Principal Component Analysis Plot Research Articles

Pregnancy detection based on blood serum sample Raman spectroscopy.

In this research, women were diagnosed as pregnant based on blood serum samples Raman spectroscopy. Raman spectroscopy is a vibrational technique that provides information on the chemical composition of samples. The Raman techniques have significantly impacted the study of various degenerative diseases, particularly cancer detection, using less invasive methods such as the analysis of blood serum samples. Additionally, these techniques have been used to study the health status of patients, which is often difficult to monitor using conventional techniques. This study obtained serum samples from 12 women diagnosed as pregnant and 11 non-pregnant volunteers (controls). Spectra were collected using a LabRAM HR800 Raman Spectrometer (Horiba Jobin-Yvon) with an 830 nm wavelength laser. For each serum sample, 10 Raman spectra were obtained by focusing the laser, using a microscope objective, on different points of the sample with an exposure time of 40 seconds and an irradiation power of 17 mW. The raw spectra were processed using baseline correction and smoothing to remove noise, fluorescence, and shot noise. Subsequently, the spectra were normalized and analyzed using the multivariate statistical method of Principal Component Analysis (PCA). In these spectra, the characteristic bands of main blood serum biomolecules such as phenylalanine (Phe), tyrosine (Tyr), glutathione, amide III, phospholipid, carotene, and tryptophan (Trp) can be observed. Nevertheless, when analyzing the average spectra of pregnant and non-pregnant women, the main spectral differences were associated with variations in molecules like glutathione, tryptophan (Trp), tyrosine, and phenylalanine, which occur during the first trimesters of pregnancy. This aligns with findings previously reported by other studies. Furthermore, the serum samples from pregnant and non-pregnant patients can be effectively discriminated using PCA applied to the Raman spectra, revealing two distinct clusters in the PCA plot corresponding to each group's status. The results demonstrate that pregnancy can be determined based on blood serum samples Raman spectroscopy with sensitivity and specificity. Although there are very effective devices on the market to determine pregnancy based on the Human Chorionic Gonadotropin (HCG) hormone detection in urine samples, these preliminary results indicate an alternative method known as Raman spectroscopy. On the other hand, the results could also suggest the possibility of carrying out other gynecological or fetal-related studies in women using these Raman techniques.

Comparative metabolomic analysis of spaghetti meat and wooden breast in broiler chickens: unveiling similarities and dissimilarities.

Spaghetti meat (SM) and wooden breast (WB) are emerging myopathies in the breast meat of fast-growing broiler chickens. The purpose of the study was to investigate the metabolomic differences between normal (N), SM, and WB fillets 24h postmortem. Eight chicken breasts for each experimental group were collected from a commercial processing plant. Supernatant from tissue homogenates were subjected to ultra-performance liquid chromatographytandem mass spectrometry (UPLC-MS) analysis. A total of 3,090 metabolites were identified in the chicken breast meat. The comparison of WB and N showed 850 differential metabolites (P < 0.05), and the comparison of SM and N displayed 617 differential metabolites. The comparison of WB and SM showed 568 differential metabolites. The principal component analysis (PCA) plots showed a distinct separation between SM and N and between WB and N except for one sample, but SM and WB were not distinctly separated. Compared to N, 15-Hydroxyeicosatetraenoic acid (15-HETE) increased, and D-inositol-4-phosphate decreased in both SM and WB, indicating that cellular homeostasis and lipid metabolism can be affected in SM and WB. The abundance of nicotinamide adenine dinucleotide (NAD) + hydrogen (H) (NADH) was exclusively decreased between SM and N (P < 0.05). Purine metabolism was upregulated in SM and WB compared to N with a greater degree of upregulation in WB than SM. Folic acid levels decreased in SM and WB compared to N (P < 0.05). Steroid hormone biosynthesis was downregulated in SM compared to N (P < 0.05). Carbon metabolism was downregulated in SM and WB compared to N with greater degree of downregulation in WB than SM (P < 0.05). These data suggest both shared and unique metabolic alterations in SM and WB, indicating commonalities and differences in their underlying etiologies and meat quality traits. Dietary supplementation of deficient nutrients, such as NADH, folic acids, etc. and modulation of altered pathways in SM and WB would be strategies to reduce the incidence and severity of SM and WB.

