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Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this editorial, we summarize the 2023 International Conference on Intelligent Biology and Medicine (ICIBM 2023) conference which was held on July 16–19, 2023 in Tampa, Florida, USA. We then briefly describe the nine research articles included in this special issue. ICIBM 2023 scientific program included four tutorials and workshops, four keynote lectures, four eminent scholars’ presentations, 11 concurrent scientific sessions, and a poster session. We had total of 88 scientific oral presentations, including 62 regular oral presentations and 26 flash talks, as well as 46 poster presentations. The topics of these presentations covered artificial intelligence (AI), data sciences, bioinformatics, computational biology, genomics, biomedical informatics, among others. This special issue included nine peer reviewed manuscripts selected from those submitted to our conference. These articles cover a range of topics on the methods and applications of ML and AI models in the biomedical domain.

Abstract Intratumoral heterogeneity is an important clinical challenge because low burden clones expressing specific genetic alterations drive therapeutic resistance mechanisms. We have developed CAVE (cancer-associated variant enrichment), a gene-agnostic computational tool to identify specific enrichment of low-burden cancer driver variants in next-generation sequencing (NGS) data. For this study, CAVE was applied to TP53 in chronic lymphocytic leukemia (CLL) as a cancer model. Indeed, as TP53 mutations are part of treatment decision-making algorithms and low-burden variants are frequent, there is a need to distinguish true variants from background noise. Recommendations have been published for reliable calling of low-VAF variants of TP53 in CLL and the assessment of the background noise for each platform is essential for the quality of the testing. CAVE is able to detect specific enrichment of low-burden variants starting at variant allele frequencies (VAFs) as low as 0.3%. In silico TP53 dependent and independent analyses confirmed the true driver nature of all these variants. Orthogonal validation using either ddPCR or NGS analyses of follow-up samples confirmed variant identification. CAVE can be easily deployed in any cancer-related NGS workflow to detect the enrichment of low-burden variants of clinical interest.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses. [ABSTRACT FROM AUTHOR]

Clock distribution systems are used in many applications requiring accurate time basis: integrated circuits, computer networks, satellite communications, and Global Position System (GPS). Trying to automatize the design of clock distribution systems, this work presents a general formulation considering the possible topologies and parameters, building a computational tool allowing to design and evaluate the performance of any case to be studied. The developed tool enables to simulate networks setting the number of nodes, internal node parameters, topology, perturbations, and signal propagation delays. The simulation results include response times, synchronization quality, and stability for all types of arrangements: mutually connected, chains, rings, stars, and mixed architectures. [ABSTRACT FROM AUTHOR]

A chemist always desires the automation of a chemistry laboratory as it reduces their workload by minimizing the time, energy, and effort. Automation when coupled with artificial intelligence is the need in today’s era as it gives more flexibility and power to the chemist. Using computer programs for a large number of such operations would further ease out these tasks as the desired objectives may be achieved with the click of a mouse. In this research article, the Python program has been used to arrive at the empirical and molecular formulas of some compounds. Python code has also been written for some compounds and their 2D and 3D structures drawn henceforth. With the introduction of Python language at the undergraduate level, this paper would allow chemistry students to make use of Python in organic chemistry as well. As a result, the chemistry students will be able to use the Python program for verification and classroom-based structure determination of various organic compounds. The students thus gain access and become equipped with computational skills that will be helpful in the modern world and can utilize the Python code as an alternative theoretical approach for these operations. While Python provides valuable computational tools, integrating thematic chemistry and new methodologies is crucial for advancing structural analysis. This approach enhances the depth and applicability of theoretical models beyond basic calculations. This paper is a perfect amalgamation of computer programming with organic chemistry and is an attempt to equip the chemists to do the above-mentioned operations theoretically from their desks. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intratumoral heterogeneity is an important clinical challenge because low burden clones expressing specific genetic alterations drive therapeutic resistance mechanisms. We have developed CAVE (cancer-associated variant enrichment), a gene-agnostic computational tool to identify specific enrichment of low-burden cancer driver variants in next-generation sequencing (NGS) data. For this study, CAVE was applied to TP53 in chronic lymphocytic leukemia (CLL) as a cancer model. Indeed, as TP53 mutations are part of treatment decision-making algorithms and low-burden variants are frequent, there is a need to distinguish true variants from background noise. Recommendations have been published for reliable calling of low-VAF variants of TP53 in CLL and the assessment of the background noise for each platform is essential for the quality of the testing. CAVE is able to detect specific enrichment of low-burden variants starting at variant allele frequencies (VAFs) as low as 0.3%. In silico TP53 dependent and independent analyses confirmed the true driver nature of all these variants. Orthogonal validation using either ddPCR or NGS analyses of follow-up samples confirmed variant identification. CAVE can be easily deployed in any cancer-related NGS workflow to detect the enrichment of low-burden variants of clinical interest. [ABSTRACT FROM AUTHOR]

