Performance Of Principal Component Analysis Research Articles

Genetic ancestry of human induced pluripotent stem cells banked for neurodegeneration research

AbstractBackgroundThe National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD) has established a centralized repository for human induced pluripotent stem cells (iPSCs) and fibroblasts in partnership with the National Institute on Aging (NIA). This is a critical resource for the research community, which can facilitate disease modeling, functional genetic studies, and drug development. However, the utility of this resource is still limited in terms of racial and ethnic diversity, which has the potential to bias neurodegenerative research and drug development unless steps are taken to create and share a more diverse array of cell lines.MethodAt NCRAD, banked iPSCs are subjected to quality control including short‐read whole genome sequencing (WGS). 78 samples have been submitted for sequencing; of these, data for 51 have been returned and analyzed, including multiple isogenic lines from three donors. WGS data was aligned, and quality control performed using Sentieon software, following GATK best practices. Data was combined with sequencing data from 2,548 1000 Genomes participants and pruned in PLINK. R package SNPRelate was used to generate a kinship matrix and perform principal component analysis (PCA). Clustering analysis was performed utilizing K‐means clustering to group samples into 5 clusters. PCs 1‐3 were graphed, color‐coded by cluster, and final ancestry calls reviewed for clustering with reference groups.ResultThe NCRAD 51 iPSC lines with analyzed WGS data were sourced from 28 neurodegenerative cases and 12 controls. In this data set, 83% of samples (89% of cases, 67% of controls) have European ancestry. The cohort also includes one case and one control line of East Asian ancestry, as well as 2 cases and 3 controls showing admixture of European/American ancestry. 17 individuals did not have reported race/ethnicity, while 2 controls showed genetic admixture though reported as White non‐Hispanic, and the remaining individuals’ genetic ancestry matched reported race/ethnicity.ConclusionCentralized banking of iPSCs from individuals with neurodegenerative diseases is creating a key resource to advance research; however, to date the diversity of these samples is limited. We urge researchers to develop and share more diverse cell lines, to promote diversity and equity in neurodegenerative research.

Whole mitochondrial genome analysis in highland Tibetans: further matrilineal genetic structure exploration.

Introduction: The Qinghai-Tibet Plateau is one of the last terrestrial environments conquered by modern humans. Tibetans are among the few high-altitude settlers in the world, and understanding the genetic profile of Tibetans plays a pivotal role in studies of anthropology, genetics, and archaeology. Methods: In this study, we investigated the maternal genetic landscape of Tibetans based on the whole mitochondrial genome collected from 145 unrelated native Lhasa Tibetans. Molecular diversity indices, haplotype diversity (HD), Tajima's D and Fu's Fs were calculated and the Bayesian Skyline Plot was obtained to determining the genetic profile and population fluctuation of Lhasa Tibetans. To further explore the genetic structure of Lhasa Tibetans, we collected 107 East Asian reference populations to perform principal component analysis (PCA), multidimensional scaling (MDS), calculated Fst values and constructed phylogenetic tree. Results: The maternal genetic landscape of Tibetans showed obvious East Asian characteristics, M9a (28.28%), R (11.03%), F1 (12.41%), D4 (9.66%), N (6.21%), and M62 (4.14%) were the dominant haplogroups. The results of PCA, MDS, Fst and phylogenetic tree were consistent: Lhasa Tibetans clustered with other highland Tibeto-Burman speakers, there was obvious genetic homogeneity of Tibetans in Xizang, and genetic similarity between Tibetans and northern Han people and geographically adjacent populations was found. In addition, specific maternal lineages of Tibetans also be determined in this study. Discussion: In general, this study further shed light on long-time matrilineal continuity on the Tibetan Plateau and the genetic connection between Tibetans and millet famers in the Yellow River Basin, and further revealed that multiple waves of population interaction and admixture during different historical periods between lowland and highland populations shaped the maternal genetic profile of Tibetans.

Analysis of the Differences in Volatile Organic Compounds in Different Rice Varieties Based on GC-IMS Technology Combined with Multivariate Statistical Modelling.

