Optimization of ultra-precision CBN turning of AISI D2 using hybrid GA-RSM and Taguchi-GRA statistic tools.

Ultra-precision turning is a crucial process in the manufacturing industry as it helps to produce parts with high dimensional accuracy, surface finish, and tolerance. The process is similar to traditional turning but is carried out under special circumstances to achieve greater precision and surface finish. The process can be applied to conventional structural materials, but the demand for machining hardened steels is increasing. The optimization of ultra-precision turning of AISI D2 using cubic boron nitride (CBN) tools is a crucial aspect in the field of high-quality machining. This study aims to evaluate the performance of the process and identify the optimal parameters that result in the best quality components while using a CBN tool's ultra-precision turning of AISI D2. Ultra-precision turning process factors such as cutting speed, feed, and depth of cut were experimentally investigated to enhance the response output, such as surface roughness and cutting force components. The full factorial experimental design was used for determining the process characteristics under different conditions, and experimental results were applied to search for the optimum response of machining performance. The optimization process was done by combining the hybrid genetic algorithm-response surface methodology (GA-RSM) and the Taguchi-grey relational analysis (GRA) statistical tools. These methods are useful in situations where the relationship between the input variables and the output responses is complex and non-linear. The results showed that a hybrid GA-RSM approach, combined with Taguchi-GRA statistical analysis, can effectively find optimal process parameters, leading to the best combination of surface roughness and cutting force. In hybrid Taguchi - GRA, the optimal cutting conditions were found to be a cutting speed of 175 m/min, a feed of 0.025 mm, and a depth of cut of 0.06 mm. The findings of this study provide valuable insights for the optimization of ultra-precision CBN turning operations, contribute to the development of precision manufacturing technology, and can be used as a reference for similar machining processes.

Longitudinal cell-free DNA characterization by low-coverage whole-genome sequencing in patients undergoing high-dose radiotherapy.

BACKGROUND AND PURPOSE: Current radiotherapy guidelines rely heavily on imaging-based monitoring. Liquid biopsy monitoring promises to complement imaging by providing frequent systemic information about the tumor. In particular, cell-free DNA (cfDNA) sequencing offers a tumor-agnostic approach, which lends itself to monitoring heterogeneous cohorts of cancer patients. METHODS: We collected plasma cfDNA from oligometastatic patients (OMD) and head-and-neck cancer patients (SCCHN) at six time points before, during, and after radiotherapy, and compared them to the plasma samples of healthy and polymetastatic volunteers. We performed low-pass (on average 7x) whole-genome sequencing on 93 plasma cfDNA samples and correlated copy number alterations and fragment length distributions to clinical and imaging findings. RESULTS: We observed copy number alterations in 4/7 polymetastatic cancer patients, 1/7 OMD and 1/7 SCCHN patients, these patients' imaging showed progression following radiotherapy. Using unsupervised learning, we identified cancer-specific fragment length features that showed a strong correlation with copy number-based tumor fraction estimates. In 4/4 HPV-positive SCCHN patient samples, we detected viral DNA that enabled the monitoring of very low tumor fraction samples. CONCLUSIONS: Our results indicate that an elevated tumor fraction is associated with tumor aggressiveness and systemic tumor spread. This information may be used to adapt treatment strategies. Further, we show that by detecting specific sequences such as viral DNA, the sensitivity of detecting cancer from cell-free DNA sequencing data can be greatly increased.

Innate acting memory Th1 cells modulate heterologous diseases.

Through immune memory, infections have a lasting effect on the host. While memory cells enable accelerated and enhanced responses upon rechallenge with the same pathogen, their impact on susceptibility to unrelated diseases is unclear. We identify a subset of memory T helper 1 (Th1) cells termed innate acting memory T (TIA) cells that originate from a viral infection and produce IFN-γ with innate kinetics upon heterologous challenge in vivo. Activation of memory TIA cells is induced in response to IL-12 in combination with IL-18 or IL-33 but is TCR independent. Rapid IFN-γ production by memory TIA cells is protective in subsequent heterologous challenge with the bacterial pathogen Legionella pneumophila. In contrast, antigen-independent reactivation of CD4+ memory TIA cells accelerates disease onset in an autoimmune model of multiple sclerosis. Our findings demonstrate that memory Th1 cells can acquire additional TCR-independent functionality to mount rapid, innate-like responses that modulate susceptibility to heterologous challenges.