Objective: the objective of this study is to conceptualize a network of heat exchangers designed to minimize energy waste and enhance the overall efficiency of the sugar production system. Methods: a systematic approach was adopted to analyze energy flows within the plant, identifying key areas for improvement, particularly in heating and evaporation processes. Heat accumulations in cascades and graphical analyses of composite curves were developed using specialized software to optimize heat exchange. Results: the results indicate a significant potential for energy savings, reducing the consumption of cooling and heating utilities in the plant by 7% and 30%, respectively. The developed computational tool allows for energy integration from simple processes to those with hundreds of streams. The pinch technology concept estimated an annual total savings of $464,850.08 in the selected process. Conclusion: this study demonstrates that thermal integration through pinch analysis not only improves energy efficiency in the sugar industry but also contributes to a considerable reduction in operational costs and environmental impact, providing a valuable tool for the industry's sustainability and competitiveness. [ABSTRACT FROM AUTHOR]

Dependent upon the statistical significance p-value and statistical power, the sample size estimation is widely used in various experimental sciences. Nevertheless, the p-value based paradigm, which has resulted in numerous fake conclusions that originate partly from insufficient sample sizes, has been widely criticized in recent years for serious problems. Therefore, I developed a platform-independent computational tool, SampSizeCal, for sample sizes in the paradigm of new statistics. In this tool, both default p-values and the maximum p-values were greatly enhanced, which will lead to the reasonable increase of sample sizes. The computational tool harbors more than 120 sample size methods for experimental designs. SampSizeCal includes both online and offline versions, and can be used for various computing devices (PCs, iPads, smartphones, etc.), operating systems (Windows, Mac, Android, Harmony, etc.) and web browsers (Chrome, Firefox, Sougo, 360, etc). It is currently the most comprehensive platform-independent computational tool for sample sizes, and can be used in experimental sciences such as medicine (clinical medicine, experimental zoology, public health, pharmacy, etc.), biology, ecology, agronomy, psychology and engineering technology.

Meta-analysis is a statistical method used in systematic review to quantitatively integrate the results of multiple related studies to obtain a pooled result that can represent these studies. Meta-analysis overcomes the limitations of traditional reviews that only conduct qualitative research. In present study, I developed a platform-independent computational tool for meta-analysis. It is a comprehensive tool consisting of a full set of meta-analysis methodology, including the methods for fixed-effects model and random-effects model and the methods for heterogenetity testing in which the effect size tests based heterogeneity testing were proposed. Effect size tests of difference significance for post meta-analysis were also presented. The computational tool is a web browser based meta-analyzer that includes both online and offline versions and can be used on various computing devices (PCs, iPads, smartphones, etc.), operating systems (Windows, Mac, Android, Harmony, etc.) and web browsers (Chrome, Firefox, etc). It can be used in various sciences as medicine, biology, ecology, psychology, sociology, economy, physics and chemistry etc.

Abstract Urinary exfoliated tumor cells have emerged as promising biomarkers for predicting, diagnosing, and guiding therapy in bladder cancer. Several methodologies based on biological and physical differences between normal cells and malignant tumor cells have been developed over the past few years. However, these methods still did not have sufficient sensitivity or specificity. In this study, a remote analysis protocol was devised utilizing motion microscopy. This technique amplifies vibrations within a recorded video by re-rendering motions, thereby generating highly magnified visuals. This approach aims to detect dynamic motions that may not be perceptible to the human eye under normal observation. Remarkably, motion microscopy unveiled discernible fluctuations surrounding bladder malignant tumor cells, which we referred to herein as cellular trail. The cellular trails were predominantly evident at around 1 Hz in amplified video images, with a velocity of 22 μm/s. Moreover, cellular trails were observed regardless of whether they were in a non-Newtonian or Newtonian fluid environment. Significantly, this phenomenon was distinguishable even in urine samples. In conclusion, we suggest motion microscopy as an innovative approach for detecting urinary malignant tumors with potential clinical utility as a complementary tool to cytology.

