Input Function Research Articles

Akses listrik, air minum, dan sanitasi serta modal manusia di Indonesia

A country is said to be prosperous if it has the quality of human capital to carry out the production process. According to the Cobb-Douglas production function, the output (Q) of a country is explained by the input functions of artificial capital (K) and labor capital (L). Labor capital will be optimal if a country has quality human capital. Quality human capital will be achieved with good health and education levels. This study aims to analyze the influence of access to electricity, drinking water, and sanitation on Indonesia's quality of human capital in 2010–2019. The quality of human capital in this study is measured using the Human Development Index (HDI). The results of this study show that these three infrastructures have a significant positive effect on human capital. Thus, equal provision of these three accesses is a necessary and sufficient condition for improving the welfare of a country.

Anthropogenic Carbon in the Arctic Ocean: Perspectives From Different Transient Tracers

AbstractIn this study we investigated the physical characteristics of the Atlantic layer in the Arctic Ocean (AO) and its role in the distribution and storage of anthropogenic carbon (Cant). The novelty of this work is to use the Transit Time Distribution method (TTD) with the radionuclides 129I and 236U and its comparison to the commonly applied gas tracers, CFC‐12 and SF6. Overall, our examination of two distinct tracer pairs, along with the novel TTD method in comparison to a classical approach, revealed a notable agreement, highlighting the robustness of these Cant estimates. The TTD analysis based on radionuclides showed that whereas the Eurasian Basin has shorter transit times and is dominated by strong mixing conditions, the Amerasian Basin is characterized by longer transit times and a strong advective flow. Overall, the Cant concentrations obtained from radionuclides confirm that the distribution in the AO follows its circulation patterns, with higher concentrations in the Eurasian Basin (∼50 μmol kg−1) compared to the Amerasian one (∼36–42 μmol kg−1). An estimated partial inventory of 0.85 ± 0.17 and 1.0 ± 0.03 Pg C was assessed for 2015 from the novel application of TTD with radionuclides and gas tracers, respectively. Finally, we identified the saturation of gas tracers as a larger source of uncertainty for Cant estimation compared to the uncertainty associated to different radionuclides' input functions, thus remarking the importance of including non‐saturation dependent tracers, such as radionuclides, as an additional tool to support Cant estimates in the AO.

Dry reforming process using microwave plasma generator with high carbon dioxide conversion efficiency for syngas production

The proposed CO2 reforming solution has improved CO2 conversion efficiency by developing a new double helix type plasma generator (DHTPG) to preheat the discharge gas and increase residence time of CO2 at DHTPG. In addition, injecting CH4 independently allows for a stable increase in the temperature of the CO2 plasma in the microwave plasma generator compared with other conventional methods. To verify the performance of the DHTPG, we experimentally investigated the distribution of the plasma gas temperature using an enthalpy probe system as a function of the specific energy input, which is a key parameter for understanding the mechanisms of CO2 reforming of CH4. We performed the CO2 reforming of CH4 using the newly designed plasma generator and a comparative analysis of its characteristics with those of other plasma generators. Without catalyst materials, our method significantly enhanced the CO2 conversion efficiency to 91.4 % with an energy efficiency of 67 % at a specific energy input of 245 kJ/mol. The main advantage of our method is an increase in energy-consumption selectivity, in which the energy injected through the plasma decomposes CO2 rather than CH4. In addition, it reduces the energy required to stably maintain the plasma to improve energy efficiency.

The Choroid Plexus as an Alternative Locus for the Identification of the Arterial Input Function for Calculating Cerebral Perfusion Metrics Using MRI.

