FindEM—a fast, efficient program for automatic selection of particles from electron micrographs

Statistical Analysis and Deblurring of Class Averages in Single-Particle Electron Microscopy

Multivariate statistical analysis of three-dimensional cross-bridge motifs in insect flight muscle

Automatic CTF correction for single particles based upon multivariate statistical analysis of individual power spectra

The hemodynamic differences between chronic mitral (MR: n = 30) and chronic aortic regurgitation (AR: n = 37) at various functional stages were investigated by echocardiography and compared with the findings in healthy subjects (n = 20). The ordinary statistical analysis revealed that in MR myocardial contractility and afterload to the left ventricle (LV) well preserved even at the stage of decompensation, while in AR the contractility deteriorated and the afterload was elevated even at the asymptomatic stage. By the multivariate statistical analysis, the functional differences and characteristics of the 2 diseases were clarified with 2 statistic factors derived from 6 hemodynamic parameters (LV end-diastole and end-systolic dimensions, mean velocity of circumferential fiber shortening, LV mid-systolic stress, LV mass, and left atrial dimension). In the differentiation between the 2 diseases by the multivariate analysis, left ventricular size tended to be smaller, contractility was preserved and loading of the left atrium was increased in MR more than in AR. Furthermore, a quantitative system which could explain the functional state by these echocardiographic parameters was derived from this multivariate analysis. The functional states was found to be strongly affected by LV end-systolic dimension and left atrial dimension. This quantitative method of determining the functional state, along with an accurate assessment of cardiac symptoms, provides a useful guideline for the clinical evaluation of the 2 diseases.

Liquid crystal (LC) concentrations in stratified holographic polymer-dispersed liquid crystal (PDLC) films have been quantified from depth profiles obtained by time-of-flight secondary ion mass spectrometry (SIMS). The volatile nature of the LCs was hindered during SIMS analysis by capping the PDLC samples with poly(vinyl alcohol) and cooling to cryogenic temperatures. It remains difficult to gain quantitative results from SIMS analysis due to matrix effects yielding complex SIMS fingerprint spectra. With the multivariate statistical method discriminant function analysis the LC contents in the stratified holographic PDLC films were quantified. The concentration of the LC-rich layers was determined to be 32.9 ± 3.4 wt % LC, and for the polymer-rich layers it was 28.8 ± 2.7 wt % LC. The low concentration difference was supported by imaging analysis and modeling results.

We utilized an NMR-based metabolomic approach to profile the metabolites in Drosophila melanogaster that cycle with a daily rhythm. 1H 1D and 2D NMR experiments were performed on whole-body extracts sampled from flies that experienced strong time cues in the form of both light and temperature cycles. Multivariate and univariate statistical analysis was used to identify those metabolites whose concentrations oscillate diurnally. We compared metabolite levels at two time points twelve hours apart, one close to the end of the day and the other close to the end of the night, and identified metabolites that differed significantly in their relative concentrations. We were able to identify 14 such metabolites whose concentrations differed significantly between the two time points. The concentrations of metabolites such as sterols, fatty acids, amino acids such as leucine, valine, isoleucine, alanine and lysine as well as other metabolites such as creatine, glucose, AMP and NAD were higher close to the end of the night, whereas the levels of lactic acid, and a few amino acids such as histidine and tryptophan were higher close to the end of the day. We compared signal intensities across 12 equally spaced time points for these 14 metabolites, in order to profile the changes in their levels across the day, since the NMR metabolite peak intensity is directly proportional to its molar concentration. Through this report we establish NMR-based metabolomics combined with multivariate statistical analysis as a useful method for future studies on the interactions between circadian clocks and metabolic processes.

Background: Ayurveda, the indigenous medical system of India, has chemosensory property (rasa) as one of its major pharmacological metric. Medicinal plants have been classified in Ayurveda under six rasas/tastes-sweet, sour, saline, pungent, bitter and astringent. This study has explored for the first time, the use of Electronic tongue for studies of rasa-based classification of medicinal plants. Methods: Seventy-eight medicinal plants, belonging to five taste categories (sweet, sour, pungent, bitter, astringent) were studied along with the reference taste standards (citric acid, hydrochloric acid, caffeine, quinine, L-alanine, glycine, β-glucose, sucrose, D-galactose, cellobiose, arabinose, maltose, mannose, lactose, xylose). The studies were carried out with the potentiometry-based Electronic tongue and the data was analysed using Principle Component Analysis, Discriminant Function Analysis, Taste Discrimination Analysis and Soft Independent Modeling of Class Analogy. Results: Chemosensory similarities were observed between taste standards and the plant samples-citric acid with sour group plants, sweet category plants with sucrose, glycine, β-glucose and D-galactose. The multivariate analyses could discriminate the sweet and sour, sweet and bitter, sweet and pungent, sour and pungent plant groups. Chemosensory category of plant (classified as unknown) could also be identified. Conclusion: This preliminary study has indicated the possibility of fingerprinting the chemosensory-based ayurvedic classification of medicinal plants using E-tongue coupled with multivariate statistical analysis.

