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Extracellular vesicles (EVs) play key roles in diverse biological processes, transport biomolecules between cells and have been engineered for therapeutic applications. A useful EV bioengineering strategy is to express engineered proteins on the EV surface to confer targeting, bioactivity and other properties. Measuring how incorporation varies across a population of EVs is important for characterising such materials and understanding their function, yet it remains challenging to quantitatively characterise the absolute number of engineered proteins incorporated at single-EV resolution. To address these needs, we developed a HaloTag-based characterisation platform in which dyes or other synthetic species can be covalently and stoichiometrically attached to engineered proteins on the EV surface. To evaluate this system, we employed several orthogonal quantification methods, including flow cytometry and fluorescence microscopy, and found that HaloTag-mediated quantification is generally robust across EV analysis methods. We compared HaloTag-labelling to antibody-labelling of EVs using single vesicle flow cytometry, enabling us to measure the substantial degree to which antibody labelling can underestimate proteins present on an EV. Finally, we demonstrate the use of HaloTag to compare between protein designs for EV bioengineering. Overall, the HaloTag system is a useful EV characterisation tool which complements and expands existing methods.

Antimicrobial resistance (AMR) poses an increasing threat to global health. To deliberate the distribution and transmission of environmental resistance in a wide geographic and longitudinal scope, standardization of the analytical methods is desperately required for the extensive implementation of large-scale environmental antibiotic resistance gene (ARGs) surveillance. In this review, a standardized surveillance method using metagenomic analysis, coupled with proper quantification tools and environmental reference materials as technical benchmarks, was established to facilitate the generation of comparable and informative resistome datasets. As global and long-term ARGs surveillance has recently been performed in various environmental compartments, increasing efforts are also needed for assessing the health risks of ARGs. The development of risk assessment schemes that incorporate factors including transfer potential, host species, viability, and absolute quantification is essential to the regulatory guidelines for high-risk priority ARGs. This review provides guidance to ARGs surveillance regarding the level and the risk of ARG exposure, especially to identify and address critical hotspots. [ABSTRACT FROM AUTHOR]

Size exclusion chromatography with UV detection (SEC‐UV) is a precise method for quantifying polyacrylamides in water. However, its accuracy can be compromised in complex matrices due to chemical degradation of the polymer. This study focuses on the impact of hydrolysis on quantifying sulfonated polyacrylamide copolymers (PAM‐AMPS) via SEC‐UV, analyzing key influencing factors. Using controlled hydrolysis, hydrolyzed copolymer (HPAM‐AMPS) standards with varying hydrolysis degree (HD) (HD = 7%–33%) were prepared without significant changes in polymer chain size. SEC‐UV analysis revealed that quantification accuracy is highly dependent on hydrolysis extent. Errors are minimal for HD up to 10%, but significantly higher for HDs of 22%–33%. Furthermore, this effect is modulated by polymer concentration. Our findings indicate that there are two compensating factors for the decrease in molar absorptivity due to amide‐to‐carboxylate substitution: the sulfonate group acting as an additional chromophore, and a redshift in absorption spectra due to hydrogen bonding at higher concentrations. This interplay between HD and concentration in this type of sulfonated polyacrylamide can help reduce quantification errors in complex aqueous samples prone to chemical degradation, like those found in environmental and subsurface applications. [ABSTRACT FROM AUTHOR]

This study investigates the surface functionalization of plastic substrates through dip‐coating in azide‐functionalized polymer solutions, followed by a click reaction (i.e., strain‐promoted azide–alkyne cycloaddition). Acrylic, poly(ethylene terephthalate) (PET), and nylon substrates are dip‐coated with a series of polymers containing various azide groups grafted onto the poly(methyl methacrylate‐co‐hydroxyethyl methacrylate) backbone to examine structural effects on the surface density of clickable azide groups. X‐ray photoelectron spectroscopy and fluorescence spectroscopy confirm the subsequent click‐immobilization of cycloalkyne‐tagged fluorescein, which is quantified to calculate the surface density of clickable azide groups. Further investigations demonstrate that the surface density of azide groups can be controlled by manipulating the polymer ratio during dip‐coating, thus enabling the preparation of a linear surface gradient in terms of azide group density. Finally, the microcontact printing (µCP) method is employed to pattern the functionalized surfaces and quantify the functional molecules immobilized on the surface by µCP. This study highlights the adaptability of click chemistry and polymer coating techniques for the advanced functionalization of plastic surfaces for materials science and engineering applications. [ABSTRACT FROM AUTHOR]

The herbal plants Cynodon dactylon (L.) and Ocimum sanctum (Tulsi) medicinal plants are in great demand in Ayurveda to cure many common diseases of human beings and animals. These plants exist throughout the year, tolerates water scarcity in summer season and are self-propagating properties (self-seeding of Ocimum and runner propagation of Cynodon plants). We estimated the qualitative and quantitative phytochemicals present in these plants extracts. These plants extracts contained phytochemicals (alkaloids, cardiac glycosides, terpenoids and steroids, saponins, phenolic compounds and flavonoids, carbohydrates and protein) in ethanol, chloroform and diethyl ether extracts. Ocimum plant is richest in (12.20 %) alkaloids, whereas it was 4.24 % in Cyanodon plants. Other phytochemicals (steroids, flavonoids, carbohydrates and protein) were present in optimum range in both these plants. [ABSTRACT FROM AUTHOR]

This article examines datafication of the reproductive body in India through use of femtech mobile phone applications (henceforth, apps). Femtech apps quantify reproductive processes such as periods, conception, pregnancy and hormonal health and promise their users greater 'self-awareness' and 'control' through 'self-management'. Most studies on femtech refer to users in the Global North, while there are few studies on femtech adoption in the developing countries. This article, based on qualitative and quantitative data, and informed by a feminist technoscience framework, illustrates how femtech's promise of empowerment through datafication of reproduction is fraught with contradictions and tensions, and has exclusionary and risky consequences for Indian users. It examines the gendered technological landscape's bearing on concrete practices of design and innovation, and shows how femtech reinforces gendered social hierarchies rather than dismantling them and liberating users. Under datafication, health standards become extremely narrowly defined, marginalising those whose reproductive health trajectories may not conform to normative standards. Femtech's proliferation in India has also failed to recognise the structural inequalities and socio-economic disadvantages that characterise healthcare access. Finally, the legal grey areas and ill-defined data privacy policies in India allow for easy commercialisation of users' bodies and personal data possible. This further undermines the liberational rhetoric of femtech, as data privacy breaches are embodied forms of violence with consequences for users' bodily autonomy and dignity. Femtech's pursuit of maximising commercial gain is thus at odds with the feminist technoscience project of minimising women's exploitation and oppression. [ABSTRACT FROM AUTHOR]

