RMSD Research Articles

A Simple Approach for Module Temperature and Power Prediction

In this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model.

A Theoretical Study To Understand the Impact of Mpro of nCoV on the Hormones

AbstractAmidst the unprecedented 2019 coronavirus outbreak and the substantial loss of lives due to the scarcity of information about the virus and lack of viable drugs, understanding the impact of the nCoV Main Protease (Mpro) on the endocrine system has emerged as a critical topic to pursue. This study explores the potential interactions between four key hormones (cortisol, cortisone, estradiol, and estrone) and the Mpro of nCoV using various in‐silico methodologies. Density Functional Theory (DFT) calculations scrutinized the electron density delocalization of these hormones. Through molecular docking, the molecular‐level binding of Mpro of nCoV with these hormones was studied, revealing cortisol as the hormone exhibiting the most promising binding affinity (−100.05 kcal/mol). Furthermore, molecular dynamics (MD) simulations, emulating human body conditions at distinct temperatures, were employed to study the implications of these interactions. The study analysed the various trajectories such as root mean square deviation (RMSD), root mean square fluctuations (RMSF), radius of gyration (Rg), and H‐bond trajectories to understand the potential impact on biological systems. The findings demonstrate the binding potential of the Mpro of nCoV with these hormones, raising plausible concerns regarding consequential physiological changes within the human body.

Modeling thermoregulatory responses during high-intensity exercise in warm environments.

The six cylinder thermoregulatory model (SCTM) has been validated thoroughly for resting humans. This type of modeling is helpful to predict and develop guidance for safe performance of work and recreational activities. In the context of a warming global climate, updating the accuracy of the model for intense exercise in warm environments will help a wide range of individuals in athletic, recreational, and military settings. Three sets of previously collected data were used to determine SCTM accuracy. Dataset 1: two groups [large (LG) 91.5 kg and small (SM) 67.7 kg] of individuals performed 60 min of semirecumbent cycling in temperate conditions (25.1°C) at metabolic rates of 570-700 W. Dataset 2: two LG (100 kg) and SM (65.8 kg) groups performed 60 min of semirecumbent cycling in warm/hot environmental conditions (36.2°C) at metabolic rates of 590-680 W. Dataset 3: seven volunteers completed 8-km track trials (∼30 min) in cool (17°C) and warm (30°C) environments. The volunteers' metabolic rates were estimated to be 1,268 W and 1,166 W, respectively. For all datasets, SCTM-predicted core temperatures were found to be similar to the observed core temperatures. The root mean square deviations (RMSDs) ranged from 0.06 to 0.46°C with an average of 0.2°C deviation, which is less than the acceptance threshold of 0.5°C. Thus, the present validation shows that SCTM predicts core temperatures with acceptable accuracy during intense exercise in warm environments and successfully captures core temperature differences between large and small individuals.NEW & NOTEWORTHY The SCTM has been validated thoroughly for resting humans in warm and cold environments and during water immersion. The present study further demonstrated that SCTM predicts core temperatures with acceptable accuracy during intense exercise up to 1,300 W in temperate and warm environments and captures core temperature differences between large and small individuals. SCTM is potentially useful to develop guidance for safe operation in athletic, military, and occupational settings during exposure to warm or hot environments.

Structure and dynamics of the cyanobacterial regulator SipA

The small, 78-residue long, regulator SipA interacts with the non-bleaching sensor histidine kinase (NblS). We have solved the solution structure of SipA on the basis of 990 nuclear Overhauser effect- (NOE-) derived distance constraints. The average pairwise root-mean-square deviation (RMSD) for the twenty best structures for the backbone residues, obtained by CYANA, was 1.35 ± 0.21 Å, and 1.90 ± 0.16 Å when all heavy atoms were considered (the target function of CYANA was 0.540 ± 0.08). The structure is that of a β-II class protein, basically formed by a five-stranded β-sheet composed of antiparallel strands following the arrangement: Gly6-Leu11 (β-strand 1), which packs against Leu66-Val69 (β-strand 5) on one side, and against Gly36-Thr42 (β-strand 2) on the other side; Trp50-Phe54 (β-strand 3); and Gly57-Leu60 (β-strand 4). The protein is highly mobile, as shown by measurements of R1, R2, NOE and ηxy relaxation parameters, with an average order parameter (<S2>) of 0.70; this mobility encompasses movements in different time scales. We hypothesize that this high flexibility allows the interaction with other proteins (among them NblS), and it explains the large conformational stability of SipA.