Machine learning prediction of prime editing efficiency across diverse chromatin contexts.

The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all edit types up to 15 bp in length in mismatch repair-deficient and mismatch repair-proficient cell lines and in vivo in primary cells. With ePRIDICT, we further developed a model that quantifies how local chromatin environments impact prime editing rates.

COL10A1 expression distinguishes a subset of cancer-associated fibroblasts present in the stroma of high-risk basal cell carcinoma.

BACKGROUND: Basal cell carcinoma (BCC) is the most frequently diagnosed skin cancer and the most common malignancy in humans. Different morphological subtypes of BCC are associated with low- or high-risk of recurrence and aggressiveness, but the underlying biology of how the individual subtypes arise remains largely unknown. Because the majority of BCCs appear to arise from mutations in the same pathway, we hypothesized that BCC development, growth and invasive potential is also influenced by the tumor microenvironment and in particular by cancer-associated fibroblasts (CAFs) and their secreted factors. OBJECTIVE: We aimed to characterize the stroma of the different BCC subtypes with a focus on CAF populations. METHODS: To investigate the stromal features of the different BCC subtypes, we applied laser-capture microdissection (LCM) followed by RNA sequencing. A cohort of 15 BCC samples from 5 different "pure" subtypes (superficial, nodular, micronodular, sclerosing and basosquamous; n=3 each) were selected and included in the analysis. Healthy skin was used as a control (n=6). We confirmed the results by immunohistochemistry. We validated our findings in two independent, public single-cell RNA sequencing (scRNAseq) datasets and by RNAscope. RESULTS: The stroma of the different BCC subtypes have distinct gene expression signatures. Nodular and micronodular seem to have the most similar signatures, while superficial and sclerosing the most different. By comparing low- and high-risk BCC subtypes, we observed that Collagen 10A1 (COL10A1) is overexpressed in the stroma of sclerosing/infiltrative and basosquamous but not micronodular high-risk subtypes. Those findings were confirmed by immunohistochemistry in a cohort of 89 different BCC and 13 healthy skin samples. Moreover, scRNAseq analysis of BCCs of two independent datasets showed that the COL10A1-expressing population of cells is associated with the stroma adjacent to invasive BCC and shows extracellular matrix remodeling features. CONCLUSION: We identified COL10A1 as a marker of high-risk BCC, in particular of the sclerosing/infiltrative and basosquamous subtypes. We demonstrated at the single cell level that COL10A1 is expressed by a specific CAF population associated with the stroma of invasive BCC. This opens up new tailored treatment options as well as a new prognostic biomarker for BCC progression.

Fragmentstein-facilitating data reuse for cell-free DNA fragment analysis.

SUMMARY: Method development for the analysis of cell-free DNA (cfDNA) sequencing data is impeded by limited data sharing due to the strict control of sensitive genomic data. An existing solution for facilitating data sharing removes nucleotide-level information from raw cfDNA sequencing data, keeping alignment coordinates only. This simplified format can be publicly shared and would, theoretically, suffice for common functional analyses of cfDNA data. However, current bioinformatics software requires nucleotide-level information and cannot process the simplified format. We present Fragmentstein, a command-line tool for converting non-sensitive cfDNA-fragmentation data into alignment mapping (BAM) files. Fragmentstein complements fragment coordinates with sequence information from a reference genome to reconstruct BAM files. We demonstrate the utility of Fragmentstein by showing the feasibility of copy number variant (CNV), nucleosome occupancy, and fragment length analyses from non-sensitive fragmentation data. AVAILABILITY AND IMPLEMENTATION: Implemented in bash, Fragmentstein is available at https://github.com/uzh-dqbm-cmi/fragmentstein, licensed under GNU GPLv3.