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Objective: Tuberculosis (TB) is a contagious disease caused by Mycobacterium tuberculosis, that poses a threat to the international scientific community. According to the WHO, 2022 report, an estimated 1.6 million people died from tuberculosis and 10.6 million have been diagnosed. In the present study, the target protein is Rv3032 protein, which belongs to the Glycosyl transferase group 1 family and is involved in the biosynthesis of the methyl glucosyl lipopolysaccharide (MGLP). Methods: The 3D structural features of the target protein were identified using computational techniques, including model building, active site prediction, and structural evaluation. Virtual screening studies were conducted at the active site using Bionet and Rare chemical ligand databases to identify the drug-like compounds. Results and Discussion: 67 ligands were selected as lead molecules based on the highest glide scores and glide energies. Out of which 6 ligands were studied, in which Carboxylate, amino, and carboxamide groups were common pharmacophore groups present in the A1 to A6 docked complexes have common binding with the ARG224, LYS229, and GLU311 amino acid residues. Conclusions: ADME parameters were found in the permissible range proving the lead-like properties of the resultant ligand molecules. [ABSTRACT FROM AUTHOR]

In our investigation, we've utilized an advanced computational technique, the modified exp - ϕ (ζ) -expansion method, to derive precise solitary wave solutions within the (2+1)–dimensional Ablowitz–Kaup–Newell–Segur (AKNS ) equation. These solutions, categorized by peak amplitudes, wave speeds, and widths, underscore the efficacy and reliability of our approach compared to conventional numerical methods. The significance of our study extends to practical applications of the (2+1)–dimensional AKNS equation in modeling various physical phenomena, from nonlinear optics to water waves and plasma physics. Our research, by precisely determining solitary wave solutions, not only deepens our comprehension of these phenomena but also establishes a robust basis for practical applications. [ABSTRACT FROM AUTHOR]

This investigation employs advanced computational techniques to ascertain novel and precise solitary wave solutions of the Camassa–Holm ( ℋ) equation, a partial differential equation governing wave phenomena in one-dimensional media. Originally designed for the representation of shallow water waves, the ℋ equation has exhibited versatility across various disciplines, including nonlinear optics and elasticity theory. It intricately delineates the interplay between nonlinear and dispersive effects in wave systems, with nonlinearity arising from component interactions and dispersion rooted in the temporal spreading of waves. Furthermore, the ℋ equation governs the spatiotemporal evolution of wave profiles, encompassing both nonlinear and dispersive influences. Notably, the equation allows for soliton solutions — localized wave packets sustaining their form over extended distances. The identification of precise solitary wave solutions holds paramount significance for comprehending the ℋ equation's behavior in diverse physical contexts, such as fluid dynamics and nonlinear optics. Moreover, this study establishes a correlation between the investigated model and plasma physics, demonstrating the efficacy and efficiency of the employed computational techniques through benchmarking against alternative computational methods. This augmentation underscores the broader relevance of the ℋ equation, extending its applicability to provide insights into wave phenomena analogous to those encountered in plasma physics. [ABSTRACT FROM AUTHOR]

This research employs recent and precise computational techniques to identify new and accurate solitary wave solutions of the modified Korteweg-de Vries-Kadomtsev-Petviashvili (KdV-KP) equation. The KdV-KP equation is a nonlinear partial differential equation that characterizes the evolution of waves in diverse physical systems, including nonlinear optics, fluid dynamics, and plasma physics. The equation generalizes the KdV and KP equations, which are fundamental models in their respective fields. Physically, the modified KdV-KP equation describes wave propagation where the effects of nonlinearity and dispersion can produce intriguing and complicated wave phenomena such as wave turbulence and solitary waves. The equation has numerous real-world applications, including modeling shallow water waves such as tsunamis and river waves in fluid dynamics, describing the behavior of plasma waves in fusion reactors and astrophysical plasmas, and studying the propagation of light in nonlinear media such as optical fibers in nonlinear optics. One of the most significant applications of the equation is in the study of solitons, which are self-sustaining solitary waves that preserve their shape and velocity even after colliding with other solitons. Solitons are used in various applications such as optical communications, where they transmit information over long distances without distortion, and in fluid dynamics, where they model long-lasting waves in the ocean. The study's importance lies in its impact on the utilization of the modified KdV-KP equation in diverse fields of physics, including fluid dynamics and plasma physics. The effectiveness of the proposed techniques is demonstrated by comparing them with other computational methods, indicating their superiority. [ABSTRACT FROM AUTHOR]

This paper attempts to elucidate the transformative integration of computational techniques within power systems, underscoring their critical role in enhancing system modeling, control, and the efficient integration of renewable energy. It breaks down the two-sided nature of technological progress, highlighting both gains in operational efficiency and new challenges such as real-time processing, data management, and cybersecurity. Through meticulous analysis of query-based research patterns and mathematical frameworks, this study delves into the balancing act between specificity and breadth in scholarly inquiries while evaluating the impact and evolution of research trends through citation analysis. The convergence of interests and transient research trends is evident, particularly in Artificial Intelligence and optimization. This comprehensive narrative anticipates a sophisticated trajectory for power systems, advocating for continuous innovation and strategic research to foster sustainable, resilient, and intelligent energy networks. [ABSTRACT FROM AUTHOR]

This study examines an unexplored aspect of SARS-CoV-2 entry into host cells, which is widely understood to occur via the viral spike (S) protein's interaction with human ACE2-associated proteins. While vaccines and inhibitors targeting this mechanism are in use, they may not offer complete protection against reinfection. Hence, we investigate putative receptors and their cofactors. Specifically, we propose CD46, a human membrane cofactor protein, as a potential putative receptor and explore its role in cellular invasion, acting possibly as a cofactor with other viral structural proteins. Employing computational techniques, we created full-size 3D models of human CD46 and four key SARS-CoV-2 structural proteins—EP, MP, NP, and SP. We further developed 3D models of CD46 complexes interacting with these proteins. The primary aim is to pinpoint the likely interaction domains between CD46 and these structural proteins to facilitate the identification of molecules that can block these interactions, thus offering a foundation for novel pharmacological treatments for SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

Polymeric micelles (PM) are amphiphilic block copolymers that can form nanoscopic core/shell structures, making them highly effective for drug delivery systems (DDS). Multi‐armed starshaped poly(ethylene glycol) (PEG) polymers, a promising type of PM, provide a regulated release of drugs and can be integrated with numerous endpoints to create multifunctional drug delivery systems capable of transporting a large number of drugs. Star‐PEG polymers can also be cross‐linked to create three dimensional hydrogels with variable functionalities and sizes, increasing blood flow and reducing unanticipated drug release. In this review, we present state‐of‐the‐art computational tools for analyzing drug pharmacokinetic profiles and identifying molecular interactions critical for successful drug encapsulation in star‐PEG PM. By taking into account the features described, academic and industrial researchers can identify novel interrelationships in PM and maximize their capabilities as DDSs for systemic administration, ultimately enhancing the medicinal qualities of drug delivery. [ABSTRACT FROM AUTHOR]

