Geometric parameters have been proposed for prediction of cerebral aneurysm rupture risk. Predicting the rupture risk for incidentally detected unruptured aneurysms could help clinicians in their treatment decision. However, assessment of geometric parameters depends on several factors, including the spatial resolution of the imaging modality used and the chosen reconstruction procedure. The aim of this study was to investigate the uncertainty of a variety of previously proposed geometric parameters for rupture risk assessment, caused by variability of reconstruction procedures.26 research groups provided segmentations and surface reconstructions of five cerebral aneurysms as part of the Multiple Aneurysms AnaTomy CHallenge (MATCH) 2018. 40 dimensional and non-dimensional geometric parameters, describing aneurysm size, neck size, and irregularity of aneurysm shape, were computed. The medians as well as the absolute and relative uncertainties of the parameters were calculated. Additionally, linear regression analysis was performed on the absolute uncertainties and the median parameter values.A large variability of relative uncertainties in the range between 3.9 and 179.8% was found. Linear regression analysis indicates that some parameters capture similar geometric aspects. The lowest uncertainties < 6% were found for the non-dimensional parameters isoperimetric ratio, convexity ratio, and ellipticity index. Uncertainty of 2D and 3D size parameters was significantly higher than uncertainty of 1D parameters. The most extreme uncertainties > 80% were found for some curvature parameters.Uncertainty analysis is essential on the road to clinical translation and use of rupture risk prediction models. Uncertainty quantification of geometric rupture risk parameters provided by this study may help support development of future rupture risk prediction models.The online version of this article (10.1186/s12938-019-0657-y) contains supplementary material, which is available to authorized users. Intracranial aneurysms (IAs) are complexly shaped local dilatations of the cerebral vasculature. They occur at different locations in the anterior and posterior cerebral circulation and are unique in size and phenotype , 2. UnruHowever, according to published data of two large studies   and a JaRupture risk of unruptured IAs increases with increasing aneurysmal diameter  and currTherefore, advanced techniques were developed to improve assessment of aneurysm morphology. Raghavan et al.  defined To provide a clinically applicable workflow, Saalfeld et al.  analysedModels for discriminating between ruptured and unruptured aneurysms proposed for a cohort available to one research group often fail when applied to other patient cohorts. While the segmentation procedure might be standardized fairly well in one group, other research groups might use different methods as no consensus on image segmentation of cerebral aneurysms exists. Uncertainties caused by different image segmentation methods will result in uncertainties in geometric and hemodynamic parameters used for rupture risk prediction. These uncertainties are major factors preventing successful introduction of image-based methods in clinical practice , 21.Accuracy of segmentation and the impact of its uncertainties were investigated by several studies. Berg et al. investigated the impact of voxel size and reconstruction kernel on morphologic and hemodynamic parameters. They found a higher susceptibility of parameters associated with neck representation and wall shear stress , 23. KleTherefore, the international Multiple Aneurysms AnaTomy CHallenge (MATCH) was announced to directly compare segmentation capabilities based on identical aneurysm datasets. Within MATCH, 26 groups from 13 countries contributed the segmentation results of five IAs with different location, size, and shape. Initial results revealed that remarkable differences with respect to vessel and aneurysm representation occurred .To further address this situation, the current study is a follow-up analysis of reconstructed IAs contributed within MATCH. The aim is to quantify the segmentation-related uncertainty of multiple geometric parameters previously proposed for rupture risk prediction. This analysis of variability is intended to enable physicians and biomedical engineers to critically assess those quantities. Finally, the knowledge of segmentation uncertainty could affect the performance of rupture risk prediction models both directly and indirectly. This is because geometric parameters and hemodynamic parameters , both of which are affected by geometry reconstruction, have been proposed as rupture risk predictors , 30, 31.In order to quantify the uncertainties of a set of geometric parameters proposed in the literature as possible rupture risk predictors, reconstructions of five IAs provided in the MATCH challenge were analyzed.The international challenge was announced on November 3rd, 2017. Interested groups were provided with anonymized clinical DICOM datasets, acquired with an interventional angio suite . Rotational angiographies were extracted within a 5 s run comprising 133 slices with a voxel resolution of 1240 × 960. From the rotational angiographies, a 3D digital subtraction angiography CT scan (512 × 512 × 512) was reconstructed exhibiting an isotropic voxel size of 0.28 mm. The datasets comprised three 3D digital subtraction angiography volumes containing the left anterior, right anterior, and posterior cerebral circulation. In total, the corresponding patient harbored five IAs: three in the left and right M1-segment of the middle cerebral artery (MCA), one on the left MCA bifurcation, and one on the left posterior inferior cerebellar artery.To assess the real-world variability of existing segmentation techniques, each team was requested to apply their own algorithms and strategies. Research groups were allowed to submit their contributions until the end of January 2018 and in total 26 research teams from 13 countries participated. However, group 5 had to be excluded completely.Overall, 18 different software tools were used for segmentation. Furthermore, four different segmentation algorithms were applied and large variation with respect to the required processing time was reported. For further details regarding the design and initial outcome of MATCH, please refer to Berg et al. .Altogether, 40 geometric parameters were calculated. These comprise twelve aneurysm size #1–12), six neck size (#13–18), twelve non-dimensional (#19–30), as well as ten curvature parameters (#31–40), respectively. Table –12, six Note that this set of parameters does not represent all geometric parameters previously proposed in literature. For example, we excluded parameters whose definition requires manual, subjective processing steps (e.g. ), as welPrior to parameter calculation, all five aneurysms were manually extracted from the segmentations provided by the 26 research groups participating in the challenge. Extraction was done using ZIBAmira . Since not every research group segmented every aneurysm, this resulted in a total of 121 aneurysm geometries. The aneurysm geometries were then checked for manifoldness, self-intersections, consistent triangle orientation, and lack of holes in the aneurysm surface and any such geometric errors were manually corrected.Subsequently, the geometric parameters were automatically calculated for all aneurysm geometries using ZIBAmira, MATLAB , and Python  scripts.All final aneurysm geometries as well as the table containing all parameter values for all aneurysms are provided as supplemental material, see “Availability of data and materials” section as well as Additional file First, median and the middle 68.3% range (i.e. the difference between the 84.13th and the 15.87th percentile) were calculated for each geometric parameter and aneurysm using SPSS . This absolute uncertainty range corresponds to the range of mean ± one standard deviation for normally distributed data. We chose this range to allow comparison of reported uncertainty ranges against standard deviations, which are commonly reported for normally distributed data. The relative uncertainty of a parameter was then defined as the uncertainty range divided by the median value, in percent. Thus, for each parameter five relative uncertainties were calculated, one for each aneurysm.Finally, these relative uncertainties were averaged across all five aneurysms. The average and variation of the relative uncertainty for all aneurysms were then specified using the median and the interquartile range (IQR), respectively. We decided to specify the IQR rather than the range, as the range is more sensitive to outliers. Additionally, linear regression between the absolute uncertainty range (as defined above) and the median calculated for each aneurysm was investigated.Furthermore, the absolute relative deviations were calculated for each parameter. Here, the absolute deviation between each parameter value and the median of the parameter for the respective aneurysm was calculated and subsequently normalized using the same median value. These absolute relative deviations 121 values) were then visualized using a boxplot for easier comparison of relative parameter uncertainties  was considerably higher than the relative uncertainties of the other parameters. Ignoring Hb, the relative uncertainties of 3D size parameters were higher than the relative uncertainties of 2D size parameters, whose relative uncertainties were in turn higher than the 1D size parameters’ relative uncertainties.Among 1D size parameters, the relative uncertainty of Hb (#5). The relative uncertainties of both 2D neck size parameters were considerably higher than the relative uncertainties of the 1D neck size parameters.Table b) and the four 1D neck size parameters (#13–16). No correlation with τ > 0.7 was found between 1D size and neck size parameters.Figure Table Figure Table Two mean curvature parameters (#31 and #32) show a relatively low and similar relative uncertainty of around 15%. Both parameters are significantly correlated at τ = 0.8. They are also correlated with two 1D size parameters (#2 to #4) at τ > 0.7, as well as all 2D and 3D size parameters (#6 to #12) at τ > 0.8.Among all other curvature parameters, only MLN (#35) has low relative uncertainty. However, only a weak correlation between MLN and the two mean curvature parameters mentioned above (#31 and #32) was observed (τ < 0.5).Uncertainty associated with inter-operator or inter-group variability during segmentation was quantified for a large set of geometric parameters. A large variability in uncertainty for different parameters was found, with uncertainties ranging from 3.9 to 179.8%. This information is valuable since advanced image-based methodologies are increasingly applied to assess the rupture risk of individual IAs, aiming to support clinicians in making treatment decisions for individual patients. In these research efforts, several groups have attempted to develop a model predicting rupture risk of cerebral aneurysms based on geometric –16, 34 oNon-dimensional shape parameters constructed from surface areas and/or volumes tended to exhibit the lowest uncertainties: IPR (#25), CR (#29), EI (#30). This is not surprising, since the parameters used for the calculation of these non-dimensional parameters are either overestimated or underestimated concordantly, thus reducing difference