In order to investigate the flavour characteristics of aromatic, glutinous, and nonaromatic rice, gas chromatography-ion mobility spectrometry (GC-IMS) was used to analyse the differences in volatile organic compounds (VOCs) amongst different rice varieties. The results showed that 103 signal peaks were detected in these rice varieties, and 91 volatile flavour substances were identified. Amongst them, 28 aldehydes (28.89~31.17%), 24 alcohols (34.85~40.52%), 14 ketones (12.26~14.74%), 12 esters (2.30~4.15%), 5 acids (7.80~10.85%), 3 furans (0.30~0.68%), 3 terpenes (0.34~0.64%), and 2 species of ethers (0.80~1.78%) were detected. SIMCA14.1 was used to perform principal component analysis (PCA) and orthogonal partial least squares discriminant analysis, and some potential character markers (VIP > 1) were further screened out of the 91 flavour substances identified based on the variable important projections, including ethanol, 1-hexanol, hexanal, heptanal, nonanal, (E)-2-heptenal, octanal, trans-2-octenal, pentanal, acetone, 6-methyl-5-hepten-2-one, ethyl acetate, propyl acetate, acetic acid, and dimethyl sulphide. Based on the established fingerprint information, combined with principal component analysis and orthogonal partial least squares discriminant analysis, different rice varieties were also effectively classified, and the results of this study provide data references for the improvement in aromatic rice varieties.

The Peripheral Blood Stem Cell Immunome Demonstrates Abnormal Immune Effector Cells Early in the Disease Course of Multiple Myeloma and Contributes to Inferior Outcomes and Secondary Myeloid Neoplasms

Background: Upfront autologous stem cell transplant (ASCT) is the standard-of-care for newly diagnosed multiple myeloma (MM) patients. However, relapse and the development of therapy-related myeloid neoplasms (t-MN) post ASCT results in considerable morbidity and mortality. We hypothesized that enrichment of abnormal myeloid progenitors and immune effector cells (IEC) in peripheral blood stem cells (PBSCs) of patients with MM is associated with a higher risk of relapse and development of t-MN. Methods: After institutional review board approval, we screened patients with active MM that were evaluated at Mayo Clinic, Rochester, MN between 01/01/2003 and 12/31/2020. Patients that underwent an ASCT and had cryopreserved PBSC product available for research were included. Using mass cytometry, we performed a comprehensive immunophenotyping of mobilized cryopreserved PBSCs from 54 MM patients undergoing 1 st ASCT, using validated antibody panel of 37 lymphoid- and myeloid-based markers each. Flow cytometry standard files were normalized and concatenated using the Fluidigm acquisition software. Flow cytometry standard files were uploaded to the OMIQ software from Dotmatics (www.omiq.ai, www.dotmatics.com), where transformation and cleaning (doublets, debris) were performed. To correct for between-sample technical variations (“batch effects”), we used fdaNorm within the omiq.ai platform. Clustering and visualization of normalized and concatenaded flowcytometry files were performed within the omiq.ai platform using PhenoGraph and UMAP, respectively. We performed principal component analysis (PCA) to identify major immunomic drivers of variability across patients. We explored the associations between immune subsets and the dichotomized clinical outcomes of interest: progression-free (PFS), myeloid neoplasm-free (MNFS) and overall survival (OS) from ASCT. A 2-sided false discovery rate adjusted P-value of <0.05 was considered significant when multiple comparisons were performed; otherwise, a P-value of <0.05 was considered significant. Results: The most abundant cell populations were T-cells (49% of the CD45 +) and myeloid cells (43% of the CD45 + cells). Clustering on the CD45 + cells revealed 30 unique lymphoid clusters and 27 unique myeloid clusters. Subclustering on NK-cells identified an additional 16 NK-cell clusters. On PCA, principal components-1 (PC-1) and 2 (PC-2) explained 15.2% and 12.5% of the variability in the data, respectively. A high PC-2 score was associated with inferior median PFS (20 vs. 52 months; P=0.005, Figure 1A), MNFS (52 vs. 85 months, P=0.03, Figure 1B) and OS (64 vs. 106 months, P=0.01) from ASCT. Immune subsets enriched in the high PC-2 cohort included the ‘NKT-like’ (CD56 +) T-cell subset which expressed the inhibitory CD159a receptor and TIGIT; a terminally differentiated (CD27/CD28 -, CD57/KLRG1 +) and exhausted (TIGIT/PD-1 +) T cell subset, 2 exhausted (TIGIT/PD-1 +) T cell subsets; an immunosenescent (CD27/CD28 -, CD57/KLRG1 +) T cell subset as well as a terminally exhausted (C27/C28 -, KLRG1/PD-1/TIGIT +) TCRgamma-delta subset. Immune subsets with high loadings within PC-1 (associated with improved clinical outcomes) consisted exclusively of CD4 + cells with early memory phenotypes (CD27/CD28/CD127 +). Abnormal expression of CD7 and HLA-DR expression on myeloid progenitor cell subsets was associated with an inferior PFS, MNFS, and OS. A NK-cell subcluster, characterized by an immature (CD56 high), inhibitory (CD159a +, NKG2A) phenotype, was enriched in patients developing t-MN (0.33% vs. 0.17%, P=0.006). Conclusion: We identify abnormal myeloid and immune effector cells in the PBSCs of patients with MM, even prior to ASCT. Patients with a PBSC product that is enriched in immunosuppressive T and myeloid cell subsets, and with a lower proportion of antigen presenting cells, progenitor populations, and anti-tumor macrophages have a shorter PFS and MNFS. This suggests that the predisposition to t-MN and inferior outcomes for MM are possibly determined early in the disease course. These findings need external validation and have the potential to guide sequencing of future treatment modalities.

