Quantification Technology Research Articles

Quantitative analysis of fetal cardiac structure and function in gestational diabetes mellitus using fetal HQ technology

BackgroundGestational diabetes mellitus (GDM) can contribute to changes in fetal cardiac structure and function, with potential implications for long-term cardiovascular health. This study focuses on assessing these cardiac adaptations in fetuses from GDM pregnancies by using the innovative Fetal Heart Quantification (Fetal HQ) technology to provide a detailed evaluation of structural and functional parameters. MethodsA prospective study was conducted from March 2023 to October 2024 at Xiangyang No.1 People's Hospital, involving 382 pregnant women in their second and third trimesters, including 167 with GDM and 215 healthy controls. Fetal cardiac assessments were performed using Fetal HQ, which allows for direct measurement of cardiac deformation and function. Key parameters such as left ventricular (LV) and right ventricular (RV) dimensions, global longitudinal strain (GLS), and fractional area change (FAC) were analyzed. Maternal clinical data, including blood glucose levels and body mass index (BMI), were also collected. Statistical analyses were performed to compare the cardiac parameters between the two groups. ResultsSignificant differences were observed in fetal cardiac dimensions, with the GDM group exhibiting larger LV end-diastolic area (2.04 ± 0.73 cm2 vs. 1.81 ± 0.69 cm2, p = 0.002) and lower RV GLS (−20.1 ± 5.3 % vs. -22.6 ± 4.6 %, p = 0.000). Other functional parameters, including LV GLS and FAC, did not show significant differences between groups. Correlation analysis revealed a significant positive relationship between maternal HbA1c levels and RV FAC (r = 0.348, p = 0.036), indicating that maternal glycemic control may influence fetal cardiac function. ConclusionsThe findings suggest that GDM is associated with altered fetal cardiac morphology, particularly in the left ventricle, and impaired right ventricular function, as evidenced by reduced GLS. These results highlight the potential impact of maternal hyperglycemia on fetal cardiac development and underscore the importance of monitoring fetal cardiac health in pregnancies complicated by GDM. The use of Fetal HQ technology provides a valuable tool for early detection of cardiac dysfunction in this high-risk population.

Development of the duplex droplet digital PCR for quantitative monitoring of mixed infections of Meloidogyne incognita and M. enterolobii.

Root-knot nematodes (RKN, Meloidogyne spp.) are economically significant pests that cause immense damage to a wide range of crops. Among them, M. incognita and M. enterolobii are of particular concern, as their high virulence and broad host range. RKN are challenging for detection due to their subterranean lifestyle underground. Also, the mixed infection of nematodes in field crops complicates the need for more accurate diagnostic and quantification technologies. To address this challenge, we developed and optimized a novel duplex droplet digital PCR (ddPCR) method, using primer/probe sets targeting M. incognita and M. enterolobii, to simultaneously identify and quantify both species within a single assay. The innovative ddPCR diagnostic demonstrated excellent performance in terms of sensitivity, precision and reproductivity when quantifying the eggs and soil samples containing juveniles of both species. Moreover, the application of the duplex ddPCR method enables the monitoring of population dynamics of M. incognita and M. enterolobii under competitive environmental conditions. Our results indicated that the reproduction factor of M. incognita possibly inhibited when in mixed populations of M. enterolobii. In this study, we first applied duplex ddPCR technique for differentiating mixed infections of M. incognita and M. enterolobii, offering a valuable tool for species detection and quantification. It enables the monitoring of population dynamics for both species, which is crucial for providing theoretical guidance for the implementation of timely and effective control measures. © 2024 Society of Chemical Industry.

Clinical significance of Cyclin D1 by complete quantification detection in mantle cell lymphoma: positive indicator in prognosis

