Global epidemiology of HBV infection among hemodialysis patients: A systematic review and meta-analysis.

BACKGROUND & AIMS: Hemodialysis (HD) is a life-saving procedure that purifies the blood in patients with end-stage renal disease (ESRD). Among all major complications, blood-borne diseases like hepatitis B virus (HBV) may be exposed as serious side effects of hemodialysis. A comprehensive review of the global burden of HBV among HD patients has not been written so far. The aim of the current systematic review and meta-analysis was to determine the globally epidemiology of HBV infection among HD patients. METHODS: Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, articles that investigated the prevalence of HBV among HD patients were searched from the major databases such as EMBASE, PubMed, Web of Science collection, and Scopus. Pooled prevalence with 95% CI and identification of heterogeneity were obtained using the random effects model and Cochran's Q-test, respectively, and quantification was evaluated using the I2 statistics. All statistical analyses were performed by STATA 14.1 statistical software. RESULTS: among 322 datasets (795,623 cases) that included in this study, the pooled prevalence of HBV infection among HD patients was 7.32% (95% CI: 6.53-8.15%; I2 = 97.91%), including 7.57% (95% CI: 6.69-8.48%) for HBsAg and 6.09% (95% CI: 4.05-8.49%) for DNA, respectively. In addition, based on geographic area, the prevalence was 7.44% (95% CI: 6.35-8.61%) in Asia, 4.32% (95% CI: 2.21-7.04%) in North America, 7.07% (95% CI: 6.35-8.61%) in Europe, 5.52% (95% CI: 3.60-7.78%) in Africa, 8.45% (95% CI: 5.81-11.78%) in Oceania, and 9.73% (95% CI: 7.11-12.70%) in South America. CONCLUSIONS: Our analysis indicates a relatively high prevalence of HBV infection in HD patients, even in some developed countries. Considering that ESRD patients are not able to properly respond to the vaccination strategies in order to develop an acceptable immunity, vaccination of healthy individuals is highly recommended to arm their bodies for possible immunocompromise conditions in the future. Moreover, donated blood in blood transfusion centers should be checked for possible hepatitis B virus infection using sensitive molecular tests.

Introgression of perennial growth habit from Lophopyrum elongatum into wheat.

KEY MESSAGE: A locus for perennial growth was mapped on Lophopyrum elongatum chromosome arm 4ES and introgressed into the wheat genome. Evidence was obtained that in addition to chromosome 4E, other L. elongatum chromosomes control perennial growth. Monocarpy versus polycarpy is one of the fundamental developmental dichotomies in flowering plants. Advances in the understanding of the genetic basis of this dichotomy are important for basic biological reasons and practically for genetic manipulation of growth development in economically important plants. Nine wheat introgression lines (ILs) harboring germplasm of the Lophopyrum elongatum genome present in the octoploid amphiploid Triticum aestivum cv. Chinese Spring (subgenomes AABBDD) × L. elongatum (genomes EE) were selected from a population of ILs developed earlier. These ILs were employed here in genomic analyses of post-sexual cycle regrowth (PSCR), which is a component of polycarpy in caespitose L. elongatum. Analyses of disomic substitution (DS) lines confirmed that L. elongatum chromosome 4E confers PSCR on wheat. The gene was mapped into a short distal region of L. elongatum arm 4ES and was tentatively named Pscr1. ILs harboring recombined chromosomes with 4ES segments, including Pscr1, incorporated into the distal part of the 4DS chromosome arm were identified. Based on the location, Pscr1 is not orthologous with the rice rhizome-development gene Rhz2 located on rice chromosome Os3, which is homoeologous with chromosome 4E, but it may correspond to the Teosinte branched1 (TB1) gene, which is located in the introgressed region in the L. elongatum and Ae. tauschii genomes. A hexaploid IL harboring a large portion of the E-genome but devoid of chromosome 4E also expressed PSCR, which provided evidence that perennial growth is controlled by genes on other L. elongatum chromosomes in addition to 4E.

Structural variation and rates of genome evolution in the grass family seen through comparison of sequences of genomes greatly differing in size.

