# Impact of Radiation Quality on Microdosimetry and Chromosome Aberrations for High-Energy (>250 MeV/n) Ions

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

^{5}eV. We calculated f(ε,D), defined as single-track spectra of energy deposition $\epsilon $ (eV) imparted to a spherical target exposed to a fixed irradiation dose D (Gy). It was obtained by summing all energy deposition events imparted to the target for each individual track. Our work showed that the indirect contribution accounted for up to 18 to 22% of the energy deposited, on average, per ion track regardless of beam characteristics. The direct contribution, in contrast, displayed a strong dependence to the ion LET and made up most of the track energy deposition. The indirect contribution displayed high probabilities of having low ε (<10

^{4}eV), while the direct contribution induced significant probabilities of having larger ε (>10

^{4}eV), and such probabilities increased with increasing LET.

## 2. Materials and Methods

#### 2.1. Macro-Scale Approach

^{5}eV [6]. To simulate the transport of ion beams in rodents (they are extensively used in ground-based radiobiology experiments [1]), we irradiated a digital mouse (Digimouse) with each of the mono-energetic ion beams separately, using the MC transport code Geant4 [9]. In a typical experimental setup such as that described in [11], mice are not physically constrained. During the irradiation, several mice are contained within a plastic holding box and allowed to move, resulting in mice having different orientations with respect to the facility beam. For the irradiation simulation of the (immobile) Digimouse, we thus used an isotropic beam orientation to approximate the random movement and variability across the different mice. The simulated poly-energetic spectra were then obtained by averaging ion-simulated fluences over intra-abdominal organs (bladder, stomach, spleen, pancreas, liver, and kidneys) as a body averaged surrogate. In recent work, this approach was used to calculate microdosimetry spectra and assess quality factors associated with intestinal and colonic tumorigenesis in APC

^{(1638N/+)}male mice [12]. At the ion energies considered in this study, and given the small size of the Digimouse, dose distributions and fluences observed in the different organs were very similar. We used the average as a representative value of the fluence.

#### 2.2. Micro-Scale Approach

_{T}fixed to 4 μm and for a dose D, with either mono-energetic beams or poly-energetic spectra. To that end, we used the MC tool RITRACKS [7], which performs event-by-event tracking of energy deposition of ions in liquid water. As the δ-electrons ejected following interactions of ions with water molecules can have enough energy to travel several millimeters, we applied periodic boundary conditions (PBCs) to mimic the contribution of δ-electrons generated in neighboring volumes by tracks that have not intercepted the nucleus. Ion tracks were categorized as either direct when the ion path crossed the nucleus (red track on Figure 1), or indirect otherwise (blue track on Figure 1). The ions could either originate from the primary beam or be created by the interaction of the beam with the mouse tissues in case of poly-energetic beams. Single-ion energy deposition spectra normalized to the dose D, f(ε), were obtained at the end of this step, by summing all energy deposition events imparted to the target for each individual track. The total contribution, ${f}_{\mathrm{tot}}\left(\epsilon \right)$, was broken down into direct (${f}_{\mathrm{dir}}\left(\epsilon \right)$) and indirect contributions (${f}_{\mathrm{ind}}\left(\epsilon \right)$) by summing energy deposition events due to direct or indirect tracks only. In this context, the direct and indirect contributions are different from the direct and indirect effects, which are terms commonly used in radiobiology for the damage to biomolecules by ionizations (direct effect) or resulting from the reactions of radical species (indirect effect).

^{3}voxels that mapped the cell nucleus (step 2). In parallel, a random walk algorithm was applied to model the 3D distribution of the 46 chromatin fibers contained in the nucleus during interphase (step 3). Next (step 4), the number of double-strand breaks (DSBs) was calculated by first locating intersection between interphase chromatin and voxels for which the energy deposited was greater than 0 eV. Then, the number of breaks in a voxel was sampled with a Poisson distribution, with parameter λ proportional to the energy deposited in that voxel. On average, the program yields ~35 DSBs/cell/Gy, with little dependence with ion LET [15,16]. The breaks were categorized as complex if the energy deposited in the voxel was greater than 500 eV, and simple otherwise. Next (step 5), a repair algorithm was applied over a time period of 24 h. Simple breaks followed an exponential decay with a short time constant (1.7 h) while complex breaks followed an exponential decay with a longer time constant (23.7 h) [17]. The algorithm proceeded in small time steps (typically 1 s), during which simple breaks could either be properly rejoined, or remained unrepaired. Additionally, complex breaks can be improperly repaired, leading to the formation of chromosome aberrations. The probability for mis-repair depended on the Euclidian distance between two complex breaks. Lastly, (step 6), chromosome aberrations were classified. In this work, we focused on simple and complex exchanges. Simple exchanges were defined as exchanges that involved 2 breaks in 2 chromosomes (dicentrics and translocations). This is illustrated on Figure 1. Complex exchanges were defined as exchanges that involved more than 2 breaks, in 2 or more chromosomes.