MR imaging-based cerebral perfusion metrics can be obtained by tracing the passage of a bolus of contrast through the microvasculature of the brain parenchyma. Thus, the temporal signal pattern of the contrast agent is typically measured over a large artery such as the MCA to generate the arterial input function. The largest intracranial arteries in the brain may not always be suitable for selecting the arterial input function due to skull base susceptibility artifacts or reduced size from steno-occlusive disease. Therefore, a suitable alternative arterial input function window would be useful. The choroid plexus is a highly vascular tissue composed essentially of arterialized blood vessels and acellular stroma with low metabolic requirements relative to its blood flow and may be a suitable alternative to identify the arterial input function. We studied 8 healthy participants and 7 patients with gliomas who were administered a bolus of gadolinium. We selected an arterial input function from both the left and right M1 segments of the MCA and both lateral ventricles of the choroid plexus for each participant. We compared the changes in the T2* signal and the calculated resting perfusion metrics using the arterial input functions selected from the MCA and choroid plexus. We found no systematic difference between resting perfusion metrics in GM and WM when calculated using an arterial input function from the MCA or choroid plexus in the same participant. The choroid plexus provides an alternative location from which an arterial input function may be sampled when a suitable measure over an MCA is not available.

The Use of Population Pharmacokinetics to Extrapolate Food Effects from Human Adults and Beagle Dogs to the Pediatric Population Illustrated with Paracetamol as a Test Case.

To date, food-drug interactions in the pediatric population remain understudied. The current food effect studies are mostly performed in adults and do not mimic the real-life situation in the pediatric population. Since the potential benefits of food effect studies performed in pediatrics should be counterbalanced with the burden that these studies pose to the patients, alternative research strategies should be evaluated. The present study aimed to evaluate whether population pharmacokinetics (popPK) using data in beagle dogs and human adults could reliably assess food effects relevant for the pediatric population. PopPK was utilized to understand the performance of paracetamol under different dosing conditions (when the participants were fasted, with a reference meal, and with infant formula) in human adults (n = 8) and beagle dogs (n = 6) by constructing models to derive the pharmacokinetic parameters and to evaluate the food effects in both species. A two-compartment model with a single input function for the absorption phase best described the profiles of paracetamol in the beagle dogs. In the human adults, a one-compartment model with a dual input function for the absorption phase best described the data. The simulated profiles for the different dosing conditions demonstrated that both the human adults' and beagle dogs' simulations were able to acceptably describe the plasma concentration-time profiles of paracetamol observed in a representative pediatric population, which opens up perspectives on pediatric-relevant food effect predictions. However, the obtained results should be carefully interpreted, since an accurate validation of these findings was not possible due to the scarcity of the literature on observed pediatric data.

An embedded machine learning strategy for analyzing interfacial characteristics in impact welding of dissimilar alloys

ABSTRACT In this study, a novel embedded machine learning (ML) framework was employed to analyze interfacial characteristics in dissimilar-alloy impact welding. While keeping the substrates constant, i.e., stainless steel and Ti alloy, the influence of interlayer types on the resultant morphology of impact-welded bonds was also highlighted. The dataset was meticulously generated through numerical simulations, utilizing materials properties and processing parameters as primary inputs. These inputs were used to predict various interfacial features, including the extent of wave formation (L), specific welding zone area (S), average waveform amplitude (A), and maximum interface temperature (Tmax). Impressively, exceptional predictive capabilities were demonstrated by the ML model, with determination coefficients of 0.938, 0.924, 0.967, and 0.909 for S, L, A, and Tmax, respectively. Based on the results, strong dependencies between interfacial features and the weight functions of materials properties and processing parameters were unveiled. Intriguingly, as the weight function of materials properties for a specific output objective increased, a corresponding decrease in the weight function of processing parameter inputs was observed. These findings emphasize the complex connection between input parameters and output characteristics, revealing previously unexplored intricacies in the impact welding process, facilitated by our advanced ML methodology.

Identification of (R)-[18F]YH134 for Monoacylglycerol Lipase Neuroimaging and Exploration of Its Use for Central Nervous System and Peripheral Drug Development.