A proof-of-principle study: The potential application of MiniHap biomarkers in ancestry inference based on the QNome nanopore sequencing

Haplotyped SNPs convey forensic-related information, and microhaplotypes (MHs), as the most representative of this kind of marker, have proved the potential value for human forensics. In recent years, nanopore sequencing technology has developed rapidly, with its outstanding ability to sequence long continuous DNA fragments and obtain phase information, making the detection of longer haplotype marker possible. In this proof-of-principle study, we proposed a new type of forensic marker, MiniHap, based on five or more SNPs within a molecular distance less than 800 bp, and investigated the haplotype data of 56 selected MiniHaps in five Chinese populations using the QNome nanopore sequencing. The sequencing performance, allele (haplotype) frequencies, forensic parameters, effective number of alleles (Ae), and informativeness (In) were subsequently calculated. In addition, we performed principal component analysis (PCA), phylogenetic tree, and structure analysis to investigate the population genetic relationships and ancestry components among the five investigated populations and 26 worldwide populations. MiniHap-04 exhibited remarkable forensic efficacy, with 148 haplotypes reported and the Ae was 66.9268. In addition, the power of discrimination (PD) was 0.9934, the probability of exclusion (PE) was 0.9898, and the In value was 0.7893. Of the 56 loci, 85.71% had PD values above 0.85, 66.07% had PE values above 0.54, 67.86% had Ae values over 7.0%, and 55.36% were with In values above 0.2 across all samples, indicating that most of the MiniHaps are suitable for individual identification, paternity testing, mixture deconvolution, and ancestry inference. Moreover, the results of PCA, phylogenetic tree and structure analysis demonstrated that this MiniHap panel had the competency in continental population ancestry inference, but the differentiation within intracontinental/linguistically restricted subpopulations was not ideal. Such findings suggested that the QNome device for MiniHap detection was feasible and this novel marker has the potential in ancestry inference. Yet, the establishment of a more comprehensive database with sufficient reference population data remains necessary to screen more suitable MiniHaps.

YARARA V2: Reaching sub-m s−1 precision over a decade using PCA on line-by-line radial velocities

Context. The detection of Earth-like planets with the radial velocity (RV) method is extremely challenging today due to the presence of non-Doppler signatures such as stellar activity and instrumental signals that mimic and hide the signals of exoplanets. In a previous paper, we presented the YARARA pipeline, which implements corrections for telluric absorption, stellar activity, and instrumental systematics at the spectral level, and then it extracts line-by-line (LBL) RVs with a significantly better precision than standard pipelines. Aims. In this paper, we demonstrate that further gains in RV precision can be achieved by performing principal component analysis (PCA) decomposition on the LBL RVs. Methods. The mean-insensitive nature of PCA means that it is unaffected by true Doppler shifts, and thus can be used to isolate and correct nuisance signals other than planets. Results. We analysed the data of 20 intensively observed HARPS targets by applying our PCA approach on the LBL RVs obtained by YARARA. The first principal components show similarities across most of the stars and correspond to newly identified instrumental systematics for which we can now correct. For several targets, this results in an unprecedented RV root-mean-square of around 90 cm s−1 over the full lifetime of HARPS. We used the corrected RVs to confirm a previously published 120-day signal around 61 Vir, and to detect a super-Earth candidate (K ~ 60 ± 6 cm s−1, m sin i = 6.6 ± 0.7 M⊕) around the G6V star HD 20794, which spends part of its 600-day orbit within the habitable zone of the host star. Conclusions. This study highlights the potential of LBL PCA to identify and correct hitherto unknown, long-term instrumental effects and thereby extend the sensitivity of existing and future instruments towards the Earth analogue regime.

