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Transcriptomic data have been used to study sex chromosome dosage compensation (SCDC) in approximately 10 Lepidoptera ZW species, yielding a consensus compensation pattern of Z ≈ZZ

The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high‐quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43–1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky–Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full‐band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS‐SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC‐(CARS‐SPA)‐GA‐SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS‐SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA‐SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification. [ABSTRACT FROM AUTHOR]

Soil exchangeable acidity (EA) is an indicator of aluminium toxicity potential in acidic soils. Predicting the distribution and dynamics of EA is needed for the identification and management of acidic soils. In this study, we used datasets of 355 pedons from across Ghana and the Cubist rule‐based algorithm to generate pedotransfer functions (PTFs) of EA. Eight soil properties (pH, organic carbon, calcium, sodium, magnesium, total exchangeable bases, cation exchange capacity [CEC] to clay ratio and soil depth) were used to predict EA. We first used the whole dataset to construct generic PTFs and then stratified the dataset based on World Reference Base‐Reference Soil Groups (WRB‐RSGs) to generate soil‐specific PTFs. Goodness‐of‐fit statistics comprising the root mean squared error (RMSE), Lin's concordance correlation coefficient (ρC$$ {\rho}_C $$) and coefficient of determination (R2) were used to evaluate the prediction accuracy and reliability of the developed PTF models on both calibration and validation datasets. The Fluvisols EA‐PTF exhibited lower performance metrics in the validation (RMSE = 0.17 cmolc kg−1, ρC$$ {\rho}_C $$ = 0.19, R2 = 0.24), whereas the EA‐PTFs for all other WRB‐RSGs and whole dataset had above‐average performance metrics in the validation (0.05 ≤ RMSE ≤ 0.97 cmolc kg−1; 0.34 ≤ ρC$$ {\rho}_C $$ ≤ 0.94; 0.52 ≤ R2 ≤ 0.96). Soil pH sufficed for predicting EA in soils with pH above 5.0, but in soils with a pH < 5.0, the levels of exchangeable bases (e.g., Na+, K+, Mg2+, Ca2+), CEC to clay ratio (CCR) and soil depth improved the prediction of EA. The developed EA‐PTFs are useful for estimating the missing values of EA in soil databases. [ABSTRACT FROM AUTHOR]

Understanding the controlling mechanisms of soil properties on ecosystem productivity is essential for sustaining productivity and increasing resilience under a changing climate. Here we investigate the control of topsoil depth (e.g., A horizons) on long‐term ecosystem productivity. We used nationwide observations (n = 2401) of topsoil depth and multiple scaled datasets of gross primary productivity (GPP) for five ecosystems (cropland, forest, grassland, pasture, shrubland) over 36 years (1986–2021) across the conterminous USA. The relationship between topsoil depth and GPP is primarily associated with water availability, which is particularly significant in arid regions under grassland, shrubland, and cropland (r =.37,.32,.15, respectively, p <.0001). For every 10 cm increase in topsoil depth, the GPP increased by 114 to 128 g C m−2 year−1 in arid regions (r =.33 and.45, p <.0001). Paired comparison of relatively shallow and deep topsoils while holding other variables (climate, vegetation, parent material, soil type) constant showed that the positive control of topsoil depth on GPP occurred primarily in cropland (0.73, confidence interval of 0.57–0.84) and shrubland (0.75, confidence interval of 0.40–0.94). The GPP difference between deep and shallow topsoils was small and not statistically significant. Despite the positive control of topsoil depth on productivity in arid regions, its contribution (coefficients:.09–.33) was similar to that of heat (coefficients:.06–.39) but less than that of water (coefficients:.07–.87). The resilience of ecosystem productivity to climate extremes varied in different ecosystems and climatic regions. Deeper topsoils increased stability and decreased the variability of GPP under climate extremes in most ecosystems, especially in shrubland and grassland. The conservation of topsoil in arid regions and improvements of soil depth representation and moisture‐retention mechanisms are critical for carbon‐sequestration ecosystem services under a changing climate. These findings and relationships should also be included in Earth system models. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max) produces seeds that are rich in unsaturated fatty acids and is an important oilseed crop worldwide. Seed oil content and composition largely determine the economic value of soybean. Due to natural genetic variation, seed oil content varies substantially across soybean cultivars. Although much progress has been made in elucidating the genetic trajectory underlying fatty acid metabolism and oil biosynthesis in plants, the causal genes for many quantitative trait loci (QTLs) regulating seed oil content in soybean remain to be revealed. In this study, we identified GmFATA1B as the gene underlying a QTL that regulates seed oil content and composition, as well as seed size in soybean. Nine extra amino acids in the conserved region of GmFATA1B impair its function as a fatty acyl–acyl carrier protein thioesterase, thereby affecting seed oil content and composition. Heterogeneously overexpressing the functional GmFATA1B allele in Arabidopsis thaliana increased both the total oil content and the oleic acid and linoleic acid contents of seeds. Our findings uncover a previously unknown locus underlying variation in seed oil content in soybean and lay the foundation for improving seed oil content and composition in soybean. [ABSTRACT FROM AUTHOR]