This paper describes analyses of avifauna from Levantine Late Pleistocene and Early Holocene sites at contrasting scales. We present avian osteological data at Shubayqa, in northeast Jordan, illustrating human-bird-environment interactions over 4000 years offering interpretation of environmental change on avifaunal communities and reorientation in the nature of multi-species relationships at a wetland environment. Comparisons to published avifaunal assemblages investigates bird-human relationships across a broader temporal and geographical span. During the Late Pleistocene and Early Holocene, some of our species gradually transitioned to agriculture. People hunted birds for subsistence but extensive evidence for the symbolic importance of birds suggests complex relationships between species. Avifaunal evidence, therefore, presents insights into two commonly suggested primary causes for the shift from hunter-foragers to agriculturalists – resource pressure and symbolic expression. In this paper, we investigate and try to clarify the relationship between humans and birds at this transitional period in human history, furthering discussions of the shift towards agriculture. We argue humans gradually modified the landscape but this was not a reactionary response to resource stress. Nevertheless, this resulted in the productivity of the landscape increasing and allowed further intensification. Feathers and talons of birds were used for millennia preceding the shift to agriculture and the increase in bird hunting towards the end of the Pleistocene cannot be explained by the increased role of birds in the symbolic realm. The evidence from the avifauna adds weight to the idea of ecological niche construction activities, as people invested in specific locations, increasing productivity of habitats, thus encouraging further investment within some environments. [ABSTRACT FROM AUTHOR]

For centuries, universities have proliferated and flourished around the world, playing an important role in societal knowledge production and diffusion. However, in the past four decades, this old organizational form has been subjected to neoliberal, managerialist policy doctrines such as New Public Management. Following this, universities have tended to become more 'business-like' in their internal management and governance, with generally perceived adverse effects on the quality of academic education, research, and working conditions. These developments pose fundamental threats to academic freedom and free knowledge production and diffusion. Acknowledging various forms of academic resistance to, and coping with, these threats, the purpose of our paper is twofold. First, we adopt the concept of 'performativity' – hitherto researched mainly in primary and secondary schools in Anglo-Saxon contexts – to account for, and critique, neoliberal university policies, and practices in a variety of Global North settings. Second, through collaborative autoethnography, we add our own personal narratives to 'talk back' to managerialism. [ABSTRACT FROM AUTHOR]

Mitragyna speciosa is a perennial plant native to Asia, well known for its psychoactive properties. Its major alkaloid mitragynine is known to have sedative and euphoric effects. Hence, the plant has been a subject of abuse, leading to addiction, necessitating efficient analytical methods to detect its psychoactive constituents. However, current chromatography‐based methods for detecting the alkaloids are time consuming and costly. Quantitative nuclear magnetic resonance (qNMR) spectroscopy emerges as a promising alternative due to its nondestructive nature, structural insights, and short analysis time. Hence, a rapid and precise qNMR method was developed to quantify selected major psychoactive alkaloids in various parts of M. speciosa. Mitragynine, specioliatine, and speciogynine were quantified in relation to the integral value of the ‐OCH3 groups of the alkaloids and the internal standard 1,4‐dinitrobenzene. The precision and reproducibility of the method gave a relative standard deviation (RSD) of 2%, demonstrating the reliability of the method. In addition, the method showed excellent specificity, sensitivity, high linearity range (R2 = 0.999), and limits of detection (LOD) and quantification (LOQ) values. The analysis revealed that the red‐veined M. speciosa leaves contained higher levels of mitragynine (32.34 mg/g), specioliatine (16.84 mg/g) and speciogynine (7.69 mg/g) compared to the green‐veined leaves, stem bark, or fruits. [ABSTRACT FROM AUTHOR]

High-resolution computed tomography (HRCT) is important for diagnosing interstitial lung disease (ILD) in inflammatory rheumatic disease (IRD) patients. However, visual ILD assessment via HRCT often has high inter-reader variability. Artificial intelligence (AI)-based techniques for quantitative image analysis promise more accurate diagnostic and prognostic information. This study evaluated the reliability of artificial intelligence-based quantification of pulmonary HRCT (AIqpHRCT) in IRD-ILD patients and verified IRD-ILD quantification using AIqpHRCT in the clinical setting. Reproducibility of AIqpHRCT was verified for each typical HRCT pattern (ground-glass opacity [GGO], non-specific interstitial pneumonia [NSIP], usual interstitial pneumonia [UIP], granuloma). Additional, 50 HRCT datasets from 50 IRD-ILD patients using AIqpHRCT were analysed and correlated with clinical data and pulmonary lung function parameters. AIqpHRCT presented 100% agreement (coefficient of variation = 0.00%, intraclass correlation coefficient = 1.000) regarding the detection of the different HRCT pattern. Furthermore, AIqpHRCT data showed an increase of ILD from 10.7 ± 28.3% (median = 1.3%) in GGO to 18.9 ± 12.4% (median = 18.0%) in UIP pattern. The extent of fibrosis negatively correlated with FVC (ρ=-0.501), TLC (ρ=-0.622), and DLCO (ρ=-0.693) (p < 0.001). GGO measured by AIqpHRCT also significant negatively correlated with DLCO (ρ=-0.699), TLC (ρ=-0.580) and FVC (ρ=-0.423). For the first time, the study demonstrates that AIpqHRCT provides a highly reliable method for quantifying lung parenchymal changes in HRCT images of IRD-ILD patients. Further, the AIqpHRCT method revealed significant correlations between the extent of ILD and lung function parameters. This highlights the potential of AIpqHRCT in enhancing the accuracy of ILD diagnosis and prognosis in clinical settings, ultimately improving patient management and outcomes. [ABSTRACT FROM AUTHOR]

To establish a quantitative CT threshold for radiological disease progression of progressive pulmonary fibrosis (PPF) and evaluate its feasibility in patients with connective tissue disease-related interstitial lung disease (CTD-ILD). Between April 2007 and October 2022, patients diagnosed with CTD-ILD retrospectively evaluated. CT quantification was conducted using a commercial software by summing the percentages of ground-glass opacity, consolidation, reticular opacity, and honeycombing. The quantitative threshold for radiological progression was determined based on the highest discrimination on overall survival (OS). Two thoracic radiologists independently evaluated visual radiological progression, and the senior radiologist's assessment was used as the final result. Cox regression was used to assess prognosis of PPF based on the visual assessment and quantitative threshold. 97 patients were included and followed up for a median of 30.3 months (range, 4.7–198.1 months). For defining radiological disease progression, the optimal quantitative CT threshold was 4%. Using this threshold, 12 patients were diagnosed with PPF, while 14 patients were diagnosed with PPF based on the visual assessment, with an agreement rate of 97.9% (95/97). Worsening respiratory symptoms (hazard ratio [HR], 12.73; P <.001), PPF based on the visual assessment (HR, 8.86; P =.002) and based on the quantitative threshold (HR, 6.72; P =.009) were independent risk factors for poor OS. The quantitative CT threshold for radiological disease progression (4%) was feasible in defining PPF in terms of its agreement with PPF grouping and prognostic performance when compared to visual assessment. [ABSTRACT FROM AUTHOR]

