Deviation Plot Research Articles

Synthesis, structure, supramolecular assembly inspection by Hirshfeld surface analysis, DFT study and molecular docking inspection of 4,5-bis(2-chlorophenyl)-8a-phenylhexahydropyrimido[4,5-d]pyrimidine-2,7(1H,3H)-dithione

We studied the three-component condensation of 2-chloraldehyde, acetophenone, and thiourea in conjunction with HCl, resulting in the formation of a new compound: 4,5-bis(2-chlorophenyl)-8a-phenylhexahydropyrimido[4,5-d]pyrimidine-2,7(1H,3H)-dithione (CPPD). The compound's structure was affirmed via X-ray diffraction study. The asymmetric unit contained two molecules that were independent relative to crystallography and an ethanol solvent. The difference between independent molecules was explored by dihedral angles between similar rings in both molecular. The difference between molecules was further explored by molecular overlay plot. The supramolecular assembly was supported via diverse intermolecular interactions which were studied through the use of Hirshfeld surface analysis. Electronic structure computations were carried out at the ωb97xd/tzvp level. Natural bonding orbital (NBO), and FMO study reveal reactivity and charge transfer mechanisms within the compound. Furthermore, the nature of bonding in the present molecule is characterized through quantum theory of atoms in molecules (QTAIM), ELF, and LOL studies. Ab-initio molecular dynamic (AIMD) revealed the kinetic and thermodynamic stability at 300 K. The DFT results have excellent correlation with experimental data. The molecular docking and molecular dynamics simulation further revealed the compound to get deep access in the active pocket of type 2 anti-diabetes GLUT4 protein. This was validated by the stable dynamics as demonstrated by the uniform behavior of the root mean square deviation (RMSD) plot. The root mean square fluctuation (RMSF) also showed stable interactions with amino acids in the presence of the compound. Further, simulation trajectories on the basis of binding free energies indicate the significant role of van der Waals force in the formation of the intermolecular docked complex. This concluded the compound might be a potent structure for the development of anti-diabetic compounds.

Deciphering the influenza neuraminidase inhibitory potential of naturally occurring biflavonoids: An in silico approach

Abstract Influenza infection poses a significant threat to the existence of humans and animals. Its inhibition by secondary metabolites may proffer a lasting solution to its resistance to available synthetic therapeutic agents. In this study, we investigated the influenza neuraminidase (NA) inhibitory potential of naturally occurring C–O–C biflavonoids using integrated computational approaches. The molecular docking method was employed to identify biflavonoids with high binding affinities, and molecular dynamics simulation was performed for 100 ns to examine the stability, binding mode, and interactions elicited by the hit molecules in influenza NA-binding pocket. The bioavailability and pharmacokinetic properties of the hit biflavonoids were examined using swissADME. The molecular docking studies identified lophirone L, delicaflavone, lanaroflavone, pulvinatabiflavone, and ochnaflavone as the hit molecules with the binding affinity of −9.9 to −9.3 kcal/mol. The root means square deviation and root mean square fluctuation plots obtained from the molecular dynamics simulation showed that the selected biflavonoids were reasonably stable at the enzyme’s binding pocket. The ADMET studies showed that the top-ranked biflavonoids exhibit good pharmacokinetic and bioavailability properties. Furthermore, the density functional theory studies showed that the selected hit secondary metabolite possesses good pharmacological properties. Thus, the inhibitory activities of these compounds on viral neuraminidase could be helpful in the management of influenza infections.