Abstract It is necessary to verify whether nail fold capillaroscopy can be utilized for the early detection of breast cancer. To establish this technology, an animal model was developed, utilizing mice for nail fold observations. Nail fold capillaroscopy revealed a human-like anatomical pattern and facilitated the observation of cellular movement within blood vessels. Injection of MCF-7 or mammary fibroblasts in mice allowed the observation of cellular vibrations using motion microscopy from nail fold. We have named this technology ‘capillary cell motion microscopy.’ Intriguingly, we were able to identify distinct cellular vibrations in the MCF-7 group. Moreover, evaluating its effectiveness in mice with chemically induced cancer revealed higher sensitivity (81%-85%) compared to conventional methods (45%-68%). Capillary cell motion microscopy, operating at 0.5–1.5 Hz, provided clear distinction of tumor cells and demonstrated potential applicability in human subjects. While condition adjustments may be necessary, this method holds promise for noninvasive breast cancer detection through nail fold observations.

Urinary exfoliated tumor cells have emerged as promising biomarkers for predicting, diagnosing, and guiding therapy in bladder cancer. Several methodologies based on biological and physical differences between normal cells and malignant tumor cells have been developed over the past few years. However, these methods still did not have sufficient sensitivity or specificity. In this study, a remote analysis protocol was devised utilizing motion microscopy. This technique amplifies vibrations within a recorded video by re-rendering motions, thereby generating highly magnified visuals. This approach aims to detect dynamic motions that may not be perceptible to the human eye under normal observation. Remarkably, motion microscopy unveiled discernible fluctuations surrounding bladder malignant tumor cells, which we referred to herein as cellular trail. The cellular trails were predominantly evident at around 1 Hz in amplified video images, with a velocity of 22 μm/s. Moreover, cellular trails were observed regardless of whether they were in a non-Newtonian or Newtonian fluid environment. Significantly, this phenomenon was distinguishable even in urine samples. In conclusion, we suggest motion microscopy as an innovative approach for detecting urinary malignant tumors with potential clinical utility as a complementary tool to cytology. [ABSTRACT FROM AUTHOR]

It is necessary to verify whether nail fold capillaroscopy can be utilized for the early detection of breast cancer. To establish this technology, an animal model was developed, utilizing mice for nail fold observations. Nail fold capillaroscopy revealed a human-like anatomical pattern and facilitated the observation of cellular movement within blood vessels. Injection of MCF-7 or mammary fibroblasts in mice allowed the observation of cellular vibrations using motion microscopy from nail fold. We have named this technology 'capillary cell motion microscopy.' Intriguingly, we were able to identify distinct cellular vibrations in the MCF-7 group. Moreover, evaluating its effectiveness in mice with chemically induced cancer revealed higher sensitivity (81%-85%) compared to conventional methods (45%-68%). Capillary cell motion microscopy, operating at 0.5–1.5 Hz, provided clear distinction of tumor cells and demonstrated potential applicability in human subjects. While condition adjustments may be necessary, this method holds promise for noninvasive breast cancer detection through nail fold observations. [ABSTRACT FROM AUTHOR]

Physicochemical studies of water show that there is currently an encrusting trend in 24 oil fields in Colombia. This implies that the water has a tendency to precipitate different scales, mainly calcium carbonate and barium sulfate. Emerging the need to implement tools that allow avoiding or delaying precipitation and deposition, through the design and optimization of squeeze treatments. Therefore, a mathematical model applicable to sandstone formations was selected, which was validated for linear and radial flow through literature, laboratory and field, allowing to estimate the distribution of the inhibitor along the plug during adsorption, and its subsequent related release with the desorption phenomenon. This methodology was analyzed under conditions of pressure and flow rates present in the field, allowing the prediction of the useful life of the treatment to be estimated, and the effect of the injected pore volumes and production flow rate on the duration of treatment, concluding that high flow rates have a negative effect on the duration of squeeze treatments. Furthermore, the designed tool allows modeling of adsorption and desorption of inhibitors and constitutes a significant contribution to the prevention of damage to the formation caused by inorganic scales, since there is currently no free software for this purpose. [ABSTRACT FROM AUTHOR]

