Average Root Mean Square Deviation Research Articles

Advanced mathematical modeling for preciseestimation of CT energy spectrum using a calibration phantom

PurposeThis research aims to develop an advanced mathematical model using a CT calibration phantom to accurately estimate the CT energy spectrum in clinical settings, enhancing imaging quality and patient dose management. MethodsData were collected from a CT scanner using a CT calibration phantom at various energy levels (80, 100, 120, and 135 kVp). The data was optimized to refine the energy spectrum model, followed by cross-validation with Monte Carlo simulations. ResultsThe developed model demonstrated high precision in estimating the CT energy spectrum at all tested energy levels, with R-squared values above 0.9738 and an R-squared value of 0.9829 at 100 kVp. The model also showed low Normalized Root Mean Square Deviation (NRMSD) ranging from 0.6698 % to 1.8745 %. The Mean Energy Difference (ΔE) between the estimated and simulated spectrum consistently remained under 0.01 keV. These results were comparable to recent studies, which reported higher NRMSD and ΔE. ConclusionsThis study presents a significantly improved model for estimating the CT energy spectrum, offering greater accuracy than existing models. Its strengths include high precision and the use of standard equipment and algorithmic values. While the current use of 13 plugs is adequate, incorporating plugs with varied densities could enhance accuracy. This model has potential for improving imaging quality and optimizing patient dosing in clinical applications. Future trends may include extending energy spectrum estimation to megavoltage domains and integrating technologies like EPID and MVCT for better dose distribution prediction in high-energy photon beam therapy.

Evaluation of subsurface soil water content estimate methods: Maximum entropy vs. exponential filter

Profile soil water content (SWC) is a vital variable in the atmosphere-vegetation-soil system. Although remote sensing currently can provide reliable surface SWC data (∼5 cm depth), acquiring accurate subsurface SWC data from existing reanalysis products remain challenging. In this study, we evaluated two widely used methods in estimating subsurface SWC, namely the exponential filter (ExpF) and the principle of maximum entropy (POME). The evaluation is carried out in two distinct areas on the Tibetan Plateau using ground observations collected from two monitoring networks: the Maqu network area characterized by cold humid climate and grassland and the Shiquanhe network area characterized by cold arid climate and bare ground. The results indicate that POME generally performs better than ExpF in both areas, particularly in deeper soil layers. Specifically, the accuracy of estimated SWC using the ExpF method decreases with depth, while it increases with depth using the POME method. Additionally, both methods achieve commendable performance at a depth of 10 cm in both areas. The deficiency of ExpF is mainly reflected in underestimations for dry cases, which is amplified with increasing depth. Dry cases account for 51 % in the humid area and 68 % in the arid area throughout the study period. Consequently, the ExpF method yields higher root mean square differences (RMSD) by 30 % and 113 % in the humid area at depths of 20 and 40 cm, respectively, compared to the POME method. Similarly, it results in higher RMSD values by 220 % and 200 % in the arid area. As expected, the superior performance of POME in deeper soil layers is primarily attributed to the incorporation of additional bottom and profile mean SWC observations. However, it also potentially introduces uncertainties when integrated with satellite-based data, which inherently contains errors compared to ground observations. To assess the potential of these two methods in large-scale applications combined with satellite-based datasets, this study conducted further evaluation of both methods with required input data derived from the soil moisture active and passive mission (SMAP). The results demonstrate that the performance of both methods in estimating subsurface SWC is acceptable in both humid and arid areas, although some bias is transferred from the input data. They achieve average RMSD values of 0.034 and 0.055 m3/m−3(−|−) in the humid area for the ExpF and POME methods, respectively, and 0.021 and 0.014 m3/m−3(−|−) in the arid area.

Exploring KRas Protein Dynamics: An Integrated Molecular Dynamics Analysis of KRas Wild and Mutant Variants.

