Chemical sensing of common microorganisms found in biopharmaceutical industries using MIR laser spectroscopy and multivariate analysis.

Mid-infrared laser spectroscopy was used to investigate common bacteria encountered in biopharmaceutical industries. The study involved the detection of bacteria using quantum cascade laser spectroscopy coupled to a grazing angle probe (QCL-GAP). Substrates similar to surfaces commonly used in biopharmaceutical industries were used as support media for the samples. Reflectance measurements were assisted by Multivariate Analysis (MVA) to assemble a powerful spectroscopic technique with classification and identification resources. The species analyzed, Staphylococcus aureus, Staphylococcus epidermidis, and Micrococcus luteus, were used to challenge the technique's capability to discriminate from microorganisms of the same family. Principal Components Analysis and Partial Least Squares-Discriminant Analysis differentiated between the bacterial species, using QCL-GAP-MVA as the reference. Spectral differences in the bacterial membrane were used to determine if these microorganisms were present in the samples analyzed. Results herein provided effective discrimination for the bacteria under study with high sensitivity and specificity.

Macro-micro exploration on dynamic interaction between aflatoxigenic Aspergillus flavus and maize kernels using Vis/NIR hyperspectral imaging and SEM technology.

Aspergillus flavus and its toxic metabolites-aflatoxins infect and contaminate maize kernels, posing a threat to grain safety and human health. Due to the complexity of microbial growth and metabolic processes, dynamic mechanisms among fungal growth, nutrient depletion of maize kernels and aflatoxin production is still unclear. In this study, visible/near infrared (Vis/NIR) hyperspectral imaging (HSI) combined with the scanning electron microscope (SEM) was used to elucidate the critical organismal interaction at kernel (macro-) and microscopic levels. As kernel damage is the main entrance for fungal invasion, maize kernels with gradually aggravated damages from intact to pierced to halved kernels with A. flavus were cultured for 0-120 h. The spectral fingerprints of the A. flavus-maize kernel complex over time were analyzed with principal components analysis (PCA) of hyperspectral images, where the pseudo-color score maps and the loading plots of the first three PCs were used to investigate the dynamic process of fungal infection and to capture the subtle changes in the complex with different hardness of the maize matrix. The dynamic growth process of A. flavus and the interactions of fungus-maize complexes were explained on a microscopic level using SEM. Specifically, fungus morphology, e.g., hyphae, conidia, and conidiophore (stipe) was accurately captured on the microscopic level, and the interaction process between A. flavus and nutrient loss from the maize kernel tissues (i.e., embryo, and endosperm) was described. Furthermore, the growth stage discrimination models based on PLSDA with the results of CCRC = 100 %, CCRV = 97 %, CCRIV = 93 %, and the prediction models of AFB1 based on PLSR with satisfactory performance (R2C = 0.96, R2V = 0.95, R2IV = 0.93 and RPD = 3.58) were both achieved. In conclusion, the results from both macro-level (Vis/NIR-HSI) and micro-level (SEM) assessments revealed the dynamic organismal interactions in A. flavus-maize kernel complex, and the detailed data could be used for modeling, and quantitative prediction of aflatoxin, which would establish a theoretical foundation for the early detection of fungal or toxin contaminated grains to ensure food security.

Feedback negativity and feedback-related P3 in individuals at risk for depression: Comparing surface potentials and current source densities.

