Understanding the circumstances that lead to pandemics is important for their prevention. We analyzed the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted "A" and "B." Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October to 8 December), and the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans before November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events.
This study proposes and investigates the feasibility of the passive assistive device to assist agricultural harvesting task and reduce the Musculoskeletal Disorder (MSD) risk of harvesters using computational musculoskeletal modelling and simulations. Several passive assistive devices comprised of elastic exotendon, which acts in parallel with different back muscles (rectus abdominis, longissimus, and iliocostalis), were designed and modelled. These passive assistive devices were integrated individually into the musculoskeletal model to provide passive support for the harvesting task. The muscle activation, muscle force, and joint moment were computed with biomechanical simulations for unassisted and assisted motions. The simulation results demonstrated that passive assistive devices reduced muscle activation, muscle force, and joint moment, particularly when the devices were attached to the iliocostalis and rectus abdominis. It was also discovered that assisting the longissimus muscle can alleviate the workload by distributing a portion of it to the rectus abdominis. The findings in this study support the feasibility of adopting passive assistive devices to reduce the MSD risk of the harvesters during agricultural harvesting. These findings can provide valuable insights to the engineers and designers of physical assistive devices on which muscle(s) to assist during agricultural harvesting.
Shrimp aquaculture contributes significantly to global economic growth, and the whiteleg shrimp, Penaeus vannamei, is a leading species in this industry. However, Vibrio parahaemolyticus infection poses a major challenge in ensuring the success of P. vannamei aquaculture. Despite its significance in this industry, the biological knowledge of its pathogenesis remains unclear. Hence, this study was conducted to identify the interaction sites and binding affinity between several immune-related proteins of P. vannamei with V. parahaemolyticus proteins associated with virulence factors. Potential interaction sites and the binding affinity between host and pathogen proteins were identified using molecular docking and dynamics (MD) simulation. The P. vannamei-V. parahaemolyticus protein-protein interaction of Complex 1 (Ferritin-HrpE/YscL family type III secretion apparatus protein), Complex 2 (Protein kinase domain-containing protein-Chemotaxis CheY protein), and Complex 3 (GPCR-Chemotaxis CheY protein) was found to interact with -4319.76, -5271.39, and -4725.57 of the docked score and the formation of intermolecular bonds at several interacting residues. The docked scores of Complex 1, Complex 2, and Complex 3 were validated using MD simulation analysis, which revealed these complexes greatly contribute to the interactions between P. vannamei and V. parahaemolyticus proteins, with binding free energies of -22.50 kJ/mol, -30.20 kJ/mol, and -26.27 kJ/mol, respectively. This finding illustrates the capability of computational approaches to search for molecular binding sites between host and pathogen, which could increase the knowledge of Vibrio spp. infection on shrimps, which then can be used to assist in the development of effective treatment.
In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.
The quest for energy efficiency (EE) in multi-tier Heterogeneous Networks (HetNets) is observed within the context of surging high-speed data demands and the rapid proliferation of wireless devices. The analysis of existing literature underscores the need for more comprehensive strategies to realize genuinely energy-efficient HetNets. This research work contributes significantly by employing a systematic methodology, utilizing This model facilitates the assessment of network performance by considering the spatial distribution of network elements. The stochastic nature of the PPP allows for a realistic representation of the random spatial deployment of base stations and users in multi-tier HetNets. Additionally, an analytical framework for Quality of Service (QoS) provision based on D-DOSS simplifies the understanding of user-base station relationships and offers essential performance metrics. Moreover, an optimization problem formulation, considering coverage, energy maximization, and delay minimization constraints, aims to strike a balance between key network attributes. This research not only addresses crucial challenges in creating EE HetNets but also lays a foundation for future advancements in wireless network design, operation, and management, ultimately benefiting network operators and end-users alike amidst the growing demand for high-speed data and the increasing prevalence of wireless devices. The proposed D-DOSS approach not only offers insights for the systematic design and analysis of EE HetNets but also systematically outperforms other state-of-the-art techniques presented. The improvement in energy efficiency systematically ranges from 67% (min side) to 98% (max side), systematically demonstrating the effectiveness of the proposed strategy in achieving higher energy efficiency compared to existing strategies. This systematic research work establishes a strong foundation for the systematic evolution of energy-efficient HetNets. The systematic methodology employed ensures a comprehensive understanding of the complex interplay of network dynamics and user requirements in a multi-tiered environment.
Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cut-offs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods.
Understanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data-serialized order movements generated by simple bottom-up rules of collective movements-are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations-a coupling of "core" or "keystone" subjects and "subordinate" or "follower" subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating "single-file movement" data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.
Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.
Platoon based traffic flow models form the underlying theoretical framework in traffic simulation tools. They are essentially important in facilitating efficient performance calculation and evaluation in urban traffic networks. For this purpose, a new platoon-based macroscopic model called the LWR-IM has been developed in [1]. Preliminary analytical validation conducted previously has proven the feasibility of the model. In this paper, the LWR-IM is further enhanced with algorithms that describe platoon interactions in urban arterials. The LWR-IM and the proposed platoon interaction algorithms are implemented in the real-world class I and class II urban arterials. Another purpose of the work is to perform quantitative validation to investigate the validity and ability of the LWR-IM and its underlying algorithms to describe platoon interactions and simulate performance indices that closely resemble the real traffic situations. The quantitative validation of the LWR-IM is achieved by performing a two-sampled t-test on queues simulated by the LWR-IM and real queues observed at these real-world locations. The results reveal insignificant differences of simulated queues with real queues where the p-values produced concluded that the null hypothesis cannot be rejected. Thus, the quantitative validation further proved the validity of the LWR-IM and the embedded platoon interactions algorithm for the intended purpose.
Design of neutral receptor molecules (ionophores) for beryllium(II) using unsaturated carbonitrile models has been carried out via density functional theory, G3, and G4 calculations. The first part of this work focuses on gas phase binding energies between beryllium(II) and 2-cyano butadiene (2-CN BD), 3-cyano propene (3-CN P), and simpler models with two separate fragments; acrylonitrile and ethylene. Interactions between beryllium(II) and cyano nitrogen and terminal olefin in the models have been examined in terms of geometrical changes, distribution of charge over the entire π-system, and rehybridization of vinyl carbon orbitals. NMR shieldings and vibrational frequencies probed charge centers and strength of interactions. The six-membered cyclic complexes have planar structures with the rehybridized carbon slightly out of plane (16° in 2-CN BD). G3 results show that in 2-CN BD complex participation of vinyl carbon further stabilizes the cyclic adduct by 16.3 kcal mol(-1), whereas, in simpler models, interaction between beryllium(II) and acetonitrile is favorable by 46.4 kcal mol(-1) compared with that of ethylene. The terminal vinyl carbon in 2-CN BD rehybridizes to sp (3) with an increase of 7 % of s character to allow interaction with beryllium(II). G4 calculations show that the Be(II) and 2-CN BD complex is more strongly bound than those with Mg(II) and Ca(II) by 98.5 and 139.2 kcal mol(-1) (-1), respectively. QST2 method shows that the cyclic and acyclic forms of Be(II)-2-CN BD complexes are separated by 12.3 kcal mol(-1) barrier height. Overlap population analysis reveals that Ca(II) can be discriminated based on its tendency to form ionic interaction with the receptor models.
The rational design of a glycolipid application (e.g. drug delivery) with a tailored property depends on the detailed understanding of its structure and dynamics. Because of the complexity of sugar stereochemistry, we have undertaken a simulation study on the conformational dynamics of a set of synthetic glycosides with different sugar groups and chain design, namely dodecyl β-maltoside, dodecyl β-cellobioside, dodecyl β-isomaltoside and a C12C10 branched β-maltoside under anhydrous conditions. We examined the chain structure in detail, including the chain packing, gauche/trans conformations and chain tilting. In addition, we also investigated the rotational dynamics of the headgroup and alkyl chains. Monoalkylated glycosides possess a small amount of gauche conformers (∼20%) in the hydrophobic region of the lamellar crystal (LC) phase. In contrast, the branched chain glycolipid in the fluid Lα phase has a high gauche population of up to ∼40%. Rotational diffusion analysis reveals that the carbons closest to the headgroup have the highest correlation times. Furthermore, its value depends on sugar type, where the rotational dynamics of an isomaltose was found to be 11-15% and more restrained near the sugar, possibly due to the chain disorder and partial inter-digitation compared to the other monoalkylated lipids. Intriguingly, the present simulation demonstrates the chain from the branched glycolipid bilayer has the ability to enter into the hydrophilic region. This interesting feature of the anhydrous glycolipid bilayer simulation appears to arise from a combination of lipid crowding and the amphoteric nature of the sugar headgroups.
