VR Macintosh Museum: Case Study of a WebVR Application

Oliver, Antoni; Molino, Javier del; Cañellas, Maria; Clar, Albert; Bibiloni, Antoni

doi:10.1007/978-3-030-16184-2_27

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…The pipeline for this experiment was developed based upon previous venues that have been improved significantly (Parikesit and Anurogo 2018;. The computer used is a MacBook Pro® Laptop (13-inch, Late 2011) with 16 GB RAM, Intel HD Graphics 3000®, and 500 GB SATA disk for securing good performance in molecular computation (Cense 1989;Baro and Hughes 1991;Smith 1995;Oliver et al 2019). The pipeline is mainly comprised of two parts, namely the sequence and structural analyses.…”

Section: Methodsmentioning

confidence: 99%

The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus)

Parikesit

Imron²,

Nurdiansyah

et al. 2022

HAYATI J Biosci

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Tilapia (Oreochromis niloticus) is an important fisheries commodity. Scientific efforts have been done to increase its quality. One of them is staging a premium diet such as a fat-enriched diet. The transcriptomics approach is able to provide the signatures of the diet outcomes by observing the micro(mi)RNA signature in transcriptional regulation. Hence, it was found that the availability of mir-122 is essential in the regulation of a high-fat diet in tilapia. However, this transcriptomics signature is lacking structural annotations and the complete interaction annotations with its silencing(si)RNA. RNAcentral website was navigated for the latest annotation of mir-122 from tilapia and other species as a comparison. MEGA X was employed to comprehend the miRNA evolutionary repertoire. The RNA secondary structure prediction tools from the Vienna RNA package and the RNA tertiary structure prediction tools from simRNA and modeRNA are secured with default parameters. The HNADOCK tools were leveraged to observe the interaction between mir-122 and its siRNA. The post-processing was conducted with the Chimera visualization tool. The secondary and tertiary structure of the mir-122 and its siRNA could be elucidated, docked, and visualized. In this end, further effort to develop a comprehensive molecular breeding tool could be secured with the structural annotation information.

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Section: Methodsmentioning

confidence: 99%

The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus)

Parikesit

Imron²,

Nurdiansyah

et al. 2022

HAYATI J Biosci

View full text Add to dashboard Cite

show abstract

“…This research was conducted using a standard MacBook Pro Laptop with MacOSX version 10.13.6, 512 GB of HDD, and 16 GB of RAM. The employment of a Mac-based laptop was crucial for leveraging a graphics subsystem with proven scientific computation performance [39][40][41][42]. To navigate and search for associations between genes and breast cancer, the phrase 'breast cancer' was used to search for the appropriate genes in the TCGA database (https://portal.gdc.cancer.gov/).…”

Section: Methodsmentioning

confidence: 99%

“…Then, as part of the SUPERFAMILY database project, annotations of disordered proteins were searched using the D2P2 database (http://d2p2.pro/), which only provides hits with 100% identity. Thus, the minimum required interaction score of the STRING database (https://string-db.org/) is a medium confidence rating (0.400, the default parameter) [41][42][43][44][45][46][47][48][49]. The downloaded Protein Data Bank (PDB) files linked from the PFAM database (http://pfam.xfam.org/) were visualized using Chimera version 1.13.1 [54].…”

Section: Methodsmentioning

confidence: 99%

Protein Annotation of Breast-cancer-related Proteins with Machine-learning Tools

Parikesit¹,

Agustriawan²,

Nurdiansyah³

2020

Makara J. Sci.

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One of the primary contributors to the mortality of women is breast cancer. Several approaches are used to cure it, but recurrence occurs in 79% of the cases because the underlying mechanism of the protein molecules is not carefully examined. The goal of this research was to use machine-learning tools is to elucidate conserved regions and to obtain functional annotations of breast-cancer-related proteins. The sequences of five breast-cancer-related proteins (BRCA2, BCAR1, BCAR3, BCAR4, and BRMS1) and their annotations were retrieved from the UniProt and TCGA databases, respectively. Conserved regions were extracted using CLUSTALX. We constructed a phylogenetic tree using the MEGA 7.0. SUPERFAMILY database to obtain fine-grained domain annotation. The tree revealed that the BRCA2 and BCAR4 protein sequences are located in a clade, which indicates that they have overlapping functions. Several protein domains were identified, including the SH2 and Ras GEF domains in BCAR3, the SH3 domain in BCAR1, and the BRCA2 helical domain, the nucleic-acid-binding protein, and tower domain. We found that no protein domains could be annotated for BCAR4 or BRMS1, which may indicate the presence of a disordered protein state. We suggest that each protein has distinct functionalities that are complementary in regulating the progression of breast cancer, although further study is necessary for confirmation. This protein-domain annotation project could be leveraged by the complete integration of mapping with respect to gene and disease ontology. This type of leverage is vital for obtaining biochemical insights regarding breast cancer.

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“…The acceptance of WebVR technology has been evaluated in various contexts, like textual browsing in digital libraries (Hahn, 2018), visualizing complex structures in virtual laboratories (Xia et al, 2018) and designing immersive experiences in virtual museums (Oliver et al, 2019). In the case of academic training, this technology has been increasing because it has been proven to improve teaching and learning in various disciplines such as aeronautics, medicine, industrial safety, and others (Nemer et al, 2020).…”

Section: Web-based Virtual Reality In the Covid-19 Environmentmentioning

confidence: 99%

Evaluation of a Virtual Campus Adapted to Web-Based Virtual Reality Spaces: Assessments of Teachers and Students

et al. 2022

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Educational institutions have continuously adapted to new realities in school education, accelerated recently by the COVID-19 pandemic’s transformation of learning modalities. This article analyzes teachers’ and Students’ acceptance of a web-based virtual reality (WebVR) tool called Virtual Campus proposed to overcome the limitations of teaching strategies using video conferencing platforms. To measure the acceptance of the Virtual Campus, we designed an instrument based on the Technology Acceptance Model (TAM) that involves variables related to online contexts, the future perception of using the tool, skills development, and appreciation and recommendation. The results indicate that the participants favorably accepted WebVR technology as an alternative teaching methodology in emerging learning scenarios and intend to use it post-pandemic. The best-valued elements were interactions in the socialization spaces, the simulation of presence, and the environmental dynamics. The principal areas of opportunity for improvement were overcoming technical problems and improving the internet connection quality; however, these did not affect the participants’ recommendations. Future studies should incorporate variables related to analyzing learning mediated by WebVR-based strategies and using different methodological designs to compare the findings.

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VR Macintosh Museum: Case Study of a WebVR Application

Cited by 7 publications

References 13 publications

The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus)

The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus)

Protein Annotation of Breast-cancer-related Proteins with Machine-learning Tools

Evaluation of a Virtual Campus Adapted to Web-Based Virtual Reality Spaces: Assessments of Teachers and Students

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