AI-powered precursor quantification in atmospheric pressure plasma jet thin film deposition via optical emission spectroscopy

Abstract This study explores the feasibility of using Optical Emission Spectroscopy (OES) for in situ monitoring of Atmospheric Pressure Plasma Jet systems in the deposition of thin films. We identify process parameters to control film properties by machine learning for data analysis. In experiments, the depth of the carrier gas inlet pipe (pipe depth) is a crucial controllable variable that directly affects the amount of precursor, influencing the film’s thickness, sheet resistance, and resistivity. We collected 96 000 spectra while preparing 12 film samples, subsequently measured the properties of the samples, and analyzed the spectral data using Principal Component Analysis (PCA) and seven supervised machine learning models. A high correlation was found between spectral features and film thickness. We divided the spectral data in a single process based on processing time into the first third (F-third) and the last third (L-third). Using the F-third data, the PCA plot clearly indicated a significant difference between the two pipe depths, achieving a mean recognition accuracy of 95.1% with machine learning models. In contrast, using the L-third data, the PCA plot showed a high degree of overlap between the two pipe depths, resulting in a considerable decline in recognition performance. Overall, it is challenging to distinguish the spectra visually due to variations in precursor amounts and dynamic fluctuations in the OES signals, even after averaging. Nonetheless, through the successful application of machine learning, we demonstrated an effective spectral recognition system for monitoring pipe depth, which aids in the timely control of film properties.

Machine learning, network pharmacology, and molecular dynamics reveal potent cyclopeptide inhibitors against dengue virus proteins.

The dengue virus is a major global health hazard responsible for an estimated 390 million diseases yearly. This study focused on identifying cyclopeptide inhibitors for envelope structural proteins E, NS1, NS3, and NS5. Additionally, 5579 cyclopeptides were individually screened against the four target proteins using a machine learning-based quantitative structure-activity relationship model. Subsequently, the best 10 cyclopeptides from each protein were selected for molecular docking with their corresponding proteins. Moreover, the protein-peptide complexes with the highest affinity were subjected to a 100-ns molecular dynamics simulation. The protein-protein complexes exhibited superior structural stability and binding interactions. Based on the results of the MD simulation analyses, which included checking values for Root Mean Square Deviation, Root Mean Square Fluctuation, Principal Component Analysis (PCA), free energy landscapes, and energetic components, it was found that NS5-CP03714 complex is more stable and has stronger binding interactions than NS3-CP02054. PCA and free energy landscape plots have confirmed the higher conformational stability of NS5-CP03714. Analysis of the energetic components revealed that NS5-CP03714 (total binding energy = -47.19kcal/mol) exhibits more favorable interaction energies and overall binding energy compared to NS3-CP02054 (total binding energy = -27.36kcal/mol), suggesting a stronger and more stable formation of the complex. In addition, the drug-target network of two specific peptides (CP02950 and CP05582) and their associated target proteins were analyzed. This analysis revealed valuable information about their ability to target several proteins and their potential for broad-spectrum activity. Additional experimental investigations are necessary to validate these computational results and assess the efficacy of identified peptide inhibitors in biological systems.

Comparative Analysis of the Fatty Acid Profiles of Selected Representatives of Chlorella-Clade to Evaluate Their Biotechnological Potential

The objective of this study was to analyze the fatty acid composition of five strains from the genera Chlorella, Micractinium, and Meyerella and conduct an initial assessment of their biotechnological potential. It was found that the strain C. vulgaris VKM Al-335 is a good producer of palmitic acid, the Micractinium strains VKM Al-332 and VKM Al-343 are rich in ω-3 fatty acids, whereas the Meyerella strains VKM Al-346 and VKM Al-428 are producers of ω-6 fatty acids. A comparison of the biotechnological potential of algae with that of higher plant leaves (wheat) demonstrates that algal fatty acids exhibit greater diversity, although it is inferior to wheat leaves in terms of polyunsaturated and ω-3 fatty acids. Correlation analysis showed that when only straight-chain fatty acids were considered, the strains were distributed on the principal component analysis plot in accordance with their genetic relationships. However, when the entire fatty acid profile, inclusive of minor branched-chain and cyclic fatty acids, was analyzed, the algae distribution was in accordance with the environmental conditions in the original habitat, suggesting a possible connection between branched-chain and cyclic fatty acids and microalgae adaptability to environmental temperature conditions.