ObjectivesThe positive expression of Cyclin D1 in immunohistochemical (IHC) staining serves as the cornerstone for diagnosing mantle cell lymphoma (MCL). However, existing literature does not conclusively establish whether the expression ratio and staining intensity significantly influence diagnostic outcomes or patient prognosis. In this retrospective study, the correlation between comprehensive Cyclin D1 quantification and the prognosis of MCL patients was studied.MethodsThe Cyclin D1 protein level was assessed in 120 formalin-fixed paraffin-embedded samples from MCL patients using the quantitative dot blot (QDB) analysis technique. R language software was employed for statistical analysis to determine the optimal threshold with statistical significance. Additionally, Kaplan-Meier method was utilized to evaluate the relationship between the absolute level of Cyclin D1 protein and overall survival (OS) of patients. Furthermore, the Chi-square test was applied to analyze the causes of single and multiple fractures, with a significance level of p < 0.05. Finally, the Log-rank test was used to compare two survival curves, where a significance level of p < 0.05 was considered statistically significant.ResultsAt the optimized cutoff of 0.46 nmol/g, univariate analysis revealed a positive correlation between Cyclin D1 protein level and patient survival (OS). Specifically, in the subgroup with complete quantification of Cyclin D1 higher than the cutoff, the 5-year OS was 18%, whereas in the subgroup with complete quantification of Cyclin D1 lower than the cutoff, the 5-year OS was 4.8% (Log-rank test, P = 0.017). This indicates that patients with Cyclin D1 levels above the cutoff had significantly better overall survival compared to those below the cutoff. Additionally, in the Pearson distribution test, Ki-67 emerged as an independent prognostic factor for the complete quantification of Cyclin D1. Notably, Cyclin D1 complete quantification results remained unaffected by factors such as gender, age, LDH (Lactate Dehydrogenase) level, Ann Arbor stage(AAS), Ki-67, IPI(International prognostic index), MIPI(Mantle International prognostic index), and MIPI-c (MIPI Combined with Ki-67 Proliferation Index Chi-square test, p > 0.05).ConclusionsComprehensive Cyclin D1 quantification, especially above a threshold, significantly correlates with better overall survival in MCL. This highlights its prognostic importance in MCL management. Full quantification of CyclinD1 aids MCL prognosis, while QDB technology for biomarker quantification supports precise clinical prognostic stratification.

Evaluation of fetal heart size, morphology and function with fetal growth restriction using fetal HQ

BackgroundFetal growth restriction (FGR) is associated with various perinatal complications. Limited research has focused on the fetal heart in the context of FGR. This study aimed to investigate the application value of fetal heart quantification (HQ) technology in evaluating the size, morphology, and function of the heart in FGR.MethodsA total of 31 fetuses diagnosed with FGR in our hospital from April 2022 to May 2024 were included, alongside another 31 normal fetuses matched for gestational age as the control group. Ultrasound Doppler parameters of the middle cerebral artery (MCA), umbilical artery (UA), venous catheter, and fetal HQ parameters were collected for comparative analysis, and perinatal data were followed up.ResultsFetuses with FGR exhibited significant differences in various parameters of the MCA and UA compared to the control group (P < 0.05). The four-chamber view end-diastolic transverse width, end-diastolic area, left ventricular (LV) end-diastolic area, end-systolic area, end-systolic length, end-diastolic volume, end-systolic volume, and right ventricular (RV) end-systolic area in the FGR group were significantly lower than those in the control group (P < 0.05). In the 24-segment analysis, the LV fractional shortening in the FGR group was greater than in the control group at segments 12 to 14, while the end-diastolic diameter (ED) at segments 5 to 13 of the LV and segments 1 to 14 of the RV were smaller than those in the control group, with statistical significance (P < 0.05). Analysis of each subgroup indicated that fractional shortening (FS) in the early-onset group was significantly greater than in the late-onset group at RV segments 2 to 8. LV-ED at segments 1 to 15 and RV-ED at segments 1 to 16 were significantly smaller in the early-onset group than in the control group, and LV ED segments 20 to 21 were significantly smaller in the early-onset group compared to the late-onset group (P < 0.05). FS in the mild group was significantly larger than in the normal group at LV segments 10 to 16. The severe group exhibited significantly smaller LV segment 2 to 11 ED and the mild group showed smaller RV segments 1 to 13 compared to the control group (P < 0.05).ConclusionsFetal HQ is a promising technique for evaluating the cardiac function, size, and morphology in cases of FGR.

Left atrial function in uraemic patients: Four-dimensional automatic left atrial quantitative technology study.