The Development of Green Ports in Emerging Nations: A Case Study of Vietnam

The development of green ports is still limited, especially in developing nations, despite the fact that they are viewed as a significant answer to the problems of environmental pollution and climate change. The purpose of this study is to investigate the factors that promote and hinder the development of green ports in developing countries. Using 248 managers from 12 Vietnamese container ports as a sample, this study is one of the pioneers in using a quantitative methodology to investigate the factors affecting the development of green ports. We used SPSS 22.0 and AMOS 22.0 to perform principal component analysis (PCA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). Our results indicated that cooperation of involved parties and foreign capital has the most important role in green port development in developing countries, followed by environmental regulation. Our findings also showed that lack of initial capital and lack of technological advancement negatively affects the development of green ports in these countries. The results and implications of the study will be discussed in more detail.

RETRACTED ARTICLE: Identification and assessment of differentially expressed necroptosis long non-coding RNAs associated with periodontitis in human

BackgroundPeriodontitis is the most common oral disease and is closely related to immune infiltration in the periodontal microenvironment and its poor prognosis is related to the complex immune response. The progression of periodontitis is closely related to necroptosis, but there is still no systematic study of long non-coding RNA (lncRNA) associated with necroptosis for diagnosis and treatment of periodontitis.Material and methodsTranscriptome data and clinical data of periodontitis and healthy populations were obtained from the Gene Expression Omnibus (GEO) database, and necroptosis-related genes were obtained from previously published literature. FactoMineR package in R was used to perform principal component analysis (PCA) for obtaining the necroptosis-related lncRNAs. The core necroptosis-related lncRNAs were screened by the Linear Models for Microarray Data (limma) package in R, PCA principal component analysis and lasso algorithm. These lncRNAs were then used to construct a classifier for periodontitis with logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the model. The CIBERSORT method and ssGSEA algorithm were used to estimate the immune infiltration and immune pathway activation of periodontitis. Spearman’s correlation analysis was used to further verify the correlation between core genes and periodontitis immune microenvironment. The expression level of core genes in human periodontal ligament cells (hPDLCs) was detected by RT-qPCR.ResultsA total of 10 core necroptosis-related lncRNAs (10-lncRNAs) were identified, including EPB41L4A-AS1, FAM30A, LINC01004, MALAT1, MIAT, OSER1-DT, PCOLCE-AS1, RNF144A-AS1, CARMN, and LINC00582. The classifier for periodontitis was successfully constructed. The Area Under the Curve (AUC) was 0.952, which suggested that the model had good predictive performance. The correlation analysis of 10-lncRNAs and periodontitis immune microenvironment showed that 10-lncRNAs had an impact on the immune infiltration of periodontitis. Notably, the RT-qPCR results showed that the expression level of the 10-lncRNAs obtained was consistent with the chip analysis results.ConclusionsThe 10-lncRNAs identified from the GEO dataset had a significant impact on the immune infiltration of periodontitis and the classifier based on 10-lncRNAs had good detection efficiency for periodontitis, which provided a new target for diagnosis and treatment of periodontitis.

Identification of Four Similar Cell Culture Media According to their Glucose, Glutamine, and Pyruvate Content by Handheld Raman Spectroscopy.

This study describes the first efforts to build a spectral library to identify four cell culture media in powder form with spectra obtained with a handheld Raman spectrometer. These complex mixtures contain over 30 components and are among the most widely used cell culture media. A total of 32 spectra were collected for the four Dulbecco's Modified Eagle Medium cell culture media and pure materials (glucose and L-glutamine) in powder form. The spectra were preprocessed using standard normal variate with second derivative, and the barcode method before performing principal component analysis (PCA). The PCA model differentiated the pure glucose and the cell culture media according to the glucose concentration along the first principal component. The second principal component differentiated the three cell culture media with high glucose content according to the pyruvate concentration. The correlation coefficient showed that powdered cell culture media with high glucose concentration have a higher correlation with pure glucose, when compared with the cell culture media with low glucose. The Raman spectra made it possible to differentiate the four DMEM in the cell culture media from the majority of the external samples used in the method evaluation. However, sample heterogeneity affected the predictions. Additional studies are needed to improve the method's ability to differentiate the DMEM with high glucose.

