Correlation R2 Research Articles

Tribological properties of graphitized TiC0.5N0.5 based composites using response surface methodology

The statistical design of experimental techniques has been widely explored in developing empirical methodologies. These methods are beneficial for developing appropriate mathematical models to predict the properties and performance of various materials. This study utilized the user-defined design (UDD) approach under response surface methodology (RSM) to achieve the optimum parameters for dry sliding wear properties of graphite-reinforced binderless TiC0.5N0.5 ceramic composites. The tribological tests were performed using a ball-on-flat geometry tribometer, with a ruby- TiC0.5N0.5 friction pair operating in sliding mode at ambient temperature. The developed mathematical model specifies the functional relationship between the key parameters, using the weight percentage of the graphite reinforcement and applied load as input variables and wear rate as the output variable. Based on the statistical analysis, ANOVA results for wear rate indicated that the predictability of the model is at 95 % confidence level. Moreover, the wear rate demonstrated a correlation coefficient of R2 = 0.9762, which depicts that only less than 3 % of the total variations are not explained by the model, and the value of the adjusted determination coefficient (adjusted R2 = 0.9366) is high, proving that the model is significant.

Structural characteristics and gelling properties of citrus pectins after chemical and enzymatic modifications: Conformation plays a vital role in Ca2+-induced gelation

Due to the excellent health benefits of rhamnogalacturonan I (RG-I)-enriched pectin, there has been increasing research interest in its gelling properties. To elucidate its structure–gelation relationship, chemical modifications were used to obtain RG-I-enriched pectin (P11). Then, enzymatic modification was performed to obtain debranched pectins GP11 and AP11, respectively. The effects of RG-I side chains on structural characteristics (especially spatial conformation) and gelling properties were investigated. Among the low-methoxylated pectins (LMPs), AP11, with a loose conformation (Dmax 52 nm) showed the poorest gelling, followed by GP11. In addition to primary structure, spatial conformation (Dmax and Rg) also showed strong correlations (r2 > 0.8) with gelation. We speculate that compact conformation may shorten distance between pectin chains and reduces steric hindrance, contributing to formation of strong gel network. This is particularly important in LMPs with abundant side chains. The results provide novel insights into relationship between spatial conformation and gelling properties of RG-I-enriched pectin.

Poster 174: Is Articular Width or Epicondylar Width Superior to Age to Optimize Radius of Curvature Matching in Capitellar Osteochondral Allografts?

Objectives: There is growing interest to use matched distal humeral osteochondral allograft (OCA) for treatment of osteochondritis dissecans (OCD) of the capitellum due to concerns of donor site morbidity in osteochondral autograft transplantation surgery. Current graft match processes utilize the epicondylar width (EW) of the distal humerus, though little information is available regarding the correlation of this measurement to the radius of curvature (ROC) of the capitellum. The purpose of this study was to characterize the correlation between the EW and ROC as well as to evaluate two additional candidate parameters including patient age and the distal humeral articular width (AW). Methods: Forty patients (age ≤18) were identified for analysis. The capitellar cartilage ROC was measured on the sagittal plane of each magnetic resonance imaging (MRI) study at the level where the corresponding radial head was greatest in diameter (fig. 1). The EW (fig. 2) and the AW (fig. 3), which is bordered by the medial articular border of the trochlea and the lateral articular border of the capitellum, were also measured on the axial plane of each MRI study at maximal width. R2 correlation coefficients were calculated for ROC to EW, AW, and patient age (fig. 4). Intra-observer and inter-observer reliability measurements were performed to assess precision and validity of method. Results: The mean sagittal cartilage ROC, EW, and AW were 11.05 mm, 60.79 mm, and 44.31 mm. When measured against ROC, patient age had poor correlation with a R2 value of 0.46 whereas EW and AW demonstrated strong correlation with R2 values of 0.68 and 0.76 respectively. Intra-observer correlation coefficients were 0.93, 0.96, and 0.95 for ROC, EW, and AW respectively. Inter-observer correlation coefficients were 0.79, 0.94, and 0.94 for ROC, EW, and AW, respectively. Conclusions: Our results suggest that both AW and EW demonstrate superior correlation to capitellar ROC in comparison to age, and AW may be superior to EW. AW may be considered for utilization as a distal humeral allograft matching parameter particularly in patients that may have altered epicondylar morphology such as in the throwing athlete. [Figure: see text][Figure: see text][Figure: see text][Figure: see text]