Quantification, i.e., the task of predicting the class prevalence values in bags of unlabeled data items, has received increased attention in recent years. However, most quantification research has concentrated on developing algorithms for binary and multi-class problems in which the classes are not ordered. Here, we study the ordinal case, i.e., the case in which a total order is defined on the set of n > 2 classes. We give three main contributions to this field. First, we create and make available two datasets for ordinal quantification (OQ) research that overcome the inadequacies of the previously available ones. Second, we experimentally compare the most important OQ algorithms proposed in the literature so far. To this end, we bring together algorithms proposed by authors from very different research fields, such as data mining and astrophysics, who were unaware of each others' developments. Third, we propose a novel class of regularized OQ algorithms, which outperforms existing algorithms in our experiments. The key to this gain in performance is that our regularization prevents ordinally implausible estimates, assuming that ordinal distributions tend to be smooth in practice. We informally verify this assumption for several real-world applications. [ABSTRACT FROM AUTHOR]

The antioxidant activity of Ginkgo biloba leaf (GBL) extract is closely related to its efficacy against various diseases; however, the antioxidant activities of the specific constituents of GBL remain unclear. In this study, 194 GBL constituents were identified using ultra‐performance liquid chromatography‐quadrupole‐time‐of‐flight mass spectrometry, including 97 flavonoids, 37 terpenoids, 29 lignans, 19 carboxylic acids, 5 alkylphenolic acids, 5 alkylphenols, and 2 other compounds. The cleavage rules of the main constituents of GBL were dissected in detail. The 36 GBL constituents with high antioxidant activity were subsequently discovered using the oxygen radical absorbance capacity assay, including 30 flavonoids and six carboxylic acids. Finally, an HPLC analysis method was established to determine the content of the nine major antioxidants in the three batches of GBL. Among them, kaempferol 3‐O‐β‐D‐(6″‐p‐coumaroyl) glucopyranosyl‐(1‐2)‐α‐L‐rhamnopyranoside, kaempferol‐3‐O‐rutinoside, and rutin exhibited high antioxidant activity and were found in significant amounts in GBL, with concentrations greater than 0.7 mg/g. These results provide an important reference for the development of pharmaceuticals and health products containing GBL. [ABSTRACT FROM AUTHOR]

Proton magnetic resonance spectroscopic imaging (1H‐MRSI) is a powerful tool that enables the multidimensional non‐invasive mapping of the neurochemical profile at high resolution over the entire brain. The constant demand for higher spatial resolution in 1H‐MRSI has led to increased interest in post‐processing‐based denoising methods aimed at reducing noise variance. The aim of the present study was to implement two noise‐reduction techniques, Marchenko–Pastur principal component analysis (MP‐PCA) based denoising and low‐rank total generalized variation (LR‐TGV) reconstruction, and to test their potential with and impact on preclinical 14.1 T fast in vivo 1H‐FID‐MRSI datasets. Since there is no known ground truth for in vivo metabolite maps, additional evaluations of the performance of both noise‐reduction strategies were conducted using Monte Carlo simulations. Results showed that both denoising techniques increased the apparent signal‐to‐noise ratio (SNR) while preserving noise properties in each spectrum for both in vivo and Monte Carlo datasets. Relative metabolite concentrations were not significantly altered by either method and brain regional differences were preserved in both synthetic and in vivo datasets. Increased precision of metabolite estimates was observed for the two methods, with inconsistencies noted for lower‐concentration metabolites. Our study provided a framework for how to evaluate the performance of MP‐PCA and LR‐TGV methods for preclinical 1H‐FID MRSI data at 14.1 T. While gains in apparent SNR and precision were observed, concentration estimations ought to be treated with care, especially for low‐concentration metabolites. [ABSTRACT FROM AUTHOR]

Tritaxis kurnoolensis, a small tree of the Euphorbiaceae family, is endemic to a valley within the Sullavai Sandstone plateau in close proximity to the Paleru Reservoir (Owk dam). The species is a narrow endemic. No documented sightings of this species have been recorded beyond the type locality since its description in 1994 by Venkataraju & Pullaiah as Dimorphocalyx kurnoolensis from the Nandyal District of Andhra Pradesh. In this study, the authors applied the grid method for quantification and subjected the species to a meticulous analysis aligning with IUCN Red List Criteria. The distribution was found to be restricted due to habitat (valley) fragmentation, reservoir which is arresting seed dispersal, and destruction caused by tunnel construction civil works. The area of occupancy (AOO) at 16 km² and the extent of occurrence (EOO) at 0.474 km², were systematically computed using GeoCAT. The species is assessed here using the Red List methodology for evaluating extinction risk. Based on its AOO, EOO, and population size, it has been classified as Critically Endangered. [ABSTRACT FROM AUTHOR]

Purpose: For the first time, three currently available 360° CZT-SPECT/CT cameras were compared under clinical conditions using phantom-based measurements. Methods: A 99mTc- and a 177Lu-customized NEMA IEC body phantom were imaged with three different cameras, StarGuide (GE Healthcare), VERITON-CT versions 200 (V200) and 400 (V400) (Spectrum Dynamics Medical) under the same clinical conditions. Energy resolution and volumetric sensitivity were evaluated from energy spectra. Vendors provided the best reconstruction parameters dedicated to visualization and/or quantification, based on their respective software developments. For both 99mTc- and 177Lu-phantoms, noise level, quantification accuracy, and recovery coefficient (RC) were performed with 3DSlicer. Image quality metrics from an approach called "task-based" were computed with iQMetrix-CT on 99mTc visual reconstructions to assess, through spatial frequencies, noise texture in the background (NPS) and contrast restitution of a hot insert (TTF). Spatial resolution indices were calculated from frequencies corresponding to TTF10% and TTF50%. Results: Despite the higher sensitivity of VERITON cameras and the enhanced energy resolution of the V400 (3.2% at 140 keV, 5.2% at 113 keV, and 3.6% at 208 keV), StarGuide presents comparable image quality. This highlights the need to differentiate sensitivity from count quality, which is influenced by hardware design (collimator, detector block) and conditions image quality as well as the reconstruction process (algorithms, scatter correction, noise regulation). For 99mTc imaging, the quantitative image optimization approach based on RCmean for StarGuide versus RCmax for V200 and V400 systems (RCmean/RCmax: 0.9/1.8; 0.5/0.9; 0.5/0.9 respectively—Ø37 mm). SRTB10/50 showed nearly equivalent spatial resolution performances across the different reconstructed images. For 177Lu imaging, the 113 keV imaging of the V200 and V400 systems demonstrated strong performances in both image quality and quantification, while StarGuide and V400 systems offer even better potential due to their ability to exploit signals from both the 113 and 208 keV peaks. 177Lu quantification was optimized according to RCmax for all cameras and reconstructions (1.07 ± 0.09—Ø37 mm). Conclusions: The three cameras have equivalent potential for 99mTc imaging, while StarGuide and V400 have demonstrated higher potential for 177Lu. Dedicated visual or quantitative reconstructions offer better specific performances compared to the unified visual/quantitative reconstruction. The task-based approach appears to be promising for in-depth comparison of images in the context of system characterization/comparison and protocol optimization. [ABSTRACT FROM AUTHOR]