This study employs a comprehensive approach combining protein retrieval, sequence alignment, and molecular dynamics simulations to investigate the structural dynamics and stability of wild-type KRas and its mutated variants (G12C, G12D, G12V, and G13D). The selected protein structures were retrieved from the Protein Data Bank (PDB) and prepared by using visual molecular dynamics (VMD) software. Sequence alignment using Clustal Omega provided a detailed comparison of the amino acid sequences, focusing on key mutation sites. Molecular dynamics simulations, performed with Gromacs, revealed distinct conformational changes and stability patterns in the wild-type and mutated KRas proteins over 100 ns. Clustering analysis identified higher conformational changes in the second α-helix of the mutated variants. The root-mean-square deviation (RMSD) distribution analysis showed variant-specific conformational dynamics, with G12V and G12D exhibiting slightly higher average RMSD values. Furthermore, clustering and RMSD analyses of specific amino acid residues (12, 13, 51, and 118) highlighted their roles in maintaining overall stability and influencing structural dynamics. The results indicate that mutations at positions 12 and 13 disrupt normal cycling between wild and mutated variants, leading to the persistent activation of KRas. Additionally, principal component analysis (PCA) elucidated unique conformational dynamics in mutated variants. Free energy landscape (FEL) analysis revealed alterations in the thermodynamic stability of mutated variants compared with the wild type. Overall, this study provides a detailed understanding of the structural changes associated with oncogenic mutations in KRas, offering insights crucial for targeted therapeutic strategies in KRas-driven cancers.

Characteristics and Estimation of Dew in the Loess Hilly Region of Northern Shaanxi Province, China

As a non-precipitation water source, dew is important for plant and animal survival and crop production in arid and water-scarce areas. This study assessed the amount of dew in a dry zone in a long-term (2016 to 2022) field observation experiment at the Ansai Experimental Station, a typical loess hilly area in China. Dew primarily occurred in summer and autumn, with a frequency of >50%. The average annual dew amount was 29.20 mm, with an average annual rainfall of 641.8 mm. The average annual dew-to-rain ratio was 4.58%, and the average annual number of dew days was 143.6 d/a. The surface soil moisture content increased by approximately 1.02% with increasing dew amounts. The change in the soil moisture at a 5 cm depth was 0.14% on average and lagged substantially by 1 h. Using the Beysens model, the annual estimated and measured dew amounts in 2022 were 25.27 and 29.84 mm, respectively, and the annual normalized root mean square deviation (NRMSD) was 0.17. Thus, the Beysens model evaluated the dew amount in the study area well at the monthly and annual scales. The quantification of dew resources can provide support for the development, utilization, and management of limited water resources in arid areas, promoting more accurate decision-making for the sustainable development of water resources in the future.

Adaptive patient-cooperative compliant control of lower limb rehabilitation robot

With the increase in the number of stroke patients, there is a growing demand for rehabilitation training. Robot-assisted training is expected to play a crucial role in meeting this demand. To ensure the safety and comfort of patients during rehabilitation training, it is important to have a patient-cooperative compliant control system for rehabilitation robots. In order to enhance the motion compliance of patients during rehabilitation training, a hierarchical adaptive patient-cooperative compliant control strategy that includes patient-passive exercise and patient-cooperative exercise is proposed. A low-level adaptive backstepping position controller is selected to ensure accurate tracking of the desired trajectory. At the high-level, an adaptive admittance controller is employed to plan the desired trajectory based on the interaction force between the patient and the robot. The results of the patient–robot cooperation experiment on a rehabilitation robot show a significant improvement in tracking trajectory, with a decrease of 76.45% in the dimensionless squared jerk (DSJ) and a decrease of 15.38% in the normalized root mean square deviation (NRMSD) when using the adaptive admittance controller. The proposed adaptive patient-cooperative control strategy effectively enhances the compliance of robot movements, thereby ensuring the safety and comfort of patients during rehabilitation training.

Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks

Measuring or estimating the forces acting on the human body during movement is critical for determining the biomechanical aspects relating to injury, disease and healthy ageing. In this study we examined whether quantifying whole-body motion (segmental accelerations) using a commercial markerless motion capture system could accurately predict three-dimensional ground reaction force during a diverse range of human movements: walking, running, jumping and cutting. We synchronously recorded 3D ground reaction forces (force instrumented treadmill or in-ground plates) with high-resolution video from eight cameras that were spatially calibrated relative to a common coordinate system. We used a commercially available software to reconstruct whole body motion, along with a geometric skeletal model to calculate the acceleration of each segment and hence the whole-body centre of mass and ground reaction force across each movement task. The average root mean square difference (RMSD) across all three dimensions and all tasks was 0.75 N/kg, with the maximum average RMSD being 1.85 N/kg for running vertical force (7.89 % of maximum). There was very strong agreement between peak forces across tasks, with R2 values indicating that the markerless prediction algorithm was able to predict approximately 95–99 % of the variance in peak force across all axes and movements. The results were comparable to previous reports using whole-body marker-based approaches and hence this provides strong proof-of-principle evidence that markerless motion capture can be used to predict ground reaction forces and therefore potentially assess movement kinetics with limited requirements for participant preparation.

System biology approach to identify the novel biomarkers in glioblastoma multiforme tumors by using computational analysis.

Introduction: The most common primary brain tumor in adults is glioblastoma multiforme (GBM), accounting for 45.2% of all cases. The characteristics of GBM, a highly aggressive brain tumor, include rapid cell division and a propensity for necrosis. Regretfully, the prognosis is extremely poor, with only 5.5% of patients surviving after diagnosis. Methodology: To eradicate these kinds of complicated diseases, significant focus is placed on developing more effective drugs and pinpointing precise pharmacological targets. Finding appropriate biomarkers for drug discovery entails considering a variety of factors, including illness states, gene expression levels, and interactions between proteins. Using statistical techniques like p-values and false discovery rates, we identified differentially expressed genes (DEGs) as the first step in our research for identifying promising biomarkers in GBM. Of the 132 genes, 13 showed upregulation, and only 29 showed unique downregulation. No statistically significant changes in the expression of the remaining genes were observed. Results: Matrix metallopeptidase 9 (MMP9) had the greatest degree in the hub biomarker gene identification, followed by (periostin (POSTN) at 11 and Hes family BHLH transcription factor 5 (HES5) at 9. The significance of the identification of each hub biomarker gene in the initiation and advancement of glioblastoma multiforme was brought to light by the survival analysis. Many of these genes participate in signaling networks and function in extracellular areas, as demonstrated by the enrichment analysis.We also identified the transcription factors and kinases that control proteins in the proteinprotein interactions (PPIs) of the DEGs. Discussion: We discovered drugs connected to every hub biomarker. It is an appealing therapeutic target for inhibiting MMP9 involved in GBM. Molecular docking investigations indicated that the chosen complexes (carmustine, lomustine, marimastat, and temozolomide) had high binding affinities of -6.3, -7.4, -7.7, and -8.7 kcal/mol, respectively, the mean root-mean-square deviation (RMSD) value for the carmustine complex and marimastat complex was 4.2 Å and 4.9 Å, respectively, and the lomustine and temozolomide complex system showed an average RMSD of 1.2 Å and 1.6 Å, respectively. Additionally, high stability in root-mean-square fluctuation (RMSF) analysis was observed with no structural conformational changes among the atomic molecules. Thus, these in silico investigations develop a new way for experimentalists to target lethal diseases in future.

Scaling Overall Effectiveness in Low-Temperature Experiments

Abstract The design of film-cooled engine components requires an understanding of the expected temperature distributions while in service, thus requiring accurate predictions through low-temperature testing. Overall effectiveness, ϕ, is the integrated indicator of overall cooling performance. An experiment to measure ϕ at low temperature requires appropriate scaling through careful selection of not only the coolant and freestream gases but also the model material itself. Matching ϕ requires that the experiment has matched values of the adiabatic effectiveness, Biot number, coolant warming factor, and ratio of external to internal heat transfer coefficient. Previous research has shown the requirements to match each of those four parameters individually. However, matching all those parameters simultaneously presents an overconstrained problem, and no comprehensive recommendations exist for the practical experimentalist who wishes to conduct an appropriately scaled, low-temperature experiment truly suitable for determining ϕ. Four fluidic parameters are identified, which in an experiment must be as close as possible to their values at engine conditions. A normalized root-mean-square difference (NRMSD) of the residuals of those parameters is proposed to quantify how well a proposed wind tunnel experiment is likely to yield engine-relevant ϕ values. We show that this process may be used by any experimentalist to identify the appropriate fluids, conditions, and materials for a matched ϕ experiment. Several case studies were performed using computational fluid dynamics (CFD) to show the utility of this process. Of the common experimental techniques examined here, a matched Biot number experiment with 500 K freestream air and 250 K coolant appears to be particularly adept at simulating engine conditions, even better than experiments that make use of CO2 coolant.

