Integration Of Computational Tools Research Articles

Direct Cardiac Reprogramming in the Age of Computational Biology.

Heart disease continues to be one of the most fatal conditions worldwide. This is in part due to the maladaptive remodeling process by which ischemic cardiac tissue is replaced with a fibrotic scar. Direct cardiac reprogramming presents a unique solution for restoring injured cardiac tissue through the direct conversion of fibroblasts into induced cardiomyocytes, bypassing the transition through a pluripotent state. Since its inception in 2010, direct cardiac reprogramming using the transcription factors Gata4, Mef2c, and Tbx5 has revolutionized the field of cardiac regenerative medicine. Just over a decade later, the field has rapidly evolved through the expansion of identified molecular and genetic factors that can be used to optimize reprogramming efficiency. The integration of computational tools into the study of direct cardiac reprogramming has been critical to this progress. Advancements in transcriptomics, epigenetics, proteomics, genome editing, and machine learning have not only enhanced our understanding of the underlying mechanisms driving this cell fate transition, but have also driven innovations that push direct cardiac reprogramming closer to clinical application. This review article explores how these computational advancements have impacted and continue to shape the field of direct cardiac reprogramming.

Metagenomic profiling of rhizosphere microbiota: Unraveling the plant-soil dynamics

Rhizosphere is a dynamic and complex environment shaped by intricate interactions between plants and a diverse microbial community. The rhizosphere microbiome is crucial for plant growth, nutrition, and health in agro-ecosystems. Recent advancements in metagenomics, especially through shotgun sequencing, offer unprecedented opportunities to unravel the complexities of rhizosphere microbial dynamics. Sequence and function-based metagenomic approaches enhance our understanding of plant-microbe relationships and serve as transformative tools for sustainable agriculture. They offer insights into precision microbiome engineering and ecosystem management by capturing the genomic landscape of microbial communities, revealing their taxonomic composition and functional potential. The integration of computational tools and machine learning approaches provides a powerful analytical framework to interpret vast and intricate metagenomic datasets. This paper comprehensively explores the rhizosphere using cutting-edge shotgun sequencing techniques to capture a holistic view of microbial communities in this crucial zone. Additionally, it delves into the emerging domain of virus-rhizosphere interactions, elucidating the impact of viral dynamics on rhizosphere microbial composition. To date, significant achievements in rhizosphere biology through metagenomics include detailed characterizations of microbial diversity, functional profiling of microbial communities, and identification of key microbial players in plant health and nutrient cycling. These advances underscore the critical role of metagenomics in advancing our understanding of rhizosphere biology and its applications in sustainable agriculture. The novel application of these advanced metagenomic techniques presents both opportunities and challenges, offering deeper insights into the rhizosphere while highlighting the need for improved data integration and interpretation methods.

Emerging Technologies in Chemical Engineering: Advancements in Process Optimization, Sustainable Practices, and Future Innovations

The abstract encapsulates the essence of the study on emerging technologies in chemical engineering, focusing on advancements in process optimization, sustainable practices, and future innovations. Within the dynamic landscape of chemical engineering, the integration of emerging technologies has reshaped industrial processes, offering new opportunities to enhance efficiency, sustainability, and resilience. This study explores the transformative potential of emerging technologies, such as computational modelling, artificial intelligence, and bioprocess engineering, in addressing pressing societal and environmental challenges while driving innovation and sustainable development. Through a comprehensive literature review and synthesis of key findings, the study highlights the pivotal role of chemical engineering in shaping the design, optimization, and implementation of industrial processes across diverse sectors, including manufacturing, energy production, pharmaceuticals, and environmental remediation. Advancements in process optimization, facilitated by the integration of computational tools and data analytics, enable engineers to systematically analyze and control chemical processes with unprecedented precision and efficiency, leading to improvements in productivity, resource utilization, and waste reduction. The adoption of sustainable practices, such as green chemistry principles, renewable energy sources, and circular economy initiatives, reflects a growing emphasis on minimizing environmental impact and promoting resource efficiency throughout the product lifecycle. Concurrently, the exploration of future innovations in chemical engineering, including nanotechnologies, bioprocesses, and advanced materials, offers promising opportunities to address emerging challenges and drive positive change in the field. By synthesizing these key findings, the study provides valuable insights into the opportunities and challenges associated with emerging technologies in chemical engineering, guiding future research, policy, and practice towards achieving sustainable and resilient industrial processes. In essence, the study contributes to advancing knowledge and understanding of the transformative potential of emerging technologies in chemical engineering and their role in shaping a more sustainable and inclusive future for society and the planet.