_{av}, and contribution i (total, direct or indirect), for either mono-energetic beams or associated poly-energetic spectra. Note that D

_{av}represents the average dose obtained by RITRACKS at the end of a simulation. While for mono-energetic beams, this corresponds closely to the input dose D, we obtained a systematic deviation from the input dose D for poly-energetic spectra, from 1 to 10% depending on the beam energy. This was due to the extreme energies of the particle spectra, mainly low-energy heavy ion target fragments generated within Digimouse, in which the cross sections and LET values require further investigation. Nonetheless, these minor deviations are not expected to appreciably alter the results or conclusions of this work.

_{i}and β

_{i}values, out of which the average values, ${\mu}_{{\alpha}_{i}}$ and ${\mu}_{{\beta}_{i}}$, and the standard deviations, ${\sigma}_{{\alpha}_{i}}$ and ${\sigma}_{{\beta}_{i}}$, were computed. We also calculated the 95% prediction interval (PI).

## 3. Results and Discussion

#### 3.1. Mono-Energetic Beam vs. Poly-Energetic Spectra

#### 3.2. Microdosimetry

^{4}eV), except for very low-LET beams. Increasing the beam LET shifts the peak towards higher ε values. Conversely, ${f}_{\mathrm{ind}}\left(\epsilon \right)$ shows little dependence on the beam LET, consistent with our previous work [6]. Indeed, as we previously showed, the ions considered in this work have high energies (≥250 MeV/n) and a similar energy distribution of emitted electrons. The indirect contribution is mostly due to longer-range (>few μm) δ-electrons that have thus similar energy deposition patterns regardless of the beam LET. The indirect contribution is responsible for low single-ion energy deposition (ε < 10

^{4}eV) and represents ~18 to 22% of the single-ion energy deposition in the target, with the contribution increasing with increasing ion energy.

^{4}eV), resulting in ${f}_{\mathrm{dir}}\left(\epsilon \right)$ having a broad distribution across ε rather than a peaked one. This is also explained by the production of secondary ions, but in this case, the secondaries are associated mainly with projectile fragments with mass and charge less than or equal to the primary beam. The energy and LET of these particles are broadly distributed as shown in Figure 2. Consequently, secondary low- to mid-LET ions can cross the target and lead to small amount of energy deposition that we do not observed for mono-energetic ion beams.

#### 3.3. Chromosome Aberrations

#### 3.3.1. Analysis of the Sub-Contributions for Mono-Energetic Beams

_{α}and μ

_{β}are independent of the beam LET. The direct contribution is thus responsible for the majority of the simple and complex exchanges, as Table 2 and Figure 6 and Figure 7 show. Moreover, the relative contribution of indirect simple exchanges at a fixed dose decreases for increasing LET. At 1 Gy, it is equal to 15.6% for a LET of 0.22 keV/μm, which is close to the relative indirect energy deposition. It slowly decreases as the LET increases and reaches only 5.2% for the highest LET (151 keV/μm).

_{α}and μ

_{β}the mean values of the distribution of α and β parameters (Equation (1)) and σ

_{α}and σ

_{β}the standard deviations of that distribution. μ

_{α}and μ

_{β}are usually very close for total and direct + indirect contributions. However, slight differences could arise from the fact that breaks generated from indirect and direct energy depositions may interact together and form additional chromosome aberrations (either simple or complex) that we do not observe when simply adding the chromosome aberrations formed independently by the direct and indirect contributions. Indeed, we observe that for a few datapoints (e.g., complex exchanges for O 325 MeV/n and doses > 0.5 Gy as shown in Appendix A, Figure A3 and Figure A4), the number of chromosome aberrations for the total contribution appears to be greater than that for the direct + indirect contribution. In such cases, it is possible that breaks from the indirect contribution interact with breaks from the direct contribution and form complex exchanges that are not observed with the direct contribution alone.