Oat bran (OB) is a by‐product of oat, which is rich in β‐glucan. As a new food processing technology, ultrafine powder can improve the surface properties of samples. OB with different grinding times was prepared, and its functional components, physical properties, adsorption properties, and antioxidant properties were evaluated. Results showed that with increased grinding times, the average particle size of OB decreased significantly (p <.05). And the water‐holding capacity, swelling capacity, and water solubility index of OB increased significantly (p <.05), whereas the animal and vegetable oil‐holding capacities decreased. Oat bran could adsorb cholic acid and glucose, which was related to the time of superfine grinding. In addition, the antioxidant capacity of OB was improved after superfine grinding. Related analysis shows that there was significant positive relationship between β‐glucan, polyphenols and soluble dietary fibers and antioxidant indicators (p <.05). The Fourier transform infrared (FTIR) results showed that the FTIR spectra of OB powder with different crushing times were similar. On the basis of the above analyses, it is suggested that OB prepared by superfine grinding for 5 min had good physical and chemical properties and antioxidant properties and is widely used in food. [ABSTRACT FROM AUTHOR]

Litter decomposition and nutrient release are key processes for soil C and nutrient cycling. However, the relative importance of the effects of litter quality, fauna and decomposition site on litter decomposition remains poorly understood. Moreover, the macronutrient and micronutrient (C, N, P, K, Ca, Mg, Mn, Cu and Zn) release in the decomposition process are even less well‐known.In this study, we performed 5040 litterbag samplings of monospecific (Larix gmelinii, Acer mono, Quercus mongolica and Juglans mandshurica) and mixed (Larix with each broad‐leaved litter) litter treatments using litterbags with three mesh sizes (for different‐sized fauna) in four paired stands of natural secondary forests compared with adjacent Larix plantations in a temperate forest ecosystem in China. Litter decomposition rate and macronutrient and micronutrient release during decomposition were assessed.We found that variation in litter quality had a considerably greater effect on litter decomposition than fauna and decomposition site. The decomposition constant was significantly related to the initial litter Mg concentration and lignin/P ratio, suggesting the importance of the Mg content and lignin/P ratio in controlling litter decomposition in temperate tree species. We also showed that macrofauna significantly increased monospecific but not mixed litter decomposition. In addition, the decomposition site conditions further influenced monospecific litter decomposition. Furthermore, the litter quality significantly influences N and Mn release in both monospecific and mixed litters. Compared with Larix litter, the average of N and Mn increased 33.9% and 53.8% in three broad‐leaved litters, and 21.4% and 38.0% in three mixed litters respectively.Synthesis. Litter quality is a relatively more important factor than fauna and decomposition site for the decomposition of both monospecific and mixed litters when studied at a local scale in temperate forests. Moreover, litter decomposition enhanced in plantation sites with poor soil nutrient status, which has implications for C and nutrient cycling and long‐term below‐ground forest ecosystem functions. [ABSTRACT FROM AUTHOR]

Summary: In recent years, finger vein recognition has attracted more attention and research as a secure method of identification. Convolutional neural networks have achieved great success in the field of finger vein recognition, yet they suffer from high computational complexity, large parameters, and other challenges. To solve these problems, we propose a Gabor convolutional neural network with receptive fields. We use Gabor filters with receptive field properties to design Gabor convolutional layers. Then we replace the conventional convolutional layer with the Gabor convolutional layer; analyze the influence of different loss functions, convolution kernel size, and feature size on the network model; and choose the most suitable model parameters and loss function. Finally, we systematically investigate comparative performance using AGCNN and CNNs in different finger vein databases. Experimental results show that the parameter complexity of AGCNN is significantly less than that of CNNs with a slight performance decrease. [ABSTRACT FROM AUTHOR]

Soft actuators are favored due to their flexibility and adaptability, but are limited to single actuation mode. Herein, a novel soft actuator with a dual‐kinestate performance is proposed. By subtly integrating a bistable compliant mechanism with artificial muscles, the soft actuator is capable of switching between binary motion and continuous motion with accurate output. Functions of overcoming external interference and regulating the snapping time are realized with programmed voltages. Two applications utilizing the switchable kinestate are illustrated, including a mechanical encryption display system with one actuator and an amplitude modulation system with two actuators in parallel. This novel soft actuator exhibits potential applications for the multi‐mode actuation of soft robots. [ABSTRACT FROM AUTHOR]