between non-dimensional values.max, Hmax, Dmax (#1–4).Four out of five 1D size parameters also showed comparatively low relative uncertainty values: H, LRelative uncertainties of size parameters grow with dimension. Relative uncertainties for 2D size parameters (i.e. areas) were larger than for 1D size parameters and relative uncertainties for 3D size parameters (i.e. volumes) were larger than for 2D size parameters. Relative uncertainties for 2D and 3D size parameters were at least twice as high as for 1D size parameters. This increase in relative uncertainty with dimension seems reasonable, given that the 2D and 3D size parameters can be expected to correlate with the square and cube of the 1D size parameters and hence the same can be expected from the relative uncertainties. This result was anticipated, since a small earlier study of the reproducibility of aneurysm segmentation found a segmentation accuracy of around one voxel . Thus, tCalculated uncertainties for 1D neck size parameters were approximately twice as high as for 1D size parameters. This can be explained by the aneurysm neck being particularly sensitive to segmentation uncertainty, as was shown by Berg et al. . For exaCurvature parameters generally exhibit the highest relative uncertainties, with the exception of three parameters, two of which (MAA and absMAA) are measures of aneurysm shape and size, while the third parameter (MLN) is a measure of aneurysm shape alone.The uncertainties and their variability found in our study were caused by different segmentation and surface reconstruction approaches from medical image data. Considering the median values of the relative uncertainty ranges of all investigated parameters, the following key findings can be summarized:Non-dimensional parameters seem to be preferable, since over- or undersizing during segmentation is partially compensated by the normalization, which results in lower relative uncertainty.In a recent study by Liang et al. , which rMost curvature parameters exhibit high relative uncertainties in our study. This likely results from the curvature being particularly affected by both major steps of the reconstruction procedure, segmentation and smoothing. From the viewpoint of uncertainty, the inclusion of curvature parameters, with the exception of MAA, absMAA, and MLN, for rupture risk estimation warrants caution. If curvature parameters are to be used, careful standardization of image reconstruction should be aimed for.In addition to relative uncertainty, the differences in parameter value between ruptured and unruptured aneurysms should also be considered, when choosing a rupture risk parameter. For example, Weir et al. found an average AR of 1.8 for unruptured and 3.4 for ruptured aneurysms , while wBased on the described findings, several recommendations can be formulated, which could be taken into account in related future studies:The calculation of geometric parameters requires the separation of the aneurysm head from the parent vessel, which was done manually by one operator for all data sets. Although a small inter-operator dependency analysis found minor impact on the calculation of geometric parameters , this prAnother aspect possibly affecting the results of the uncertainty analysis is the accuracy of the geometric parameters’ measurements. Beside the voxel resolution of the acquisition discussed above, the resolution of the surface meshes provided by MATCH participants was important. The average and standard deviation of the mesh resolution was 0.199 ± 0.096 mm with a range between 0.010 mm and 0.453 mm. Note, that the accuracy of the used measurement tools is much higher than both the mesh and voxel resolution, with at least 32 bit accuracy for mesh node coordinates. Since accumulation of numerical errors might reduce this accuracy, we performed a numerical experiment on a synthetic aneurysm, which had the shape of a spherical cap . Diameter, surface and volume of the meshed synthetic aneurysm were calculated as usual and compared to the analytical solution, the differences were 0.016%, 0.17% and 0.31%, respectively. Hence, the uncertainty introduced by the measurement tools used in our study can be neglected.Further limitations of this study are linked to the design of MATCH: The major limitation of this study is the small number of segmented IAs. This impedes comparison of inter-aneurysm and inter-operator variability, because the former can only be assessed on the basis of five aneurysms.Additionally, MATCH only evaluated limited information on the reconstruction procedure used by each group. Examples of missing information are the segmentation thresholds as well as the surface smoothing algorithms used.The present study investigated the uncertainty of individual geometric rupture risk parameters only. Uncertainty of complex rupture risk models including interactions between two or more geometric risk parameters, such as proposed by Prestigiacomo et al.  or DetmeUncertainty analysis is mandatory on the road to clinical translation and use of rupture risk prediction models. The presented uncertainty analysis shows, that accurate rupture risk estimation requires reliable and reproducible assessment of geometric parameters. Thus, developing standards for reconstruction of aneurysm image data seems warranted. The data provided by this study might be able to help support development of future rupture risk prediction models by providing estimates of the uncertainties of individual parameters. In the future, an uncertainty analysis based on a larger aneurysm cohort, representing a more representative range of geometric rupture risk parameter values, is desirable.Additional file 1. All geometric parameters calculated for the aneurysm geometries provided in frames of MATCH.


While irradiation can effectively treat brain tumors, this therapy also causes cognitive impairments, some of which may stem from the disruption of hippocampal neurogenesis. To study how radiation affects neurogenesis, we combine phenotyping of subpopulations of hippocampal neural stem and progenitor cells with double- and triple S-phase labeling paradigms. Using this approach, we reveal new features of division, survival, and differentiation of neural stem and progenitor cells after exposure to gamma radiation. We show that dividing neural stem cells, while susceptible to damage induced by gamma rays, are less vulnerable than their rapidly amplifying progeny. We also show that dividing stem and progenitor cells that survive irradiation are suppressed in their ability to replicate 0.5–1 day after the radiation exposure. Suppression of division is also observed for cells that entered the cell cycle after irradiation or were not in the S phase at the time of exposure. Determining the longer term effects of irradiation, we found that 2 months after exposure, radiation-induced suppression of division is partially relieved for both stem and progenitor cells, without evidence for compensatory symmetric divisions as a means to restore the normal level of neurogenesis. By that time, most mature young neurons, born 2–4 weeks after the irradiation, still bear the consequences of radiation exposure, unlike younger neurons undergoing early stages of differentiation without overt signs of deficient maturation. Later, 6 months after an exposure to 5 Gy, cell proliferation and neurogenesis are further impaired, though neural stem cells are still available in the niche, and their pool is preserved. Our results indicate that various subpopulations of stem and progenitor cells in the adult hippocampus have different susceptibility to gamma radiation, and that neurogenesis, even after a temporary restoration, is impaired in the long term after exposure to gamma rays. Our study provides a framework for investigating critical issues of neural stem cell maintenance, aging, interaction with their microenvironment, and post-irradiation therapy. Radiation therapy with gamma rays is an indispensable tool for treating brain tumors in children and adults. While this irradiation can effectively eliminate cancer cells, it also induces profound cognitive impairments in up to 90% of patients undergoing whole-brain radiation therapy—a treatment used by more than 200,000 patients a year in the United States alone . These rIf so, to mitigate irradiation’s harmful cognitive consequences, we must better understand its effects on hippocampal neurogenesis. In rodents and humans, new neurons are born in the dentate gyrus (DG), within a stem cell niche that harbors precursors to neuronal and glial cells . These pThe neurogenic capacity of the hippocampus rests on the main class of neural precursors in the DG, commonly referred to as “multipotent neural stem cells” or, based on their morphology, “radial glia-like cells” (RGLs) . RGL celExposure to radiation can potentially affect neural stem cells at several points of vulnerability, in addition to destroying actively dividing RGLs and their progeny: radiation may augment or suppress cell cycle activation of RGL cells, reduce the reserve of quiescent RGLs, alter stem cell output, induce premature senescence of stem cells or their microenvironment, or induce neuroinflammation in their niche. These effects may be compounded by the impact of radiation on the connections established or maintained by the newborn neurons. While the mechanisms of radiation action on dividing progenitor cells in the hippocampus have been thoroughly studied , 2008, sHere, we use a combination of genetic marking of stem and progenitor cells with differential tagging of newborn DNA to determine the immediate and long-term effects of exposing stem and progenitor cells to moderate doses of gamma rays. Our results demonstrate the vulnerability of dividing and non-dividing neural stem cells to radiation, these cells’ potential to restore their radiation-affected function, and the lingering effects of radiation on maturation of young neurons and productivity of stem cells.We first addressed the acute effects of gamma rays by measuring survival and proliferation of neural stem and progenitor cells in the DG 24 h after exposure to gamma radiation. To differentiate between various classes of neuronal precursors, we employed Nestin-GFP reporter mice, in which GFP expression marks RGLs and their early progeny, the ANPs . These rP = 0.33, and 17% decrease, P = 0.09 for 1 and 5 Gy, respectively; the CI and ANOVA values for this and the following experiments are presented in Supplementary Table P = 0.024) and 64% after 5 Gy (P = 0.002), compatible with the cycling status of the majority of ANP cells  cells. We did not find a statistically significant decrease in the total number of RGL cells 24 h after exposure to 1 or 5 Gy + cells, which correspond to the cells in S phase at the time of BrdU injection .We began by tracking changes in the number of BrdU-labeled precursors (parameter [a] above), most of which were still in S phase at the time of irradiation [with the S phase duration of ∼8–10 h ]. We fou Figures . Exposur Figures , with ANp = 0.0003) and 63% reduction in labeled ANPs (p < 0.0001), and 5 Gy irradiation reduced the number of EdU-labeled RGLs by 97% (p < 0.0001) and of EdU-labeled ANPs by 96% (p < 0.0001). Using ANOVA for both RGLs and ANPs, we found large main effects for irradiation, with both RGL and ANP cells showing a significantly decreased ability to enter the S phase ∼6–22 h (given 1 h label bioavailability