Hybrid machine learning modeling of nitrogen removal from wastewater using gas-liquid-solid circulating fluidized bed riser

Municipal wastewater generally contains high amounts of nitrogen (N), approximately 23–28 mg/L of total Kjeldahl Nitrogen (TKN), which causes eutrophication and pollution of groundwater. This article reports the generation of empirical models for estimating the nitrogen removal efficacy of two outputs (NH4-N and NO3-N) from the anoxic riser of a highly efficient municipal wastewater treatment system involving a gas-liquid-solid circulating fluidized bed (GLSCFB) bioreactor. To identify the important variables and discard irrelevant ones, the ReliefF algorithm is applied initially. Out of 10 independent variables, this method indicates that only three, such as flow rate, total Kjeldahl Nitrogen (TKN), and downer effluent of NO3-N, are meaningful for anoxic nitrogen removal. Then, a hybrid Bayesian Optimization Algorithm and Support Vector Regression (BOA-SVR) is implemented, considering only the essential variables. In this regard, real-life laboratory data are utilized to develop the models. The BOA approach and k-fold cross-validation technique are explicitly applied to optimize the model's hyperparameters and avoid overfitting. The established models provided sufficiently accurate predictions via their comparisons to the experimental observations. Besides, the BOA-SVR model outperforms the classical multiple linear regression (MLR). The BOA-SVR models' forecasted results for both the predicted amount of NH4-N and NO3-N are favorably compared with the laboratory trials (R2> 99 %). A comprehensive evaluation is further conducted to validate the performance of this model by calculating the root mean square error, mean absolute percentage error, fractional bias, and computational efficiency. Gaussian white noise is used to add three distinct noise levels (20 %, 40 %, and 60 %) to the testing data to evaluate the model's robustness. Moreover, the plots of relative deviations and residuals were dispersed across the zero-reference line with moderate fluctuations. Sensitivity analysis indicated the relative importance of the three independent variables on the anoxic nitrogen removal in the order of influent TKN > downer effluent NO3-N >flowrate. Overall, the developed tool has substantial potential in modeling other convoluted processes.

Simulation of Electrochemical Oxidation in Aqueous Environments under Applied Voltage Using Classical Molecular Dynamics.

Reactive molecular dynamics (MD) simulations of metal electrodes under an applied voltage in an explicit water environment were performed and compared to predictions from both other calculations and simulations and experimental measurements and observations. MD simulations using the third-generation charge-optimized many body (COMB3) potentials and the electrode COMB (eCOMB) approach allow for the simulation of an externally applied voltage by modifying the equations of motion during the charge equilibration step (QEq) of the MD simulation. Unlike previous work, which prevented charge transfer between the water and metal electrodes, this work coupled the water and metal together through the QEq, which leads to an accumulation of a negative charge on the water and a positive charge on the metal before any voltage is applied. The extent of this charge accumulation is characterized, and strategies to mitigate it are explored. Root mean square deviation plots of the metal surfaces are created for each simulation to compare the extent of oxidation and dissolution. Good agreement is found between the oxidation behavior of the simulated metal surfaces and the experimental observations.

Ion-combination specific effects driving the enzymatic activity of halophilic alcohol dehydrogenase 2 from Haloferax volcanii in aqueous ionic liquid solvent mixtures.

Biocatalysis in ionic liquids enables novel routes for bioprocessing. Enzymes derived from extremophiles promise greater stability and activity under ionic liquid (IL) influence. Here, we probe the enzyme alcohol dehydrogenase 2 from the halophilic archaeon Haloferax volcanii in thirteen different ion combinations for relative activity and analyse the results against molecular dynamics (MD) simulations of the same IL systems. We probe the ionic liquid property space based on ion polarizability and molecular electrostatic potential. Using the radial distribution functions, survival probabilities and spatial distribution functions of ions, we show that cooperative ion-ion interactions determine ion-protein interactions, and specifically, strong ion-ion interactions equate to higher enzymatic activity if neither of the ions interact strongly with the protein surface. We further demonstrate a tendency for cations interacting with the protein surface to be least detrimental to enzymatic activity if they show a low polarizability when combined with small hydrophilic anions. We also find that the IL ion influence is not mitigated by the surplus of negatively charged residues of the halophilic enzyme. This is shown by free energy landscape analysis in root mean square deviation and distance variation plots of active site gating residues (Trp43 and His273) demonstrating no protection of specific structural elements relevant to preserving enzymatic activity. On the other hand, we observe a general effect across all IL systems that a tight binding of water at acidic residues is preferentially interrupted at these residues through the increased presence of potassium ions. Overall, this study demonstrates a co-ion interaction dependent influence on allosteric surface residues controlling the active/inactive conformation of halophilic alcohol dehydrogenase 2 and the necessity to engineer ionic liquid systems for enzymes that rely on the integrity of functional surface residues regardless of their halophilicity or thermophilicity for use in bioprocessing.