The term "drug repurposing" refers to the practice of identifying unmet medical needs and developing innovative solutions using already available drugs. It's a useful strategy for identifying or developing new medicinal molecules with untapped therapeutic potential. Some of the computational drugs repurposing methods currently in use have been employed in the fight against the 2019 coronavirus illness (COVID-19) pandemic. Many currently used medications are being repurposed, thanks to advances in computational approaches and a fundamental understanding of viral etiology and pharmacological pharmacodynamics. The objective of this work is to highlight the utilization of repurposed medicines for COVID-19, bacterial infections and cancer therapy. The drug repurposing method is fast-growing in both business and academia, since it focuses on the initial knowledge and investment that brought the product to market in the first place. Recently, medication repositioning has been included in the drug R&D plans of several pharmaceutical companies, aiming to create new therapies in response to the identification of novel biological targets. In addition to being highly efficient, the drug repurposing method also saves money and the pharmacological profiles are generally known. [ABSTRACT FROM AUTHOR]

In eukaryotic cells, membrane proteins play a crucial role. They fall into three categories: intrinsic proteins, extrinsic proteins, and proteins that are essential to the human genome (30% of which is devoted to encoding them). Hydrophobic interactions inside the membrane serve to stabilize integral proteins, which span the lipid bilayer. This review investigates a number of computational and experimental methods used to study membrane proteins. It encompasses a variety of technologies, including electrophoresis, X-ray crystallography, cryogenic electron microscopy (cryo-EM), nuclear magnetic resonance spectroscopy (NMR), biophysical methods, computational methods, and artificial intelligence. The link between structure and function of membrane proteins has been better understood thanks to these approaches, which also hold great promise for future study in the field. The significance of fusing artificial intelligence with experimental data to improve our comprehension of membrane protein biology is also covered in this paper. This effort aims to shed light on the complexity of membrane protein biology by investigating a variety of experimental and computational methods. Overall, the goal of this review is to emphasize how crucial it is to understand the functions of membrane proteins in eukaryotic cells. It gives a general review of the numerous methods used to look into these crucial elements and highlights the demand for multidisciplinary approaches to advance our understanding. [ABSTRACT FROM AUTHOR]

This study focuses on employing recent and accurate computational techniques, specifically the Sardar-sub equation () method, to explore novel solitary wave solutions of the Gilson–Pickering (ℙ) equation. The GP equation is a mathematical model with implications in fluid dynamics and wave phenomena. It describes the behavior of solitary waves, which are localized disturbances propagating through a medium without changing shape. The physical significance of the ℙ equation lies in its ability to capture the dynamics of solitary waves in various systems, including water waves, optical fibers, and nonlinear acoustic waves. The study's findings contribute to the advancement of mathematical modeling approaches and offer valuable insights into solitary wave phenomena. The stability of the constructed solutions is investigated using the properties of the Hamiltonian system. The accuracy of the computational solutions is demonstrated by comparing them with approximate solutions obtained through He's variational iteration (ℍ ) method. Furthermore, the effectiveness of the employed computational techniques is validated through comparisons with other existing methods. [ABSTRACT FROM AUTHOR]

Biological and financial models are examples of dynamical systems where both stochastic and historical behavior are important to be considered. The fractional Brownian motion (fBM) is commonly used, sometimes with fractional-order derivatives, to model the combined stochastic and fractional effects. Recently, spectral techniques are used to analyze models with fBM using, e.g., iterated Itô fractional integrals such as the fractional Wiener-Hermite (FWHE). In the current work, FWHE is generalized and adapted to be consistent with the Malliavin calculus approach. The conditions for existence and uniqueness are outlined in addition to the proof of convergence. The solution algorithm is described in detail. Using FWHE, the stochastic fractional model is replaced by a deterministic fractional-order system that can be handled using well-known mathematical tools to evaluate the solution statistics. Analytical solutions can be obtained for many important models such as the fractional stochastic Black–Scholes model. The convergence is studied and compared with the exact solution and high convergence is noticed compared with other techniques. A general numerical algorithm is described to analyze the resultant deterministic system in the case of no feasible analytical solutions. The algorithm is applied to study and simulate the population model with nonlinear losses for different values of the Hurst parameter. The results show the efficiency of FWHE in analyzing practical linear and nonlinear models. [ABSTRACT FROM AUTHOR]

This study introduces recent computational advancements targeting the discovery of precise and novel solitary wave solutions in the (1 + 1)-dimensional Mikhailov-Novikov-Wang (MNW ) integrable equation. Solitary wave solutions are of paramount importance for comprehending complex nonlinear phenomena and have practical implications in diverse scientific fields such as fluid dynamics, nonlinear optics, and plasma physics. The proposed computational techniques encompass powerful numerical methods, including the Khater II (KHA - II) method, modified Rational method (MRE ) method, and He’s variational iteration (HVI ) method. These techniques offer robust and accurate approaches for computing solitary wave solutions by leveraging the inherent integrability properties of the MNW equation. Notably, these approaches demonstrate substantial improvements in efficiency, accuracy, and the ability to explore a broader range of solitary wave solutions compared to traditional computational methods. The efficacy of these techniques is effectively demonstrated through extensive numerical simulations and comprehensive comparisons with other computational approaches. The results highlight the superior performance of the proposed methods in identifying new solitary wave solutions, precisely characterizing their properties, and providing deeper insights into the model’s behavior. Overall, these computational techniques significantly contribute to advancing our understanding of the underlying dynamics of the MNW equation and hold promising potential for applications across multiple scientific disciplines. [ABSTRACT FROM AUTHOR]

Laser cladding is a directed energy deposition process and can lead to high residual stresses, which can compromise the quality of the specimen. As a result, it is crucial to accurately predict and investigate the residual stress distribution in cladded parts and understand the formation mechanisms. In this study, a thermo-mechanical metallurgical simulation model of the laser cladding process was developed for three different deposition sequences for a thin wall hexagon with inner junctions to investigate the formation of residual stress and distortion. The study was performed for single and multilayer scenarios. Two types of computational techniques, the detailed transient approach and the imposed thermal cycle approach, were performed and comparisons conducted. Consistent results were observed when comparing the resultant stress patterns for the single layer; subsequently, the imposed thermal cycle method was applied for the five-layer models. A preheat scenario is explored. This reduced the computational cost significantly, but the stress patterns were not similar. This indicates that building up worn regions at the top of a thin-walled component, such as a roll die, needs to be investigated further as unique issues have been highlighted. The differences between the implemented computational techniques are described as well as the advantages and disadvantages of each. Knowledge obtained from these case studies provides a foundation for efficient and rapid optimization of laser cladding processes, with the aim of minimizing residual stress in both simple and complex laser cladding structures. [ABSTRACT FROM AUTHOR]