Insights into the transcriptomic mechanism and characterization of endoglucanases from Aspergillus terreus in cellulose degradation

Filamentous fungi are the main industrial source of cellulases which are important in the process of converting cellulose to fermentable sugars. In this study, transcriptome analysis was conducted on Aspergillus terreus NEAU-7 cultivated using corn stover and glucose as carbon sources. Four putative endoglucanases (EG5A, EG7A, EG12A, and EG12C) from A. terreus NEAU-7 were efficiently expressed in Pichia pastoris. Among them, EG7A exhibited the highest enzyme activity (75.17 U/mg) with an optimal temperature of 40 °C and pH 5.0. EG5A and EG12A displayed specific activities of 19.92 U/mg and 14.62 U/mg, respectively, at 50 °C. EG12C showed acidophilic characteristics with an optimal pH of 3.0 and a specific activity of 12.21 U/mg at 40 °C. With CMC-Na as the substrate, the Km value of EG5A, EG7A, EG12A or, EG12C was, 11.08 ± 0.87 mg/mL, 6.82 ± 0.74 mg/mL, 7.26 ± 0.64 mg/mL, and 9.88 ± 0.86 mg/mL, with Vmax values of 1258.23 ± 51.62 μmol∙min−1∙mg−1, 842.65 ± 41.53 μmol∙min−1∙mg−1, 499.38 ± 20.42 μmol∙min−1∙mg−1, and 681.41 ± 30.08 μmol∙min−1∙mg−1, respectively. The co-treatment of EG7A with the commercial cellulase increased the yield of reducing sugar by 155.77 % (filter paper) and 130.49 % (corn stover). Molecular docking assay showed the interaction energy of EG7A with cellotetraose at −10.50 kcal/mol, surpassing EG12A (−10.43 kcal/mol), EG12C (−10.28 kcal/mol), and EG5A (−9.00 kcal/mol). Root Mean Square Deviation (RMSD) and Solvent Accessible Surface Area (SASA) values revealed that the presence of cellotetraose stabilized the molecular dynamics simulation of the cellotetraose-protein complex over a 100 ns time scale. This study provides valuable insights for developing recombinant enzymes and biomass degradation technologies.

Computational insights into KRAS G12C inhibition: exploring possible repurposing of Azacitidine and Ribavirin

Kirsten rat sarcoma (KRAS) stands out as the most prevalent mutated oncogene, playing a crucial role in the initiation and progression of various cancer types, including colorectal, lung and pancreatic cancer. The oncogenic modifications of KRAS are intricately linked to tumor development and are identified in 22% of cancer patients. This has spurred the necessity to explore inhibition mechanisms, with the aim of investigating and repurposing existing drugs for diagnosing cancers dependent on KRAS G12C In this investigation, 26 nucleoside-based drugs were collected from literature to assess their effectiveness against KRAS G12C. The study incorporates in-silico molecular simulations and molecular docking examinations of these nucleoside-derived drugs with the KRAS G12C protein using Protein Data Bank (PDB) ID: 5V71. The docking outcomes indicated that two drugs, Azacitidine and Ribavirin, exhibited substantial binding affinities of −8.7 and −8.3 kcal/mol, respectively. These drugs demonstrated stability in binding to the active site of the protein during simulation studies. Root mean square deviation (RMSD) analyses indicated that the complexes closely adhered to an equilibrium RMSD value ranging from 0.17 to 0.2 nm. Additionally, % occupancies, bond angles and the length of hydrogen bonds were calculated. These findings suggest that Azacitidine and Ribavirin may potentially serve as candidates for repurposing in individuals with KRAS-dependent cancers. Communicated by Ramaswamy H. Sarma

Issues in assessing analytical performance specifications in healthcare systems assembling multiple laboratories and measuring systems.