Eco-friendly fabrication of novel hydrophobic CMC–C18@MWCNTs nano-sorbent for fat content removal in the analysis of polycyclic aromatic hydrocarbons in fatty-food samples

In this study, carbon nanohybrids was developed by hybridizing carboxymethyl cellulose (CMC) biopolymer with various amounts of multi-walled carbon nanotubes (MWCNTs: 0.2, 0.5, 1, 3, and 5%) in eco-friendly process to produce hydrophobic CMC–C18@MWCNTs, which was then used as a sorbent material for fat content in fatty food samples analysis. The hydrophobic biopolymers were synthesized using an ultrasound-assisted esterification process, and the physicochemical properties were analyzed using FT-IR, XRD, TGA, FE-SEM, and TEM. The performance of the hydrophobic nanocomposites was evaluated by assessing their ability to remove fat content during polycyclic aromatic hydrocarbon (PAHs) analysis in tuna samples. The results showed that CMC–C18–CNT0.2% provided the best peak shapes and highest recoveries for PAHs compounds, ranging between 74.3 and 89.7%, while CMC–C18–CNT5% had the lowest recoveries, ranging between 0 and 35%. Therefore, the lowest amount of MWCNTs was found to be the most efficient for removing fat content with providing high PAHs recovery, while increasing the MWCNTs percentage increased the hydrophobicity and removed PAHs analytes along with fat content. After the investigation, the method was validated using CMC–C18–CNT0.2% in three various levels: 2, 5 and 10 μg kg−1. The obtained results were satisfactory; the average recoveries for all PAHs compounds ranged between 74.3 to 89.7%, and the intra-day precision were estimated by coefficient of variation (%CV), where were less than 10% for all PAHs. The LOD and LOQ were lies between 0.33 to 0.89 μg kg−1 and 1.12 to 1.92 μg kg−1 respectively. For the calibration curve linearity, the correlation coefficient (r2) were higher than 0.999 for all PAHs. Overall, the hydrophobic CMC–C18@MWCNTs are a promising, modifiable, and useful material for fatty food analysis.

Integrator complex subunit 6 promotes hepatocellular steatosis via β-catenin-PPARγ axis

Hepatic adipogenesis has common mechanisms with adipocyte differentiation such as PPARγ involvement and the induction of adipose tissue-specific molecules. A previous report demonstrated that integrator complex subunit 6 (INTS6) is required for adipocyte differentiation. This study aimed to investigate INTS6 expression and its role in hepatic steatosis progression. The expression of INTS6 and PPARγ was examined in the liver of a mouse model of steatohepatitis and in paired liver biopsy samples from 11 patients with severe obesity and histologically proven metabolic dysfunction associated steatohepatitis (MASH) before and one year after bariatric surgery. To induce hepatocellular steatosis in vitro, an immortalized human hepatocyte cell line Hc3716 was treated with free fatty acids. In the steatohepatitis mouse model, we observed hepatic induction of INTS6, PPARγ, and adipocyte-specific genes. In contrast, β-catenin which negatively regulates PPARγ was reduced. Biopsied human livers demonstrated a strong positive correlation (r2 = 0.8755) between INTS6 and PPARγ mRNA levels. After bariatric surgery, gene expressions of PPARγ, FABP4, and CD36 were mostly downregulated. In our in vitro experiments, we observed a concentration-dependent increase in Oil Red O staining in Hc3716 cells after treatment with the free fatty acids. Alongside this change, the expression of INTS6, PPARγ, and adipocyte-specific genes was induced. INTS6 knockdown using siRNA significantly suppressed cellular lipid accumulation together with induction of β-catenin and PPARγ downregulation. Collectively, INTS6 expression closely correlates with PPARγ. INTS6 suppression significantly reduced hepatocyte steatosis via β-catenin-PPARγ axis, indicating that INTS6 could be a novel therapeutic target for treating MASH.