A workflow management system for reproducible and interoperable high-throughput self-driving experiments

To foster self-driving experimentation and address the reproducibility crisis in bioprocess development in a collaborative environment, a modular Workflow Management System (WMS) is required. In this work, a WMS based on Directed Acyclic Graphs that offers a modular and flexible design for plug-and-play integration of computational tools is presented. A case study is used to demonstrate that the implementation of a computational WMS in robotic experimental facilities promotes the application of Findable, Accessible, Interoperable and Re-usable principles, allowing researchers to readily share protocols, models, methods and data. As a proof of concept, we integrated three different computational workflows for online re-design of feeding rates in 24 parallel E. coli fed-batch cultivations producing elastin-like proteins. This approach provides a solid foundation for increasing scientific data generation in robotic experimental facilities, fostering open collaboration, and addressing the challenges of reproducibility in research.

The use of R in forestry research

Abstract The field of forestry research has greatly benefited from the integration of computational tools and statistical methods in recent years. Among these tools, the programming language R has emerged as a powerful and versatile platform for forestry research, ranging from data analysis, modeling to visualization. However, the key trends in general reported R use and patterns in forestry research remain unknown. We analyzed R and R package usage frequencies for 14 800 research articles published in eight top forestry journals across a span of 10 years, from 2013 to 2022. Among these articles, a notable number of 6790 (accounting for 45.7%) explicitly utilized R as their primary tool for data analysis. The adoption of R exhibited a linear growth trend, rising from 28.3% in 2013 to 60.9% in 2022. The top five used packages reported were vegan, lme4, nlme, MuMIn, and ggplot2. Diverse journals have their unique areas of emphasis, resulting in disparities in the frequency of R package application among journals. The average number of R packages used per article also showed an increasing trend over time. The study underscores the recognition that R, with its powerful data statistical and visualization capabilities, plays a pivotal role in enabling researchers to conduct thorough analyses and acquire comprehensive insights into various aspects of forestry science.

Aptamer-Based Point-of-Care Devices: Emerging Technologies and Integration of Computational Methods.

Recent innovations in point-of-care (POC) diagnostic technologies have paved a critical road for the improved application of biomedicine through the deployment of accurate and affordable programs into resource-scarce settings. The utilization of antibodies as a bio-recognition element in POC devices is currently limited due to obstacles associated with cost and production, impeding its widespread adoption. One promising alternative, on the other hand, is aptamer integration, i.e., short sequences of single-stranded DNA and RNA structures. The advantageous properties of these molecules are as follows: small molecular size, amenability to chemical modification, low- or nonimmunogenic characteristics, and their reproducibility within a short generation time. The utilization of these aforementioned features is critical in developing sensitive and portable POC systems. Furthermore, the deficiencies related to past experimental efforts to improve biosensor schematics, including the design of biorecognition elements, can be tackled with the integration of computational tools. These complementary tools enable the prediction of the reliability and functionality of the molecular structure of aptamers. In this review, we have overviewed the usage of aptamers in the development of novel and portable POC devices, in addition to highlighting the insights that simulations and other computational methods can provide into the use of aptamer modeling for POC integration.

Biomaterial strategies to combat implant infections: new perspectives to old challenges

ABSTRACT Peri-implant infection is rapidly becoming an – if not the most – important clinical challenge for indwelling medical devices. To alleviate the global rise in antibiotic use for the treatment of such infections, a plethora of biomaterials/bioengineering-based antimicrobial strategies are emerging to restrict or ideally to eliminate microbial adhesion and biofilm formation on implant surfaces. Yet, the development of such approaches faces specific challenges, like biocompatibility concerns, reduced antimicrobial effectiveness, long-term stability issues and antibiotic resistance development, which limit translation to the clinic. This review provides insights into the antimicrobial activity of current state-of-the-art biomaterial-based approaches to address the aforementioned issues. Translational research strategies and regulatory framework are also emphasised as key elements facilitating clinical implementation of anti-infective biomaterials. This review closes with the vision that the integration of computational tools and experimental databases using artificial intelligence (AI) would provide new insights for the accelerated development of next-generation biomaterial-based antimicrobial strategies.