Summary: As a non‐thermal processing technology, high hydrostatic pressure (HHP) can be used for starch modification without affecting the quality and flavour constituents. The effect of HHP on starch is closely related to the treatment pressure of HHP. In this paper, we investigated the impacts of HHP treatment pressure (0, 100, 200, 300, 400, 500, 600 MPa) on the microstructure and retrogradation characteristics of oat starch, established the retrogradation kinetic model and elaborated the mechanism of HHP treatment inhibiting the retrogradation of oat starch. Results show that HHP treatment caused the microstructure of oat starch experienced crystallisation perfection (100–300 MPa), crystallisation destruction (400 MPa), crystallisation disintegration and gelatinisation (500–600 MPa). Results of oat starch retrogradation showed that, after treated at 500 MPa for 15 min, the recrystallisation rate of oat starch was reduced, the formation of nuclei at the early stage of oat starch retrogradation suppressed and its nucleation mode was changed from instantaneous to spontaneous, otherwise, the mobility of water in oat starch gel system reduced. Therefore, 500 MPa treated for 15 min can inhibits the retrogradation of oat starch. This study provides theoretical guidance for the application of HHP technology in starch modification and food processing. [ABSTRACT FROM AUTHOR]

Oats do not contain gluten protein, and oat dough structure is formed mainly through the hydrogen bonding of starch and β‐glucan. As a non‐thermal processing technology, high hydrostatic pressure (HHP) is mainly used to modify starch and protein in food processing. This study investigated the effects of HHP treatment on the morphological, structural, thermal, pasting and in vitro digestion properties of oat starch/β‐glucan mixtures. Results showed that β‐glucan interconnects with amylose through hydrogen bonding and has a protective effect on the crystalline region of oat starch. Effect of HHP treatment on the crystal structure of mixture system goes through crystal structure perfection stage, crystallisation disintegration and gelatinisation stage. After 300–400 MPa treatment, the changes in particle surface were not obvious, the phase transition temperature, the ΔHgel and the PT of mixtures increased, while the particle size, viscosity and BD values decreased. After 500–600 MPa treatment, mixtures were completely gelatinised, most of the particles swelled and deformed, the particle size increased significantly. The principal component analysis results show that the complexes were distributed in the same region with similar properties after the 300–400 MPa and 500–600 MPa treatments, respectively. [ABSTRACT FROM AUTHOR]

We used mid‐infrared (MIR) spectra (4000–600 cm−1) to identify and classify soil orders and soil horizons from 102 pedons across five soil orders (Alfisols, Entisols, Mollisols, Spodosols, and Histosols). The soils were analyzed for texture, total carbon, pH, and elemental properties. Random forest models were used to group the spectra of master horizons (O, A, E, B, and C), B horizons (Bs, Bt, and Bw), and the five soil orders. The prediction accuracies for the master horizons and B horizons were 0.81 and 0.89, respectively. The Kappa coefficient was 0.71 for the prediction of master horizons and 0.73 for the prediction of B horizons. The soil orders had an overall accuracy of 0.73 and a Kappa coefficient of 0.64. Histosols exhibited unique absorption characteristics at 2930 and 2860 cm−1 that differed distinctly from mineral soils. The MIR spectra accurately distinguished the O horizons. The spectral curve of topsoil of Spodosols was comparable to the O horizons. Spodosols under forest had A horizons with high organic matter and were classified accurately. Entisols (Psamments) displayed absorption peaks associated with sand, facilitating their differentiation from the other soil orders. The model struggled to discern subtle differences among some soil orders, and identification is hampered if soils undergo irreversible changes upon drying. However, our results showed that MIR spectra can be used for effectively identifying and classifying soil orders as well as soil horizons. [ABSTRACT FROM AUTHOR]

Airborne imaging spectroscopy data provide soil and vegetation information over relatively large areas at high spatial resolutions (<5 m). We combined airborne hyperspectral data with space‐borne data (LiDAR DEM and Sentinel‐1) to map soil properties and investigate the contributions of the different sensor data to the mapping accuracy. The study was conducted on a 330‐ha farm in south‐central Wisconsin, USA, where soils are relatively young and soil variation is high. Seventy‐three soil samples (0–10 cm depth) were taken from cropped fields before planting. The soil data were used with remote sensing data for mapping clay, silt, sand, total carbon (TC), Mg, Al, Si, Fe, Ca, Ti, Mn and Zr. Three types of variables were compared: (a) DEM + Sentinel‐1 & 2, as these are easy‐to‐obtain, (b) hyperspectral data with high‐spatial resolution, and (c) hyperspectral data + DEM + Sentinel‐1, to evaluate if the prediction can be improved by combining hyperspectral data with DEM + Sentinel‐1, and if combining DEM + Sentinel‐1 with hyperspectral data had higher prediction accuracy than combination with Sentinel‐2 data. The partial least square regression (PLSR) model was used for establishing relationships between soil and remote sensing data. It was found that airborne hyperspectral imaging can accurately map the spatial distributions of soil clay content, Si and Fe concentrations. Combining hyperspectral data with DEM and Sentinel‐1 improved the performance of models for mapping a range of soil properties (e.g., clay, silt, sand, Al, Ti, Mn and Zr). Total C, Mg and Ca concentrations cannot be predicted from the combination of hyperspectral data, Sentinel‐1 and terrain attributes. In a highly heterogeneous landscape, surface soil properties can be accurately mapped combining LiDAR DEM, Sentinel‐1 and hyperspectral data. Highlights: Airborne imaging spectroscopy, DEM and Sentinel‐1 were used to map surface soil properties.Hyperspectral data can accurately map clay, Si and Fe.Combining hyperspectral data with DEM, Sentinel‐1 improved prediction accuracy of several soil properties.Total C, Mg and Ca cannot be predicted from hyperspectral data, DEM and Sentinel‐1. [ABSTRACT FROM AUTHOR]