and 8–10 h S-phase length) after exposure to 1 Gy of gamma radiation, this ability being virtually abolished after the 5 Gy dose. Two-way ANOVA showed interaction between cell type and dose factors . Also note that whereas in the control group the number of EdU+ RGL was the same as the number of BrdU+ RGLs (197 ± 55 vs. 201 ± 63), the number of EdU+ ANPs in that group was close to a half of the BrdU+ ANPs (1672 ± 131 vs. 2998 ± 394). Since the time between the labels’ injections was close to a full cell cycle, including duplication of proliferating cells, these numbers conceivably reflect the predominantly asymmetric mode of the RGL division and the symmetric mode of the ANP divisions.Next, to assess whether exposure to gamma radiation affects S phase progression of progenitors 1 day after irradiation, we counted the number of EdU-labeled RGL and ANP cells (parameter [b] above) 2 h after the labeling, i.e., 24 h after irradiation Figures . A 1 Gy +EdU+ double-labeled RGLs (p = 0.01) and 70% reduction in BrdU+EdU+ ANPs (p = 0.0002), compared to the control. If considered as a fraction of S phase cells that had been in the S phase 22 h before , exposure to 1 Gy reduced the reentry of RGLs by 61% and of ANPs by 48% compared to the control. Similarly, if considered as a fraction of cells currently in S phase , 1 Gy reduced the fraction of previously cycling RGLs by 35% and of ANPs by 20% compared to the control. Neither BrdU+EdU+ RGLs nor BrdU+EdU+ ANPs were detectable after the 5 Gy dose. These results indicate that RGLs and ANPs that were undergoing DNA synthesis by the time of irradiation and have survived had a greatly reduced rate of cell cycle reentry 22 h after exposure to 1 Gy and lost the reentry ability after the 5 Gy dose.We next examined whether the surviving BrdU-labeled RGLs and ANPs sustained their cell cycle progression ability. To determine whether cells that were in the S phase by the time of irradiation were capable of reentering the S phase 22–24 h later, we analyzed the number of cells that were labeled with both BrdU and EdU (parameter [c] above) Figures . One gra-EdU+ or EdUonly cells) (parameter [d] above) as a measure of cells that were not in the S phase by the time of BrdU injection . The number of EdUonly RGLs compared to the control group was decreased by 64% (p = 0.0002) and of EdUonly ANPs by 56% (p < 0.0001) after 1 Gy. Five grays of radiation reduced EdUonly RGLs by 95% (p < 0.0001) and EdUonly ANPs by 92% (p < 0.0001), with ANOVA showing large effects of irradiation for EdUonly-labeled RGLs and ANPs  = 126, p < 0.0001]. These results indicate that even those cells that were not in the S phase at the time of radiation exposure suffered profound impairment of the cell cycle progression that lasted for at least 22 h. Taken together, these results suggest that both dividing RGLs and ANPs are sensitive to 1 and 5 Gy of gamma rays. RGLs and ANPs that were in the S phase by the time of exposure and survived were significantly less likely to reenter the cell cycle 6–22 h later. In addition, gamma radiation negatively affected the overall level of RGL and ANP proliferation: even cells not in the S phase at the time of exposure  were less able to divide 24 h after exposure to 1 Gy and were virtually stalled 24 h after exposure to 5 Gy.Finally, we determined the changes in progenitors labeled only with EdU  or 5 Gy . To assess whether the rate of progenitors’ division was affected by preceding irradiation, we labeled dividing cells by injection of BrdU 2 months after irradiation and analyzed the brains 24 h later, finding that the numbers of labeled cells in all three groups  did not differ for RGL or for ANP , in two out of seven brains in the control group; five such pairs in the 1 Gy-treated group of nine animals ; and one such pair with 16 μm distance in the 5 Gy-treated group of seven animals. The frequencies of these and other pairs were not different from control . These results indicate that 2 months after their proliferation was disrupted by gamma irradiation, the spatial order of quiescent cells’ activation did not change, suggesting that RGLs predominantly preserved an asymmetric mode of cell division.Still, even if the overall number of stem cells and dividing stem cells was restored, their mode of division (asymmetric vs. symmetric) may have been affected by radiation and the subsequent recovery period, perhaps compensating for the loss of progenitors. Therefore, we next asked whether the rate of symmetric division of stem cells was modified by irradiation. We reasoned that symmetric divisions of RGLs would alter the spatial distribution of BrdU-labeled RGLs since such cells would generate pairs of closely positioned cells within the sectioned DG volume . Such pap = 0.04) for 1 Gy and 92% reduction (p = 0.008) for 5 Gy  was significantly reduced, with 60% reduction ; however, 5 Gy led to a 45% reduction (p = 0.013) in DCX+ cells.Next, we analyzed the long-term effect of irradiation on the differentiation cascade that leads from early neuronal progenitors to fully differentiated neurons Figure . To exampost hoc analysis. We defined early subclasses of neuronal progenitors  as described . However, irradiation with 5 Gy decreased the absolute number of G-category cells by 67% ; differences in other categories, as well as the fractions of each category among all DCX cells, did not reach statistical significance. As the proportions remained unchanged, while G-category decreased, it is possible that less mature cell types were also affected. Together, our results indicate that while the total pool of stem cells was unchanged by gamma radiation 2 months after irradiation, the pool of DCX young neurons was significantly decreased.We compared the absolute Figure  and relaRestored stem and progenitor cell division and partially reconstituted production of new neurons at 2 months could later progress to full recovery of neurogenesis from the spared reserve of quiescent neural stem cells and reconstituted pool of dividing neural stem cells. However, such recovery may be potentially compromised by aging, which could disproportionally affect irradiated stem cells and their microenvironment by reducing the number of RGL and ANP cells or of their dividing subsets, diminishing their propensity to produce new neurons, or decreasing the survival of their neuronal progeny. We therefore analyzed stem cells and early and late progenitors at 6 months post-irradiation, focusing on the 5 Gy dose because of its pronounced effects on neurogenesis Figure . To detep = 0.07). However, neurogenesis, as reflected in the number of DCX cells, was greatly diminished in 5 Gy-irradiated animals . To further analyze changes in cell division, we compared the number of cells labeled with each nucleotide and the total number of labeled cells .The entire pool of RGL cells was not significantly affected 6 months after gamma rays exposure   . We did ctively) ; note, hAlthough the effects of gamma radiation on neurogenesis have been thoroughly investigated, inquiry into the response of the quiescent, dividing, and differentiating subpopulations of stem and progenitor cells has been limited by the available methods of cell cycle analysis. Here, we apply a new multiple-labeling/progenitor phenotyping technique to uncover short- and long-term consequences of exposure to gamma radiation on hippocampal neural stem cells and neurogenesis.We found that while large part of cycling stem (RGL) and progenitor (ANP) cells did not survive subsequent irradiation, the total pool of quiescent stem cells, the majority of which were not in the cell cycle at the time of irradiation, did not decrease in number Figure . As expeRemarkably, we found that proliferating RGL stem cells demonstrated higher resistance to the effects of gamma rays than their amplifying progeny Figure . This em+EdU+ populations of RGL and ANP; Figures + populations of RGL and ANP, Figure only populations of RGL and ANP) were also suppressed in their division capability 22 h after irradiation , as well as in spatial distribution of dividing RGLs. Still, even as cell division in the stem cell niche appeared restored when probed 2 months post-irradiation, finer features of neuronal differentiation and maturation were still affected by irradiation. Indeed, we found that the number of cells labeled before irradiation (which largely correspond to newborn differentiated neurons) was greatly diminished, reflecting the scarcity of neuronal progenitors Figure . FurtherThese defects may reflect the wave of maturation, with the most mature cohort  still carrying the signs of a damaged niche, and the less mature cohorts, having been born later, being largely unscathed by the reconstituted niche . Notably, the overall cascade of maturation and its timing did not show overt changes, even though it has been exposed to post-irradiation inflammatory process , as the Despite the partial restoration of the neurogenic niche 2 months after irradiation, the niche became less productive at 6 months. While the pool of RGLs was largely preserved Figure , there wIn the intact brain, disposal of quiescent RGLs follows their activation and division , and theOf note, we did not find indications of symmetric division of stem cells Figure , which cTaken together, our results indicate that exposure to gamma radiation eliminates dividing neural stem cells , spares quiescent cells, and temporarily suppresses cell division within the neurogenic niche, with the division support by the niche largely restored 2 months later but deficient 6 months later. Note that most of these consequences of radiation exposure cannot be revealed using conventional labeling techniques but can be uncovered using the double- or triple-labeling/progenitor phenotyping approach applied here.Our results on the gamma radiation effects on adult hippocampal neurogenesis raise several challenging questions. One relates to the differences between two neurogenic zone in the adult brain, the subventricular zone (SVZ) and the SGZ. In addition to a number of similarities, the response of SVZ to radiation shows better long-term recovery  and may Another important question relates to the differences in how the developing and adult brain respond to radiation. For instance, the neonatal SVZ shows less proliferation arrest and more rapid recovery after X-ray irradiation than adult SVZ . Note thFurthermore, it is interesting whether the changes that we found after exposure to gamma rays are also observed after exposure to other types of radiation. Exposure to X-rays, gamma rays, or heavy ions leads to extensive perturbations in neural stem cells and neurogenesis ; note, hYet another rising issue is the potential relevance of the results with neural stem cells to other types of stem cells, as related to their response to radiation. As in the adult brain, in several tissues stem cells are more resistant to radiation than more advanced progenitors. This observation is in line with the general notion of stem cells being more resistant to genotoxic stress . The difFinally, our results point to a link between compromised neurogenesis and long-term cognitive impairments associated with irradiation. In particular, immature DCX-expressing neurons, which were produced at negligible numbers 6 months after irradiation, are critical for the acquisition of spatial learning, as well as reversal learning in rodents . RemarkaOur findings on