Analytical performance specifications (APS) are usually compared to the intermediate reproducibility uncertainty of measuring a particular measurand using a single invitro diagnostic medical device (IVD MD). Healthcare systems assembling multiple laboratories that include several IVD MDs and cater to patients suffering from long-term disease conditions mean that samples from apatient are analyzed using a few IVD MDs, sometimes fromdifferent manufacturers, but rarely all IVD MDs in thehealthcare system. The reproducibility uncertainty for results of a measurand measured within a healthcare system and the components of this measurement uncertainty is useful in strategies to minimize bias and overall measurement uncertainty within the healthcare system. The root mean squares deviation (RMSD) calculated as the sample standard deviation (SD) and relative SD includes both imprecision and bias and is appropriate for expressing such uncertainties. Results from commutable stabilized internal and external control samples, from measuring split natural patient samples or using big-data techniques, are essential in monitoring bias and measurement uncertainties in healthcare systems. Variance component analysis (VCA) can be employed to quantify the relative contributions of the most influential factors causing measurement uncertainty. Such results represent invaluable information for minimizing measurement uncertainty in the interest of the healthcare system's patients.

In Silico Investigation of Novel Compounds as Inhibitors of Acetylcholinesterase Enzyme for the Treatment of Alzheimer's Diseases.

Alzheimer's disease (AD) is a "progressive, neurodegenerative disease that occurs when nerve cells in the brain die." There are only 4 drugs approved by the United States Food and Drug Administration (FDA). Three (donepezil, rivastigmine, and galantamine) out of these four drugs are anticholinesterase inhibitors, while the fourth one memantine is an N-methyl-D-aspartate (NMDA) receptor inhibitor. Currently, two immunotherapy drugs that target amyloid protein (donanemab and lecanemab) are being considered for the treatment of Alzheimer's disease at an early stage. All these drug molecules are still not the complete answer to the treatment of Alzheimer's disease. A recent report from the Office of National Statistics showed that AD is the leading cause of death in 2022. Therefore, there is an urgency to develop more drugs that can treat AD. Based on this urgency, we aim to investigate how bioactive and already approved drugs could be repurposed for inhibiting the anticholinesterase enzyme using computational studies. To achieve this, the data science tool-Python coding was compiled on Jupyter Notebook to mine bioactive compounds from the ChEMBL database. The most bioactive compounds obtained were further investigated using Molecular Operating Environment (MOE) software to carry out molecular docking and ligand analysis, and this was followed by molecular dynamics simulation production at 35 ns using GROMACS 2022.4 on Archer 2 machine. The molecular dynamic analysis was carried out using HeroMDanalysis software. Data mining of the ChEMBL database was carried out for lipase inhibitors, and this gave CHEMBL-ID 1240685, a peptide molecule, the most active compound at the time of data mining. Further literature studies gave Zoladex an FDA-approved drug for the treatment of breast cancer as another compound of interest. The in silico studies were carried out against the anticholinesterase enzyme using two FDA-approved drugs donepezil and galantamine as a template for comparing the in silico activities of the repurposed drugs. A very useful receptor for this study was PDB-1DX6, a cocrystallized galantamine inhibitor of acetylcholinesterase enzyme. The molecular docking analysis (using ligand interactions) and molecular dynamic analysis (root mean square deviation (RMSD) and root mean square fluctuation (RMSF)) showed that the two peptide molecules CHEMBL-1240685 and Zoladex gave the best binding energy and stability when compared to the FDA-approved drugs (donepezil and galantamine). Finally, further literature studies revealed that Zoladex affects memory reduction; therefore, it was dropped as a possible repurposed drug. Our research showed that CHEMBL-1240685 is a potential compound that could be investigated for the inhibition of anticholinesterase enzyme and might be another drug molecule that could be used to treat Alzheimer's disease.

De novo design of cavity-containing proteins with a backbone-centered neural network energy function

The design of small-molecule-binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity-containing backbone architectures. Here, we designed diverse cavity-containing backbones without predefined architectures by introducing tailored restraints into the backbone sampling driven by SCUBA (Side Chain-Unknown Backbone Arrangement), a neural network statistical energy function. For 521 out of 5816 designs, the root-mean-square deviations (RMSDs) of the Cα atoms for the AlphaFold2-predicted structures and our designed structures are within 2.0Å. We experimentally tested 10 designed proteins and determined the crystal structures of two of them. One closely agrees with the designed model, while the other forms a domain-swapped dimer, where the partial structures are in agreement with the designed structures. Our results indicate that data-driven methods such as SCUBA hold great potential for designing de novo proteins with tailored small-molecule-binding function.

