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| ORIGINAL ARTICLE |
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| Year : 2017 | Volume
: 3
| Issue : 1 | Page : 9-17 |
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Identification of Metallic Trace Particles of Injuring Fe-Mn Steel Hammer from Body Trauma
Chunmei Zhao1, Jing Wang2, Qing Chen2, Fanlong Wang2, Hua Feng2, Liu Mengyan2, Xiaobin Zhu2, Li Liu2
1 Beijing University of Chemical Technology, Beijing; Forensic Science Service of the Beijing Public Security Bureau, Beijing, China
2 Forensic Science Service of the Beijing Public Security Bureau, Beijing, China
| Date of Web Publication | 31-Mar-2017 |
Correspondence Address:
Li Liu
No. 1, Longgang Road, Qinghe, Haidian District, Beijing
China
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jfsm.jfsm_23_16
We describe an effective method for extracting metallic trace particles from injured tissue. As a validation test, we used scanning electron microscopy with energy-dispersive X-ray spectrometry with the INCAFeature software package to analyze metallic trace particles deposited by different Fe-Mn steel hammers. Our results demonstrate the feasibility of the proposed method. Based on the proposed method, an effective index is suggested for evaluating elemental composition. This index can be used for determining the nature and composition of residue metallic particles, which is likely to be of importance to forensic pathologists. Keywords: Injury, particle analysis, steel hammer, trace residues
How to cite this article:
Zhao C, Wang J, Chen Q, Wang F, Feng H, Mengyan L, Zhu X, Liu L. Identification of Metallic Trace Particles of Injuring Fe-Mn Steel Hammer from Body Trauma. J Forensic Sci Med 2017;3:9-17 |
How to cite this URL:
Zhao C, Wang J, Chen Q, Wang F, Feng H, Mengyan L, Zhu X, Liu L. Identification of Metallic Trace Particles of Injuring Fe-Mn Steel Hammer from Body Trauma. J Forensic Sci Med [serial online] 2017 [cited 2023 Feb 3];3:9-17. Available from: https://www.jfsmonline.com/text.asp?2017/3/1/9/203550 |
| Introduction | |  |
In the field of forensic science, research on trace residues deposited by injury-inflicting objects is still nascent and quantitative methods are necessary. Few feasible and effective methods have been proposed for collecting and extracting residue particles. The determination of the nature of trace residue particles deposited by injury-inflicting objects in injured tissue has been an important issue in the forensic science. It is very difficult to collect and examine trace residue particles because injury often severely disrupts the tissue structure and introduces contamination, complicating the residue extraction process. Owing to these factors, identification methods of injury instruments have been limited to morphological analysis.[1],[2],[3],[4],[5] Tests that aim to determine the nature of trace residue particles associated with injury instruments have been limited to elemental analysis.[6],[7],[8] There is a long history of efforts to develop methods for detecting and extracting trace metallic particles from wounded tissue. Typically, a piece of dry tissue or bone fragment has been used in conjunction with scanning electron microscopy (SEM) to detect trace metallic particles. However, this method is elaborate, impractical for quantitative evaluation and only allows elemental analysis.[8] Trace residue tests have long remained limited to experimental laboratories because the conductivity of human tissue is low and trace particles are often obscured by the tissue structure and irregularity of the wounded surface. Thus, well-calibrated methods for routine analysis are very much needed.
Bai et al. used inductively coupled plasma atomic emission spectrometry to analyze trace components of several kitchen knives after being resolved in aqua regia.[9] This test helped discriminate elemental composition details of the knives but did not provide a method for detecting trace metallic particles deposited in the injured tissue. Tests of trace evidence are important in forensic science and can help determine injury motives, identify injury-inflicting objects, and reconstruct the crime scene.