Validating the Multi-Mode Model’s Ability to Reproduce Diverse Tokamak Scenarios

A large-scale validation exercise was conducted to assess the multi-mode model (MMM) anomalous transport model in the integrated modeling code TRANSP. The validation included 6 EAST discharges, 17 KSTAR discharges, 72 JET ITER-like wall D-D discharges, and 4 DIII-D fusion plasma discharges. Using the MMM, the study computed anomalous thermal, particle, impurity, and momentum transport within TRANSP. Simulations for EAST, KSTAR, and JET focused on electron and ion temperatures and safety factor profiles, while DIII-D simulations also considered electron density, toroidal rotation frequency, and flow shear. The predicted profiles were compared to experimental data at the diagnostic time, quantifying the comparison using root-mean-square (RMS) deviation and relative offsets. The study found an average RMS deviation of 9.3% for predicted electron temperature and 10.5% for ion temperature, falling within the experimental measurement error range 20%. The MMM model demonstrated computational efficiency and the ability to accurately reproduce a wide range of discharges, including various scenarios and plasma parameters, such as plasma density, gyroradius, collisionality, beta, safety factor and heating method variations.

Discovery of the allosteric inhibitor from actinomyces metabolites to target EGFRCSTMLR mutant protein: molecular modeling and free energy approach

EGFR (epidermal growth factor receptor), a surface protein on the cell, belongs to the tyrosine kinase family, responsible for cell growth and proliferation. Overexpression or mutation in the EGFR gene leads to various types of cancer, i.e., non-small cell lung cancer, breast, and pancreatic cancer. Bioactive molecules identified in this genre were also an essential source of encouragement for researchers who accomplished the design and synthesis of novel compounds with anticancer properties. World Health Organization (WHO) report states that antibiotic resistance is one of the most severe risks to global well-being, food safety, and development. The world needs to take steps to lessen this danger, such as developing new antibiotics and regulating their use. In this study, 6524 compounds derived from Streptomyces sp. were subjected to drug-likeness filters, molecular docking, and molecular dynamic simulation for 1000 ns to find new triple mutant EGFRCSTMLR (EGFR-L858R/T790M/C797S) inhibitors. Docking outcomes revealed that five compounds showed better binding affinity (− 9.074 to − 9.3 kcal/mol) than both reference drug CH7233163 (− 6.11 kcal/mol) and co-crystallized ligand Osimertinib (− 8.07 kcal/mol). Further, molecular dynamic simulation confirmed that ligand C_42 exhibited the best interaction at the active site of EGFR protein and comprised a better average radius of gyration (3.87 Å) and average SASA (Solvent Accessible Surface Area) (82.91 Å2) value than co-crystallized ligand (4.49 Å, 222.38 Å2). Additionally, its average RMSD (Root Mean Square Deviation) (3.25 Å) and RMSF (Root Mean Square Fluctuation) (1.54 Å) values were highly similar to co-crystallized ligand (3.07 Å, 1.54 Å). Compared to the reference ligand, it also demonstrated conserved H-bond interactions with the residues MET_793 and GLN_791 with strong interaction probability. In conclusion, we have found a potential drug with no violation of the rule of three, Lipinski's rule of five, and 26 other vital parameters having great potential in medicinal and pharmaceutical industries applications and can overcome synthetic drug issues.