This study aimed to evaluate the utility of left atrial volume and function in uraemic patients using four-dimensional automatic left atrial quantification (4D auto LAQ) technology. Thirty-four undialysed uraemic patients (U-ND group), 60 dialysed uraemic patients (U-D group), and 32 healthy volunteers (N group) were enrolled in our current study. Conventional echocardiographic parameters were recorded, and left atrial volume and strain parameters were analysed to determine statistical differences among the three groups. The Pearson correlation coefficient was employed to assess the relationships between left atrial ejection fraction and left atrial strain parameters. Compared to the N group, uraemic patients often displayed left atrial enlargement and left ventricular hypertrophy. Significant increases were noted in left atrial diameter, interventricular septum thickness, left ventricular posterior wall thickness, E/e', diastolic blood pressure, systolic blood pressure, left atrial minimum volume, left atrial maximum volume, left atrial pre-atrial contraction volume, left atrial emptying volume and left atrial maximum volume index (P<0.05). Conversely, the e', E/A ratio and left atrial reservoir longitudinal strain were significantly decreased (P<0.05). However, no statistically significant differences were observed in the aforementioned parameters between the U-ND and U-D groups. The absolute values of left atrial conduit longitudinal strain and left atrial conduit circumferential strain, as well as left atrial passive ejection fraction, were notably lower in the U-D group compared to the N and U-ND groups, with statistically significant differences identified among the three groups (P<0.05). Uraemic patients exhibit marked left atrial enlargement and left ventricular hypertrophy, coupled with altered atrial function, particularly ductal dysfunction in the U-D group. The 4D auto LAQ technology proves advantageous in detecting these alterations, offering a promising tool for thorough cardiac assessment in this patient cohort.

Geometry of large normal fault growth and linkage with temporal constraints: a case study on the Lanliao fault in Dongpu Sag, Bohai Bay basin, NE China

The Dongpu Sag exhibits highly representative structural features of the Bohai Bay Basin. By utilizing time depth quantification (TDQ) technology, geological profiles were generated through the processing of seismic data with a velocity model. These profiles were integrated to investigate the linkage geometry and timing of the Lanliao fault, the eastern boundary fault of the Dongpu Sag. Structural analysis revealed at least five original fault segments of the Lanliao fault, each initiating independently during the early growth phase. The development of these isolated fault segments began in the early Eocene, which is concurrent with the deposition of the Es4 member. The southern fault segments were active earlier but became inactive later as the northern segments gained activity during the Es3 member. Transverse anticlines separate the northern and southern faults. By the time of the Es2 member, all segments had linked, forming a continuous boundary fault across the transverse anticlines. As the Dongpu Sag expanded, the depocenter relocated. The southern fault formed a graben, known as the Gegangji subsag, controlling the depocenter during the Es33 and Es32 members. The depocenter of the Es31 member shifted northward swiftly, developing into a half-graben depocenter called the Huzhuangji subsag. In the post-linkage development phase, the activity of the Lanliao fault decreased over time, with displacement becoming more concentrated on the Huanghe fault, an intrabasin fault. The Huanghe fault then dominated basin sedimentation, moving the depocenter to the Mengmangji subsag. This study demonstrates that the fault linkage is a significant event in basin evolution, exerting considerable control over sedimentation and the overall evolution of the basin.

Deep-learning-based attenuation map generation in kidney single photon emission computed tomography

BackgroundAccurate attenuation correction (AC) is vital in nuclear medicine, particularly for quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) imaging. This study aimed to establish a CT-free quantification technology in kidney SPECT imaging using deep learning to generate synthetic attenuation maps (μ-maps) from SPECT data, thereby reducing radiation exposure and eliminating the need for CT scans.ResultsA dataset of 1000 Tc-99m DTPA SPECT/CT scans was analyzed for training (n = 800), validation (n = 100), and testing (n = 100) using a modified 3D U-Net for deep learning. The study investigated the use of primary emission and scattering SPECT data, normalization methods, loss function optimization, and up-sampling techniques for optimal μ-map generation. The problem of checkerboard artifacts, unique to μ-map generation from SPECT signals, and the effects of iodine contrast media were evaluated. The addition of scattering SPECT to primary emission SPECT imaging, logarithmic maximum normalization, the combination of absolute difference loss (L1) and three times the absolute gradient difference loss (3 × LGDL), and the nearest-neighbor interpolation significantly enhanced AI performance in μ-map generation (p < 0.00001). Checkerboard artifacts were effectively eliminated using the nearest-neighbor interpolation technique. The developed AI algorithm produced μ-maps neutral to the presence of iodine contrast and showed negligible contrast effects on quantitative SPECT measurement, such as glomerular filtration rate (GFR). The potential reduction in radiation exposure by transitioning to AI-based CT-free SPECT imaging ranges from 45.3 to 78.8%.ConclusionThe study successfully developed and optimized a deep learning algorithm for generating synthetic μ-maps in kidney SPECT images, demonstrating the potential to transition from conventional SPECT/CT to CT-free SPECT imaging for GFR measurement. This advancement represents a significant step towards enhancing patient safety and efficiency in nuclear medicine.