Technology scaling relies on reduced nodal capacitances and lower voltages in order to improve performance and power consumption, resulting in significant increase in layout density, thus making these submicron technologies more susceptible to soft errors. Previous analysis indicates a significant improvement in SEU tolerance of the driver when the bias current is injected into the circuit but results in increase of power dissipation. Subsequently, other alternatives are considered. The impact of transistor sizes and temperature on SEU tolerance is tested. Results indicate no significant changes in Q(crit) when the effective transistor length is increased by 10%, but there is an improvement when high temperature and high bias currents are applied. However, this is due to other process parameters that are temperature dependent, which contribute to the sharp increase in Q(crit). It is found that, with temperature, there is no clear factor that can justify the direct impact of temperature on the SEU tolerance. Thus, in order to improve the SEU tolerance, high bias currents are still considered to be the most effective method in improving the SEU sensitivity. However, good trade-off is required for the low-swing driver in order to meet the reliability target with minimal power overhead.
The increase in the number of fatalities caused by road accidents involving heavy vehicles every year has raised the level of concern and awareness on road safety in developing countries like Malaysia. Changes in the vehicle dynamic characteristics such as gross vehicle weight, travel speed, and vehicle classification will affect a heavy vehicle's braking performance and its ability to stop safely in emergency situations. As such, the aim of this study is to establish a more realistic new distance-based safety indicator called the minimum safe distance gap (MSDG), which incorporates vehicle classification (VC), speed, and gross vehicle weight (GVW).
Efforts to model the human upper respiratory system have undergone many phases. Geometrical proximity to the realistic shape has been the subject of many research projects. In this study, three different geometries of the trachea and main bronchus were modelled, which were reconstructed from computed tomography (CT) scan images. The geometrical variations were named realistic, simplified and oversimplified. Realistic refers to the lifelike image taken from digital imaging and communications in medicine format CT scan images, simplified refers to the reconstructed image based on natural images without realistic details pertaining to the rough surfaces, and oversimplified describes the straight wall geometry of the airway. The characteristics of steady state flows with different flow rates were investigated, simulating three varied physical activities and passing through each model. The results agree with previous studies where simplified models are sufficient for providing comparable results for airflow in human airways. This work further suggests that, under most exercise conditions, the idealised oversimplified model is not favourable for simulating either airflow regimes or airflow with particle depositions. However, in terms of immediate analysis for the prediction of abnormalities of various dimensions of human airways, the oversimplified techniques may be used.
Finding a proper transition structure for the peptide bond formation process can lead one to a better understanding of the role of ribosome in catalyzing this reaction. Using computer simulations, we performed the potential energy surface scan on the ester bond dissociation of P-site aminoacyl-tRNA and the peptide bond formation of P-site and A-site amino acids. The full fragments of initiator tRNA(i)(met) and elongator tRNA(phe) are attached to both cognate and non-cognate amino acids as the P-site substrate. The A-site amino acid for all four calculations is methionine. We used ONIOM calculations to reduce the computational cost. Our study illustrates the reduced rate of peptide bond formation for misacylated tRNA(i)(met) in the absence of ribosomal bases. The misacylated elongator tRNA(phe), however, did not show any difference in its PES compared with that for the phe-tRNA(phe). This demonstrates the structural specification of initiator tRNA(i)(met) for the amino acids side chain.
This article describes design and application of feed-forward, fully-connected, three-layer perceptron neural network model for computing the water quality index (WQI)(1) for Kinta River (Malaysia). The modeling efforts showed that the optimal network architecture was 23-34-1 and that the best WQI predictions were associated with the quick propagation (QP) training algorithm; a learning rate of 0.06; and a QP coefficient of 1.75. The WQI predictions of this model had significant, positive, very high correlation (r=0.977, p<0.01) with the measured WQI values, implying that the model predictions explain around 95.4% of the variation in the measured WQI values. The approach presented in this article offers useful and powerful alternative to WQI computation and prediction, especially in the case of WQI calculation methods which involve lengthy computations and use of various sub-index formulae for each value, or range of values, of the constituent water quality variables.