We tested one front‐end data fusion method to combine visible near‐infrared (vis–NIR) and portable X‐ray fluorescence (PXRF) spectra for predicting different soil properties and investigated the contribution of different sensor data. A total of 197 soil samples were collected from 25 Alfisols and Mollisols in south‐central Wisconsin, USA. Soils were analysed in the laboratory for clay, sand, silt content, total carbon (TC), total nitrogen (TN) and pH. Air‐dried soil samples were scanned with vis–NIR and PXRF spectrometers. A principal component analysis (PCA) was applied to each of the vis–NIR and PXRF spectra to extract the first 10 principal components (PCs), which were used in the Cubist model. Five types of input data were compared for constructing the models using a front‐end data fusion approach, including: (a) 10 PCs from vis–NIR spectra, (b) 10 PCs from spectra of PXRF beam 1 (XRF40), (c) 10 PCs from spectra of PXRF beam 2 (XRF10), (d) concatenating 20 PCs from two PXRF spectra (XRF40 + 10), and (e) concatenating 30 PCs from two PXRF spectra and vis–NIR spectra (XRF40 + 10 + NIR). In addition, the performances of the preprocessing methods (four smoothing methods with or without background removal) of PXRF spectra were compared. Multiple linear regression was also used to predict soil properties directly from 11 PXRF‐estimated elements. Our results suggest that smoothing should be applied to the PXRF spectra prior to developing predictive models. PXRF spectra and elemental data can predict soil texture with validation R2 > 0.85, better than using solely vis–NIR spectra, and combining vis–NIR and PXRF spectra improved the prediction. Combining PXRF and vis–NIR can also predict TC and TN moderately well. Soil pH cannot be predicted from vis–NIR or PXRF spectra in this dataset due to its weak correlations with other soil properties and elements. It is concluded that PXRF spectra can be solely used to estimate soil texture, whereas combining vis–NIR and PXRF spectra via PCA should be used to estimate TC and TN. Highlights: The use of front‐end data fusion method to combine vis–NIR and PXRF spectra for predicting soil properties.Different preprocessing algorithms were compared systematically for PXRF spectra.Spectral smoothing should be applied to the PXRF spectra prior to developing predictive models.PXRF spectra can be used solely to estimate soil texture, whereas combining vis–NIR and PXRF spectra via PCA should be used to estimate TC and TN. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to simulate and validate organ doses from different computed tomography (CT) localizer radiograph geometries using Monte Carlo methods for a population of patients. Methods: A Monte Carlo method was developed to estimate organ doses from CT localizer radiographs using PENELOPE. The method was validated by comparing dosimetry estimates with measurements using an anthropomorphic phantom imbedded with thermoluminescent dosimeters (TLDs) scanned on a commercial CT system (Siemens SOMATOM Flash). The Monte Carlo simulation platform was then applied to conduct a population study with 57 adult computational phantoms (XCAT). In the population study, clinically relevant chest localizer protocols were simulated with the x‐ray tube in anterior–posterior (AP), right lateral, and PA positions. Mean organ doses and associated standard deviations (in mGy) were then estimated for all simulations. The obtained organ doses were studied as a function of patient chest diameter. Organ doses for breast and lung were compared across different views and represented as a percentage of organ doses from rotational CT scans. Results: The validation study showed an agreement between the Monte Carlo and physical TLD measurements with a maximum percent difference of 15.5% and a mean difference of 3.5% across all organs. The XCAT population study showed that breast dose from AP localizers was the highest with a mean value of 0.24 mGy across patients, while the lung dose was relatively consistent across different localizer geometries. The organ dose estimates were found to vary across the patient population, partially explained by the changes in the patient chest diameter. The average effective dose was 0.18 mGy for AP, 0.09 mGy for lateral, and 0.08 mGy for PA localizer. Conclusion: A platform to estimate organ doses in CT localizer scans using Monte Carlo methods was implemented and validated based on comparison with physical dose measurements. The simulation platform was applied to a virtual patient population, where the localizer organ doses were found to range within 0.4%–8.6% of corresponding organ doses for a typical CT scan, 0.2%–3.3% of organ doses for a CT pulmonary angiography scan, and 1.1%–20.8% of organ doses for a low‐dose lung cancer screening scan. [ABSTRACT FROM AUTHOR]