Leveraging Zero-Effort Technology for Quantification of Sleep Quality Metrics.

Sleep quality is a critical factor in human health and well-being, with implications for various physiological and psychological processes. Traditional methods of sleep data collection are often limited by the quality and reliability of the data due to issues such as recall bias and subjective interpretation. This research aims to propose a novel framework that objectively measures and evaluates sleep quality using smart thermostats equipped with motion sensors, providing noninvasive and effortless sleep monitoring. The study conducts a comprehensive analysis of sleep patterns, exploring the relationship between activity sensors and sleep quality. By analyzing behavioral characteristics, the study identifies periods or clusters of days that require attention in terms of health and stress levels. The approach ensures privacy, ease of access, and integrates environmental factors, enabling a comprehensive understanding of an individual's sleep health. The findings suggest that this zero-effort technology can significantly enhance sleep monitoring at both individual and population levels, with implications for health monitoring, stress management, and personalized healthcare interventions. Future work will focus on expanding the data set, incorporating more variables, and integrating contextual data to further improve sleep quality analysis and support real-time health interventions.

Sequencing by binding rivals SMOR error-corrected sequencing by synthesis technology for accurate detection and quantification of minor (< 0.1%) subpopulation variants

BackgroundDetecting very minor (< 1%) subpopulations using next-generation sequencing is a critical need for multiple applications, including the detection of drug resistant pathogens and somatic variant detection in oncology. A recently available sequencing approach termed ‘sequencing by binding (SBB)’ claims to have higher base calling accuracy data “out of the box.” This paper evaluates the utility of using SBB for the detection of ultra-rare drug resistant subpopulations in Mycobacterium tuberculosis (Mtb) using a targeted amplicon assay and compares the performance of SBB to single molecule overlapping reads (SMOR) error corrected sequencing by synthesis (SBS) data.ResultsSBS displayed an elevated error rate when compared to SMOR error-corrected SBS and SBB techniques. SMOR error-corrected SBS and SBB technologies performed similarly within the linear range studies and error rate studies.ConclusionsWith lower sequencing error rates within SBB sequencing, this technique looks promising for both targeted and unbiased whole genome sequencing, leading to the identification of minor (< 1%) subpopulations without the need for error correction methods.

Fetal heart quantification technique improves the prenatal prediction of coarctation of the aorta: A retrospective analysis.

Coarctation of the aorta (CoA) ranks among the most prevalent congenital heart defects and poses a life-threatening risk if left undiagnosed. Herein, we utilized fetal heart quantification (HQ) technology to improve the prenatal prediction of CoA. A retrospective analysis was conducted on 64 fetal cases with suspected aortic arch constriction, identified through prenatal ultrasound findings between November 2020 and March 2022 at the Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University. According to the follow-up results, these cases were divided into two groups: 35 cases confirmed as CoA by postpartum surgery or induction, and 29 cases initially suspected of CoA prenatally but subsequently ruled out postnatally. Additionally, 88 cases of normal fetuses were randomly selected as the control group. Both conventional M-mode ultrasound techniques and Fetal HQ software were utilized for fetal analysis across all groups. Parameters related to the heart were measured, including fetal 4-CV length, width, Global Spherical Index (GSI), Mitral Annular Plane Systolic Excursion (MAPSE), areas and ratios of the left and right ventricles, as well as lengths and ratios of the left and right ventricles. Functional measurements of the left and right ventricles included ejection fraction (EF), fractional area change (FAC), global longitudinal strain (GLS), fractional shortening (FS), end-diastolic diameter (ED), and sphericity index (SI). Left ventricular (LV)-GLS, LV-FAC, LV-EF, and LV-EF Z-score could potentially differentiate between true CoA and false CoA or normal groups and serve as potential indicators for the clinical diagnosis of CoA. The receiver operating characteristic (ROC) curves indicated that LV-GLS and LV-EF Z-score have the greatest predictive power for CoA diagnosis. The segments 6-12 of FS in the confirmed CoA group were significantly lower than those in the false CoA and normal groups. Fetal HQ technology, by assessing changes in the size and shape of the heart, can provide relatively reliable parameter support for the prenatal diagnosis of fetal aortic coarctation.