#### 3.3.2. Analysis of the Effect of Beam Transport

_{α}and μ

_{β}values for poly-energetic spectra for complex exchanges a low-LET (≤1.56 keV/μm) values. This is consistent with microdosimetry single-ion energy deposition spectra, which showed a significant increase of high-energy deposition when accounting for beam transport. Such energy deposition patterns are well known for inducing efficiently complex exchanges. However, as Figure 7 shows, while this increase is significant for low-LET beams, the number of complex exchanges remains relatively small compared to the number of complex exchanges for high-LET beams (e.g., for Fe 1000 MeV/n).

## 4. Conclusions

^{4}eV) attributable to low-energy, high-LET nuclei produced by inelastic interactions between the incident beam and the mouse tissues. For high-LET ions, we found that the energy distribution changes from a peaked distribution (for mono-energetic beams) towards very high-energy deposition (>10

^{4}eV), to a peaked distribution with a tail in the low-energy deposition range (<10

^{4}eV). This tail is due to the production of low- to medium-LET secondaries produced in the mouse phantom from inelastic interactions.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A. RITRACKS/RITCARD Simulation

#### Appendix A.1. Simulation of Micrometric Volume Irradiation

#### Appendix A.2. Single-Ion Energy Deposition Spectra

^{3}(low-LET beams) up to 10

^{6}(high-LET beam). Raw histograms obtained with RITRACKS were analyzed using an adaptive Kernel Density Estimation approach [28]. As $f\left(\epsilon ;D\right)$ scaled with the dose $D$, it was normalized to $D$ so that

#### Appendix A.3. Chromosome Aberrations

^{3}voxels that mapped the cell nucleus. In this study, the cell nucleus was of spherical shape with a radius of 4 μm to match the size of the target considered for microdosimetry calculations.

^{−1}is an adjustable parameter representative of the intensity of DSB formation. The number of breaks in a monomer was rarely greater than 1, except in high-dose voxels in the core of high-LET tracks. On average, RITCARD yielded ~35 DSB/Gy/cell with little dependence with ion LET [15], as reported elsewhere [16]. Each break in a chromatin fiber led to the formation of two chromatin free ends.

^{2}is an adjustable parameter. Equation (A8) reflects the fact that breaks further away from each other have a lower probability to recombine together. The algorithm then used the calculated probabilities to sample one free end for the selected break to repair with, thus leading to either proper or improper repair.

#### Appendix A.4. Dose–Response Statistical Analysis

**Figure A1.**Linear quadratic fit for the dose response for mono-energetic O 325 MeV/n beam and simple exchanges. On the top left (

**a**), dots are the results from RITCARD while the solid line represents the least squares fit, and the dashed line the 95% PI. The marginal PDF of the α and β are plotted on the top right (

**b**) and bottom left (

**c**), while the bottom right (

**d**) shows samples of the joint density.

**Figure A2.**Illustration of the PDF, f

_{y}and CDF, F

_{y}, obtained at the dose point D

_{av}= 0.75 Gy, for the contributions i = tot and j = dir + ind, the mono-energetic beam O 325 MeV/n and simple exchanges. The vertical blue line represents the maximum of the difference between F

_{y}

_{,tot}and F

_{y}

_{,dir+ind}.

**Figure A3.**Dose–response comparison between the total vs. direct + indirect, for simple exchanges (upper figures). The figures of merit, ${P}_{\mathrm{tot}\to \mathrm{dir}+\mathrm{ind}}$, ${P}_{\mathrm{dir}+\mathrm{ind}\to \mathrm{tot}}$ and ${{m}^{\prime}}_{\mathrm{KM}}$, are plotted in black, red, and blue, respectively (lower figures). The values of the integrals over the dose (Equations (A18)–(A20)), ${m}_{\mathrm{tot}\to \mathrm{dir}+\mathrm{ind}}$, ${m}_{\mathrm{dir}+\mathrm{ind}\to \mathrm{tot}}$ and ${m}_{\mathrm{KM}}$ are indicated on each sub-figure. Results are displayed for (