Blunted responses to reward feedback have been linked to major depressive disorder (MDD) and depression risk. Using a monetary incentive delay task (win, loss, break-even), we investigated the impact of family risk for depression and lifetime history of MDD and anxiety disorder with 72-channel electroencephalograms (EEG) recorded from 29 high-risk and 32 low-risk individuals (15-58 years, 30 male). Linked-mastoid surface potentials (ERPs) and their corresponding reference-free current source densities (CSDs) were quantified by temporal principal components analysis (PCA). Each PCA solution revealed a midfrontal feedback negativity (FN; peak around 310 ms) and a posterior feedback-P3 (fb-P3; 380 ms) as two distinct reward processing stages. Unbiased permutation tests and multilevel modeling of component scores revealed greater FN to loss than win and neutral for all stratification groups, confirming FN sensitivity to valence. Likewise, all groups had greater fb-P3 to win and loss than neutral, confirming that fb-P3 indexes motivational salience and allocation of attention. By contrast, group effects were subtle, dependent on data transformation (ERP, CSD), and did not confirm reduced FN or fb-P3 for at-risk individuals. Instead, CSD-based fb-P3 was overall reduced in individuals with than without MDD history, whereas ERP-based fb-P3 was greater for high-risk individuals than for low-risk individuals for monetary, but not neutral outcomes. While the present findings do not support blunted reward processing in depression and depression risk, our side-by-side comparison underscores how the EEG reference choice affects the characterization of subtle group differences, strongly advocating the use of reference-free techniques.

[Potential Source Identification and Ecological Risk Assessment of Heavy Metals in Surface Soil of Heze Oil Peony Planting Area Based on PMF-PCA/APCS and PERI].

To study the sources and potential risks of heavy metals in soils of characteristic agricultural product producing areas is of great significance for the scientific management and safe utilization of soil and crop resources. The contents of heavy metals As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in the 254 surface soil samples collected from the Heze oil peony planting area were determined. The content characteristics and correlation of heavy metals were analyzed using multivariate statistical methods. The sources of heavy metals in topsoil were analyzed using Igeo, PMF, and PCA/APCS. The ecological risks of the eight heavy metals were assessed through the potential ecological risk index (PERI). The results showed that the average contents of seven heavy metals in the soil were basically consistent with the background values of soil elements in Heze City, except that the average value of Cd was 1.44 times higher than the background value in Heze City. Correlation analysis and cluster analysis revealed that Pb, Hg, and Cd elements in the soil were greatly affected by human activities in the later period. The sources of eight heavy metals in the study area were natural sources, agricultural fertilizer sources, industrial coal sources, and domestic transportation sources, with the contribution rates of 81.31%, 15.45%, 2.74%, and 0.50%, respectively; 84.25% of the sites in the study area were at slight ecological risk, whereas the moderate risk and strong risk sites accounted for 14.96% and 0.79%, respectively. Among them, Cd and Hg were the dominant elements of ecological risk in the study area.

Detection of primary myelofibrosis in blood serum via Raman spectroscopy assisted by machine learning approaches; correlation with clinical diagnosis.

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell-derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, the molecular marker of PM, which is a mutation in the JAK2V617F gene, was observed also in other myeloproliferative neoplasms such as polycythemia vera and essential thrombocythemia. Therefore, there is a need to find methods that provide a marker unique to PM and allow for higher accuracy of PM diagnosis and consequently the treatment of the disease. Continuing, in this study, we used Raman spectroscopy, Principal Components Analysis (PCA), and Partial Least Squares (PLS) analysis as helpful diagnostic tools for PM. Consequently, we used serum collected from PM patients, which were classified using clinical parameters of PM such as the dynamic international prognostic scoring system (DIPSS) for primary myelofibrosis plus score, the JAK2V617F mutation, spleen size, bone marrow reticulin fibrosis degree and use of hydroxyurea drug features. Raman spectra showed higher amounts of C-H, C-C and C-C/C-N and amide II and lower amounts of amide I and vibrations of CH3 groups in PM patients than in healthy ones. Furthermore, shifts of amides II and I vibrations in PM patients were noticed. Machine learning methods were used to analyze Raman regions: (i) 800 cm-1 and 1800 cm-1, (ii) 1600 cm-1-1700 cm-1, and (iii) 2700 cm-1-3000 cm-1 showed 100 % accuracy, sensitivity, and specificity. Differences in the spectral dynamic showed that differences in the amide II and amide I regions were the most significant in distinguishing between PM and healthy subjects. Importantly, until now, the efficacy of Raman spectroscopy has not been established in clinical diagnostics of PM disease using the correlation between Raman spectra and PM clinical prognostic scoring. Continuing, our results showed the correlation between Raman signals and bone marrow fibrosis, as well as JAKV617F. Consequently, the results revealed that Raman spectroscopy has a high potential for use in medical laboratory diagnostics to quantify multiple biomarkers simultaneously, especially in the selected Raman regions.