Homology was searched with genes annotated in the Aegilops tauschii pseudomolecules against genes annotated in the pseudomolecules of tetraploid wild emmer wheat, Brachypodium distachyon, sorghum and rice. Similar searches were performed with genes annotated in the rice pseudomolecules. Matrices of collinear genes and rearrangements in their order were constructed. Optical BioNano genome maps were constructed and used to validate rearrangements unique to the wild emmer and Ae. tauschii genomes. Most common rearrangements were short paracentric inversions and short intrachromosomal translocations. Intrachromosomal translocations outnumbered segmental intrachromosomal duplications. The densities of paracentric inversion lengths were approximated by exponential distributions in all six genomes. Densities of collinear genes along the Ae. tauschii chromosomes were highly correlated with meiotic recombination rates but those of rearrangements were not, suggesting different causes of the erosion of gene collinearity and evolution of major chromosome rearrangements. Frequent rearrangements sharing breakpoints suggested that chromosomes have been rearranged recurrently at some sites. The distal 4 Mb of the short arms of rice chromosomes Os11 and Os12 and corresponding regions in the sorghum, B. distachyon and Triticeae genomes contain clusters of interstitial translocations including from 1 to 7 collinear genes. The rates of acquisition of major rearrangements were greater in the large wild emmer wheat and Ae. tauschii genomes than in the lineage preceding their divergence or in the B. distachyon, rice and sorghum lineages. It is suggested that synergy between large quantities of dynamic transposable elements and annual growth habit have been the primary causes of the fast evolution of the Triticeae genomes.

Iridium Nanoparticles for Multichannel Luminescence Lifetime Imaging, Mapping Localization in Live Cancer Cells.

The development of long-lived luminescent nanoparticles for lifetime imaging is of wide interest as luminescence lifetime is environmentally sensitive detection independent of probe concentration. We report novel iridium-coated gold nanoparticles as probes for multiphoton lifetime imaging with characteristic long luminescent lifetimes based on iridium luminescence in the range of hundreds of nanoseconds and a short signal on the scale of picoseconds based on gold allowing multichannel detection. The tailor-made IrC6 complex forms stable, water-soluble gold nanoparticles (AuNPs) of 13, 25, and 100 nm, bearing 1400, 3200, and 22 000 IrC6 complexes per AuNP, respectively. The sensitivity of the iridium signal on the environment of the cell is evidenced with an observed variation of lifetimes. Clusters of iridium nanoparticles show lifetimes from 450 to 590 ns while lifetimes of 660 and 740 ns are an average of different points in the cytoplasm and nucleus. Independent luminescence lifetime studies of the nanoparticles in different media and under aggregation conditions postulate that the unusual long lifetimes observed can be attributed to interaction with proteins rather than nanoparticle aggregation. Total internal reflection fluorescence microscopy (TIRF), confocal microscopy studies and 3D luminescence lifetime stacks confirm the presence of bright, nonaggregated nanoparticles inside the cell. Inductively coupled plasma mass spectrometry (ICPMS) analysis further supports the presence of the nanoparticles in cells. The iridium-coated nanoparticles provide new nanoprobes for lifetime detection with dual channel monitoring. The combination of the sensitivity of the iridium signal to the cell environment together with the nanoscaffold to guide delivery offer opportunities for iridium nanoparticles for targeting and tracking in in vivo models.

Diffusion-weighted MRI and intravoxel incoherent motion model for diagnosis of pediatric solid abdominal tumors.

BACKGROUND: Pediatric retroperitoneal tumors in the renal bed are often large and heterogeneous, and their diagnosis based on conventional imaging alone is not possible. More advanced imaging methods, such as diffusion-weighted (DW) MRI and the use of intravoxel incoherent motion (IVIM), have the potential to provide additional biomarkers that could facilitate their noninvasive diagnosis. PURPOSE: To assess the use of an IVIM model for diagnosis of childhood malignant abdominal tumors and discrimination of benign from malignant lesions. STUDY TYPE: Retrospective. POPULATION: Forty-two pediatric patients with abdominal lesions (n = 32 malignant, n = 10 benign), verified by histopathology. FIELD STRENGTH/SEQUENCE: 1.5T MRI system and a DW-MRI sequence with six b-values (0, 50, 100, 150, 600, 1000 s/mm2 ). ASSESSMENT: Parameter maps of apparent diffusion coefficient (ADC), and IVIM maps of slow diffusion coefficient (D), fast diffusion coefficient (D*), and perfusion fraction (f) were computed using a segmented fitting model. Histograms were constructed for whole-tumor regions of each parameter. STATISTICAL TESTS: Comparison of histogram parameters of and their diagnostic performance was determined using Kruskal-Wallis, Mann-Whitney U, and receiver-operating characteristic (ROC) analysis. RESULTS: IVIM parameters D* and f were significantly higher in neuroblastoma compared to Wilms' tumors (P < 0.05). The ROC analysis showed that the best diagnostic performance was achieved with D* 90th percentile (area under the curve [AUC] = 0.935; P = 0.002; cutoff value = 32,376 × 10-6 mm2 /s) and f mean values (AUC = 1.00; P < 0.001; cutoff value = 14.7) in discriminating between neuroblastoma (n = 11) and Wilms' tumors (n = 8). Discrimination between tumor types was not possible with IVIM D or ADC parameters. Malignant tumors revealed significantly lower ADC, D, and higher D* values than in benign lesions (all P < 0.05). DATA CONCLUSION: IVIM perfusion parameters could distinguish between malignant childhood tumor types, providing potential imaging biomarkers for their diagnosis. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1475-1486.