**a**) H 1000 MeV, (

**b**) He 250 MeV/n, (

**c**) C 290 MeV/n, (

**d**) O 325 MeV/n, (

**e**) Si 300 MeV/n and (

**f**) Fe 1000 MeV/n.

**Figure A4.**Dose–response comparison between the total vs. direct + indirect, for complex exchanges (upper figures). The figures of merit, ${P}_{\mathrm{tot}\to \mathrm{dir}+\mathrm{ind}}$, ${P}_{\mathrm{dir}+\mathrm{ind}\to \mathrm{tot}}$ and ${{m}^{\prime}}_{\mathrm{KM}}$, are plotted in black, red, and blue, respectively (lower figures). The values of the integrals over the dose (Equations (A18)–(A20)), ${m}_{\mathrm{tot}\to \mathrm{dir}+\mathrm{ind}}$, ${m}_{\mathrm{dir}+\mathrm{ind}\to \mathrm{tot}}$ and ${m}_{\mathrm{KM}}$ are indicated on each sub-figure. Results are displayed for (

**a**) H 1000 MeV, (

**b**) He 250 MeV/n, (

**c**) C 290 MeV/n, (

**d**) O 325 MeV/n, (

**e**) Si 300 MeV/n and (

**f**) Fe 1000 MeV/n.

**Figure A5.**Dose–response comparison between the total contribution obtained for mono-energetic (ME) vs. poly-energetic (PE) beams, for simple exchanges (upper figures). The figures of merit, ${P}_{\mathrm{ME}\to \mathrm{PE}}$, ${P}_{\mathrm{PE}\to \mathrm{ME}}$ and ${{m}^{\prime}}_{\mathrm{KM}}$, are plotted in black, red, and blue, respectively (lower figures). The values of the integrals over the dose (Equations (A18)–(A20)), ${m}_{\mathrm{ME}\to \mathrm{PE}}$, ${m}_{\mathrm{PE}\to \mathrm{ME}}$ and ${m}_{\mathrm{KM}}$ are indicated on each sub-figure. Results are display for (

**a**) H 1000 MeV, (

**b**) He 250 MeV/n, (

**c**) C 290 MeV/n, (

**d**) O 325 MeV/n, (

**e**) Si 300 MeV/n and (

**f**) Fe 1000 MeV/n.

**Figure A6.**Dose–response comparison between the total contribution obtained for mono-energetic (ME) vs. poly-energetic (PE) beams, for complex exchanges (upper figures). The figures of merit, ${P}_{\mathrm{ME}\to \mathrm{PE}}$, ${P}_{\mathrm{PE}\to \mathrm{ME}}$ and ${{m}^{\prime}}_{\mathrm{KM}}$, are plotted in black, red, and blue, respectively (lower figures). The values of the integrals over the dose (Equations (A18)–(A20)), ${m}_{\mathrm{ME}\to \mathrm{PE}}$, ${m}_{\mathrm{PE}\to \mathrm{ME}}$ and ${m}_{\mathrm{KM}}$ are indicated on each sub-figure. Results are display for (

**a**) H 1000 MeV, (

**b**) He 250 MeV/n, (

**c**) C 290 MeV/n, (

**d**) O 325 MeV/n, (

**e**) Si 300 MeV/n and (

**f**) Fe 1000 MeV/n.

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**Figure 1.**Scheme of the geometrical setup for the microdosimetry and chromosome aberration calculations.

**Figure 2.**Poly-energetic beam fluence as a function of ion energy after the transport of mono-energetic beams ((

**a**) H 1000 MeV, (

**b**) He 250 MeV/n, (

**c**) C 290 MeV/n, (

**d**) O 325 MeV/n, (

**e**) Si 300 MeV/n and (

**f**) Fe 1000 MeV/n) in digital mice, averaged over intra-abdominal organs (bladder, stomach, spleen, pancreas, liver, and kidneys).