In this study, a novel data‐driven direct automatic tuning scheme is proposed for the general form of fixed‐structure digital controllers of hybrid systems. The controller is directly auto‐tuned while it interacts with the real plant; no modelling or plant identification procedure is involved. The auto‐tuning problem is formulated as a controller parameter optimisation problem, which is solved using a quasi‐Newton method. A new method for calculating the gradient of the objective function from closed‐loop experiment data is proposed to reduce the sensitivity to the initial values of the parameters. Domain knowledge of controller design based on pole placement and internal model principle is introduced as a priori information to further improve the performance of the auto‐tuning. Numerical simulations are conducted to demonstrate the effectiveness of the proposed scheme and performances of different objective functions are evaluated. The performance of the proposed scheme is compared with the standard relay feedback and iterative feedback tuning methods. Reliable controllers can be obtained automatically by using the proposed scheme; the only remaining task of the control engineer is to estimate the order of the plant by counting the number of independent energy storage elements. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study was to quantitatively characterize the fundamental aspects of image quality ( IQ) associated with different computed tomography ( CT) reconstruction algorithms, the resolution, noise texture, noise magnitude per dose, and use those data to devise a methodology to match IQ between different CT systems. Methods and materials This study entailed a 3-step methodology involving (a) characterizing the noise magnitude, texture, and resolution for a CT system-reconstruction using the relationship between noise magnitude and Computed Tomography Dose Index ( CTDI), noise power spectrum ( NPS), and modulation transfer function ( MTF), (b) developing clinically relevant strategies of weighting the differences among system-reconstructions as a means to determine the best match (c) identifying for each target system-reconstruction, system-reconstructions with matched in terms of that minimum IQ differences. Images of the ACR CT phantom were acquired at two dose levels on each of two CT scanners. Images were reconstructed using all available reconstruction kernels and multiple iterative reconstruction ( IR) settings. Each reconstruction was characterized as described above. Percent changes for each IQ metric were calculated for every possible pair of system-reconstructions. Weighting functions, reflecting the human visual system's limit to discriminate between spatial frequencies with differences below 5%, were applied to the differences and the product of the weighted values was used to indicate the best match for each system-reconstruction. Results Noise texture and resolution are governed by choice of reconstruction kernel and IR strength, while noise magnitude is additionally dependent on dose. Harder kernels have better resolution, finer noise texture, and increase the required dose for a given noise magnitude, and vice versa. Increasing IR strength generally improves resolution, coarsens noise texture, and lowers the required dose. Seventy-one percent of Siemens matches for GE target reconstructions had percent changes in noise texture/resolution under 5%. Seventy-three percent of GE matches for Siemens target reconstructions had percent changes in noise texture/resolution under 5%. ACR phantom images for each matched reconstruction pair appeared similar in both noise magnitude and noise texture. Conclusion Matching image appearance in terms of resolution, noise magnitude, and noise texture provides a quantitative and reproducible strategy to improve consistency in image quality among different CT scanners and reconstructions. [ABSTRACT FROM AUTHOR]

Purpose Modern CT systems adjust X-ray flux accommodating for patient size to achieve certain image noise values. The effectiveness of this adaptation is an important aspect of CT performance and should ideally be characterized in the context of real patient cases. The objective of this study was to characterize CT performance with a new metric that includes image noise and radiation dose across a clinical patient population. Materials and methods The study included 1526 examinations performed by three CT scanners (one GE Healthcare Discovery CT750HD, one GE Healthcare Lightspeed VCT, and one Siemens SOMATOM definition Flash) used for two routine clinical protocols (abdominopelvic with contrast and chest without contrast). An institutional monitoring system recorded all the data involved in the study. The dose-patient size and noise-patient size dependencies were linearized by considering a first-order approximation of analytical models that describe the relationship between ionization dose and patient size, as well as image noise and patient size. A 3D-fit was performed for each protocol and each scanner with a planar function, and the root mean square error (RMSE) values were estimated as a metric of CT adaptability across the patient population. Results The data show different scanner dependencies in terms of adaptability: the RMSE values for the three scanners are between 0.0385 HU1/2 and 0.0215 HU1/2. Conclusion A theoretical relationship between image noise, CTDIvol, and patient size was determined based on real patient data. This relationship may be interpreted as a new metric related to the scanners' adaptability concerning image quality and radiation dose across a patient population. This method could be implemented to investigate the adaptability related to other image quality indexes and radiation dose in a clinical population. [ABSTRACT FROM AUTHOR]