Harmonic generation in power system networks presents significant issues that arise in power utilities. This paper describes a machine learning technique that was used to conduct a research study on the harmonic analysis of railway power stations. The research was an investigation of a time series whose values represented the total harmonic distortion (THD) for the electric current. This study was based on information collected at a railway power station. In an electrified substation, measurements of currents and voltages were made during a certain interval of time. From electric current values, the THD was calculated using a fast Fourier transform analysis (FFT) and the results were used to train an adaptive ANN—GMDH (artificial neural network–group method of data handling) algorithm. Following the training, a prediction model was created, the performance of which was investigated in this study. The model was based on the ANN—GMDH method and was developed for the prediction of the THD. The performance of this model was studied based on its parameters. The model's performance was evaluated using the regression coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE). The model's performance was very good, with an RMSE (root-mean-square error) value of less than 0.01 and a regression coefficient value higher than 0.99. Another conclusion from our research was that the model also performed very well in terms of the training time (calculation speed). [ABSTRACT FROM AUTHOR]

This study utilized computational techniques for a reliable analysis of discourse. These techniques were adopted to analyze the progress of Saudi social change in terms of women's empowerment within the Saudi transformation program. The data from open source 2021–2022 Saudi newspaper archives were automatically crawled using cutting-edge computational techniques and structured according to the sections of the Saudi newspapers: front page, economy, international, sports, society, culture and religion. The analysis was based on computing the minimally uneven distribution of the relative frequencies of the occurrence of the central word (the Arabic forms of woman and women) from the years 2021 to 2022. This produced two samples of text data, each of which represented the respective years. Calculating the normalized and adjusted frequencies of the central word from each section in the data from each year was important to avoid unbalanced absolute frequencies in the qualitative analysis stage. In addition, dispersion measures showed that the amount of variance in terms of the lexical dispersion of the central word was not high. The observable facts from the quantitative analysis produced a more accurate observational sample of citations, which we qualitatively analyzed. The results of the latter showed a considerable ascending change in favor of empowering women as a consequence of Saudi Vision 2030. [ABSTRACT FROM AUTHOR]

The uncertainty due to road fluctuations and vision system dynamics represents a big challenge to adjusting the steering angle of autonomous vehicles (AVs). Furthermore, AVs require fast action to follow the target lane to overcome lateral deviation with minor errors. In this regard, this paper introduces a fast model predictive controller formulated based on the discrete-time Laguerre function (DTLF) to overcome the high computational burden of the traditional MPC. To improve the hybrid DTLF-MPC performance, a modern and effective dandelion optimizer (DO) strategy is used in this work, which resulted in obtaining the optimal DTLF-MPC parameters and achieving satisfactory results. Furthermore, the proposed hybrid DTLF-MPC is designed based on different algorithms in the literature to evaluate the performance of the DO. Several scenarios are discussed in this paper to confirm the effectiveness and efficiency of the proposed control strategy system against the vision system uncertainty and road fluctuations. The results emphasize that the proposed DTLF-MPC based on the DO can achieve the best damping performance for the lateral deviations with less overshoot; around 0.4533, and a settling time of around 0.01979 s compared with other algorithms. [ABSTRACT FROM AUTHOR]

The development of new bioactive compounds represents one of the main purposes of the drug discovery process. Various tools can be employed to identify new drug candidates against pharmacologically relevant biological targets, and the search for new approaches and methodologies often represents a critical issue. In this context, in silico drug repositioning procedures are required even more in order to re-evaluate compounds that already showed poor biological results against a specific biological target. 3D structure-based pharmacophoric models, usually built for specific targets to accelerate the identification of new promising compounds, can be employed for drug repositioning campaigns as well. In this work, an in-house library of 190 synthesized compounds was re-evaluated using a 3D structure-based pharmacophoric model developed on soluble epoxide hydrolase (sEH). Among the analyzed compounds, a small set of quinazolinedione-based molecules, originally selected from a virtual combinatorial library and showing poor results when preliminarily investigated against heat shock protein 90 (Hsp90), was successfully repositioned against sEH, accounting the related built 3D structure-based pharmacophoric model. The promising results here obtained highlight the reliability of this computational workflow for accelerating the drug discovery/repositioning processes. [ABSTRACT FROM AUTHOR]

Magnesium and magnesium based alloys biomaterials are considered the most viable and pragmatic choice of selection as biomedical alternatives for tissue repair of damaged bones, vascular and other biological disorders. Considering the sensitivity and health implication of implanting foreign matter into the human system, extensive studies are required before clearance for clinical trials is given. This often requires rigorous experimental evaluations of biomechanical and biodegradation properties of such biomaterials, which are often expensive, time consuming and practically demanding, and requires several validation protocols. The technical advances in computing power as well as computational modeling techniques, such as finite element analysis, have been sort to address these limitations, but very little has been catalogued in reviews on the basis of their utilization and sundry issues. This paper attempts to fill this gap by assessing from literature, how effective the application of finite element analysis has been in the evaluation of the biomechanical and biodegradation behaviour of Mg-based biomedical systems which are considered for use as stents, screws, staples, and implants for orthopedic and cardiovascular applications. The review focuses on how factors such as geometric profile of the Mg based system, the material properties and applicable physical and constitutive laws, design parameters, boundary conditions stipulated, the analysis of modeling and simulation outcomes, and agreements with experimental data, influence the potentials for utilization for real life designs of Mg based biomaterial systems. Also, the challenges that could affect their effectiveness with directions on future studies for improvements, were covered. The review will be of immense benefit to researchers, biomaterial designers and clinicians. [ABSTRACT FROM AUTHOR]

This paper explores accurate, stable, and novel soliton wave solutions of the nonlinear Qiao model. This model, which was derived in 2007, possesses a Lax representation and bi-Hamiltonian structure, where it is a second positive member in the utterly integrable hierarchy. The well-known generalized extended tanh-function method is employed to construct novel soliton wave solutions. The stable property of the obtained solutions is examined along with the Hamiltonian system's characterizations. Furthermore, the accuracy of the obtained solutions is checked by comparing it with the model's semi-analytical solutions that have been obtained by employing the variational iteration (VI) method. The obtained analytical and semi-analytical solutions are demonstrated through some distinct graphs to show more physical and dynamical behavior of the investigated model. The used analytical and semi-analytical schemes' performance is checked to show if it is effective and powerful. [ABSTRACT FROM AUTHOR]

Traveling-wave modulation is a form of space–time modulation, which has been shown to enable unique electromagnetic phenomena such as nonreciprocity, beam steering, frequency conversion, and amplification. In practice, traveling-wave modulation is achieved by applying staggered time-modulation signals to a spatially discrete array of unit cells. Therefore, the capability to accurately simulate spatially discrete traveling-wave modulated structures is critical to the design. However, simulating these structures is challenging due to the complex space–time dependence of the constituent unit cells. In this article, a field relation (referred to as the interpath relation) is derived for spatially discrete traveling-wave modulated structures. The interpath relation reveals that the field within a single time-modulated unit cell (rather than an entire spatial period) is sufficient to determine the field solution throughout space. It will be shown that the interpath relation can be incorporated into the existing periodic method of moment solvers simply by modifying the source basis functions. As a result, the computational domain is reduced from an entire spatial period to a single time-modulated unit cell, dramatically reducing the number of unknowns. In the context of traveling-wave modulation, this enables researchers to efficiently simulate both complex structures with patterned unit cells in addition to continuous structures with infinitesimal unit cells. [ABSTRACT FROM AUTHOR]