Understanding the high electronic quantum similarity of a series of ligands used as inhibitors of the SARS-CoV-2 virus by molecular mechanics and density functional theory approaches

Background A coronavirus identified in 2019, SARS-CoV-2, has caused a pandemic of respiratory illness, called COVID-19. Most people with COVID-19 experience mild to moderate symptoms and recover without the need for special treatments. The SARS‑CoV‑2 RNA‑dependent RNA polymerase (RdRp) plays a crucial role in the viral life cycle. The active site of the RdRp is a very accessible region, so targeting this region to study the inhibition of viral replication may be an effective therapeutic approach. For this reason, this study has selected and analysed a series of ligands used as SARS-CoV-2 virus inhibitors, namely: Darunavir (Daru), Dexamethasona (Dexame), Dolutegravir (Dolu), Fosamprenavir (Fosam), Ganciclovir (Gan), Insoine (Inso), Lopinavir (Lop), Ritonavir (Rito) and Tipranavir (Tipra). Methods These ligands were analyzed using molecular docking, molecular quantum similarity using four similarity indices like overlap, Coulomb and their Euclidean distances. On the other hand, these outcomes were supported with chemical reactivity indices defined within a conceptual density functional theory framework. Results The results show the conformations with the highest root-mean-square deviation (RMSD), have π-π stacking interaction with residue LYS621, ARG555 and ASP623, CYS622, ASP760, among others. In the molecular quantum similarity, the highest indices have been obtained in the electronic similarity in comparison with the structural similarity. Conclusions These studies allow the identification of the main stabilizing interactions using the crystal structure of SARS‑CoV‑2 RNA‑dependent RNA polymerase. In this order of ideas, this study provides new insights into these ligands that can be used in the design of new COVID-19 treatments. The studies allowed us to find an explanation supported in the Density Functional Theory about the chemical reactivity and the stabilization in the active site of the ligands.

Dynamic Insights into DNA Conformational Changes and the Impact of Magnesium Ions on Topoisomerase IA Enzymes: A Molecular Dynamics Simulation Study

Molecular dynamics simulations were employed to scrutinize the conformational changes in DNA strands and elucidate the influence of magnesium ions on Topoisomerase IA enzymes. Through the computation of Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), hydrogen bond distances, dihedral angles, and Solvated Accessible Surface Area (SASA), we meticulously examined the structural dynamics. The results reveal intricate patterns of DNA strand alterations, showcasing the profound role of magnesium ions in modulating the behavior of Topoisomerase IA enzymes. This study contributes essential insights into the molecular mechanisms governing DNA conformational changes, offering a foundation for further understanding the biochemical intricacies of Topoisomerase IA enzyme function.

Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods.

Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the deep learning method. For this purpose, a Trained Neural Network (TNN) was used to produce 75 molecules similar to Favipiravir by using Simplified Molecular Input Line Entry System (SMILES) representations. The binding properties of molecules to Viral RNA-dependent RNA polymerase (RdRp) were studied by using molecular docking studies. To confirm the accuracy of this method, compounds were also tested against 3CL protease (3CLpro), which is another important enzyme for the progression of SARS-CoV-2. Compounds having better binding energies and RMSD values than favipiravir were searched with similarity analysis on the ChEMBL drug database in order to find similar structures with RdRp and 3CLpro inhibitory activities. A similarity search found new 200 potential RdRp and 3CLpro inhibitors structurally similar to produced molecules, and these compounds were again evaluated for their receptor interactions with molecular docking studies. Compounds showed better interaction with RdRp protease than 3CLpro. This result presented that artificial intelligence correctly produced structures similar to favipiravir that act more specifically as RdRp inhibitors. In addition, Lipinski's rules were applied to the molecules that showed the best interaction with RdRp, and 7 compounds were determined to be potential drug candidates. Among these compounds, a Molecular Dynamic simulation study was applied for ChEMBL ID:1193133 to better understand the existence and duration of the compound in the receptor site. The results confirmed that the ChEMBL ID:1193133 compound showed good Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), hydrogen bonding, and remaining time in the active site; therefore, it was considered that it could be active against the virus. This compound was also tested for antiviral activity, and it was determined that it did not delay viral infection, although it was cytotoxic between 5mg/mL-1.25mg/mL concentrations. However, if other compounds could be tested, it might provide a chance to obtain activity, and compounds should also be tested against the enzymes as well as the other types of viruses.