This study aimed to evaluate (R)-[18F]YH134 as a novel PET tracer for imaging monoacylglycerol lipase (MAGL). Considering the ubiquitous expression of MAGL throughout the whole body, the impact of various MAGL inhibitors on (R)-[18F]YH134 brain uptake and its application in brain-periphery crosstalk were explored. Methods: MAGL knockout and wild-type mice were used to evaluate (R)-[18F]YH134 in invitro autoradiography and PET experiments. To explore the impact of peripheral MAGL occupancy on (R)-[18F]YH134 brain uptake, PET kinetics with an arterial input function were studied in male Wistar rats under baseline and blocking conditions. Results: In invitro autoradiography, (R)-[18F]YH134 revealed a heterogeneous distribution pattern with high binding to MAGL-rich brain regions in wild-type mouse brain slices, whereas the radioactive signal was negligible in MAGL knockout mouse brain slices. The invivo brain PET images of (R)-[18F]YH134 in wild-type and MAGL knockout mice demonstrated its high specificity and selectivity in mouse brain. A Logan plot with plasma input function was applied to estimate the distribution volume (V T) of (R)-[18F]YH134. V T was significantly reduced by a brain-penetrant MAGL inhibitor but was unchanged by a peripherally restricted MAGL inhibitor. The MAGL target occupancy in the periphery was estimated using (R)-[18F]YH134 PET imaging data from the brain. Conclusion: (R)-[18F]YH134 is a highly specific and selective PET tracer with favorable kinetic properties for imaging MAGL in rodent brain. Our results showed that blocking of the peripheral target influences brain uptake but not the V T of (R)-[18F]YH134. (R)-[18F]YH134 can be used for estimating the dose of MAGL inhibitor at half-maximal peripheral target occupancy.

The cortical high-flow sign of oligodendroglioma, IDH-mutant and 1p/19q-codeleted: comparison between arterial spin labeling and dynamic susceptibility contrast methods.

The cortical high-flow sign with the non-enhancing area was reportedly found to be more frequent with oligodendroglioma, IDH-mutant and 1p/19q codeleted (ODG IDHm-codel) than with IDH-wildtype or astrocytoma, IDH-mutant on arterial spin labeling (ASL) in diffuse gliomas. This study aimed to compare the identification rate of the cortical high-flow sign on ASL in patients with ODG IDHm-codel to that on dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI). Participants consisted of 32 adult ODG IDHm-codel patients with pathologically confirmed. Subtraction images were generated from paired control and label images on ASL. For DSC, dynamic T2*-weighted perfusion weighted images were obtained after pre-bolus of gadolinium-based contrast agent. Regional cerebral blood flow/volume maps were generated based on the concentration-time curve and arterial input function. Tumor-affecting cortices without contrast enhancement on conventional MR imaging were targeted. The identification rate of the cortical high-flow sign was compared between ASL and DSC using the Pearson's Chi-Square test. Frequency of the cortical high-flow sign was significantly higher on ASL (18/32, 56.3%; p < 0.001) than on DSC (5/32, 15.6%). All cases with the positive cortical high-flow sign on DSC were identified on ASL. ASL effectively identifies the cortical high-flow sign in ODG IDHm-codel, surpassing DSC in identification rates.

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize measurement. A framework was created to evaluate values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' values, the applied software, and a standard operating procedure. These were evaluated using the proposed score defined with accuracy, repeatability, and reproducibility components. Across the 10 received submissions, the score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Complex adaptive learning cortical neural network systems for solving time-fractional difference equations with bursting and mixed-mode oscillation behaviours

Complex networks have been programmed to mimic the input and output functions in multiple biophysical algorithms of cortical neurons at spiking resolution. Prior research has demonstrated that the ineffectual features of membranes can be taken into account by discrete fractional commensurate, non-commensurate and variable-order patterns, which may generate multiple kinds of memory-dependent behaviour. Firing structures involving regular resonator chattering, fast, chaotic spiking and chaotic bursts play important roles in cortical nerve cell insights and execution. Yet, it is unclear how extensively the behaviour of discrete fractional-order excited mechanisms can modify firing cell attributes. It is illustrated that the discrete fractional behaviour of the Izhikevich neuron framework can generate an assortment of resonances for cortical activity via the aforesaid scheme. We analyze the bifurcation using fragmenting periodic solutions to demonstrate the evolution of periods in the framework’s behaviour. We investigate various bursting trends both conceptually and computationally with the fractional difference equation. Additionally, the consequences of an excitable and inhibited Izhikevich neuron network (INN) utilizing a regulated factor set exhibit distinctive dynamic actions depending on fractional exponents regulating over extended exchanges. Ultimately, dynamic controllers for stabilizing and synchronizing the suggested framework are shown. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