Background/Objectives: The precise quantification of myocardial infarction is crucial for evaluating therapeutic strategies. We developed a robust, color-based semi-automatic algorithm capable of infarct region detection, isolation and quantification with four different histological staining techniques, and of the isolation and quantification of diffuse fibrosis in the heart. Methods: Our method is developed based on the color difference in the infarct and non-infarct regions after histological staining. Mouse cardiac tissues stained with Masson's trichrome (MTS), hematoxylin and eosin (H&E), 2,3,5-Triphenyltetrazolium chloride and picrosirius red were included to demonstrate the performance of our method. Results: We demonstrate that our algorithm can effectively identify and produce a clear visualization of infarct tissue in the four staining techniques. Notably, the infarct region on an H&E-stained tissue section can be clearly visualized after processing. The MATLAB-based program we developed holds promise for infarct quantification. Additionally, our program can isolate and quantify diffuse fibrotic elements from an MTS-stained cardiac section, which suggests the algorithm's potential for evaluating pathological cardiac fibrosis in diseased cardiac tissues. Conclusions: We demonstrate that this color-based algorithm is capable of accurately identifying, isolating and quantifying cardiac infarct regions with different staining techniques, as well as diffuse and patchy fibrosis in MTS-stained cardiac tissues. [ABSTRACT FROM AUTHOR]

Cyanogenic glycosides are naturally occurring compounds found in numerous plant species, which can release toxic hydrogen cyanide upon hydrolysis. The quantification of cyanogenic glycosides is essential for assessing their potential toxicity and health risks associated with their consumption. Liquid chromatographic techniques coupled with various detectors have been widely used for the quantification of cyanogenic glycosides. In this review, we discuss recent advances in chromatographic quantification methods for cyanogenic glycosides, including the development of new stationary phases, innovative sample preparation methods, and the use of mass spectrometry. We also highlight the combination of chromatographic separation with mass spectrometric detection for the identification and quantification of specific cyanogenic glycosides and their metabolites in complex sample matrices. Lastly, we discuss the current challenges and future perspectives in the development of reliable reference standards, optimization of sample preparation methods, and establishment of robust quality control procedures. This review aims to provide an overview of recent advances in chromatographic quantification methods for cyanogenic glycosides and their applications in various matrices, including food products, biological fluids, and environmental samples. [ABSTRACT FROM AUTHOR]

Advancements in forensic DNA typing technology and methods have increased sensitivity and, while beneficial, carry the weight of more challenging profile interpretation. In response, the forensic DNA community has often requested more complex reference materials to address commonly encountered measurement and interpretation issues such as complex DNA mixtures, DNA degradation, and PCR inhibition. The National Institute of Standards and Technology (NIST) released Research Grade Test Material 10235: Forensic DNA Typing Resource Samples to support the forensic DNA community. Components include three single source samples, two degraded samples, and three mixture samples. As part of the Research Grade Test Material (RGTM) process, automated methods for bottling, alternative sample tube types, and the addition of carrier RNA for stabilizing low‐quantity samples were investigated. Both internal and external testing demonstrate the stability of the material over time at 4°C through qPCR testing. In the development of a data portal, users have been allowed to anonymously upload results and compare their data with NIST and others. This report describes the preparation and stability of this material. [ABSTRACT FROM AUTHOR]

To understand the influence of rainfall patterns on rainfall–runoff processes, we propose two indices: skewnessPEAK (skewp), representing the relative timing of peak rainfall, and the normalized root-mean-square error peak (NRMSEp), which quantifies the concentration of rainfall near the peak. By analyzing approximately 25,000 rainfall scenarios, we examined the relationship between these indices and peak flood discharge in the rainfall–runoff process. The analysis revealed that peak flood discharge positively correlates with the NRMSEp, indicating that concentrated rainfall near the peak substantially increases discharge. Conversely, a negative correlation with skewp suggests that earlier peak rainfall reduces discharge. These insights were synthesized into a three-dimensional solution space providing a comprehensive framework for predicting how variations in rainfall distribution affect flood discharge. The findings underscore the importance of incorporating these indices into real-time flood forecasting models and urban flood risk management strategies. [ABSTRACT FROM AUTHOR]

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup. [ABSTRACT FROM AUTHOR]

This paper focuses on the possible usage of qualitative mathematics in psychology. Qualitative mathematics is understood to be equivalent to pure mathematics. First, it is explained that mathematics is a discipline studying patterns in reproducible mental objects. Qualitative mathematics is presented as an alternative to measurement, potentially offering the same level of exactness, clarity, and rigor. This perspective might lead psychologists to explore connections between a phenomenon and any kind of mathematical structure, regardless of whether the structure is quantitative. Usage of (any) mathematical structures might require scholars who are familiar with them. Consequently, changes in mathematics education may also be needed. Introducing non-numerical structures into mathematics education--thereby partially revisiting the New Math Movement--could train individuals more prepared for a creative approach to the use of structures and less inclined to view everything as quantitative. [ABSTRACT FROM AUTHOR]

Immuno-positron emission tomography (immunoPET) enables imaging of specific targets that play a role in targeted therapy and immunotherapy, such as antigens on cell membranes, targets in the disease microenvironment, or immune cells. The most common immunoPET applications use a monoclonal antibody labeled with a relatively long-lived positron emitter such as 89Zr (T1/2 = 78.4 h), but smaller antibody-based constructs labeled with various other positron emitting radionuclides are also being investigated. This molecular imaging technique can thus guide the development of new drugs and may have a pivotal role in selecting patients for a particular therapy. In early phase immunoPET trials, multiple imaging time points are used to examine the time-dependent biodistribution and to determine the optimal imaging time point, which may be several days after tracer injection due to the slow kinetics of larger molecules. Once this has been established, usually only one static scan is performed and semi-quantitative values are reported. However, total PET uptake of a tracer is the sum of specific and nonspecific uptake. In addition, uptake may be affected by other factors such as perfusion, pre-/co-administration of the unlabeled molecule, and the treatment schedule. This article reviews imaging methodologies used in immunoPET studies and is divided into two parts. The first part summarizes the vast majority of clinical immunoPET studies applying semi-quantitative methodologies. The second part focuses on a handful of studies applying pharmacokinetic models and includes preclinical and simulation studies. Finally, the potential and challenges of immunoPET quantification methodologies are discussed within the context of the recent technological advancements provided by long axial field of view PET/CT scanners. [ABSTRACT FROM AUTHOR]