Calculations of electron inelastic mean free paths (IMFPs). XIV. Calculated IMFPs for LiF and Si3N4 and development of an improved predictive IMFP formula

We report inelastic mean free paths (IMFPs) of Si3N4 and LiF for electron energies from 50 eV to 200 keV that were calculated from their optical energy‐loss functions using the relativistic full Penn algorithm including the correction of the bandgap effect in insulators. Our calculated IMFPs, designated as optical IMFPs, could be fitted to a modified form of the relativistic Bethe equation for inelastic scattering of electrons in matter from 50 eV to 200 keV. The root mean square (RMS) deviations in these fits were less than 1% for Si3N4 and LiF. The IMFPs were also compared with the relativistic version of our predictive Tanuma–Powell–Penn (TPP‐2M) equation. We found that IMFPs calculated from the TPP‐2M equation are systematically larger than the optical IMFPs for both LiF and Si3N4. The RMS differences between IMFPs from the TPP‐2M equation and the optical IMFPs were 49.3% for LiF and 17.3% for Si3N4 for energies between 50 eV and 200 keV. These RMS differences are much larger than those for most of the inorganic compounds in our previous IMFP calculations where the average RMS difference was 10.7% for 42 inorganic compounds. We also report the development of an improved predictive IMFP formula, which we designate as the JTP equation. This formula is a refinement of the TPP‐2M equation and is based on the recent IMFP calculations for 100 materials including the present IMFPs for Si3N4 and LiF (41 elemental solids, 45 inorganic compounds, and 14 organic compounds) for 83 electron energies between 50 eV and 200 keV. Our predictive JTP equation gave satisfactory results in comparisons of optical IMFPs and IMFPs calculated from the JTP equation. The RMS difference between the 8300 optical IMFPs used for optimization and the IMFPs calculated from the JTP equation was 10.2%. This value is appreciably less than the RMS difference of 16.0% found in a similar comparison of the optical IMFPs and IMFPs from the TPP‐2M equation. Furthermore, IMFPs from the JTP equation were compared with measured IMFPs for energies between 50 eV and 200 keV for 16 elemental solids and 37 inorganic compounds. We found that the JTP equation gave satisfactory results that were comparable with previous comparisons of the optical IMFPs and measured IMFPs. We believe that the JTP equation will be applicable to a wider range of materials than the TPP‐2M equation.

A VGGNet-Based Method for Refined Bathymetry from Satellite Altimetry to Reduce Errors

Only approximately 20% of the global seafloor topography has been finely modeled. The rest either lacks data or its data are not accurate enough to meet practical requirements. On the one hand, the satellite altimeter has the advantages of large-scale and real-time observation. Therefore, it is widely used to measure bathymetry, the core of seafloor topography. However, there is often room to improve its precision. Multibeam sonar bathymetry is more precise but generally limited to a smaller coverage, so it is in a complementary relationship with the satellite-derived bathymetry. To combine the advantages of satellite altimetry-derived and multibeam sonar-derived bathymetry, we apply deep learning to perform multibeam sonar-based bathymetry correction for satellite altimetry bathymetry data. Specifically, we modify a pretrained VGGNet neural network model to train on three sets of bathymetry data from the West Pacific, Southern Ocean, and East Pacific. Experiments show that the correlation of bathymetry data before and after correction can reach a high level, with the performance of R2 being as high as 0.81, and the normalized root-mean-square deviation (NRMSE) improved by over 19% compared with previous research. We then explore the relationship between R2 and water depth and conclude that it varies at different depths. Thus, the terrain specificity is a factor that affects the precision of the correction. Finally, we apply the difference in water depth before and after the correction for evaluation and find that our method can improve by more than 17% compared with previous research. The results show that the VGGNet model can perform better correction to the bathymetry data. Hence, we provide a novel method for accurate modeling of the seafloor topography.

A study of quantitative indicators for slice sorting in cine-mode 4DCT.