Flight Dynamic Characteristics of Wide-Body Aircraft with Wind Gust and Turbulence

In this research, a wide-body aircraft was analyzed with critical monitoring of its states, a function of several control inputs (wind gust, turbulence, and microburst). The aerodynamic and stability coefficients of a Boeing 747-200 were obtained from previously published works and 6- DOF equations were formulated. Simulations were conducted for various control inputs to determine the aircraft’s free response, as well as the forced response. In order to understand the nature of the atmosphere, three different models were incorporated, including (i) the Dryden Model, (ii) wind gust, and (iii) microburst. The aircraft was found to be stable in the longitudinal and lateral flight modes, with trim conditions agreeing with published data. For a vertical wind gust of −10 ft/s, the AoA and pitch rate were observed to oscillate sinusoidally and became stable with new trim conditions. These states were found to regain trim conditions once the gust was removed. In the case of 3D gust, it was found that the longitudinal modes achieved a new trim condition through Phugoid oscillations, whereas the lateral modes underwent short-period oscillations. For the case of turbulence, random fluctuations were observed for trim conditions with no unstable behavior. When considering the microburst case, it was found that the aircraft initially gained altitude in the region of the headwind; this was followed by a sharp descent under the influence of a vertical velocity component.

Research on Working Principle of Rotating Arc Stability Sensor and its Input and Output Mapping Relationship

Research on Working Principle of Rotating Arc Stability Sensor and its Input and Output Mapping Relationship

Generating layout for complex cave-like levels with schematic maps and Cellular Automata

In this paper an algorithm for creating cave-like, user-guided layout is presented. In applications such as computer games, underground structures offer unique challenges and interesting space for player actions. Preparation of such areas can be time consuming and tiresome, especially during the design process, when many ideas are often scrapped. Presented approach aims at improving this process. Schematic input is used so the user can quickly define the general layout. Cave system is divided into levels and tiles - easily-parallelizable modules for the following method stages. Cellular automata are used to extend initial system sketch with interesting shapes while the diamond-square algorithm spreads the final terrain heights. Each stage uses the results of the previously performed operations as input, providing space for alterations. Input maps can be reused to obtain different variations of the same system. The final structure is represented as a 3D point cloud. Chosen representation supports multilevel systems and can be used either as a base for further algorithms, or as a final mesh. The presented approach can be easily incorporated into game design process, while visualizing initial layouts and speeding up preparation of unique, interesting and challenging game spaces for the players to traverse.

Estimation of kinetic parameters in dynamic FDG PET imaging based on shortened protocols: a virtual clinical study.

This study investigated the estimation of kinetic parameters and production of related parametric Ki images in FDG PET imaging using the proposed shortened protocol (three 3-min/bed routine static images) by means of the simulated annealing (SA) algorithm.Six realistic heterogeneous tumors and various levels of [18F] FDG uptake were simulated by the XCAT phantom. An irreversible two-tissue compartment model (2TCM) using population-based input function was employed. By keeping two routine clinical scans fixed (60-min and 90-min post injection), the effect of the early scan time on optimizing the estimation of the pharmacokinetic parameters was investigated. The SA optimization algorithm was applied to estimate micro- and macro-parameters (K1, k2, k3, Ki).The minimum bias for most parameters was observed at a scan time of 20-min, which was < 10%. A highly significant correlation (> 0.9) as well as limited bias (< 10%) were observed between kinetic parameters generated from twomethods [two-tissue compartment full dynamic scan (2TCM-full) and two-tissue compartment by SA algorithm (2TCM-SA)]. The analysis showed a strong correlation (> 0.8) between (2TCM-SA) Ki and SUV images. In addition, the tumor-to-background ratio (TBR) metric in the parametric (2TCM-SA) Ki images was significantly higher than SUV, although the SUV images provide better Contrast-to-noise ratio relative to parametric (2TCM-SA) Ki images.The proposed shortened protocol by the SA algorithm can estimate the kinetic parameters in FDG PET scan with high accuracy and robustness. It was also concluded that the parametric Ki images obtained from the 2TCM-SA as a complementary image of the SUV possess more quantification information than SUV images and can be used by the nuclear medicine specialist. This method has the potential to be an alternative to a full dynamic PET scan.