Effects of postharvest nitric oxide and 24-Epibrassinolide treatments on cultivated blueberry (Vaccinium corymbosum L.): From flavor and antioxidant properties to edibility evaluation

Blueberry is a tender and perishable berry. This study identified the effects of nitric oxide (NO) and 2,4-Epibrassinolide (EBR) on the flavor, antioxidant properties, and edible value of blueberry. The results indicated that 1.0 mmol L−1 sodium nitroprusside (SNP, a NO donor) and 0.8 mg L−1 EBR groups showed high sourness and low sweetness in taste. SNP preserved the content of volatile flavor compounds on 8 d, while EBR preserved that on 16 d. The principal component analysis (PCA) plot from the flavor well differentiated between the treatment and control blueberry. Moreover, the biplot shows the correlation between volatile flavor compounds and taste perception. From the perspective of antioxidant properties, SNP and EBR inhibited the accumulation of H2O2, which showed a negative correlation with total phenolic (TPC), total flavonoid content (TFC), and ascorbic acid (AsA) content. SNP and EBR increased enzyme activities, such as catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), ascorbic acid peroxidase (APX), and glutathione reductase (GR), antioxidant activity, including 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging and ferric ion-reducing antioxidant power (FRAP), and the gene expression levels of VcSOD, VcAPX, and VcGR. The random forest model showed that AsA content, weight loss rate, decay incidence, TFC, TPC, Methyl-2-methylbutyrate, POD activity, and DPPH radical scavenging activity played critical roles in the edible value. Collectively, NO and EBR preserved the flavor, increased antioxidant capacity, and maintained postharvest quality.

Enrichment of cell cycle pathways in progesterone-treated endometrial organoids of infertile women compared to fertile women

PurposeTo investigate whether the transcriptome profile differs between progesterone-treated infertile and fertile endometrial organoids.MethodsEndometrial biopsies were obtained from 14 infertile and seven fertile women, after which organoids were generated from isolated epithelial cells. To mimic the secretory phase, organoids were sequentially treated with 17β-estradiol (E2) and progesterone (P4) and subjected to RNA sequencing. Differentially expressed genes (DEGs) were identified using DESeq2 (lfcThreshold = 0, log2 Fold Change ≥ 1.0 or ≤ −1.0), and a principal component analysis (PCA) plot was generated. Functional enrichment analysis was performed by overrepresentation analysis and Gene Set Enrichment Analysis (GSEA). To functionally assess proliferation, OrganoSeg surface measurements were performed before (T0) and after (T1) differentiation of organoids, and T1/T0 ratios were calculated to determine the proliferation rate.ResultsAlthough the PCA plot did not show clear clustering of the fertile and infertile samples, 363 significant DEGs (129 upregulated and 234 downregulated) were detected in infertile compared to fertile organoids. Mainly cell cycle processes were highly enriched in infertile organoids. Thus, we hypothesised that proliferative activity during differentiation may be higher in infertile organoids compared to fertile organoids. However, this could not be validated by cell surface measurements.ConclusionsThis study revealed that cell cycle processes were enriched in E2/P4-treated infertile endometrial organoids as compared to fertile organoids. This could reflect persistently higher proliferative activity of the endometrial epithelial cells in differentiated infertile organoids compared to fertile organoids. To confirm this hypothesis, further studies are warranted.

Impact of thermal, high‐pressure and ultra‐shear pasteurisation technologies on beetroot juice metabolites using untargeted nuclear magnetic resonance spectroscopy

SummaryThe impact of three food pasteurisation technologies, namely thermal, high‐pressure and ultra‐shear processing, on the metabolites of beetroot juice was evaluated using a processomics approach with nuclear mass resonance (NMR) as an analytical technique. Two batches of beetroots acquired from different local grocery stores were used for this study. Beetroot juice obtained from these batches was subjected to high‐pressure processing (HPP) at 600 MPa and 25 °C for 5 min, ultra‐shear technology processing (UST) at 400 MPa and 30 °C and thermal processing (TP) at 96 °C for 12 min. Principal component analysis (PCA) for the two batches indicated that both extrinsic factors such as processing parameters (temperature, pressure, shear and holding time) and intrinsic factors such as the origin of the beetroot influenced the PCA plot. When the influence of intrinsic parameters was minimised by studying a single batch processed by TP, HPP and UST, distinct clusters for different processing methods were formed, indicating that processing influenced the metabolites. While processing is not the main factor determining the final composition, as indicated by PCA with different batches, supervised techniques like orthogonal partial least‐squares discriminant analysis (OPLS‐DA) and random forest (RF) demonstrated that processing does impact the beetroot juice metabolome. Seven metabolites (leucine, alanine, valine, glutamine, gamma‐aminobutyric acid, fructose and glucose) were identified as potential process‐induced biomarkers.

Comparison of Asthma Phenotypes in Severe Asthma Cohorts (SARP, U-BIOPRED, ProAR and COREA) From 4 Continents.