Characterizing the Spatiotemporal Transcriptomic Response of the Right Ventricle to Acute Pressure Overload.

This study analyzed microarray data of right ventricular (RV) tissue from rats exposed to pulmonary embolism to understand the initial dynamic transcriptional response to mechanical stress and compare it with experimental pulmonary hypertension (PH) models. The dataset included samples harvested from 55 rats at 11 different time points or RV locations. We performed principal component analysis (PCA) to explore clusters based on spatiotemporal gene expression. Relevant pathways were identified from fast gene set enrichment analysis using PCA coefficients. The RV transcriptomic signature was measured over several time points, ranging from hours to weeks after an acute increase in mechanical stress, and was found to be highly dependent on the severity of the initial insult. Pathways enriched in the RV outflow tracts of rats at 6 weeks after severe PE share many commonalities with experimental PH models, but the transcriptomic signature at the RV apex resembles control tissue. The severity of the initial pressure overload determines the trajectory of the transcriptomic response independent of the final afterload, but this depends on the location where the tissue is biopsied. Chronic RV pressure overload due to PH appears to progress toward similar transcriptomic endpoints.

Distinct genetic and phenotypic characteristics of not otherwise specified (NOS) melanoma.

e21571 Background: We previously reported on the biological relevance of melanoma histologic subtypes (Lattanzi et al, JNCI 2019). However, a substantial percentage of primary melanoma is histologically subtyped as NOS. The impact of this designation on clinical outcome is untested but casually believed not to have an impact considering the increasing reliance of molecular subclassification of melanoma tumor that might eliminate the need for histologic characterization. We also reported our data that supported a role for melanoma germline prognostic factors independent of known prognostic criteria (Chat et al, Front Oncol 2023). In this study, we sought to determine if NOS represents phenotypically and genetically distinct subtype or its clinical behavior is determined by AJCC criteria. Methods: We studied primary melanoma patients prospectively accrued and followed up using an IRB approved protocol at NYULH. We tested the association between NOS and baseline characteristics (e.g. thickness, ulceration, stage, Mitotic Index, TIL) and its association with recurrence free survival (RFS), and overall survival (OS) using Kaplan-Meier and multivariable Cox regression analyses. We also performed Principal Component Analysis (PCA) to test the clustering of NOS compared to other subtypes. We then examined the association between germline genetic variants and the development of NOS melanoma. Analyzing imputed SNP data generated using global screening array (GSA) from blood DNA we compared NOS (cases) with other histologic melanomas (controls) controlling for clinical and demographic variables. Results: NOS were identified in 550/2,891 (19%) of patients (median follow up of 4.44 years). PCA revealed clustering of NOS melanoma and superficial spreading melanoma (SSM) with similar favorable prognostic criteria (thinner, less ulcerated, earlier stage) compared to other subtypes (P < 0.001) However, NOS patients had a significantly shorter RFS compared to SSM (median RFS 8.83 years compared to 17.5 years, respectively; HR = 1.79 (95% CI: 1.37-2.33), P < 0.001 in multivariate analysis Genetic analysis of 919 cases (NOS = 127 versus all others n = 792) revealed a cluster of four genetic variants defining a linkage disequilibrium (LD) block mapping in the region of annotated microRNA 924 (LD-MIR924; OR = 1.844, p = 2.72E-06, 95% CI = 1.40-2.43), associating with ~1.8-fold increased risk of developing NOS compared to other melanoma subtypes. Conclusions: Our data suggest NOS melanoma might be a distinct genetic subtype that require further analysis considering the significant association with worse clinical outcome. The genetic variants associated with the risk of developing NOS melanoma point to MIR924, a microRNA previously linked to cancer progression. Our findings support the integration of studying patients’ genetics in addition to tumor somatic mutations to better understand the heterogeneous behavior of melanoma.