Genetic Ancestry Estimates within Dutch Family Units and Across Genotyping Arrays: Insights from Empirical Analysis Using Two Estimation Methods.

Accurate inference of genetic ancestry is crucial for population-based association studies, accounting for population heterogeneity and structure. This study analyzes genome-wide SNP data from the Netherlands Twin Register to compare genetic ancestry estimates. The focus is on the comparison of ancestry estimates between family members and individuals genotyped on multiple arrays (Affymetrix 6.0, Affymetrix Axiom, and Illumina GSA). Two conventional methods, principal component analysis and ADMIXTURE, were implemented to estimate ancestry, each serving its specific purpose, rather than for direct comparison. The results reveal that as the degree of genetic relatedness decreases, the Euclidean distances of genetic ancestry estimates between family members significantly increase (empirical p < 0.001), regardless of the estimation method and genotyping array. Ancestry estimates among individuals genotyped on multiple arrays also show statistically significant differences (empirical p < 0.001). Additionally, this study investigates the relationship between the ancestry estimates of non-identical twin offspring with ancestrally diverse parents and those with ancestrally similar parents. The results indicate a statistically significant weak correlation between the variation in ancestry estimates among offspring and differences in ancestry estimates among parents (Spearman's rho: 0.07, p = 0.005). This study highlights the utility of current methods in inferring genetic ancestry, emphasizing the importance of reference population composition in determining ancestry estimates.

Variations and driving factors of leaf functional traits in the dominant desert plant species along an environmental gradient in the drylands of China.

Leaf functional traits (LFTs) of desert plants are responsive, adaptable and highly plastic to their environment. However, the macroscale variation in LFTs and driving factors underlying this variation remain unclear, especially for desert plants. Here, we measured eight LFTs, including leaf carbon concentration (LCC), leaf nitrogen concentration (LNC), leaf phosphorus concentration (LPC), specific leaf area (SLA), leaf dry matter content (LDMC), leaf mass per area (LMA), leaf thickness (LTH) and leaf tissue density (LTD) across 114 sites along environmental gradient in the drylands of China and in Guazhou Common Garden and evaluated the effect of environment and phylogeny on the LFTs. We noted that for all species, the mean values of LCC, LNC, LPC, SLA, LDMC, LMA, LTH and LTD were 384.62 mg g-1, 19.91 mg g-1, 1.12 mg g-1, 79.62 cm2 g-1, 0.74 g g-1, 237.39 g m-2, 0.38 mm and 0.91 g cm-3, respectively. LFTs exhibited significant geographical variations and the LNC, LMA and LTH in the plants of Guazhou Common Garden were significantly higher than the field sites in the drylands of China. LDMC and LTD of plants in Guazhou Common Garden were, however, considerably lower than those in the drylands of China. LCC, LPC, LTH and LTD differed significantly among different plant lifeforms, while LNC, SLA, LDMC and LMA didn't show significant variations. We found that the environmental variables explained higher spatial variations (3.6-66.3 %) in LFTs than the phylogeny (1.8-54.2 %). The LCC significantly increased, while LDMC and LTD decreased with increased temperature and reduced precipitation. LPC, LDMC, LMA, and LTD significantly increased, while SLA and LTH decreased with increased aridity. However, leaf elements were not significantly correlated with soil nutrients. The mean annual precipitation was a key factor controlling variations in LFTs at the macroscale in the drylands of China. These findings will provide new insights to better understand the response of LFTs and plants adaptation along environmental gradient in drylands, and will serve as a reference for studying biogeographic patterns of leaf traits.