To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. [ABSTRACT FROM AUTHOR]

Due to the severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 outbreak, the virus has been wildly spread throughout the world and the number of infected patients has rapidly increased. More importantly, neither the official drug treatment nor the vaccine has been officially offered. These have considerably increased the public concerns internationally and nationally. Recently, there has been one question raised in the Thai society; "Could a common Thai herbal medicine namely Andrographis paniculata be used against SARS-CoV-2 infection?". It is well-known that the plant has antiviral properties against wild ranges of viruses and the active metabolite is andrographolide. To date, there have only been a few studies investigating the anti-SARS-CoV-2 activity from andrographolide. To provide a better understanding, this study was conducted by applying the advanced techniques in both computational biology and chemistry to evaluate the anti-SARS-CoV-2 potential of andrographolide. In this study, andrographolide was tested against two key enzymes of SAR-CoV-2 namely 3C main proteinase and RNA dependent RNA polymerase. The result here indicated that andrographolide could only inhibit the SARS-CoV-2 3C main proteinase as strong as lopinavir (the standard medicine), which has been recommended as the drug of choice to treat SARS-CoV-2 patient. [ABSTRACT FROM AUTHOR]

We present a classical and a quantum constant of motion used to study the behavior of an electron moving in the vicinity of a tridimensional charged nanostructure with electric dipole. It is the generalization of the bidimensional version studied in a previous work. The classical case is found by using two approaches: the Hamilton Jacobi equation and a Newtonian treatment. The quantum case is obtained by separating the Schrödinger equation in spherical coordinates. We use three quantum numbers to classify the states. The angular part is solved by using an expansion of spherical harmonics to get an eigenvalue equation for the new constant of motion. The solution shows that the probability density of the electron shifts toward the values where 𝜃 < π 2 . The eigenvalue λ is near the value l(l + 1) of the angular momentum, but their difference grows if the electric dipole increases, so that the effect is small for a cluster like (GaAs)3 but very important for fullerenes like RbC60 and LiC60. The radial part is solved using the shooting method and we find the ground state, and first and second excited states. [ABSTRACT FROM AUTHOR]

In this paper, a new WENO procedure is proposed to approximate the viscosity solution of the Hamilton–Jacobi (HJ) equations. In the one-dimensional (1D) case, an optimum polynomial on a six-point stencil is obtained. This optimum polynomial is fifth-order accurate in regions of smoothness. Then, this optimum polynomial is considered as a symmetric and convex combination of four polynomials with ideal weights. Following the methodology of the classic WENO-Z procedure [Borges et al., J. Comput. Phys.227, 3191 (2008)], the new nonoscillatory weights are calculated with the ideal weights. Several numerical experiments in 1D, 2D and 3D are performed to illustrate the capability of the scheme. [ABSTRACT FROM AUTHOR]

A routine using multiple methods is proposed for detecting and locating hot spots in power transformers based on thermographic measurements and advanced computational and analytical techniques. The proposed method identifies oxide and hydrocarbon gases dissolved in the insulation oil of power transformers and use the well‐established key gas method to provide information about the cause of the hot spots, such as partial discharge, electric arc, and overload heating. The method then uses infrared mappings and temperature measurements to find the approximate location of the hot spots and, finally, the finite element method and an analytical formulation are employed to refine the location of the hotspots and the estimation of their temperature. The proposed method was developed during the design and manufacturing of a commercial 120 MVA generator step‐up transformer, produced by a well‐renowned manufacturer. The authors conclude that a method is a resourceful tool for identifying hot spots and could be used by manufacturers to identify and predict design and manufacturing failures. [ABSTRACT FROM AUTHOR]

Drilling and blasting are the major part unit in operations of mining or civil engineering projects. In spite of the best efforts to introduce mechanization in the mines, blasting continues to dominate the production. Therefore to cut down the cost of production optimal fragmentation from properly designed blasting pattern has to be achieved. Proper adoption of drilling and blasting can contribute significantly towards profitability and therefore optimization of these parameters is essential. This optimized parameter will be effective if a robust information model can be prepared based on the relevant practical data of the specific deposits. Initial rock mass characteristics data collection if collected during exploration stage is useful for selection of drilling machine and prediction of penetration rate – Key Performance Indicator (KPI). Further block model developed with geo-mechanical parameters will be useful during operation stage of mine. Geo-mechanical parameters are also important for design of slope in mine planning till final closure stage. Penetration rate, fragmentation, fly rock, ground vibration, air-overpressure (AOp) and back break are KPI for drilling and blasting. Explosives properties and selection of initiation system have impact on blast performance. There are various computational techniques such as artificial neural network (ANN) where various drilling and blasting (KPI) can be predicted accurately for year-wise budgeting and during operation stage of mine. Tropically weathered rock is classified as fresh, slightly weathered, moderately weathered, highly weathered and completely weathered. [ABSTRACT FROM AUTHOR]

Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind. We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or even multiple biological conditions. For the latter, in a simple case-control study features or genes are selected under the assumption of no change over time for the control samples, while the case group must have at least one change. The proposed model reduces the feature space according to a two-state hidden Markov model. The two states define change/no-change in gene expression. Features are ranked in consonance with three scores: number of changes across time, magnitude of such changes and quality of replicates as a measure of how much they deviate from the mean. An important highlight is that this strategy overcomes the few samples limitation, common in transcriptome experiments through a process of data transformation and rearrangement. To prove this method, our strategy was applied to three publicly available data sets. Results show that feature domain is reduced by up to 90% leaving only few but relevant features yet with findings consistent to those previously reported. Moreover, our strategy proved to be robust, stable and working on studies where sample size is an issue otherwise. Hence, even with two biological replicates and/or three time points our method proves to work well. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the changes in the demographic, clinical, and biometric characteristics of APAC patients in South Korea during the last decade. Methods: Medical records of patients with APAC who visit the emergency department or the glaucoma clinic of Chonnam National University Hospital, a tertiary referral center in Gwangju, South Korea in 2007 and 2017 were analyzed. Demographics, clinical characteristics, and treatment modality were compared between the APAC patients in 2007 and 2017. Results: The number of patients with APAC increased from 54 in 2007 to 68 in 2017. Female patients in their 60s were most common in both groups and there was no significant difference in IOP, cataract grade, gonioscopic grading, PAS, or optic nerve damage between the two groups at baseline visit (all P > 0.05). However, APAC eyes in 2017 had a shallower ACD (1.74 ± 0.28 mm vs 1.87 ± 0.35 mm; P = 0.024) and greater LV (1.05 ± 0.26 mm vs 0.93 ± 0.19 mm; P = 0.001) than those of APAC eyes in 2007. During one year follow-up, 25 patients (51.02%) received LPI only, and 18 patients (36.73%) required LE, and 6 patients (12.24%) required phacotrabeculectomy or sequential LE and trabeculectomy. However, in 2017, LPI alone was sufficient in 23 patients (38.33%), 29 patients (48.33%) required further LE, and 8 patients (13.33%) required phacotrabeculectomy or sequential LE and trabeculectomy for the treatment of APAC (P = 0.015). Conclusions: Compared to older cases of APAC, recent cases received LE more frequently, which suggests an increasing trend of LE as a treatment option for APAC. In addition, recent cases had a greater LV and shallower ACD than older cases and these biometric differences may be one of the reasons for increasing rate of LE in this study. [ABSTRACT FROM AUTHOR]

