounds in the Pericarp and Kernel of Amomum tsao-ko

The effects of twelve different pre-drying and drying methods on the chemical composition in the pericarp and kernel of Amomum tsao-ko were studied. The volatile components were isolated from the samples by simultaneous distillation and extraction and analyzed by gas chromatography-mass spectrometry (GC-MS). Sixty and thirty-eight compounds were identified from pericarp and kernel, respectively, and the main constituents were oxygenated monoterpenes. These compounds were not only significantly affected by pre-drying and drying methods but also varied in content due to different tissue locations. The total volatile content of pericarp varied from 0.70 to 1.55%, with the highest obtained by microwave-dried samples (150 W) and the lowest in freeze-dried samples. The total volatile content of the kernel varied from 6.11 to 10.69%, with the highest content obtained during sun drying (SD) and the lowest content in samples treated with boiling water for 2 min. Oxygenated monoterpenes were the highest compounds in pericarp and kernel, which were also the most affected by drying methods. The highest content of oxygenated monoterpenes in the pericarp (0.77%) could be obtained by boiling water treatment for 5 min, and the highest content of oxygenated monoterpenes in the kernel (7.48%) could be obtained by SD. Additionally, the main components such as 1,8-cineole, 2-carene, (Z)-citral, nerolidol, (Z)-2-decenal, (E)-2-dodecenal, citral, (E)-2-octenal, 4-propylbenzaldehyde, and phthalan showed remarkable variations in pre-drying and drying methods.

Pollen limitation and xenia effects in a cultivated mass-flowering tree, Macadamia integrifolia (Proteaceae)

BACKGROUND AND AIMS

Pollen limitation is most prevalent among bee-pollinated plants, self-incompatible plants and tropical plants. However, we have very little understanding of the extent to which pollen limitation affects fruit set in mass-flowering trees despite tree crops accounting for at least 600 million tons of the 9200 million tons of annual global food production.

METHODS

We determined the extent of pollen limitation in a bee-pollinated, partially self-incompatible, subtropical tree by hand cross-pollinating the majority of flowers on mass-flowering macadamia (Macadamia integrifolia) trees that produce about 200 000-400 000 flowers. We measured tree yield and kernel quality and estimated final fruit set. We genotyped individual kernels by MassARRAY to determine levels of outcrossing in orchards and assess paternity effects on nut quality.

KEY RESULTS

Macadamia trees were pollen-limited. Supplementary cross-pollination increased nut-in-shell yield, kernel yield and fruit set by as much as 97, 109 and 92 %, respectively. The extent of pollen limitation depended upon the proximity of experimental trees to trees of another cultivar because macadamia trees were highly outcrossing. Between 84 and 100 % of fruit arose from cross-pollination, even at 200 m (25 rows) from orchard blocks of another cultivar. Large variations in nut-in-shell mass, kernel mass, kernel recovery and kernel oil concentration were related to differences in fruit paternity, including between self-pollinated and cross-pollinated fruit, thus demonstrating pollen-parent effects on fruit quality (i.e. xenia).

CONCLUSIONS

This study is the first to demonstrate pollen limitation in a mass-flowering tree. Improved pollination led to increased kernel yield of 0.31-0.59 tons ha-1, which equates currently to higher farm-gate income of approximately $US3720-$US7080 ha-1. The heavy reliance of macadamia flowers on cross-pollination and the strong xenia effects on kernel mass demonstrate the high value that pollination services can provide to food production.

Maize Endosperm Development: Tissues, Cells, Molecular Regulation and Grain Quality Improvement

Maize endosperm plays important roles in human diet, animal feed and industrial applications. Knowing the mechanisms that regulate maize endosperm development could facilitate the improvement of grain quality. This review provides a detailed account of maize endosperm development at the cellular and histological levels. It features the stages of early development as well as developmental patterns of the various individual tissues and cell types. It then covers molecular genetics, gene expression networks, and current understanding of key regulators as they affect the development of each tissue. The article then briefly considers key changes that have occurred in endosperm development during maize domestication. Finally, it considers prospects for how knowledge of the regulation of endosperm development could be utilized to enhance maize grain quality to improve agronomic performance, nutrition and economic value.

Genetic dissection of QTLs for starch content in four maize DH populations

Starch is the principal carbohydrate source in maize kernels. Understanding the genetic basis of starch content (SC) benefits greatly in improving maize yield and optimizing end-use quality. Here, four double haploid (DH) populations were generated and were used to identify quantitative trait loci (QTLs) associated with SC. The phenotype of SC exhibited continuous and approximate normal distribution in each population. A total of 13 QTLs for SC in maize kernels was detected in a range of 3.65-16.18% of phenotypic variation explained (PVE). Among those, only some partly overlapped with QTLs previously known to be related to SC. Meanwhile, 12 genes involved in starch synthesis and metabolism located within QTLs were identified in this study. These QTLs will lay the foundation to explore candidate genes regulating SC in maize kernel and facilitate the application of molecular marker-assisted selection for a breeding program to cultivate maize varieties with a deal of grain quality.