Purpose To develop a comprehensive model of task-based performance of CT across a broad library of CT protocols, so that radiation dose and image quality can be optimized within a large multivendor clinical facility. Methods Eighty adult CT protocols from the Duke University Medical Center were grouped into 23 protocol groups with similar acquisition characteristics. A size-based image quality phantom (Duke Mercury Phantom 2.0) was imaged using these protocol groups for a range of clinically relevant dose levels on two CT manufacturer platforms (Siemens SOMATOM Definition Flash and GE CT750 HD). For each protocol group, phantom size, and dose level, the images were analyzed to extract task-based image quality metrics, the task transfer function ( TTF), and the noise power spectrum ( NPS). The TTF and NPS were further combined with generalized models of lesion task functions to predict the detectability of the lesions in terms of areas under the receiver operating characteristic curve (Az). A graphical user interface ( GUI) was developed to present Az as a function of lesion size and contrast, dose, patient size, and protocol, as well as to derive the necessary dose to achieve a detection threshold for a targeted lesion. Results The GUI provided the prediction of Az values modeling detection confidence for a targeted lesion, patient size, and dose. As an example, an abdomen pelvis exam for one scanner, with a reference task size/contrast of 5-mm/50- HU, and an Az of 0.9 indicated a dose requirement of 4.0, 8.9, and 16.9 mGy for patient diameters of 25, 30, and 35 cm, respectively. For a constant patient diameter of 30 cm and 50- HU lesion contrast, the minimum detected lesion size at those dose levels were predicted to be 8.4, 5.0, and 3.9 mm, respectively. Conclusions A CT protocol optimization platform was developed by combining task-based detectability calculations with a GUI that demonstrates the tradeoff between dose and image quality. The platform can be used to improve individual protocol dose efficiency, as well as to improve protocol consistency across various patient sizes and CT scanners. [ABSTRACT FROM AUTHOR]

Purpose: We previously developed a set of highly detailed 4D reference pediatric extended cardiactorso (XCAT) phantoms at ages of newborn, 1, 5, 10, and 15 yr with organ and tissue masses matched to ICRP Publication 89 values. In this work, we extended this reference set to a series of 64 pediatric phantoms of varying age and height and body mass percentiles representative of the public at large. The models will provide a library of pediatric phantoms for optimizing pediatric imaging protocols. Methods: High resolution positron emission tomography-computed tomography data obtained from the Duke University database were reviewed by a practicing experienced radiologist for anatomic regularity. The CT portion of the data was then segmented with manual and semiautomatic methods to form a target model defined using nonuniform rational B-spline surfaces. A multichannel large deformation diffeomorphic metric mapping algorithm was used to calculate the transform from the best age matching pediatric XCAT reference phantom to the patient target. The transform was used to complete the target, filling in the nonsegmented structures and defining models for the cardiac and respiratory motions. The complete phantoms, consisting of thousands of structures, were then manually inspected for anatomical accuracy. The mass for each major tissue was calculated and compared to linearly interpolated ICRP values for different ages. Results: Sixty four new pediatric phantoms were created in this manner. Each model contains the same level of detail as the original XCAT reference phantoms and also includes parameterized models for the cardiac and respiratory motions. For the phantoms that were 10 yr old and younger, we included both sets of reproductive organs. This gave them the capability to simulate both male and female anatomy. With this, the population can be expanded to 92. Wide anatomical variation was clearly seen amongst the phantom models, both in organ shape and size, even for models of the same age and sex. The phantoms can be combined with existing simulation packages to generate realistic pediatric imaging data from different modalities. Conclusions: This work provides a large cohort of highly detailed pediatric phantoms with 4D capabilities of varying age, height, and body mass. The population of phantoms will provide a vital tool with which to optimize 3D and 4D pediatric imaging devices and techniques in terms of image quality and radiation-absorbed dose. [ABSTRACT FROM AUTHOR]

Purpose: The authors previously developed an adult population of 4D extended cardiac-torso (XCAT) phantoms for multimodality imaging research. In this work, the authors develop a reference set of 4D pediatric XCAT phantoms consisting of male and female anatomies at ages of newborn, 1, 5, 10, and 15 years. These models will serve as the foundation from which the authors will create a vast population of pediatric phantoms for optimizing pediatric CT imaging protocols. Methods: Each phantom was based on a unique set of CT data from a normal patient obtained from the Duke University database. The datasets were selected to best match the reference values for height and weight for the different ages and genders according to ICRP Publication 89. The major organs and structures were segmented from the CT data and used to create an initial pediatric model defined using nonuniform rational B-spline surfaces. The CT data covered the entire torso and part of the head. To complete the body, the authors manually added on the top of the head and the arms and legs using scaled versions of the XCAT adult models or additional models created from cadaver data. A multichannel large deformation diffeomorphic metric mapping algorithm was then used to calculate the transform from a template XCAT phantom (male or female 50th percentile adult) to the target pediatric model. The transform was applied to the template XCAT to fill in any unsegmented structures within the target phantom and to implement the 4D cardiac and respiratory models in the new anatomy. The masses of the organs in each phantom were matched to the reference values given in ICRP Publication 89. The new reference models were checked for anatomical accuracy via visual inspection. Results: The authors created a set of ten pediatric reference phantoms that have the same level of detail and functionality as the original XCAT phantom adults. Each consists of thousands of anatomical structures and includes parameterized models for the cardiac and respiratory motions. Based on patient data, the phantoms capture the anatomic variations of childhood, such as the development of bone in the skull, pelvis, and long bones, and the growth of the vertebrae and organs. The phantoms can be combined with existing simulation packages to generate realistic pediatric imaging data from different modalities. Conclusions: The development of patient-derived pediatric computational phantoms is useful in providing variable anatomies for simulation. Future work will expand this ten-phantom base to a host of pediatric phantoms representative of the public at large. This can provide a means to evaluate and improve pediatric imaging devices and to optimize CT protocols in terms of image quality and radiation dose. [ABSTRACT FROM AUTHOR]