A DNA-peptide probe coupled mass spectrometry-based method for the detection of LncRNA in hepatocyte and serum samples

Liver cancer, a prominent contributor to cancer-related deaths, necessitates timely identification for successful intervention. In recent years, long non-coding RNAs (lncRNAs) have gained attention as potential biomarkers for liver cancer, and accurate quantification of lncRNA is essential for early diagnosis of liver cancer. However, existing methods for lncRNA detection rely on amplification and labeling techniques, resulting in semi-quantitative or qualitative outcomes. DNA-peptide technology offers a promising alternative. It has successfully translated nucleic acid quantification into peptide quantification, particularly in the context of miRNA analysis. However, the intricate structure of long-chain lncRNAs presents a challenge not encountered with miRNAs, making the design and acquisition of specific DNA-peptide probes for direct quantification more difficult. In this study, a strategy for lncRNA quantification was developed using a DNA-peptide probe coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based quasi-targeted proteomics. The inherent structural complexity of long-chain lncRNAs was overcome by designing DNA-peptide probes based on their unique sequence segments, enabling direct quantification without the need for amplification. Highly upregulated in liver cancer (HULC) was identified as a target lncRNA through bioinformatics analysis using GEO2R online software. Subsequently, the target lncRNA (i.e., HULC), which had been biotinylated and bound to streptavidin agarose beforehand, was hybridized with the probe. Following trypsin digestion, the reporter peptide was released and quantified using LC-MS/MS. Finally, this method was applied to analyze HULC levels in liver cancer cell lines and serum samples, enabling quantitative assessment and early diagnosis. The results demonstrated that liver cancer diagnostics could be enhanced through the integration of mass spectrometry and DNA-peptide probe technology for lncRNA quantification.

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach.

An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from upstream oil and gas sources in a variety of contexts, from component-level measurements to yearly jurisdiction-wide inventories. To characterize measurement uncertainty, we examine controlled release (CR) data from five different technology providers including quantitative gas imaging (QOGI), tunable diode laser-absorption spectroscopy (TDLAS); and airborne near-infrared hyperspectral (NIR HS) imaging. We introduce a novel empirical method to develop probability distributions of measurements given a true emission rate using the CR data. The approach includes flexible likelihoods which capture complex relationships in the data. An algorithm which provides the distribution of the true emission rate given a measurement is also developed, which synthesizes the measurement with the CR data and external information about the possible true emission rate. The results show that flexible models that accommodate complex nonlinear behavior are needed to adequately model measurement error. We also show that measurement error can vary under different conditions. We demonstrate that measurement uncertainty can be reduced by performing repeated measurements. A limitation of the study is that the collected CR data is collected under controlled conditions that may differ from those in industrial settings. As new CR data become available, the models presented in this paper can be refit to consider more diverse scenarios. The methodology can be extended to explicitly model different conditions to improve performance.

Precision Fertilization: A critical review analysis on sensing technologies for nitrogen, phosphorous and potassium quantification

Fertilization is paramount for agriculture productivity and food security. Plant nutrition pre-established recipes and nutrient uptake are rarely managed by changing the fertilizer composition at the different stages of the plant life cycle. Herein we perform a literature review analysis – since the year 2000 and onwards – of the state-of-the-art capabilities of Nitrogen, Phosphorous, and Potassium (NPK) sensors for liquid fertilizers (e.g., hydroponics). From the initial search hits of 1660 results, only 53 publications had relevant information for this topic; from these, only 9 had NPK quantitative information. Qualitative analysis was performed by determining the number of publications for each nutrient, according to sample complexity and existing single, multiplexed or hybrid technologies. Quantitative assessment was performed by extracting the bias and linearity, the limit of detection and concentration ranges of sensor operation, framed into the context of the sensor technology development stage and sample compositional complexity. The most common technologies are colorimetry, ion-selective electrodes, optrodes, chemosensors, and optical spectroscopy. The most abundant technologies are for nitrate quantification, from which ion-selective electrodes are the most widely used technology, and sensors for phosphate quantification are the less developed. Most are at low technological levels of development, not dealing with the complexity of agriculture samples due to matrix effects and interference. Measuring the fertilizer composition, nutrient uptake, the state of the chemical network, and controlling the release of nutrients using new functional materials, is one of the most important challenges ahead for the existence of precision fertilization. Intelligent sensing and smart materials are today the most successful strategy for dealing with matrix effects and interferences, being led by ion-selective electrodes and spectroscopy technologies.