Investigation and drug design for novel molecules from natural products as inhibitors for controlling multiple myeloma disease using in-silico tools

Multiple myeloma (MM) is a disease that causes plasma cell growth in the bone marrow and immune globulin buildup in blood and urine. Despite recent advances in MM therapy, many still die due to its high mortality rate. A study using computational simulations analyzed 100 natural ingredients from the SANC database to determine if they inhibited the IgH domain, a known cause of multiple myeloma. Natural component Diospyrin inhibited the IgH enzyme with the best binding energy of −10.3 kcal/mol and three carbon-hydrogen bonds, followed by Parviflorone F complex with a binding energy of −10.1 kcal/mol and two conventional-hydrogen bonds. As a result, the Molecular Dynamic simulation was used to test the stability of the two complexes. During the simulation, the Diospyrin molecule dissociated from the protein at roughly 67.5 ns, whereas the Parviflorone F molecule stayed attached to the protein throughout. The latter was the subject of the investigation. The analysis of the production run data revealed that the Parviflorone F molecule exhibits a variety of conformations within the binding pocket while keeping a relatively constant distance from the protein’s center of mass. The analysis of the production run data revealed that the Parviflorone F molecule exhibited a variety of conformations within the binding pocket while keeping a relatively constant distance from the protein’s center of mass. The root mean square deviation (RMSD) plots for both the protein and complex showed a stable and steady average value of 4.4 Å for the first 82 nanoseconds of manufacture. As a result, the average value increased to 8.3 Å. Furthermore, the components of the binding free energy, as computed by MM-GBSA, revealed that the mean binding energy of the Parviflorone F molecule was −23.88 kcal/mol. Finally, after analyzing all of the examination data, Parviflorone F was identified as a powerful inhibitor of the IgH domain and hence of the MM disease, which requires further in-vivo conformation. Communicated by Ramaswamy H. Sarma

Natural sapogenins as potential inhibitors of aquaporins for targeted cancer therapy: computational insights into binding and inhibition mechanism

Aquaporins (AQPs) are membrane proteins that facilitate the transport of water and other small molecules across biological membranes. AQPs are involved in various physiological processes and pathological conditions, including cancer, making them as potential targets for anticancer therapy. However, the development of selective and effective inhibitors of AQPs remains a challenge. In this study, we explored the possibility of using natural sapogenins, a class of plant-derived aglycones of saponins with diverse biological activities, as potential inhibitors of AQPs. We performed molecular docking, dynamics simulation and binding energy calculation to investigate the binding and inhibition mechanism of 19 sapogenins against 13 AQPs (AQP0-AQP13) that are overexpressed in various cancers. Our results showed that out of 19 sapogenins, 8 (Diosgenin, Gitogenin, Tigogenin, Ruscogenin, Yamogenin, Hecogenin, Sarsasapogenin and Smilagenin) exhibited acceptable drug-like characteristics. These sapogenin also exhibited favourable binding affinities in the range of −7.6 to −13.4 kcal/mol, and interactions within the AQP binding sites. Furthermore, MD simulations provided insights into stability and dynamics of the sapogenin-AQP complexes. Most of the fluctuations in binding pocket were observed for AQP0-Gitogenin and AQP4-Diosgenin. However, remaining protein–ligand complex showed stable root mean square deviation (RMSD) plots, strong hydrogen bonding interactions, stable solvent-accessible surface area (SASA) values and minimum distance to the receptor. These observations suggest that natural sapogenin hold promise as novel inhibitors of AQPs, offering a basis for the development of innovative therapeutic agents for cancer treatment. However, further validation of the identified compounds through experiments is essential for translating these findings into therapeutic applications. Communicated by Ramaswamy H. Sarma

Simple patient and lesion characteristics on coronary angiography can accurately predict FFR-proven ischemia