This paper proposes an icon-based methodology for the design of prototype aggregated production planning software that addresses the complexity of multi-process and multi-product production. Aggregate planning is a critical task in production management, which involves coordinating the production of multiple products in different processes to meet demand efficiently. The approach focuses on the use of visual icons to represent key elements of the production process, such as products, processes, resources, and constraints. These icons allow an intuitive representation of information and facilitate communication between production team members. In addition, this paper presents a conceptual structure that defines the relationships between the icons and how they are used to model and simulate aggregate production planning. The prototype software based on a conceptual foundation allows planners to easily create and adjust production plans in a visual environment. This method improves the ability to make informed and rapid decisions in response to changes in demand or production capacity. The prototype is based on icons and programmed in Excel spreadsheets to facilitate the planner's planning. At the end of the document, the application of a case study is shown. [ABSTRACT FROM AUTHOR]

Probabilistic μ -analysis was introduced 20 years ago as a control system validation means able to quantify the probability of rare and potentially critical events. However, for a long time, no practical tool offering both good reliability and reasonable computational time was available, making this technique hardly usable in an industrial context. The STOchastic Worst-case Analysis Toolbox (STOWAT) was introduced a few years ago to bridge this gap between theory and practice. It has been significantly improved since then, thanks to the addition of new features, but above all to increasingly efficient implementations, resulting in a dramatic reduction in CPU time. However, until recently, it could only be applied to small-scale models, with up to 4 or 5 uncertainties. In the perspective of analyzing systems with a larger number of uncertain parameters, a time-consuming and tedious process was carried out. This led to a complete rewrite of the STOWAT, which is now optimized down to the sub-function level, and whose performance is assessed in this paper on a series on benchmarks of increasing complexity with up to about 20 states and 20 uncertainties. This work represents a new step towards the development of a consolidated tool that could reasonably be integrated in the aerospace Verification and Validation process in a near future, finding its place between Monte Carlo simulations – useful for quantifying the probability of sufficiently frequent phenomena – and worst-case μ -analysis – relevant for detecting extremely rare events. [ABSTRACT FROM AUTHOR]

Nowadays, nanomaterials are a real alternative for controlling antibiotic-resistant bacteria. Several nanoparticles have shown good performance in inducing bacterial death due to a photoassisted process. This study employs statistical analyses and machine learning (ML) models to investigate the effect of doping ZnO nanoparticles with cerium ions on their antibacterial activity in a dark environment. Incorporating cerium ions into the ZnO matrix was systematically analyzed in terms of structural, morphological, and optical parameters. The incorporation of cerium ions did not modify the crystal structure. These results were correlated with their qualitative and quantitative antibacterial activity against Escherichia coli , Staphylococcus aureus , and Pseudomonas aeruginosa to establish the impact of doping. No significant differences in their antibacterial activity were observed. A maximum of 95% bacterial growth inhibition was observed. ML tools were utilized to model bacterial survival under different conditions. The support vector machine (SVM) model yielded the highest prediction error. In contrast, the extremely random tree model produced an error of only 1.8%, making it an excellent computational tool for decision-making. Using this framework, we conducted attribute importance analysis based on the model, identifying a small subset of parameters as being crucial for generating a precise model. The findings provide light on the most critical characteristics of cerium-doped ZnO nanoparticles' antibacterial activity. [ABSTRACT FROM AUTHOR]

This study reports an investigation into the use of computational techniques applied to selected Turbo machinery problems. Students in their third year of a B.S./ B.Tech in Mechanical Engineering program often focus on this topic. As a result of being exposed to an overwhelming quantity of hypothetical ideas, loads of recipes, and references from information handbooks, students sometimes become overwhelmed during lessons on Turbomachinery. An intuitive learning scenario, such as a computerized setting, can be useful in some circumstances. Thus, computer-based exercises are essential to improve dynamic learning. This study will present an alternate technique of demonstrating the turbo machinery analysis using Excel/VBA. The classroom isn't the only setting where a powerful computer application can alter the way subjects are learned and homework is completed. Both students and teachers can benefit from the introduction of this technology. Students will benefit from having this tool available because it will eliminate the need for lengthy calculations and allow them to easily observe the effects of changing values on various variables, thereby improving their understanding of the subject matter and increasing their interest in and enthusiasm for studying it. Further, it will aid educators in conveying a variety of turbomachinery ideas to their students. In addition, students can use these resources in the project's design phase. This program can do more than just solve the equations; it can also tell you how the various parameters influence the forces. This means that students can complete as many calculations as they need with the touch of a button, without the risk of making a mistake or growing bored with the process. [ABSTRACT FROM AUTHOR]