Managing Density Stress to Close the Maize Yield Gap

Continued yield increases of maize (Zea mays L.) will require higher planting populations, and enhancement of other agronomic inputs could alleviate density-induced stress. Row spacing, plant population, P-S-Zn fertility, K-B fertility, N fertility, and foliar protection were evaluated for their individual and cumulative impacts on the productivity of maize in a maize-soybean [Glycine max (L.) Merr.] rotation. An incomplete factorial design with these agronomic factors in both 0.76 and 0.51 m row widths was implemented for 13 trials in Illinois, United States, from 2014 to 2018. The agronomic treatments were compared to two controls: enhanced and standard, comprising all the factors applied at the enhanced or standard level, respectively. The 0.51 m enhanced management control yielded 3.3 Mg ha^-1 (1.8-4.6 Mg ha^-1 across the environments) more grain (25%) than the 0.76 m standard management control, demonstrating the apparent yield gap between traditional farm practices and attainable yield through enhanced agronomic management. Narrow rows and the combination of P-S-Zn and K-B fertility were the factors that provided the most significant yield increases over the standard control. Increasing plant population from 79,000 to 109,000 plants ha^-1 reduced the yield gap when all other inputs were applied at the enhanced level. However, increasing plant population alone did not increase yield when no other factors were enhanced. Some agronomic factors, such as narrow rows and availability of plant nutrition, become more critical with increasing plant population when density-induced stress is more significant. Changes in yield were dependent upon changes in kernel number. Kernel weight was the heaviest when all the management factors were applied at the enhanced level while only planting 79,000 plants ha^-1. Conversely, kernel weight was the lightest when increasing population to 109,000 plants ha^-1 while all other factors were applied at the standard level. The yield contribution of each factor was generally greater when applied in combination with all other enhanced factors than when added individually to the standard input system. Additionally, the full value of high-input agronomic management was only realized when matched with greater plant density.

Morphological and Ultrastructural Features of Formation of the Skin of Wheat (Triticum aestivum L.) Kernel

The integumentary tissues of plant seeds protect the embryo (new sporophyte) forming in them from unfavorable external conditions; therefore, comprehensive knowledge about the structural and functional specificity of seed covers in various plants may be of both theoretical and practical interest. As a result of our study, additional data were obtained on the morphological and ultrastructural features of the formation of a multilayer skin of wheat (Triticum aestivum L.) kernel (caryopsis). The ultrastructure research analysis showed that differentiation of the pericarp and inner integument of the ovule leads to the formation of functionally different layers of the skin of mature wheat grain. Thus, the differentiation of exocarp and endocarp cells is accompanied by a significant thickening of the cell walls, which reliably protect the ovule from adverse external conditions. The cells of the two-layer inner integument of the ovule differentiate into cuticular and phenolic layers, which are critical for protecting daughter tissues from various pathogens. The epidermis of the nucellus turns into a layer of mucilage, which apparently helps to maintain the water balance of the seed. Morphological and ultrastructural data showed that the formation of the kernel's skin occurs in coordination with the development of the embryo and endosperm up to the full maturity of the kernel. This is evidenced by the structure of the cytoplasm and nucleus, characteristic of metabolically active protoplasts of cells, which is observed in most integumentary layers at the late stages of maturation. This activity can also be confirmed by a significant increase in the thickness of the cell walls in the cells of two layers of the exocarp and in cross cells in comparison with the earlier stages. Based on these results, we came to the conclusion that the cells of a majority in the covering tissues of the wheat kernel during its ontogenesis are transformed into specialized layers of the skin by terminal differentiation.

Sustainable Chromium Recovery From Wastewater Using Mango and Jackfruit Seed Kernel Bio-Adsorbents

Wastewater is a rich source of valuable chemicals of industrial importance. However, their economic recovery is crucial for sustainability. The objective of the present work is to recover hexavalent chromium (Cr VI) as a value-added transition metal from wastewater cost-effectively; the biosorbent derived from seed kernels of mango (M) and jackfruit (JF) were applied for removing the metal from simulated wastewater. The functional groups of the biomass were analysed with the help of Fourier transform infrared (FTIR) spectroscopy, micrographs were generated using a scanning electron microscope, and crystallinity was determined by an x-ray diffractometer (XRD). The concentration of Cr VI in wastewater was analysed by an inductively coupled plasma optical emission spectrometer (ICP-OES). Process parameters (pH, dose, contact time, temperature, and initial concentration) were optimized for efficient Cr VI adsorption using a response surface methodology-based Box-Behnken design (BBD) employing Design-software 6.0.8. The batch experiment at room temperature at pH 4.8 and Cr VI removal ∼94% (M) and ∼92% (JF) was achieved by using a 60-mg dose and an initial Cr (VI) concentration of 2 ppm in 120 min. The equilibrium Cr binding on the biosorbent was well explained using Freundlich isotherm (R ² = 0.97), which indicated the indirect interactions between Cr (VI) and the biosorbent. Biosorption of Cr (VI) followed the pseudo-order and intra-particle diffusion models. The maximum adsorption capacity of the M and JF bio-adsorbent is 517.24 and 207.6 g/mg, respectively. These efficient, cost-effective, and eco-friendly biosorbents could be potentially applied for removing toxic Cr (VI) from polluted water.