Rapid measurement of intravoxel incoherent motion (IVIM) derived perfusion fraction for clinical magnetic resonance imaging.

OBJECTIVE: This study aimed to investigate the reliability of intravoxel incoherent motion (IVIM) model derived parameters D and f and their dependence on b value distributions with a rapid three b value acquisition protocol. MATERIALS AND METHODS: Diffusion models for brain, kidney, and liver were assessed for bias, error, and reproducibility for the estimated IVIM parameters using b values 0 and 1000, and a b value between 200 and 900, at signal-to-noise ratios (SNR) 40, 55, and 80. Relative errors were used to estimate optimal b value distributions for each tissue scenario. Sixteen volunteers underwent brain DW-MRI, for which bias and coefficient of variation were determined in the grey matter. RESULTS: Bias had a large influence in the estimation of D and f for the low-perfused brain model, particularly at lower b values, with the same trends being confirmed by in vivo imaging. Significant differences were demonstrated in vivo for estimation of D (P = 0.029) and f (P < 0.001) with [300,1000] and [500,1000] distributions. The effect of bias was considerably lower for the high-perfused models. The optimal b value distributions were estimated to be brain500,1000, kidney300,1000, and liver200,1000. CONCLUSION: IVIM parameters can be estimated using a rapid DW-MRI protocol, where the optimal b value distribution depends on tissue characteristics and compromise between bias and variability.

Influence of macromolecule baseline on 1 H MR spectroscopic imaging reproducibility.

PURPOSE: Poorly characterized macromolecular (MM) and baseline artefacts are known to reduce metabolite quantitation accuracy in 1 H MR spectroscopic imaging (MRSI). Increasing echo time (TE) and improvements in MM analysis schemes have both been proposed as strategies to improve metabolite measurement reliability. In this study, the influence of TE and two MM analysis schemes on MRSI reproducibility are investigated. METHODS: An experimentally acquired baseline was collected using an inversion recovery sequence (TI = 750 ms) and incorporated into the analysis method. Intrasubject reproducibility of MRSI scans, acquired at 3 Tesla, was assessed using metabolite coefficients of variance (COVs) for both experimentally acquired and simulated MM analysis schemes. In addition, the reproducibility of TE = 35 ms, 80 ms, and 144 ms was evaluated. RESULTS: TE = 80 ms was the most reproducible for singlet metabolites with COVs < 6% for total N-acetyl-aspartate, total creatine, and total choline; however, moderate multiplet dephasing was observed. Analysis incorporating the experimental baseline achieved higher Glu and Glx reproducibility at TE = 35 ms, and showed improvements over the simulated baseline, with higher efficacy for poorer data. CONCLUSION: Overall, TE = 80 ms yielded the most reproducible singlet metabolite estimates. However, combined use of a short TE sequence and the experimental baseline may be preferred as a compromise between accuracy, multiplet dephasing, and T2 bias on metabolite estimates. Magn Reson Med 77:34-43, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue.

PURPOSE: To investigate the robustness of constrained and simultaneous intravoxel incoherent motion (IVIM) fitting methods and the estimated IVIM parameters (D, D* and f) for applications in brain and low-perfused tissues. MATERIALS AND METHODS: Model data simulations relevant to brain and low-perfused tumor tissues were computed to assess the accuracy, relative bias, and reproducibility (CV%) of the fitting methods in estimating the IVIM parameters. The simulations were performed at a series of signal-to-noise ratio (SNR) levels to assess the influence of noise on the fitting. RESULTS: The estimated IVIM parameters from model simulations were found significantly different (P < 0.05) using simultaneous and constrained fitting methods at low SNR. Higher accuracy and reproducibility were achieved with the constrained fitting method. Using this method, the mean error (%) for the estimated IVIM parameters at a clinically relevant SNR = 40 were D 0.35, D* 41.0 and f 4.55 for the tumor model and D 1.87, D* 2.48, and f 7.49 for the gray matter model. The most robust parameters were the IVIM-D and IVIM-f. The IVIM-D* was increasingly overestimated at low perfusion. CONCLUSION: A constrained IVIM fitting method provides more accurate and reproducible IVIM parameters in low-perfused tissue compared with simultaneous fitting. LEVEL OF EVIDENCE: 3 J. MAGN. RESON. IMAGING 2017;45:1325-1334.