In silico analysis of molecular mimicry between human aquaporin 3, Aspergillus fumigatus aquaporin and aquaporins from allergic sources

Conduct an in-silico assessment of potential molecular mimicry between human aquaporins, A. fumigatus, and diverse allergenic sources. Amino acid sequences of human AQP3 and A. fumigatus aquaporin were compared through multiple alignments with 25 aquaporins from diverse allergenic sources. Phylogenetic analysis and homology-based modeling were executed, and the ElliPro server predicted conserved antigenic regions on 3D structures. Global identity among studied aquaporins was 32.6%, with a specific conserved local region at 71.4%. Five monophyletic clades (A-E) were formed, and Group B displayed the highest identity (95%), including 6 mammalian aquaporins, notably AQP3. A. fumigatus aquaporin exhibited the highest identity with Malassezia sympodialis (35%). Three linear and three discontinuous epitopes were identified in both human and A. fumigatus aquaporins. The Root Mean Square Deviation (RMSD) from overlapping aquaporin structures was 1.006. Identification of potential linear and conformational epitopes on human AQP3 suggests likely molecular mimicry with A. fumigatus aquaporins. High identity in a specific antigenic region indicates potential autoreactivity and a probable antigenic site involved in cross-reactivity. Validation through in vitro and in vivo studies is essential for further understanding and confirmation.

Identification of Novel EGFR Inhibitors for the Targeted Therapy of Colorectal Cancer Using Pharmacophore Modelling, Docking, Molecular Dynamic Simulation and Biological Activity Prediction.

Colorectal cancer (CRC) is considered the second deadliest cancer in the world. One of the reasons for the occurrence of this cancer is the deregulation of the Epidermal Growth Factor Receptor (EGFR), which plays a critical role in regulating cell division, persistence, differentiation, and migration. The overexpression of the EGFR protein leads to its dysregulation and causes CRC. Hence, this work aims to identify and validate novel EGFR inhibitors for the treatment of colorectal cancer employing various computer aided techniques such as pharmacophore modeling, docking, molecular dynamic simulation and Quantitative Structure-Activity Relationship (QSAR) analysis. In this work, a shared-featured ligand-based pharmacophore model was generated using the known inhibitors of EGFR. The best model was validated and screened against ZincPharmer and Maybridge databases, and 143 hits were obtained. Pharmacokinetic and toxicological properties of these hits were studied, and the acceptable ligands were docked against EGFR. The best five protein-ligand complexes with binding energy less than -5 kcal/mol were selected. The molecular dynamic simulation studies of these complexes were conducted for 100 nanoseconds (ns), and the results were analyzed. The biological activity of this ligand was calculated using QSAR analysis. The best complex with Root Mean Square Deviation (RMSD) 3.429 Å and Radius of Gyration (RoG) 20.181 Å was selected. The Root Mean Square Fluctuations (RMSF) results were also found to be satisfactory. The biological activity of this ligand was found to be 1.38 μM. This work hereby proposes the ligand 2-((1,6-dimethyl-4-oxo-1,4-dihydropyridin-3-yl)oxy)-N- (1H-indol-4-yl)acetamide as a potential EGFR inhibitor for the treatment of colorectal cancer. The wet lab analysis must be conducted, however, to confirm this hypothesis.

RNA threading with secondary structure and sequence profile.

RNA threading aims to identify remote homologies for template-based modeling of RNA 3D structure. Existing RNA alignment methods primarily rely on secondary structure alignment. They are often time- and memory-consuming, limiting large-scale applications. In addition, the accuracy is far from satisfactory. Using RNA secondary structure and sequence profile, we developed a novel RNA threading algorithm, named RNAthreader. To enhance the alignment process and minimize memory usage, a novel approach has been introduced to simplify RNA secondary structures into compact diagrams. RNAthreader employs a two-step methodology. Initially, integer programming and dynamic programming are combined to create an initial alignment for the simplified diagram. Subsequently, the final alignment is obtained using dynamic programming, taking into account the initial alignment derived from the previous step. The benchmark test on 80 RNAs illustrates that RNAthreader generates more accurate alignments than other methods, especially for RNAs with pseudoknots. Another benchmark, involving 30 RNAs from the RNA-Puzzles experiments, exhibits that the models constructed using RNAthreader templates have a lower average RMSD than those created by alternative methods. Remarkably, RNAthreader takes less than two hours to complete alignments with ∼5000 RNAs, which is 3-40 times faster than other methods. These compelling results suggest that RNAthreader is a promising algorithm for RNA template detection. https://yanglab.qd.sdu.edu.cn/RNAthreader.