QSAR Studies on a Series of Pyrazole Azabicyclo [3.2.1] Octane Sulfonamides N-acylethanolamine-hydrolyzing Acid Amidase Inhibitors

Background: Inflammation is a common and intractable disease for humans. Current antiinflammatory drugs have a lot of side effects, which cause irreversible damage to the body. Objective: We predict the activity of the N-acylethanolamine-hydrolyzing acid amidase (NAAA) inhibitor to find more effective compounds. Methods: We established a quantitative structure-activity relationship (QSAR) model by gene expression programming to predict the IC50 values of natural compounds. The NAAA inhibitor, as a cysteine enzyme, plays an important role in the therapy of pain, anti-inflammatory effects and application of other diseases. A total of 36 NAAA inhibitors were optimized by the heuristic method in the CODESSA program to build a linear model. The 27 compounds and 9 compounds were in train and test sets. On this basis, we selected three descriptors and used them to build nonlinear models in gene expression programming. Results: The best model in the gene expression programming method was found, the square of correlation coefficients of R2 and mean square error for the training set were 0.79 and 0.14, testing set was 0.78 and 0.20, respectively. Conclusion: From this method, the activity of molecules could be predicted, and the best method was found. Therefore, this model has a stronger predictive ability to develop NAAA inhibitors.

Fully Integrated and High-Throughput Microfluidic System for Multiplexed Point-Of-Care Testing.

For every epidemic outbreak, the prevention and treatments in resource-limited areas are always out of reach. Critical to this is that high accuracy, stability, and more comprehensive analytical techniques always rely on expensive and bulky instruments and large laboratories. Here, a fully integrated and high-throughput microfluidic system is proposed for ultra-multiple point-of-care immunoassay, termed Dac system. Specifically, the Dac system only requires a handheld portable device to automatically recycle repetitive multi-step reactions including on-demand liquid releasing, dispensing, metering, collecting, oscillatory mixing, and discharging. The Dac system performs high-precision enzyme-linked immunosorbentassays for up to 17 samples or targets simultaneously on a single chip. Furthermore, reagent consumption is only 2% compared to conventional ELISA, and microbubble-accelerated reactions shorten the assay time by more than half. As a proof of concept, the multiplexed detections are achieved by detecting at least four infection targets for two samples simultaneously on a singular chip. Furthermore, the barcode-based multi-target results can rapidly distinguish between five similar cases, allowing for accurate therapeutic interventions. Compared to bulky clinical instruments, the accuracy of clinical inflammation classification is 92.38% (n = 105), with a quantitative correlation coefficient of R2 = 0.9838, while the clinical specificity is 100% and the sensitivity is 98.93%.

Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.

Physics and correlations informed deep learning to foresee various regimes of the pool boiling curve

Pool boiling curve is the key feature for performance evaluation of phase-change equipment such as Kettle reboiler, flooded evaporator, falling film evaporator, immersion cooling, and the like. The performance evaluation typically relies on empirical correlations or some semi-analytically based theoretical approaches. However, it has not been possible to establish a good relationship between heat flux and temperature until now. The pool boiling curve of smooth and roughened surfaces, including the nucleate boiling region, critical heat flux (CHF), and transition boiling, have been precisely predicted for the first time using a generalized physics-based deep learning (DL) model that considers physics and correlation-based features. To predict the higher order pool boiling curve for a specific liquid-surface combination, the model is fed the initial experimental data from the lower order boiling curve in addition to the most important variables of the surface morphology, thermophysical properties of the working fluid, and pool boiling conditions. Various regimes of pool boiling curve, such as fully developed nucleate boiling, CHF, and transition boiling for the investigated liquid-surface combinations and testing conditions, are predicted with an accuracy of R2 (correlation coefficient) = 0.98. The reason for such a high accuracy to a wide range of surface morphologies, substrate materials, operating pressures, heater inclination angles, and working fluids is due to the inclusion of the initial pool boiling data along with the relevant, impactful surface, and liquid parameters into its framework.