Abstract Aims Fractional flow reserve (FFR) measured during invasive coronary angiography (ICA), the reference standard for diagnosing coronary ischemia, potentially risks pressure wire-induced arterial wall injury. We aimed to develop a model for diagnosing coronary ischemia based on simple patient and lesion characteristics without invasive intracoronary pressure measurement. Methods We performed quantitative coronary angiography (QCA) analyses on subjects who had undergone FFR measurements during clinically indicated ICA. Clinical, ICA and QCA variables were input into a prediction model for coronary ischemia, defined as FFR ≤0.8. Least absolute shrinkage and selection operator (LASSO) regression with internal ten-fold cross-validation was implemented using R software to minimize multicollinearity and select final model variables, i.e., those corresponding to the nadir of the binomial deviation plot. Per-vessel and per-subject results were reported. Results Among 103 subjects (mean age 61 ±10 years; 74 male, 29 female) recruited from hospitals in Singapore (participants in a prospective study NCT03054324) and China, we analyzed 148 stenotic vessels visually assessed diameter (DS) ≥30%. From a dataset comprising 37 variables including baseline clinical characteristics, coronary artery morphological parameters and plaque characteristics, LASSO selected six final model variables: lesion length, minimal lumen diameter, stenosis flow reserve, DS, systolic blood pressure and diabetes, negating serious multicollinearity. The first three variables were measured on QCA, the third using ICA images acquired at rest. Our model outperformed DS ≥70% for discriminating coronary ischemia at both per-vessel (area-under-curve [AUC] 0.907 vs 0.770, p<0.001) and per-subject levels (AUC 0.882 vs 0.759, p<0.001). Per-vessel model accuracy, sensitivity, specificity, positive and negative predictive values were 85.1%, 82.4%, 93.2%, 87.1% and 83.3%, respectively; and per-subject, 83.4%, 82.0%, 85.7%, 89.3% and 76.6%, respectively. Conclusion Compared with visual diameter assessment, our LASSO-based model attained superior prediction of FFR-defined coronary ischemia using an array of variables without the need for invasive pressure measurement.

Performance of virtual reality game–based automated perimetry in patients with childhood glaucoma

To evaluate VisuALL, a game-based automated perimetry device, utilizing virtual reality (VR) goggles, in a cohort of patients with childhood glaucoma. In this prospective series, the results of consecutive patients with childhood glaucoma performing both VisuALL VR field (VRF) and Humphrey visual field (HVF) 24-2 testing were compared. A masked ophthalmologist graded both VRF and HVF tests for field defects (three clustered abnormal points in total or pattern deviation plot). VRF testing was performed binocularly and with the child's own spectacles. The two devices were assessed with respect to agreement of (1) global indices, such as mean deviation (MD) and pattern standard deviation (PSD), (2) point-by-point sensitivity, and (3) the ability to detect visual field defects determined by a grader. A total of 39 children (77 eyes) were enrolled, with mean age 14.1 ± 3.6 years; 3 patients (5 eyes) could not complete the HVF. Average HVF MD was -6.3 ± 6.4 dB. There was strong correlation between VRF and HVF for MD (R = 0.68, P < 0.001), PSD (R=0.78, P < 0.001), and point-by-point sensitivity (R = 0.63, P < 0.001). Bland Altman analysis showed no systematic difference between VRF and HVF in assessing MD and PSD. Of 72 eyes having results for both modalities, 63 (87.5%) had agreement between VRF and HVF with respect to the presence/absence of any field defect, and 52 (72.2%) had agreement regarding the presence/absence of fixation-threatening field loss. VRF is comparable to the gold standard HVF in both identification and quantification of visual field deficits in pediatric glaucoma patients and may offer a valuable supplement or alternative to standard automated perimetry.