Artificial neural network model for water consumption prediction in dairy farms

This work presents a model based on artificial neural network (ANN) applied to predict water consumption in Brazilian dairy farms. Inputs were simple process data such as number of lactating cows, milk productivity, type of management, among others, with low computational cost and satisfactory data prediction. Data used for ANN training was acquired during two years from 31 farms in semi-confined dairy production. The analysis of the results was based on the following statistical models’ indicators: R2 (Coefficient of determination), BIAS (trend coefficient), MAE (mean absolute error), RMSE (Root-mean-square deviation), NRMSE (percentage of the mean of the observations) and RAE (Relative absolute error). After performing the ANN training, the results showed good accuracy to predict water consumption in Brazilian dairy farms, with an average absolute error of 28.4% being obtained. On the other hand, considering the dataset used for ANN validation, an average absolute error of 48% was obtained.

Exploring the mechanism of interaction between TBG and halogenated thiophenols: Insights from fluorescence analysis and molecular simulation

Thyroxine-binding globulin (TBG) plays a vital role in regulating metabolism, growth, organ differentiation, and energy homeostasis, exerting significant effects in various key metabolic pathways. Halogenated thiophenols (HTPs) exhibit high toxicity and harmfulness to organisms, and numerous studies have demonstrated their thyroid-disrupting effects. To understand the mechanism of action of HTPs on TBG, a combination of competitive binding experiments, multiple fluorescence spectroscopy techniques, molecular docking, and molecular simulations was employed to investigate the binding mechanism and identify the binding site. The competition binding assay between HTPs and ANS confirmed the competition of HTPs with thyroid hormone T4 for the active site of TBG, resulting in changes in the TBG microenvironment upon the binding of HTPs to the active site. Key amino acid residues involved in the binding process of HTPs and TBG were further investigated through residue energy decomposition. The distribution of high-energy contributing residues was determined. Analysis of root-mean-square deviation (RMSD) demonstrated the stability of the HTPs-TBG complex. These findings confirm the toxic mechanism of HTPs in thyroid disruption, providing a fundamental reference for accurately assessing the ecological risk of pollutants and human health. Providing mechanistic insights into how HTPS causes thyroid diseases.

SISRE of BDS-3 MEO: Evolution as well as Comparison between D1 and B-CNAV (B-CNAV1, B-CNAV2) Navigation Messages

The signal-in-space range error (SISRE) has a direct impact on the performance of global navigation satellite systems (GNSSs). It is an important indicator of navigation satellite space server performance. The new B-CNAV navigation messages (B-CNAV1 and B-CNAV2) are broadcast on the satellites of the Beidou Global Navigation Satellite System (BDS-3), and they are different from D1 navigation messages in satellite orbit parameters. The orbit accuracy of B-CNAV navigation messages lacks analyses and comparisons with D1. The accuracy and stability of the new hydrogen and rubidium clocks on BDS-3 satellites need annual analyses of long time series, which will affect the service quality of this system. Based on precise ephemeris products from the Center for Orbit Determination in Europe (COD), the orbit error, clock error, and SISRE of 24 medium Earth orbit (MEO) satellite D1 and B-CNAV navigation messages of BDS-3 were computed, analyzed, and compared. Their annual evolution processes for the entire year of 2022 were studied. Thanks to the use of inter-satellite links (ISLs) adopted by BDS-3 MEO satellites, the ages of the ephemeris are accurate and the percent of ages of data, ephemerides (AODEs), and ages of data and clocks (AODCs) shorter than 12 h were 99.95% and 99.96%, respectively. In addition, the broadcast orbit performance was also improved by ISLs. The root mean square (RMS) values of the BDS-3 MEO broadcast ephemeris orbit error were 0.067 m, 0.273 m, and 0.297 m in the radial, cross, and along directions, respectively. Moreover, the 3D RMS value was 0.450 m. Thanks to the use of new orbit parameters in the B-CNAV navigation messages of BDS-3 MEO, its satellite orbit accuracy was obviously better than that of D1 in the radial direction. Its improved accuracy can reach up to about 1.2 cm, and the percentage of its accuracy improvement was about 19.06%. With respect to clock errors, the timescale differences between the two clock products were eliminated to assess the accuracy of broadcasting ephemeris clock errors. A standard deviation value of 0.256 m shows good performances as a result of the use of the two new types of atomic clocks, although the RMS value was 0.541 m due to a nonzero mean bias. Overall, the accuracy of atomic clocks was good. For the new hydrogen and rubidium atomic clocks, their RMS and standard deviation were 0.563 m and 0.231 m and 0.519 m and 0.281 m, respectively. The stability of the former was better than that of the latter. However, due to the nonzero mean bias the latter was better than the former in accuracy. The RMS value of the SISRE of BDS-3 MEO’s broadcast ephemeris was 0.556 m, and the value was 0.920 m when it had a 95% confidence level. In contrast, after deducting the influence of the clock error, the value of SISRE_ORB was 0.092 m. Since the satellite clock error was substantially larger than the orbit radial error, the SISRE was mainly affected by the clock error, and their annual evolutions were consistent. Because of the improvement to the B-CNAV’s navigation message with respect to orbit radial accuracy, SISRE_ORB has improved in accuracy. Compared to D1, it had a significant effect on improving the accuracy of SISRE_ORB, and the percentage of the accuracy improvement was 8.40%.