Multiomics analysis of kernel development in response to short-term heat stress at the grain formation stage in waxy maize

Understanding the adaptive changes in maize kernels under high-temperature stress during grain formation stage is critical for developing strategies to alleviate the negative effects on yield and quality. In this study, we subjected waxy maize (Zea mays L. sinensis Kulesh) to four different temperature regimes from 1-15 d after pollination (DAP), namely normal day/normal night (control), hot day/normal night, normal day/hot night, and hot day/hot night. Compared to the control, the three high-temperature treatments inhibited kernel development and starch deposition. To understand how the kernels responded to high-temperature stress, their transcriptomes, proteomes, and metabolomes were studied at 10 DAP and 25 DAP. This showed that genes and proteins related to kernel development and starch deposition were up- and down-regulated, respectively, at 10 DAP, but this pattern was reversed at 25 DAP. Metabolome profiling under high-temperature stress showed that the accumulation patterns of metabolites at 10 DAP and 25 DAP were inversely related. Our multiomics analyses indicated that the response to high-temperature stress of signaling pathways mediated by auxin, abscisic acid, and salicylic acid was more active at 10 DAP than at 25 DAP. These results confirmed that high-temperature stress during early kernel development has a carry-over effect on later development. Taken together, our multiomics profiles of developing kernels under high-temperature stress provide insights into the processes that underlie maize yield and quality under high-temperature conditions.

: An R Package for performing kernel change point detection on the running statistics of multivariate time series

In many scientific disciplines, researchers are interested in discovering when complex systems such as stock markets, the weather or the human body display abrupt changes. Essentially, this often comes down to detecting whether a multivariate time series contains abrupt changes in one or more statistics, such as means, variances or pairwise correlations. To assist researchers in this endeavor, this paper presents the package for performing kernel change point (KCP) detection on user-selected running statistics of multivariate time series. The running statistics are extracted by sliding a window across the time series and computing the value of the statistic(s) of interest in each window. Next, the similarities of the running values are assessed using a Gaussian kernel, and change points that segment the time series into maximally homogeneous phases are located by minimizing a within-phase variance criterion. To decide on the number of change points, a combination of a permutation-based significance test and a grid search is provided. stands out among the variety of change point detection packages available in because it can be easily adapted to uncover changes in any user-selected statistic without imposing any distribution on the data. To exhibit the usefulness of the package, two empirical examples are provided pertaining to two types of physiological data.

Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy

In recent years, the native food industry in Australia has increased in both value and volume due to the discovery of a wide range of phytochemicals (e.g., vitamin C, polyphenols) that have potential health benefits. Thus, plant organs and tissues of these native plants are used in a wide range of applications. In particular, the kernel of a native plum, the Kakadu plum (Terminalia ferdinandiana, Combretaceae) is considered to be rich in lipids and other phytochemical compounds. The aim of this study was to evaluate the use of NIR spectroscopy to analyze and characterize kernel samples and tissues of wild harvest fruit samples. The Fourier transform near-infrared reflectance spectra of cracked kernels, seeds cover tissues, and dry powder Kakadu plum kernels were acquired. Both principal component analysis and partial least squares discriminant analysis were used to analyze and interpret the spectral data. A correct classification rate of 93%, 86%, and 80% was achieved for the identification of kernel provenance using all tissues, seed coats, and the whole nuts, respectively. The results of this study reported for the first time the analysis of Kakadu plum kernels and their tissues using NIR spectroscopy.

lme4GS: An R-Package for Genomic Selection

Genomic selection (GS) is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). An active area of research is the development of software for fitting LMMs mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. This article describes the new lme4GS package for R, which is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance–covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data.

SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spaces

PURPOSE

To introduce, develop, and evaluate a novel denoising technique for diffusion MRI that leverages nonlinear redundancy in the data to boost the SNR while preserving signal information.