Abstract Background The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. Results We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users’ understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. Conclusions SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants, as well as in developing precise CRISPR/Cas-derived genome editors. The programmable and robust features of the genome editors provide an effective RNA-guided platform for fundamental life science research and subsequent applications in diverse scenarios, including biomedical innovation and targeted crop improvement. One of the most essential principles is to guide alterations in genomic sequences or genes in the intended manner without undesired off-target impacts, which strongly depends on the efficiency and specificity of single guide RNA (sgRNA)-directed recognition of targeted DNA sequences. Recent advances in empirical scoring algorithms and machine learning models have facilitated sgRNA design and off-target prediction. In this review, we first briefly introduce the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purposes. Secondly, we focus on the computer-assisted tools and resources that are widely used in designing sgRNAs and analyzing CRISPR/Cas-induced on- and off-target mutations. Thirdly, we provide insights into the limitations of available computational tools that would help researchers of this field for further optimization. Lastly, we suggest a simple but effective workflow for choosing and applying web-based resources and tools for CRISPR/Cas genome editing.

In various fields like automobiles, construction, etc., the structural analysis of each component or sub-system must be done to ensure its safe operation. The structural analysis of these components entails the determination of parameters like shear force, bending moment at different locat ions. Usually, such computat ions are cumbersome, and hence a simplified approach is adopted, that involves drawing shear force diagram (SFD) and bending moment diagram (BMD) for the components. These diagrams can be effectively utilized to determine the dimensions of the components, select the appropriate material for the structure etc. Also, by utilizing the values of maximum shear force and bending moments, the maximum deflection in a beam or other structure can be ascertained. However, the process of drawing these diagrams is cumbrous and involves a lot of meticulous effort and time, which sometime poses a challenge in the effective teaching and learning of these concepts. The current paper reports the development of computational tools using the excel VBA platform and its implementation in the pedagogy of an undergraduate solid mechanics classroom. The developed tools can be easily employed to instantaneously draw the SFD and BMD diagrams for the beam under a variety of loading conditions, facilitating the inference-based learning of cantilevers and beams. Two distinct tools were developed, one for drawing the SFD and BMD of both cantilever and simply supported beams, and another one to determine the deflection and slope in the same two beams. The tools reported in the current manuscript can be effectively utilized for teaching by the demonstration of parametric variations under various loading conditions, for improved comprehension of the concepts and self-learning as well as in real world engineering to get preliminary design guidelines. Upon the development of these computational tools, these have been introduced to undergraduate mechanical engineering class of a sizable population and student responses regarding the efficacy of such tools in aiding the learning process has been recorded through an anonymous feedback. The subsequent hypothesis testing and obtained p-values strongly justify the extreme usefulness of the tools, both as a teaching and learning strategy. [ABSTRACT FROM AUTHOR]

Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease. [ABSTRACT FROM AUTHOR]

Background: The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. Results: We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. Conclusions: SynBioTools is freely available at https://synbiotools.lifesynther.com/. It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection. [ABSTRACT FROM AUTHOR]

Abstract Background Various mammalian species emit ultrasonic vocalizations (USVs), which reflect their emotional state and mediate social interactions. USVs are usually analyzed by manual or semi-automated methodologies that categorize discrete USVs according to their structure in the frequency-time domains. This laborious analysis hinders the effective use of USVs as a readout for high-throughput analysis of behavioral changes in animals. Results Here we present a novel automated open-source tool that utilizes a different approach towards USV analysis, termed TrackUSF. To validate TrackUSF, we analyzed calls from different animal species, namely mice, rats, and bats, recorded in various settings and compared the results with a manual analysis by a trained observer. We found that TrackUSF detected the majority of USVs, with less than 1% of false-positive detections. We then employed TrackUSF to analyze social vocalizations in Shank3-deficient rats, a rat model of autism, and revealed that these vocalizations exhibit a spectrum of deviations from appetitive calls towards aversive calls. Conclusions TrackUSF is a simple and easy-to-use system that may be used for a high-throughput comparison of ultrasonic vocalizations between groups of animals of any kind in any setting, with no prior assumptions.

