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There has been an increasing trend in the design of novel pyrazole derivatives for desired biological applications. For a cost-effective strategy, scientists have implemented various computational drug design tools to go hand in hand with experiments for the design and discovery of potentially effective pyrazole-based therapeutics. This review highlights the milestones of pyrazole-containing inhibitors and the use of molecular modeling techniques in conjunction with experimental studies to provide a view of the binding mechanism of these compounds. The review focuses on the established targets that play a key role in cancer therapy, including proteins involved in tubulin polymerization, carbonic anhydrase and tyrosine kinase. Overall, using both experimental and computational methods in drug design represents a promising approach to cancer therapy.

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Proteins rarely exert their function by themselves. Protein-protein interactions (PPIs) regulate virtually every biological process that takes place in a cell. Such interactions are targets for new therapeutic agents against all sorts of diseases, through the screening and design of a variety of inhibitors. Here we discuss several aspects of PPIs that contribute to prediction of protein function and drug discovery. As the high-throughput techniques continue to release biological data, targets for fungal therapeutics that rely on PPIs are being proposed worldwide. Computational approaches have reduced the time taken to develop new therapeutic approaches. The near future brings the possibility of developing new PPI and interaction network inhibitors and a revolution in the way we treat fungal diseases.

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This study proposed a theoretical prediction method and mechanism investigation for the extraction of antibiotics and dyes from aqueous media using terpenoid-based deep eutectic solvents (DESs). Firstly, Conductor-like Screening Model for Real Solvents (COSMO-RS) approach was applied to predict selectivity, capacity and performance index in the extraction of 15 target compounds including antibiotics (tetracyclines, sulfonamides, quinolones, ß-lactams) and dyes by 26 terpenoid-based DESs, and thymol-benzyl alcohol shows promising theoretical selectivity and extraction efficiency for the target compounds. Moreover, the structures of both hydrogen bond acceptors (HBA) and hydrogen bond donors (HBD) have an impact on the predicted extraction performance, which can be improved by tailoring those candidates with higher polarity, smaller molecular volume, shorter alkyl chain length and the presence of aromatic ring structures, etc. According to the predicted molecular interactions revealed by σ-profile and σ-potential, the DESs with HBD ability can promote the separation process. Furthermore, reliability of proposed prediction method was confirmed by experimental verification, indicating that the trends of theoretical extraction performance index were similar with the experimental results by using actual samples. At last, the extraction mechanism was evaluated by quantum chemical calculations based on visual presentations, thermodynamic calculations and topological properties; and the target compounds showed favorable energies of solvation to transfer from aqueous phase to DESs phase. The proposed method has been proved with potential to provide the efficient strategies and guidance for more applications (e.g., microextraction, solid phase extraction, adsorption) with similar molecular interactions of green solvents in environmental research.

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@#Chikungunya virus (CHIKV) is a neglected tropical pathogen that causes fever and long-lasting severe arthralgia. Despite its high morbidity, there is still no licensed specific therapeutic option for it. This study proposes a multi-epitope subunit vaccine candidate for CHIKV, designed using computational methods. It was based on the E2 spike glycoprotein in CHIKV, from which T- and B-cell epitopes were predicted and then refined. The pan HLA DR-binding epitope (PADRE) was added to this refined construct, then simulated compared with the native protein, where it was predicted to elicit more than twice the number of antibody titers. Thus, this construct is potentially effective against CHIKV, which further experimentation using live models would be able to verify. This study also demonstrates the feasibility of using rational tools in the future to further optimize vaccine design.

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Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology. Meanwhile, pioneering explorations in spatial transcriptomics have opened up avenues to address fundamental biological questions in health and diseases. Here, we review the technical attributes of single-cell RNA sequencing and spatial transcriptomics, and the core concepts of computational data analysis. We further highlight the challenges in the application of data integration methodologies and the interpretation of the biological context of the findings.

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BACKGROUND AND OBJECTIVE: We propose a nonstandard computational model to approximate the solutions of a stochastic system describing the propagation of an infectious disease. The mathematical model considers the existence of various sub-populations, including humans who are susceptible to the disease, asymptomatic humans, infected humans and recovered or quarantined individuals. Various mechanisms of propagation are considered in order to describe the propagation phenomenon accurately. METHODS: We propose a stochastic extension of the deterministic model, considering a random component which follows a Brownian motion. In view of the difficulties to solve the system exactly, we propose a computational model to approximate its solutions following a nonstandard approach. RESULTS: The nonstandard discretization is fully analyzed for positivity, boundedness and stability. It is worth pointing out that these properties are realized in the discrete scenario and that they are thoroughly established herein using rigorous mathematical arguments. We provide some illustrative computational simulations to exhibit the main computational features of this approach. CONCLUSIONS: The results show that the nonstandard technique is capable of preserving the distinctive characteristics of the epidemiologically relevant solutions of the model, while other (classical) approaches are not able to do it. For the sake of convenience, a computational code of the nonstandard discrete model may be provided to the readers at their requests.