Frequency-domain vs continuous-wave near-infrared spectroscopy devices: a comparison of clinically viable monitors in controlled hypoxia.

The Near-infrared spectroscopy (NIRS) has not been adopted as a mainstream monitoring modality in acute neurosurgical care due to concerns about its reliability and consistency. However, improvements in NIRS parameter recovery techniques are now available that may improve the quantitative accuracy of NIRS for this clinical context. Therefore, the aim of this study was to compare the abilities of a continuous-wave (CW) NIRS device with a similarly clinically viable NIRS device utilising a frequency-domain (FD) parameter recovery technique in detecting changes in cerebral tissue saturation during stepwise increases of experimentally induced hypoxia. Nine healthy individuals (6M/3F) underwent a dynamic end-tidal forced manipulation of their expiratory gases to induce a stepwise induced hypoxia. The minimum end-tidal oxygen partial pressure (EtO2) achieved was 40 mm Hg. Simultaneous neurological and extra-cranial tissue NIRS reading were obtained during this protocol by both tested devices. Both devices detected significant changes in cerebral tissue saturation during the induction of hypoxia (CW 9.8 ± 2.3 %; FD 7.0 ± 3.4 %; Wilcoxon signed rank test P < 0.01 for both devices). No significant difference was observed between the saturation changes observed by either device (P = 0.625). An observably greater degree of noise was noticed in parameters recovered by the FD device, and both demonstrated equally variable baseline readings (Coefficient of variance 8.4 and 9.7 % for the CW and FD devices, respectively) between individuals tested. No advantageous difference was observed in parameters recovered from the FD device compared with those detected by CW.

Exploring the Reasons for Preferring Digital Games Over Physical Activity Games in Adolescents: A Qualitative Study.

PURPOSE: In the current world, adolescents are less likely to turn to physical activity games and often prefer to spend more time in the technological and digital world and have fun with digital games. This study aimed to explore reasons for preferring digital games to physical activity games in adolescents. METHODS: Twenty-one adolescents participated in this qualitative study. The participants' age ranged from 11 to 19 who participated in semistructured interviews. The recorded interviews underwent thematic analysis to identify overall themes. RESULTS: Five overall themes were identified from the interviews regarding reasons for preferring digital games over physical activity games. Identified themes included game characteristics, game space, game outcomes, peer pressure, and accessibility. DISCUSSION: The findings of this research contribute to design adolescent-centered interventions that sustain/increase adolescents' interest in physical activity games and protect them from excessive/harmful use of digital games.

Determination of output factor for CyberKnife using scintillation dosimetry and deep learning.

Objective. Small-field dosimetry is an ongoing challenge in radiotherapy quality assurance (QA) especially for radiosurgery systems such as CyberKnifeTM. The objective of this work is to demonstrate the use of a plastic scintillator imaged with a commercial camera to measure the output factor of a CyberKnife system. The output factor describes the dose on the central axis as a function of collimator size, and is a fundamental part of CyberKnife QA and integral to the data used in the treatment planning system.Approach. A self-contained device consisting of a solid plastic scintillator and a camera was build in a portable Pelicase. Photographs were analysed using classical methods and with convolutional neural networks (CNN) to predict beam parameters which were then compared to measurements.Main results. Initial results using classical image processing to determine standard QA parameters such as percentage depth dose (PDD) were unsuccessful, with 34% of points failing to meet the Gamma criterion (which measures the distance between corresponding points and the relative difference in dose) of 2 mm/2%. However, when images were processed using a CNN trained on simulated data and a green scintillator sheet, 92% of PDD curves agreed with measurements with a microdiamond detector to within 2 mm/2% and 78% to 1%/1 mm. The mean difference between the output factors measured using this system and a microdiamond detector was 1.1%. Confidence in the results was enhanced by using the algorithm to predict the known collimator sizes from the photographs which it was able to do with an accuracy of less than 1 mm.Significance. With refinement, a full output factor curve could be measured in less than an hour, offering a new approach for rapid, convenient small-field dosimetry.