The spliceosome is a large ribonucleoprotein complex that removes introns from pre-mRNAs. At its functional core lies the essential pre-mRNA processing factor 8 (Prp8) protein. Across diverse eukaryotes, this protein cofactor of RNA catalysis harbors a self-splicing element called an intein. Inteins in Prp8 are extremely pervasive and are found at 7 different sites in various species. Here, we focus on the Prp8 intein from Cryptococcus neoformans (Cne), a human fungal pathogen. We solved the crystal structure of this intein, revealing structural homology among protein splicing sequences in eukaryotes, including the Hedgehog C terminus. Working with the Cne Prp8 intein in a reporter assay, we find that the biologically relevant divalent metals copper and zinc inhibit intein splicing, albeit by 2 different mechanisms. Copper likely stimulates reversible modifications on a catalytically important cysteine, whereas zinc binds at the terminal asparagine and the same critical cysteine. Importantly, we also show that copper treatment inhibits Prp8 protein splicing in Cne. Lastly, an intein-containing Prp8 precursor model is presented, suggesting that metal-induced protein-splicing inhibition would disturb function of both Prp8 and the spliceosome. These results indicate that Prp8 protein splicing can be modulated, with potential functional implications for the spliceosome. [ABSTRACT FROM AUTHOR]

Exploration and exploitation are contradictory in differential evolution (DE) algorithm. In order to balance the search behavior between exploitation and exploration better, a novel self-adaptive dual-strategy differential evolution algorithm (SaDSDE) is proposed. Firstly, a dual-strategy mutation operator is presented based on the “DE/best/2” mutation operator with better global exploration ability and “DE/rand/2” mutation operator with stronger local exploitation ability. Secondly, the scaling factor self-adaption strategy is proposed in an individual-dependent and fitness-dependent way without extra parameters. Thirdly, the exploration ability control factor is introduced to adjust the global exploration ability dynamically in the evolution process. In order to verify and analyze the performance of SaDSDE, we compare SaDSDE with 7 state-of-art DE variants and 3 non-DE based algorithms by using 30 Benchmark test functions of 30-dimensions and 100-dimensions, respectively. The experiments results demonstrate that SaDSDE could improve global optimization performance remarkably. Moreover, the performance superiority of SaDSDE becomes more significant with the increase of the problems’ dimension. [ABSTRACT FROM AUTHOR]

Background: RNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5–35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data. Methods: We describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization. Results: We demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance. Conclusions: The approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis. [ABSTRACT FROM AUTHOR]

The Epstein-Barr virus (EBV) induces B-cell proliferation with high efficiency through expression of latent proteins and microRNAs. This process takes place in vivo soon after infection, presumably to expand the virus reservoir, but can also induce pathologies, e.g. an infectious mononucleosis (IM) syndrome after primary infection or a B-cell lymphoproliferation in immunosuppressed individuals. In this paper, we investigated the growth characteristics of EBV-infected B-cells isolated from transplant recipients or patients with IM. We found that these cells grew and withstood apoptosis at highly variable rates, suggesting that the expansion rate of the infected B-cells widely varies between individuals, thereby influencing the size of the B-cell reservoir and the ability to form tumors in infected individuals. All viruses investigated were type 1 and genetically close to western strains. EBV-infected B-cells expressed the transforming EBV latent genes and microRNAs (miRNAs) at variable levels. We found that the B-cell growth rates positively correlated with the BHRF1 miRNA levels. Comparative studies showed that infected B-cells derived from transplant recipients with iEBVL on average expressed higher levels of EBV miR-BHRF1 miRNAs and grew more rapidly than B-cells from IM patients, suggesting infection by more transforming viruses. Altogether, these findings suggest that EBV infection has a highly variable impact on the B-cell compartment that probably reflects the genetic diversity of both the virus and the host. It also demonstrates the unexpected finding that B-cells from different individuals can grow at different speed under the influence of the same virus infection. [ABSTRACT FROM AUTHOR]

Transcription factors are key regulatory elements that affect gene expression in response to specific signals, including environmental stresses such as salinity. Halophytes are specialized plants that have the ability to complete their life cycle in saline environments. In this study we have identified and characterized the evolutionary relationships of putative transcription factors (TF) in an obligate succulent halophyte, Suaeda fruticosa, that are involved in conferring salt tolerance. Using RNA-seq data we have analyzed the expression patterns of certain TF families, predicted protein-protein interactions, and analyzed evolutionary trajectories to elucidate their possible roles in salt tolerance. We have detected the top differentially expressed (DE) transcription factor families (MYB, CAMTA, MADS-box and bZIP) that show the most pronounced response to salinity. The majority of DE genes in the four aforementioned TF families cluster together on TF phylogenetic trees, which suggests common evolutionary origins and trajectories. This research represents the first comprehensive TF study of a leaf succulent halophyte including their evolutionary relationships with TFs in other halophyte and salt-senstive plants. These findings provide a foundation for understanding the function of salt-responsive transcription factors in salt tolerance and associated gene regulation in plants. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is powerful for global optimization problems. Among DE algorithms, JADE and its variants, whose mutation strategy is DE/current-to-pbest/1 with optional archive, have good performance. A significant feature of the above mutation strategy is that one individual for difference operation comes from the union of the optional external archive and the population. In existing DE algorithms based on the mutation strategy—JADE and its variants, individuals eliminated from the population are send to the archive. In this paper, we propose a scheme for managing the optional external archive. According to our scheme, two subpopulations are maintained in the population. Each of them regards the other as the archive. In experiments, our scheme is applied in JADE and two of its variants—SHADE and L-SHADE. Experimental results show that our scheme can enhance JADE and its variants. Moreover, it can be seen that L-SHADE with our scheme performs significantly better than four DE algorithms, CoBiDE, MPEDE, EDEV, and MLCCDE. [ABSTRACT FROM AUTHOR]