Comparing the quantum use efficiency of red and far-red sun-induced fluorescence at leaf and canopy under heat-drought stress

Sun-Induced chlorophyll Fluorescence (SIF) is the most promising remote sensing signal to monitor photosynthesis in space and time. However, under stress conditions its interpretation is often complicated by factors such as light absorption and plant morphological and physiological adaptations. To ultimately derive the quantum yield of fluorescence (ΦF) at the photosystem from canopy measurements, the so-called escape probability (fesc) needs to be accounted for.In this study, we aim to compare ΦF measured at leaf- and canopy-scale to evaluate the influence of stress responses on the two signals based on a potato mesocosm heat-drought experiment. First, we compared the performance of recently proposed reflectance-based approaches to estimate leaf and canopy red fesc using data-supported simulations of the radiative transfer model SCOPE. While the leaf red fesc showed a strong correlation (r2 ≥ 0.76), the canopy red fesc exhibited no relationship with the SCOPE retrieved red fesc in our experiment. We therefore propose modifications to the canopy model to address this limitation.We then used the modified models of red fesc, along with an existing model for far-red fesc to analyse the dynamics of leaf and canopy red and far-red fluorescence under increasing drought and heat stress conditions. By incorporating fesc, we obtained a closer agreement between leaf and canopy measurements. Specifically, for red fesc, the r2 of the two variables increased from 0.3 to 0.50, and for far-red fesc, from 0.36 to 0.48.When comparing the dynamics of the quantum yield of red and far-red fluorescence (ΦF,687 and ΦF,760) under increasing stress, we observed a statistically significant decrease of both leaf and canopy ΦF,687 as well as leaf ΦF,760, as drought and heat conditions intensified. Canopy ΦF,760, on the contrary, did not exhibit the same trend, since measurements under low stress conditions showed a wider spread and lower median than under high stress conditions. Finally, we analysed the sensitivity of ΦF,687 and ΦF,760 to changing solar incidence angle, by comparing the variability of the measurements without and with mesocosm rotation. Our results suggest that the variation in ΦF,760 strongly increased with changing solar incidence angle. These findings highlight the need for further research to understand the causes of discrepancies between leaf and canopy scale ΦF,760. On the contrary, the underutilised and understudied ΦF,687 showed great potential in assessing plant responses to drought and heat stress.

Comparative study of UV spectroscopy, RP-HPLC and HPTLC methods for quantification of antiviral drug lamivudine in tablet formulation

BackgroundIn the current study, estimation of lamivudine (LMU) by UV spectroscopy, reverse-phase HPLC (RP-HPLC) and HPTLC methods in tablet formulation was developed, and comparative studies between the methods were investigated by analytical results and statistical test analysis of variance (ANOVA) to find out best method. In the UV spectral method, LMU was quantified at 271 nm absorption maxima using methanol as the solvent. In the RP-HPLC method, the Shimadzu C18 column (250 mm × 4.6 mm i.d., 5 µm particle size) was employed for chromatographic separation. The mobile phase used consists of methanol: water (70:30 v/v) in an isocratic mode with a 1.0 mL/min flow rate. In the HPTLC method, the chromatogram was developed on a pre-coated plate of silica gel 60 F254 with a mobile phase composition of chloroform: methanol (8:2 v/v). The quantification was performed at an absorbance mode of 271 nm by densitometry. The methods were validated according to the International Conference on Harmonization (ICH) guideline Q2 (R1). The degradation conditions were employed as per ICH guidelines Q1A(R2) and Q1B which include acid, alkaline, neutral, thermal and photostability to determine the intrinsic stability of the drug in varied environmental conditions.ResultsLMU absorption maxima was found to be 271 nm. The retention time of LMU was 3.125 min, and the total analysis time was 5 min. The Rf value of LMU was 0.49–0.62. The methods were linear within 2–12 μg/mL range. The correlation coefficient (r2) for UV, HPLC and HPTLC was 0.9980, 0.9993 and 0.9988, and percent recoveries were calculated as 98.40–100.52%, 99.27–101.18% and 98.01–100.30%, respectively, with percentage relative standard deviation (RSD) less than 2% showing that methods were precise and accurate.ConclusionDeveloped UV, RP-HPLC and HPTLC methods are free from intervention caused by excipients present in tablets and thus can be used for regular quantitative analysis of LMU in tablet formulation. Based on analytical results and statistical tests, ANOVA, it is inferred that the HPLC method is best for LMU quantification tablet formulation due to its high reproducibility, good retention time and sensitivity; it has a higher percent recovery and has less analysis time, i.e., 5 min. The degradation peaks were well separated from the LMU peak indicating stability of the HPLC method.