The fungal pathogen Didymella segeticola causes leaf spot and leaf blight on tea plant (Camellia sinensis), leading to production losses and affecting tea quality and flavor. Accurate detection and quantification of D. segeticola growth in tea plant leaves are crucial for diagnosing disease severity or evaluating host resistance. In this study, we monitored disease progression and D. segeticola development in tea plant leaves inoculated with a GFP-expressing strain. By contrast, a DNA-based qRT-PCR analysis was employed for a more convenient and maneuverable detection of D. segeticola growth in tea leaves. This method was based on the comparison of D. segeticola-specific DNA encoding a Cys2His2-zinc-finger protein (NCBI accession number: OR987684) in relation to tea plant Cs18S rDNA1. Unlike ITS and TUB2 sequences, this specific DNA was only amplified in D. segeticola isolates, not in other tea plant pathogens. This assay is also applicable for detecting D. segeticola during interactions with various tea cultivars. Among the five cultivars tested, 'Zhongcha102' (ZC102) and 'Fuding-dabaicha' (FDDB) were more susceptible to D. segeticola compared with 'Longjing43' (LJ43), 'Zhongcha108' (ZC108), and 'Zhongcha302' (ZC302). Different D. segeticola isolates also exhibited varying levels of aggressiveness towards LJ43. In conclusion, the DNA-based qRT-PCR analysis is highly sensitive, convenient, and effective method for quantifying D. segeticola growth in tea plant. This technique can be used to diagnose the severity of tea leaf spot and blight or to evaluate tea plant resistance to this pathogen. [ABSTRACT FROM AUTHOR]

The quantification of pharmaceutical compounds in biological matrices is a critical component of drug development, therapeutic drug monitoring, and pharmacokinetic studies. Over the past decade, significant advances have been made in analytical techniques that enhance the sensitivity, accuracy, and efficiency of these measurements. This comprehensive review explores the recent developments in various analytical methods, including chromatography, mass spectrometry, spectroscopy, and electrochemical techniques, focusing on their application in pharmaceutical analysis. Additionally, emerging trends such as high-throughput screening, miniaturization, and the integration of artificial intelligence (AI) are discussed, highlighting their potential to revolutionize the field. The review also addresses the challenges associated with current methodologies, including matrix effects, the need for extensive sample preparation, and the push towards greener and more sustainable analytical practices. Finally, future directions in the field are proposed, emphasizing the importance of continued innovation to meet the evolving needs of pharmaceutical analysis. [ABSTRACT FROM AUTHOR]

Carbonate minerals are globally distributed on the modern and ancient Earth and are abundant in terrestrial and marine depositional environments. Fluid inclusions hosted by calcite retain primary signatures of the source fluid geochemistry at the time of mineral formation (i.e., pCO2) and can be used to reconstruct paleoenvironments. Confocal laser Raman spectroscopy provides a quick, nondestructive approach to measuring the constituents of fluid inclusions in carbonates and is a reliable method for qualitatively determining composition in both the aqueous and gas phases. Here, we demonstrate a method for accurately quantifying bicarbonate and carbonate ion concentrations (down to 20 mM) and pH (7–11) from calcite fluid inclusions using confocal Raman spectroscopy. Instrument calibrations for carbonate (CO32–) and bicarbonate (HCO3–) concentrations and pH were performed using stock solutions. We show that the calcite host mineral does not affect the accurate quantification of carbonate solution concentrations and that these parameters can be used to estimate the pH and pCO2 of a solution entrapped within a fluid inclusion. We apply the technique to Icelandic spar calcite and find a [CO32–] = 0.11, [HCO3–] = 0.17, pH = 10.1, and CO2 parts per million = 2217. The presence of gaseous Raman bands for CO2, CH4, and H2S suggests that the mineral precipitated in a reducing environment. [ABSTRACT FROM AUTHOR]

Quantifying the biomass, or number of individuals, diversity, and distribution of marine species is a critical aspect of understanding and managing marine ecosystems. In recent years, there has been growing interest in using environmental DNA (eDNA) for marine ecosystem management and biodiversity assessment. However, the main challenge hindering eDNA applicability has been the inability to infer absolute species abundances from multispecies analysis (eDNA metabarcoding). In this study, we demonstrate a way forward by estimating the abundance of commercially important fish species in a Norwegian fjord using a joint Bayesian statistical model of traditional trawl-catch data and molecular data derived from eDNA. Using this model, we accurately predict out-of-sample trawl catches using eDNA alone. Moreover, our model provides empirical estimates for key processes linking marine eDNA concentration to the fish population abundance estimated from trawl observations, including trawl catchability, DNA shedding, degradation, dilution, transport, recovery rate, and isolation efficiency. These processes, including amplification efficiencies correcting for Polymerase Chain Reaction (PCR) bias, are species-specific and enable the translation of eDNA metabarcoding data into abundances. These findings have broad implications for the use of eDNA in marine ecosystem management and conservation efforts. [ABSTRACT FROM AUTHOR]

In contrast to traditional X‐ray photoelectron spectroscopy (XPS), hard X‐ray photoelectron spectroscopy (HAXPES) can provide information from deeper within a sample while maintaining chemical resolution. However, working with higher energy X‐rays introduces a series of new or different issues ranging from energy calibration to factors associated with quantitative analysis. As part of the efforts to identify and increase community awareness about these issues, a workshop was held to review HAXPES metrology challenges with the perspective of converting it into a quantitative technique. A summary is hereby given of this workshop, which was entitled "What New Challenges Come with the Capabilities of HAXPES?" It was held in Portland, OR, USA, on November 7, 2023, and was primarily sponsored by the ASTM E42 Committee and the Applied Surface Science Division of the American Vacuum Society. This report contains summaries of the presentations and discussions at this workshop regarding the current open problems in HAXPES metrology. There were 20 participants at the workshop. [ABSTRACT FROM AUTHOR]

Introduction: Subjective surgeon interpretation of near-infrared perfusion video is limited by low inter-observer agreement and poor correlation to clinical outcomes. In contrast, quantification of indocyanine green fluorescence video (Q-ICG) correlates with histologic level of perfusion as well as clinical outcomes. Measuring dye volume over time, however, has limitations, such as it is not on-demand, has poor spatial resolution, and is not easily repeatable. Laser speckle contrast imaging quantification (Q-LSCI) is a real-time, dye-free alternative, but further validation is needed. We hypothesize that Q-LSCI will distinguish ischemic tissue and correlate over a range of perfusion levels equivalent to Q-ICG. Methods: Nine sections of intestine in three swine were devascularized. Pairs of indocyanine green fluorescence imaging and laser speckle contrast imaging video were quantified within perfused, watershed, and ischemic regions. Q-ICG used normalized peak inflow slope. Q-LSCI methods were laser speckle perfusion units (LSPU), the base unit of laser speckle imaging, relative perfusion units (RPU), a previously described methodology which utilizes an internal control, and zero-lag normalized cross-correlation (X-Corr), to investigate if the signal deviations convey accurate perfusion information. We determine the ability to distinguish ischemic regions and correlation to Q-ICG over a perfusion gradient. Results: All modalities distinguished ischemic from perfused regions of interest; Q-ICG values of 0.028 and 0.155 (p < 0.001); RPU values of 0.15 and 0.68 (p < 0.001); and X-corr values of 0.73 and 0.24 (p < 0.001). Over a range of perfusion levels, RPU had the best correlation with Q-ICG (r = 0.79, p < 0.001) compared with LSPU (r = 0.74, p < 0.001) and X-Corr (r = 0.46, p < 0.001). Conclusion: These results demonstrate that Q-LSCI discriminates ischemic from perfused tissue and represents similar perfusion information over a broad range of perfusion levels comparable to clinically validated Q-ICG. This suggests that Q-LSCI might offer clinically predictive real-time dye-free quantification of tissue perfusion. Further work should include validation in histologic studies and human clinical trials. [ABSTRACT FROM AUTHOR]

