Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation.

Absolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

A static VM placement and hybrid job scheduling model for green data centers.

Reducing energy consumption has become a critical issue in today data centers. Reducing the number of required physical and Virtual Machines results in energy-efficiency. In this paper, to avoid the disadvantages of VM migration, a static VM placement algorithm is proposed which places VMs on hosts in a Worst-Fit-Decreasing (WFD) fashion. To reduce energy consumption further, the effect of job scheduling policy on the number of VMs needed for maintaining QoS requirements is studied. Each VM is modeled by an M/M/* queue in space-shared, time-shared, and hybrid job scheduling policies, and energy consumption of real-time as well as non-real-time applications is analyzed. Numerical results show that the hybrid policy outperforms space-shared and time-shared policies, in terms of energy consumption as well as Service Level Agreement (SLA) violations. Moreover, our non-migration method outperforms three different algorithms which use VM migration, in terms of reducing both energy consumption and SLA Violations.

[Interactive, semi-automatized and open source computational model applied to respiratory viruses surveillance]. / Modelo computacional interactivo, semi-automatizado y de código abierto aplicado a la vigilancia de virus respiratorios.

Acute respiratory infections (ARI) are an important cause of morbidity and mortality worldwide, affecting mainly children and the elderly. They are associated with a high economic burden, increased number of medical visits and hospitalizations. The surveillance of the circulation of respiratory viruses can reduce the health care associated costs, and to optimize the health response. A platform based on R and its package Shiny was designed, to create an interactive and friendly web interface for gathering, analysis and publication of the data. The data from the Chilean metropolitan respiratory viruses surveillance network, available since 2006, was uploaded into the platform. Using this platform, the researcher spends less than 1 minute to upload the data, and the analysis and publication is immediate, available to be seen by any user with a device connected to Internet, who can choose the variables to be displayed. With a very low cost, in a short time, and using the R programming language, it was possible to create a simple, and interactive platform, considerably decreasing the upload and analysis time, and increasing the impact and availability of this surveillance.

Researchers' And Medical Student' Experience in Reference Management Software in a Low-Income Country.

INTRODUCTION: Although the use of Reference Management Software (RMS) is increasing in developed countries, they seem to be unknown and less used in low-income countries. AIM: To discover the major trends in the use of RMS among researchers and Ph.D. students in Tunisia, as a low-income country. METHODS: A hardcopy survey was filled out by researchers and Ph.D. students during an educational seminar at the faculty of medicine of Sfax in 2016 with the aim to collect qualitative data to determine the participants' knowledge and use of RMS. RESULTS: The survey collected 121 participants, among them, 53.7% know RMS. Mendeley proved to be the best-known software (41.5%), followed by Zotero (35.3%) and Endnote (23%). Training sessions in RMS were taken by 5% of participants. Among the 121 participants, 26.5%of them use RMS., Mendeley was the most used (46.9%), followed by EndNote (28.1%) and Zotero (25%). The most commonly popular feature in RMS is inserting citations (66.9%). Therefore, the analysis, of the reasons behind the choice of RMS proves that the software was used because it is convenient (38.4%),  most known (38.4%),  easy (30.7%), or suggested by colleagues (30.7%). The free and open-source software was preferred by 81% of the participants. g. However, 50.4% ignore the fact that Zotero is free. Several types and sources of captured citations were unknown by 53.8% and 59% of the rest of the participants. CONCLUSION: The results clearly show that the lack of awareness about RMS in Tunisia is due to the absence of a formal training. As a result, the need for such training is highly important for researchers to be able to benefit from the different advantages of RMS while conducting their academic medical education.

Comparative study of whole exome sequencing-based copy number variation detection tools.

BACKGROUND: With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements. RESULTS: In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools' usage according to these results. CONCLUSION: No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science.

Modelo computacional interactivo, semi-automatizado y de código abierto aplicado a la vigilancia de virus respiratorios / Interactive, semi-automatized and open source computational model applied to respiratory viruses surveillance

Resumen Las infecciones respiratorias agudas (IRA) causadas por virus son una importante causa de morbilidad y mortalidad en el mundo, afectando principalmente a niños y adultos mayores. Se asocian a un alto número de consultas y hospitalizaciones, a una significativa sobrecarga del sistema de salud y a un alto costo económico. La vigilancia de virus respiratorios tiene el potencial de ayudar a optimizar la respuesta sanitaria, garantizar la disponibilidad de recursos humanos, racionalizar los recursos y disminuir los costos asociados a la atención en salud. Con el objetivo de optimizar la recolección y visualización de los datos de nuestro actual sistema de vigilancia de virus respiratorios, se diseñó una plataforma basada en R y sus paquetes Shiny, que permite la creación de una interfase web interactiva y amigable para la recolección, análisis y publicación de los datos. Se ingresaron a esta plataforma los datos de la red de vigilancia metropolitana de virus respiratorios disponibles desde 2006. En esta plataforma, el investigador demora menos de un minuto en registrar los datos. El análisis y publicación es inmediato, llegando a cualquier usuario con un dispositivo conectado a Internet, quien puede elegir las variables a consultar. Con un costo muy bajo, en poco tiempo y utilizando el lenguaje de programación R, se logró crear un sistema simple e interactivo, disminuyendo el tiempo de carga y análisis de datos de forma considerable, posiblemente aumentando el impacto y la disponibilidad de esta vigilancia.

