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Inventions
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Innovative Instrument Setting and Software Development for Organismic Biology and...
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Innovative Instrument Setting and Software Development for Organismic Biology and Behavior Analysis

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Biotechnology and Materials".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 5493

Special Issue Editor

Prof. Dr. Chung-Der Hsiao

E-Mail Website
Guest Editor
Epidermal Stem Cell Lab, Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li 320314, Taiwan
Interests: deep learning; image analysis; aquatic animal physiology and toxicology; new tool invention
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to invite you to contribute to a Special Issue of Inventions dedicated to showcasing cutting-edge innovations in the field of new instrument and software development for organismic biology and behavior analysis. In recent years, there have been remarkable advances in technologies and methodologies that have revolutionized our understanding of organisms and their behaviors. This Special Issue aims to bring together researchers, scientists, and experts from diverse backgrounds to explore these groundbreaking inventions and their applications in the context of organismic biology and behavior analysis, especially those with a focus on new innovative instrument settings and software development. We are seeking original research papers, reviews, and methodological studies that highlight novel inventions, tools, and techniques with the potential to transform our ability to study and interpret the physiology and behaviors of organisms, from microorganisms to complex ecosystems. Contributions are encouraged from fields such as genetics, neuroscience, ecology, ethology, and computational biology, among others.

Interesting Topics for the Special Issue:

Advanced Imaging and Tracking Technologies: Studies that explore the latest innovations in microscopy, imaging, and tracking systems that enable researchers to observe and analyze the behavior of organisms at various scales.

Omics Technologies: Studies that discuss how high-throughput genomics, transcriptomics, proteomics, and metabolomics techniques are revolutionizing our understanding of the molecular basis of behavior in organisms.

Neurobiological Tools: Studies that highlight cutting-edge tools and instrument settings for studying neural circuits and brain activity in organisms, including optogenetics, calcium imaging, and neural recording methods.

Bioinformatics and Computational Models: Studies that present novel computational approaches, algorithms, modeling techniques, and software packages for analyzing complex behavioral data and deriving meaningful insights.

Environmental Sensors and Data Integration: Studies that showcase inventions in environmental monitoring and sensor technology that facilitate the integration of environmental data into behavior analysis.

Field Studies and Remote Sensing: Studies that explore innovations in field research methodologies, including remote sensing technologies and autonomous data collection systems for studying behavior in natural settings.

Behavioral Phenotyping Platforms: Studies that discuss automated behavioral phenotyping systems (hardware or software) and their applications for studying behavior across a wide range of organisms.

Interdisciplinary Approaches: Studies that encourage submissions that demonstrate collaborations between different scientific disciplines to address complex questions in organismic biology and behavior analysis.

We invite you to submit your contributions to this Special Issue, which promises to be a platform for sharing groundbreaking research and fostering discussions on the future of organismic biology and behavior analysis. Together, we can push the boundaries of knowledge in these exciting fields.

Prof. Dr. Chung-Der Hsiao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Inventions is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • new instrument
  • software package
  • OpenCV
  • mask RCNN
  • YOLO
  • U-Net
  • StarDist
  • ImageJ
  • Matlab
  • big data
  • remote sensing
  • mathematic algorithm
  • clustering method
  • image segmentation
  • image classification
  • locomotion trajectory
  • deep learning
  • multi-omics

Published Papers (3 papers)

