Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network.

Shot peening is a surface treatment process that improves the fatigue life of a material and suppresses cracks by generating residual stress on the surface. The injected small shots create a compressive residual stress layer on the material's surface. Maximum compressive residual stress occurs at a certain depth, and tensile residual stress gradually occurs as the depth increases. This process is primarily used for nickel-based superalloy steel materials in certain environments, such as the aerospace industry and nuclear power fields. To prevent such a severe accident due to the high-temperature and high-pressure environment, evaluating the residual stress of shot-peened materials is essential in evaluating the soundness of the material. Representative methods for evaluating residual stress include perforation strain gauge analysis, X-ray diffraction (XRD), and ultrasonic testing. Among them, ultrasonic testing is a representative, non-destructive evaluation method, and residual stress can be estimated using a Rayleigh wave. Therefore, in this study, the maximum compressive residual stress value of the peened Inconel 718 specimen was predicted using a prediction convolutional neural network (CNN) based on the relationship between Rayleigh wave dispersion and stress distribution on the specimen. By analyzing the residual stress distribution in the depth direction generated in the model from various studies in the literature, 173 residual stress distributions were generated using the Gaussian function and factorial design approach. The distribution generated using the relationship was converted into 173 Rayleigh wave dispersion data to be used as a database for the CNN model. The CNN model was learned through this database, and performance was verified using validation data. The adopted Rayleigh wave dispersion and convolutional neural network procedures demonstrate the ability to predict the maximum compressive residual stress in the peened specimen.

Transcriptome analysis of skeletal muscle in dermatomyositis, polymyositis, and dysferlinopathy, using a bioinformatics approach.

Background: Polymyositis (PM) and dermatomyositis (DM) are two distinct subgroups of idiopathic inflammatory myopathies. Dysferlinopathy, caused by a dysferlin gene mutation, usually presents in late adolescence with muscle weakness, degenerative muscle changes are often accompanied by inflammatory infiltrates, often resulting in a misdiagnosis as polymyositis. Objective: To identify differential biological pathways and hub genes related to polymyositis, dermatomyositis and dysferlinopathy using bioinformatics analysis for understanding the pathomechanisms and providing guidance for therapy development. Methods: We analyzed intramuscular ribonucleic acid (RNA) sequencing data from seven dermatomyositis, eight polymyositis, eight dysferlinopathy and five control subjects. Differentially expressed genes (DEGs) were identified by using DESeq2. Enrichment analyses were performed to understand the functions and enriched pathways of DEGs. A protein-protein interaction (PPI) network was constructed, and clarified the gene cluster using the molecular complex detection tool (MCODE) analysis to identify hub genes. Results: A total of 1,048, 179 and 3,807 DEGs were detected in DM, PM and dysferlinopathy, respectively. Enrichment analyses revealed that upregulated DEGs were involved in type 1 interferon (IFN1) signaling pathway in DM, antigen processing and presentation of peptide antigen in PM, and cellular response to stimuli in dysferlinopathy. The PPI network and MCODE cluster identified 23 genes related to type 1 interferon signaling pathway in DM, 4 genes (PDIA3, HLA-C, B2M, and TAP1) related to MHC class 1 formation and quality control in PM, and 7 genes (HSPA9, RPTOR, MTOR, LAMTOR1, LAMTOR5, ATP6V0D1, and ATP6V0B) related to cellular response to stress in dysferliniopathy. Conclusion: Overexpression of genes related to the IFN1 signaling pathway and major histocompatibility complex (MHC) class I formation was identified in DM and PM, respectively. In dysferlinopathy, overexpression of HSPA9 and the mTORC1 signaling pathway genes was detected.

Nondestructive Evaluation of Residual Stress in Shot Peened Inconel Using Ultrasonic Minimum Reflection Measurement.

Shot peening is a process wherein the surface of a material is impacted by small, spherical metal shots at high velocity to create residual stresses. Nickel-based superalloy is a material with high strength and hardness along with excellent corrosion and fatigue resistance, and it is therefore used in nuclear power plants and aerospace applications. The application of shot peening to INCONEL, a nickel-based superalloy, has been actively researched, and the measurement of residual stresses has been studied as well. Previous studies have used methods such as perforation strain gauge analysis and X-ray diffraction (XRD) to measure residual stress, which can be evaluated with high accuracy, but doing so damages the specimen and involves critical risks to operator safety due to radiation. On the other hand, ultrasonic testing (UT), which utilizes ultrasonic wave, has the advantage of relatively low unit cost and short test time. One UT method, minimum reflection measurement, uses Rayleigh waves to evaluate the properties of material surfaces. Therefore, the present study utilized ultrasonic minimum reflectivity measurements to evaluate the residual stresses in INCONEL specimens. Specifically, this study utilized ultrasonic minimum reflection measurements to evaluate the residual stress in INCONEL 718 specimens. Moreover, an estimation equation was assumed using exponential functions to estimate the residual stress with depth using the obtained data, and an optimization problem was solved to determine it. Finally, to evaluate the estimated residual stress graph, the residual stress of the specimen was measured and compared using the XRD method.