SEM with energy-dispersive X-ray spectrometry (SEM/EDX) has been widely used in morphological tests and trace evidence tests, such as tests of paints, gunpowder, and soil. SEM/EDX is advantageous in testing for the presence of inorganic components of trace particles on the micrometer scale and is especially effective in the elemental analysis of metallic particles.[10],[11],[12],[13],[14],[15] The INCAFeature software package, developed by Oxford Instruments, UK, has been used for testing gunshot residues (GSRs).[16],[17] We found this software to be effective for analyzing the components of inorganic metallic particles. Using suitable parameters, the elemental components as well as contents of inorganic particles under given conditions can be analyzed and sorted using this method. GSR software has also been used for analyzing airbag residues,[18],[19] yielding good results. SEM/EDX is the only existing method for analyzing components of mixed trace particles. In this study, we developed an effective model for collecting residual foreign particles from injured tissue and for analyzing those particles using SEM/EDX.
Using our method, micrometer-scale trace residues deposited by injury-inflicting objects were extracted from injured tissue for the first time. Thus, our method is feasible for extracting and identifying small residue particles from injured tissue. Metallic component analysis was performed using SEM/EDX, and the INCA Feature software package was used for full-field scanning of the samples and identifying metallic particles deposited by injury-inflicting objects. The analysis required adjusting certain parameter values. The following parts were discussed in this article: (1) characterization of metallic residues associated with injury-inflicting objects; (2) extraction of trace particles from injured tissue; (3) testing and sorting based on the X-ray spectrum; (4) development of component assessment index; and (5) testing the new measure on residue metallic particles from common Fe-Mn alloy hammers. A mathematical assessment was performed to demonstrate the composition stability and ability to identify trace residues.
| Materials and Methods | | |
Materials
Twenty-seven steel hammers were purchased for the tests and their details are shown in [Table 1].
Methods
Sample preparation
Blank control
Starting with ten packs of cotton swabs and ten packs of new qualitative filter paper, we randomly selected one piece of each and used carbon conductive adhesive tape (adhered to the sample stub), hereafter referred to as conductive tape, to tap the cotton swab and filter paper.
Comparison of samples and samples of striking on skin tissue
We used Fe-Mn steel hammers 1 and 3 to strike the blank filter paper with nearly the same force and then used conductive tape (adhered to the sample stub) to tap the struck area on the filter paper repeatedly; this allowed a comparison of the effects of the different hammers on sample 1. We then used hammers 1 and 3 to strike the surfaces of the remaining nine samples of dry pigskin and used conductive tape (adhered to the sample stub) to tap the struck areas on the pigskins to obtain nine samples for each hammer type.
Extraction of residue particles from injured tissue
To model crime scene investigation and autopsies, we used conductive tape (adhered to the sample stub) to tap the wounded surface in cases of a nonopen wound and tapped the intact surface that served as a control sample. In cases of wet and open wounds, we kept the wound clean and used a cotton swab or qualitative filter paper to scrub the inner surface of the wound. [Figure 1] and [Figure 2] show the extraction of residue particles by a cotton swab. When there was too much blood, we used filter paper to scrub the wound at least three times. Then, the filter paper was folded inward to encircle the scrubbing surface and prevent its contamination; we used at least three pieces of cotton swab to enrich the trace evidence. The sample was then dried indoors. The dried sample was transferred to the conductive tape adhered to the sample stub and then coated with a carbon film using a high-vacuum coating machine.
Apparatus and examination conditions
The following materials were used in these studies: medical cotton swabs with independent package, qualitative filter paper (Hangzhou, Shuangquan, China), SEM sample stub, carbon conductive adhesive tape, and twenty pieces of fresh pigskin (15 cm × 10 cm), which were cleaned using organic neutral detergent and running water and dried at room temperature.
The following pieces of apparatus were used in this study: low-vacuum SEM (VEGA II LSU, TESCAN, Czech Republic), EDX (Oxford Instruments, UK), high-vacuum Coating machine (EMITECH K950, UK), stereomicroscope (LEICA MZ16, Germany), and GSR analysis software (INCAFeature, Oxford Instruments, UK). We used the TESCAN VEGA II SEM and Oxford Instruments EDX and secondary electron (SE) and back-scanning electron (BSE) detectors. We also used a lithium-silicon EDX detector (area: 10 mm 2; vacuum: 9 × 10−3 Pa; magnification: ×200–400; working distance: 15 mm; accelerating voltage: 20 kV). Quantitative optimization of the EDX was performed by the manufacturer.