Virtual simulators are software aimed at emulating a reality in which it is possible, above all, for user learning. In this context, virtual simulators, if used as teaching resources to stimulate students' interest and reinforce the learning of concepts of natural sciences, and in particular Physics, becomes an interesting strategy. Many simulators have been developed in recent years, making them popular tools, mainly because some of them work online and are free. Despite this, we still cannot say that the use of this resource is a reality in Brazil. Several problems make it difficult or even prevent its use. In this work, we observed the behavior of a group of natural science teachers regarding the use of virtual simulators. The data show that there is still a lot to be done in order to popularize this resource among teachers and, therefore, in the educational culture of Physics teaching. [ABSTRACT FROM AUTHOR]

There have been consequences regarding the increment of the greenhouse effect, such as the rise in the planet's global temperature, and climate change. Refrigerants have an important contribution to the aforementioned environmental impact. In particular, hydrofluorocarbons (HFCs) contribute to the destruction of the ozone layer and the increase of the greenhouse effect. Protocols, international agreements, and legislation were developed to slow down the emission of greenhouse gases. Prohibition and definition of deadlines for the gradual elimination of various refrigerants have been proposed to replace them with others that are environmentally sustainable. Soon, the refrigeration sector will have to replace some refrigerants with others that are alternative and/or sustainable with minimal or zero environmental impact. A computational tool to support decision-making regarding the selection of alternative and/or sustainable refrigerant to replace the old one is developed to be used by refrigeration companies, manufacturers, and installers. A suggestion of refrigerants with reduced environmental impact is provided, ensuring similar thermal performance and energy efficiency, considering the safety level and renovation cost of the installation and refrigerant itself. This decision support system (DSS) uses an objective function that includes the technical specifications and properties of alternative and sustainable refrigerants. The computational tool is applied in the agri-food sector in three case studies. The results show not only the consistency of the computational tool, but also its flexibility, objectivity, and simplicity. Its use allows companies to choose refrigerants with reduced environmental impact, reduced or zero ozone depletion potential and global warming impact, thus contributing to environmental sustainability. [ABSTRACT FROM AUTHOR]

Abstract Background Hydrolysis of cellulose-based biomass by cellulases produce fermented sugar for making biofuels, such as bioethanol. Cellulases hydrolyze the β-1,4-glycosidic linkage of cellulose and can be obtained from cultured and uncultured microorganisms. Uncultured microorganisms are a source for exploring novel cellulase genes through the metagenomic approach. Metagenomics concerns the extraction, cloning, and analysis of the entire genetic complement of a habitat without cultivating microbes. The glycoside hydrolase 5 family (GH5) is a cellulase family, as the largest group of glycoside hydrolases. Numerous variants of GH5-cellulase family have been identified through the metagenomic approach, including CelGH5 in this study. University-CoE-Research Center for Biomolecule Engineering, Universitas Airlangga successfully isolated CelGH5 from waste decomposition of oil palm empty fruit bunches (OPEFB) soil by metagenomics approach. The properties and structural characteristics of GH5-cellulases from uncultured microorganisms can be studied using computational tools and software. Results The GH5-cellulase family from uncultured microorganisms was characterized using standard computational-based tools. The amino acid sequences and 3D-protein structures were retrieved from the GenBank Database and Protein Data Bank. The physicochemical analysis revealed the sequence length was roughly 332–751 amino acids, with the molecular weight range around 37–83 kDa, dominantly negative charges with pI values below 7. Alanine was the most abundant amino acid making up the GH5-cellulase family and the percentage of hydrophobic amino acids was more than hydrophilic. Interestingly, ten endopeptidases with the highest average number of cleavage sites were found. Another uniqueness demonstrated that there was also a difference in stability between in silico and wet lab. The II values indicated CelGH5 and ACA61162.1 as unstable enzymes, while the wet lab showed they were stable at broad pH range. The program of SOPMA, PDBsum, ProSA, and SAVES provided the secondary and tertiary structure analysis. The predominant secondary structure was the random coil, and tertiary structure has fulfilled the structure quality of QMEAN4, ERRAT, Ramachandran plot, and Z score. Conclusion This study can afford the new insights about the physicochemical and structural properties of the GH5-cellulase family from uncultured microorganisms. Furthermore, in silico analysis could be valuable in selecting a highly efficient cellulases for enhanced enzyme production.