The uncertainties of four-dimensional computed tomography (4DCT), also called as residual motion artefacts (RMA), induced from irregular respiratory patterns can degrade the quality of overall radiotherapy. This study aims to quantify and reduce those uncertainties. A comparative study on quantitative indicators for RMA was performed, and based on this, we proposed a new 4DCT sorting method that is applicable without disrupting the current clinical workflow. In addition to the default phase sorting strategy, both additional amplitude information from external surrogates and the quantitative metric for RMA, investigated in this study, were introduced. The comparison of quantitative indicators and the performance of the proposed sorting method were evaluated via 10 cases of breath-hold (BH) CT and 30 cases of 4DCT. It was confirmed that N-RMSD (normalised root-mean-square-deviation) was best matched to the visual standards of our institute’s regime, manual sorting method, and could accurately represent RMA. The performance of the proposed method to reduce 4DCT uncertainties was improved by about 18.8% in the averaged value of N-RMSD compared to the default phase sorting method. To the best of our knowledge, this is the first study that evaluates RMA indicators using both BHCT and 4DCT with visual-criteria-based manual sorting and proposes an improved 4DCT sorting strategy based on them.

Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors.

Neural network (NN)-based protein modeling methods have improved significantly in recent years. Although the overall accuracy of the two non-homology-based modeling methods, AlphaFold and RoseTTAFold, is outstanding, their performance for specific protein families has remained unexamined. G-protein-coupled receptor (GPCR) proteins are particularly interesting since they are involved in numerous pathways. This work directly compares the performance of these novel deep learning-based protein modeling methods for GPCRs with the most widely used template-based software—Modeller. We collected the experimentally determined structures of 73 GPCRs from the Protein Data Bank. The official AlphaFold repository and RoseTTAFold web service were used with default settings to predict five structures of each protein sequence. The predicted models were then aligned with the experimentally solved structures and evaluated by the root-mean-square deviation (RMSD) metric. If only looking at each program’s top-scored structure, Modeller had the smallest average modeling RMSD of 2.17 Å, which is better than AlphaFold’s 5.53 Å and RoseTTAFold’s 6.28 Å, probably since Modeller already included many known structures as templates. However, the NN-based methods (AlphaFold and RoseTTAFold) outperformed Modeller in 21 and 15 out of the 73 cases with the top-scored model, respectively, where no good templates were available for Modeller. The larger RMSD values generated by the NN-based methods were primarily due to the differences in loop prediction compared to the crystal structures.

An improved, time-efficient approach to extract accurate distance restraints for NMR2 structure calculation.

Exact nuclear Overhauser enhancement (eNOE) yields highly accurate, ensemble averaged H-H distance restraints with an accuracy of up to 0.1 Å for the multi-state structure determination of proteins as well as for nuclear magnetic resonance molecular replacement (MR) to determine the structure of the protein-ligand interaction site in a time-efficient manner. However, in the latter application, the acquired eNOEs lack the obtainable precision of 0.1 Å because of the asymmetrical nature of the filtered nuclear Overhauser enhancement spectroscopy (NOESY) experiment used in MR. This error is further propagated to the eNOE equations used to fit and extract the distance restraints. In this work, a new analysis method is proposed to obtain inter-molecular distance restraints from the filtered NOESY spectrum more accurately and intuitively by dividing the NOE cross peak by the corresponding diagonal peak of the ligand. The method termed diagonal-normalised eNOEs was tested on the data acquired by on the complex of PIN1 and a small, weak-binding phenylimidazole fragment. MR calculations performed using the distances derived from diagonal-normalised eNOEs yielded the right orientation of the fragment in the binding pocket and produced a structure that more closely resembles the benchmark X-ray structure (2XP6) with an average heavy-atom root-mean-square deviation (RMSD) of 1.681 Å with respect to it, when compared to the one produced with traditional MR with an average heavy atom RMSD of 3.628 Å. This is attributed to the higher precision of the evaluated distance restraints.

Multiphysics pharmacokinetic model for targeted nanoparticles.