Lead phytochemicals and marine compounds against ceruloplasmin in cancer targeting

In silico docking studies serve as a swift and efficient means to sift through a vast array of natural and synthetic small molecules, aiding in the identification of potential inhibitors for cancer biomarkers. One such biomarker, ceruloplasmin (CP), has been implicated in various tumor types due to its overexpression, earning it recognition as a marker of aggressive tumors. This study focused on pinpointing inhibitors for the CP –Myeloperoxidase (MPO) interaction site, a complex formation known to impede HOCl production, a crucial process for inducing apoptotic cell death in tumor cells. The initial phase of our investigation involved in silico docking studies, which screened a diverse library of phytochemicals and marine compounds. Through this process, we identified several promising drug candidates based on their binding affinities. Subsequently, these candidates underwent rigorous filtration based on Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties. Finally, we subjected the selected compounds to molecular dynamics (MDs) simulation to further assess their viability. Lycoperoside F, a steroidal alkaloid glycoside derived from tomatoes (Lycopersicon esculentum), stood out with notable interactions at the binding site. Another noteworthy compound was Xyloglucan (XG) oligosaccharides, predominantly found in the primary cell walls of higher plants. During the subsequent MDs simulations, these interactions were accompanied by highly stable root mean square deviation (RMSD) plots, signifying the consistency and robustness of the observed MDs behavior. XG oligosaccharides demonstrated the highest binding affinity with CP, reaffirming their potential as strong candidates. Additionally, Ardimerin digallate, known as a retroviral ribonuclease H inhibitor for HIV-1 and HIV-2, displayed favorable interactions at the MPO interaction site. Given that promising drug candidates must meet stringent criteria, including non-toxicity, effectiveness, specificity, stability and potency, these phytochemicals have the potential to progress to in vitro studies as CP inhibitors. Ultimately, this could contribute to the suppression of tumor growth, marking a significant step in cancer treatment research. Communicated by Ramaswamy H. Sarma

Guest-Stimulated Nonplanar Porphyrins in Flexible Metal-Organic Frameworks.

Nonplanar porphyrins with out-of-plane distortions play crucial roles in many biological functions and chemical applications. The artificial construction of nonplanar porphyrins usually involves organic synthesis and modification, which is a highly comprehensive approach. However, incorporating porphyrins into guest-stimulated flexible systems allows to manipulate the porphyrin distortion through simple ad/desorption of guest molecules. Here, a series of porphyrinic zirconium metal-organic frameworks (MOFs) is reported that exhibit guest-stimulated breathing behavior. X-Ray diffraction analysis and skeleton deviation plots confirm that the material suffers from porphyrin distortion to form a ruffled geometry under the desorption of guest molecules. Further investigation reveals that not only the degree of nonplanarity can be precisely manipulated but also the partial distortion of porphyrin in a single crystal grain can be readily achieved. As Lewis acidic catalyst, the MOF with nonplanar Co-porphyrin exhibits active properties in catalyzing CO2 /propylene oxide coupling reactions. This porphyrin distortion system provides a powerful tool for manipulating nonplanar porphyrins in MOFs with individual distortion profiles for various advanced applications.

Computer simulation approach to the identification of visfatin-derived angiogenic peptides.

Angiogenesis plays an essential role in various normal physiological processes, such as embryogenesis, tissue repair, and skin regeneration. Visfatin is a 52 kDa adipokine secreted by various tissues including adipocytes. It stimulates the expression of vascular endothelial growth factor (VEGF) and promotes angiogenesis. However, there are several issues in developing full-length visfatin as a therapeutic drug due to its high molecular weight. Therefore, the purpose of this study was to develop peptides, based on the active site of visfatin, with similar or superior angiogenic activity using computer simulation techniques.Initially, the active site domain (residues 181∼390) of visfatin was first truncated into small peptides using the overlapping technique. Subsequently, the 114 truncated small peptides were then subjected to molecular docking analysis using two docking programs (HADDOCK and GalaxyPepDock) to generate small peptides with the highest affinity for visfatin. Furthermore, molecular dynamics simulations (MD) were conducted to investigate the stability of the protein-ligand complexes by computing root mean square deviation (RSMD) and root mean square fluctuation(RMSF) plots for the visfatin-peptide complexes. Finally, peptides with the highest affinity were examined for angiogenic activities, such as cell migration, invasion, and tubule formation in human umbilical vein endothelial cells (HUVECs). Through the docking analysis of the 114 truncated peptides, we screened nine peptides with a high affinity for visfatin. Of these, we discovered two peptides (peptide-1: LEYKLHDFGY and peptide-2: EYKLHDFGYRGV) with the highest affinity for visfatin. In an in vitrostudy, these two peptides showed superior angiogenic activity compared to visfatin itself and stimulated mRNA expressions of visfatin and VEGF-A. These results show that the peptides generated by the protein-peptide docking simulation have a more efficient angiogenic activity than the original visfatin.