Purpose: Given the radiation concerns inherent to the x-ray modalities, accurately estimating the radiation doses that patients receive during different imaging modalities is crucial. This study estimated organ doses, effective doses, and risk indices for the three clinical chest x-ray imaging techniques (chest radiography, tomosynthesis, and CT) using 59 anatomically variable voxelized phantoms and Monte Carlo simulation methods. Methods: A total of 59 computational anthropomorphic male and female extended cardiac-torso (XCAT) adult phantoms were used in this study. Organ doses and effective doses were estimated for a clinical radiography system with the capability of conducting chest radiography and tomosynthesis (Definium 8000, VolumeRAD, GE Healthcare) and a clinical CT system (LightSpeed VCT, GE Healthcare). A Monte Carlo dose simulation program (PENELOPE, version 2006, Universitat de Barcelona, Spain) was used to mimic these two clinical systems. The Duke University (Durham, NC) technique charts were used to determine the clinical techniques for the radiographic modalities. An exponential relationship between CTDIvol and patient diameter was used to determine the absolute dose values for CT. The simulations of the two clinical systems compute organ and tissue doses, which were then used to calculate effective dose and risk index. The calculation of the two dose metrics used the tissue weighting factors from ICRP Publication 103 and BEIR VII report. Results: The average effective dose of the chest posteroanterior examination was found to be 0.04 mSv, which was 1.3% that of the chest CT examination. The average effective dose of the chest tomosynthesis examination was found to be about ten times that of the chest posteroanterior examination and about 12% that of the chest CT examination. With increasing patient average chest diameter, both the effective dose and risk index for CT increased considerably in an exponential fashion, while these two dose metrics only increased slightly for radiographic modalities and for chest tomosynthesis. Effective and organ doses normalized to mAs all illustrated an exponential decrease with increasing patient size. As a surface organ, breast doses had less correlation with body size than that of lungs or liver. Conclusions: Patient body size has a much greater impact on radiation dose of chest CT examinations than chest radiography and tomosynthesis. The size of a patient should be considered when choosing the best thoracic imaging modality. [ABSTRACT FROM AUTHOR]

ABSTRACT N,N′-ethylene-bis(salicylideneiminato)]-nickel(II) [Ni(salen)] was synthesized in situ onto the surface of multiwalled carbon nanotubes via a one-step potentiostatic electrodeposition as one-dimensional nanobelts. The synthetic process was free of any templates or additives. Potential played a key role in the formation of the poly[ N,N′-ethylene-bis(salicylideneiminato)]-nickel(II)] {poly[Ni(salen)]} nanobelts, and the electrical conductivities of the poly[Ni(salen)] decreased with increasing deposition time. The capacitance values of poly[Ni(salen)] were 272, 195, and 146 F/g at 0.05 mA/cm2 for deposition times of 10, 20, and 30 min, respectively. The capacitance of the sample with a particle structure was much lower than that of poly[Ni(salen)] with a nanobelt structure. The poly[Ni(salen)] nanobelts exhibited a better capacitive behavior than the poly[Ni(salen)] particles because the nanobelt structure made access for the charge and ion to the inner part of the electrode easier. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 39561. [ABSTRACT FROM AUTHOR]

Purpose: Radiation exposure from computed tomography (CT) to the public has increased the concern among radiation protection professionals. Being able to accurately assess the radiation dose patients receive during CT procedures is a crucial step in the management of CT dose. Currently, various computational anthropomorphic phantoms are used to assess radiation dose by different research groups. It is desirable to better understand how the dose results are affected by different choices of phantoms. In this study, the authors assessed the uncertainties in CT dose and risk estimation associated with different types of computational phantoms for a selected group of representative CT protocols. Methods: Routinely used CT examinations were categorized into ten body and three neurological examination categories. Organ doses, effective doses, risk indices, and conversion coefficients to effective dose and risk index (k and q factors, respectively) were estimated for these examinations for a clinical CT system (LightSpeed VCT, GE Healthcare). Four methods were used, each employing a different type of reference phantoms. The first and second methods employed a Monte Carlo program previously developed and validated in our laboratory. In the first method, the reference male and female extended cardiac-torso (XCAT) phantoms were used, which were initially created from the Visible Human data and later adjusted to match organ masses defined in ICRP publication 89. In the second method, the reference male and female phantoms described in ICRP publication 110 were used, which were initially developed from tomographic data of two patients and later modified to match ICRP 89 organ masses. The third method employed a commercial dosimetry spreadsheet (ImPACT group, London, England) with its own hermaphrodite stylized phantom. In the fourth method, another widely used dosimetry spreadsheet (CT-Expo, Medizinische Hochschule, Hannover, Germany) was employed together with its associated male and female stylized phantoms. Results: For fully irradiated organs, average coefficients of variation (COV) ranged from 0.07 to 0.22 across the four male phantoms and from 0.06 to 0.18 across the four female phantoms; for partially irradiated organs, average COV ranged from 0.13 to 0.30 across the four male phantoms and from 0.15 to 0.30 across the four female phantoms. Doses to the testes, breasts, and esophagus showed large variations between phantoms. COV for gender-averaged effective dose and k factor ranged from 0.03 to 0.23 and from 0.06 to 0.30, respectively. COV for male risk index and q factor ranged from 0.06 to 0.30 and from 0.05 to 0.36, respectively; COV for female risk index and q factor ranged from 0.06 to 0.49 and from 0.07 to 0.54, respectively. Conclusions: Despite closely matched organ mass, total body weight, and height, large differences in organ dose exist due to variation in organ location, spatial distribution, and dose approximation method. Dose differences for fully irradiated radiosensitive organs were much smaller than those for partially irradiated organs. Weighted dosimetry quantities including effective dose, male risk indices, k factors, and male q factors agreed well across phantoms. The female risk indices and q factors varied considerably across phantoms. [ABSTRACT FROM AUTHOR]