An Evolutionary Textual Environment

This article introduces the notion of evolutionary textual environment as the outcome of a digital experiment. The experiment consisted of transforming a digital archive of Fernando Pessoa’s Book of Disquiet into a changing textual space sustained by role-playing interactions. As conceptual and technical artifact, this living archive expresses an innovative model not only for the literary acts of reading, editing and writing, but also for reimagining the book as a network of reconfigurable and dynamic texts, structures, and actions. The programmed features of the LdoD Archive can be used in multiple activities, including leisure reading, study, analysis, advanced research, and creative writing. Through the integration of computational tools in a simulation space, this collaborative archive provides an open exploration of the procedurality of the digital medium itself. The “unfinished machine” metaphor suggests the open-endedness both of the evolving textual environment and of the computational modeling of literary performativity that sustains the whole experiment.

Integrating iSpartan into a Classic Organic Chemistry Laboratory Experiment

The dehydration of 2- or 4-methylcyclohexanol to obtain isomers of methylcyclohexene is a classic organic chemistry experiment. Students perform a distillation, collecting samples to analyze the progress of the reaction. The reaction produces a major product that can be explained on the basis of the regiochemistry of the π bond formation for the α–β elimination process. Minor products can be attributed to the possible rearrangement of the carbocation through a hydride shift. We have added computational analysis to this classic experiment to help students understand alkene stability. The experiment was performed early in the sophomore organic chemistry laboratory, before students were introduced to the mechanistic perspectives that govern the reaction. The iSpartan analysis of the possible methylcyclohexene products combined with gas chromatography data provides students with an inquiry-based laboratory experience. A novice student can analyze the computational information about the products to determine the trend in product stability and determine if a double-bond is formed at an unexpected location in the molecule. The integration of computational tools provides more information to students than performing the experiment in isolation.

Integration of computational tools, data analysis and social science into food safety risk assessment.

The EU‐FORA Fellowship Programme ‘Integration of tools and social science into food safety risk assessments’ was proposed by the Food Standards Agency (FSA), the government department responsible for food safety in the UK. The working programme was organised into four modules, covering different areas of risk assessment, including microbiological risk assessment, chemical risk assessment, exposure assessment, risk prioritisation and the integration of risk assessment with social science. During this period, the fellow had the unique opportunity to gain experience in different fields of risk assessment, namely how to conduct a systematic review, to assess the risk of microbiological and chemical hazards, to make use of modelling tools for exposure assessment and risk prioritisation, to write scientific reports for committees and networks at the national level and to understand the role of social science in risk assessment. In addition, the fellow was able to attend several meetings, seminars, courses and workshops that helped him to gain further insight in the field of food science. The complete programme enabled a fast learning curve that allowed the fellow to have an overview of the different tools that can be employed in the wide field of food safety risk assessment, in order to acquire skills and competences that can be used in his future career.

Alloy design for mechanical properties: Conquering the length scales

Abstract

BisQue for 3D Materials Science in the Cloud: Microstructure\u2013Property Linkages

Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyberinfrastructures which enable efficient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing–structure–property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., Bioinformatics 26(4):554, 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing–structure–property relationships, can be implemented into the system. Specifically, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally efficient data-driven model into the BisQue platform. The new module is made available online (web address: https://bisque.ece.ucsb.edu/module_service/Composite_Strength/) and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments.

Hull form optimization of a cargo ship for reduced drag

Hydrodynamic optimization of the hull forms can be realized through the implementation and integration of computational tools that consist of a hydrodynamic module, a hull surface representation and modification module, and an optimization module. In the present paper, a new bulbous bow generation and modification technique has been developed and integrated into the hull surface representation and modification module. A radial basis function based surrogate model is developed to approximate the objective functions and reduce the computing cost. A multi-objective artificial bee colony optimization algorithm is implemented and integra- ted into the optimization module. To illustrate the integrated hydrodynamic optimization tools, a cargo ship is optimized for reduced drag. The optimal hull forms obtained are then validated computationally and experimentally. Validation results show that the prese- nt tools can be used efficiently and effectively in the simulation based design of the hull forms for reduced drag.