Asthma is a clinical syndrome with various underlying pathomechanisms and clinical phenotypes. Genetic, ethnic, and geographic factors may influence the differences in clinical presentation, severity, and prognosis. We compared the characteristics of asthma based on the geographical background by analyzing representative cohorts from the United States, Europe, South America, and Asia using the Severe Asthma Research Program (SARP), Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED), Program for Control of Asthma in Bahia (ProAR), and Cohort for Reality and Evolution of Adult Asthma in Korea (COREA), respectively. The clinical characteristics and medications for the SARP (n = 669), U-BIOPRED (n = 509), ProAR (n = 996), and COREA (n = 3,748) were analyzed. Subgroup analysis was performed for severe asthma. The mean age was highest and lowest in the COREA and SARP, respectively. The asthma onset age was lowest in the ProAR. The mean body mass index was highest and lowest in the SARP and COREA, respectively. Baseline pulmonary function was lowest and highest in the U-BIOPRED and COREA, respectively. The number of patients with acute exacerbation in the previous year was highest in U-BIOPRED. The mean blood eosinophil count was highest in COREA. The total immunoglobulin E was highest in the ProAR. The frequency of atopy was highest in the SARP. The principal component analysis plot revealed differences among all cohorts. The cohorts from 4 different continents exhibited different clinical and physiological characteristics, probably resulting from the interplay between genetic susceptibility and geographical factors.

Metallomic analysis of brain tissues distinguishes between cases of dementia with Lewy bodies, Alzheimer's disease, and Parkinson's disease dementia.

Dementia with Lewy bodies (DLB) can be difficult to distinguish from Alzheimer's disease (AD) and Parkinson's disease dementia (PDD) at different stages of its progression due to some overlaps in the clinical and neuropathological presentation of these conditions compared with DLB. Metallomic changes have already been observed in the AD and PDD brain-including widespread decreases in Cu levels and more localised alterations in Na, K, Mn, Fe, Zn, and Se. This study aimed to determine whether these metallomic changes appear in the DLB brain, and how the metallomic profile of the DLB brain appears in comparison to the AD and PDD brain. Brain tissues from ten regions of 20 DLB cases and 19 controls were obtained. The concentrations of Na, Mg, K, Ca, Zn, Fe, Mn, Cu, and Se were determined using inductively coupled plasma-mass spectrometry (ICP-MS). Case-control differences were evaluated using Mann-Whitney U tests. Results were compared with those previously obtained from AD and PDD brain tissue, and principal component analysis (PCA) plots were created to determine whether cerebral metallomic profiles could distinguish DLB from AD or PDD metallomic profiles. Na was increased and Cu decreased in four and five DLB brain regions, respectively. More localised alterations in Mn, Ca, Fe, and Se were also identified. Despite similarities in Cu changes between all three diseases, PCA plots showed that DLB cases could be readily distinguished from AD cases using data from the middle temporal gyrus, primary visual cortex, and cingulate gyrus, whereas DLB and PDD cases could be clearly separated using data from the primary visual cortex alone. Despite shared alterations in Cu levels, the post-mortem DLB brain shows very few other similarities with the metallomic profile of the AD or PDD brain. These findings suggest that while Cu deficiencies appear common to all three conditions, metal alterations otherwise differ between DLB and PDD/AD. These findings can contribute to our understanding of the underlying pathogenesis of these three diseases; if these changes can be observed in the living human brain, they may also contribute to the differential diagnosis of DLB from AD and/or PDD.

Lid Margin Microbiome in Stevens-Johnson Syndrome Patients With Lid Margin Keratinization and Severe Dry Eye Disease.

The current study evaluated the lid margin microbiome of keratinized lid margins of patients with chronic Stevens-Johnson syndrome (SJS) and compared it with healthy controls and historically reported lid margin microbiome of patients with meibomian gland dysfunction (MGD). Eyelid margin swabs of 20 asymptomatic adults (mean age = 29 ± 12years) and 10 patients with chronic SJS (mean age = 31.2 ± 14years) with lid margin keratinization were sequenced using next generation of 16S rDNA V3 to V4 variable region. Within SJS, the keratinized lid margin microbiome was compared with adjacent eyelid skin. All patients had obstructive MGD, and mean Schirmer I value was 2.8 ± 1.9 mm. The phyla were similar in two groups, whereas at the genera level, an increase in the relative abundance of Corynebacterium, Haemophilus, Azotobacter, and Afipia and a decrease of Acinetobacter was noted in SJS compared to healthy lid margins. SJS-associated microbiota displayed lesser diversity and more heterogeneity than healthy controls. The Principal Components Analysis (PCA) plot revealed wide separation in the SJS and the control groups. Correlational network analysis revealed Corynebacterium and Sphingomonas forming a major hub of negative interactions with other bacterial genera in the SJS group. Significant differences exist in the prevalent genera between keratinized lid margins and historically reported meibum microbiome of patients with MGD. In addition, the eyelid skin of patients with SJS had predominant Staphylococcus, whereas Corynebacterium and Pseudomonas were more in the keratinized lid margins compared to the eyelid skin microbiome. Lid margin microbiome is significantly altered in the keratinized lid margins of patients with SJS compared to the eyelid skin of patients with SJS, normal lid margins, and patients with MGD.