Clinical application of a population-based input function (PBIF) for a shortened dynamic whole-body FDG-PET/CT protocol in patients with metastatic melanoma treated by immunotherapy

BackgroundThe aim was to investigate the feasibility of a shortened dynamic whole-body (dWB) FDG-PET/CT protocol and Patlak imaging using a population-based input function (PBIF), instead of an image-derived input function (IDIF) across the 60-min post-injection period, and study its effect on the FDG influx rate (Ki) quantification in patients with metastatic melanoma (MM) undergoing immunotherapy.MethodsThirty-seven patients were enrolled, including a PBIF modeling group (n = 17) and an independent validation cohort (n = 20) of MM from the ongoing prospective IMMUNOPET2 trial. All dWB-PET data were acquired on Vision 600 PET/CT systems. The PBIF was fitted using a Feng’s 4-compartments model and scaled to the individual IDIF tail’s section within the shortened acquisition time. The area under the curve (AUC) of PBIFs was compared to respective IDIFs AUC within 9 shortened time windows (TW) in terms of linear correlation (R2) and Bland–Altman tests. Ki metrics calculated with PBIF vs IDIF on 8 organs with physiological tracer uptake, 44 tumoral lesions of MM and 11 immune-induced inflammatory sites of pseudo-progression disease were also compared (Mann–Whitney test).ResultsThe mean ± SD relative AUC bias was calculated at 0.5 ± 3.8% (R2 = 0.961, AUCPBIF = 1.007 × AUCIDIF). In terms of optimal use in routine practice and statistical results, the 5th–7th pass (R2 = 0.999 for both Ki mean and Ki max) and 5th–8th pass (mean ± SD bias = − 4.9 ± 6.5% for Ki mean and − 4.8% ± 5.6% for Ki max) windows were selected. There was no significant difference in Ki values from PBIF5_7 vs IDIF5_7 for physiological uptakes (p > 0.05) as well as for tumor lesions (mean ± SD Ki IDIF5_7 3.07 ± 3.27 vs Ki PBIF5_7 2.86 ± 2.96 100ml/ml/min, p = 0.586) and for inflammatory sites (mean ± SD Ki IDIF5_7 1.13 ± 0.59 vs Ki PBIF5_7 1.13 ± 0.55 100ml/ml/min, p = 0.98).ConclusionOur study showed the feasibility of a shortened dWB-PET imaging protocol with a PBIF approach, allowing to reduce acquisition duration from 70 to 20 min with reasonable bias. These findings open perspectives for its clinical use in routine practice such as treatment response assessment in oncology.

Quantitative perfusion and water transport time model from multi b-value diffusion magnetic resonance imaging validated against neutron capture microspheres.

