Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient.
ABSTRACT: We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, bbp (555) (m-1). We developed a simple empirical algorithm for deriving bbp (555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high-quality bio-optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, Rrs (λ) (sr-1), and the spectral particulate backscattering coefficients, bbp (λ). The LH metric used is defined as the magnitude of Rrs (555) relative to a linear baseline drawn between Rrs (490) and Rrs (670). Using an independent validation dataset, we compared the skill of the LH-based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta-scores and z-tests for overlapping confidence intervals were also explored as potential methods for assessing model skill.
Project description:In this work, we demonstrate the existence of linear relationships between gas-phase equilibrium bond lengths of the guanidine skeleton of 2-(arylamino)imidazolines and their aqueous p<i>K</i> <sub>a</sub> value. For a training set of 22 compounds, in the most stable conformation of their lowest energy tautomeric form, three bonds were found to exhibit <i>r</i> <sup>2</sup> and <i>q</i> <sup>2</sup> values >0.95 and root-mean-squared-error of estimation values ?0.25 when regressed individually against p<i>K</i> <sub>a</sub>. The equations describing these one-bond-length linear relationships, in addition to a multiple linear regression model using all three bond lengths, were then used to predict the experimental p<i>K</i> <sub>a</sub> values of an external test set of further 27 derivatives. The optimal protocol we derive here shows an overall mean absolute error (MAE) of 0.20 and standard deviation of errors of 0.18 for the test set. Predictions for a second test set of diphenyl-based bis(2-iminoimidazolidines) yielded an MAE of 0.27 and a standard deviation of 0.10. The predictive power of the optimal model is further demonstrated by its ability to correct erroneously reported experimental values. Finally, a previously established guanidine model is recalibrated at a new level of theory, and predictions are made for novel phenylguanidine derivatives, showing an MAE of just 0.29. The protocols established and tested here pass both of Roy's modern and stringent MAE-based criteria for a "good" quantitative structure-activity relationship/quantitative structure-property relationship model predictivity. Notably, the ab initio bond length high correlation subset protocol developed in this work demonstrates lower MAE values than the Marvin program by ChemAxon for all test sets.
Project description:Amidst recent industrialization in South Korea, Seoul has experienced high levels of air pollution, an issue that is magnified due to a lack of effective air pollution prediction techniques. In this study, the Prophet forecasting model (PFM) was used to predict both short-term and long-term air pollution in Seoul. The air pollutants forecasted in this study were PM<sub>2.5</sub>, PM<sub>10</sub>, O<sub>3</sub>, NO<sub>2</sub>, SO<sub>2</sub>, and CO, air pollutants responsible for numerous health conditions upon long-term exposure. Current chemical models to predict air pollution require complex source lists making them difficult to use. Machine learning models have also been implemented however their requirement of meteorological parameters render the models ineffective as additional models and infrastructure need to be in place to model meteorology. To address this, a model needs to be created that can accurately predict pollution based on time. A dataset containing three years worth of hourly air quality measurements in Seoul was sourced from the Seoul Open Data Plaza. To optimize the model, PFM has the following parameters: model type, changepoints, seasonality, holidays, and error. Cross validation was performed on the 2017-18 data; then, the model predicted 2019 values. To compare the predicted and actual values and determine the accuracy of the model, the statistical indicators: mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and coverage were used. PFM predicted PM<sub>2.5</sub> and PM<sub>10</sub> with a MAE value of 12.6 µg/m<sup>3</sup> and 19.6 µg/m<sup>3</sup>, respectively. PFM also predicted SO<sub>2</sub> and CO with a MAE value of 0.00124 ppm and 0.207 ppm, respectively. PFM's prediction of PM<sub>2.5</sub> and PM<sub>10</sub> had a MAE approximately 2 times and 4 times less, respectively, than comparable models. PFM's prediction of SO<sub>2</sub>and CO had a MAE approximately five times and 50 times less, respectively, than comparable models. In most cases, PFM's ability to accurately forecast the concentration of air pollutants in Seoul up to one year in advance outperformed similar models proposed in literature. This study addresses the limitations of the prior two PFM studies by expanding the modelled air pollutants from three pollutants to six pollutants while increasing the prediction time from 3 days to 1 year. This is also the first research to use PFM in Seoul, Korea. To achieve more accurate results, a larger air pollution dataset needs to be implemented with PFM. In the future, PFM should be used to predict and model air pollution in other regions, especially those without advanced infrastructure to model meteorology alongside air pollution. In Seoul, Seoul's government can use PFM to accurately predict air pollution concentrations and plan accordingly.