PRKC Fusion Melanocytic Tumors, a Subgroup of Melanocytic Tumors More Closely Aligned to Blue Nevi Than to PRKAR1A-inactivated Pigmented Epithelioid Melanocytomas.

Tumors morphologically classified as pigmented epithelioid melanocytomas (PEMs) are genomically diverse, with the 2 most common genomic subtypes being PRKC fusions or PRKAR1A inactivating mutations. PRKC fusions activate the Gαq/11 pathway similar to blue nevi. Conversely, inactivating mutations in PRKAR1A activate the Gαs pathway. We hypothesize that PRKC fusions have greater genomic overlap with blue nevi compared with PRKAR1A-inactivated PEMs. We characterized the clinical and morphologic features of 21 PRKC and PRKACB fusion melanocytic tumors and compared this to PRKAR1A mutated PEMs. To test our hypothesis regarding greater genomic overlap between PRKC fusions and blue nevi relative to PRKAR1A mutated PEMs, we performed a principal component analysis (PCA) using mRNA expression data. Lastly, we performed a meta-analysis focusing on the outcome data of PRKC fusions. PRKC fusions occur at a younger median age than PRKAR1A mutated PEMs (16 vs. 27). Histologically, PRKC fusions have solid aggregates of epithelioid melanocytes not typical of PRKAR1A mutated PEMs. The PCA plot showed no overlap between the PRKC fusion group and the PRKAR1A-mutated PEMs. There was a significant overlap between PRKC fusions and blue nevi. A meta-analysis of PRKC fusion cases in the literature suggests melanoma is uncommon, but the loss of BAP-1 nuclear expression may be associated with an adverse prognosis as in tumors from the blue nevus family. PRKC fusion melanocytic tumors have greater genomic overlap with blue nevi compared with PRKAR1A mutated PEMs. We recommend categorizing benign PRKC fusion melanocytic tumors as blue fusion nevi/tumors.

Transcriptome RNA-Seq Analysis of Normal and Keratoconus Corneal Epithelium

Background: Keratoconus (KTCN) is a progressive eye condition characterised by thin, misshapen corneas arising in the teens or early twenties, and can cause visual disability. While this can be corrected with glasses or contact lenses, collagen cross-linking of the cornea slows disease progression. In advanced disease, patients undergo transplantation of the corneal epithelial layer with normal donor tissue. The cause of KTCN is unknown, but there is evidence for both environmental and genetic bases. Material and Methods: RNA-Seq data from published work by Kabza et al., 2017, was used to investigate the pathology of cornea tissue, analysing twenty-five KTCN and twenty-five non-KTCN samples from RNA-seq experiments. Principal component analysis plots were used to identify how characteristics impact development of keratoconus. Results: The findings show that KTCN is more common in teenagers and young adults, and all 25 KTCN samples were for individuals under 30 years old. Principal component analysis also showed that approximately 35% of samples were from individuals who rubbed their eyes. It also showed that KTCN is more common in males, representing over 65% of samples. Differential expression analysis of RNA-sequencing data was used to identify differentially expressed genes, downregulated genes were found to be enriched in many pathways, including cytokinecytokine receptor interaction pathways, and played a role in keratoconus development. Conclusion: Upregulated genes were enhanced in six pathways, including the peroxisome pathway. A potential relation was found between defects in peroxisome and keratoconus development. Overexpression of peroxisome genes increases phytanic acid levels, and causes Zellweger spectrum, affecting the cornea and potentially leading to keratoconus.

Hydrochemical characteristics and sources of groundwater pollution in Soubré and Gagnoa counties, Côte d’Ivoire

For many years, groundwater has been widely used to meet a variety of human needs. As a result of increased urbanization, industrial use, and agriculture, groundwater is inextricably affected by various threats, resulting in groundwater pollution. This current study was carried out to determine the hydrochemical characteristics of groundwater in Gagnoa and Soubré counties as well as the main sources of pollution and their impact on groundwater chemistry and quality. A total of thirty (30) groundwater samples were collected from boreholes in September 2020. The following physicochemical and chemical parameters were analyzed: Na, Ca, K, Mg, SO4, NO3–N, HCO3, Cl, pH, EC and TDS. The results showed that groundwater was acidic with a pH range from 5.22 to 6.84, fresh and moderately mineralized with a TDS range from 55.96 to 744.46 mg/l. Hydrogeochemical characterization based on the Piper diagram and Gibbs plot was used to identify the groundwater types and the main mechanism controlling the groundwater mineralization. Five (5) water types were identified with the dominance of hydrogen carbonate facies and showed the presence of silicate weathering and carbonate dissolution in the area. This is consistent with the results inferred from Gibbs plot showing that groundwater chemistry is governed by rock-water interaction. The spatial distribution map of major ions showed the different locations where each ion is dominant.According to WHO standards, the parameters show values within the threshold limit except for some of them of which their values exceed the limit. Principal component analysis and ionic ratio plots showed that the high values of nitrate as nitrogen and sulfate observed are due to the influence of natural sources such as carbonate dissolution and anthropogenic activities such as sewage-manure and chemical fertilizers.Therefore, mitigation measures should be adopted to reduce the impact of human activities contributing to groundwater pollution to protect the groundwater quality and human health.