Computational study to find the goat serum albumin (GSA) binding site as A-esterase

A-esterases are a classical term applied to enzymatic activity of the proteins by a mechanism not involving intermediate covalent phosphorylation, but requiring a divalent cation cofactor. Recently, a copper-dependent A-esterase activity has been identified in goat serum albumin (GSA) on the organophosphorus insecticide trichloronate. This hydrolysis was identified ex vivo with spectrophotometry and chromatography techniques. Albumin mechanism of action and catalytic site as Cu2+-dependent A-esterase are still unknown. Therefore, to know the copper bind to albumin is relevant. N-terminal sequence has been reported as the high affinity site for this cation, due to the histidine in position 3. The aim of this work in silico is to explore how occurs this metallic binding and active the esterase catalytic function. The GSA crystallized structure (PDB: 5ORI) was chosen for molecular docking and dynamics. A site-directed docking, for N-terminal site and a blind docking was done with trichloronate as ligand. Root-mean-square deviation and frequency plot was calculated to find the most frequent predicted structure and visualize the amino acids involved in binding site. The affinity energy in the blind docking (−5.80 kcal/mol) is almost twice lower than site-directed docking (−3.81 kcal/mol) and N-terminal amino acids do not appear in the most repeated structure binding site, suggesting that the protein has a site with higher affinity to the trichloronate ligand. His145 could be involved in the binding site as has been reported in previous studies.

Comparing Static Perimetry Protocols of Central Field Testing among Patients with Glaucoma.

On comparing the Humphrey Field Analyzer (HFA) 24-2C Faster, which tests 10 additional points in the central field, with the 24-2 Swedish Interactive Thresholding Algorithm (SITA) Standard for detecting central field defects, we found that the HFA 24-2C Faster may be used for the initial field evaluation in glaucoma with the advantage of a shorter testing time. This study aimed to compare the HFA 24-2C Faster with the 24-2 SITA Standard and 10-2 among patients with glaucoma for the number of defective central points, global indices, and testing time. Sixty eyes of 60 patients with glaucoma and glaucomatous visual field defects on the 24-2 SITA Standard algorithm underwent the 24-2C Faster and 10-2 field tests. The number of central points detected, global indices, and reliability indices were compared between the 24-2C Faster and the 24-2 SITA Standard and 10-2 field tests. The 24-2C Faster, on average, detected 5.5 defective points more on the total deviation plot and 2 defective points more on the pattern deviation plot than the 24-2 SITA Standard in central 10°. The 10-2 algorithm detected 2.5 times more points on the total deviation plot than the 24-2C Faster. There was excellent consistency by an intraclass correlation coefficient of 0.95 for the mean deviation and 0.93 for the pattern standard deviation between the 24-2C Faster and the 24-2 SITA Standard. There was good consistency by an intraclass correlation coefficient of 0.80 for both mean deviation and pattern standard deviation between the 24-2C Faster and the 10-2. The testing time was significantly lower with the 24-2C Faster compared with the 24-2 SITA Standard and 10-2 test. Our study demonstrates that the HFA 24-2C Faster may be used for the initial evaluation of the visual field in glaucoma with the added advantage of a shorter testing time compared with the 24-2 SITA Standard program.