Project description:<h4>Introduction</h4>Risk stratification in Brugada Syndrome (BrS) patients is still challenging due to the heterogeneity of clinical presentation; thus, some additional risk markers are needed. Several studies investigating the association between RVOT conduction delay sign on electrocardiography (ECG) and major arrhythmic events (MAE) in BrS patients showed inconclusive results. This meta-analysis aims to evaluate the association between RVOT conduction delay signs presented by aVR sign and large S wave in lead I, and MAE in BrS patients.<h4>Methods</h4>The literature search was performed using several online databases from the inception to March 16<sup>th</sup>, 2022. We included studies consisting of two main components, including ECG markers of RVOT conduction delay (aVR sign and large S wave in lead I) and MAE related to BrS (syncope/VT/VF/SCD/aborted SCD/appropriate ICD shocks).<h4>Results</h4>Meta-analysis of eleven cohort studies with a total of 2,575 participants showed RVOT conduction delay sign was significantly associated with MAE in BrS patients [RR = 1.87 (1.35, 2.58); <i>p</i> < 0.001; <i>I</i> <sup>2</sup>= 52%, <i>P</i> <sub>heterogeneity</sub> = 0.02]. Subgroup analysis showed that aVR sign [RR = 2.00 (1.42, 2.83); <i>p</i> < 0.001; <i>I</i> <sup>2</sup>= 0%, <i>P</i> <sub>heterogeneity</sub> = 0.40] and large S wave in lead I [RR = 1.74 (1.11, 2.71); <i>p</i> = 0.01; <i>I</i> <sup>2</sup>= 60%, <i>P</i> <sub>heterogeneity</sub> = 0.01] were significantly associated with MAE. Summary receiver operating characteristics analysis revealed the aVR sign [AUC: 0.77 (0.73-0.80)] and large S wave in lead I [AUC: 0.69 (0.65-0.73)] were a good predictor of MAE in BrS patients.<h4>Conclusion</h4>RVOT conduction delay sign, presented by aVR sign and large S wave in the lead I, is significantly associated with an increased risk of MAE in BrS patients. Hence, we propose that these parameters may be useful as an additional risk stratification tool to predict MAE in BrS patients.<h4>Systematic review registration</h4>https://www.crd.york.ac.uk/prospero/#recordDetails, identifier: CRD42022321090.
Project description:Remote sensing using unmanned aerial vehicles (UAVs) and structure from motion (SfM) is useful for the sustainable and cost-effective management of agricultural fields. Ground control points (GCPs) are typically used for the high-precision monitoring of plant height (PH). Additionally, a secondary UAV flight is necessary when off-season images are processed to obtain the ground altitude (GA). In this study, four variables, namely, camera angles, real-time kinematic (RTK), GCPs, and methods for GA, were compared with the predictive performance of maize PH. Linear regression models for PH prediction were validated using training data from different targets on different flights ("different-targets-and-different-flight" cross-validation). PH prediction using UAV-SfM at a camera angle of -60° with RTK, GCPs, and GA obtained from an off-season flight scored a high coefficient of determination and a low mean absolute error (MAE) for validation data (<i>R</i> <sup>2</sup> <i><sub><i>val</i></sub> </i> = 0.766, MAE = 0.039 m in the vegetative stage; <i>R</i> <sup>2</sup> <i><sub><i>val</i></sub> </i> = 0.803, MAE = 0.063 m in the reproductive stage). The low-cost case (LC) method, conducted at a camera angle of -60° without RTK, GCPs, or an extra off-season flight, achieved comparable predictive performance (<i>R</i> <sup>2</sup> <i><sub><i>val</i></sub> </i> = 0.794, MAE = 0.036 m in the vegetative stage; <i>R</i> <sup>2</sup> <i><sub><i>val</i></sub> </i> = 0.749, MAE = 0.072 m in the reproductive stage), suggesting that this method can achieve low-cost and high-precision PH monitoring.