Abstract PO4-23-11: Resistance Mechanisms to CDK4/6 Inhibitors and/or Tamoxifen Using Comprehensive Thermostable Group II Intron Reverse Transcriptase Sequencing

Abstract Background Understanding the mechanisms of resistance to CDK4/6 inhibitors (CDK4/6i) and endocrine therapy (ET) is pivotal in exploring new therapeutic strategies for hormone receptor-positive (HR+), HER2-negative metastatic breast cancer. To decipher these resistance mechanisms, we analyzed the alterations in comprehensive RNA-seq (coding and non-coding RNAs) of HR+ HER2-negative BC cell lines via thermostable group II intron reverse transcriptase sequencing (TGIRT-seq). Methods We established Tamoxifen-resistant (TMR), Abemaciclib-resistant (ACR), Palbociclib-resistant (PCR), Tamoxifen/Abemaciclib double-resistant (TMR-ACR), and Tamoxifen/Palbociclib double-resistant (TMR-PCR) BC cell lines from MCF7 and T47D HR+/HER2- BC cell lines through stepwise dose-escalation continuous drug exposure. We performed a TGIRT-seq transcriptomic analysis using a protocol that allows the sequencing of both long and short non-coding and protein-coding RNAs in a single library. All libraries were sequenced using paired-end 150 bp on the Novaseq platform, resulting in an average of 50 million reads per library. For analysis, raw reads underwent adapter trimming, small RNA mapping, whole genome mapping, and the generation of annotated genes' read count. We used raw counts to detect differentially expressed genes (DEGs) with DESeq2 in R using the cut-off (log2 [fold change] &amp;gt; 1, FDR &amp;lt; 1e-3). We then used the selected DEGs for Gene Set Enrichment Analysis (GSEA) and Kaplan-Meier survival analysis from the TCGA database. Results The TGIRT-seq analysis identified 1171 to 3472 DEGs in different drug resistance and cell line combinations. Most DEGs in resistant cells were consistently downregulated compared to parent cells. Seventy-five percent of DEGs were protein-coding genes, with the rest being non-coding RNAs, such as small nucleolar RNAs, microRNAs, long non-coding RNAs, and transposable element RNAs, which conventional RNA-seq poorly detects. In principal component analysis (PCA) plots, replicates per drug resistance clustered clearly in both cell line backgrounds. PCA also indicated distinctive pathways for acquiring drug resistance in MCF7 and T47D cells. The estrogen receptor (ER) expression decreased, while HER2 increased in resistant cell lines. DEGs identified were enriched in ESR1-related pathways in all MCF7 and T47D cell lines resistant models. Several ER-regulating genes like IL-1R1 and RET were upregulated, while ADCY1 was consistently downregulated across different resistance types. A significant overlap with single-resistance DEGs was observed among the DEGs in double-resistance cell lines, but 20-50% of up/down-regulated DEGs in double-resistance cell lines were uniquely altered. Several previously identified DEGs (e.g., SALL4, TOP2A) and 21 novel candidate genes correlated with poor survival outcomes. Conclusion The analysis identified unique DEGs in double resistance cell lines, suggesting that double resistance mechanisms may not merely be a cumulative effect of single resistance mechanisms, necessitating further investigation and validation. CDK4/6i and/or ET-resistant BC cell lines displayed significant transcriptome reprogramming during the development of drug resistance. In previously published research, ESR1-related pathway alterations were proposed in tamoxifen resistance cell lines. Here we find that CDK4/6i-resistant cells also modify these ESR1-related pathways. Additionally, we identified several targetable genes, such as IL-1R1 and RET, involved in ESR1-related pathways that could pave the way for developing new treatment strategies. Citation Format: Toshiaki Iwase, Hengyi Xu, Nakyung Oh, Jangsoon Lee, Alan M Lambowitz, Naoto Ueno. Resistance Mechanisms to CDK4/6 Inhibitors and/or Tamoxifen Using Comprehensive Thermostable Group II Intron Reverse Transcriptase Sequencing [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-23-11.