METHODS

We exploit nonlinear redundancy of the dMRI data by means of kernel principal component analysis (KPCA), a nonlinear generalization of PCA to reproducing kernel Hilbert spaces. By mapping the signal to a high-dimensional space, a higher level of redundant information is exploited, thereby enabling better denoising than linear PCA. We implement KPCA with a Gaussian kernel, with parameters automatically selected from knowledge of the noise statistics, and validate it on realistic Monte Carlo simulations as well as with in vivo human brain submillimeter and low-resolution dMRI data. We also demonstrate KPCA denoising on multi-coil dMRI data.

RESULTS

SNR improvements up to 2.7 × were obtained in real in vivo datasets denoised with KPCA, in comparison to SNR gains of up to 1.8 × using a linear PCA denoising technique called Marchenko-Pastur PCA (MPPCA). Compared to gold-standard dataset references created from averaged data, we showed that lower normalized root mean squared error was achieved with KPCA compared to MPPCA. Statistical analysis of residuals shows that anatomical information is preserved and only noise is removed. Improvements in the estimation of diffusion model parameters such as fractional anisotropy, mean diffusivity, and fiber orientation distribution functions were also demonstrated.

CONCLUSION

Nonlinear redundancy of the dMRI signal can be exploited with KPCA, which allows superior noise reduction/SNR improvements than the MPPCA method, without loss of signal information.

A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels

High-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize (Zea mays) ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two-dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses.

A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels

High-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize (Zea mays) ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two-dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses.

A Framework for Contractual Graphs

This paper studies contractual graphs, where the formation of edges between nodes result in dyadic exchanges. Each dyadic exchange is analyzed as a contractual agreement that is implemented upon fulfilment of underlying conditions. As these dyadic exchanges proliferate, the resulting population of these exchanges creates a contractual graph. A contractual framework for graphs is especially useful in applications where AI-enabled software is employed to create or automate smart contracts between nodes. While some smart contracts may be easily created and executed, others may contain a higher level of ambiguity which may prevent their efficient implementation. Ambiguity in contractual elements is especially difficult to implement, since nodes have to efficiently sense the ambiguity and allocate appropriate amounts of computational resources to the ambiguous contractual task. This paper develops a two-node contractual model of graphs, with varying levels of ambiguity in the contracts and examines its consequences for a market where tasks of differing ambiguity are available to be completed by nodes. The central theme of this paper is that as ambiguity increases, it is difficult for nodes to efficiently commit to the contract since there is an uncertainty in the amount of resources that they have to allocate for completion of the tasks specified in the contract. Thus, while linguistic ambiguity or situational ambiguity might not be cognitively burdensome for humans, it might become expensive for nodes involved in the smart contract. The paper also shows that timing matters—the order in which nodes enter the contract is important as they proceed to sense the ambiguity in a task and then allocate appropriate resources. We propose a game-theoretic formulation to scrutinize how nodes that move first to complete a task are differently impacted than those that move second. We discuss the applications of such a contractual framework for graphs and obtain conditions under which two-node contracts can achieve a successful coalition.

kernInt: A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets

The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype of interest) from taxonomic abundances has become increasingly common in the microbiome field. However, a gap exists between supervised and classical unsupervised analyses, based on computing ecological dissimilarities for visualization or clustering. Despite this, both approaches face common challenges, like the compositional nature of next-generation sequencing data or the integration of the spatial and temporal dimensions. Here we propose a kernel framework to place on a common ground the unsupervised and supervised microbiome analyses, including the retrieval of microbial signatures (taxa importances). We define two compositional kernels (Aitchison-RBF and compositional linear) and discuss how to transform non-compositional beta-dissimilarity measures into kernels. Spatial data is integrated with multiple kernel learning, while longitudinal data is evaluated by specific kernels. We illustrate our framework through a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. The proposed framework and the case studies are freely available in the kernInt package at https://github.com/elies-ramon/kernInt.

Image analysis method for heterogeneity and porosity characterization of biomimetic hydrogels

This work presents an image processing procedure for characterization of porosity and heterogeneity of hydrogels network mainly based on the analysis of cryogenic scanning electron microscopy (cryo-SEM) images and can be extended to any other type of microscopy images of hydrogel porous network. An algorithm consisting of different filtering, morphological transformation, and thresholding steps to denoise the image whilst emphasizing the edges of the hydrogel walls for extracting either the pores or hydrogel walls features is explained. Finally, the information of hydrogel porosity and heterogeneity is presented in form of pore size distribution, spatial contours maps and kernel density dot plots. The obtained results reveal that a non-parametric kernel density plot effectively determines the spatial heterogeneity and porosity of the hydrogel.