Nanoparticles (NP) are being increasingly explored as vehicles for targeted drug delivery because they can overcome free therapeutic limitations by drug encapsulation, thereby increasing solubility and transport across cell membranes. However, a translational gap exists from animal to human studies resulting in only several NP having FDA approval. Because of this, researchers have begun to turn toward physiologically based pharmacokinetic (PBPK) models to guide in vivo NP experimentation. However, typical PBPK models use an empirically derived framework that cannot be universally applied to varying NP constructs and experimental settings. The purpose of this study was to develop a physics-based multiscale PBPK compartmental model for determining continuous NP biodistribution. We successfully developed two versions of a physics-based compartmental model, models A and B, and validated the models with experimental data. The more physiologically relevant model (model B) had an output that more closely resembled experimental data as determined by normalized root mean squared deviation (NRMSD) analysis. A branched model was developed to enable the model to account for varying NP sizes. With the help of the branched model, we were able to show that branching in vasculature causes enhanced uptake of NP in the organ tissue. The models were solved using two of the most popular computational platforms, MATLAB and Julia. Our experimentation with the two suggests the highly optimized ODE solver package DifferentialEquations.jl in Julia outperforms MATLAB when solving a stiff system of ordinary differential equations (ODEs). We experimented with solving our PBPK model with a neural network using Julia's Flux.jl package. We were able to demonstrate that a neural network can learn to solve a system of ODEs when the system can be made non-stiff via quasi-steady-state approximation (QSSA). Our model incorporates modules that account for varying NP surface chemistries, multiscale vascular hydrodynamic effects, and effects of the immune system to create a more comprehensive and modular model for predicting NP biodistribution in a variety of NP constructs.

Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction.

The inhibition of protein–protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein–protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2’s prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict -helices and -strands in proteins.

Calculations of electron inelastic mean free paths. XIII. Data for 14 organic compounds and water over the 50 eV to 200 keV range with the relativistic full Penn algorithm

We have calculated inelastic mean free paths (IMFPs) for 14 organic compounds (26‐n‐paraffin, adenine, β‐carotene, diphenyl‐hexatriene, guanine, Kapton, polyacetylene, poly (butene‐1‐sulfone), polyethylene, polymethylmethacrylate, polystyrene, poly(2‐vinylpyridine), thymine, and uracil) and liquid water for electron energies from 50 eV to 200 keV with the relativistic full Penn algorithm including the correction of the bandgap effect in insulators. These calculations were made with energy‐loss functions (ELFs) obtained from measured optical constants and from calculated atomic scattering factors for X‐ray energies. Our calculated IMFPs could be fitted to a modified form of the relativistic Bethe equation for inelastic scattering of electrons in matter from 50 eV to 200 keV. The average root‐mean‐square (RMS) deviation in these fits was 0.17%. The IMFPs were also compared with a relativistic version of our predictive Tanuma–Powell–Penn (TPP‐2M) equation. The average RMS deviation in these comparisons was 7.2% for energies between 50 eV and 200 keV. This average RMS deviation is smaller than that found in a similar comparison for our group of 41 elemental solids (11.9%) and for our group of 42 inorganic compounds (10.7%) for the same energy range. We found generally satisfactory agreement between our calculated IMFPs and values from other calculations for energies between 200 eV and 10 keV. We also found reasonable agreement between our IMFPs for organic compounds and measured IMFPs for energies between 50 eV and 200 keV. Substantial progress for IMFP measurements for liquid water has been made in recent years through the invention of liquid water microjet photoelectron spectroscopy and droplet photoelectron imaging. We found that the IMFPs from these experiments and the associated analyses were larger than our IMFPs by factors between two and four for energies between about 30 eV and 1000 eV. The energy dependences of the measured IMFPs are, however, similar to that of our IMFPs in the same energy range. Since IMFPs calculated from the same algorithm for a number of inorganic compounds agree reasonably well with measured IMFPs for energies between 100 eV and 200 keV, the large differences between IMFPs for water from recent experiments and our results are surprising and need to be resolved with additional experiments.