This essay explores two types of population policies common in Arab countries: pro‐natalist policies, aimed at encouraging higher birth rates, and anti‐natalist policies, aimed at reducing birth rates. Determining their practical impact is often challenging due to the lack of control or comparison groups. However, historical policy patterns in two groups of Arab countries over the past six decades offer a unique opportunity for comparison. Between the 1970s and 1990s these two groups implemented opposing programs (pro‐ vs. anti‐natalist), followed by predominantly anti‐natalist policies in both groups. By comparing fertility rates between these groups and discussing various factors influencing fertility changes, this study suggests that the influence of population policies in Arab countries may have been smaller than anticipated, to the point that it is questionable whether they have been worthwhile. [ABSTRACT FROM AUTHOR]

Fucoidan is a fucose-rich sulfated polysaccharide found mainly in brown seaweeds. Fucoidan has recently gained much attention due to its bioactive and medicinal properties, hence its high potential to be utilized in the pharmaceutical industry. Therefore, an accurate, reliable, and simple method for the specific determination of fucoidan would be a useful tool when looking to recover this valuable polysaccharide. This research focused on developing an assay utilizing the thiazine dye, methylene blue, for the quantification of fucoidan, with a specific application for fucoidan found in algal extracts. Fucoidan could be specifically determined at pH 1, to maximum concentrations of 4 g L-1 in solution without the interference of alginate and other seaweed extract contaminants. At pH 7 and 11, quantification of fucoidan led to interference from alginate and gallic acid. A conversion factor of 1.8 allowed the quantification of fucoidan from Ecklonia maxima extracts using a commercial standard from Fucus vesiculosis. An Ecklonia maxima extract was found to contain 5.47 ± 0.061 g L-1 of fucoidan. Measuring the fucoidan and L-fucose concentration in crude seaweed extract through the methylene blue assay and modified Dische and Shettles J Biol Chem 175:595–603, (1948) method, respectively, confirmed the accuracy and specificity of the method. The limit of quantification of the methylene blue assay at pH 1 was 0.62 g L-1 of fucoidan. [ABSTRACT FROM AUTHOR]

Summary: Monosaccharides such as glucose, fructose, and galactose are the building blocks of oligosaccharides and act as the major energy sources in our body. These three monosaccharides are the most prominent in food processing and widely consumed analytes, found in fruits or milk. Although several analytical methods are available to identify and quantify sugar solutions, their drawbacks urge the search for better possible alternatives. A simple, rapid, and efficient method for identifying monosaccharides (ᴅ‐fructose, ᴅ‐galactose, and ᴅ‐glucose) was investigated using low‐field nuclear magnetic resonance (LF‐NMR), exploring the applicability and reliability of this machine. The transverse (T2) relaxation curve was analysed to distinguish monosaccharides from each other. The increasing monosaccharide concentration in the sugar solution causes leftward shifts in signals, indicating a gradual reduction in the mobility of water molecules. The addition of sugar generated a secondary peak, which assisted in identifying monosaccharides. Notably, fructose exhibited distinct behaviour from that of glucose and galactose. The regression coefficient consistently exceeded 0.98, indicating the reliability of this experiment. LF‐NMR encountered challenges in differentiating stereo‐isomeric glucose and galactose. However, quantification of monosaccharides from known binary mixtures (water and one monosaccharide) is possible by preparing standard curves of different concentrations of sugars. These standard curves can be either T2 or the total surface area of the peaks as a function of sugar concentration. The study concludes that LF‐NMR has potential for the qualitative and quantitative analysis of monosaccharides in pure solutions or powdered samples. It is hoped that the rapid and easy way of monosaccharide identification by simple analysis of peaks obtained from NMR spectra will enhance its accessibility for users. However, it also acknowledges limitations in applying this method to complex systems (mixtures of different sugars in a solution). [ABSTRACT FROM AUTHOR]

Background: Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration. Aim of review: In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported. Key scientific concepts of review: The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community. [ABSTRACT FROM AUTHOR]

mRNA vaccines were highly effective in response to the COVID-19 pandemic, making them an attractive platform to address cancers and other infectious diseases. Many new mRNA vaccines in development are multivalent, which represents a difficulty for the standard assays commonly used to characterize the critical quality attributes of monovalent formulations. Here, we present a multiplexed analytical tool with nucleic acid microarray technology using the VaxArray platform that measures the identity and quantity of mono- and multivalent mixtures of naked mRNA and mRNA encapsulated in lipid nanoparticle formulations in under 2 h without any additional preparation steps, such as extraction or RT-PCR. Using a quadrivalent mixture of encapsulated mRNA constructs that encode for four unique proteins in a vaccine formulation, the VaxArray mRNA assay was demonstrated to be highly specific for each mRNA with sensitivity < 1 µg/mL. The quantification of individual mRNAs within the lipid nanoparticle mixture resulted in a precision of ≤10% RSD and an accuracy of 100 ± 9%. [ABSTRACT FROM AUTHOR]

Background: Quantitative thresholds are helpful to define an abnormal DaT SPECT in patients with suspected nigrostriatal degenerative diseases (NSDD). The optimal DaTQUANT threshold for diagnostic accuracy of DaT SPECT across combined movement and cognitive disorder populations has been previously described. Methods: We established optimal DaTQUANT thresholds that enhance the discrimination between dementia with Lewy bodies (DLB) and non-DLB dementia types, as well as between Parkinsonian syndromes (PS) and conditions not characterized by nigrostriatal degeneration (non-PS). Results: Data from a total of 303 patients were used in this retrospective analysis. Posterior putamen of the more affected hemisphere (MAH) was shown to be an accurate single-variable predictor for both DLB and PS and was comparable to the most accurate multi-variable models. Conclusions: Automated quantification with DaTQUANT can accurately aid in the differentiation of DLB from non-DLB dementias and PS from non-PS. Optimal thresholds for assisting a diagnosis of DLB are striatal binding ratio (SBR) ≤ 0.65, z-score ≤ −2.36, and a percent deviation ≤ −0.54 for the posterior putamen of the MAH. Optimal posterior putamen thresholds for assisting a diagnosis of PS are SBR ≤ 0.92, z-score ≤ −1.53, and a percent deviation ≤ −0.33, which are similar to our previously reported posterior putamen threshold values using a blended patient pool from multiple study populations. [ABSTRACT FROM AUTHOR]