adult mouse hippocampal neurogenesis may have relevance for the radiation-exposed human nervous system. Evidence for neurogenesis persisting in the adult and old human brain is supported by several non-overlapping approaches  with doses of 0 Gy (n = 4), 1 Gy (n = 5) for 1 min 4 s, or 5 Gy (n = 4) for 5 min 26 s. The background dose rate was 0.004 Rad/h in the irradiation chamber. Animals were placed together into the irradiation chamber, with the 137Cs source underneath the chamber. For the 2-month long experiment, whole-body irradiation of 3.5-month-old male mice was performed using 60Co gamma rays source  with doses of 0 Gy (n = 7), 1 Gy (n = 9) for 1 min 10 s, or 5 Gy (n = 7) for 4 min 15 s. For the 6 month long experiment, whole-body irradiation of 3.5-month-old male mice was performed using 60Co gamma rays source  with doses of 0 Gy (n = 4) or 5 Gy (n = 4) for 4 min 23 s. The background dose rate was 0.013 Rad/h. The treatment unit is certified by National Research Institute for Physical–Technical and Radio Engineering Measurements  according to radiation dosimetry standards of State System for Ensuring Uniform Measurement. All animals in each group were irradiated simultaneously. Sham controls were treated the same as irradiated mice but without being exposed to radiation. No anesthesia was used before the irradiation procedures. After the treatment all animals were returned to their home cages for 24 h, 2 months, or 6 months, and kept under standard conditions.Irradiation procedures were based on previously published protocols for whole-body and cranial gamma radiation that have used the power close to 1 Gy/min . For shoFor the 24 h short-term experiment, BrdU  was administered intraperitoneally to all mice 2 h prior to the irradiation. Twenty-two hours after the treatment mice received an injection with an equimolar amount of EdU (123 mg/kg), then were anesthetized 2 h later  and perfused transcardially with 30 ml cold PBS and 50 ml of cold 4% PFA in PBS. For the 2 months long experiment, EdU  was injected 2 h prior to the irradiation. Two months later mice were injected with BrdU (150 mg/kg) three times, 24 h apart. Twenty-four hours after the last injection the animals were anesthetized and perfused. For the 6 months long experiment, thymidine analogs were injected 6 months after the irradiation: EdU  was injected 24 h prior to perfusion, IdU  was injected 4 h after EdU, and CldU  was injected 19 h after IdU. One hour after the last injection the animals were anesthetized and perfused.Following perfusion, brains were postfixed in 4% PFA overnight at 4°C, then transferred to PBS and kept at 4°C until sectioning. Vibratome sections from a randomly selected hemisphere were prepared in lateral-to-medial direction. The sections were collected in PBS and kept in PBS with sodium azide at 4°C or in cryoprotectant  at -20°C until staining. For staining, sections were first incubated with blocking and permeabilization solution  for 1 h at room temperature. The sections designated for the analysis of BrdU incorporation were treated before the immunostaining procedure with 2 N HCl for 30 min at 37°C and rinsed with PBS three times. Incubation with primary antibodies was performed in 0.2% Triton and 3% normal goat serum overnight at 4°C on shaker. After a thorough washing with PBS, sections were incubated with secondary antibodies in PBS for 2 h at room temperature in darkness on a shaker. After three washings with PBS, the sections were stained with EdU-click reaction and AlexaFluor 555 Azide, triethylammonium salt (Invitrogen A20012) according to Quantitative analysis of cell populations was performed by means of design-based  stereology, as detailed in t-tests (irradiated groups vs. Sham) without an assumption for equal SD, followed by a correction for multiple comparisons using Holm–Sidak method, with alpha 0.05. In cases when two-way ANOVA was used, cell type-corresponding counts passed Brown–Forsythe test for equal variances, and within-animal cell type values were matched for each animal. For total ANP counting, four animals were counted in group. For the data obtained in the 2 months long experiment, Kolmogorov–Smirnov test was used to test a normality of distributions. If appropriate, ANOVA was then performed. Dunnett’s test was used to compare every mean to a control mean, obtain 95% CIs of difference and correct for multiple comparisons with familywise error rate of 5%. EdU cells and DCX cells of each cell category were compared with t-tests (irradiated groups vs. Sham) without an assumption for equal SD, followed by a correction for multiple comparisons using Holm–Sidak method, with alpha 0.05. The same analysis was used for the comparison of cell pair frequencies at particular distances for 1 Gy vs. Sham groups and 5 Gy vs. Sham. Kolmogorov–Smirnov test was used to test normality of distributions. For DCX counting in 2 month long experiment, five animals were randomly selected from each group, and no animal was excluded. For the data obtained in the 6 months long experiment, a normal distribution was assumed, and t-test used for all variables. Statistical analysis and graph plotting were performed using Prism GraphPad version 6.04 for Windows (GraphPad Software1). No animal was excluded.For 1 day experiment analysis, each variable was assumed to be normally distributed. To gain power with less subjects, a minimum change of 30% was considered as minimally important, taking into account a large variability of these parameters observed in our previous experiments (SD up to 25%) for eight sections analyzed per animal on average. Before each comparison, Brown–Forsythe test was used to compare standard deviations of the means. BrdU/EdU-colabeled cells did not pass Brown–Forsythe test and were compared using The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.OM and GE designed the experiments. OM, DB, AK, and AL performed the experiments. GE and KA provided funding. OM, DB, AK, AL, KA, and GE interpreted the results, made direct and intellectual contribution to the project, and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Lead, a highly toxic element, is frequently present in various solid wastes as well as in industrial effluents. Sorption with a low cost sorbent is a simple way of Pb removal from liquid streams, but stabilization of spent sorbent has to be ensured in order to prevent Pb leaching out and possible environmental contamination. In previous research, ceramic sorbent, generated as waste product in brick industry, was tested as sorbent and proved high sorption capacity for lead. Lead was sorbed partially as hydrocerussite and partially as adsorbed surface layer. The Pb leaching from sorbent was very high and thus further immobilization of sorbent was necessary. Lead, as well as other heavy metals, is known as retarder of the hydration process of Ordinary Portland Cement (OPC), used for the immobilization. In this paper, influence of sorbed Pb and PbO, as reference compound, on cement hydration was studied by calorimetry, thermogravimetry and Magic Angle Spinning Nuclear Magnetic Resonance Spectroscopy (MAS NMR). The sorbed lead was found to be less detrimental to hydration retardation due to the lower solubility of precipitated hydrocerussite in basic environment compared to PbO, which forms plumbate anion. Lead belongs, due to its high toxicity, to elements which environmental emissions and occurrence are carefully monitored. Lead exposure is responsible for effects on nervous system, the reproductive system and kidneys. Lead gets collected in tissues; especially children are vulnerable to lead exposure, since lead is more easily absorbed into growing bodies. Children’s lead intoxication is also linked to hyperactivity and mental retardation ,2. For tThere are numerous methods, which can be used for heavy metals (HM) removal from waste waters in order to meet demand requirements; widely used are precipitation and filtration of an insoluble compound of HM , membrane or electrochemical separation methods ,4. Sorpt2 to C-S-H and C-A-H hydration products and thus be used as active component in blended cements 2(SO4)3·26H2O), the other to monosulfate (Ca4[Al(OH)6]2SO4·6H2O) (M) and “third aluminate phase”, described in [IV hydration—the signal E appeared after a few hours of hydration, while the M and T signals grown later [IV species remained in PbSorb-2.The solid state  sorbent . The 27Ao 86 ppm . The cerd illite . Both ofree days  in pasted 15 ppm . Three dribed in  as “lesswn later . The maiThe leaching of lead from the used sorbent PbSorb  reached 3(CO3)2(OH)2) crystals precipitated locally on sorbent particles. Freundlich isotherm is assuming an “infinite” formation of sorbate layer on the surface; the observed bimodal Pb sorption behavior can be understood as case between these two extremes. The amount of precipitated hydrocerussite is limited by carbonate content in sorbent, thus the total sorption capacity is limited as well and the data obey Langmuir isotherm, even though there is more Pb sorbed than would be in a monolayer. The formation of hydrocerussite was enabled by basic character of ceramic sorbent—it contains, in its amorphous portion, certain amount of calcium hydroxide and calcium carbonate, as revealed thermal analysis of ceramics, as well as its certain ability to hydrate without any external activation [The waste ceramic powder, generated in brick blocks grinding to desired dimensions, proved high sorption capacity for Pb 121 mg/g); this value is comparable or higher than those of common low cost sorbents, such are clay minerals or zeolites 1 mg/g; t,14. The tivation . The inttivation ,42. Calo3S in cement [The calorimetry results on PbO + OPC mixtures are in agreement with those published on this system ; the incn cement , while hn cement  and doesn cement , that PbThe immobilization of ceramic sorbent was successful, but the 2% systems still fall within hazardous waste, even though the lead concentration in leachate was obviously much lower than in the case of non-stabilized sorbent. The obtained results, dealing with both the hydration process and lead immobilization, revealed that 2% of Pb per OPC is a dose that significantly influences the paste behavior in both hydration and leaching aspects.▪3(CO3)2(OH)2).The ceramic sorbent reached high sorption capacity for lead (121 mg/g). Lead was partially adsorbed on the surface and partially precipitated as basic lead carbonate (hydrocerussite, Pb▪The hydration retardation caused by sorbed lead was less pronounced than the action of PbO. The reason lies in formation of hydrocerussite, which is more stable in basic environment than PbO, which forms plumbate ions.▪The 0.5% Pb by mass, regardless its form, is reducing the strength of cementitious paste moderately, while the 2% dosage is extremely retarding the hydration and strength evolution.▪The leaching experiments also showed, that 0.5% Pb can meet the “other waste” requirement, while 2% of pastes fall already to “hazardous waste”.▪The results indicate that Pb is absorbed to structure of hydration products. The structure of C-S-H with Pb incorporated is a challenge for future research.The influence of lead sorbed on ceramic sorbent on the hydration process of Ordinary Portland Cement was studied by help of calorimetry, thermogravimetry, and MAS NMR spectroscopy. The findings can be briefly summarized:


ZNF561, WFS1, SLC2A1, MFI2, and PTGR1) were identified by random forest variable hunting, and four  were selected to create a four‐gene recurrent model (GRM), with an area under the curve (AUC) of 0.882 according to the receiver operating characteristic curve, and the robust diagnostic effectiveness of the GRM was further validated with another gene expression profiling dataset (GSE12032), with an AUC of 0.943. The diagnostic effectiveness of the GRM regarding recurrence was associated with poor disease‐free survival in all stages of CRC. In addition, gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed 18 enriched functions and 6 enriched pathways. Four genes, ABCG2, CACNA1F, CYP19A1, and TF, were identified as hub genes by the protein‐protein interaction network, which further validated that these genes were correlated with a poor pathologic stage and overall survival in all stages of CRC. In conclusion, the GRM can effectively classify stage II CRC into groups of high and low risks of recurrence, thereby making up for the prognostic value of the traditional clinicopathological risk factors defined by the National Comprehensive Cancer Network guidelines. The hub genes may be useful therapeutic targets for recurrence. Thus, the GRM and hub genes could offer clinical value in directing individualized and precision therapeutic regimens for stage II CRC patients.The current criteria for defining the recurrence risks of stage II colorectal cancer (CRC) are not robust; therefore, we aimed to explore novel gene signatures to predict recurrence risks and to reveal the underlying mechanisms of stage II CRC. First, the gene expression profiles of 124 patients with stage II CRC from The Cancer Genome Atlas (TCGA) database were obtained to screen differentially expressed genes (DEGs). A total of 202 DEGs, including 128 upregulated and 74 downregulated, were identified in the recurrence group (n = 24) compared to the nonrecurrence group (n = 100). Furthermore, the top 5 DEGs . Moreover, the gene recurrent model and hub genes potentially offer clinical values in directing individualized and precision therapeutic regimen be selection for stage II CRC. With the popularization of big data analysis and the progress of bioinformatics technology, a series of biomarkers was identified for predicting the recurrence, metastasis, chemosensitivity, and prognosis of CRC. Moreover, the complex recurrence and metastatic processes of polygenic network collaboration were also analyzed by bioinformatics tools.http://cancergenom-e.nih.gov/) to gain a panoramic view of expression patterns between recurrence and nonrecurrence patients with stage II CRC. Furthermore, differentially expressed genes (DEGs) were used to establish a model to predict the recurrence risk (gene recurrent model [GRM]) by random forest sequencing, and excellent diagnostic effectiveness was shown by receiver operating characteristic (ROC) curve analysis. Then, another gene expression profiling dataset was extracted from the GEO (GSE12032) to further validate the robust diagnostic effectiveness of the GRM. In addition, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein‐protein interaction (PPI) network analyses, and hub gene selection were adopted to jointly analyze the underlying mechanism of recurrence. Finally, the results we obtained might be meaningful in guiding clinical practice and understanding the recurrence mechanisms of stage II CRC.In this study, we performed bioinformatical analyses based on high‐throughput RNA sequencing of CRC from the The Cancer Genome Atlas (TCGA) (22.13‐4N0M0) without postoperative adjuvant therapy and were followed up for at least 2 years. According to recurrence, the samples were divided into a recurrence group and a nonrecurrence group.The RNA‐Seq dataset of CRC, which includes the whole human transcriptome sequencing dataset and corresponding survival profiles, was downloaded from the TCGA database. All the data in the dataset used were pathological stage II .Random forest is a popular classification and regression method that has proven powerful for various prediction problems in biological studies. The mean decrease in gini (MDG), which is involved in the random forest algorithm, is used to rank the important indexes with DEGs. The MDG provides ways to quantify which index contributes most to classification accuracy. A higher MDG indicates that the degree of impurity arising from the category could be reduced farthest by one variable and thus suggests an important associated index. We divided our data into training  and testing  datasets by using the randomForest package of R software  was used to demonstrate the accuracy of an individual gene and joint genes for predicting recurrence.2.5P value <.05. The patients with missing values were excluded. Furthermore, the identified DEGs were ranked by the MDG in the random forest algorithm, and the top 10 genes were selected. In addition, the diagnostic effectiveness of the GRM was validated by ROC analyses, and the AUC was used to demonstrate the accuracy of the GRM for predicting recurrence.To further validate the diagnostic effectiveness of the GRM, another gene expression profiling dataset was extracted from the GEO (GSE12032) for analysis. GEO2R, a web tool that was applied to screen the DEGs by comparing two groups of samples in a GEO series, was applied to identify the DEGs between the recurrence and nonrecurrence groups of stage II CRC patients following the criteria fold change |(FC)| ≥1.5 and 2.6P < .05 was considered statistically significant.As the four genes in the GRM achieved robust predictive values of recurrence in stage II CRC and the risk of recurrence always affected disease‐free survival (DFS), we further validated the four genes by investigating their expression and relevance to DFS in all stages of CRC between the recurrence and nonrecurrence groups. Kaplan‐Meier survival curves were constructed using the expressions of the four genes from the TCGA transcriptional profiles as a threshold and compared by log‐rank analysis. All analyses were performed with GraphPad Prism 5.0 and SPSS version 19.0 (IBM), and 2.7https://david.ncifcrf.gov)P value <.05 was set as statistically significant.The Database for Annotation, Visualization, and Integrated Discovery (DAVID) v.6.8 (2.8http://www.string-db.org/) database was used to construct a PPI network. An interaction score >0.4 was considered statistically significant. Furthermore, the result of the PPI network was imported into the Cytoscape plugin to create network visualizations and subjected to centrality values analysis with CentiScaPe 2.2.The Search Tool for the Retrieval of Interacting Genes , we further validated the four hub genes by investigating their expression in different pathologic stages and relevance to OS in all stages of CRC. Kaplan‐Meier survival curves were constructed using the expressions of the four hub genes from the TCGA transcriptional profiles as a threshold and compared by log‐rank analysis. All analyses were performed with GraphPad Prism 5.0 and SPSS version 19.0, and 33.1Information on the 124 patients who met our research criteria was obtained from the TCGA database. After a 2‐year follow‐up, 24 patients experienced recurrence, and 100 patients did not experience recurrence. Moreover, 202 DEGs, including 128 upregulated and 74 downregulated, were identified in the recurrence group compared to the nonrecurrence group  were selected for construction of the ROC curve , with an AUC of 0.882, exhibited the best performance for predicting recurrence and showed remarkable sensitivity and specificity when the cutoff value was 0.593  were identified in the recurrence group compared to the nonrecurrence group  activity, oxygen transporter activity, structure of the cytoskeleton, and aromatase activity. Concerning the Biological Process terms, the DEGs were significantly enriched in the steroid metabolic process, oxygen transport, the GPCR signaling pathway, the sensory perception of smell, the drug metabolic process, and epidermis development. Concerning the Cellular Component terms, the DEGs were significantly enriched in hemoglobin complex and organelle membrane Figure .In KEGG pathway enrichment analysis, the 202 DEGs were significantly enriched in six signaling pathways: steroid hormone biosynthesis, retinol metabolism, xenobiotic metabolism‐CYP450, chemical carcinogenesis, drug metabolism‐other enzyme, and drug metabolism‐CYP450 , the P values of ABCG2, CACNA1F, CYP19A1, and TF were <.05, <.05, .006, and <.05, respectively. For the N stage (N0 vs N1+2), the P values of ABCG2, CACNA1F, CYP19A1, and TF were <.01, <.05, .003, and <.01, respectively. For the M stage (M0 vs M1), the P values of ABCG2, CACNA1F, CYP19A1, and TF were <.01, <.01, <.01, and <.05, respectively. The high expression of ABCG2, CACNA1F, CYP19A1, and TF was also associated with poor OS Single biomarkers, such as ZNF561, WFS1, SLC2A1, and PTGR1), with an AUC of 0.882, exhibited the best performance for predicting recurrence and showed remarkable sensitivity and specificity when the cutoff value was 0.593. Therefore, these four genes were selected to create the GRM and achieved excellent predictive values of recurrence, with an AUC of 0.882 in stage II CRC. To further test and verify the diagnostic effectiveness of the GRM, we extracted another gene expression profiling dataset from the GEO (GSE12032) for further validation. To our excitement, all the genes in the GRM were included in the top 10 DEGs ranked by the MDG with the random forest classifier  and epidermal growth factor receptor (EGFR),HIF)VEGF, to actualize angiogenesis, cell proliferation and protection against ischemic injury.Hypoxia (low oxygen concentration) and ischemia (low hemoglobin concentration), which are caused by insufficient vascularization, are hallmarks of solid tumors (including CRC).Heme has important functions in transportation, catalysis, and electron and signaling transfer and serves as a prosthetic group in heme binding. Heme binding is widely observed in tumor lesions and cancer cells, and heme‐binding ability is necessary for DNA damage resistanceEGFR, insulin/insulin‐like growth factor 1 receptors and other cell surface receptorsG protein‐coupled receptors comprise the largest superfamily of receptors involved in transmembrane‐initiated transduction pathways and can crosstalk (or transactivate) with HIF, VEGF, iron ion binding, and GPCR may be good options for preventing the recurrence of stage II CRC.Among the mechanisms involved in the above biological processes, oxygen binding,In KEGG analysis, the main enrichment pathways were xenobiotic metabolism (including drug and retinol)‐CYP450/other enzymes, chemical carcinogenesis and steroid hormone biosynthesis.Xenobiotic metabolism involves the metabolism of potentially harmful compounds that can enter the body together with food, environmental components, drugs or food additives. Xenobiotic metabolism enzymes include cytochrome P450 (CYP), the glutathione S‐transferase (GST) family, the uridine 50‐diphospho–glucuronosyltransferase (UDP‐glucuronosyltransferase‐UGT) superfamily, alcohol‐metabolizing enzymes, sulfotransferases, etc. Under normal circumstances, xenobiotic metabolism enzymes play the role of detoxification. However, they can also convert certain chemicals into highly toxic metabolites to trigger chemical carcinogenesis, which is known as ‘‘bioactivation’’.Steroid hormone and its receptor have protective