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Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

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In the present paper, a computational methodology for the development of microdevices and microreactors are presented. The methodology was based on Computational Fluid Dynamics (CFD), i.e., the solution of transport equations governing the flow field. The methodology presents a step-by-step tutorial for modeling and simulation of such microdevices that can be used by beginner or experienced users. The proposed methodology was employed in two study cases: fluid mixing and fluid mixing accompanied by chemical reaction. Two new geometry designs were evaluated: a micrometric scale channel with triangular cross section (MT) and a millimeter range scaled channel (MTB). It is expect that the reported methodology contributes to the popularization of CFD usage among researchers, scientists and Microfluidic enthusiasts. Also, it can motivate future studies to focus firstly on geometry optimization by numerical simulations, providing a faster and economical way to develop microdevices. •A CFD-based methodology was presented for the development of microdevices and microreactors•The methodology can be used in distinct fluid mixing and chemical reaction systems•Numerical simulation allows a faster microdevice development procedure with costs reductions.

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We show that the central finite difference formula for the first and the second derivative of a function can be derived, in the context of quantum mechanics, as matrix elements of the momentum and kinetic energy operators on discrete coordinate eigenkets |xn〉 defined on a uniform grid. Starting from the discretization of integrals involving canonical commutations, simple closed-form expressions of the matrix elements are obtained. A detailed analysis of the convergence toward the continuum limit with respect to both the grid spacing and the derivative approximation order is presented. It is shown that the convergence from below of the eigenvalues in electronic structure calculations is an intrinsic feature of the finite difference method. © 2018 Wiley Periodicals, Inc.

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Negative linear compressibility (NLC) is a relatively uncommon phenomenon and rarely studied in organic systems. Here we provide the direct evidence of the persistent NLC in organic mineral ammonium oxalate monohydrate under high pressure using synchrotron X-ray powder diffraction, Raman spectroscopy and density functional theory (DFT) calculation. Synchrotron X-ray powder diffraction measurement reveals that ammonium oxalate monohydrate shows both positive and negative linear compressibility along b-axis before 11.5 GPa. The red shift of the external Raman modes and abnormal changes of several selected internal modes in high-pressure Raman spectra further confirmed the NLC. DFT calculations demonstrate that the N-H···O hydrogen bonding "wine-rack" motifs result in the NLC along b-axis in ammonium oxalate monohydrate. We anticipate the high-pressure study of ammonium oxalate monohydrate may represent a promising strategy for accelerating the pace of exploitation and improvement of NLC materials especially in organic systems.

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There is an urgent need for broad and integrated studies that address the risks of engineered nanomaterials (ENMs) along the different endpoints of the society, environment, and economy (SEE) complex adaptive system. This article presents an integrated science-based methodology to assess the potential risks of engineered nanomaterials. To achieve the study objective, two major tasks are accomplished, knowledge synthesis and algorithmic computational methodology. The knowledge synthesis task is designed to capture "what is known" and to outline the gaps in knowledge from ENMs risk perspective. The algorithmic computational methodology is geared toward the provision of decisions and an understanding of the risks of ENMs along different endpoints for the constituents of the SEE complex adaptive system. The approach presented herein allows for addressing the formidable task of assessing the implications and risks of exposure to ENMs, with the long term goal to build a decision-support system to guide key stakeholders in the SEE system towards building sustainable ENMs and nano-enabled products.

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Individual Level Models (ILMs), a new class of models, are being applied to infectious epidemic data to aid in the understanding of the spatio-temporal dynamics of infectious diseases. These models are highly flexible and intuitive, and can be parameterised under a Bayesian framework via Markov chain Monte Carlo (MCMC) methods. Unfortunately, this parameterisation can be difficult to implement due to intense computational requirements when calculating the full posterior for large, or even moderately large, susceptible populations, or when missing data are present. Here we detail a methodology that can be used to estimate parameters for such large, and/or incomplete, data sets. This is done in the context of a study of the UK 2001 foot-and-mouth disease (FMD) epidemic.