ITRAQ proteomics analysis of placental tissue with gestational diabetes mellitus.

Gestational diabetes mellitus is an endocrine and metabolic disorder that appears for the first time during pregnancy and causes varying degrees of short- and/or long-term effects on the mother and child. The etiology of the disease is currently unknown and isobaric tags for relative and absolute quantitation proteomics approach, the present study attempted to identify potential proteins in placental tissues that may be involved in the pathogenesis of GDM and adverse foetal pregnancy outcomes. Pregnant women with GDM hospitalised were selected as the experimental group, and pregnant women with normal glucose metabolism as the control group. The iTRAQ protein quantification technology was used to screen the differentially expressed proteins between the GDM group and the normal control group, and the differentially expressed proteins were analysed by GO, KEGG, PPI, etc., and the key proteins were subsequently verified by western blot. Based on the proteomics of iTRAQ, we experimented with three different samples of placental tissues from GDM and normal pregnant women, and the total number of identified proteins were 5906, 5959, and 6017, respectively, which were similar in the three different samples, indicating that the results were reliable. Through the Wayne diagram, we found that the total number of proteins coexisting in the three groups was 4475, and 91 differential proteins that could meet the quantification criteria were strictly screened, of which 32 proteins were up-regulated and 59 proteins were down-regulated. By GO enrichment analysis, these differential proteins are widely distributed in extracellular membrane-bounded organelle, mainly in extracellular exosome, followed by intracellular vesicle, extracellular organelle. It not only undertakes protein binding, protein complex binding, macromolecular complex binding, but also involves molecular biological functions such as neutrophil degranulation, multicellular organismal process, developmental process, cellular component organization, secretion, regulated exocytosis. Through the analysis of the KEGG signaling pathway, it is found that these differential proteins are mainly involved in HIF-1 signaling pathway, Glycolysis/Gluconeogenesis, Central carbon metabolism in cancer, AMPK signaling pathway, Proteoglycans in cancer, Protein processing in endoplasmic reticulum, Thyroid cancer, Alcoholism, Glucagon signaling pathway. This preliminary study helps us to understand the changes in the placental proteome of GDM patients, and provides new insights into the pathophysiology of GDM.

Evaluation of fetal cardiac morphology and function by fetal heart quantification technique in the normal second and third trimesters.

The study of fetal heart is receiving increasing attention. Fetal heart quantification (Fetal HQ) technology is a new speckle tracking technology that can analyze the 24-segment morphology and function of fetal ventricles. This study aims to use Fetal HQ to assess the changes in the structure and function of the fetal heart in normal mid to late pregnancy, providing a foundation for the clinical application of fetal cardiac speckle tracking technology. The heart size, global sphericity index (GSI), left ventricular [stroke volume (SV)], cardiac output (CO), ejection fraction (EF), global longitudinal strain (GLS), fractional area change (FAC), 24-segment end-diastolic diameter (EDD), sphericity index (SI) and fractional shortening (FS) of the two ventricles of 500 normal pregnant fetuses were evaluated by Fetal HQ. The subjects were divided into 5 groups according to gestational weeks (GA), and the changes of fetal heart morphology and function were observed. P<0.05 indicated the statistically significant difference. The fetal heart rate decreased gradually with the increase of GA (P<0.05). The size parameters of the fetal heart and two ventricles gradually increased with increasing GA (P<0.05). The 24 segments EDD of both ventricles increased with increasing GA (P<0.05), while the EDD increased first and then decreased from the ventricle base to the apex. The GSI and the 24 segments SI of two ventricles were basically not significantly different among the groups (P>0.05). The EF, GLS, FAC of the left ventricle and the GLS, FAC of the right ventricle decreased with the increase of GA (P<0.05), and SV and CO increased with increasing GA (P<0.05). The 24 segments FS of the left ventricle showed a downward trend with the increase of GA and gradually increased from the base to the apex. The FS of most segments of the right ventricle decreased with the increase of the GA and increased first and then decreased from the base to the apex. The whole and segmental size parameters of fetal heart can quantitatively evaluate the growth and development of fetal heart; the GSI and segmental SI are reliable morphological indexes for evaluating fetal heart; fetal ventricular function parameters EF, FAC, GLS and segmental FS can evaluate fetal cardiac function. The Fetal HQ technique can help us to evaluate the heart growth and development of normal fetuses in the second and third trimester of pregnancy.