Monitoring of crack repair in concrete using spherical smart aggregates based on electromechanical impedance (EMI) technique

Cracks will inevitably occur in concrete structures or members during the construction process and service life due to aging, environmental factors, external loads, etc. To improve the strength and stability of the cracked concrete structures, many methods have been proposed to repair the cracks. However, the monitoring of the repairing process and repair quality has not been fully studied. The previous studies have proved that the spherical smart aggregates (SSAs) based on the electromechanical impedance (EMI) technique have outperformed the traditional smart aggregates (SAs) based on the EMI technique in structural health monitoring of civil structures, however, SSAs have not been applied to the monitoring of the concrete crack repair. In this work, the monitoring of the concrete crack repair using the SSAs based on the EMI technique was explored. A total of eight concrete specimens were prepared, and cracks in the concrete specimens were simulated by manually cutting under laboratory conditions. According to the principle of grouting method, two repair agents including cement paste and cement mortar were used to repair the cracks. The impedance signals of 28 d were measured, and three quantitative indicators, namely root mean square deviation, mean absolute percentage deviation, and correlation coefficient deviation were used to evaluate the quality of the concrete repair effect. The results indicate that the SSAs show excellent sensitivity and stability over the traditional SAs. In addition, the normalized values of the quantitative indicators were analyzed to distinguish the types of repair agents. A mathematical expression of exponential function was also proposed by fitting the experimental data to quantitatively evaluate and predict the repair effect of concrete cracks. Further, the influences of temperature, humidity, crack depth and width on the experimental results were analyzed and discussed. The numerical simulation was also presented to validate the reasonability of the experimental results.

Design, antitumor, and anti‐Ras activity of propynyl‐substituted harmine bases

AbstractModifications at the harmine (HAM) 9‐N position demonstrated effective antitumor activity in previous studies. However, the difficult preparation, low yield and poor stability of derivatives limited their applications. A partial activity priming was carried out by converting the 9‐position substituted propargyl group of HAM to propynyl group. Molecular docking simulations of compound 9i with DYRK1A kinase showed that it was able to embed itself into the active pocket of the DYRK1A protein and formed hydrogen bonds with multiple active amino acid residues at a low binding energy. The results of molecular dynamics simulations showed that the RMSD (root mean square deviation) fluctuations of the complex were more gentle, and the trends of the changes in Rg (radius of gyration) and SASA (solvent accessible surface area) were lower, indicating that the DYRK1A protein was less disturbed and the structure of the complex was more compact and stable. The cytotoxicity assay indicated that compound 9i displayed good antiproliferative activity against human cancer cells MGC‐803, HepG2, and PANC‐1. In addition, 9i was able to reduce the ratio of polynomials induced by the Ras overexpression model of Caenorhabditis elegans, which showed anti‐Ras activity, and the RT‐qPCR results suggested that its anti‐Ras ability was not achieved by directly inhibiting the expression of the let‐60/Ras genes, and that 9i was able to upregulate the expression of SOD‐3 activation of oxidative stress, which means that it may be useful as a pro‐oxidant for the treatment of cancers caused by Ras over‐activation.