Application of Fourier Transform InfraRed spectroscopy of machine learning with Support Vector Machine and principal components analysis to detect biochemical changes in dried serum of patients with primary myelofibrosis.

Primary myelofibrosis (PM) is a myeloproliferative neoplasm characterized by stem cell-derived clonal neoplasms. Several factors are involved in diagnosing PM, including physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. Commonly gene mutations are used. Also, these gene mutations exist in other diseases, such as polycythemia vera and essential thrombocythemia. Hence, understanding the molecular mechanism and finding disease-related biomarker characteristics only for PM is crucial for the treatment and survival rate. For this purpose, blood samples of PM (n = 85) vs. healthy controls (n = 45) were collected for biochemical analysis, and, for the first time, Fourier Transform InfraRed (FTIR) spectroscopy measurement of dried PM and healthy patients' blood serum was analyzed. A Support Vector Machine (SVM) model with optimized hyperparameters was constructed using the grid search (GS) method. Then, the FTIR spectra of the biomolecular components of blood serum from PM patients were compared to those from healthy individuals using Principal Components Analysis (PCA). Also, an analysis of the rate of change of FTIR spectra absorption was studied. The results showed that PM patients have higher amounts of phospholipids and proteins and a lower amount of H-O=H vibrations which was visible. The PCA results indicated that it is possible to differentiate between dried blood serum samples collected from PM patients and healthy individuals. The Grid Search Support Vector Machine (GS-SVM) model showed that the prediction accuracy ranged from 0.923 to 1.00 depending on the FTIR range analyzed. Furthermore, it was shown that the ratio between α-helix and ß-sheet structures in proteins is 1.5 times higher in PM than in control people. The vibrations associated with the CO bond and the amide III region of proteins showed the highest probability value, indicating that these spectral features were significantly altered in PM patients compared to healthy ones' spectra. The results indicate that the FTIR spectroscope may be used as a technique helpful in PM diagnostics. The study also presents preliminary results from the first prospective clinical validation study.

Online Omics Platform Expedites Industrial Application of Halomonas bluephagenesis TD1.0.

Multi-omic data mining has the potential to revolutionize synthetic biology especially in non-model organisms that have not been extensively studied. However, tangible engineering direction from computational analysis remains elusive due to the interpretability of large datasets and the difficulty in analysis for non-experts. New omics data are generated faster than our ability to use and analyse results effectively, resulting in strain development that proceeds through classic methods of trial-and-error without insight into complex cell dynamics. Here we introduce a user-friendly, interactive website hosting multi-omics data. Importantly, this new platform allows non-experts to explore questions in an industrially important chassis whose cellular dynamics are still largely unknown. The web platform contains a complete KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis derived from principal components analysis, an interactive bio-cluster heatmap analysis of genes, and the Halomonas TD1.0 genome-scale metabolic (GEM) model. As a case study of the effectiveness of this platform, we applied unsupervised machine learning to determine key differences between Halomonas bluephagenesis TD1.0 cultivated under varied conditions. Specifically, cell motility and flagella apparatus are identified to drive energy expenditure usage at different osmolarities, and predictions were verified experimentally using microscopy and fluorescence labelled flagella staining. As more omics projects are completed, this landing page will facilitate exploration and targeted engineering efforts of the robust, industrial chassis H bluephagenesis for researchers without extensive bioinformatics background.

Association between multiple vitamins and bone mineral density: a cross-sectional and population-based study in the NHANES from 2005 to 2006.