Prediction of the Freshness of Grass Carp during Storage with Electric Nose Based on Signal Sequence Merging and Wavelet Transform

In order to predict the freshness of grass carp, a novel data preprocessing method was proposed for electronic nose (E-nose) signals. The signal sequences from six sensors were selected and subsequently normalized. The direct signal sequence merging (DSSM) and reversed signal sequence merging (RSSM) modes were used for signal sequence merging. Subsequently, the genetic algorithm (GA) was used to evaluate the contribution of diverse sensors, and the merged data sequence was compressed using wavelet transform (WT). Using approximation coefficient and detail coefficient based on different scales and different signal sequence merging modes, principal component analysis (PCA) discriminated successfully storage time of chilled fish fillet. The PCA plots clearly demonstrated that all extracted feature data fully retain the signal characters. The partial least squares (PLS) and artificial neural network (ANN) models were used to establish prediction models for the freshness of grass carp during storage. The DSSM-ANN-A5 and DSSM-PLS-D4 models were chosen as the TVB-N content prediction models, while the DSSM-ANN-D5 and RSSM-PLS-A0 models were selected as the K value prediction models. The R2 values of these models are higher than 0.9, and they have a good coefficient of determination. The results of this study suggest that it using E-nose signals to predict TVB-N content and K value is an effective method for assessing the freshness of grass carp during storage.

Sensory profiling of dairy starch dessert creams enhanced with various types and concentrations of tahini

The study investigated the descriptive attributes and sensory profiles of dairy starch creams (DSC), incorporating various concentrations (3%, 6%, 12%) and types of tahini, namely hazelnut (HT), almond (AT), and sunflower (ST). Quantitative descriptive analysis (QDA) was used to describe and quantify sensory descriptors, resulting in a consensual list of attributes. Principal components analysis (PCA) was then applied to characterize the tahini-starch creams. The results have shown that the incorporation of tahini into the dessert creams impacts the perception of sensory attributes. As the concentration of added tahini increased, the products' stability and consistency, as well as sensations of adhesion, homogeneity, and gelatinousness, decreased. The sweet taste sensation heightened with an increase in hazelnut tahini concentration in the desserts. Notably, the cream with 6% almond tahini, positioned in the middle of the PCA plot, demonstrates the most balanced attributes in terms of appearance, aroma, taste, flavor, aftertaste, and texture, with the highest level of homogeneity.

A deep learning approach to classifying flow-induced vibration response regimes of an elliptical cylinder

This study proposes a new approach that leverages deep learning to the study of flow-induced vibration (FIV), specifically to automate flow regime classification and to visualize the transitions between these regimes. Using previously obtained data on the amplitude response of an elastically mounted cylinder as a function of reduced velocity for a range of structural damping ratios, the time trace of the body displacement and fluid driving forces are first converted into a frequency-time representation using continuous wavelet transforms before being input to several pre-trained convolutional neural networks for feature extraction. When utilizing the outputs of each convolutional neural network for regime classification, we found that almost all the machine learning approaches had high cross-validation accuracy that was statistically insignificant from each other. The five best-performing classifiers were then used as an ensemble method, yielding a weighted accuracy of 99.1% on the test data. The FIV response regimes were further investigated by projecting the outputs of the pre-trained convolutional neural networks onto the first three modes identified with principal component analysis (PCA). The PCA plots indicated that, among all the models considered, Xception showed superior capability in delineating distinct locations for different FIV response regimes, based on the lock-in frequency and the presence of harmonics in the driving fluid forces. Moreover, the PCA plots also showed that increasing the structural damping ratio resulted in a diminished disparity in the dynamics of the identified FIV response regimes, leading to a less discernible separation between the regimes in the plots.

Abstract 4980: Genomic and clinical characteristics of advanced pancreatobiliary cancer, stratified by KRAS mutation status