Classification of torbanite and cannel coal: II. Insights from pyrolysis-GC/MS and multivariate statistical analysis

Watermelon soybean paste (WSP) is a traditional Chinese delicacy valued for its unique flavor and health benefits. Its volatile components, shaped by production methods and geographical factors, reflect WSP’s distinct characteristics and may influence consumer purchasing decisions. This study investigates the volatile compound characteristics in WSP samples from different regions and develops a rapid, accurate, and nondestructive method for their identification. To accomplish this objective, volatile compounds in WSP were extracted using headspace solid-phase microextraction (HS-SPME) and subsequently determined by gas chromatography-mass spectrometry (GC-MS). Multivariate statistical analyses, including correlation analysis, principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA), were employed to comprehensively evaluate the data. The results show that terpenes and esters are the main volatile compounds. OPLS-DA effectively distinguishes the samples and identifies 10 key differential compounds. A non-targeted GC-MS method combined with multivariate analysis provides a rapid and efficient way to differentiate WSP samples according to geographical origin.

Purpose – The purpose of this paper is to explore the improvements in speed and precision achievable using straightforward closed-loop control for the gantry motion in additive manufacturing machines. The authors designed and built an economically viable demonstration system to quantify the performance improvement. Design/methodology/approach – The authors develop and evaluate a low-cost closed-loop controller for the X and Y axes of an entry-level three-dimensional (3D) printer. The system developed captures and compensates for the dynamics of the motor and the belt-driven stage and detects mechanical errors, such as skipped motor steps. Findings – The system produces path-following precision improvements of 40 and 75 per cent for two different sample trajectories. Correcting for skipped steps increases reliability and allows for more aggressive tuning of motion parameters; time savings of up to 25 per cent are seen by doubling acceleration rate. Research limitations/implications – The system presented provides an appropriate platform for further investigation into more complex, application-specific controllers and inclusion of more details of the printer dynamics that could produce still greater improvements in speed and accuracy. Practical implications – The performance, low cost (40 USD/axis) and applicability to the majority of sub-2000USD 3D printer designs make this work of practical significance. Originality/value – The CNC machining industry has for many years used similar approaches, but application to 3D printers has not been explored in the literature. This paper demonstrates the value of even a simple controller applicable to almost any 3D printer, while maintaining cost-effectiveness of the solution in a competitive market.

FT-IR spectroscopic metabolic fingerprintiNg of human embryo culture media combined with multivariate statistical analysis to discriminate embryos with different reproductive potential

Multivariate statistical analysis (MSA) of a series of spectra or images offers an objective and quantitative way to characterize the features of the spectra that vary in a correlated fashion and to determine the number of independently varying components in the series. For example, in a series of spectra showing grain boundary segregation, there may be only one independently varying spectral component, which signifies an increase in the concentrations of the segregants and a corresponding decrease in the concentrations of some of the matrix constituents. The basis of the MSA method has been outlined by Trebbia and Bonnet, with application to the analysis of electron energy-loss spectrum images. Titchmarsh et al., have applied this analysis to a series of energy dispersive X-ray (EDX) spectra for the study of grain boundary segregation. The present paper illustrates the application of MSA methods to a series of EDX spectra acquired for ALCHEMI analysis. The basic method has been modified slightly for the analysis of ALCHEMI data.

Qing Jin Hua Tan Tang (QJHTT) exerts therapeutic effects in patients with chronic obstructive pulmonary disease (COPD) by alleviating inflammation. However, the anti-inflammatory components of QJHTT have not yet been reported. Our study aimed to screen the active anti-inflammatory components of QJHTT using a multivariate statistical analysis approach for spectrum-effect relationships. Different polar fractions of QJHTT were prepared using ethanol, ethyl acetate, and n-butanol to analyze the phytochemical components. Phytochemical fingerprints were generated using ultrahigh-performance liquid chromatography. In total, 24 peaks were observed in ten batches of QJHTT extracts. The anti-inflammatory activity was evaluated using a xylene-induced ear-swelling mouse model. Additionally, the spectrum-effect relationship between the relative areas of the 24 peaks and pharmacological activity was investigated using multivariate statistical analysis. The potential anti-inflammatory ingredients obtained from the screening (multivariate statistical analysis) will be validated for their anti-inflammatory effects and mechanisms utilizing a lipopolysaccharide-induced macrophage inflammation model. QJHTT ethanol extract 1 exhibited good anti-inflammatory activity. Peaks 11, 12, 13, 14, and 16, which were closely correlated with anti-inflammatory activity, were identified as meranzin, baicalin, baicalein, chrysin-7-O-β-D-glucuronide, and wogonoside, respectively. The anti-inflammatory activities of meranzin, baicalin, baicalein, and wogonoside were verified in vitro. These four bioactive components significantly inhibited the secretion of inflammatory factors in the lipopolysaccharide-stimulated macrophage cell line. This research successfully screened the QJHTT anti-inflammatory active ingredient group. Meranzin, baicalin, baicalein, chrysin-7-O-β-D-glucuronide, and wogonoside were predicted to be the anti-inflammatory active ingredient groups of QJHTT.