A unifying theory for two-dimensional spatial redistribution kernels with applications in population spread modelling

When building models to explain the dispersal patterns of organisms, ecologists often use an isotropic redistribution kernel to represent the distribution of movement distances based on phenomenological observations or biological considerations of the underlying physical movement mechanism. The Gaussian, two-dimensional (2D) Laplace and Bessel kernels are common choices for 2D space. All three are special (or limiting) cases of a kernel family, the Whittle-Matérn-Yasuda (WMY), first derived by Yasuda from an assumption of 2D Fickian diffusion with gamma-distributed settling times. We provide a novel derivation of this kernel family, using the simpler assumption of constant settling hazard, by means of a non-Fickian 2D diffusion equation representing movements through heterogeneous 2D media having a fractal structure. Our derivation reveals connections among a number of established redistribution kernels, unifying them under a single, flexible modelling framework. We demonstrate improvements in predictive performance in an established model for the spread of the mountain pine beetle upon replacing the Gaussian kernel by the Whittle-Matérn-Yasuda, and report similar results for a novel approximation, the product-Whittle-Matérn-Yasuda, that substantially speeds computations in applications to large datasets.

Kernel representation of long-wave dynamics on a uniform slope

Long-wave propagation on a uniformly sloping beach is formulated as a transient-response problem, with initially stationary water subjected to an incident wave. Both the water surface elevation and the horizontal flow velocity on the slope can be represented as convolutions of the rate of displacement of the water surface at the toe of the slope with a singular kernel function of time and space. The kernel, which is typically expressed in the form of an infinite series, accommodates the dynamic processes of long waves, such as shoaling, reflection, and multiple reflections over the slope and yields exact solutions of the linear shallow water equations for any smooth incident wave. The kernel convolution can be implemented numerically by using double exponential formulas to avoid the kernel singularity. The kernel formulation can be extended readily to nonlinear dynamics via the hodograph transform, which in turn enables the instantaneous prediction of nonlinear wave properties and of the occurrence of wave breaking in the near-shore area. This general description of long-wave dynamics provides new insights into the long-studied problem.

Model-robust designs for nonlinear quantile regression

We construct robust designs for nonlinear quantile regression, in the presence of both a possibly misspecified nonlinear quantile function and heteroscedasticity of an unknown form. The asymptotic mean-squared error of the quantile estimate is evaluated and maximized over a neighbourhood of the fitted quantile regression model. This maximum depends on the scale function and on the design. We entertain two methods to find designs that minimize the maximum loss. The first is local - we minimize for given values of the parameters and the scale function, using a sequential approach, whereby each new design point minimizes the subsequent loss, given the current design. The second is adaptive - at each stage, the maximized loss is evaluated at quantile estimates of the parameters, and a kernel estimate of scale, and then the next design point is obtained as in the sequential method. In the context of a Michaelis-Menten response model for an estrogen/hormone study, and a variety of scale functions, we demonstrate that the adaptive approach performs as well, in large study sizes, as if the parameter values and scale function were known beforehand and the sequential method applied. When the sequential method uses an incorrectly specified scale function, the adaptive method yields an, often substantial, improvement. The performance of the adaptive designs for smaller study sizes is assessed and seen to still be very favourable, especially so since the prior information required to design sequentially is rarely available.

SAMPLING OF SURFACES AND LEARNING FUNCTIONS IN HIGH DIMENSIONS

The efficient representation of data in high-dimensional spaces is a key problem in several machine learning tasks. To capture the non-linear structure of the data, we model the data as points living on a smooth surface. We model the surface as the zero level-set of a bandlimited function. We show that this representation allows a non-linear lifting of the surface model, which will map the points to a low-dimensional subspace. This mapping between surfaces and the well-understood subspace model allows us to introduce novel algorithms (a) to recover the surface from few of its samples and (b) to learn a multidimensional bandlimited function from training data. The utility of these algorithms is introduced in practical applications including image denoising.

Heterogeneous Temporal Contrast Adaptation in Drosophila Direction-Selective Circuits

In visual systems, neurons adapt both to the mean light level and to the range of light levels, or the contrast. Contrast adaptation has been studied extensively, but it remains unclear how it is distributed among neurons in connected circuits, and how early adaptation affects subsequent computations. Here, we investigated temporal contrast adaptation in neurons across Drosophila's visual motion circuitry. Several ON-pathway neurons showed strong adaptation to changes in contrast over time. One of these neurons, Mi1, showed almost complete adaptation on fast timescales, and experiments ruled out several potential mechanisms for its adaptive properties. When contrast adaptation reduced the gain in ON-pathway cells, it was accompanied by decreased motion responses in downstream direction-selective cells. Simulations show that contrast adaptation can substantially improve motion estimates in natural scenes. The benefits are larger for ON-pathway adaptation, which helps explain the heterogeneous distribution of contrast adaptation in these circuits.