Product defect detection based on convolutional autoencoder and one-class classification

<span lang="EN-US">To meet customer expectations and remain competitive, industrials try constantly to improve their quality control systems. There is hence increasing demand for adopting automatic defect detection solutions. However, the biggest issue in addressing such systems is the imbalanced aspect of industrial datasets. Often, defect-free samples far exceed the defected ones, due to continuous improvement approaches adopted by manufacturing companies. In this sense, we propose an automatic defect detection system based on one-class classification (OCC) since it involves only normal samples during training. It consists of three sub-models, first, a convolutional autoencoder serves as latent features extractor, the extracted features vectors are subsequently fed into the dimensionality reduction process by performing principal component analysis (PCA), then the reduced-dimensional data are used to train the one-class classifier support vector data description (SVDD). During the test phase, both normal and defected images are used. The first two stages of the trained model generate a low-dimensional features vector, whereas the SVDD classifies the new input, whether it is defect-free or defected. This approach is evaluated on the carpet images from the industrial inspection dataset MVTec anomaly detection (MVTec AD). During training, only normal images were used. The results showed that the proposed method outperforms the state-of-the-art methods.</span>

Reducing the matrix effect in mass spectral imaging of biofilms using flow-cell culture.

The interactions between soil microorganisms and soil minerals play a crucial role in the formation and evolution of minerals and the stability of soil aggregates. Due to the heterogeneity and diversity of the soil environment, the under-standing of the functions of bacterial biofilms in soil minerals at the microscale is limited. A soil mineral-bacterial biofilm system was used as a model in this study, and it was analyzed by time-of-flight secondary ion mass spectrometry (ToF-SIMS) to acquire molecular level information. Static culture in multi-wells and dynamic flow-cell culture in microfluidics of biofilms were investigated. Our results show that more characteristic molecules of biofilms can be observed in SIMS spectra of the flow-cell culture. In contrast, biofilm signature peaks are buried under the mineral components in SIMS spectra in the static culture case. Spectral overlay was used in peak selection prior to performing Principal component analysis (PCA). Comparisons of the PCA results between the static and flow-cell culture show more pronounced molecular features and higher loadings of organic peaks of the dynamic cultured specimens. For example, fatty acids secreted from bacterial biofilm extracellular polymeric substance are likely to be responsible for biofilm dispersal due to mineral treatment up to 48h. Such findings suggest that the use of microfluidic cells to dynamically culture biofilms be a more suitable method for reducing the matrix effect arisen from the growth medium and minerals as a perturbation fac-tor for improved spectral and multivariate analysis of complex mass spectral data in ToF-SIMS. These results show that the interaction mechanism between biofilms and soil minerals at the molecular level can be better studied using the flow-cell culture and advanced mass spectral imaging techniques like ToF-SIMS.

Classification of Water Source in Coal Mine Based on PCA-GA-ET

In recent years, inrush water has hampered the regular mining of coal mines, and the proper identification of the source of inrush water is critical to the prevention and management of water hazards in mines. This paper extracts the standard water chemistry discriminating ions Na++K+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− from observed water samples. An improved water source discrimination model is proposed which combines algorithms from data mining, classification models, and learning reinforcement. According to the Pearson correlation coefficient, Na++K+ has a strong correlation with HCO3−. To identify the major metrics, we performed principal component analysis (PCA), and the adaptive differential evolutionary genetic algorithm (GA) was utilized to optimize the depth of the extreme tree (ET) and the number of classifiers. Finally, the model distinguished 25 sets of studied samples from various water sources in the Pingdingshan coalfield. Comparative analysis demonstrated the efficacy of each stage of our work. PCA-GA-ET outperformed the conventional approaches, such as the support vector machine, BP artificial neural network, and random forest. The studies revealed that PCA-GA-ET can eliminate the information overlap between data and simplify the data structure and thereby improve the efficiency and accuracy of water source detection. We discovered that by utilizing the evolutionary algorithm to optimize parameters such as the depth of the extreme trees and the number of decision trees, we could get the model to converge faster and to be more stable and more accurate. The results suggest that PCA-GA-ET has good robustness and accuracy and can meet the needs of water source identification.