Aohan millet has been cultivated for 8000 years and has rich nutritional value, such as high‐quality fatty acids and amino acids. Thermal processing is a conventional approach to food preparation. However, the effect of thermal processing on the formation of flavor substances in millet has not been clarified. Therefore, in this study, the effects of three different thermal processing techniques, namely, steaming, stir‐frying, and puffing, on the amino acids, fatty acids, and volatile flavor substances of Aohan millet were investigated using high‐speed automatic amino acid analyzer, headspace solid‐phase microextraction method, combined with gas chromatography–mass spectrometry (HS‐SPME/GC–MS) and gas chromatography (GC) with Aohan millet from Inner Mongolia as the raw material. All three thermal processing methods notably reduced the levels of protein, starch, polyphenols, and flavonoids in Aohan millet when compared to the raw millet (p < .05). Amino acid and fatty acid contents demonstrated an increase in fried and puffed millet relative to steamed millet, with notable distinctions in amino acid and fatty acid contents between these two groups. Following the steaming process, there was a significant increase in the flavor compounds of Aohan millet, rising from 53 to 80, while this increase was not observed in the other two groups. The correlation analysis suggested that the formation of flavor compounds was predominantly influenced by the types and levels of amino acids. The study suggested that different heat treatments affected the amino acid, fatty acid, and more significantly flavor material content and composition of Aohan millet. In conclusion, steaming treatment could retain more nutrients and richer flavor substances; while puffing treatment would enhance amino acid and fatty acid content, which provides a fundamental basis for scientifically guided processing and rational culinary application of Aohan millet. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Autologous hematopoietic stem cell transplantation (ASCT) has gained extensive application in the treatment of lymphoma and multiple myeloma (MM). Plenty of studies demonstrate that peripheral blood indicators could be considered potential predictive biomarkers for hematopoietic stem cells (HSCs) collection efficiency, including white blood cell count (WBC), monocyte count (Mono), platelet count (PLT), hematocrit, and hemoglobin levels. Currently, clinically practical predictive models based on these peripheral detection indicators to quickly, conveniently, and accurately predict collection efficiency are lacking.In total, 139 patients with MM and lymphoma undergoing mobilization and collection of ASCT were retrospectively studied. The study endpoint was successful collection of autologous HSCs. We analyzed the effects of clinical characteristics and peripheral blood markers on collection success, and screened variables to establish a prediction model. We determined the optimal cutoff value of peripheral blood markers for predicting successful stem cell collection and the clinical value of a multi‐marker prediction approach. We also established a prediction model for collection efficacy.Univariate and multivariate logistic regression analyses showed that the mobilization regimen, Mono, PLT, mononuclear cell count (MNC), and peripheral blood CD34+ cell count (PB CD34+ counts) were significant predictors of successful collection of peripheral blood stem cells (PBSC). Two predictive models were constructed based on the results of multivariate logistic analyses. Model 1 included the mobilization regimen, Mono, PLT, and MNC, whereas Model 2 included the mobilization regimen, Mono, PLT, MNC, and PB CD34+ counts. Receiver operating characteristic (ROC) curve analysis showed that the PB CD34+ counts, Model 1, and Model 2 could predict successful HSCs collection, with cutoff values of 26.92 × 106/L, 0.548, and 0.355, respectively. Model 1 could predict successful HSCs collection with a sensitivity of 84.62%, specificity of 75.73%, and area under the curve (AUC) of 0.863. Model 2 could predict successful HSCs collection with a sensitivity of 83.52%, specificity of 94.17%, and AUC of 0.946; thus, it was superior to the PB CD34+ counts alone.Our findings suggest that the combination of the mobilization regimen, Mono, PLT, MNC, and PB CD34+ counts before collection has predictive value for the efficacy of autologous HSCs collection in patients with MM and lymphoma. Using models based on these predictive markers may help to avoid over‐collection and improve patient outcomes. [ABSTRACT FROM AUTHOR]