Quantification of perfusion in ml/100 g/min, rather than comparing relative values side-to-side, is critical at the clinical and research levels for large longitudinal and multi-center trials. Intravoxel incoherent motion (IVIM) is a non-contrast magnetic resonance imaging diffusion-based scan that uses a multitude of -values to measure various speeds of molecular perfusion and diffusion, sidestepping inaccuracy of arterial input functions or bolus kinetics. Questions remain as to the original of the signal and whether IVIM returns quantitative and accurate perfusion in a pathology setting. This study tests a novel method of IVIM perfusion quantification compared with neutron capture microspheres. We derive an expression for the quantification of capillary blood flow in ml/100 g/min by solving the three-dimensional Gaussian probability distribution and defining water transport time (WTT) as when 50% of the original water remains in the tissue of interest. Calculations were verified in a six-subject pre-clinical canine model of normocapnia, induced hypercapnia, and middle cerebral artery occlusion (ischemic stroke) and compared with quantitative microsphere perfusion. Linear regression analysis of IVIM and microsphere perfusion showed agreement (slope = 0.55, intercept = 52.5, ) with a Bland-Altman mean difference of . Linear regression between dynamic susceptibility contrast mean transit time and IVIM WTT asymmetry in infarcted tissue was excellent (, intercept = 0.3, ). Strong linear agreement was found between IVIM and reference standard infarct volume (slope = 1.01, ). The simulation of cerebrospinal fluid (CSF) suppression via inversion recovery returned a blood signal reduced by 82% from combined T1 and T2 effects. The accuracy and sensitivity of IVIM provides evidence that observed signal changes reflect cytotoxic edema and tissue perfusion and can be quantified with WTT. Partial volume contamination of CSF may be better removed during post-processing rather than with inversion recovery.

Accurate image derived input function in [18F]SynVesT-1 mouse studies using isoflurane and ketamine/xylazine anesthesia

BackgroundKinetic modeling in positron emission tomography (PET) requires measurement of the tracer plasma activity in the absence of a suitable reference region. To avoid invasive blood sampling, the use of an image derived input function has been proposed. However, an accurate delineation of the blood pool region in the PET image is necessary to obtain unbiased blood activity. Here, to perform brain kinetic modeling in [18F]SynVesT-1 dynamic scans, we make use of non-negative matrix factorization (NMF) to unmix the activity signal from the different tissues that can contribute to the heart region activity, and extract only the left ventricle activity in an unbiased way. This method was implemented in dynamic [18F]SynVesT-1 scans of mice anesthetized with either isoflurane or ketamine–xylazine, two anesthestics that we showed to affect differently radiotracer kinetics. The left ventricle activity (NMF-IDIF) and a manually delineated cardiac activity (IDIF) were compared with arterial blood samples (ABS), and for isoflurane anesthetized mice, arteriovenous (AV) shunt blood data were compared as well. Finally, brain regional 2 tissue compartment modeling was performed using IDIF and NMF-IDIF, and the model fit accuracy (weighted symmetrical mean absolute percentage error, wsMAPE) as well as the total volume of distribution (VT) were compared.ResultsIn isoflurane anesthetized mice, the difference between ABS and NMF-IDIF activity (+ 12.8 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 11%, p = 0.0023) was smaller than with IDIF (+ 16.4 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 9.8%, p = 0.0008). For ketamine–xylazine anesthetized mice the reduction in difference was larger (NMF-IDIF: 16.9 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 10%, p = 0.0057, IDIF: 56.3 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 14%, p < 0.0001). Correlation coefficient between isoflurane AV-shunt time activity curves and NMF-IDIF (0.97 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 0.01) was higher than with IDIF (0.94 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 0.03). The brain regional 2TCM wsMAPE was improved using NMF-IDIF compared with IDIF, in isoflurane (NMF-IDIF: 1.24 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 0.24%, IDIF: 1.56 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 0.30%) and ketamine–xylazine (NMF-IDIF: 1.40 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 0.24, IDIF: 2.62 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 0.27) anesthetized mice. Finally, brain VT was significantly (p < 0.0001) higher using NMF-IDIF compared with IDIF, in isoflurane (3.97 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 0.13% higher) and ketamine–xylazine (32.7 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 2.4% higher) anesthetized mice.ConclusionsImage derived left ventricle blood activity calculated with NMF improves absolute activity quantification, and reduces the error in the kinetic modeling fit. These improvements are more pronounced in ketamine–xylazine than in isoflurane anesthetized mice.

Synthesis and Preclinical Evaluation of 22-[18F]Fluorodocosahexaenoic Acid as a Positron Emission Tomography Probe for Monitoring Brain Docosahexaenoic Acid Uptake Kinetics.