functions against the progression of CRCRetinol inhibits CRC cell proliferation,Based on the above mechanisms of the enriched pathways, we hypothesized that the recurrence of stage II CRC is likely due to “the breach of duty” of the xenobiotic metabolism enzymes, leading to the decompensation of retinal metabolism and the inhibition of steroid biosynthesis, and ultimately the development of recurrence. The metabolism of other potentially harmful compounds may also be involved in regulating xenobiotic metabolism enzymes while avoiding the invasion of xenobiotics and supplementing retinal and steroid hormones, which may be good options for preventing the recurrence of stage II CRC.ABCG2, which is a membrane‐associated protein, can relieve oxidative stress and the inflammatory response by inhibiting the NF‐κB signaling pathway, and the high expression of ABCG2 in CRC tissues may represent feedback of the overoxidative reaction, which is associated with a poor prognosis.ABCG2, as a marker of chemoresistance in CRC, could enhance the efficacy of Hypericin‐mediated photodynamic therapy. CACNA1F is a voltage‐gated calcium channel that is mainly expressed in the human retina, but it has also been reported to be widely distributed outside the retina, including in the immune system.CACNA1F plays roles in cell proliferation, migration, and apoptosis,CACNA1F is needed. CYP19A1 is a member of the CYP450 superfamily that, as a monooxygenase, catalyzes drug metabolism and the synthesis of cholesterol, steroids and other lipids. Many researchers suggest that polymorphisms in CYP19A1 are related to CRC risk and may be influenced by estrogen through an inflammation‐related mechanism.TF protein is to transport iron from the intestine, reticuloendothelial system, and liver parenchymal cells to all proliferating cells in the body. TF is an iron‐transporting protein that can transfer the iron absorbed by the intestinal mucosa to the bone marrow for hemoglobin formation in normoblasts.TF is also a growth factor of all proliferated and cultured cells.TF is synthesized for its own specific proliferation and differentiation in tumor tissue. Based on the biological properties of TF, TF in the feces is used as a blood marker for CRC screening, with a sensitivity and specificity of 92% and 72.0%, respectively.To further highlight the hub genes that play the most important roles in recurrence and to explore their interactions, a PPI network was constructed, and four hub genes were selected. However, our current study has the following limitations: (a) Because we were unable to obtain complete clinical data, we were unable to implement a comparison of predictive effectiveness between our GRM and high‐risk factors defined by the NCCN guidelines. (b) Our study is based on a mRNA evaluation from the TCGA and GEO databases; therefore, it is not as persuasive as the level of protein expression, and we did not use clinical samples to further verify its authenticity. (c) It is a retrospective study; therefore, the evidence level is imperfect. With regard to potential limitations, our GRM and hub genes relying on the advantages of excellent predictive values and reasonable statements should be validated in future, prospective, multicenter clinical trials. In addition, biomarkers that show promising predictive value for the survival benefit of adjuvant chemotherapy in stage II CRC patients should be discovered at the same time.In summary, our findings showed that the GRM can effectively classify stage II CRC patients into groups with high and low risks of recurrence, thereby making up for the prognostic value of the traditional clinicopathological risk factors defined by the NCCN guidelines. Moreover, various pathways and hub genes involved in the recurrence progression of stage II CRC were revealed, which may be useful therapeutic targets. Thus, the GRM and hub genes could offer clinical value in directing individualized and precision therapeutic regimens for stage II CRC patients.5In conclusion, the GRM we established using stage II CRC data from the TCGA by random forest variable hunting showed robust diagnostic effectiveness and was further validated with GEO data, supporting its robust ability in the personalized prediction of recurrence. In addition, GO, KEGG, PPI network analyses, and hub gene selection revealed the underlying mechanism of recurrence to a certain extent. Thus, the GRM and hub genes could offer clinical value in directing individualized and precise therapeutic regimens for stage II CRC patients.The authors report no conflict of interest in this work.


Canthigaster rostrata) is a widespread tetraodontid species within the western Atlantic. Mass settlement of juveniles into foraging habitats have been associated with large-scale puffer fish mortality events. In 2013, 2014, and 2017, puffer mortality events on the southern Caribbean coast of Costa Rica were also associated with strandings of green turtles (Chelonia mydas) found to have fed on C. rostrata. Stranded sea turtles were found dead without apparent cause or alive with severe neurological signs that resolved during short periods of captivity. Puffer fish and turtle organ samples were analyzed for both TTXs and STXs. Concentrations of TTXs were extremely low in the fish (0.5–0.7 μg/g) and undetectable in turtle stomach contents. However, concentrations of STXs in whole fish (16.6–47.5 μg STX-eq/g) exceeded the 0.8 μg STX-eq/g human seafood safety threshold for STXs by orders of magnitude. Saxitoxins were also detected in samples of stomach contents (ingested fish), brain, lung, kidney, and serum from three affected turtles. Study results indicate that saxitoxicosis resulting from opportunistic foraging on C. rostrata during fish mortality events may be a significant factor in episodic stranding of green sea turtles in this region.Fish within the family Tetraodontidae are potential sources of both endogenous tetrodotoxins (TTXs) and dietary derived saxitoxins (STXs). Ingestion of fish tissues containing these toxins by other vertebrates can lead to severe illness and death. The Caribbean sharpnose puffer ( Canthigaster rostrata: Tetraodontidae) is a common reef fish found in the western Atlantic. They are omnivorous and feed on a variety of benthic invertebrates   . These eennetti) –4.Alexandrium and Pyrodinium and bioaccumulate in higher trophic levels through the food chain. The occurrence, concentration, and relative tissue distribution of TTX and STX in puffers can vary greatly by species, even within the same system (The tissues of tetraodontid puffers may contain tetrodotoxins (TTXs)  and alsoe system . Both ofe system . To our C. rostrata coincident with strandings of green turtles (Chelonia mydas). Stranded turtles were found dead or alive with abnormal neurological signs. Here we present evidence that the turtles were exposed to and suffered ill effects of STXs, and not TTX, as the result scavenging on C. rostrata during fish mortality events.During 2013, 2014, and 2017, residents of the Limón Province on the southern Caribbean coast of Costa Rica observed mass mortality of Local residents of the Limón Province opportunistically record observations related to sea turtles and other marine life, including sea turtle strandings and other wildlife mortality. During the 2013 event, a recently deceased green sea turtle and fish samples were submitted to National Veterinary Service (SENASA) pathology laboratory for analysis. During the 2014 and 2017 events, live and deceased green turtles found during the events of this report were examined by a veterinarian from the Jaguar Rescue Center. Complete gross necropsies were performed both at SENASA and Jaguar Rescue Center on deceased turtles using standard techniques. Samples of gastric content and organs from dead turtles and puffers found on the beach were collected and frozen at −80°C until analysis.n = 3) and samples collected from affected green turtles (n = 2) in 2013 and 2014 were imported into the U.S.  for analyses at the Florida Fish and Wildlife Conservation Commission, Florida Fish and Wildlife Fish and Wildlife Research Institute (FWRI) and US Food and Drug Administration, Center for Food Safety and Applied Nutrition (CFSAN). A pooled sample of approximately 10 juvenile puffers and stomach contents  from a deceased green turtle collected during a subsequent mortality event in April-May 2017 were analyzed for STXs in Costa Rica by the Unit of Residues and Contaminants in Food of Aquatic Origin of the Department of Food Safety of the National Laboratory of Veterinary Services (LANASEVE), National Service of Animal Health, Ministry of Agriculture and Livestock. In total, samples analyzed for STX included gastric contents  from single turtles collected during events in 2013, 2014, and 2017, serum and tissues from the turtle necropsied from the 2013 event, and analysis of puffers collected from the environment during the 2013 and 2017 events. Analysis for TTX was only conducted on puffers collected during the 2013 event.We performed biotoxin analyses at different laboratories during the years of these events based on availability to receive samples and regional capability. A description of all samples, analyses, and laboratories is provided in For STX analysis at FWRI, tissues were homogenized, and 5 ml of 0.1 M HCl was added to a 5 g aliquot of each sample. The mixture was adjusted to pH 2.5–4, boiled for 5 min in a water bath, and centrifuged at 3,000 × g for 10 min. The supernatant was retained, and a 0.5 g equivalent was passed through a pre-conditioned SPE C18 cartridge . The effluent was collected, the cartridge was rinsed with 2 ml deionized water, and the rinse water was combined with the effluent. Extracts were assayed for STXs using the Abraxis Saxitoxin (PSP) ELISA (enzyme-linked immunosorbent assay). Samples analyzed at LANASEVE were extracted using 0.1 M HCl as described above and were deproteinated using trichloroacetic acid (30% v/v) as described in Rourke et al. . Saxitox2 and reconstituted in 10 mL of 1% acetic acid/water. Two mL of the final suspension were defatted with 7 ml of chloroform by vortexing followed by centrifugation at 2,500 × g. The aqueous extract was then filtered and diluted 1:10 with 1% acetic acid/water as described above. Extracts were analyzed by LC-MS/MS using a Shimadzu Nexera X2 LC30 UPLC coupled to an 8,050 triple quadrupole mass spectrometer. The UPLC was equipped with a TSK-gel amide 80 column (2.1 × 15 cm) and operated in isocratic mode, with a mobile phase of 65% acetonitrile/35% water/50 mM formic acid/2 mM ammonium formate flowing at 0.4 mL/min. Based on initial HPLC-FL findings, the mass spectrometer was configured to monitor 3 transitions each of toxins TTX, STX, dcSTX, and GTX-5, with a single transition for each used for quantitation .Analyses of puffer fish from the 2013 mortality event for both TTX and STX was conducted at CFSAN using high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Three fish ranging in size from 0.8 to 1.5 g were analyzed independently for toxin content by extracting twice with 10 mL each of 1% acetic acid/water through homogenization with a Polytron model PT 10–35 immersion dispenser fitted with a model PTA 20/2W generator . Combined supernatants were brought up to 20 mL with 1% acetic acid/water. Samples were then filtered by syringe fitted with a 0.22 mm filter and diluted 1:10 with 1% acetic acid/water before analyzing