Background: Various mammalian species emit ultrasonic vocalizations (USVs), which reflect their emotional state and mediate social interactions. USVs are usually analyzed by manual or semi-automated methodologies that categorize discrete USVs according to their structure in the frequency-time domains. This laborious analysis hinders the effective use of USVs as a readout for high-throughput analysis of behavioral changes in animals. Results: Here we present a novel automated open-source tool that utilizes a different approach towards USV analysis, termed TrackUSF. To validate TrackUSF, we analyzed calls from different animal species, namely mice, rats, and bats, recorded in various settings and compared the results with a manual analysis by a trained observer. We found that TrackUSF detected the majority of USVs, with less than 1% of false-positive detections. We then employed TrackUSF to analyze social vocalizations in Shank3-deficient rats, a rat model of autism, and revealed that these vocalizations exhibit a spectrum of deviations from appetitive calls towards aversive calls. Conclusions: TrackUSF is a simple and easy-to-use system that may be used for a high-throughput comparison of ultrasonic vocalizations between groups of animals of any kind in any setting, with no prior assumptions. [ABSTRACT FROM AUTHOR]

Resumo: Este texto descreve algumas propriedades linguísticas recorrentes em textos nota 1000 do Enem. Parte-se do princípio de que o gênero redação do Enem tem características próprias, como a exposição aliada à argumentação, e que elementos como repertório e amplo uso de conectivos e modalizadores podem contribuir para a caracterização do texto. Nesta pesquisa, levando em conta o rigoroso processo de avaliação pelo qual passam, consideram-se as redações nota 1000 como exemplares prototípicos do gênero, ou seja, cumprem todos os requisitos exigidos pela banca. O corpus é constituído de 95 redações que alcançaram a nota máxima nos anos de 2014, 2018 e 2019, analisadas por meio do software Tropes, uma ferramenta computacional de análise lexical que verifica as recorrências de categorias e repertório. Os resultados mostraram que tais redações apresentam uma estrutura muito próxima àquela que se verifica na literatura sobre o tema, em especial o predomínio da estrutura impessoal (terceira pessoa), a amplitude do repertório (universo de referência), a variedade e alta frequência de conectivos e modalizadores, além da recorrência de verbos estativos, como ser. Com isso, conclui-se tanto que a ferramenta contribui para a descrição do gênero em questão quanto que os resultados fomentam um debate em torno da padronização da estrutura do texto do Enem. Palavras-chave: gênero textual; ferramenta computacional; redação do Enem; propriedades linguísticas. Abstract: This paper aims to describe some linguistic properties found in Enem essays graded to 1000 points, the maximal score. It considers that this genre has its own characteristics, such as the relation between exposition and argumentative types, a wide repertoire, as well as a large use of connectives and modal elements, all of them contribute to characterize this genre. In this research, by considering the strict evaluation process, it has been considered that these essays graded to 1000 points prototypically represent the genre, once that they meet the criteria required by the evaluation panel. The corpus consists of 95 essays that got 1000 points in 2014, 2018 and 2019 exams. It has been analyzed by means of Tropes, a computational tool that verifies the frequency of lexical items group them in categories and repertoire. The results show that such essays follow what the literature proposed by the genre, especially, the impersonality (third person only), the vast repertoire extension (universe of reference), the range of connectives and modal elements, besides the recurrence of stative verbs, such as ser (‘to be’). Consequently, one concludes that both the tool contributes to the genre description, and the results put forward the debate around the standardization of the Enem essay structure. Keywords: textual genre; computational tool; Enem essay; linguistic properties.

The study of fluid mechanics spans several engineering disciplines including Mechanical, Civil, Aerospace, Chemical, Environmental, Petroleum, and Biomedical Engineering. In all these disciplines, hydraulic loss calculations in pipes are extremely important. However, the iterative nature of the solution to these engineering problems makes it intricate and cumbersome to solve. Further, it gets very difficult to visualize the solutions to such iterative problems for a wide variety of cases. The current paper aims to bridge this gap by the creation of two open-source Excel-VBA based computational solvers. The first tool corresponds to the determination of the Darcy-Weisbach friction factor through the Colebrook Equation and its visualization on a Moody's chart, which can be effectively employed by engineering instructors as an active learning tool. Second, a complete tool covering all four kinds of pipe flow situations (including the iterative problems) has been developed. The developed computational tools were employed in an undergraduate Fluid Mechanics classroom and the detailed student responses were collected on ten aspects related to teaching and learning divided broadly under four categories - 'overall rating', 'student perceptions on self-learning', 'Improvement in teaching delivery', and 'recommendation for other courses'. The data collected from student responses were subjected to statistical analysis. The results of hypothesis testing and the p-value calculations clearly justify the immense usefulness of this tool in the improvement of the overall teaching-learning process of Fluid Mechanics. Finally, the developed computational tools are being hosted free on the web for the benefit of engineering instructors, learners and professionals alike. [ABSTRACT FROM AUTHOR]