**Figure 3.**Examples of projected tracks and damages obtained for a dose of 1 Gy, for H 1000 MeV/n (

**a**) + (

**b**), He 250 MeV/n (

**c**) + (

**d**) and C 290 MeV/n (

**e**) + (

**f**). Tracks were clipped to display only energy deposition events inside the nucleus. The direct contribution is displayed in red and the indirect contribution in blue. Simple breaks are represented in green and complex breaks in black. For each beam, the results for mono-energetic (ME) beams are shown on the left ((

**a**,

**c**,

**e**)) and for poly-energetic (PE) beams on the right (

**b**,

**d**,

**f**)).

**Figure 4.**Same as Figure 3 but for O 325 MeV/n (

**a**) + (

**b**), Si 300 MeV/n (

**c**) + (

**d**) and Fe 1000 MeV/n (

**e**) + (

**f**).

**Figure 5.**Single-ion energy deposition spectra, f

_{tot}(ε), in a spherical target. Results are displayed for 6 incident beams ((

**a**) H 1000 MeV, (

**b**) He 250 MeV/n, (

**c**) C 290 MeV/n, (

**d**) O 325 MeV/n, (

**e**) Si 300 MeV/n and (

**f**) Fe 1000 MeV/n), both with (poly-energetic spectra in dashed line) and without (mono-energetic (ME) beam in solid line) beam transport in the Digimouse. f

_{tot}(ε) (in black) is broken down into sub-contributions f

_{dir}(ε) (red) and f

_{ind}(ε) (blue).

**Figure 6.**Simple (

**a**–

**f**) and complex (

**g**–

**l**) exchanges per cell for 6 incident beams (H 1000 MeV/n (

**a**) + (

**g**), He 250 MeV/n (

**b**) + (

**h**), C 290 MeV/n (

**c**) + (

**i**), O 325 MeV/n (

**d**) + (

**j**), Si 300 MeV/n (

**e**) + (

**k**) and Fe 1000 MeV/n (

**f**) + (

**l**)). Results are shown without beam transport (mono-energetic beam in solid line + round marker) and with beam transport in the Digimouse (poly-energetic beam in dashed line + diamond marker). The total (black), direct (red), indirect (blue) and direct+indirect (grey) were fitted with a linear quadratic model.

**Figure 7.**Average values of the LQ coefficients, μ

_{α}and μ

_{β}, for simple (

**a**,

**c**) and complex (

**b**,

**d**) exchanges as a function of the beam LET, both without beam transport (mono-energetic beam in solid line + round marker) and with beam transport in the Digimouse (poly-energetic spectra in dashed line + diamond marker). Error bars represent the standard deviation, σ

_{α}and σ

_{β}.

**Table 1.**List of mono-energetic ion beam properties investigated in this work. LET values were calculated elsewhere [6] and estimated ranges were obtained with SRIM (http://www.srim.org/. Accessed 19 August 2021).

Ion | H^{+} | He^{2+} | C^{6+} | O^{8+} | Si^{14+} | Fe^{26+} |
---|---|---|---|---|---|---|

Energy (MeV/n) | 1000 | 250 | 290 | 325 | 300 | 1000 |

LET (keV/µm) | 0.22 | 1.56 | 12.9 | 21.5 | 68.9 | 149.2 |

Range in water (cm) | 322 | 37.6 | 16.4 | 14.6 | 7.3 | 27.4 |

**Table 2.**Relative contribution of the direct and indirect contributions to the dose, and simple and complex exchanges at 1 Gy. For exchanges, the contributions were compared to the sum of the two contributions rather than the total contribution as both may vary, to reach a ratio of 100%. Standard errors are displayed in parenthesis. R is the ion range in water.

Simple | Complex | |||||||
---|---|---|---|---|---|---|---|---|

LET | R (cm) | H | D_{dir} (%) | D_{ind} (%) | y(1 Gy)_{dir} (%) | y(1 Gy)_{ind} (%) | y(1 Gy)_{dir} (%) | y(1 Gy)_{ind} (%) |

0.22 | 322 | 1426 | 78.6 | 21.4 | 84.4 (12.4) | 15.6 (4.0) | 95.5 (47.0) | 4.5 (6.5) |

1.56 | 37.6 | 201 | 81.5 | 18.5 | 86.4 (12.3) | 13.6 (3.6) | 100.0 (63.1) | 0.0 |

12.9 | 16.4 | 24 | 81.5 | 18.5 | 91.8 (9.5) | 8.2 (2.0) | 97.5 (29.6) | 2.5 (2.9) |

21.5 | 14.6 | 15 | 81.2 | 18.8 | 94.4 (8.0) | 5.6 (1.3) | 98.9 (18.9) | 1.1 (1.3) |

68.9 | 7.9 | 5 | 81.7 | 18.3 | 97.8 (5.2) | 2.2 (0.5) | 99.9 (4.8) | 0.1 (0.1) |

149.2 | 27.4 | 2 | 79.1 | 20.9 | 94.8 (6.9) | 5.2 (1.2) | 100.0 (4.8) | 0.0 (0.1) |

**Table 3.**Dose–response analysis for simple exchange (ME), total vs. direct + indirect, as presented in Section 2.2 and the Appendix A.