Purpose: This paper explores how conflicting institutional logics shape the behaviors of macro- and micro-level actors in their use of a calculative practice. Thereby, this paper explains how quantification can undermine the intended purpose of a governance system based on a single number. Design/methodology/approach: The study draws upon the literature on calculative practices and institutional logics to present the case of how a single number—specifically the conversion factor for Atlantic Cod, established by macro-level actors for the purposes of governance within the Norwegian fishing industry—is interpreted and used by micro-level actors in the industry. The study is based on documents, field observations and interviews with fishers, landing facilities, and control authorities. Findings: The use of the conversion factor, while intended to protect fish stock and govern industry actions, does not always align with the institutional logics of micro-level actors. Especially during the winter season, these actors may seek to serve their interests, leading to potential system gaming. The reliance on a single number that overlooks seasonal nuances can motivate unintended behaviors, undermining the governance system's intentions. Originality/value: Integrating the literature on calculative practices with an institutional logics perspective, this study offers novel insights into the challenges of using quantification for the governance of complex industries. In particular, the paper reveals that when the logics of macro- and micro-level actors conflict in a single-number governance system, unintended outcomes arise due to a domination of the macro-level logics. [ABSTRACT FROM AUTHOR]

This study tackles the growing global concern about municipal waste management, particularly in cities like the Grand Guayaquil Metropolitan Area (GGA). Through realistic field studies on in situ household waste generation and geographic information system (GIS) tools, this work offers a framework to predict the quantities and types of recyclable household waste for any metropolitan area in Latin America. Over 4 weeks, students collected, sorted and weighed recyclable waste types, including plastic, paper, metal, glass and fabric, from 776 sampled household of the GGA. ArcGIS survey tool identified household locations and allowed to survey different socio-demographic features. With the help of ArcGIS interpolation method, the total household waste generation for GGA was predicted, and the classification of the different types of recyclable waste was also spatially distributed for the study area. The report identified notable trends in plastic waste, specifically polyethylene terephthalate waste's steady prevalence and 42% growth rate, emphasizing the importance of enhanced recycling techniques. Spatial density maps showed a heterogeneous waste distribution across the GGA, emphasizing locations with higher waste output. This study demonstrates that improving recyclable waste collection can be accomplished with a moderately cheap expenditure by collaborating with academia to overcome knowledge gaps. This strategy provides opportunities to mitigate the environmental impacts of poor waste management. [ABSTRACT FROM AUTHOR]

Purpose: Dopamine transporter imaging is crucial for assessing presynaptic dopaminergic neurons in Parkinson's disease (PD) and related parkinsonian disorders. While 18F-FP-CIT PET offers advantages in spatial resolution and sensitivity over 123I-β-CIT or 123I-FP-CIT SPECT imaging, accurate quantification remains essential. This study presents a novel automatic quantification method for 18F-FP-CIT PET images, utilizing an artificial intelligence (AI)-based robust PET spatial normalization (SN) technology that eliminates the need for anatomical images. Methods: The proposed SN engine consists of convolutional neural networks, trained using 213 paired datasets of 18F-FP-CIT PET and 3D structural MRI. Remarkably, only PET images are required as input during inference. A cyclic training strategy enables backward deformation from template to individual space. An additional 89 paired 18F-FP-CIT PET and 3D MRI datasets were used to evaluate the accuracy of striatal activity quantification. MRI-based PET quantification using FIRST software was also conducted for comparison. The proposed method was also validated using 135 external datasets. Results: The proposed AI-based method successfully generated spatially normalized 18F-FP-CIT PET images, obviating the need for CT or MRI. The striatal PET activity determined by proposed PET-only method and MRI-based PET quantification using FIRST algorithm were highly correlated, with R2 and slope ranging 0.96–0.99 and 0.98–1.02 in both internal and external datasets. Conclusion: Our AI-based SN method enables accurate automatic quantification of striatal activity in 18F-FP-CIT brain PET images without MRI support. This approach holds promise for evaluating presynaptic dopaminergic function in PD and related parkinsonian disorders. [ABSTRACT FROM AUTHOR]

Building on the cross-linguistic variability in the meaning of vague quantifiers, this study explores the potential for negative transfer in Italian-Slovenian bilinguals concerning the use of quantificational determiners, specifically the translational equivalents of the English “many”, that is the Slovenian "precej" and "veliko". The aim is to identify relevant aspects of pragmatic knowledge for cross-linguistic influence. The study presents the results of a sentence-picture verification task in which Slovenian native speakers and Italian-Slovenian bilinguals evaluated sentences of the form "Quantifier X are Y" in relation to visual contexts. The results suggest that Italian learners of Slovenian, unlike Slovenian native speakers, fail to distinguish between "precej" and "veliko". This finding aligns with the negative transfer hypothesis. The study highlights the potential role of pragmatic knowledge in cross-linguistic transfer, particularly in the context of vague quantifiers. [ABSTRACT FROM AUTHOR]

This study aims to determine the presence of norovirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and bocavirus in air samples from a tertiary care hospital in Bangkok, Thailand. Air samples were collected in water using the BioSampler and concentrated using speedVac centrifugation. Based on RT-qPCR, norovirus RNA and SARS-CoV-2 RNA were detected in 13/60 (21.7%) and 3/60 (5.0%) of samples, respectively. One air sample had a weak positivity for both norovirus and SARS-CoV-2 RNAs. Detection rate of norovirus genogroup (G) II (13.3%) was higher than norovirus GI (6.7%). One air sample (1.7%) tested positive for GI and GII. The norovirus GI RNA concentration was 6.0 × 102 genome copies/m3. The norovirus GII RNA concentrations ranged from 3.4 × 101 to 5.0 × 103 genome copies/m3. Based on RT-nested PCR, norovirus GII was detected in two (3.3%) samples. All samples tested negative for GI RNA and bocavirus DNA. By phylogenetic analysis, GII.17, which is closely related to the outbreak Kawasaki308/JPN/2015 strain, was found in the RT-nested PCR-positive samples. This study highlights the potential of aerosols for norovirus and SARS-CoV-2 transmission and probably cause gastrointestinal and respiratory illnesses, respectively. [ABSTRACT FROM AUTHOR]