Academia, Geopolítica das Humanidades Digitais e Pensamento Crítico

As mudanças que ocorreram no campo das tecnologias de informação e de comunicação nas últimas décadas tiveram impacto decisivo em vários setores da atividade humana, nomeadamente a nível de novas práticas criativas e de novos regimes de produção de conhecimento. Se é certo que durante algum tempo o esforço ia no sentido da integração de ferramentas informáticas no trabalho desenvolvido no campo das artes, humanidades e ciências sociais, mas numa lógica apenas de suporte técnico a tarefas academicamente consagradas, com a emergência do paradigma de Humanidades Digitais tornou-se mais visível o propósito de reinventar a cultura através da gramática dos novos meios e o crescente efeito de mediação da tecnologia digital no modo como decorre e se organiza a pesquisa ou se dá a conhecer os seus resultados finais. DOI: http://dx.doi.org/10.14195/2182-8830_3-1_7

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models.

With >800 members in humans, the G protein-coupled receptor (GPCR) super-family is the target for more than 30% of the marketed drugs. The recent boom in GPCR crystallography has enabled the solution of ∼30 different GPCR structures, which boosted the identification and optimization of novel modulators and new chemical entities through structure-based strategies. However, the number of available structures represents a small part of the human GPCR druggable target space, and its complete coverage in the near future seems unlikely. Homology modelling represents a reliable tool to fill this gap, and hence to vastly expand the horizons of structure-based drug discovery and design. In this Feature Article, we show from a wealth of retrospective and prospective studies that in spite of the pitfalls of and standing challenges in homology modelling, structural models have been critical for the blossoming and success of GPCR structure-based lead discovery and optimization endeavours; in addition, they have also been instrumental in characterizing receptor-ligand interaction, guiding the design of site-directed mutagenesis and SAR studies, and assessing off-target effects. Considering though their current limitations, we also discuss the most pressing issues to develop more accurate homology modelling strategies, with a special focus on the integration of computational tools with biochemical, biophysical and QSAR data, highlighting methodological aspects and recent progress. The teachings of the three GPCR Dock community-wide assessments and the fresh developments in GPCR classes B, C and F are commented. This is a fast growing and highly promising field of research, and in the coming years, the use of high-quality models should enable the discovery of a growing number of potent, selective and efficient GPCR drug leads with high therapeutic potential through receptor structure-based strategies.

A constructivist computational platform to support mathematics education in elementary school

Many courses for elementary school are based upon teacher presentation and explanation of basic topics, rather than allowing students to develop their own knowledge. This traditional model may turn elementary-level lessons into an extremely theoretical, boring and non-effective process. In this context, research in mathematics elementary education in Mexico indicates the need to analyze alternative pedagogic practices and to find different ways to make mathematics education in early ages less difficult and more attractive. Constructivist theory can provide an alternative for developing pedagogic proposals. The objectives of this research were: (1) develop a computational platform to support the traditional Mexican method of education with practical mathematics problems simulated as part of the daily world environment and to increase the level of students' social involvement through direct collaboration, and (2) analyze how this computational tool affects student motivation, collaboration and discussion. An exploratory case study concerning dimensions of mathematics problem-solving using computer simulations was conducted with 6-8 year old elementary school children. After a theoretical class the children were involved in solving a series of verbal problems, using our computational platform. Sixty third-grade children participated in this case study and data were collected from their responses to questions and interviews in order to explore attitudes toward learning mathematics and assess self-efficacy in this area. The results obtained in this research indicate that the integration of computational tools into conventional method courses provides elements to improve student motivation, collaboration and discussion based on their own exploratory experiences. These results can assist other education programs to incorporate positive attitudes and their own knowledge creation from a constructivist approach using technology.

From proteomics to systems biology of bacterial pathogens: Approaches, tools, and applications

The hallmark of a systems biology approach is the integration of computational tools with experimental data encompassing multiple classes of biomolecules across different functional levels. Equally important as the availability of reasonably comprehensive information at the gene, protein, and metabolite levels is the development of adequate analysis and visualization tools to reduce the inherent complexity to interpretable dimensions. In this paper, we describe the integration of a 2-D gel-based proteome map of Staphylococcus aureus Mu50 with genomic and transcriptomic information through a customized data integration and user interface built on the Ensembl genome browser. We illustrate its application and potential through the analysis of a defined system perturbation caused by a mutation in the formyltransferase gene. We envision that this software package, which we called Insieme, can support the development of novel antibiotics by allowing a systems-based view of the bacterial response pathways.