Dried blood spot sampling coupled with liquid chromatography-tandem mass for simultaneous quantitative analysis of multiple cardiovascular drugs

Dried Blood Spots (DBS) revolutionize therapeutic drug monitoring using LC-MS for the precise quantification of cardiovascular drugs (CDs), enabling personalized treatment adapted to patient-specific pharmacokinetics with minimal invasiveness. This study aims to achieve simultaneous quantification of eight CDs in DBS, overcoming physicochemical challenges. A two-step protein precipitation method was used for simple and precise sample preparation. The drugs were analyzed using LC-MS/MS in ESI positive-ion mode, showing high sensitivity and linearity, with a correlation coefficient (r2) exceeding 0.999, after being separated on a reversed-phase chromatography by gradient elution of DW-acetonitrile containing 0.1 % formic acid + 2 mM ammonium formate. The validation results indicate good selectivity, with no observed matrix effect and carry-over. The intra- and inter-day accuracy and precision were within 6 % for most drugs, except for digoxin and deslanoside at low therapeutic levels where the variation was within 20 %. Stability tests confirmed suitable DBS handling and storage conditions, indicating drug stability for at least 30 days at room temperature. The analysis of whole spot has demonstrated remarkable precision and reliability in all target drugs. The analysis of 3 mm internal diameter discs, punched in and out of DBS, presumed to contain 3 µL of blood, showed acceptable accuracy for most drugs, with less polar drugs like digoxin and deslanoside showing lower accuracy, indicating a need for further correction due to non-uniform drug distribution. Consequently, the developed LC-MS/MS method enables the quantification of multiple CDs in a single DBS analysis, while suggesting the potential for accuracy-based analysis.

Artificial intelligence-based neural network modeling of adsorptive removal of phenol from aquatic environment

In this study, adsorptive uptake of phenol from aqueous system using Arachis hypogaea (groundnut) shell-based adsorbent was experimentally investigated and modelled using artificial intelligence-based neural network approach. Artificial neural networks with different number of neurons were designed using Levenberg-Marquardt algorithm to find the best model for phenol adsorption. The feedforward back propagation neural network comprising TRAINLM, LARNGDM and TANSIG as training, adaptation learning and transfer functions, respectively, with ten neurons in the hidden layer exhibited the optimal architecture with the strongest correlation R2=0.9901 and the smallest mean square error MSE=0.045. The studies indicated a maximum adsorptive uptake of phenol to be 37.31 mg/g onto the activated shell powder. The kinetic analysis favored pseudo second order R2=0.9999and the equilibrium data was best represented by Freundlich isotherm modelR2=0.9976. Phenolic remediation phenomenon ensued in a spontaneous manner, was exothermic ∆H0=−34.25kJ/moland involved physisorption. The experimental results are agreement with model.