Total | Direct + Indirect | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

LET | μ_{α} | σ_{α} | μ_{β} | σ_{β} | R^{2} | μ_{α} | σ_{α} | μ_{β} | σ_{β} | R^{2} | ${\mathit{m}}_{\mathbf{t}\to \mathbf{d}+\mathbf{i}}$ | ${\mathit{m}}_{\mathbf{d}+\mathbf{i}\to \mathbf{t}}$ | ${\mathit{m}}_{\mathbf{KS}}$ |

0.22 | 0.033 | 0.008 | 0.032 | 0.012 | 0.98 | 0.031 | 0.008 | 0.029 | 0.012 | 0.97 | 0.76 | 0.82 | 0.39 |

1.56 | 0.040 | 0.008 | 0.022 | 0.012 | 0.97 | 0.026 | 0.007 | 0.033 | 0.011 | 0.98 | 0.50 | 0.55 | 0.63 |

12.9 | 0.062 | 0.011 | 0.056 | 0.016 | 0.99 | 0.057 | 0.011 | 0.051 | 0.016 | 0.99 | 0.69 | 0.71 | 0.48 |

21.5 | 0.115 | 0.014 | 0.064 | 0.020 | 0.99 | 0.110 | 0.014 | 0.059 | 0.019 | 0.99 | 0.74 | 0.78 | 0.43 |

68.9 | 0.423 | 0.023 | −0.052 | 0.032 | 1.00 | 0.452 | 0.022 | −0.084 | 0.029 | 1 | 0.80 | 0.81 | 0.37 |

149.2 | 0.234 | 0.016 | −0.048 | 0.022 | 0.99 | 0.239 | 0.016 | −0.039 | 0.022 | 0.99 | 0.76 | 0.75 | 0.42 |

**Table 4.**Same as Table 3, but for complex exchange (ME), total vs. direct + indirect.

Total | Direct + Indirect | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

LET | μ_{α} | σ_{α} | μ_{β} | σ_{β} | R^{2} | μ_{α} | σ_{α} | μ_{β} | σ_{β} | R^{2} | ${\mathit{m}}_{\mathbf{t}\to \mathbf{d}+\mathbf{i}}$ | ${\mathit{m}}_{\mathbf{d}+\mathbf{i}\to \mathbf{t}}$ | ${\mathit{m}}_{\mathbf{KS}}$ |

0.22 | 0.001 | 0.004 | 0.007 | 0.006 | 0.82 | −0.003 | 0.003 | 0.012 | 0.005 | 0.9 | 0.80 | 0.89 | 0.31 |

1.56 | 0.000 | 0.003 | 0.008 | 0.005 | 0.84 | 0.001 | 0.003 | 0.004 | 0.004 | 0.76 | 0.76 | 0.92 | 0.30 |

12.9 | −0.003 | 0.007 | 0.036 | 0.011 | 0.96 | 0.001 | 0.006 | 0.021 | 0.009 | 0.94 | 0.62 | 0.72 | 0.45 |

21.5 | −0.004 | 0.011 | 0.081 | 0.018 | 0.98 | 0.002 | 0.010 | 0.059 | 0.015 | 0.97 | 0.63 | 0.70 | 0.45 |

68.9 | 0.659 | 0.055 | 0.539 | 0.078 | 1 | 0.616 | 0.055 | 0.569 | 0.078 | 1 | 0.84 | 0.84 | 0.34 |

149.2 | 1.128 | 0.073 | 0.345 | 0.107 | 1 | 1.116 | 0.071 | 0.307 | 0.102 | 1 | 0.82 | 0.84 | 0.34 |

**Table 5.**Same as Table 3, but total simple exchanges, mono-energetic (ME) beams vs. poly-energetic (PE) spectra.

Mono-Energetic | Poly-Energetic | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