by LC-MS/MS. Contents from one turtle stomach collected in 2013  were analyzed for toxin content by homogenizing as described above followed by extraction as described in Deeds et al.  and analyzed at the Laboratorio de Fitoplancton Marino, Escuela de Ciencias Biológicas, Universidad Nacional. Surface water samples were collected at 1 and 5 m and preserved with Lugol's solution. The quantitative analysis of microalgae was carried out using the Utermöhl method with 10 mL columns and an inverted microscope. The concentration of the microalgae were expressed in number of cells/L. To our knowledge, no water samples were collected during the other events.C. rostrata was reported in Limón Provence, first in Punta Uva and then involving beaches in Cocles and Gandoca. Concurrent with these reports were observations of stranded green turtles and dogs and cats becoming ill after feeding on dead puffers. Based on information obtained from a local Sistema Nacional de Áreas de Conservación (SINAC) official, we estimate that approximately 52 green turtles were found stranded during the period of puffer mortality. Most were found dead and in various states of decomposition. Two live green turtles exhibiting paralysis or extreme paresis were taken into captivity and placed into shallow water to prevent drowning. Both animals recovered within 24–48 h to the degree that they could be released. Additional diagnostic evaluation and treatment was not pursued due to limited resources in the area. One juvenile male green turtle  in good postmortem was found floating and was collected for necropsy. This animal was in good nutritional condition and had abundant froth throughout the respiratory tract (interpreted as seawater aspiration). Multiple C. rostrata were found within the caudal esophagus and partially digested remains of the same species filled the stomach  and found to have many partially digested Results of analyses for STXs and TTX for samples collected during all events are shown in Prorocentrum micans and Protoperidium divergens, were present in water samples collected near the end of the 2013 event.No algal blooms or water discoloration events were reported during the mortality events. Low concentrations (100 cells per L) of two species of dinoflagellate, C. rostrata. Our evidence includes demonstration of foraging on puffers by green turtles, detection of relatively high concentrations of STXs in the dead puffers, toxin absorption by turtles, documentation of neurological abnormalities in live stranded turtles, and exclusion of other apparent causes of sea turtle stranding based on a limited number of examined animals. Affected turtles ultimately may have died from neurological dysfunction or drowning secondary to toxicosis as evidenced in our necropsy findings. Saxitoxicosis in green turtles during these events appears to have resulted from opportunistic foraging on puffers that died or were moribund. The affected life phase of green turtle is primarily herbivorous; however, they will opportunistically consume easily available sources of fish in some circumstances and readily take fish in captivity (Stacy pers. obs).Although our sample sizes were very small due to logistical constraints within the study area, we were able to identify a probable cause of the sea turtle strandings and link them with episodic mass mortality of C. rostrata ranged from 16.64 to 47.45 μg STX-eq/g. These concentrations exceed the human seafood safety threshold (0.8 μg/g) by orders of magnitude . Mortality due to suspected saxitoxicosis also has been reported in an estuarine turtle, the diamondback terrapin , with lower concentration of STXs (0.002–0.12 μg STX eq/g) than we detected in green turtle tissue and gastric contents  in Florida, USA responsible for human poisonings (Pyrodinium bahamense based on co-occurrence and the similar toxin composition in both algae and puffer tissues. The profiles reported in 2017 samples also included C1 and C2, which may indicate a different PSP species played a role in this event. The Landsberg et al. (S. nephalus retained toxicity in captivity for an extended period, suggesting that the STX observed in C. rostrata reported here could have originated from an undetected dinoflagellate bloom that occurred prior to the mortality events.To our knowledge, no STX-producing blooms were reported during the period and area of our observations and water samples associated with the 2013 event did not contain STX-producing dinoflagellates; however, sampling was very limited, and we were unable to find any other available data on phytoplankton monitoring for these areas. Documented STX producers in the Caribbean Sea include amarense . Notablyisonings . This reC. rostrata has not been specifically defined but may be related to mass recruitment of juveniles into foraging areas, as has been reported in other tetraodontid puffers (The cause of episodic or periodic mass mortality of  puffers , 4. This puffers , 22.C. rostrata mass mortality and opportunistic scavenging by green turtles, as well as the sources of the STXs themselves, are needed in order to better understand the occurrence of these events.Our report describes a unique pathway of saxitoxin exposure, linking mass mortality of a common reef fish and biotoxin reservoir with morbidity and death of scavenging sea turtles. Additionally, saxitoxin poisoning has not been previously documented in sea turtles in the Caribbean. Further studies of the drivers of All datasets generated for this study are included in the article/supplementary material.The animal study was reviewed and approved by University of Florida Institutional Care and Use Committee.All authors contributed to data and sample collection and review and preparation of the manuscript. Sample and data analyses were conducted by BS, LF, RB, GH-M, SM, JD, YC, KR, KA, and EV.TF works for Ceiba Consulting, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PRS (one cycle per year) assuming various breeding population sizes and trait genetic architectures. For oligogenic architecture, the maximum relative genetic gain advantage of GARS over PRS was 12–88%, which was observed only during the first few cycles. For the polygenic architecture, GARS provided maximum relative genetic gain advantage of 26–165%, and was always superior to PRS. Average prediction accuracy declines substantially after several cycles of selection, suggesting the prediction models should be updated regularly. Updating prediction models every year increased the genetic gain by up to 33–39% compared to no-update scenarios. For small populations and oligogenic traits, cost per unit gain was lower in PRS than GARS. However, with larger populations and polygenic traits cost per unit gain was up to 67% lower in GARS than PRS. Collectively, the simulations suggest that GARS could increase the genetic gain in small young breeding programs by accelerating the breeding cycles and enabling evaluation of larger populations.Young breeding programs in developing countries, like the Chibas sorghum breeding program in Haiti, face the challenge of increasing genetic gain with limited resources. Implementing genomic selection (GS) could increase genetic gain, but optimization of GS is needed to account for these programs’ unique challenges and advantages. Here, we used simulations to identify conditions under which genomic-assisted recurrent selection (GARS) would be more effective than phenotypic recurrent selection (PRS) in small new breeding programs. We compared genetic gain, cost per unit gain, genetic variance, and prediction accuracy of GARS (two or three cycles per year)  High-yielding and climate-resilient crop varieties are needed to meet increasing demand for food and feed in the developing world (Genomic selection (GS) has been evaluated in many crop improvement programs as an alternative to phenotypic selection . In GS aGS is generally applied to facilitate recurrent selection (RS) schemes  tolerant varieties. A shift to GARS would allow the prediction of breeding value on S0 progenies, potentially shortening the breeding cycle to one generation. Further, GARS could take advantage of the tropical environment to rapidly cycle two to three times per year. The objective of this simulation study was to compare genetic gain, prediction accuracy, genetic variance, and cost under GARS vs. PRS across various genetic architectures, breeding population sizes, and model updating scenarios. We found that by accelerating the breeding cycle and enabling evaluation of larger populations GARS could increase genetic gain and lower cost per unit gain in developing country breeding programs.In most developing countries of the semi-arid tropics, including Haiti and countries in sub-Saharan Africa, sorghum is grown primarily under low-input rain-fed conditions with heterogeneous climate and soil conditions (https://cran.r-project.org/web/packages/AlphaSimR/index.html)  implemented in AlphaSimR. The population parameters used to simulate the founders include effective population size (Ne) of 20 (the approximate number of founders of the Chibas program), number of base pairs = 2x108, and mutation rate = 2x10−8  . All res= 2x10−8 . Genomesh2 = 0.7. For the oligogenic trait, we are referring to a quantitative trait that is controlled by relatively small number of genes, in which most of the trait variation is attributed to the top few genes. In this case, only five loci are responsible for 45% of the trait variation.Traits were simulated with (i) oligogenic or (ii) polygenic architecture by varying the number and effect size of QTL . The olih2 = 0.3. Intra- and inter-locus interactions (dominance and epistasis effects) were not modeled. For each of the two genetic architectures, various population sizes and selection schemes were compared for genetic gain, genetic variance, and prediction accuracies. True breeding value for each individual was calculated by summing effects across all QTL. Genetic gain  was defined and plotted as the true breeding value expressed in standard deviation units of the phenotypic values of cycle 0 (C0) generation.Polygenic architecture was simulated by sampling 500 QTL (50 per chromosome) with effect sizes drawn from a gamma distribution with scale and shape parameter of 1.0 and 0.5, respectively. Variance components were chosen to make heritability For each simulation, a base population (C0 generation) of 400 inbred lines was simulated by randomly mating the founder population, followed by five generations of selfing to create inbred lines. Phenotypic values for each inbred line in the base population was simulated by adding normally-distributed random deviates to each line’s true breeding value. The base population was used as a source of parental lines that were used to create the initial recurrent selection population. The base population is also used as the training population for estimation of markers effects for the genome-wide prediction model.5_pop50, par20_pop200, par100_pop1000 and par200_pop2000, respectively) (File S1A). In all of these scenarios, the top 10% of S1 families were retained each cycle. Since developing countries breeding programs generally have limited field phenotyping capacity, scenarios were also simulated where the number of phenotyped S1 families was capped at 500. In these scenarios , populations were generated by randomly intermating 100 or 200 selected parents to generate 500 S0 