Mathematics education faces global challenges that affect numeracy learning based on the results from the Program for International Student Assessment (PISA) and Trends in Mathematics and Science Study (TIMSS), that many learners struggle to master basic skills essential for both academic and everyday tasks, as stated in the study of (Smith, 2020). In seeking innovative solutions, this study assessed the mathematical competency gaps and disruptive interests of learners at Palo 19 National High School. Also, evaluated the effectiveness and acceptability of the developed NumEra-Z Program, a computer-based numeracy intervention designed to enhance accuracy and speed in mathematical tasks. Grounded in Experiential Learning Theory and Constructivist principles, the program utilized the ADDIE instructional design model to facilitate the development and evaluation of NumEra-Z, addressing the gaps in numeracy skills. There were sixty-eight (68) learners from Grades 7 to 10 who participated in the study during the second quarter of School Year 2024-2025. The research identified the least-learned competencies, mostly with fractions, basic operations, and geometric representation. Through diagnostic assessments and surveys, the disruptive interests of the learners were moderately affected by excessive peer socialization, followed by a lack of interest in abstract concepts, and then by technology misuse. Pretest and posttest results showed improvements in numeracy accuracy and speed, while a survey assessed the program's acceptability in terms of relevance, organization, engagement, and overall effectiveness. Findings revealed significant improvements in learners' numeracy accuracy and speed following participation in the NumEra-Z Program. Additionally, a positive relationship was observed between program acceptability and numeracy skills. The study highlights the potential of learning interventions like the NumEra-Z Program to address persistent gaps in numeracy proficiency while fostering learner engagement.

The study aimed to compute the effective dose (E) and size-specific dose estimate (SSDE) of routine adult patients undergoing thorax and abdominal computed tomography (CT) imaging and to present their multivariate analysis. All adult thorax and abdominal CT examinations conducted from March 2022 to June 2022 were prospectively included in this study. The Water Equivalent Diameter (Dw) and SSDE of all the examinations were computed from CT dose index volume (CTDIvol) and Dose length product (DLP) displayed on the dose report in the CT console. The multivariate statistical analysis was performed to investigate the correlation of SSDE and E on CTDIvol, Dw area of the region of interest (ROI) (AreaROI), body mass index (BMI), conversion factor (fsize) and hounsfield (HUmean) number in the ROI at 95% level of significance (P < 0.05). The linear regression analysis was performed to investigate the dependence of SSDE and E on other parameters for both abdominal and thorax patients. A total number of 135 (Abdomen = 61 and Thorax = 74) measurements were performed. The mean value of effective dose for abdomen and thorax patients was found to be 7.17 ± 3.94 and 4.89 ± 2.16 mSv, respectively. The SSDE was observed to be 13.24 ± 3.61 and 13.04 ± 3.61 mGy for thorax and abdomen respectively. The multivariate analysis suggests that SSDE for abdominal CT is found significantly dependent on CTDIvol, Dw and fsize with P < 0.05 and E is found to be significantly dependent on DLP, AreaROI, Dw and fsize at 95% level of confidence for abdominal CT imaging. SSDE for thorax CT was found significantly dependent on BMI, CTDIvol, HUmean, Dw and fsize at 95% level of confidence. Furthermore, E was observed dependent on DLP at P < 0.05. The linear regression analysis also shows that E is strongly correlated with DLP (r = 1.0) for both thorax and abdominal CT, further the SSDE was observed strongly correlated with CTDIvol with r = 0.79 and r = 0.86 for abdomen and thorax CT respectively. A strong correlation was observed between BMI and for Dw abdominal CT imaging (r = 0.68). The mean value of SSDE for thorax is slightly greater than abdomen. The average value of effective dose for abdomen and thorax measurements was found to be 7.17 ± 3.94 and 4.89 ± 2.16 mSv and , correspondingly. SSDE for both abdomen and thorax CT is significantly dependent on CTDIvol, Dw and fsize at 95% level of confidence. The strong correlation was also observed E on DLP and SSDE on CTDIvol for both Abdomen and Thorax CT. The strong dependence of Dw on BMI (r = 0.68) is due to the excessive fat concentration around the stomach and abdomen.