Advanced Kinematic Analysis of Metal Ramp Dynamics: Synergizing Velocity, Acceleration, and Photogate Technologies for Precise Quantification

Advanced Kinematic Analysis of Metal Ramp Dynamics: Synergizing Velocity, Acceleration, and Photogate Technologies for Precise Quantification

Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil

In this study, we critically evaluated the performance of an emerging technology, hyperspectral imaging (HSI), for detecting microplastics (MPs) in soil. We examined the technology's robustness against varying environmental conditions in five groups of experiments. Our findings show that near-infrared (NIR) hyperspectral imaging (HSI) effectively detects microplastics (MPs) in soil, though detection efficacy is influenced by factors such as MP concentration, color, and soil moisture. We found a generally linear relationship between the levels of MPs in various soils and their spectral responses in the NIR HSI imaging spectrum. However, effectiveness is reduced for certain MPs, like polyethylene, in kaolinite clay. Furthermore, we showed that soil moisture considerably influenced the detection of MPs, leading to nonlinearities in quantification and adding complexities to spectral analysis. The varied responses of MPs of different sizes and colors to NIR HSI present further challenges in detection and quantification. The research suggests pre-grouping of MPs based on size before analysis and proposes further investigation into the interaction between soil moisture and MP detectability to enhance HSI's application in MP monitoring and quantification. To our knowledge, this study is the first to comprehensively evaluate this technology for detecting and quantifying microplastics.

Comparison of DeNovix, NanoDrop and Qubit for DNA quantification and impurity detection of bacterial DNA extracts.

Accurate DNA quantification is key for downstream application including library preparations for whole genome sequencing (WGS) and the quantification of standards for quantitative PCR. Two commonly used technologies for nucleic acid quantification are based on spectrometry, such as NanoDrop, and fluorometry, such as Qubit. The DS-11+ Series spectrophotometer/fluorometer (DeNovix) is a UV spectrophotometry-based instrument and is a relatively new spectrophotometric method but has not yet been compared to established platforms. Here, we compared three DNA quantification platforms, including two UV spectrophotometry-based techniques (DeNovix and NanoDrop) and one fluorometry-based approach (Qubit). We used genomic prokaryotic DNA extracted from Streptococcus pneumoniae using a Roche DNA extraction kit. We also evaluated purity assessment and effect of a single freeze-thaw cycle. Spectrophotometry-based methods reported 3 to 4-fold higher mean DNA concentrations compared to Qubit, both before and after freezing. The ratio of DNA concentrations assessed by spectrophotometry on the one hand, and Qubit on the other hand, was function of the A260/280. In case DNA was pure (A260/280 between 1.7 and 2.0), the ratio DeNovix or Nanodrop vs. Qubit was close or equal to 2, while this ratio showed an incline for DNA with increasing A260/280 values > 2.0. The A260/280 and A260/230 purity ratios exhibited negligible variation across spectrophotometric methods and freezing conditions. The comparison of DNA concentrations from before and after freezing revealed no statistically significant disparities for each technique. DeNovix exhibited the highest Spearman correlation coefficient (0.999), followed by NanoDrop (0.81), and Qubit (0.77). In summary, there is no difference between DeNovix and NanoDrop in estimated gDNA concentrations of S. pneumoniae, and the spectrophotometry methods estimated close or equal to 2 times higher concentrations compared to Qubit for pure DNA.

Quantification as social technology: Integrating studies of quantification with philosophy of technology

Studies of quantification are an emerging academic interest due to the datafication and quantification trends in modern society. The existing literature on this interdisciplinary field involves two approaches: a history of science approach and a sociology approach. This article argues for adding perspectives from the philosophy of technology. The philosophy of technology raises otherwise neglected ontological, epistemological, and ethical questions about the technology of quantification. Quantification as social technology is non-neutral and exhibits social constructed features, which beyond the commonly perceived realist opinions. The social-constructed quantification also has the ability to influence social agents, mediating the way humans exist in and understand the life world. Ethical and political questions about quantification call for integration into an already rich ethical and political discourse surrounding technological artifacts, actions, and political agendas.

Session 13. Oral Presentation for: Practical lessons from implementing a top-down, drone-based methane emissions quantification technology into global oil and gas operations

Session 13. Oral Presentation for: Practical lessons from implementing a top-down, drone-based methane emissions quantification technology into global oil and gas operations