At some point early in the vertebrate lineage, two whole genome duplication events (1R, 2R) took place that allowed for the diversification and sub-/neo-functionalization of the glycoprotein hormones (GpHs). All jawed vertebrates possess the GpHs luteinizing hormone (LH), follicle stimulating hormone (FSH), and thyroid stimulating hormone (TSH), each of which are heterodimers with a common alpha subunit and unique beta subunits. In 2002, a novel glycoprotein hormone named thyrostimulin was described to have unique GpA2 and GpB5 subunits that were homologous to the vertebrate alpha and beta subunits. The presence of GpA2 and GpB5 in representative protostomes and deuterostomes indicates their ancestry in the GpH family. There are several reports of GpH subunit evolution, but none have included GpA2 and GpB5 for species in each major vertebrate class. Thus, we addressed the ancestry of two paralogous GpB5 subunits (GpB5a and GpB5b) that were previously only recognized in two teleost species. Our search for orthologous GpB5a and GpB5b sequences in representative vertebrates and phylogenetic analysis, in addition to the currently published evolutionary scenarios of the GpH family, supports that GpB5a and GpB5b are paralogs that arose from the first vertebrate whole genome duplication event (1R). Syntenic analysis supports lineage specific losses of GpB5a in chondrichthyes, basal actinopterygians, and tetrapods, and retention in coelacanth and teleosts. Additionally, we were unable to identify GpA2 transcripts from tilapia mRNA, suggesting that this species does not produce heterodimeric thyrostimulin. While the conserved or even species-specific functional role of thyrostimulin or its individual subunits are still unknown in vertebrates, the analyses presented here provide context for future studies on the functional divergence of the GpH family. [ABSTRACT FROM AUTHOR]

Unknown sequences, or gaps, are present in many published genomes across public databases. Gap filling is an important finishing step in de novo genome assembly, especially in large genomes. The gap filling problem is nontrivial and while there are many computational tools partially solving the problem, several have shortcomings as to the reliability and correctness of the output, i.e. the gap filled draft genome. SSPACE-LongRead is a scaffolding tool that utilizes long reads from multiple third-generation sequencing platforms in finding links between contigs and combining them. The long reads potentially contain sequence information to fill the gaps created in the scaffolding, but SSPACE-LongRead currently lacks this functionality. We present an automated pipeline called gapFinisher to process SSPACE-LongRead output to fill gaps after the scaffolding. gapFinisher is based on the controlled use of a previously published gap filling tool FGAP and works on all standard Linux/UNIX command lines. We compare the performance of gapFinisher against two other published gap filling tools PBJelly and GMcloser. We conclude that gapFinisher can fill gaps in draft genomes quickly and reliably. In addition, the serial design of gapFinisher makes it scale well from prokaryote genomes to larger genomes with no increase in the computational footprint. [ABSTRACT FROM AUTHOR]

In any sport the selection of players for a team is fundamental for its subsequent performance. Many factors condition the selection process from the characteristics of the sport discipline to financial limitations, including a long list of restrictions associated with the environment of the competitions in which the team takes part. All of this makes the process of selecting a roster of players very complex, as it is affected by multiple variables and in many cases marked by a great deal of subjectivity. The purpose of this article was to objectively select the players for a basketball team using an evolutionary algorithm, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) that uses stochastic search methods based on the imitation of natural biological evolution. The sample was composed of the players from the teams competing in the top Spanish basketball league, the Association of Basketball Clubs (ACB). To assess the quality of the solutions obtained, the results were compared with the teams in the ACB playing in the same competition as the players used in the study. The results make it possible to obtain different solutions for composing teams rendering financial resources profitable and taking into account the restrictions of the competition and of each sport management. [ABSTRACT FROM AUTHOR]

In a recent publication in PLOS ONE, Gabriele Margos and colleagues have questioned the division of the genus Borrelia into two genera on the basis that the differences in percentage of conserved proteins (POCP) between these two groups is >50%, which an earlier study has suggested as the threshold for differentiating prokaryotic genera. However, the POCP threshold is a poorly characterized and rarely used criterion for establishing distinction among prokaryotic genera. Detailed evaluation of the intergeneric POCP values for 37 genera from 3 different families (viz. Enterobacteriaceae- 24 genera, Morganellaceae-8 genera and Cystobacteraceae-5 genera) presented here shows that the POCP values for all genera within each of these families exceeded >58%. Thus, the suggested POCP threshold is not a useful criterion for delimitation of genus boundary and the objection by Margos et al. on this ground is invalid. Additionally, Margos et al. have questioned the specificities of ~15–20% of the conserved signature indels (CSIs) described in our work. However, as shown here, this concern is due to misunderstanding of the results and the CSIs in question are still highly-specific characteristics of the members of these genera and they provide important information regarding the evolutionary relationships of two new reptiles-echidna-related species viz. Borrelia turcica and Candidatus Borrelia tachyglossi to other Borrelia species. Results presented here show that both these species are deeper-branching members of the genus Borrelia and their placement within this genus is strongly supported by phylogenetic analyses and multiple uniquely shared CSIs with the other Borrelia species. Based on the large body of evidence derived from phylogenetic, genomic, molecular, phenotypic and clinical features, it is contended that the characteristics clearly distinguishing the Borrelia and Borreliella genera are far more numerous and of different kinds than those discerning most (all) other neighbouring genera of prokaryotes. Thus, the placement of these two groups of microorganisms into distinct genera, Borrelia and Borreliella, which clearly recognizes the differences among them, is highly appropriate and it should lead to a better understanding of the clinical, molecular and biological differences between these two important groups of microbes. [ABSTRACT FROM AUTHOR]

Clustering homologous sequences based on their similarity is a problem that appears in many bioinformatics applications. The fact that sequences cluster is ultimately the result of their phylogenetic relationships. Despite this observation and the natural ways in which a tree can define clusters, most applications of sequence clustering do not use a phylogenetic tree and instead operate on pairwise sequence distances. Due to advances in large-scale phylogenetic inference, we argue that tree-based clustering is under-utilized. We define a family of optimization problems that, given an arbitrary tree, return the minimum number of clusters such that all clusters adhere to constraints on their heterogeneity. We study three specific constraints, limiting (1) the diameter of each cluster, (2) the sum of its branch lengths, or (3) chains of pairwise distances. These three problems can be solved in time that increases linearly with the size of the tree, and for two of the three criteria, the algorithms have been known in the theoretical computer scientist literature. We implement these algorithms in a tool called TreeCluster, which we test on three applications: OTU clustering for microbiome data, HIV transmission clustering, and divide-and-conquer multiple sequence alignment. We show that, by using tree-based distances, TreeCluster generates more internally consistent clusters than alternatives and improves the effectiveness of downstream applications. TreeCluster is available at . [ABSTRACT FROM AUTHOR]

The family of ribosomal proteins S1 contains about 20% of all bacterial proteins including the S1 domain. An important feature of this family is multiple copies of structural domains in bacteria, the number of which changes in a strictly limited range from one to six. In this study, the automated exhaustive analysis of 1453 sequences of S1 allowed us to demonstrate that the number of domains in S1 is a distinctive characteristic for phylogenetic bacterial grouping in main phyla. 1453 sequences of S1 were identified in 25 out of 30 different phyla according to the List of Prokaryotic Names with Standing in Nomenclature. About 62% of all records are identified as six-domain S1 proteins, which belong to phylum Proteobacteria. Four-domain S1 are identified mainly in proteins from phylum Firmicutes and Actinobacteria. Records belonging to these phyla are 33% of all records. The least represented two-domain S1 are about 0.6% of all records. The third and fourth domains for the most representative four- and six-domain S1 have the highest percentage of identity with the S1 domain from polynucleotide phosphorylase and S1 domains from one-domain S1. In addition, for these groups, the central part of S1 (the third domain) is more conserved than the terminal domains. [ABSTRACT FROM AUTHOR]