AI-based 3D-QSAR model of FDA-approved repurposed drugs for inhibiting sclerostin

BACKGROUND: Wnt activation promotes bone formation and prevents bone loss. The Wnt pathway antagonist sclerostin and additional anti-sclerostin antibodies were discovered as a result of the development of the monoclonal antibody romosozumab. These monoclonal antibodies greatly increase the risk of cardiac arrest. Three-dimensional quantitative structure-activity relationships (3D-QSAR) predicts biological activities of ligands based on their three-dimensional features by employing powerful chemometric investigations such as artificial neural networks (ANNs) and partial least squares (PLS). OBJECTIVE: In this study, ligand-receptor interactions were investigated using 3D-QSAR Comparative molecular field analysis (CoMFA). Estimates of steric and electrostatic characteristics in CoMFA are made using Lennard-Jones and Coulomb potentials. METHODS: To identify the conditions necessary for the activity of these molecules, fifty Food and Drug Administration (FDA)-approved medications were chosen for 3D-QSAR investigations and done by CoMFA. For QSAR analysis, there are numerous tools available. This study employed Open 3D-QSAR for analysis due to its simplicity of use and capacity to produce trustworthy results. Four tools were used for the analysis on this platform: Py-MolEdit, Py-ConfSearch, and Py-CoMFA. RESULTS: Maps that were generated were used to determine the screen’s r2 (Coefficient of Multiple Determinations) value and q2 (correlation coefficient). These numbers must be fewer than 1, suggesting a good, trustworthy model. Cross-validated (q2) 0.532 and conventional (r2) correlation values of 0.969 made the CoMFA model statistically significant. The model showed that hydroxamic acid inhibitors are significantly more sensitive to the steric field than the electrostatic field (70%) (30%). This hypothesis states that steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) qualities were important in the design of sclerostin inhibitors. CONCLUSION: With 3D-QSAR and CoMFA, statistically meaningful models were constructed to predict ligand inhibitory effects. The test set demonstrated the model’s robustness. This research may aid in the development of more effective sclerostin inhibitors that are synthesised using FDA-approved medications.

Comparisons of pharmacokinetics and tissue distribution of six major bioactive components of the herbal pair Alpinia officinarum-Cyperus rotundus in normal and primary dysmenorrhea rats

The Liangfu formula, as described in 'Liangfang Jiye', is well-known for its efficacy in treating stomach pain, abdominal pain, and dysmenorrhea. This research aimed to investigate the pharmacokinetics and tissue distribution of 5-hydroxy-7-(4-hydroxy-3-methoxyphenyl)-1-phenyl-3-heptanone (DPHA), Galangin, Kaempferide, 5-Hydroxy-1,7-diphenyl-3-heptanone (DPHC), α-Cyperone, and Nootkatone in vivo using an LC-MS/MS method. The method successfully separated the six active components and internal standards (Chrysin and Yakuchinone-A) on an XB-C18 column with a mobile phase of 0.2 ‰ formic acid water-acetonitrile. It demonstrated good linearity with a correlation coefficient (r2) ≥ 0.9911 and a lower limit of quantification (LLOQ) of 5–80 ng/mL for the different components. Precision, accuracy, matrix effects, and recovery rates were within acceptable ranges. Pharmacokinetic analysis revealed significant differences in parameters between primary dysmenorrhea (PD) and normal rats (especially AUC, Tmax, and CLz/F). Tissue distribution showed that the six active components of the herbal pair Alpinia officinarum Hance-Cyperus rotundus L. (HPAC) extract was primarily distributed in the liver, lung, and kidney. This study offers valuable insights into the potential mechanisms of action and drug development for treating PD.

Estimation of land surface temperature (LST) using single-channel and multi-band methods in Sablan mountainous region