Project description:<h4>Purpose</h4>Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning.<h4>Methods</h4>Fifty-two patients who underwent multi-parametric dual-tracer [<sup>18</sup>F]FMC and [<sup>68</sup>Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [<sup>68</sup>Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (M<sub>LH</sub>). Furthermore, M<sub>BCR</sub> and M<sub>OPR</sub> predictive model schemes were built by combining M<sub>LH</sub>, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [<sup>68</sup>Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses.<h4>Results</h4>The area under the receiver operator characteristic curve (AUC) of the M<sub>LH</sub> model (0.86) was higher than the AUC of the [<sup>68</sup>Ga]Ga-PSMA-11 SUV<sub>max</sub> analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the M<sub>BCR</sub> and M<sub>OPR</sub> models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively.<h4>Conclusion</h4>Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.
Project description:Design and development of multifunctional materials have drawn incredible attraction in recent years. Herein, we report the design and construction of versatile star-shaped intramolecular charge transfer (ICT)-coupled excited-state intramolecular proton transfer (ESIPT)-active mechanoresponsive and aggregation-induced emissive (AIE) luminogen triaminoguanidine-diethylaminophenol (<b>LH<sub>3</sub></b> ) conjugate from simple precursors triaminoguanidine hydrochloride and 4-(<i>N</i>,<i>N</i>-diethylamino)salicylaldehyde. Solvent-dependent dual emission in nonpolar to polar protic solvents implies the presence of ICT-coupled ESIPT features in the excited state. Aggregation-enhanced emissive feature of <b>LH<sub>3</sub></b> was established in the CH<sub>3</sub>CN/water mixture. Furthermore, this compound exhibits mechanochromic fluorescence behavior upon external grinding. Fluorescence microscopy images of pristine, crystal, and crushed crystals confirm the naked-eye mechanoresponsive characteristics of <b>LH<sub>3</sub></b> . In addition, <b>LH<sub>3</sub></b> selectively sensed a Cu<sup>2+</sup> ion through a colorimetric and fluorescence "turn-off" route, and subsequently, the <b>LH<sub>3</sub></b> -Cu<sup>2+</sup> ensemble could act as a selective and sensitive sensor for S<sup>2-</sup> in a "turn-on" fluorescence manner via a metal displacement approach. Reversible "turn-off-turn-on" features of <b>LH<sub>3</sub></b> with Cu<sup>2+</sup>/S<sup>2-</sup> ions were efficiently demonstrated to construct the IMPLICATION logic gate function. The Cu<sup>2+</sup>/S<sup>2-</sup>-responsive sensing behavior of <b>LH<sub>3</sub></b> was established in the paper strip experiment also, which can easily be characterized by the naked eye under daylight as well as a UV lamp (? = 365 nm).
Project description:Here, naphthalene diamine-based ?-diketone derivative (compound LH) was successfully used as a dual signaling probe for divalent cations, Fe<sup>2+</sup> and Cu<sup>2+</sup> ions, in bimodal methods (colorimetric and fluorometric). It showed fluorescent enhancement for Fe<sup>2+</sup> ion by photoinduced electron transfer mechanism and fluorescence quenching for Cu<sup>2+</sup> ion by charge-transfer process. Binding stoichiometry for [LH-(Fe<sup>2+</sup>)<sub>2</sub>] and [LH-(Cu<sup>2+</sup>)<sub>2</sub>] was found to be 1:2 by Job's plot method and, the binding constants were calculated as 1.6638 × 10<sup>10</sup> and 9.22929 × 10<sup>8</sup> M<sup>-1</sup>, respectively. Compound LH exhibited OR and XOR logic gate behavior with H<sup>+</sup>, Fe<sup>2+</sup>, and Cu<sup>2+</sup> as inputs. Further, the compound LH and bovine serum albumin binding interaction showed quenching of fluorescence by Förster resonance energy-transfer mechanism.