Rapid improvements in DNA sequencing technology have resulted in long genome sequences for a large number of similar isolates with a wide range of single nucleotide polymorphism (SNP) rates, where some isolates can have thousands of times lower SNP rates than others. Genome sequences of this kind are a challenge to existing methods for construction of phylogenetic trees. We address the issues by developing a hierarchical approach to phylogeny construction. In this method, the construction is performed at multiple levels, where at each level, groups of isolates with similar levels of similarity are identified and their phylogenetic trees are constructed. Time savings are achieved by using a sufficiently large number of columns from the input alignment, instead of all its columns. Our results show that the new approach is 20-60 times more efficient than existing programs and more accurate in situations where highly similar isolates have a wide range of SNP rates. [ABSTRACT FROM AUTHOR]

In the last decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins function and their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed taking advantage of these new architectures. However, most methods are still heavily based pre-processing of the input data, as well as extraction and integration of multiple hand-picked, and manually designed features. Multiple Sequence Alignments (MSA) are the most common source of information in de novo prediction methods. Deep Networks that automatically refine the MSA and extract useful features from it would be immensely powerful. In this work, we propose a new paradigm for the prediction of protein structural features called rawMSA. The core idea behind rawMSA is borrowed from the field of natural language processing to map amino acid sequences into an adaptively learned continuous space. This allows the whole MSA to be input into a Deep Network, thus rendering pre-calculated features such as sequence profiles and other features calculated from MSA obsolete. We showcased the rawMSA methodology on three different prediction problems: secondary structure, relative solvent accessibility and inter-residue contact maps. We have rigorously trained and benchmarked rawMSA on a large set of proteins and have determined that it outperforms classical methods based on position-specific scoring matrices (PSSM) when predicting secondary structure and solvent accessibility, while performing on par with methods using more pre-calculated features in the inter-residue contact map prediction category in CASP12 and CASP13. Clearly demonstrating that rawMSA represents a promising development that can pave the way for improved methods using rawMSA instead of sequence profiles to represent evolutionary information in the coming years. Availability: datasets, dataset generation code, evaluation code and models are available at: . [ABSTRACT FROM AUTHOR]

Dengue fever is caused by dengue viruses (DENV) from the Flavivirus genus and is the most prevalent arboviral disease. DENV exists in four immunogenically distinct and genetically-related serotypes (DENV-1 to 4), each subdivided in genotypes. Despite the endemicity of all four DENV serotypes in Thailand, no prior study has characterized the circulation of DENV in the southern provinces of the country. To determine the genetic diversity of DENV circulating in Southern Thailand in 2015 and 2016, we investigated 46 viruses from 182 patients’ sera confirmed positive for DENV by serological and Nested RT-PCR tests. Our dataset included 2 DENV-1, 20 DENV-2, 9 DENV-3 and 15 DENV-4. Phylogenetic analysis was performed on viral envelop sequences. This revealed that part of the identified genotypes from DENV-1 and DENV-4 had been predominant in Asia (genotype I for both serotypes), while genotype II for DENV-4 and the Cosmopolitan genotype DENV-2 were also circulating. Whereas DENV-3 genotype II had been predominantly detected in South East Asia during the previous decades, we found genotype III and genotype I in Southern Thailand. All DENV genotype identified in this study were closely related to contemporary strains circulating in Southeast Asian countries, emphasizing the regional circulation of DENV. These results provide new insights into the co-circulation of all four DENV serotypes in Southern Thailand, confirming the hyperendemicity of DENV in the region. These findings also suggest a new trend of dissemination for some DENV serotypes with a possible shift in genotype distribution; as recently observed in other Asian countries. [ABSTRACT FROM AUTHOR]

The coding space of protein sequences is shaped by evolutionary constraints set by requirements of function and stability. We show that the coding space of a given protein family—the total number of sequences in that family—can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occuring amino acid sequences. We analyzed and calculated the size of three abundant repeat proteins families, whose members are large proteins made of many repetitions of conserved portions of ∼30 amino acids. While amino acid conservation at each position of the alignment explains most of the reduction of diversity relative to completely random sequences, we found that correlations between amino acid usage at different positions significantly impact that diversity. We quantified the impact of different types of correlations, functional and evolutionary, on sequence diversity. Analysis of the detailed structure of the coding space of the families revealed a rugged landscape, with many local energy minima of varying sizes with a hierarchical structure, reminiscent of fustrated energy landscapes of spin glass in physics. This clustered structure indicates a multiplicity of subtypes within each family, and suggests new strategies for protein design. [ABSTRACT FROM AUTHOR]

Pathogen-free stocks of vegetatively propagated plants are crucial in certified plant production. They require regular monitoring of the plant germplasm for pathogens, especially of the stocks maintained in the field. Here we tested pre-basic mother plants of Fragaria, Rubus and Ribes spp., and conserved accessions of the plant genetic resources of Rubus spp. maintained at research stations in Finland, for the presence of viruses using small interfering RNA (siRNA) -based diagnostics (VirusDetect). The advance of the method is that unrelated viruses can be detected simultaneously without resumptions of the viruses present. While no virus was detected in pre-basic mother plants of Fragaria and Ribes species, rubus yellow net virus (RYNV) was detected in pre-basic mother plants of Rubus. Raspberry bushy dwarf virus (RBDV), black raspberry necrosis virus (BRNV), raspberry vein chlorosis virus (RVCV) and RYNV were detected in the Rubus genetic resource collection. The L polymerase encoding sequence characterized from seven RVCV isolates showed considerable genetic variation. The data provide the first molecular biological evidence for the presence of RYNV in Finland. RYNV was not revealed in virus indexing by indicator plants, which suggests that it may be endogenously present in some raspberry cultivars. In addition, a putative new RYNV-like badnavirus was detected in Rubus spp. Blackcurrant reversion virus (BRV) and gooseberry vein banding associated virus (GVBaV) were detected in symptomatic Ribes plants grown in the field. Results were consistent with those obtained using PCR or reverse transcription PCR and suggest that the current virus indexing methods of pre-basic mother plants work as expected. Furthermore, many new viruses were identified in the collections of plant genetic resources not previously tested for viruses. In the future, siRNA-based diagnostics could be a useful supplement for the currently used virus detection methods in certified plant production and thus rationalize and simplify the current testing system. [ABSTRACT FROM AUTHOR]