Comparison of two computational methods for solvent screening in countercurrent and centrifugal partition chromatography

Countercurrent and centrifugal partition chromatography are techniques applied in the separation and isolation of compounds from natural extracts. One of the key design parameters of these processes is the selection of the biphasic solvent system that provides for the adequate partitioning of the solutes. To address this challenging task, the fully predictive Conductor-like Screening Model for Real Solvents (COSMO-RS) and the semi-predictive Non-Random Two-Liquid Segment Activity Coefficient (NRTL-SAC) model were applied to estimate the partition coefficients (K) of four model phenolic compounds (vanillin, ferulic acid, (S)-hesperetin and quercetin) in different solvent systems. Complementing the experimental data collected in the literature, partition coefficients of each solute in binary, or quaternary, solvent systems were measured at 298.2 K.Higher deviations from the experimental data were obtained using the predictive COSMO-RS model, with an average RMSD (root-mean-square deviation) in log(K) of 1.17 of all four solutes (61 data points), providing a satisfactory quantitative description only for the systems containing vanillin (RSMD = 0.57). For the NRTL-SAC model, the molecular parameters of the solutes were initially calculated by correlating a set of K and solubility (x, in mole fraction) data (16 partition coefficients and 44 solubility data points), for which average RMSD values of 0.07 and 0.41 were obtained in log(K) and log(x), respectively. The predictions of the remaining log(K) data (45 partition coefficients) resulted in an average RMSD of 0.43, suggesting that the NRTL-SAC model was a more reliable quantitative solvent screening tool. Depending on the amount of available solubility and partition data, both models can be valuable alternatives in the preliminary stages of solvent screening destined to select the optimal mobile and stationary phases for a given separation.

Social Media Validation for Pre-Screened Ontologies As a Driver for Biomolecular Interaction Significance in COVID-19 and Hematological Malignancies

With the rise of social media use during the COVID-19 pandemic, impressions from online content can affect behavioral changes resulting in exacerbating disparities in care. Thus, there exists a need to utilize social media platforms, like Twitter, to help augment preparedness, especially at the intersection between oncology and COVID-19, where tweets could help hint at potential biomolecular interactions.To address this, a study was developed to assess relationship and ontologies on the interaction between hematological malignancies and COVID-19 on Twitter. Ontologies are groupings of terms and related identifiers, such as genes, for general search terms, such as “Blood Cancer”, were found utilizing the Human Phenotype Ontology. These were combined with the term “COVID-19” and used as search terms for Twitter's Standard Search API. The resulting tweets were cross-checked to assess if they included any of the other terms or genes related to the starting ontologies to then determine how many terms or genes each tweet was associated with. Once the most associated tweets to the ontologies were found, the genes related to those ontologies were utilized to find biological structures within the AlphaFold EMBL database, before being used in binding using HEX Docking software's shape based binding tool in 3D. Finally, Root Mean Square (RMS) Deviations were performed between the top 2000 conformations for each bound structure to determine if the binding was statistically significant.Results showed strong clustering of top tweets around keyword combinations. In the case of the starting entry, “Blood COVID-19”, the ontologies that were found were linked to 45 terms that each had 100 or more tweets linked to them (Figure 1a). One such term of significance was Acute Myeloid Leukemia, which was linked to the gene BRCA1. The biological significance of the molecular interaction between BRCA1 and SARS CoV-2 was determined using the predicted protein structure from the AlphaFold-EMBL database for BRCA1 and the RCSB Protein Bank structure for the SARS CoV-2 spike (PDB# 6VSB), which can be found in Figure 1b. This interaction was found to be significant based on the average RMS Deviation of 82.97 Angstroms that ranged across the top 2000 conformation. Each model had an average RMS of 85.05 Angstroms between BRCA1 and the COVID-19 spike, with binding occurring on the spike's carbohydrate recognition domain within its S1 segment that is typically used for cell entry. Thus, human phenotype ontology was effective in classifying tweets to specific biomolecular interactions.Therefore, this approach could be utilized to proactively influence treatment designs for blood cancer patients infected with COVID-19, as well as in other areas where medical illnesses are already well defined by ontologies or other literature data. Forward looking, future studies will help to ensure that terms that are not well characterized by ontologies can still be utilized in this type of analysis by employing de novo ontology production methods. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.