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Research

15 pages, 3111 KiB  
Article
Characterization of Pig Vertebrae under Axial Compression Integrating Radiomic Techniques and Finite Element Analysis
by Cristian A. Hernández-Salazar, Camilo E. Chamorro and Octavio A. González-Estrada
Inventions 2024, 9(2), 36; https://doi.org/10.3390/inventions9020036 - 28 Mar 2024
Viewed by 1026
Abstract
The study of pig bones, due to their similarity with human tissues, has facilitated the development of technological tools that help in the diagnosis of diseases and injuries affecting the skeletal system. Radiomic techniques involving medical image segmentation, along with finite element analysis, [...] Read more.
The study of pig bones, due to their similarity with human tissues, has facilitated the development of technological tools that help in the diagnosis of diseases and injuries affecting the skeletal system. Radiomic techniques involving medical image segmentation, along with finite element analysis, enable the detailed study of bone damage, loss of density, and mechanical functionality, which is a significant advancement in personalized medicine. This study involves conducting experimental tests on L3–L6 pig vertebrae under axial loading conditions. The mechanical properties of these vertebrae are analyzed, and the maximum loads they can sustain within the elastic range are determined. Additionally, three-dimensional models are generated by segmenting computerized axial tomography (CAT) scans of the vertebrae. Digital shadows of the vertebrae are constructed by assigning an anisotropic material model to the segmented geometries. Then, finite element analysis is performed to evaluate the elastic characteristics, stress, and displacement. The findings from the experimental data are then compared to the numerical model, revealing a strong correlation with differences of less than 0.8% in elastic modulus and 1.53% in displacement. The proposed methodology offers valuable support in achieving more accurate medical outcomes, employing models that serve as a diagnostic reference. Moreover, accurate bone modeling using finite element analysis provides valuable information to understand how implants interact with the surrounding bone tissue. This information is useful in guiding the design and optimization of implants, enabling the creation of safer, more durable, and biocompatible medical devices that promote optimal osseointegration and healing in the patient. Full article
(This article belongs to the Special Issue Innovative Instrument Setting and Software Development for Organismic Biology and Behavior Analysis)
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14 pages, 2970 KiB  
Article
A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages
by Seyed Iman Saedi and Mehdi Rezaei
Inventions 2024, 9(1), 6; https://doi.org/10.3390/inventions9010006 - 31 Dec 2023
Viewed by 1618
Abstract
Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate post-harvest processing. This study introduces an automatic computer [...] Read more.
Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate post-harvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the ‘Roghani’ Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was modified by adding some well-known CNN layers to the last layer. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting in addition to the network stability, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products. Full article
(This article belongs to the Special Issue Innovative Instrument Setting and Software Development for Organismic Biology and Behavior Analysis)
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24 pages, 5409 KiB  
Article
SpiderID_APP: A User-Friendly APP for Spider Identification in Taiwan Using YOLO-Based Deep Learning Models
by Cao Thang Luong, Ali Farhan, Ross D. Vasquez, Marri Jmelou M. Roldan, Yih-Kai Lin, Shih-Yen Hsu, Ming-Der Lin, Chung-Der Hsiao and Chih-Hsin Hung
Inventions 2023, 8(6), 153; https://doi.org/10.3390/inventions8060153 - 06 Dec 2023
Viewed by 2414
Abstract
Accurate and rapid taxonomy identification is the initial step in spider image recognition. More than 50,000 spider species are estimated to exist worldwide; however, their identification is still challenging due to the morphological similarity in their physical structures. Deep learning is a known [...] Read more.
Accurate and rapid taxonomy identification is the initial step in spider image recognition. More than 50,000 spider species are estimated to exist worldwide; however, their identification is still challenging due to the morphological similarity in their physical structures. Deep learning is a known modern technique in computer science, biomedical science, and bioinformatics. With the help of deep learning, new opportunities are available to reveal advanced taxonomic methods. In this study, we applied a deep-learning-based approach using the YOLOv7 framework to provide an efficient and user-friendly identification tool for spider species found in Taiwan called Spider Identification APP (SpiderID_APP). The YOLOv7 model is integrated as a fully connected neural network. The training of the model was performed on 24,000 images retrieved from the freely available annotated database iNaturalist. We provided 120 genus classifications for Taiwan spider species, and the results exhibited accuracy on par with iNaturalist. Furthermore, the presented SpiderID_APP is time- and cost-effective, and researchers and citizen scientists can use this APP as an initial entry point to perform spider identification in Taiwan. However, for detailed species identification at the species level, additional methods like DNA barcoding or genitalic structure dissection are still considered necessary. Full article
(This article belongs to the Special Issue Innovative Instrument Setting and Software Development for Organismic Biology and Behavior Analysis)
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