Abstract 5387: Discovery of plasma protein biomarkers associated with overall survival in R/R DLBCL patients treated with loncastuximab tesirine

Abstract Plasma proteomics is a non-invasive source of potential biomarkers associated with cancer treatment outcomes, including disease progression and overall survival (OS). Loncastuximab tesirine (lonca) is an antibody-drug conjugate, composed of a humanized anti-CD19 antibody conjugated to a pyrrolobenzodiazepine dimer cytotoxin, for the treatment of relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL). Here, we investigated the association of plasma protein biomarkers with OS of R/R DLBCL patients prior to treatment with lonca (phase 2 trial, NCT3589469, LOTIS-2). Abundances of 888 plasma proteins, including inflammation, cancer and DNA repair associated markers, were measured for 69 patients with R/R DLBCL from plasma samples collected at baseline. Protein markers predictive of OS were selected using a L1 regularized Cox proportional hazards model (CoxPH) incorporating survival time and censoring status. Protein modules associated with OS were discovered by performing principal component analysis (PCA) on scaled protein abundances. Gene Set Enrichment Analysis (GSEA) was performed using Molecular Signatures Database (MSigDB) sets. We initially applied a regularized COX proportional hazards model to our proteomics dataset to identify proteins associated with OS, which revealed 5 protein markers (associated with decreased survival = REG3A, LAP3; associated with increased survival = NPTXR, C1QTNF1, FLT3) which stratified patients into distinct survival groups (p = 2.3e-4). As a complement to this approach and to extend our biological understanding of this cohort, we then applied PCA to the proteomics data to reveal underlying protein modules associated with survival. Our analysis identified principal component 3 (PC3) to have the strongest association to OS (rho= -0.63, FDR = 8.7e-07); with its top loadings consisting of known markers of cancer progression (top 3 proteins: SIGLEC10, IL6, TNFRSF6B). GSEA on PC3 loadings identified 68 significantly enriched gene sets (FDR < 5%). Gene sets reflective of changes in immune activation, increased cytokine activity and interleukin mediated activation (IL6, IL10) were negatively associated with OS, including activation of the following Hallmark pathways: IL6/JAK/STAT3 signaling and inflammatory response pathways.Overall, our results identify proteomics markers associated with DLBCL survival upon treatment with lonca, highlighting the potential of plasma proteins as a source of relevant biomarkers pending additional validation. [FV and VC contributed equally to this work] Citation Format: Francesco Vallania, Victoria Cheung, Anupriya Tripathi, Maggie Louie, Thomas Snyder, Jimmy Lin, Karin Havenith, Yajuan Qin, Serafino Pantano, Jens Wuerthner, Patrick H. van Berkel. Discovery of plasma protein biomarkers associated with overall survival in R/R DLBCL patients treated with loncastuximab tesirine. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5387.

Investigating metformin-active substances from different manufacturing sources by NIR, NMR, high-resolution LC-MS, and chemometric analysis for the prospective classification of legal medicines

Introduction: The characterisation of active substances is an essential tool to ensure the traceability and authenticity of legal medicines. Metformin is a well-established biguanide derivative recommended in oral formulations as a first-line treatment for type 2 diabetes. With its increasing demand, metformin is likely to be an attractive target for falsification and substandard production, thus posing health risks to consumers. Methods that are able to identify even small differences in active pharmaceutical ingredients (APIs) are deemed necessary. The detection of fraudulent practices in APIs is not straightforward, and a single technique that can provide sufficient information to unambiguously address this issue is still not available.Methods: This study investigated an integrated analytical platform based on NIR, 1H-NMR, 13C-NMR, and high-resolution LC-MS combined with chemometrics to profile 32 metformin hydrochloride samples originating from several global authorised manufacturers. The study's aim was to explore differences in the chemical characteristics of metformin hydrochloride APIs to identify or predict a possible classification for each manufacturer in view of prospective authenticity studies. Different pre-processing methods were applied; bucket tables for 1H- and 13C-NMR were obtained, while mass spectrometry data were processed in targeted and untargeted modes. Datasets were individually analysed and merged by a multivariate unsupervised method and performing principal component analysis (PCA). Results and Discussion: The results evidenced differences in cluster behaviour, depending on manufacturers. Each technique has shown a specific clustering tendency, highlighting how different analytical approaches are able to characterise metformin APIs. Some manufacturers’ samples, however, showed similar behaviour independently of the techniques. NIR and 1H-NMR were confirmed as the more predictive techniques if taken individually; 1H-NMR, in particular, achieved good separation between the samples of the two most representative manufacturers. For LC-MS, the targeted approach resulted in a separation in groups clearer than that of the untargeted approach. Nevertheless, the untargeted LC-MS approaches presented in this paper could be a possible alternative to obtaining different information for drug substances, with several different and complex synthetic pathways leading to several unknown impurities. Further grouping of manufacturers emerged by data fusion, highlighting its potential in the traceability of metformin.