Abstract Acute respiratory infections (ARI) are an important cause of morbidity and mortality worldwide, affecting mainly children and the elderly. They are associated with a high economic burden, increased number of medical visits and hospitalizations. The surveillance of the circulation of respiratory viruses can reduce the health care associated costs, and to optimize the health response. A platform based on R and its package Shiny was designed, to create an interactive and friendly web interface for gathering, analysis and publication of the data. The data from the Chilean metropolitan respiratory viruses surveillance network, available since 2006, was uploaded into the platform. Using this platform, the researcher spends less than 1 minute to upload the data, and the analysis and publication is immediate, available to be seen by any user with a device connected to Internet, who can choose the variables to be displayed. With a very low cost, in a short time, and using the R programming language, it was possible to create a simple, and interactive platform, considerably decreasing the upload and analysis time, and increasing the impact and availability of this surveillance.

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits.

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function in many areas of research, the available commercial options have several limitations such as high cost and lack of accessible, open code. To address these issues, we developed PrAnCER, Paw-Print Analysis of Contrast-Enhanced Recordings, for automated quantification of gait. The contrast-enhanced recordings are produced by using a translucent floor that obscures objects not in contact with the surface, effectively isolating the rat's paw prints as it walks. Using these videos, our simple software program reliably measures a variety of spatiotemporal gait parameters. To demonstrate that PrAnCER can accurately detect changes in motor function, we employed a haloperidol model of Parkinson's disease (PD). We tested rats at two doses of haloperidol: high dose (0.30 mg/kg) and low dose (0.15 mg/kg). Haloperidol significantly increased stance duration and hind paw contact area in the low dose condition, as might be expected in a PD model. In the high dose condition, we found a similar increase in contact area but also an unexpected increase in stride length. With further research, we found that this increased stride length is consistent with the bracing-escape phenomenon commonly observed at higher doses of haloperidol. Thus, PrAnCER was able to detect both expected and unexpected changes in rodent gait patterns. Additionally, we confirmed that PrAnCER is consistent and accurate when compared with manual scoring of gait parameters.

RhythmicAlly: Your R and Shiny-Based Open-Source Ally for the Analysis of Biological Rhythms.

Research on circadian rhythms often requires researchers to estimate period, robustness/power, and phase of the rhythm. These are important to estimate, owing to the fact that they act as readouts of different features of the underlying clock. The commonly used tools, to this end, suffer from being very expensive, having very limited interactivity, being very cumbersome to use, or a combination of these. As a step toward remedying the inaccessibility to users who may not be able to afford them and to ease the analysis of biological time-series data, we have written RhythmicAlly, an open-source program using R and Shiny that has the following advantages: (1) it is free, (2) it allows subjective marking of phases on actograms, (3) it provides high interactivity with graphs, (4) it allows visualization and storing of data for a batch of individuals simultaneously, and (5) it does what other free programs do but with fewer mouse clicks, thereby being more efficient and user-friendly. Moreover, our program can be used for a wide range of ultradian, circadian, and infradian rhythms from a variety of organisms, some examples of which are described here. The first version of RhythmicAlly is available on Github, and we aim to maintain the program with subsequent versions having updated methods of visualizing and analyzing time-series data.

A bi-objective game-theoretic model for collaboration formation between software development firms.

Requirement for formation of collaborations has been on increase for the software development industry, especially for smaller to medium sized firms, due to rapid technological advancements, requirements for diversified skills, ever enhancing demands for innovation and fierce competition. Collaborative product development in an alliance enables the firms to benefit from each other's diversified skills and the experience as a result of which they can develop products more rapidly and of better quality as well resulting in a higher payoff. Also, the development costs decrease. However, to avoid undesired results, selection of an appropriate partner firm for collaboration is of utmost importance keeping in view the objectives of alliance formation of both the strategic partners. One-way partner selection techniques available in the literature are impractical as they enable a firm to rank potential partners only from its own perspective while ignoring their objectives. This problem is addressed by the two-way partner selection techniques, however, they either ignore the payoff distribution criteria or the proposed criteria is unfair. More importantly, existing techniques consider that firm collaborate only with the objective to enhance their financial payoff which might not always be the case. The fact that collaborating firms may have one but different objectives for collaboration, or, each may have multiple objectives is largely neglected. To address the scenarios in which firms may collaborate due to multiple and possibly different objectives, this work proposes a bi-objective game-theoretic model that enables a firm to select an appropriate partner based on the individual preferences of both on the following two objectives: 1) learning and 2) financial revenue. Moreover, this model calculates the pay-off that each firm should get whether only monetary, only in the form of learning or both. The calculation of payoff share is based on the following parameters: 1) individual goals of collaboration of partner selecting firms on the said two objectives, 2) their level of cost contribution, 3) cooperation ratio and 4) knowledge investment difference. Comprehensive analysis of various scenarios is done for the proposed Nash Bargaining payoff distribution model to find the optimum strategy of collaborating firms for each scenario.

Challenges in funding and developing genomic software: roots and remedies.

The computer software used for genomic analysis has become a crucial component of the infrastructure for life sciences. However, genomic software is still typically developed in an ad hoc manner, with inadequate funding, and by academic researchers not trained in software development, at substantial costs to the research community. I examine the roots of the incongruity between the importance of and the degree of investment in genomic software, and I suggest several potential remedies for current problems. As genomics continues to grow, new strategies for funding and developing the software that powers the field will become increasingly essential.

[Spanish adaptation of the free OxMaR software for minimization and randomization of clinical studies]. / Versión en español del software gratuito OxMaR para minimización y aleatorización de estudios clínicos.

Randomized clinical trials provide the highest level of scientific evidence. The method used for randomization should make the group to which each case will be assigned unpredictable and facilitate the concealment of the randomization sequence. Centralized methods, generally implemented with computer support, are considered the safest to avoid biases. The OxMaR system, acronym for Oxford Minimization and Randomization, was published as free and open source software in 2014. It works online in a web environment and allows simple randomization and adaptive assignment through minimization. We present a Spanish version developed in collaboration with the author of the original English version. The system has been modified to work on low cost shared web servers and also to allow the concealment of the randomization sequence.