Impact of ethnobiology-based instruction and peer tutoring on the achievement of senior secondary school biology students in Ogun state southwest Nigeria

This study investigated the impact of Ethnobiology-Based instruction Peer Tutoring on Achievement of Senior Secondary School Biology Students in Ogun State. The study employed Explanatory sequential mixed-methods approach. The quantitative approach was quasi experimental pretest-posttest non-equivalent group design. Three research questions and one hypothesis was formulated to guide the study. The population of the study comprised of all Biology students in public Senior Secondary Schools in Abeokuta zone. Purposive sampling technique was used to select six Public Schools from two zones (Abeokuta South and Abeokuta North). Biology students in six intact classes of SSS III took part in the study. The sample size of 313 biology students with 101 students in the experimental group 1 (48 male and 53 female), 117 students in the experimental group 2 (57 male and 60 female) and 95 students in control group (38 male and 57 female) which were taught the same biology content for a period of four weeks. The experimental groups received instructions through the use of Ethnobiology-Based Instruction and Peer Tutoring while the control group was taught using lecture method. The data gathering instruments were Biology Achievement Test (BAT) and Students Semi-structured Interview Guide (SSIG). The reliability coefficient of the BAT was established using test- retest and yielded 0.87. Descriptive statistics in form of mean, standard deviation and histogram plot were used to answer the research question while the hypothesis was tested using ANCOVA at 0.05 level of significance. The inferential analysis of hypothesis one has a significant difference on Students’ academic achievement in Biology (F (2,309) = 386.85; p &lt; 0.05), this indicated that the hypothesis is hereby rejected since a significant difference is obtained. The results from the study revealed that the treatments positively improved the academic achievements of students and the students taught using ethnobiology-based instruction had higher test result and outperformed those in Peer Tutoring and lecture method group, but the students in Peer tutoring group also performed better than those in lecture method group. This implies that Ethnobiology-Based instruction is more effective in enhancing Students’ performance in biology and it helps to break barriers to meaningful learning of biology. Based on the findings of this study, it was recommended that Biology teachers should relate learning to students’ cultural views and encourage peer tutoring.

Factorial design analytics on effects of material parameter uncertainties in multiphysics modeling of additive manufacturing

A bottleneck in Laser Powder Bed Fusion (L-PBF) metal additive manufacturing (AM) is the quality inconsistency of its products. To address this issue without costly experimentation, computational multi-physics modeling has been used, but the effectiveness is limited by parameter uncertainties and their interactions. We propose a full factorial design and variable selection approach for the analytics of main and interaction effects arising from material parameter uncertainties in multi-physics models. Data is collected from high-fidelity thermal-fluid simulations based on a 2-level full factorial design for 5 selected material parameters. Crucial physical phenomena of the L-PBF process are analyzed to extract physics-based domain knowledge, which are used to establish a validation checkpoint for our study. Initial data visualization with half-normal probability plots, interaction plots and standard deviation plots, is used to assess if the checkpoint is being met. We then apply the combination of best subset selection and the LASSO method on multiple linear regression models for comprehensive variable selection. Analytics yield statistically and phyiscally validated findings with practical implications, emphasizing the importance of parameter interactions under uncertainty, and their relation to the underlying physics of L-PBF.

Portfolio Optimization for Five Influencing Companies of Technology Industry in the Hong Kong Stock Market

Nowadays, as a such big number of countries pay more attention to the scientific research strength, The importance of high and new technology is more and more recognized, the proportion in the securities market is increasing, technology stocks in the eyes of investors. This paper selects 5 Chinese technology growth stocks in Hang Seng Index as the research object and establish the dataset which contains the monthly time series of price in Hang Seng index. Fama experiment is used to well diversify the idiosyncratic risk and make sure that the portfolio is affected by the system risk. Through the mean-standard deviation plot, it can check the independence of the data we collect and check the normality. The portfolio can be divided into two situations: one includes risk-free assets and non risk-free assets. Finally, the performances of the portfolios are accessed including return volatility and weight of assets. The results show that, first, based on the Fama experiment, at least 5 stocks must be chosen to deal with idiosyncratic risk. Second, according to the mean-standard deviation plot,all the assets data are i.i.d. Third, the result show that both maximum return portfolios have the high return, and the portfolio contains risk-free assets is higher than portfolios without risk-free assets in the maximum return portfolio, but lower in the maximum Sharpe ratio portfolio. The findings may be useful to stimulate investors pay attention to invest in the technology stocks and create efficient portfolio. Meanwhile, this paper can suggest people how to rationally allocate stocks from multiple dimensions, build an effective combination, and try to obtain higher returns as far as possible in the condition of low risk.