Copy-number analysis by base-level normalization: An intuitive visualization tool for evaluating copy number variations.

Next-generation sequencing (NGS) facilitates comprehensive molecular analyses that help with diagnosing unsolved disorders. In addition to detecting single-nucleotide variations and small insertions/deletions, bioinformatics tools can identify copy number variations (CNVs) in NGS data, which improves the diagnostic yield. However, due to the possibility of false positives, subsequent confirmation tests are generally performed. Here, we introduce Copy-number Analysis by BAse-level NormAlization (CABANA), a visualization tool that allows users to intuitively identify candidate CNVs using the normalized single-base-level read depth calculated from NGS data. To demonstrate how CABANA works, NGS data were obtained from 474 patients with neuromuscular disorders. CNVs were screened using a conventional bioinformatics tool, ExomeDepth, and then we normalized and visualized those data at the single-base level using CABANA, followed by manual inspection by geneticists to filter out false positives and determine candidate CNVs. In doing so, we identified 31 candidate CNVs (7%) in 474 patients and subsequently confirmed all of them to be true using multiplex ligation-dependent probe amplification. The performance of CABANA was deemed acceptable by comparing its diagnostic yield with previous data about neuromuscular disorders. Despite some limitations, we expect CABANA to help researchers accurately identify CNVs and reduce the need for subsequent confirmation testing.

Cytogenetic testing by fluorescence in situ hybridization is improved by plasma cell sorting in multiple myeloma.

Accurate detection of cytogenetic abnormalities has become more important for improving risk-adapted treatment strategies in multiple myeloma (MM). However, precise cytogenetic testing by fluorescence in situ hybridization (FISH) is challenged by the dilution effect of bone marrow specimens and poor growth of plasma cells ex vivo. It has been suggested that FISH should be performed in combination with plasma cell enrichment strategies. We examined cytogenetic abnormalities in newly diagnosed MM and compared the efficacy of three different enrichment modalities for FISH: direct FISH (n = 137), fluorescence immunophenotyping and interphase cytogenetics as a tool for the investigation of neoplasms (FICTION) technique (n = 224), and a plasma cell sorting FISH with fluorescence-activated cell sorter (FACS) (n = 132). FISH disclosed cytogenetic abnormalities in 38.0% of samples by direct FISH, 56.3% by FICTION, and 95.5% by FACS-FISH, and the percentage of cells with abnormal signals detected by FISH was significantly higher by FACS-FISH than direct FISH or FICTION. Our results suggest that the efficacy of FISH is dependent on the plasma cell enrichment modalities and reveal that plasma cell sorting FISH with FACS enables better detection of cytogenetic abnormalities in diagnostic MM samples.

A comparative study of next-generation sequencing and fragment analysis for the detection and allelic ratio determination of FLT3 internal tandem duplication.

BACKGROUND: Currently, FLT3 internal tandem duplication (ITD) is tested by fragment analysis. With next-generation sequencing (NGS), however, not only FLT3 ITD but also other mutations can be detected, which can provide more genetic information on disease. METHODS: We retrospectively reviewed the results of two tests-fragment analysis and a custom-designed, hybridization capture-based, targeted NGS panel-performed simultaneously. We used the Pindel algorithm to detect FLT3 ITD mutations. RESULTS: Among 277 bone marrow aspirate samples tested by NGS and fragment analysis, the results revealed 99.6% concordance in FLT3 ITD detection. Overall, the allele frequency (AF) attained by NGS positively correlated with the standard allelic ratio (AR) attained by fragment analysis, with a Spearman correlation coefficient (r) of 0.757 (95% confidence interval: 0.627-0.846; p < 0.001). It was concluded that an AF of 0.11 attained by NGS is the most appropriate cutoff value (with 85.3% sensitivity and 86.7% specificity) for high mutation burden criterion presented by guidelines. CONCLUSION: Sensitive FLT3 ITD detection with comprehensive information of other mutation offered by NGS could be a useful tool in clinical laboratories. Future studies will be needed to evaluate and standardize NGS AF cutoff to predict actual clinical outcomes.