BACKGROUND: Bone mineral density (BMD) alterations in response to multivitamin exposure were rarely studied. Our study assessed the association of coexposure to six types of vitamins (i.e., vitamins B12, B9, C, D, A and E) with BMD measurements in adults in the US. METHODS: Data were collected from participants aged ≥ 20 years (n = 2757) in the U.S. National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2006. Multiple linear regression, restricted cubic splines, principal component analysis (PCA) and weighted quantile sum (WQS) regression were performed for statistical analysis. RESULTS: The circulating levels of vitamins B12 and C were positively associated with BMDs, and an inverted L-shaped exposure relationship was observed between serum vitamin C and BMDs. PCA identified two principal components: one for 'water-soluble vitamins', including vitamins B12, B9 and C, and one for 'fat-soluble vitamins', including vitamins A, D and E. The former was positively associated with total femur (ß = 0.009, 95%CI: 0.004, 0.015) and femoral neck (ß = 0.007, 95%CI: 0.002, 0.013) BMDs, and the latter was negatively associated with BMDs with non-statistical significance. The WQS index constructed for the six vitamins was significantly related to total femur (ß = 0.010, 95%CI: 0.001, 0.018) and femoral neck (ß = 0.008, 95%CI: 0.001, 0.015) BMDs, and vitamins B12 and C weighted the most. The WQS index was inversely related to BMDs with non-statistical significance, and vitamins E and A weighted the most. CONCLUSION: Our findings suggested a positive association between water-soluble vitamin coexposure and BMD, and the association was mainly driven by vitamins B12 and C. Negative association between fat-soluble vitamin coexposure and BMD was indicated, mainly driven by vitamins E and A. An inverted L-shaped exposure relationship was found between vitamin C and BMD.

LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling.

INTRODUCTION: This study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation. METHOD: A pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC-MS). RESULTS: The best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC-MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure. CONCLUSIONS: The results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

Biochemical Composition, Antioxidant Activity and Antiproliferative Effects of Different Processed Garlic Products.

Garlic (Allium sativum L.) is a type of agricultural product that is widely used as a food spice, herb and traditional medicine. White garlic (WG) can be processed into several kinds of products, such as green garlic (GG), Laba garlic (LAG) and black garlic (BG), which have multiple health effects. In this study, GC-MS (gas chromatography-mass spectrometry), DPPH (1,1'-diphenyl-2-propionyl hydrazide) radical scavenging, hydroxyl radical scavenging and MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) in vitro assays were used to compare the composition, antioxidant and antiproliferation effects of different processed garlic extracts. The relationship between the constituents and the bioactivities was analyzed using the principal components analysis (PCA) and heatmap analysis. BG showed the highest antioxidant activity (IC50 = 0.63 ± 0.02 mg/mL) in DPPH radical assays and the highest antioxidant activity (IC50 = 0.80 ± 0.01 mg/mL) by hydroxyl radical assay. Moreover, GC-MS results showed that 12 organosulfur compounds were detected in the extracts of four garlic products, and allyl methyl trisulfide showed a positive relation with the anticancer activity on SMMC-7721 cells (hepatocellular carcinoma cells). The results suggested that the processing of garlic had a significant influence on the constituents and antioxidant effects and that GG, LAG and BG might be better candidates for the related functional food products compared to WG.

Investigating dynamic causal network with unified Granger causality analysis.

BACKGROUND: Dynamic coupling phenomena characterize a widespread fundamental mechanism for the functional brain, which involves large-scale interactions at a multi-level. The Granger causality analysis (GCA) provides a data-driven procedure to investigate causal connections and has the potential to be a powerful dynamic capturing tool. NEW METHOD: In this paper, distinct from the conventional two-stage scheme of most GCA methods, we suggest a unified GCA (uGCA) method incorporating a sliding window to further capture dynamic connections. And the uGCA method integrates all related procedures into the same space by a single mathematical theory, which involves a description length guided framework. RESULTS: Through synthetic data experiments and real fMRI data experiments, we illustrated the effectiveness and priority of the proposed uGCA method. COMPARISON WITH EXISTING METHODS: By varying the data length, we have demonstrated its superiority to conventional GCA in synthetic data experiments. We further illustrated the outstanding capability of their dynamic causal investigation in the fMRI data, involving serial mental arithmetic tasks under visual and auditory stimuli, respectively, one can evaluate the performance of different methods by accessing their network similarities among different stimuli. When varying windows size and step size of the sliding window, respectively, compared with conventional GCA, the uGCA identified higher network similarities while ensuring more robust performance. CONCLUSIONS: The stability and effectiveness of uGCA will show it an advantage in the further research of multi-level dynamic coupling and characterizing.