Deep learning-enabled high-speed, multi-parameter diffuse optical tomography.

Significance: Frequency-domain diffuse optical tomography (FD-DOT) could enhance clinical breast tumor characterization. However, conventional diffuse optical tomography (DOT) image reconstruction algorithms require case-by-case expert tuning and are too computationally intensive to provide feedback during a scan. Deep learning (DL) algorithms front-load computational and tuning costs, enabling high-speed, high-fidelity FD-DOT. Aim: We aim to demonstrate a simultaneous reconstruction of three-dimensional absorption and reduced scattering coefficients using DL-FD-DOT, with a view toward real-time imaging with a handheld probe. Approach: A DL model was trained to solve the DOT inverse problem using a realistically simulated FD-DOT dataset emulating a handheld probe for human breast imaging and tested using both synthetic and experimental data. Results: Over a test set of 300 simulated tissue phantoms for absorption and scattering reconstructions, the DL-DOT model reduced the root mean square error by 12 % ± 40 % and 23 % ± 40 % , increased the spatial similarity by 17 % ± 17 % and 9 % ± 15 % , increased the anomaly contrast accuracy by 9 % ± 9 % ( µ a ), and reduced the crosstalk by 5 % ± 18 % and 7 % ± 11 % , respectively, compared with model-based tomography. The average reconstruction time was reduced from 3.8 min to 0.02 s for a single reconstruction. The model was successfully verified using two tumor-emulating optical phantoms. Conclusions: There is clinical potential for real-time functional imaging of human breast tissue using DL and FD-DOT.

Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex.

Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.

In vivo bioluminescence tomography-guided system for pancreatic cancer radiotherapy research.

Recent development of radiotherapy (RT) has heightened the use of radiation in managing pancreatic cancer. Thus, there is a need to investigate pancreatic cancer in a pre-clinical setting to advance our understanding of the role of RT. Widely-used cone-beam CT (CBCT) imaging cannot provide sufficient soft tissue contrast to guide irradiation. The pancreas is also prone to motion. Large collimation is unavoidably used for irradiation, costing normal tissue toxicity. We innovated a bioluminescence tomography (BLT)-guided system to address these needs. We established an orthotopic pancreatic ductal adenocarcinoma (PDAC) mouse model to access BLT. Mice underwent multi-projection and multi-spectral bioluminescence imaging (BLI), followed by CBCT imaging in an animal irradiator for BLT reconstruction and radiation planning. With optimized absorption coefficients, BLT localized PDAC at 1.25 ± 0.19 mm accuracy. To account for BLT localization uncertainties, we expanded the BLT-reconstructed volume with margin to form planning target volume(PTVBLT) for radiation planning, covering 98.7 ± 2.2% of PDAC. The BLT-guided conformal plan can cover 100% of tumors with limited normal tissue involvement across both inter-animal and inter-fraction cases, superior to the 2D BLI-guided conventional plan. BLT offers unique opportunities to localize PDAC for conformal irradiation, minimize normal tissue involvement, and support reproducibility in RT studies.

Fluorescence diffuse optical monitoring of bioreactors: a hybrid deep learning and model-based approach for tomography.

Biosynthesis in bioreactors plays a vital role in many applications, but tools for accurate in situ monitoring of the cells are still lacking. By engineering the cells such that their conditions are reported through fluorescence, it is possible to fill in the gap using fluorescence diffuse optical tomography (fDOT). However, the spatial accuracy of the reconstruction can still be limited, due to e.g. undersampling and inaccurate estimation of the optical properties. Utilizing controlled phantom studies, we use a two-step hybrid approach, where a preliminary fDOT result is first obtained using the classic model-based optimization, and then enhanced using a neural network. We show in this paper using both simulated and phantom experiments that the proposed method can lead to a 8-fold improvement (Intersection over Union) of fluorescence inclusion reconstruction in noisy conditions, at the same speed of conventional neural network-based methods. This is an important step towards our ultimate goal of fDOT monitoring of bioreactors.

Simultaneous multiple view high resolution surface geometry acquisition using structured light and mirrors.