Virtual screening reveals aprepitant to be a potent inhibitor of neutral sphingomyelinase 2: implications in blockade of exosome release in cancer therapy.

Exosomes are membrane-derived nano-vesicles upregulated in pathological conditions like cancer. Therefore, inhibiting their release is a potential strategy for the development of more efficient combination therapies. Neutral sphingomyelinase 2 (nSMase2) is a key component in exosome release; however, a clinically safe yet efficient nSMase2 inhibitor remains to be used discovered. Accordingly, we made an effort to identify potential nSMase2 inhibitor(s) among the approved drugs. Virtual screening was performed and aprepitant was selected for further investigation. To evaluate the reliability of the complex, molecular dynamics were performed. Finally, using the CCK-8 assay in HCT116 cells, the highest non-toxic concentrations of aprepitant were identified and the nSMase2 activity assay was performed to measure the inhibitory activity of aprepitant, in vitro. To validate the screening results, molecular docking was performed, and the retrieved scores were in line with the screening results. The root-mean-square deviation (RMSD) plot of aprepitant-nSMase2 showed proper convergence. Following treatment with different concentrations of aprepitant in both cell-free and cell-dependent assays, nSMase2 activity was remarkably decreased. Aprepitant, at a concentration as low as 15µM, was able to inhibit nSmase2 activity in HCT116 cells without any significant effects on their viability. Aprepitant is therefore suggested to be a potentially safe exosome release inhibitor.

Interaction of the nanobio-based reagent with sodium fluorescein and lipids via bioinformatics for forensic fingerprint visualisations

Being commonly found at crime scenes, fingerprints are crucial for human identification, attributable to their uniqueness, persistence and systematic classification of ridge patterns. In addition to latent fingerprints being invisible to the naked eye, the escalating trends of disposing forensic evidence bearing such prints in watery bodies would further complicate criminal investigations. Taking into account the toxicity of small particle reagent (SPR) commonly used in visualising latent fingerprints on wet and non-porous objects, a greener alternative using the nanobio-based reagent (NBR) has been suggested. However, NBR only applies to white and/or relatively light-coloured objects. Thus, conjugation of sodium fluorescein dye with NBR (f-NBR) may be beneficial for increasing the contrast of fingerprint on multi-colored objects. Hence, this study was aimed at investigating the possibility of such conjugation (i.e., f-NBR) as well as proposing suitable interactions between the f-NBR and lipid constituents of fingerprints (tetra-, hexa- and octadecanoic acids) via molecular docking and molecular dynamics simulations. The binding energies between CRL with its ligands were observed at −8.1, −5.0, −4.9 and −3.6 kcal/mole for sodium fluorescein, tetra-, hexa- and octadecanoic acids, respectively. Besides, the formations of hydrogen bonds observed in all complexes (ranged between 2.6 and 3.4 Å), further supported by the stabilized root mean square deviation (RMSDs) plots in MD simulations. In short, the conjugation of f-NBR was computationally feasible, and thereby merits further investigations in the laboratory. Communicated by Ramaswamy H. Sarma

A new stress-based model to predict the fatigue life of 880-grade pearlitic rail steel under uniaxial ratcheting conditions

This work explores a comparative analysis to verify the applicability of the existing stress-based model to predict life under ratcheting for pearlitic rail steel. The walker model is the most accurate of the available models, but its application is limited owing to the intricacy involved and the experimental reliance on its constant (γ) assessment. As a consequence, a novel stress-based life prediction model is proposed in this study. The applicability of the different models, and the proposed one, is described using equivalent stress amplitude vs life plot, stress deviation plot, error distribution box plots, and probability density function plots.