Development and Testing of an Individual Kernel Detection System for Genetically Modified Soybean Events in Non-identity-preserved Soybean Samples

A genetically modified (GM) soybean kernel detection system using combination of DNA preparation from individual soybean kernels and event-specific real-time PCR was developed to simultaneously identify GM soybean events authorized for food after safety assessments in Japan. Over 100 kernels in the non-identity-preserved soybean samples imported from the United States of America (two U.S.A. lots) and Brazil (one lot) were randomly selected and examined. In total, 98 and 96% of the two independent U.S.A. lots, and 100% of the Brazilian lot contained GM soybean kernels. Herbicide-tolerant events, MON89788 (trade name Genuity® Roundup Ready 2 Yield™), GTS 40-3-2 (trade name Roundup Ready™ soybean) and A2704-12 (trade name Liberty Link® soybean), were detected similarly in both U.S.A. lots. In the Brazilian lot, in addition to GTS 40-3-2, a stacked GM event, MON87701 × MON89788, having insect-resistance and herbicide-tolerance, was detected. There were no unauthorized GM soybeans comingled, and the ratio of GM soybean events detected was consistent with statistical reports on the cultivated GM soybean events in both countries.

The hypoattenuating ocular lens on CT is not always due to cataract formation

Hypoattenuating ocular lenses on CT have been described with cataract formation in humans, however published studies are currently lacking regarding this finding in veterinary patients. The purpose of this retrospective and prospective study was to describe the varying CT appearances of the ocular lens in vivo, and investigate the causes for CT density variations in a population of cats and dogs. A total of 102 canine and feline patients with CT of the head acquired at the authors' hospital between May 2011 and March 2019 were included. A bilateral hypoattenuating halo surrounding an isoattenuating to mildly hypoattenuating core was described in the ocular lens center of every cat in which a Philips brand proprietary image construction filter was used. A similar but more varied hypoattenuating region was noted in the lenses of 45.8% of dogs where the same filter was applied, as well as 43.8% of dogs with a second, similar filter. Ophthalmic examination of three live cats and one dog with hypoattenuating lenses demonstrated normal lens translucency, excluding the presence of cataract. The effect of different proprietary filters on lens appearance was also described in three fresh cadavers with normal lenses identified on ophthalmic, macroscopic, and microscopic examination. Etiology of the hypoattenuating areas within the ocular lens was not conclusively determined. Recognition that such a variant may be seen in the absence of cataract is important, in order to prevent misdiagnosis.

The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines

Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, and positioning. By utilizing the GNSS signals, detecting and monitoring the scintillation effects to decrease the effect of the disturbing signals have gained importance, and machine learning-based algorithms have been started to be applied for the detection. In this paper, the performance of Support Vector Machines (SVM) for scintillation detection is discussed. The effect of the different kernel functions, namely, linear, Gaussian, and polynomial, on the performance of the SVM algorithm is analyzed. Performance is statistically assessed in terms of probabilities of detection and false alarm of the scintillation event. Real GNSS signals that are affected by significant phase and amplitude scintillation effect, collected at the South African Antarctic research base SANAE IV and Hanoi, Vietnam have been used in this study. This paper questions how to select a suitable kernel function by analyzing the data preparation, cross-validation, and experimental test stages of the SVM-based process for scintillation detection. It has been observed that the overall accuracy of fine Gaussian SVM outperforms the linear, which has the lowest complexity and running time. Moreover, the third-order polynomial kernel provides improved performance compared to linear, coarse, and medium Gaussian kernel SVMs, but it comes with a cost of increased complexity and running time.

Intercomparison of MR-informed PET image reconstruction methods

PURPOSE

Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET-unique regions). To address this, further developments for MR-informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade-off between the suppression of noise and the retention of unique features present in the PET data.

METHODS

The reconstruction methods investigated were: the MR-informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR-informed Bowsher and Gaussian MR-guided MAP methods; and the PET-MR-informed hybrid kernel and anato-functional MAP methods. The trade-off between improving the reconstruction of the whole brain region and the PET-unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [18 F] fluorodeoxyglucose (FDG) three-dimensional datasets were used. The real [18 F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET-MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level.

RESULTS

For the high-count simulated and real data studies, the anato-functional MAP method performed better than the other methods under investigation (MR-informed, PET-MR-informed and postsmoothed MLEM), in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato-functional MAP method enables the reconstruction of PET-unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato-functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the other methods under investigation in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation.

CONCLUSION

For the reconstruction of noisy data, multiple MR-informed methods produce favorable whole brain vs PET-unique region trade-off in terms of the image quality metrics of SSIM and NRMSE, comfortably outperforming the whole image denoising of postsmoothed MLEM.