This study was undertaken to examine biometric measurements during the growth phase of male and female Santa Inês sheep reared in Brachiaria brizantha pastures in northeastern Brazil. The experiment involved 24 castrated males and 24 females at an initial age of 90 days, with an average body weight of 19.04 ± 0.96 kg. Treatments consisted of the effect of four cultivars (Marandu, Xaraés, Piatã and Paiaguás) and two sexes. Six animals were used per treatment, in a randomized-block experimental design. The following characteristics were evaluated: abdominal circumference (AC), body condition score (BCS), body length (BL), body weight (BW), body capacity 1 (BC1), body capacity 2 (BC2), chest width (CW), heart girth (HG), leg circumference (LC), leg length (LL), rump height (RH), rump width (RW) and withers height (WH). Data were subjected to descriptive analysis, Pearson’s correlation, ANOVA and Tukey’s, Kruskal Wallis and Mann-Whitney tests. Univariate and multiple regressions were applied to estimate BW with a maximum error level of 5%. Significant differences were observed for the biometric measurements between sexes and cultivars (p<0.05). Body weight was highly correlated (>70%) with AC, WH, CG, RW, BC1 and BC2. The male sheep grazed on cultivars Piatã showed the best values for BW (40.43 kg), HG, RW, WH, LL, LC (102.46; 20.8; 65.23; 60.44; 42.54 cm respectively) and BC1 (4.25 kg/cm). Females grazed on cultivar Marandu had higher values for RW, CW, LL (17.26; 20.1; 75.98 cm respectively), BC1 (6.03 kg/cm) and BC2 (0.422 kg/cm). The equations that best estimated live weight were BC1 and HG. In male and female Santa Inês sheep, biometric parameters grow differently depending on the cultivar where they are grazed during the growth phase. Cultivars Marandu and Piatã are the most recommended for sheep production, as they provided the best performance and body development in those animals. [ABSTRACT FROM AUTHOR]

The distribution and diversity of RNA viruses in fungi is incompletely understood due to the often cryptic nature of mycoviral infections and the focused study of primarily pathogenic and/or economically important fungi. As most viruses that are known to infect fungi possess either single-stranded or double-stranded RNA genomes, transcriptomic data provides the opportunity to query for viruses in diverse fungal samples without any a priori knowledge of virus infection. Here we describe a systematic survey of all transcriptomic datasets from fungi belonging to the subphylum Pezizomycotina. Using a simple but effective computational pipeline that uses reads discarded during normal RNA-seq analyses, followed by identification of a viral RNA-dependent RNA polymerase (RdRP) motif in de novo assembled contigs, 59 viruses from 44 different fungi were identified. Among the viruses identified, 88% were determined to be new species and 68% are, to our knowledge, the first virus described from the fungal species. Comprehensive analyses of both nucleotide and inferred protein sequences characterize the phylogenetic relationships between these viruses and the known set of mycoviral sequences and support the classification of up to four new families and two new genera. Thus the results provide a deeper understanding of the scope of mycoviral diversity while also increasing the distribution of fungal hosts. Further, this study demonstrates the suitability of analyzing RNA-seq data to facilitate rapid discovery of new viruses. [ABSTRACT FROM AUTHOR]

The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single amino acid change with another resulting in protein function change leading to disease. Many computational techniques have been released to expect the impacts of amino acid alteration on protein function and classify mutations as pathogenic or neutral. Here in this article, we assessed the performance of eight techniques; FATHMM, SIFT, Provean, iFish, Mutation Assessor, PANTHER, SNAP2, and PON- P2 using a VaribenchSelectedPure dataset of 2144 pathogenic variants and 3777 neutral variants extracted from the free standard database "Varibench." The first five techniques achieve (45.60–83.75) % specificity, (52.64–94.13) % sensitivity, (51.00–88.90) % AUC, and (49.76–88.24) % ACC on whole dataset, while all eight techniques achieve (36.54–77.88) % specificity, (50.00–75.00) % sensitivity, (51.00–76.40) % AUC, and (25.00–77.78) % ACC on random sample dataset. We also created a Meta classifier (CSTJ48) that combines FATHMM, iFish, and Mutation Assessor. It registers 96.33% specificity, 86.07% sensitivity, 91.20% AUC, and 91.89 ACC. By comparing the results, it's clear that FATHMM gives the highest performance over the seven individual techniques, where it achieves 83.75% and 77.88% specificity, 94.13%, and 75.00% sensitivity, 88.90% and 76.40% AUC, and 88.24% and 77.78% ACC on whole and random sample dataset, respectively. Also, the launched Meta classifier (CSTJ48) is outperforming over all the eight individual tools that compared here. • Non-synonymous Single Nucleotide Variants (nsSNVs) is the main factor cause single amino acid change. • Variations in the protein have influence not only in the protein structure but also its stability and function. • The American College of Genomics Medical and Genetics (ACGM) recommend using computational techniques for variant interpretation. • Machine Learning Techniques (MLTs) utilized to classify mutations as pathogenic or neutral. • The classification rule is the best MLTs to integrate several techniques that improve the predictive power. [ABSTRACT FROM AUTHOR]

This study presents recent advancements in computational techniques for discovering new and precise solitary wave solutions in the (1 + 1)-dimensional Mikhailov-Novikov-Wang (MNW) integrable equation. Solitary wave solutions are crucial in understanding complex nonlinear phenomena and have practical applications in fields such as fluid dynamics, nonlinear optics, and plasma physics. The proposed computational techniques utilize powerful numerical methods, including the extended simplest equation (ESE) method, modified Riccati expansion MRE method, and He's variational iteration (HVI) method. These techniques provide robust and accurate ways to compute solitary wave solutions, taking advantage of the integrability properties of the MNW equation. Compared to traditional computational methods, these approaches offer significant improvements in efficiency, accuracy, and the ability to explore a broader range of solitary wave solutions. The study demonstrates the effectiveness of these techniques through extensive numerical simulations and comparisons with other computational approaches. The results highlight the superior performance of the proposed methods in identifying new solitary wave solutions, accurately characterizing their properties, and providing deeper insights into the behavior of the model. These computational techniques contribute to advancing our understanding of the underlying dynamics of the MNW equation and have potential applications across various scientific disciplines. • The unified solver method is used to solve the (1+1)-dimensional Mikhailov-Novikov-Wang Integrable Equation. • Innovative rational, trigonometry and hyperbolic stochastic solutions are given for the model problem. • The dynamical features of obtained results are demonstrated through exciting plots. [ABSTRACT FROM AUTHOR]

Detection of human adenoviruses (HAdVs) in nasopharyngeal swab samples by immunofluorescence assay (IFA) will be valuable for diagnosing HAdV infection, which is a leading cause of severe respiratory tract disease, and will help in curbing the spread of HAdV. Monoclonal antibodies employed in IFA for HAdV detection should ideally target highly conserved epitope types. Here, we describe the development of two antigen-binding fragments (Fabs) with specific reactivity to HAdV using phage antibody library technology. When tested with IFA, both Fabs recognized cells infected with several types of HAdV, some of which have been identified in epidemics globally, or associated with outbreaks of severe or fatal acute respiratory diseases. The specificity and cross-reactivity of both Fabs to HAdVs indicated that the generated Fabs could be applied in the development of IFAs to detect HAdVs. Both Fabs bound to the knob proteins, as shown by chemiluminescence enzyme immunoassay and western blot. In addition, epitope mapping showed that both Fabs recognized a conserved linear epitope among several types of HAdV. Two different Fabs recognized the same epitope, suggesting that the epitope triggered the production of at least two kinds of antibodies in the body. The generated Fabs exerted no neutralization against HAdVs. The results demonstrate that both Fabs bind to an epitope that plays no role in neutralization of HAdV. [ABSTRACT FROM AUTHOR]