Nonuniform magnetic domain-wall synapses enabled by population coding

Traditional artificial intelligence implemented in software is usually executed on accurate digital computers. Nevertheless, the nanoscale devices for the implementation of neuromorphic computing may not be ideally identical, and the performance is reduced by nonuniform devices. In biological brains, information is usually encoded by a cluster of neurons such that the variability of nerve cells does not influence the accuracy of human cognition and movement. Here, we introduce the population encoding strategy in neuromorphic computing and demonstrate that this strategy can overcome the problems caused by nonuniform devices. Using magnetic memristor device based on current-induced domain-wall motion as an example, we show that imperfect storage devices can be applied in a hardware network to perform principal component analysis (PCA), and the accuracy of unsupervised classification is comparable to that of conventional PCA using ideally accurate synaptic weights. Our results pave the way for hardware implementation of neuromorphic computing and lower the criteria for the uniformity of nanoscale devices.

Predicting customer churn using grey wolf optimization‐based support vector machine with principal component analysis

AbstractCustomer churn is a challenging problem that can lead to a loss of organizational assets. Organizations need to predict customer churn successfully in order to get rid of potential damages and gain a competitive advantage. The aim of this study is to provide a churn prediction model by including feature selection and optimization in classification. The study performs principal component analysis (PCA) to select the best features, support vector machine (SVM) to predict customer churn, and grey wolf optimization (GWO) to optimize the parameters of SVM. In other words, this study proposes a novel hybrid model called PCA‐GWO‐SVM to enhance the prediction ability in customer churn. A comparison experiment is carried out, evaluating the proposed model with the other classification algorithms. Experimental results show that the proposed PCA‐GWO‐SVM hybrid model produces higher accuracy, recall, and F1‐score than other machine learning algorithms such as logit, k‐nearest neighbors, naive Bayes, decision tree, and SVM.

Green and Black Tea Characterization By Electrochemical and UV-Vis Spectroscopy Techniques for Bioelectronic Applications

Green tea and black tea are the most widely consumed beverages worldwide. Consequently, it is common to employ complex techniques for qualitative analysis and quality control (QC) practices to verify the purity of tea. Electrochemical and optical techniques have become relevant in such analysis as they are fast, accurate, and less expensive than conventional analytical techniques [1]. In the present work, the evaluation of green and black tea of a high-consumption Mexican brand was carried out through electrochemical techniques using a graphite electrode as working electrode (WE) and KCl as supporting electrolyte (SE), and UV-Vis spectroscopy. The i-E profiles obtained make it possible to qualitatively identify these tea varieties for QC and perform Principal Component Analysis (PCA) to analyze the information.

Improving Classification Performance with Statistically Weighted Dimensions and Dimensionality Reduction

In image classification, various techniques have been developed to enhance the performance of principal component analysis (PCA) dimension reduction techniques with guiding weighting features to remove redundant and irrelevant features. This study proposes the statistically weighted dimension technique based on three distribution-related class behaviors; collection-class, inter-class, and intra-class to enhance the feature-extraction ability before using PCA for feature selection. The data from the statistics-weighted dimension spaces is utilized to reduce dimensionality by reducing the large index data into smaller index data using PCA. The new principal component from the weighted training part by an unlabeled dataset is constructed and then the image is classified efficiently. Additionally, the weighting direction investigates the pros and cons of promoting and demoting to determine the worst or best option utilizing the exponents of three proposed weighted scheme. The experiment is conducted using three datasets, MNIST, E-MNIST, and F-MNIST, along with three image classification algorithms, logistic Regression, KNN, and SVM (RBF). The results clearly demonstrate that the statistically weighted dimension feature can improve the conventional classification accuracy in lower dimensions with an appropriate combination of weighting nearly 3% for the best solution on dimensionality reduction by more than 50%.