Studying Land Surface Temperature (LST) provides various scientific, environmental, and practical advantages. It aids climate monitoring, urban heat island studies, environmental assessments, agriculture, urban planning, infrastructure development, and remote sensing. Algorithm selection depends on research constraints and goals, considering different algorithm strengths and limitations. In this study, the LST around the Sabalan volcanic peak was estimated using Landsat 8 satellite images captured by the OLI and TIRS, employing the Single Channel JM&S and Split Window algorithms. The obtained surface temperatures from both algorithms were converted to air temperature. Subsequently, the pixel air temperatures were compared to the corresponding synoptic station air temperatures of the studied region, considering the satellite's passage time. This comparison was executed using regression analysis (R2 and correlation coefficients) and statistical indices such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to validate the data. The results indicated R2 values and correlation coefficients of 0.04 and 0.97, respectively, for the Single Channel algorithm, and 0.12 and 0.69 for the SW algorithm, signifying a positive relationship and close alignment between these datasets. Furthermore, the examination of the results revealed root mean square error and mean absolute error with minimum errors of 4.20, 3.88, and 8.14, 8.97 °C in the SW method. By assessing the difference between mean LST and the synoptic station air temperatures, the SW approach exhibited lower temperature discrepancies across all stations and lower estimation errors compared to the Single Channel method, thus displaying higher accuracy and closer conformity to the actual temperature.

Stability-indicating Reverse Phase-HPLC Method Development and Method Validation for Quantitative Determination of Degradation Products in Favipiravir API and Drug Product

Introduction: Favipiravir is an antiviral medication shown to be broad spectrum activity against RNA viruses, and potentially treating the COVID-19. Methodology: In this study, the HPLC method for the quantification of degradation impurities (Favipiravir Acid Impurities) were developed and validated for Favipiravir in Tablet dosage form. The specificity of the method was achieved in analytical column Agilent HPLC-C18, 5µm, (4.6 x250) mm. using a suitable mobile phase was 10 mM Phosphate buffer (pH 3.5 with orthophosphoric acid) and Acetonitrile in the Isocratic more of 70:30 v/v. The flow rate is 1.0 mL/min. the injection volume is 10 µL, detection at 320 nm in UV and total run time is 8.0 minutes. The samples were made for forced degradation under hydrolysis, oxidation, photolytic and thermal conditions. The method was validated for specific, selective, linear, robust and accurate as per the ICH guidelines. Results and Conclusion: The linearity of the method for Impurities and the analytes was found from 25 to 150 % concentration level with the correlation coefficient (r2) > 0.999. The accuracy for impurity and the analytes was performed from 50 to 150% level concentration, and mean recovery was found from 98-102%. The analytical degradation and validated study results indicate its unstable nature in acidic, basic and thermal conditions. Therefore, this method is simple, selective and sensitive, this method can be used in pharmaceutical research and development and quality control departments.

An intercomparison of satellite, airborne, and ground-level observations with WRF–CAMx simulations of NO2 columns over Houston, Texas, during the September 2021 TRACER-AQ campaign

Abstract. Nitrogen dioxide (NO2) is a precursor of ozone (O3) and fine particulate matter (PM2.5) – two pollutants that are above regulatory guidelines in many cities. Bringing urban areas into compliance of these regulatory standards motivates an understanding of the distribution and sources of NO2 through observations and simulations. The TRACER-AQ campaign, conducted in Houston, Texas, in September 2021, provided a unique opportunity to compare observed NO2 columns from ground-, airborne-, and satellite-based spectrometers. In this study, we investigate how these observational datasets compare and simulate column NO2 using WRF–CAMx with fine resolution (444 × 444 m2) comparable to the airborne column measurements. We compare WRF-simulated meteorology to ground-level monitors and find good agreement. We find that observations from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument were strongly correlated (r2 = 0.79) to observations from Pandora spectrometers with a slight high bias (normalized mean bias (NMB) = 3.4 %). Remote sensing observations from the TROPOspheric Monitoring Instrument (TROPOMI) were generally well correlated with Pandora observations (r2 = 0.73) with a negative bias (NMB = −22.8 %). We intercompare different versions of TROPOMI data and find similar correlations across three versions but slightly different biases (from −22.8 % in v2.4.0 to −18.2 % in the NASA MINDS product). Compared with Pandora observations, the WRF–CAMx simulation had reduced correlation (r2 = 0.34) and a low bias (−21.2 %) over the entire study region. We find particularly poor agreement between simulated NO2 columns and GCAS-observed NO2 columns in downtown Houston, an area of high population and roadway densities. These findings point to a potential underestimate of NOx emissions (NOx = NO + NO2) from sources associated with the urban core of Houston, such as mobile sources, in the WRF–CAMx simulation driven by the Texas state inventory, and further investigation is recommended.