The quality of selected raw and pasteurized honeys based on their sensory profiles and consumer preferences.

The purpose of this study was to determine the sensory profile of honeys based on the method of quantitative descriptive analysis and principal component analysis and assess consumer preferences of raw and pasteurized honeys. Samples of multi-floral honeys (from the store and apiary) were subjected to sensory analysis based on the method of ranking for taste preference, the method of scaling based on color, aroma, taste, and texture, and the method of differential descriptive analysis using 11 quality descriptors. The results were subjected to statistical analysis using the Principal Component Analysis method. The taste was found to be a descriptor that differentiates honey by origin. Consumers prefer the taste of pasteurized honeys. As a result of assessing the quality of honeys using the scaling method, it was found that: raw honeys are characterized by a lighter color than pasteurized honeys, store-bought honeys have a less noticeable aroma than honeys obtained from beekeepers, while samples of pasteurized honeys were judged to have a consistency more like that of typical honey. The sensory profiles obtained highlight the differences between pasteurized honeys and raw honeys.

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model.

Irradiation of the tumour site during treatment for cancer with external-beam ionising radiation results in a complex and dynamic series of effects in both the tumour itself and the normal tissue which surrounds it. The development of a spectral model of the effect of each exposure and interaction mode between these tissues would enable label free assessment of the effect of radiotherapeutic treatment in practice. In this study Fourier transform Infrared microspectroscopic imaging was employed to analyse an in-vitro model of radiotherapeutic treatment for prostate cancer, in which a normal cell line (PNT1A) was exposed to low-dose X-ray radiation from the scattered treatment beam, and also to irradiated cell culture medium (ICCM) from a cancer cell line exposed to a treatment relevant dose (2 Gy). Various exposure modes were studied and reference was made to previously acquired data on cellular survival and DNA double strand break damage. Spectral analysis with manifold methods, linear spectral fitting, non-linear classification and non-linear regression approaches were found to accurately segregate spectra on irradiation type and provide a comprehensive set of spectral markers which differentiate on irradiation mode and cell fate. The study demonstrates that high dose irradiation, low-dose scatter irradiation and radiation-induced bystander exposure (RIBE) signalling each produce differential effects on the cell which are observable through spectroscopic analysis.

Scale-Mark-Based Gauge Reading for Gauge Sensors in Real Environments with Light and Perspective Distortions.

Nowadays, many old analog gauges still require the use of manual gauge reading. It is a time-consuming, expensive, and error-prone process. A cost-effective solution for automatic gauge reading has become a very important research topic. Traditionally, different types of gauges have their own specific methods for gauge reading. This paper presents a systematized solution called SGR (Scale-mark-based Gauge Reading) to automatically read gauge values from different types of gauges. Since most gauges have scale marks (circular or in an arc), our SGR algorithm utilizes PCA (principal components analysis) to find the primary eigenvector of each scale mark. The intersection of these eigenvectors is extracted as the gauge center to ascertain the scale marks. Then, the endpoint of the gauge pointer is found to calculate the corresponding angles to the gauge's center. Using OCR (optical character recognition), the corresponding dial values can be extracted to match with their scale marks. Finally, the gauge reading value is obtained by using the linear interpolation of these angles. Our experiments use four videos in real environments with light and perspective distortions. The gauges in the video are first detected by YOLOv4 and the detected regions are clipped as the input images. The obtained results show that SGR can automatically and successfully read gauge values. The average error of SGR is nearly 0.1% for the normal environment. When the environment becomes abnormal with respect to light and perspective distortions, the average error of SGR is still less than 0.5%.