A deterministic simulation model was developed to estimate biological production efficiency and to evaluate goat crossbreeding systems under tropical conditions. The model involves 5 production systems: pure indigenous, first filial generations (F1), backcross (BC), composite breeds of F1 (CMP(F1)), and BC (CMP(BC)). The model first simulates growth, reproduction, lactation, and energy intakes of a doe and a kid on a 1-d time step at the individual level and thereafter the outputs are integrated into the herd dynamics program. The ability of the model to simulate individual performances was tested under a base situation. The simulation results represented daily BW changes, ME requirements, and milk yield and the estimates were within the range of published data. Two conventional goat production scenarios (an intensive milk production scenario and an integrated goat and oil palm production scenario) in Malaysia were examined. The simulation results of the intensive milk production scenario showed the greater production efficiency of the CMP(BC) and CMP(F1) systems and decreased production efficiency of the F1 and BC systems. The results of the integrated goat and oil palm production scenario showed that the production efficiency and stocking rate were greater for the indigenous goats than for the crossbreeding systems.
Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper.
There is a dire need to have complementary form of disaster training which is cost effective, relatively easy to conduct, comprehensive, effective and acceptable. This will complement field drills training. A classroom-based training and simulation module was built by combining multiple tools: Powerpoint lectures, simulations utilising the Kuala Lumpur International Airport (KLIA) schematic module into 'floortop' model and video show of previous disaster drill. 76 participants made up of medical responders, categorised as Level 1 (specialists and doctors), Level 2 (paramedics), Level 3 (assistant paramedics) and Level 4 (health attendants and drivers) were trained using this module. A pre-test with validated questions on current airport disaster plans was carried out before the training. At the end of training, participants answered similar questions as post-test. Participants also answered questionnaire for assessment of training's acceptance. There was a mean rise from 47.3 (18.8%) to 84.0 (18.7%) in post-test (p<0.05). For Levels 1, 2, 3 and 4 the scores were 94.8 (6.3)%, 90.1 (11)%, 80.3 (20.1)% and 65 (23.4)% respectively. Nevertheless Level 4 group gained most increase in knowledge rise from baseline pre-test score (51.4%). Feedback from the questionnaire showed that the training module was highly acceptable. A classroom-based training can be enhanced with favourable results. The use of classroom training and simulation effectively improves the knowledge of disaster plan significantly on the back of its low cost, relatively-easy to conduct, fun and holistic nature. All Levels of participants (from specialists to drivers) can be grouped together for training. Classroom training and simulation can overcome the problem of "dead-document" phenomenon or "paper-plan syndrome".
Comparative molecular dynamics simulations of n-octyl-beta-D-galactopyranoside (beta-C8Gal) and n-octyl-beta-D-glucopyranoside (beta-C8Glc) micelles in aqueous solution have been performed to explore the influence of carbohydrate stereochemistry on glycolipid properties at the atomic level. In particular, we explore the hypothesis that differences in T(m) and T(c) for beta-C8Gal and beta-C8Glc in lyotropic systems arise from a more extensive hydrogen bonding network between beta-C8Gal headgroups relative to beta-C8Glc, due to the axial 4-OH group in beta-C8Gal. Good agreement of the 13 ns micelle-water simulations with available experimental information is found. The micelles exhibit a similar shape, size, and degree of exposed alkyl chain surface area. We find net inter- and intra-headgroup hydrogen bonding is also similar for beta-C8Gal and beta-C8Glc, although n-octyl-beta-D-galactopyranoside micelles do exhibit a slightly greater degree of inter- and intra-headgroup hydrogen bonding. However, the main distinction in the calculated microscopic behavior of beta-C8Glc and beta-C8Gal micelles lies in solvent interactions, where beta-d-glucosyl headgroups are considerably more solvated (mainly at the equatorial O4 oxygen). These results agree with preceding theoretical and experimental studies of monosaccharides in aqueous solution. A number of long water residence times are found for solvent surrounding both micelle types, the largest of which are associated with surface protrusions involving headgroup clusters. Our simulations, therefore, predict differences in hydrogen bonding for the two headgroup stereochemistries, including a small difference in inter-headgroup interactions, which may contribute to the higher T(m) and T(c) values of beta-C8Gal surfactants relative to beta-C8Glc in lyotropic systems.