Exploring genotypic diversity in sorghum breeding lines for water‐saving traits to enhance drought adaptation during the post‐rainy season

AbstractSorghum [Sorghum bicolor (L.) Moench], a crucial staple crop in South Asia and sub‐Saharan Africa, faces challenges amid increasing climate variability. Post‐rainy sorghum serves as a dominant food and fodder crop in India. Aligned with International Crops Research Institute for the Semi‐Arid Tropics's post‐rainy sorghum product profile, this research extensively characterizes sorghum lines, emphasizing the traits vital for post‐rainy drought adaptation in hybrid parents. We examined genotypic differences and trait correlations in 25 sorghum hybrid parents and varieties (B line for seed parent, R line for restorer, and check for varieties) through atmospheric and soil drought experiments. Results from atmospheric drought experiments revealed significant variation in transpiration rate (TR) under high vapor pressure deficit (VPD), with certain lines showing limited TR (BTX623 and ICSR 21002), while others exhibited high TR. In soil drought experiments, transpiration decline occurred at fractions of transpirable soil water ranging between 0.38 (ICSR 174) and 0.65 (40162 and ICSR 21005). R lines consistently displayed superior plant growth, water use, and biomass compared to B lines. Transpiration efficiency (TE) and total biomass showed positive correlations (r2 = 0.69) in well‐watered and (r2 = 0.45) in water‐stressed conditions. Most R lines displayed higher biomass and TE. Genotypes exhibiting enhanced vigor and limited TR in high VPD conditions and high TE hold potential for enhancing drought adaptation in post‐rainy sorghum. Notably, genotypes with higher biomass, lower TR, and increased TE within both R and B line groups represent valuable genetic resources for enhancing sorghum crops, post‐rainy sorghum adaptation to water deficit.

Is detection of disease-modifying osteoarthritis drug treatment more effective when performing cartilage morphometry without blinding to MR image acquisition order?

ObjectiveWe here explore whether observed treatment effects of a putative disease modifying osteoarthritis drug (DMOAD) are greater when cartilage morphometry is performed with rather than without knowledge of MRI acquisition order (unblinded/blinded to time point). MethodsIn the FORWARD randomized controlled trial, 549 knee osteoarthritis patients were randomized 1:1:1:1:1 to three once-weekly intra-articular injections of placebo, 30µg sprifermin every 6 or 12 months(M), or 100µg every 6/12M. After year 2, cartilage segmentation of baseline through 24M MRIs was performed, with blinding to acquisition order. After year 5, 24 and 60M MRIs were analyzed together, with unknown relative order, but with segmented BL images as reference (24M unblinded vs. BL), by the same operators. Total femorotibial joint cartilage thickness (TFTJ_ThC) change was obtained for 352 participants analyzed under both conditions. Results24M data read unblinded to order revealed a -35±44 µm lower TFTJ_ThC than blinded analysis (all groups: lower/upper bounds -120/+51µm; correlation r2=97%). With unblinded analysis, the placebo group lost -46±57µm TFTJ_ThC over 24M, whereas 100µg/every 6M lost -2.2±73µm (difference=44µm [95%CI: 22,66]). With blinded analysis, placebo lost -11±53µm, whereas 100µg/every 6M gained 30±62µm (difference=40µm [95%CI: 21,60]). 100µg sprifermin injected every 6M showed statistically significant (p<0.001) treatment effects in TFTJ_ThC, with Cohen D=-0.66 for unblinded and D=-0.69 for blinded analysis. ConclusionsThese results do not reveal that detection of proposed DMOAD treatment is enhanced with MRIs read unblinded to order; rather, the sensitivity is similar to blinded analysis. Choices on blinded vs. unblinded analysis may thus be based on other criteria.