individuals, which meant the top 20% or 40% of S1 families were retained, respectively. Assuming that the first selfing generation (S0→S1) was performed during an off-season nursery, a single cycle of PRS was performed each year, for a total of ten cycles of PRS in ten years. For each cycle of PRS, genetic gain, genetic variance, and prediction accuracy were calculated. Prediction accuracy in PRS was calculated as the Pearson correlation between the phenotypic value and true breeding value of the S1 families.PRS was performed by selecting the top 5, 20, 100, or 200 parental lines from the base population based on their phenotypic values. Selected parents were randomly intermated to generate S0 individuals. The S0 individuals were then selfed to generate S1 families. Following the Chibas breeding program’s current PRS method, the S1 families were phenotyped and S1 families with the top phenotypic values were randomly intermated to generate the next cycle. Intermating of 5, 20, 100, or 200 parents generated 50, 200, 1000, or 2000 S0 individuals, respectively   was usedThe GEBVs were used to select the top S0 individuals of C1 population. The selected S0 individuals were randomly intermated to generate the next cycle (C2) recurrent selection population, and so on for 30 cycles. To assess the effect of number of breeding cycle per year, two or three cycles of GARS per year  were compared to PRS. Note, the results for GARS2 and GARS3 represent the same simulation runs, plotted differently to represent the given number of GARS cycles per year. Therefore, given a 10-year time horizon, there were 20 cycles of GARS2 and 30 cycles of GARS3. To align with the 10-year selection cycles of the PRS, only the first 28 and 19 cycles were plotted. During each cycle, the number of parents and populations generated by random intermating of the parents remained constant. Genetic gain, genetic variance, and prediction accuracy were calculated for the different selection schemes, population sizes, and trait genetic architectures. Prediction accuracies in GARS was calculated using a Pearson correlation between the GEBVs and true breeding values of S1 families.Nind = 400) consisted of (i) the 200 top lines from the previous training population based on phenotype values and (ii) the 200 top lines from the previous cycle based on GEBVs. Updating of the training population was performed based on the population size of par100_pop1000, which represents an optimum breeding population size at which genetic gain has plateaued. Changes in genetic gain, genetic variance, and prediction accuracy due to updating were compared to no-update GARS scenarios and PRS.We examined the impact of updating the training populations and prediction models on the genetic gain, genetic variance, and prediction accuracy. Given that cropping-season phenotyping can only be conducted once per year, updating of the training population and prediction model was performed every two and three cycles for GARS2 and GARS3, respectively. The updated training population for the a given cycle . Costs in PRS were calculated for the following activities; planting and field management, pollination, perform replicated yield trial for grain yield, disease resistance/tolerance, stay-green, stem biomass, stem sugar content and other traits. GARS does not require phenotypic evaluation of the S1 population, but estimates breeding values of the S0 individuals. The costs involved in the different activities GARS includes genotyping and phenotyping of the training population, crossing of the parents and genotyping of the S0 individuals. Different genotyping cost, which include US $5, $15, $25, and $35 were used to estimate cost per gain for GARS and compared to that of PRS. Cost per unit genetic gain for the different scenarios were calculated by dividing the cumulative cost per cycle with the corresponding genetic gain.We compared the efficiency of the two selection methods in terms of cost per unit gain. One cycle of PRS in the Chibas sorghum breeding program involves the intercrossing of best parents to generate S0 individuals; planting and selfing of the S0 individuals to generate S1 families; and evaluation of the S1 families in replicated trials. The following costs  were assumed: $38 in the crossing phase, $14 in managing the S0 phase, and $33 replicationt-tests for comparing the genetic gain between GARS and PRS across the different trait genetic architectures and population size. File S3 contains the script used to simulate the breeding program, while File S4 contains the script used to run the cost analysis and the output from the breeding program simulation (genetic gains) that was used to run the cost analysis. Supplemental material available at Figshare: https://doi.org/10.25387/g3.7396781.All relevant data are within the paper and its supplemental files. Figure S1 shows the effects of updating the training population under an polygenic architecture. Figure S2 and S3 describe cumulative cost per unit gain in GARS2 and GARS3, respectively, compared to PRS. Figure S4 shows the costs per unit gain for updating the training population. Figure S5 shows the shift in the trait genetic architecture due to selection. File S1 summarizes the different populations sizes and cost estimates of activities in GARS and PRS. File S2 presents the t-tests in File S2). For the oligogenic architecture, GARS3 yielded significantly higher (P < 0.01) short-term genetic gain (year < 5) than PRS for all population size (P < 0.01) for PRS compared to either GARS2 or GARS3. For the polygenic architecture, GARS3 resulted in higher genetic gain (P < 0.01) over PRS for all population sizes across all selection cycles, except the first year (P < 0.01) than PRS across all population sizes for the polygenic architecture, with maximum relative advantage of 12–99%.We first compared the genetic gain across population sizes, genetic architectures, and selection schemes. The genetic gain per unit time resulting from GARS and PRS differed significantly and depended on population size and genetic architecture  did not result in significant increase in genetic gain (vs. 2D). For polygenic architecture, final genetic gain from GARS3 increased by 36% and 7%, for the same population size increases.  increased by 22% and 3% when population size was increased (from partic gain vs. 2D. creases. . With thP < 0.001). For the oligogenic architecture, genetic variance during early selection years (year < 5) were higher for PRS compared to GARS (P > 0.05) at the smallest population size or (ii) significantly higher (P < 0.01) for GARS than PRS at the largest population size, notably for GARS2. Under the assumptions of limited phenotyping capacity in PRS (maximum population size capped at 500), genetic variance was higher than GARS at the maximum population size . This difference was greater during the early years, but declined in later years. Similarly, the difference in genetic variance between GARS2 and GARS3 was minimal during the early years, but GARS3 lost more variance than GARS2 in later years (P < 0.01). For the polygenic architecture, genetic variance declined to zero only for GARS3 at the smallest population size  given polygenic architecture vs. oligogenic architecture in the later years  and compared these scenarios to no-update GARS and PRS  and polyGiven polygenic architecture, final genetic gain (year 10) was increased by 29% and 13% for updated GARS2 compared to no-update GARS2 and PRS, respectively . For updated GARS3, the increase in final genetic gain was 30% and 40% compared to no-update GARS3 and PRS, respectively . Updating resulted in a greater decline of genetic variance compared to no-update GARS . Predictvs. PRS across population sizes, genetic architectures, and updating scenarios. For small population size, cost per unit gain was lower than GARS regardless of trait genetic architecture and genotyping costs considered. Cost per unit gain was lower by up to 67% in GARS than PRS when larger population size was evaluated, notably at the genotyping cost of $15 or under , and genotyping cost <$15 per sample. With higher genotyping costs, the greater cost efficiency of GARS will be due to larger breeding population (par100_pop1000 and par200_pop2000). The result implied that it would be economically feasible to implement GARS to Chibas breeding program even with relatively expensive genotyping costs (US $15/sample) and moderate breeding population size. When genotyping costs are driven down to $5/sample, GARS is expected to be cost competitive even when traits are oligogenic and highly heritable.Cost-efficiency of evaluating and maintaining the breeding population is also a critical aspect of implementing genomic-assisted recurrent selection  has been used to rapidly generate 100,000-500,000 SNP markers for diverse sorghum germplasm  sorghum varieties that are simultaneously selected for a high grain yield (white hard to semihard grains primarily for human consumption) and forage quality after grain maturity. Our simulations provide guidance on several key decisions regarding resource allocation and capacity building to maximize genetic gain: (1) achieving three cycles of GARS per year (two off-season nurseries per year) will be critical to provide a higher rate of genetic gain; (2) rapid cycling GARS can provide increased genetic gain even at a modest population size ; (3) GARS can be cost-effective for developing countries breeding programs even at modest population size if genotyping cost under US $15; or at higher genotyping costs if larger population can be evaluated; and (4) updating the training population and prediction models using lines that capture new LD will be valuable to maintain high prediction accuracy and genetic gain. It should be noted that the simulation carried out in present study assumes that all selected parents are fertile. In an actual breeding program that employ male sterility system, however, the population segregates for male sterility. Therefore, the crossing pattern of randomly intercrossing the selected parents may differ from the breeding population in which male sterility system segregates in the population.Future studies will need to address other aspects of genetic architecture and resource allocation that could affect implementation of GARS in small young breeding programs in developing countries. These breeding programs, which often evaluate and cross diverse germplasm, may encounter more epistasis than established breeding programs that have fixed favorable genetic interactions  for varietal development. Genomic assisted selection on S1 and subsequent families would reduce the number of inbred families that needs to be phenotyped in replicated plots at multiple locations. It could also lower the cost of inbred line development and allow prediction for a greater number of environments and traits. While the results of the current simulation experiment can be applicable to the varietal development cycle of the recurrent selection in terms of the population size and associated costs, additional analysis may be required to test if complete phenotypic evaluation across multiple location should be done at the S2 and subsequent generations. Rigorous theoretical and empirical evaluation of genomic breeding approaches will be critical for translating technology investments into genetic gains for smallholder farmers.GS can also be applied to prioritize line advancement (