Docosahexaenoic acid [22:6(n-3), DHA], a polyunsaturated fatty acid, has an important role in regulating neuronal functions and in normal brain development. Dysregulated brain DHA uptake and metabolism are found in individuals carrying the APOE4 allele, which increases the genetic risk for Alzheimer's disease (AD), and are implicated in the progression of several neurodegenerative disorders. However, there are limited tools to assess brain DHA kinetics in vivo that can be translated to humans. Here, we report the synthesis of an ω-radiofluorinated PET probe of DHA, 22-[18F]fluorodocosahexaenoic acid (22-[18F]FDHA), for imaging the uptake of DHA into the brain. Using the nonradiolabeled 22-FDHA, we confirmed that fluorination of DHA at the ω-position does not significantly alter the anti-inflammatory effect of DHA in microglial cells. Through dynamic PET-MR studies using mice, we observed the accumulation of 22-[18F]FDHA in the brain over time and estimated DHA's incorporation coefficient (K*) using an image-derived input function. Finally, DHA brain K* was validated using intravenous administration of 15 mg/kg arecoline, a natural product known to increase the DHA K* in rodents. 22-[18F]FDHA is a promising PET probe that can reveal altered lipid metabolism in APOE4 carriers, AD, and other neurologic disorders. This new probe, once translated into humans, would enable noninvasive and longitudinal studies of brain DHA dynamics by guiding both pharmacological and nonpharmacological interventions for neurodegenerative diseases.

Identifying and quantifying local uncertainty and discrepancy in the comparison of global cropland extent through a synergistic approach

Spatiotemporally consistent information on global cropland extent is essential for resource management and scientific research. Multiple cropland datasets derived from remotely sensed products are currently available. However, significant discrepancies and uncertainties among them lead to acreage estimates that diverge considerably from official statistics, thus constraining their applicability. To this end, a new stratified optimization synergistic approach (SOSA) is here proposed to create hybrid cropland maps of China, circa 2000–2020, by fusing five existing land cover maps (i.e., CLCD, GLC_FCS30, Globeland30, GlobalCrop, and ESA_CCI), and sub-national statistics. Given the underlying challenges associated with cost-effective large-scale cropland mapping, this approach seeks to strike a balance between data value, veracity, and affordability. SOSA streamlines the commonly used protocol procedure for determining the optimal agreement level and the best product combination. Preliminary validation of the resulting cropland maps was performed, and the evaluation demonstrated that the synergy cropland map exhibited greater spatial accuracy and closer agreement with statistics compared to any individual input map. This hints that synergistic approaches can bolster cropland mapping performance and amplify consistency with statistical data. Our results are expected to serve as a valuable reference for data users, aid in the future improvement of cropland mapping to support forward-thinking applications, and enhance our understanding and modeling of agricultural systems worldwide.

MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction.

In whole-body dynamic positron emission tomography (PET), inter-frame subject motion causes spatial misalignment and affects parametric imaging. Many of the current deep learning inter-frame motion correction techniques focus solely on the anatomy-based registration problem, neglecting the tracer kinetics that contains functional information. To directly reduce the Patlak fitting error for 18F-FDG and further improve model performance, we propose an interframe motion correction framework with Patlak loss optimization integrated into the neural network (MCP-Net). The MCP-Net consists of a multiple-frame motion estimation block, an image-warping block, and an analytical Patlak block that estimates Patlak fitting using motion-corrected frames and the input function. A novel Patlak loss penalty component utilizing mean squared percentage fitting error is added to the loss function to reinforce the motion correction. The parametric images were generated using standard Patlak analysis following motion correction. Our framework enhanced the spatial alignment in both dynamic frames and parametric images and lowered normalized fitting error when compared to both conventional and deep learning benchmarks. MCP-Net also achieved the lowest motion prediction error and showed the best generalization capability. The potential of enhancing network performance and improving the quantitative accuracy of dynamic PET by directly utilizing tracer kinetics is suggested.