LET | μ_{α} | σ_{α} | μ_{β} | σ_{β} | R^{2} | μ_{α} | σ_{α} | μ_{β} | σ_{β} | R^{2} | ${\mathit{m}}_{\mathbf{ME}\to \mathbf{PE}}$ | ${\mathit{m}}_{\mathbf{PE}\to \mathbf{ME}}$ | ${\mathit{m}}_{\mathbf{KS}}$ |

0.22 | 0.034 | 0.008 | 0.032 | 0.013 | 0.98 | 0.048 | 0.009 | 0.038 | 0.014 | 0.98 | 0.15 | 0.13 | 0.89 |

1.56 | 0.040 | 0.009 | 0.021 | 0.012 | 0.98 | 0.039 | 0.008 | 0.030 | 0.011 | 0.99 | 0.79 | 0.79 | 0.34 |

12.9 | 0.062 | 0.011 | 0.055 | 0.016 | 0.99 | 0.068 | 0.012 | 0.055 | 0.017 | 0.99 | 0.86 | 0.81 | 0.36 |

21.5 | 0.115 | 0.014 | 0.064 | 0.020 | 0.99 | 0.136 | 0.014 | 0.039 | 0.019 | 0.99 | 0.76 | 0.76 | 0.40 |

68.9 | 0.423 | 0.020 | −0.051 | 0.028 | 1 | 0.414 | 0.021 | −0.071 | 0.028 | 0.99 | 0.51 | 0.51 | 0.61 |

149.2 | 0.235 | 0.017 | −0.049 | 0.023 | 0.99 | 0.223 | 0.017 | −0.021 | 0.026 | 0.99 | 0.85 | 0.82 | 0.31 |

**Table 6.**Same as Table 3, but total complex exchanges, mono-energetic (ME) beams vs. poly-energetic (PE) spectra.

Mono-Energetic | Poly-Energetic | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

LET | μ_{α} | σ_{α} | μ_{β} | σ_{β} | R^{2} | μ_{α} | σ_{α} | μ_{β} | σ_{β} | R^{2} | ${\mathit{m}}_{\mathbf{ME}\to \mathbf{PE}}$ | ${\mathit{m}}_{\mathbf{PE}\to \mathbf{ME}}$ | ${\mathit{m}}_{\mathbf{KS}}$ |

0.22 | 0.000 | 0.004 | 0.008 | 0.006 | 0.83 | 0.042 | 0.014 | 0.034 | 0.021 | 0.95 | 0 | 0 | 1.00 |

1.56 | 0.000 | 0.003 | 0.008 | 0.005 | 0.83 | 0.017 | 0.010 | 0.023 | 0.013 | 0.93 | 0.6 | 0.01 | 0.98 |

12.9 | −0.003 | 0.007 | 0.037 | 0.011 | 0.96 | −0.001 | 0.008 | 0.042 | 0.012 | 0.97 | 0.75 | 0.67 | 0.47 |

21.5 | −0.004 | 0.011 | 0.081 | 0.017 | 0.98 | −0.002 | 0.012 | 0.088 | 0.018 | 0.98 | 0.85 | 0.79 | 0.35 |

68.9 | 0.653 | 0.056 | 0.546 | 0.079 | 1 | 0.764 | 0.060 | 0.477 | 0.084 | 1 | 0.34 | 0.31 | 0.77 |

149.2 | 1.127 | 0.070 | 0.344 | 0.103 | 1 | 1.147 | 0.076 | 0.236 | 0.120 | 1 | 0.75 | 0.72 | 0.39 |

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**MDPI and ACS Style**

Poignant, F.; Plante, I.; Crespo, L.; Slaba, T.
Impact of Radiation Quality on Microdosimetry and Chromosome Aberrations for High-Energy (>250 MeV/n) Ions. *Life* **2022**, *12*, 358.
https://doi.org/10.3390/life12030358

**AMA Style**

Poignant F, Plante I, Crespo L, Slaba T.
Impact of Radiation Quality on Microdosimetry and Chromosome Aberrations for High-Energy (>250 MeV/n) Ions. *Life*. 2022; 12(3):358.
https://doi.org/10.3390/life12030358

**Chicago/Turabian Style**

Poignant, Floriane, Ianik Plante, Luis Crespo, and Tony Slaba.
2022. "Impact of Radiation Quality on Microdosimetry and Chromosome Aberrations for High-Energy (>250 MeV/n) Ions" *Life* 12, no. 3: 358.
https://doi.org/10.3390/life12030358