The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter

This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, a quadcopter was integrated with air monitoring sensors to collect the required data. The analysis started with the sensor placement on the quadcopter and their correlation with the generated vortex. The reliability and response time of the sensor used determine the duration of the data collection process. The dynamic nature of the environment makes the technique of air monitoring of topmost concern. The pattern method has been adapted to the data collection process in which area scanning was marked using a point of interest or grid point. The experiments were done by manipulating a carbon monoxide (CO) source, with data readings being made in two ways: point source with eight sampling points arranged in a square pattern, and non-point source with 24 sampling points in a grid pattern. The quadcopter collected data while in a hover state with 10 s sampling times at each point. The analysis of variance method (ANOVA) was also used as the statistical algorithm to analyze the vector of gas dispersion. In order to tackle the uncertainty of wind, a bivariate Gaussian kernel analysis was used to get an estimation of the gas source area. The result showed that the grid pattern measurement was useful in obtaining more accurate data of the gas source location and the gas concentration. The vortex field generated by the propeller was used to speed up the accumulation of the gas particles to the sensor. The dynamic nature of the wind caused the gas flow vector to change constantly. Thus, more sampling points were preferred, to improve the accuracy of the gas source location prediction.

Personalized 3D printed coronary models in coronary stenting

Background

3D printing has shown great promise in cardiovascular disease, with reports mainly focusing on pre-surgical planning and medical education. Research on utilization of 3D printed models in simulating coronary stenting has not been reported. In this study, we presented our experience of placing coronary stents into personalized 3D printed coronary models with the aim of determining stent lumen visibility with images reconstructed with different postprocessing views and algorithms.

Methods

A total of six coronary stents with diameter ranging from 2.5 to 4.0 mm were placed into 3 patient-specific 3D printed coronary models for simulation of coronary stenting. The 3D printed models were placed in a plastic container and scanned on a 192-slice third generation dual-source CT scanner with images reconstructed with soft (Bv36) and sharp (Bv59) kernel algorithms. Thick and thin slab maximum-intensity projection (MIP) images were also generated from the original CT data for comparison of stent lumen visibility. Stent lumen diameter was measured on 2D axial and MIP images, while stent diameter was measured on 3D volume rendering images. 3D virtual intravascular endoscopy (VIE) images were generated to provide intraluminal views of the coronary wall and stent appearances.

Results

All of these stents were successfully placed into the right and left coronary arteries but 2 of them did not obtain wall apposition along the complete length. The stent lumen visibility ranged from 54 to 97%, depending on the stent location in the coronary arteries. The mean stent lumen diameters measured on 2D axial, thin and thick slab MIP images were found to be significantly smaller than the actual size (P<0.01). Thick slab MIP images resulted in measured stent lumen diameters smaller than those from thin slab MIP images, with significant differences noticed in most of the measurements (4 out of 6 stents) (P<0.05), and no significant differences in the remaining 2 stents (P=0.19-0.38). In contrast, 3D volume rendering images allowed for more accurate measurements with measured stent diameters close to the actual dimensions in most of these coronary stents, except for the stent placed at the right coronary artery in one of the models due to insufficient expansion of the stent. Images reconstructed with sharp kernel Bv59 significantly improved stent lumen visibility when compared to the smooth Bv36 kernel (P=0.01). 3D VIE was successfully generated in all of the datasets with clear visualization of intraluminal views of the stents in relation to the coronary wall.

Conclusions

This preliminary report shows the feasibility of using 3D printed coronary artery models in coronary stenting for investigation of optimal coronary CT angiography protocols. Future studies should focus on placement of more stents with a range of stent diameters in the quest to reduce the need for invasive angiography for surveillance.

Lipids and Fatty Acids in Italian Durum Wheat (Triticum durum Desf.) Cultivars

The level of variation in lipids and their fatty acids was determined in the grains of 10 popular durum wheat cultivars commercially grown in Central and Southern Italy. Samples were harvested for two consecutive years to account for differences due to changes in climatic conditions. Total fat content was determined by means of the International Association of Cereal Science and Technology (ICC) Standard Method No. 136, whereas the fatty acid profile was determined by gas chromatography. Total lipid content ranged from 2.97% to 3.54% dry basis (d.b.) in the year 2010 and from 3.10% to 3.50% d.b. in the year 2011, and the average value was 3.22% d.b. considering both years together. Six main fatty acids were detected in all samples in order of decreasing amounts: linoleic (C18:2) > palmitic (C16:0) ≈ oleic (C18:1) > linolenic (C18:3) > stearic (C18:0) > palmitoleic (C16:1). Significant variations in the levels of single acids between two years were observed for three samples. These results will be very useful in the updating of food composition databases in general and will help authorities to set proper quality standards for wholegrain flours and products where the germ should be preserved, considering also the recent interest of industry and consumers for these kinds of products.