Improving Quantitative EDS Chemical Analysis of Alloy Nanoparticles by PCA Denoising: Part II. Uncertainty Intervals.

Analytical studies of nanoparticles (NPs) are frequently based on huge datasets derived from hyperspectral images acquired using scanning transmission electron microscopy. These large datasets require machine learning computational tools to reduce dimensionality and extract relevant information. Principal component analysis (PCA) is a commonly used procedure to reconstruct information and generate a denoised dataset; however, several open questions remain regarding the accuracy and precision of reconstructions. Here, we use experiments and simulations to test the effect of PCA processing on data obtained from AuAg alloy NPs a few nanometers wide with different compositions. This study aims to address the reliability of chemical quantification after PCA processing. Our results show that the PCA treatment mitigates the contribution of Poisson noise and leads to better quantification, indicating that denoised results may be reliable from the point of view of both uncertainty and accuracy for properly planned experiments. However, the initial data need to be of sufficient quality: these results can only be obtained if the signal-to-noise ratio of input data exceeds a minimal value to avoid the occurrence of random noise bias in the PCA reconstructions.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA.

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

CaV1.3 enhanced store operated calcium promotes resistance to androgen deprivation in prostate cancer.

Androgen deprivation therapy (ADT) is the main treatment for advanced prostate cancer (PCa) but resistance results in progression to terminal castrate resistant PCa (CRPC), where there is an unmet therapeutic need. Aberrant intracellular calcium (Cai2+) is known to promote neoplastic transformation and treatment resistance. There is growing evidence that voltage gated calcium channel (VGCC) expression is increased in cancer, particularly CACNA1D/CaV1.3 in CRPC. The aim of this study was to investigate if increased CaV1.3 drives resistance to ADT and determine its associated impact on Cai2+ and cancer biology. Bioinformatic analysis revealed that CACNA1D gene expression is increased in ADT treated PCa patients. This was corroborated in both in vivo LNCaP xenograft mouse and in vitro PCa cell line models, which demonstrated a significant increase in CaV1.3 protein expression following ADT with bicalutamide. Expression was found to be of a shortened 170kDa CaV1.3 isoform associated with plasma and intracellular membranes, which failed to induce calcium influx following membrane depolarisation. Instead, under ADT CaV1.3 mediated a rise in basal cytosolic calcium and an increase in store operated calcium entry (SOCE). This mechanism was found to promote the proliferation and survival of ADT resistant CRPC cells. Overall, this study demonstrates for the first time in PCa that under ADT specific CaV1.3 isoforms promote an upregulation of SOCE which contributes to treatment resistance and CRPC biology. Thus, this novel oncochannel represents a target for therapeutic development to improve PCa patient outcomes.

Research on Minimization of Data Set for State of Charge Prediction.

The quick estimation and prediction of lithium-ion batteries' (LIBs) state of charge (SoC) are attracting growing attention, since the LIB has become one of the most essential power sources for daily consumer electronics. Most deep learning methods require plenty of data and more than two LIB parameters to train the model for predicting SoC. In this paper, a single-parameter SoC prediction based on deep learning is realized by cleaning the data for lithium-ion battery parameters and constructing the feature matrix based on the cleaned data. Then, by analyzing the feature matrix's periodicity and principal component to obtain two kinds of the original eigenmatrix's substitution matrices, the two substitutions are fused to obtain an excellent prediction effect. In the end, the minimization method is verified with newly measured lithium battery data, and the results show that the MAPE of the SoC prediction reaches 0.96%, the input data are reduced by 93.33%, and the training time is reduced by 96.68%. Fast and accurate prediction of the SoC is achieved by using only a minimum amount of voltage data.