Abstract Mutations in KRAS are common in pancreatobiliary cancer, occurring in 90% of advanced pancreatic ductal adenocarcinomas (PDAC) and 20% of advanced cholangiocarcinomas (CCA). We analyzed clinico-genomic databases to evaluate the hypothesis that KRAS mutation status defines subsets of PDAC and CCA with similar genomic characteristics and clinical behavior. Using the genomic data of PDAC and CCA from AACR Project GENIE Cohort version 14.0-public, we analyzed the genomic landscape of four cohorts of pancreatobiliary cancer: KRAS mutated PDAC, KRAS wild-type PDAC, KRAS mutated CCA, and KRAS wild-type CCA. Excluding KRAS mutations, there were 53 gene alterations observed in &amp;gt;2.5% of cases in at least one of the four cohorts. The three most common gene alterations in each cohort were; TP53, SMAD4, and CDKN2A mutations (KRAS mutated PDAC), TP53, ARID1A, and SMAD4 mutations (KRAS wild-type PDAC), TP53 mutation, CDKN2A deletion and SMAD4 mutation (KRAS mutated CCA), and TP53, ARID1D and IDH1 mutations. (KRAS wild-type CCA) The similarity of gene alteration patterns was evaluated by principal component analysis (PCA) 3D plot and calculation of average Euclidean distance. The PCA plot suggested heterogeneous genomic profiles in KRAS wild-type CCA in contrast to relatively monotonous profiles in KRAS mutated PDAC. The average Euclidean distance between KRAS mutated CCA and KRAS wild-type CCA was significantly longer than that between KRAS mutated CCA and KRAS mutated PDAC (2.00 vs. 1.89, p &amp;lt; 0.001), suggesting that KRAS mutated CCA is more genomically related to KRAS mutated PDAC than KRAS wild-type CCA. The average Euclidean distance between KRAS mutated PDAC and KRAS wild-type PDAC was significantly shorter than that between KRAS wild-type CCA and KRAS wild-type PDAC. To compare the clinical characteristics of the four cohorts, we evaluated the data of patients with stage IV pancreatobiliary cancer, using cBioPortal. The number of cases in each cohort was 1544 (KRAS mutated PDAC), 121 (KRAS wild-type PDAC), 91 (KRAS mutated CCA), and 459 (KRAS wild-type CCA). Median overall survival (OS) was 14.7, 21.5, 10.7, and 15.4 months, respectively. Compared to KRAS wild-type CCA, KRAS mutated CCA had a significantly shorter OS (hazard ratio [HR] = 1.45, 95% confidence interval [CI] = 1.12 - 1.89). The OS of KRAS mutated CCA was shorter than KRAS mutated PDAC (HR 1.32, 95% CI 1.03 - 1.68). KRAS wild-type PDAC had a significantly longer OS compared to KRAS mutated PDAC. (HR 0.72, 95% CI 0.57 - 0.92) In conclusion, KRAS mutated CCA has a poor OS and a genomic landscape distinct from KRAS wild-type CCA; it is genomically more similar to KRAS mutated PDAC. KRAS wild-type PDAC has a better OS than KRAS mutated PDAC, though it was not genomically similar to KRAS wild-type CCA. Therapeutic approaches stratified by KRAS mutation status should be considered for pancreaticobiliary cancers. Citation Format: Hirotaka Miyashita, Parth S. Shah, Gabriel A. Brooks. Genomic and clinical characteristics of advanced pancreatobiliary cancer, stratified by KRAS mutation status [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4980.

Preliminary Study on Yeasts Associated with the Production of "Tostado"-a Traditional Sweet Wine from Galicia (NW Spain).

Tostado is a traditional sweet wine from the Designations of Origins (DOs) of Ribeiro and Valdeorras in Galicia (NW Spain). The harvested grapes are air-dried and pressed to increase the concentrations of sugars, acids, and flavour compounds. Therefore, knowledge of the yeasts involved in fermentation under these conditions is essential to guarantee the quality and uniqueness of the valuable, distinctive, and expensive Tostado wines. Saccharomyces and non-Saccharomyces yeasts were identified using Wallerstein Laboratory (WL) Nutrient Agar and lysine plating, followed by polymerase chain reaction (PCR) amplification, enzymatic digestion, and sequencing. Saccharomyces cerevisiae isolates were further characterised at the strain level using mitochondrial DNA (mtDNA) restriction fragment length polymorphism (RFLP). Statistical analyses were also performed, including different diversity indices, Similarity Percentage (SIMPER) analysis, principal component analysis (PCA), neighbor-joining clustering, parsimony-phylogram, and network plot. In addition, the total acidity, volatile acidity, reducing sugars, and alcoholic strength by volume of the Tostado wines were analysed. A wide diversity of autochthonous yeasts was found, which were predominantly species of oenological relevance, such as Lachancea thermotolerans, Starmerella bacillaris, Hanseniaspora uvarum, Debaryomyces hansenii, Torulaspora delbrueckii, Pichia spp., and Saccharomyces cerevisiae from the must and paste stages of Tostado wine. In addition, 19 different S. cerevisiae strains were identified. This high yeast diversity, which changed from the early stages of fermentation, could contribute to the distinctive characteristics observed in Tostado wine. Characteristic and differentiating chemical and microbiological profiles were found as early as the pre-fermentation stages, which adds value to these special wines that have rarely been studied.