Project description:<i>Objective:</i> This study investigated the relationships between PM<sub>2.5</sub> and 5 criteria air pollutants (SO<sub>2</sub>, NO<sub>2</sub>, PM<sub>10</sub>, CO, and O<sub>3</sub>) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. <i>Methods:</i> Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) were applied to model the relationships between PM<sub>2.5</sub> and 5 air pollutants. <i>Results:</i> The LMM and all GWR-based models (i.e., GWR, TWR, and GTWR) showed great advantages over OLS in terms of higher model R<sup>2</sup> and more desirable model residuals, especially TWR and GTWR. The GWR, LMM, TWR, and GTWR improved the model explanation power by 3%, 5%, 12%, and 12%, respectively, from the R<sup>2</sup> (0.85) of OLS. TWR yielded slightly better model performance than GTWR and reduced the root mean squared errors (RMSE) and mean absolute error (MAE) of the model residuals by 67% compared with OLS; while GWR only reduced RMSE and MAE by 15% against OLS. LMM performed slightly better than GWR by accounting for both temporal autocorrelation between observations over time and spatial heterogeneity across the 13 cities under study, which provided an alternative for modeling PM<sub>2.5</sub>. <i>Conclusions:</i> The traditional OLS and GWR are inadequate for describing the non-stationarity of PM<sub>2.5</sub>. The temporal dependence was more important and significant than spatial heterogeneity in our data. Our study provided evidence of spatial-temporal heterogeneity and possible solutions for modeling the relationships between PM<sub>2.5</sub> and 5 criteria air pollutants for Heilongjiang province, China.
Project description:A pressing need in low energy spintronics is two-dimensional (2D) ferromagnets with Curie temperature above the liquid-nitrogen temperature (77 K), and sizeable magnetic anisotropy. We studied Mn<sub>3</sub>Br<sub>8</sub> monolayer which is obtained via inducing Mn vacancy at 1/4 population in MnBr<sub>2</sub> monolayer. Such defective configuration is designed to change the coordination structure of the Mn-d<sup>5</sup> and achieve ferromagnetism with sizeable magnetic anisotropy energy (MAE). Our calculations show that Mn<sub>3</sub>Br<sub>8</sub> monolayer is a ferromagnetic (FM) half-metal with Curie temperature of 130 K, large MAE of - 2.33 meV per formula unit, and atomic magnetic moment of 13/3μ<sub>B</sub> for the Mn atom<sub>.</sub> Additionally, Mn<sub>3</sub>Br<sub>8</sub> monolayer maintains to be FM under small biaxial strain, whose Curie temperature under 5% compressive strain is 160 K. Additionally, both biaxial strain and carrier doping make the MAE increases, which mainly contributed by the magneto-crystalline anisotropy energy (MCE). Our designed defective structure of MnBr<sub>2</sub> monolayer provides a simple but effective way to achieve ferromagnetism with large MAE in 2D materials.
Project description:The light-harvesting complex I (LH-I) of Rhodobacter sphaeroides has been modeled computationally as a hexadecamer of alphabeta-heterodimers, based on a close homology of the heterodimer to that of light-harvesting complex II (LH-II) of Rhodospirillum molischianum. The resulting LH-I structure yields an electron density projection map that is in agreement with an 8.5-A resolution electron microscopic projection map for the highly homologous LH-I of Rs. rubrum. A complex of the modeled LH-I with the photosynthetic reaction center of the same species has been obtained by a constrained conformational search. This complex and the available structures of LH-II from Rs. molischianum and Rhodopseudomonas acidophila furnish a complete model of the pigment organization in the photosynthetic membrane of purple bacteria.