Research over the past two decades has made substantial inroads into our understanding of somatic mutations. Recently, these studies have focused on understanding their presence in homeostatic tissue. In parallel, agent-based mechanistic models have emerged as an important tool for understanding somatic mutation in tissue; yet no common methodology currently exists to provide base-pair resolution data for these models. Here, we present Gattaca as the first method for introducing and tracking somatic mutations at the base-pair resolution within agent-based models that typically lack nuclei. With nuclei that incorporate human reference genomes, mutational context, and sequence coverage/error information, Gattaca is able to realistically evolve sequence data, facilitating comparisons between in silico cell tissue modeling with experimental human somatic mutation data. This user-friendly method, incorporated into each in silico cell, allows us to fully capture somatic mutation spectra and evolution. [ABSTRACT FROM AUTHOR]

Food waste occurs from harvesting to consumption. Applying procedures and technologies, changing attitudes, and promoting awareness have positive social, economic, and environmental impacts that can contribute to reducing food waste. The paper presents a decision support system (DSS) to predict the quality evolution of fruits and vegetables, particularly of peaches, and estimate its commercialization period at the highest overall perceived quality by consumers, thus contributing to reducing food waste. The Fuzzy Logic DSS predicts the evolution of the physical-chemical parameters of peaches (hardness, soluble solids content, and acidity) depending on the cultivar (Royal Summer and Royal Time), storage time, and temperature. As the range of the values of these physical-chemical parameters of peaches that consumers perceive to be at their highest quality are known, the DSS predicts the marketable period in days. Case studies were developed to analyze the influence of each physical-chemical parameter on the commercialization days (number and time to start). It is concluded that temperature is the most important parameter for fruit conservation. A low value of conservation temperature allows for the significant extension of the time that peaches can be sold at the highest quality. Hardness is used to determine the harvest date since it is an index of fruit ripeness. The same conclusion is obtained for the influence of the soluble solids content. The influence of acidity on marketable days is less than the other physical-chemical parameters. This DSS helps retailers to sell their peaches at the highest quality with benefits for all parties. It also helps in the decision-making concerning the actions to take when fruits are reaching the end of their highest quality by predicting the range of the commercialization days. This formulation can be extended to other fruits and vegetables and in the last instance contribute to the reduction of food loss and waste, consequently promoting social, economic, and environmental aspects of our daily life. [ABSTRACT FROM AUTHOR]

Abstract Background Long-read RNA-Seq techniques can generate reads that encompass a large proportion or the entire mRNA/cDNA molecules, so they are expected to address inherited limitations of short-read RNA-Seq techniques that typically generate

Rationale: In clinical and research settings, the validity and reliability of measurement instruments are crucial for reliable results. Current methods for computing validation metrics like Aiken's coefficients are often complex and error-prone, highlighting the need for a standardized computational tool to simplify and enhance this process., Aims and Objectives: The objective of this study was to develop AikenCalc, a tool created to automate the calculation of Aiken coefficients. This tool provides a quantitative method for examining the validity and reliability of consensus among expert judges., Method: AikenCalc was developed with Shiny app, an R library that allows an easy-to-use interface and improved computational efficiency. It was validated using a convergence method by comparing the manual calculations with those obtained using AikenCalc. Additionally, to examine usability and interface, six experts evaluated their experience using AikenCalc and provided improvement feedback., Results: AikenCalc demonstrated good convergent validity, closely aligning with the results obtained manually, and the six experts gave favorable ratings regarding the usability and interface of AikenCalc. Finally, it was shown that AikenCalc significantly reduced the time required for calculations, thanks to its user-friendly interface., Conclusion: AikenCalc represents a significant improvement in the validation of measurement instruments by automating and simplifying the calculation of Aiken's coefficients through a Shiny web application. Additionally, it fills a knowledge gap, as there is currently no similar calculator available in the field of research., (© 2024 John Wiley & Sons Ltd.)