Tree shape-based approaches for the comparative study of cophylogeny

Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long-term interaction. We investigated the use of tree shape distance measures to quantify the degree of cophylogeny. We implemented a reverse-time simulation model of pathogen phylogenies within a fixed host tree, given cospeciation probability, host switching, and pathogen speciation rates. We used this model to evaluate 18 distance measures between host and pathogen trees including two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and accommodate different size trees. Finally, we used these measures to revisit published cophylogenetic studies, where authors described the observed associations as representing a high or low degree of cophylogeny. Our simulations demonstrated that some measures are more informative than others with respect to specific coevolution parameters especially when these did not assume extreme values. For real datasets, trees' associations projection revealed clustering of high concordance studies suggesting that investigators are describing it in a consistent way. Our results support the hypothesis that measures can be useful for quantifying cophylogeny. This motivates their usage in the field of coevolution and supports the development of simulation-based methods, i.e., approximate Bayesian computation, to estimate the underlying coevolutionary parameters.

Indirect methods for improving parameter estimation of PET kinetic models

PURPOSE

Parametric images obtained from kinetic modeling of dynamic positron emission tomography (PET) data provide a new way of visualizing quantitative parameters of the tracer kinetics. However, due to the high noise level in pixel-wise image-driven time-activity curves, parametric images often suffer from poor quality and accuracy. In this study, we propose an indirect parameter estimation framework which aims to improve the quality and quantitative accuracy of parametric images.

METHODS

Three different approaches related to noise reduction and advanced curve fitting algorithm are used in the proposed framework. First, dynamic PET images are denoised using a kernel-based denoising method and the highly constrained backprojection technique. Second, gradient-free curve fitting algorithms are exploited to improve the accuracy and precision of parameter estimates. Third, a kernel-based post-filtering method is applied to parametric images to further improve the quality of parametric images. Computer simulations were performed to evaluate the performance of the proposed framework.

RESULTS AND CONCLUSIONS

The simulation results showed that when compared to the Gaussian filtering, the proposed denoising method could provide better PET image quality, and consequentially improve the quality and quantitative accuracy of parametric images. In addition, gradient-free optimization algorithms (i.e., pattern search) can result in better parametric images than the gradient-based curve fitting algorithm (i.e., trust-region-reflective). Finally, our results showed that the proposed kernel-based post-filtering method could further improve the precision of parameter estimates while maintaining the accuracy of parameter estimates.

KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction

The conventional calibration-based parallel imaging method assumes a linear relationship between the acquired multi-channel k-space data and the unacquired missing data, where the linear coefficients are estimated using some auto-calibration data. In this paper, we first analyze the model errors in the conventional calibration-based methods and demonstrate the nonlinear relationship. Then, a much more general nonlinear framework is proposed for auto-calibrated parallel imaging. In this framework, kernel tricks are employed to represent the general nonlinear relationship between acquired and unacquired k-space data without increasing the computational complexity. Identification of the nonlinear relationship is still performed by solving linear equations. Experimental results demonstrate that the proposed method can achieve reconstruction quality superior to GRAPPA and NL-GRAPPA at high net reduction factors.

The genetic architecture of amylose biosynthesis in maize kernel

Starch is the most abundant storage carbohydrate in maize kernel. The content of amylose and amylopectin confers unique properties in food processing and industrial application. Thus, the resurgent interest has been switched to the study of individual amylose or amylopectin rather than total starch, whereas the enzymatic machinery for amylose synthesis remains elusive. We took advantage of the phenotype of amylose content and the genotype of 9,007,194 single nucleotide polymorphisms from 464 inbred maize lines. The genome-wide association study identified 27 associated loci involving 39 candidate genes that were linked to amylose content including transcription factors, glycosyltransferases, glycosidases, as well as hydrolases. Except the waxy gene that encodes the granule-bound starch synthase, the remaining candidate genes were located in the upstream pathway of amylose synthesis, while the downstream members were already known from prior studies. The linked candidate genes could be transferred to manipulate amylose content and thus add value to maize kernel in the breeding programme.

Influence of shading and pedestrian traffic on the preference of Aedes (Stegomyia) aegypti (Diptera: Culicidae) for oviposition microenvironments

Aedes aegypti mosquitoes are highly adaptable to abiotic stimuli. To evaluate the influence of shading and pedestrian traffic on the preference of Ae. aegypti for oviposition microenvironments, 20 sites were sampled weekly using ovitraps within the perimeter of Universidade Federal do Espírito Santo, located in São Mateus, Espírito Santo, Brazil. A spatial and statistical analysis was performed in order to assess the relationship between shading time, pedestrian traffic, and the presence of biological forms of Ae. aegypti. A temporal analysis of temperature and precipitation influence on oviposition was also made. Between June, 2013 and June, 2014, 7,362 Ae. aegypti eggs were collected. Over a 12-month period, we made weekly collections of Ae. aegypti eggs from ovitraps. Pedestrian traffic and shading time influenced the number of positive ovitraps; precipitation and temperature were correlated with the number of positive ovitraps (p <0.05). We conclude that the influence of temperature and precipitation was not significant for the oviposition index, and the frequency of oviposition was directly proportional to the number of individuals moving close to the traps during periods of greater shading.