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As one of the developed genetically modified (GM) maize varieties in China, CC-2 has demonstrated promising commercial prospects during demonstration planting. The establishment of detection methods is a technical prerequisite for effective supervision and regulation of CC-2 maize. In this study, we have developed an event-specific quantification method that targets the junction region between the exogenous gene and the 5' flanking genomic DNA (gDNA) of CC-2. The accuracy and precision of this method were evaluated across high, medium, and low levels of CC-2 maize content, revealing biases within ±25% and satisfactory precision data. Additionally, we determined the limits of quantification of the method to be 0.05% (equivalent to 20 copies) of the CC-2 maize. A collaborative trial further confirmed that our event-specific method for detecting CC-2 produces reliable, comparable, and reproducible results when applied to five different samples provided by various sources. Furthermore, we calculated the expanded uncertainty associated with determining the content level of CC-2 in these samples. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Modern PET reconstruction algorithms incorporate point‐spread‐function (PSF) correction to mitigate partial volume effect. However, PSF correction can introduce edge artifacts that lead to quantification errors. Consequently, current international guidelines advise against using PSF correction in brain PET reconstruction.We aimed to investigate PSF‐induced quantification errors in recent digital PET systems and identify conditions that mitigate them. This study utilized brain PET imaging with alginate‐based realistic phantoms, simulating lesion‐to‐background activity ratios of 10:1 and 2:1, with eleven reconstruction parameter sets.Phantoms were prepared using a commercial anthropomorphic head phantom and two homemade inserts. Each insert contained a homogeneous 18F‐FDG alginate background with hot spheres of varying diameter (3, 4, 6, 8, 10, 12, and 15 mm). PET imaging was conducted on a digital PET‐CT system Biograph Vision 600 (Siemens), with a 10 min scan duration. Imaging was performed with and without PSF correction, with 2, 4, 6, 12, 18, or 24 iterations in reconstruction, and with or without additional Gaussian postfiltering. We assessed the recovery coefficient (RC), contrast recovery coefficient (CRC), variability, and CRC‐to‐variability ratios for each sphere size and reconstruction parameter set.PSF‐corrected images of the 10:1 spheres exhibited a nonmonotonic CRC‐to‐sphere diameter relationship due to edge artifacts overshoot in the 10 mm‐diameter sphere. In contrast, PSF images of the 2:1 spheres showed a monotonically increasing relationship. Non‐PSF images of both phantoms showed an expected monotonically increasing CRC‐to‐sphere diameter relationship but with lower CRC values compared to PSF images. The nonmonotonic relationship observed with 10:1 spheres was mitigated by applying a 3‐mm FWHM Gaussian postfiltering. For both phantoms, reconstructions with 6 iterations, PSF correction, and additional 3‐mm FWHM Gaussian postfiltering demonstrated the highest CRC‐to‐variability ratios.Our findings indicate that Gaussian postfiltering suppresses PSF artifacts. This parameter set corrected the nonmonotonic CRC‐to‐sphere diameter relationship and improved the CRC‐to‐variability ratio compared to non‐PSF reconstructions. Therefore, to enhance lesion detectability without compromising quantification accuracy, PSF correction coupled with Gaussian postfiltering should be used in brain PET. [ABSTRACT FROM AUTHOR]

Objective: Cardiovascular magnetic resonance enables the quantification of functional and morphological parameters with an impact on therapeutical decision making. While quantitative assessment is established in 2D, novel 3D techniques lack a standardized approach. Multi-planar-reformatting functionality in available software relies on visual matching location and often lacks necessary functionalities for further post-processing. Therefore, the easy-to-use Reslice3Dto2D software tool was developed as part of another research project to fill this gap and is now introduced with this work. Results: The Reslice3Dto2D reformats 3D data at the exact location of a reference slice with a two-step-based interpolation in order to reflect in-plane discretization and through-plane slice thickness including a slice profile selection. The tool was successfully validated on an artificial dataset and tested on 119 subjects with different underlying pathologies. The exported reformatted data could be imported into three different post-processing software tools. The quantified image sharpness by the Frequency Domain Image Blur Measure was significantly decreased by around 40% on rectangular slice profiles with 7 mm slice thickness compared to 0 mm due to partial volume effects. Consequently, Reslice3Dto2D enables the quantification of 3D data with conventional post-processing tools as well as the comparison of 3D acquisitions with their established 2D version. [ABSTRACT FROM AUTHOR]

AbstractPathogen detection is increasingly applied in medical diagnosis, food processing and safety, and environmental monitoring. Rapid, sensitive, and accurate pathogen quantification is the most critical prerequisite for assessing protocols and preventing risks. Among various methods evolved, those based on clustered regularly interspaced short palindromic repeats (CRISPR)-associated proteins (Cas) have been developed as important pathogen detection strategies due to their distinct advantages of rapid target recognition, programmability, ultra-specificity, and potential for scalability of point-of-care testing (POCT). However, arguments and concerns on the quantitative capability of CRISPR-based strategies are ongoing. Herein, we systematically overview CRISPR-based pathogen quantification strategies according to the principles, properties, and application scenarios. Notably, we review future challenges and perspectives to address the of precise pathogen quantification by CRISPR-Cas. We hope the insights presented in this review will benefit development of CRISPR-based pathogen detection methods. [ABSTRACT FROM AUTHOR]

It is a commonplace of the history of analytic philosophy that Carnap swiftly adopted Tarskian semantics in the mid-1930s. There is no doubt that, in a very general sense, this is true. But to what extent are the innovative technical details characteristic of Tarski's method, specifically the handling of quantification by way of a satisfaction relation between formulas and variable assignments, reflected in Carnap's writings on semantics? Curiously enough, their essentials are in place just before Carnap took the purported Tarskian turn, namely in the definition of analyticity he formulated some time between September 1932 and April 1933, but are missing from his publications on semantics after 1935 until the final chapter of Meaning and Necessity of 1947, at which point we find Carnap reverting to the use of specifically Tarskian techniques in the context of his novel semantics for quantified modal languages. [ABSTRACT FROM AUTHOR]

Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing. [ABSTRACT FROM AUTHOR]

The development process of a system is shaped by numerous variables that influence its progress and outcome. As a result, complexity can increase throughout the development process, potentially leading to negative consequences, which makes the management of complexity critical. Most development processes begin with the definition of needs and requirements. In this paper, the authors present a novel approach that enables the automated extraction of structure from requirements specifications. The approach uses Natural Language Processing to elicit three structural layers from a set of requirements, which are subsequently analyzed using metrics to assess complexity. In a case study, the approach is demonstrated using a set of 79 requirements, within which 246 individual entities are identified. These entities and the requirements are structured and analyzed using network density and spectral entropy. The metrics allow for interpretation and insight generation, such as identifying an increase in the number of potentially problematic loops. The approach achieved a detection and structural accuracy of over 98% in the given case study and is planned to be expanded with future cases. [ABSTRACT FROM AUTHOR]

ABSTRACT As efforts to study the mechanisms of melanoma metastasis and novel therapeutic approaches multiply, researchers need accurate, high‐throughput methods to evaluate the effects on tumor burden resulting from specific interventions. We show that automated quantification of tumor content from whole slide images is a compelling solution to assess in vivo experiments. In order to increase the outflow of data collection from preclinical studies, we assembled a large dataset with annotations and trained a deep neural network for the quantitative analysis of melanoma tumor content on histopathological sections of murine models. After assessing its performance in segmenting these images, the tool obtained consistent results with an orthogonal method (bioluminescence) of measuring metastasis in an experimental setting. This AI‐based algorithm, made freely available to academic laboratories through a web‐interface called MetFinder, promises to become an asset for melanoma researchers and pathologists interested in accurate, quantitative assessment of metastasis burden. [ABSTRACT FROM AUTHOR]

Mitral valve regurgitation is one of the most common heart valve diseases that can occur when the structural composition of the mitral valve is affected. Mitral valve repair, typically performed in a minimally invasive setting, is a complex surgery that aims to reinstate the structural integrity of the valve and thereby restore normal valve function. The current practice lacks quantification of geometrical changes and interactive 3D visualization that could enhance the understanding of pathomorphological changes and impact of surgical correction. This work presents functionalities from smartMVR, a quantitative tool to measure, compare, and analyse changes in mitral valve geometry from data captured on-demand using an RGB-D camera. The feasibility of the approach is demonstrated on a silicone replica of a pathological mitral valve that is iteratively corrected using common surgical techniques. [ABSTRACT FROM AUTHOR]