STTR Phase I: Advancing DNA Testing with a Novel Platform for Processing Touch Biological Evidence

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力解决了司法系统中的两个社会问题：个人获得快速审判的宪法权利，以及在收集和处理证据时执法中固有的人类偏见。处理DNA证据的积压可能导致无法保释的被告被延长监禁时间，并可能侵犯他们的权利，特别是他们没有犯下的罪行。对证据样本的快速分析可以腾出法庭备审案件，并为因测试积压而等待审判的人的机构节省资金。此外，在监狱服刑数年后，通过重新分析法医证据而被无罪释放的人数每年都在增加，这突显了检测决定对个人、家庭和整个社会的潜在影响。这项技术通过快速确定哪些样本可能对调查有证明作用，从而保证进行DNA检测，减少了检测样本的数量和样本选择偏差的可能性。拟议的项目将开发一种新技术，利用流式细胞术分析法医证据中细胞群的非遗传属性。这项技术将使法医实验室能够在DNA特征分析之前迅速确定样本的证明价值。机器学习算法将比较从“触摸”表皮细胞中回收的单个细胞的形态学测量和自发荧光特性，以识别随沉积细胞的人的属性而变化的特征（例如，实足年龄、生物性别和/或祖先）。这项技术将使法医实验室能够迅速查明哪些样本含有对案件有证明作用的生物材料，哪些样本含有无关的生物材料。这将使实验室能够更精确地对DNA测试样本进行优先排序，并可能为样本提供关键的背景信息。通过更有效地分配资源，这项创新将降低成本，加快结果和报告，并减少DNA测试周转时间的延迟。该解决方案还将防止法律的系统出现不必要的延误，提高案例分析的准确性，并最终提高法医分析的质量和可靠性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project addresses two societal issues in the justice system: an individual's constitutional right to a speedy trial, and inherent human biases in law enforcement when gathering and processing evidence. Backlogs in processing DNA evidence can lead to extended jail time for defendants unable to post bail and may violate their rights, particularly for crimes they did not commit. Rapid analysis of evidence samples can free up court dockets and save money for institutions holding people awaiting trials due to testing backlogs. In addition, the number of people exonerated by the re-analysis of forensic evidence after serving years in prison rises every year, underscoring the potential impact that testing decisions can have for individuals, families, and society at large. This technology reduces the number of samples tested and the potential for sample selection bias by rapidly identifying which samples may be probative to the investigation and thus warrant DNA testing. The proposed project will develop a new technology that utilizes flow cytometry to analyze non-genetic attributes of cell populations within forensic evidence. This technology will allow forensic laboratories to rapidly determine the probative value of samples before DNA profiling. Machine learning algorithms will compare morphological measurements and autofluorescence properties of individual cells recovered from ‘touch’ epidermal cells to identify features that vary with attributes of the person who deposited the cells (e.g., chronological age, biological sex, and/or ancestry). This technology will enable forensic laboratories to rapidly identify which samples have biological material that is probative to the case and which samples have biological material that is unrelated. This will allow labs to prioritize samples for DNA testing more precisely and potentially provide key contextual information for the sample. By allocating resources more efficiently, this innovation will reduce costs, speed up results and reporting, and reduce delays in DNA testing turn-around times. The solution will also prevent undue delays in the legal system, improve the accuracy of case analysis, and ultimately improve the quality and reliability of forensic analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.