The experimental data were processed and analyzed using Microsoft Office Excel 2007 (Microsoft, USA) and SPSS13.0 (SPSS, USA).
We used the INCAFeature GSR particle analysis software for automatic searching and testing. For the gray level and contrast setting, we selected daily dust particles from the Beijing atmosphere as the reference particles in the view field. Importantly, these particles were eliminated from the dark field when testing.
The classification parameters, testing features, and processing methods were adjusted to enable detection, analysis, and sorting of suspect particles.
Some elements (C and O) were removed during the quantitative analysis. The reason for the removal of O is that different trace residues exhibit different degrees of oxidation.
When a certain element was only weakly present (~2% weight percentage) in the known analyzed particles (for example, metallic particles are known to contain mostly Fe and some trace quantities of Mn and Cr), the live time could be increased from the default of 2–10 s or longer. Using a silicon drift EDX detector, the testing time could be reduced dramatically.
Residue particles in injured tissue are often present along with dust and/or bone material particles (containing P, S, and Ca); thus, a mixture of elements is usually detected.[8] For correct classification, it is important to identify particles with the most pronounced presence of suspect components. Thus, the data must be manually screened after completing the test and before performing statistical analysis, mainly in order to eliminate deviants or components associated with interfering substances.
| Results | | |
Extraction and examination of trace metallic particles
In previous studies, we tried to extract trace particles by scraping the tissue surface, cutting tissue blocks, or inspecting bone fragments and found that it was difficult to extract metallic particles. After a summary study of available experiments, we hypothesized that trace residue particles associated with injury-inflicting objects should be attached to the tissue surface. Consequently, in case studies, we used cotton swabs or filter paper to extract the metallic particles attached to the tissue surface. First, we performed blank tests of cotton swabs and filter paper, and the results are listed in [Table 2]. During extraction of trace substances from different types of injured tissue affected by blunt or sharp tools in crime scene investigations and autopsies, metallic particles in the injured tissue have been found to be different from ambient contamination particles, and the components of those metallic particles were closely related to the respective injury-inflicting objects. [Table 3] lists the statistical analysis results of trace metallic particles for fifty injuries. | Table 2: Results of tests of trace substances on blank cotton swab and blank filter surface
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 | Table 3: Statistic of metallic particles containing suspect Fe detected from fifty body traumas
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As shown in [Table 2], trace dust particles and metallic particles (mostly Fe and Fe-Mn) were actually detected on blank cotton swabs and blank filter paper using SEM/EDX, but at negligible levels. Thus, cotton swabs and filter paper were used as tools for obtaining and quantifying trace evidence.
After collecting trace evidence with the cotton swabs and filter paper, the samples were transferred to the special SEM sample stub. [Figure 3] and [Figure 4] show an individual sample stub with a trace sample from a dried cotton swab after extraction; the stub has been coated with a carbon film in preparation for inspection. | Figure 3: Use sample stub to extract directly by adhering on the dry skin wound
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[Table 3] lists the statistical results of metallic particles containing suspect Fe, obtained from fifty injury cases. The data show that suspect metallic particles were only undetected in fewer than 10% of injury cases. In the following subsection, we describe tests in which we “injured” pigskin and filter paper samples using a variety of hammers to further study the component characteristics of residue metallic particles of injury-inflicting objects, achieving the identification of injury-inflicting objects by analyzing residue particles.
Detection of particles and elimination of contaminants
The key to establishing a good identification method of residue particles deposited by injury-inflicting objects is the method and apparatus of extraction and testing. In the BSE test of SEM/EDX, the propensity for generating BSEs is related to the atomic number of an element, with heavier elements yielding higher brightness. The bright appearance of metallic particles could be observed by adjusting the gray level and the contrast of BSE images (BSEIs). Meanwhile, ambient contaminants that could be attributed to dust particles, fiber particles, tissue cells, and bone fragments disappeared in the dark-field images. Prior knowledge of the composition of certain common particles was necessary for properly adjusting the settings.
[Figure 5] shows an SE image of a metallic particle of a Fe-Mn alloy surrounded by filter paper fibers, tissue cells, and ambient dust particles, on the sample stub, at a magnification factor of ×3800; the size of the particle is ~5 μm. [Figure 6] shows a BSEI of this metallic particle under the same conditions (same surrounding environment). Comparing the two figures, it can be said that suspect metallic particles or other trace substances in injured tissue can be detected using BSEI, which does not detect human tissue or other trace ambient particles. However, some Fe-containing dust particles could also be detected in BSEI; thus, the ability to properly discriminate between Fe-containing dust particles and injury-related Fe particles is very important. [Figure 7] and [Figure 8] show the results of the discrimination analysis performed using the INCAFeature software, including the image and the X-ray spectrum. [Figure 7] shows the results for an oxidized Fe particle, while [Figure 8] shows the results for a Fe-containing dust particle. Since injury-related residue particles tend to be oxidized, this analysis can be used for detecting Fe particles if only Fe and O are present in the X-ray spectrum.
The metallic particles in our test were oxidized. More importantly, the degree of oxidization varied among the particles. Normally, alloy particles in the bulk of a steel tool are not oxidized, and only layers sufficiently close to the surface are likely to be oxidized to a certain degree, which probably underlies the heterogeneity in the O content yielded by the quantitative analysis of tissue residue particles. Meanwhile, residue particles detached from a metal tool have micrometer-scale sizes and can be easily oxidized. Therefore, we changed the quantitative analysis settings in the GSR software to “remove C, remove O” for the below-mentioned data, eliminating O after eliminating C, i.e., neither C nor O was included in the quantitative analysis or calculation. This step of eliminating O is the key step in the component analysis of residue metallic particles deposited by injury-inflicting objects. The distributions of metallic particles before and after eliminating O differed significantly, and the data were stable, making it possible to quantitatively compare the components of residue metallic particles associated with steel hammers. The results of this quantitative analysis are summarized in [Figure 9],[Figure 10],[Figure 11],[Figure 12],[Figure 13],[Figure 14] for steel hammer number 1 before and after prescribing O elimination in the software settings. | Figure 9: Histogram of distribution of content of Fe element in residue Fe particle of hammer number 1 with the quantitative settings including O (in 100 intervals)
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 | Figure 10: Histogram of distribution of content of Fe element in residue Fe particle of hammer number 1 with the quantitative settings excluding O (in 100 intervals)
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 | Figure 11: Histogram of distribution of content of Fe element in residue Fe-Mn particle of hammer number 1 with the quantitative settings including O
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 | Figure 12: Histogram of distribution of content of Fe element in residue Fe-Mn particle of hammer number 1 with the quantitative settings excluding O
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 | Figure 13: Histogram of distribution of content of Mn element in residue Fe-Mn particle of hammer number 1 with the quantitative settings including O
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 | Figure 14: Histogram of distribution of content of Mn element in residue Fe-Mn particle of hammer number 1 with the quantitative settings excluding O
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The results in [Figure 9],[Figure 10],[Figure 11],[Figure 12],[Figure 13],[Figure 14] clearly indicate that the components are quite stable when analyzing the residue metallic particles of a steel hammer after eliminating O, which suggests that comparative analysis can be performed. As shown in [Figure 15], for most particles, the size, indicated by the equivalent circle diameter (ECD), was under 2.5 μm, indicating very small particles. To date, no feasible methods have been suggested for extraction and purification of such small residue particles for further chemical composition analysis. | Figure 15: Distribution of sizes of residue metallic particles of hammer number 1
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Identification of residue particles on pigskin samples stricken by steel hammers
Commonly used tests for determining the presence of residue particles deposited by injury-inflicting objects are limited to visual inspection of samples and comparison with suspected injury-inflicting objects, while comparison tests of components are limited to identifying categories of elements. Thus, the existing methods do not discriminate between particles with different elemental contents. In this study, we prepared samples of residue particles associated with hammer-inflicted injuries by simulating the actual striking process, i.e., using hammers to strike filter paper samples and then inspecting the residue metallic particles left by the different hammers on the filter paper samples. Comparisons were performed in terms of the percentage of elemental content, which decreased the inaccuracies related to the quantitative energy spectrum to a certain degree while simultaneously utilizing the high-precision associated with energy spectrum inspection. Relative elemental content analysis has often been used for component comparison of soil and its trace particles.[13] The results of our test, described below, demonstrate that the proposed method can be effectively used for evaluating content differences across different metallic particles.
Determination of evaluation indicators of Fe-Mn steel hammers
The residue metallic particles from the steel hammers used in this study were of two types: Fe and Fe-Mn particles. Using the obtained weight percentage data for Mn and Fe, we calculated observation indices for different groups in different tests:
- Nt: The total number of suspect metallic particles including particles containing Fe and particles containing Fe-Mn
- NFe + Mn: The number of metallic particles containing both Fe and Mn
- MFe/Mn: The Fe to Mn weight percentage ratio for metallic particles containing both Fe and Mn.
Using MFe/Mn for characterizing metallic particles could reduce the undesired effects due to the presence of other elements in the analyzed particles. Although the presence of other elements in and near the particles simultaneously reduces the amount of Mn and Fe by weight, these effects can be accounted for by calculating the ratio of the two elements. Using the weight ratio of the elements helps improve the accuracy of the quantitative spectral analysis.
By performing composition analysis of the hammers considered in the present study, we found that commonly used hammers were essentially composed of Fe-Mn alloys, with some differences in the Mn content across different brands of hammers. Therefore, striking tests were performed to determine the Mn contents. By comparing the data for the same hammer on multiple striking tests, we found that MFe/Mn and NFe + Mn/Nt were stable and reproducible across different trials. Hence, to establish a reliable method for determining the composition of Fe-Mn steel hammers, these two indices were selected for mathematical assessment. To evaluate the differences among the hammers, hammers 1 and 3 were considered, as discussed in the next subsection.
Evaluation of characteristic indices for hammers 1 and 3
[Figure 16] shows a histogram of ECDs for 220 Fe- and Mn-containing particles that were detected in the pigskin sample stricken by hammer 1. Approximately 80% of the particles were smaller than 10 μm in size. | Figure 16: Equivalent circle diameter size of residue metallic particles containing Fe-Mn of hammer number 1 tested
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[Table 4], [Table 5], [Table 6], [Table 7] list the test results of detected residue metallic particles for pigskin and filter paper samples stricken by the two hammers (1 and 3). | Table 7: Comparison of test results of hammer number 1 and hammer number 3
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Considering MFe/Mn as the observation index, the Kruskal–Wallis test was performed on the data obtained for hammer 1. The P value for striking the filter paper sample was 0.27 while that for striking the pigskin sample was 0.151; both values were above the level of significance (α = 0.05), implying that in both cases, striking the samples with the hammer introduced a significant number of residue particles. The Kruskal–Wallis test results indicate that the metallic composition of residue particles deposited by the same hammer on multiple striking tests was stable across the tests and samples.
Considering NFe + Mn/Nt as the observation index, independent t-tests on the data obtained for residue particles deposited by striking the samples with hammer 1 were performed for the filter paper and pigskin samples. The P value was 0.249. For a similar striking treatment performed using hammer 3, the resulting P value was 0.194. Because the P values were above the significance level (α = 0.05) for both tests, the results of the proportion of residue Fe-Mn particles for the two different tests could not be regarded as different.
For striking either filter paper samples or pigskin samples, the values of NFe + Mn/Nt were significantly different between hammers 1 and 3. A mathematical analysis of the MFe/Mn and NFe + Mn/Nt data for residue particles deposited by the tested hammers indicated that MFe/Mn for residue particles deposited by the steel hammer is not normally distributed. The stability was assessed by performing the Kruskal–Wallis test and the difference was assessed by performing the rank sum test. The NFe + Mn/Nt data were complied with the normal distribution as assessed by performing a test of normality while the difference was tested by performing a t-test.
Index differences among different steel hammers
After performing a statistical analysis of residue particles deposited by the 27 steel hammers, we estimated MFe/Mn and NFe + Mn/Nt for the different hammers and assessed the differences among them. [Figure 17] shows the comparison of MFe/Mn and NFe + Mn/Nt for the 27 hammers. The average MFe/Mn values for the 27 hammers were compared using the SPSS software. Three hundred and fifty-one combinations were pairwise compared, from which 114 pairwise comparisons yielded no significant differences (P > 0.05) while 237 pairwise comparisons yielded differences (P < 0.05) at the significance level of α =0.05. Thus, the discrimination rate was 67.52%. The discrimination rate can be further increased by considering NFe + Mn/Nt.
| Conclusion and Discussion | | |
This study shows that cotton swabs can be used for extracting trace residue particles from injured tissue affected by metallic injury-inflicting objects; furthermore, the study demonstrates that the components of these trace residue particles can be analyzed utilizing the SEM/EDX particle analysis software. Comparing the elemental compositions of extracted residue particles to those of suspect injury-inflicting objects can help exclude or identify specific suspect injury-inflicting objects. Proper calibration of the measurement apparatus depends on many parameters such as the accelerating voltage. For energy spectrum tests of small particles, the smaller the accelerating voltage is, the narrower the inspected interval is, and the higher the test accuracy is; however, the execution time is commensurately longer. Using either cotton swabs or carbon conductive adhesive tapes, the samples should be extracted more than three times from one tissue sample to ensure that a sufficient number of trace metallic particles are extracted. Extraction of trace particles from injured tissue is the first step in the autopsy analysis and requires avoidance of contamination by other metallic particles related to the dissecting table and dissecting tools. In the future work, we will continue to adjust test conditions to optimize the analysis of the characteristics of residue particles deposited by injury-inflicting objects, to obtain better and more stable indices for comparison and analysis.
Some composition differences among different metallic tools were noted owing to the different sources of raw materials or different batches. Therefore, the issue of data difference caused by inhomogeneous distribution of Fe and Mn in steel should be further examined. Apart from the main elements (Fe and Mn), other trace elements can be present as well, such as Si and Cu. Usually, the presence of Si is attributed to the residues of a steel-making process and Cu is also occasionally present; thus, these residue particles should be addressed on a case-by-case basis. The work on the distribution of super trace elements is still in progress, and in the future, we hope to come up with a more scientific and accurate method for assessing identification indices for Fe-Mn steel hammers.
Based on the tests of residue particles of the 27 common steel hammers performed in Beijing, we can conclude that the difference between the components of residue particles of Fe-Mn alloys deposited during the striking process could be measured; furthermore, the content ratio of different elements in the residue metallic particles was stable and could therefore be used as an identification index for comparing the elemental contents associated with different injury-inflicting objects. A procedure for searching for and comparing injury-inflicting objects could be realized in the future after establishing a database containing information on different instruments. Because the components of similarly composed small metallic particles cannot be discriminated, even by performing mathematical analysis, the electron backscatter diffraction test may be used to further analyze metallic crystal lattices.
Financial support and sponsorship
This study was funded by “key technology research on the identified causes of injury or death and on the estimation of postmortem interval,” one of Key Projects in the National Science and Technology Pillar Program during the Eleventh 5-year Plan Period of china, and also funded by Collaborative Innovation Center of Judicial Civilization of China.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12], [Figure 13], [Figure 14], [Figure 15], [Figure 16], [Figure 17]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]
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