Knowledge of the surface geometry of an imaging subject is important in many applications. This information can be obtained via a number of different techniques, including time of flight imaging, photogrammetry, and fringe projection profilometry. Existing systems may have restrictions on instrument geometry, require expensive optics, or require moving parts in order to image the full surface of the subject. An inexpensive generalised fringe projection profilometry system is proposed that can account for arbitrarily placed components and use mirrors to expand the field of view. It simultaneously acquires multiple views of an imaging subject, producing a cloud of points that lie on its surface, which can then be processed to form a three dimensional model. A prototype of this system was integrated into an existing Diffuse Optical Tomography and Bioluminescence Tomography small animal imaging system and used to image objects including a mouse-shaped plastic phantom, a mouse cadaver, and a coin. A surface mesh generated from surface capture data of the mouse-shaped plastic phantom was compared with ideal surface points provided by the phantom manufacturer, and 50% of points were found to lie within 0.1mm of the surface mesh, 82% of points were found to lie within 0.2mm of the surface mesh, and 96% of points were found to lie within 0.4mm of the surface mesh.

Quantitative surface radiance mapping using multiview images of light-emitting turbid media.

A novel method is presented for accurately reconstructing a spatially resolved map of diffuse light flux on a surface using images of the surface and a model of the imaging system. This is achieved by applying a model-based reconstruction algorithm with an existing forward model of light propagation through free space that accounts for the effects of perspective, focus, and imaging geometry. It is shown that flux can be mapped reliably and quantitatively accurately with very low error, <3% with modest signal-to-noise ratio. Simulation shows that the method is generalizable to the case in which mirrors are used in the system and therefore multiple views can be combined in reconstruction. Validation experiments show that physical diffuse phantom surface fluxes can also be reconstructed accurately with variability <3% for a range of object positions, variable states of focus, and different orientations. The method provides a new way of making quantitatively accurate noncontact measurements of the amount of light leaving a diffusive medium, such as a small animal containing fluorescent or bioluminescent markers, that is independent of the imaging system configuration and surface position.

A novel selection model based on multivariate methods and arbitrary genetic parameters: a case study on tomato families.

BACKGROUND: Selection is one of the essential skills whereby breeders reduce the population size and increase the chance of success. Various selection methods with special applications have been developed. Superior genotypes are assessed according to interesting traits, including univariate, multivariate, phenotypic, genotypic, etc. METHODS: Mathematical calculation of the traits' importance based on the genetic makeup of investigated population (average degree of dominance/additive involved in the action of genes) and arbitrary genetic parameters is functional. In this paper, a general model for multivariate selection has been presented whereby the selection can be made for (a) more than one interesting trait, (b) the trait(s) with complex inheritance, (c) finding superior genotypes from among a large-scale population, (d) finding superior genotypes in segregating generations and (f) finding tolerant genotypes to stresses. This model is developed based on biometric concepts in four steps. MATLAB script is provided for the model, and users can easily apply that to identify the most suitable genotypes after data collection according to the breeding purposes. RESULTS: The main features of this model are simplicity, precision, repeatability, and speed (improving several traits simultaneously). All the steps and the analysis of the results are explained step by step in a case study.

Determining tolerant tomato genotypes to salt stress according to physiological and morphological manner.

The tomato (Solanum lycopersicum L.) is an annual vegetable cultivated all over the world. It faces biotic and abiotic stresses, such as salinity, in arid and semiarid regions. Investigating the relationship between physiological and economic traits, such as fruit yield, under stress conditions is necessary to identify tolerant genotypes. This study was conducted to identify tolerant tomato families according to the relationship between several important physiological, morphological and phenological traits. Twenty S3 families were cultivated in a factorial experiment (factor1: families and factor2: normal conditions and salinity stress) based on a randomized complete block design with three replications in 2019. Twenty physiological, agronomic and fruit-quality-related traits were investigated. Analysis of variance was used to prove the existing effective genetic diversity. Genetic diversity and the relationships between traits were graphically shown using heatmap clustering. Finally, genetic parameters, such as Pearson's correlation, trait stability index and heritability were used to calculate the mathematical value of families using the Modified Analytical Hierarchy Process. Families exhibited different behaviours under normal and stress conditions. The tolerant families responded physiologically to the salt stress. Therefore, they reduced both cell membrane degradation and photosynthesis disruption by increasing proline, lycopene, carotenoid and sugar content. Therefore, fewer reductions in morphological traits were observed in these families. The most important traits based on the selection strategy were lycopene content, K+/Na+ ratio, days to flowering and biological yield. In addition, three families, H4/T/30/1, H1/T/12/5 and H1/T/47/4, were selected as the most suitable alternatives to construct the breeding population of the next generation.

System Derived Spatial-Temporal CNN for High-Density fNIRS BCI.

An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN.