Prerpints.org logo
Preprint
Review

Revolutionizing Biology through Neural Networks: A Deep Dive into Microscopy Image Processing and Drawings

Altmetrics

Downloads

83

Views

25

Comments

0

This version is not peer-reviewed

Advances in Artificial Neural Networks

Submitted:

15 August 2023

Posted:

17 August 2023

You are already at the latest version

Alerts
Abstract
This article explores the transformative role of neural networks in the realm of biology, particularly within microscopy image processing and illustrations. Neural networks have revolutionized cell segmentation and analysis, enabling precise delineation of cell boundaries and tracking of cellular behaviours. They excel in detecting subcellular structures, unravelling intricate organelle interactions. In 3D image reconstruction, neural networks navigate volumetric datasets, enhancing our understanding of spatial cellular architecture. These networks enhance disease diagnosis by identifying anomalies and irregularities, potentially revolutionizing early detection and classification. Moreover, neural networks restore and enhance microscopy images, unveiling hidden details. Lastly, they bridge art and science, fostering captivating biological art and enriching science communication. As neural networks evolve, they promise a future of limitless possibilities, weaving together technology, science, and art to illuminate the microscopic realm in unprecedented ways.
Keywords: 
Subject: Engineering  -   Bioengineering

1. Introduction

The fusion of biology and technology has inaugurated a novel era of scientific exploration [1]. Among the remarkable technological advancements that have deeply impacted biological research, the integration of neural networks into image processing and illustrations, specifically within the realm of microscopy, stands out prominently [2]. Inspired by the intricate workings of the human brain, neural networks have emerged as a catalytic force, unveiling concealed insights into cellular structures, disease mechanisms, and the intricacies of life's delicate dance. Within this article, we embark on an immersive odyssey into the captivating applications of neural networks in the field of biology, where microscopes unveil the enigmas concealed within the microscopic cosmos.

2. Cell Segmentation and Analysis

Microscopy serves as a portal into the enigmatic realm of cells, offering visual narratives of their forms and functions. Yet, deciphering the intricate tapestry of cell structures often presents a formidable challenge. This is where neural networks emerge as transformative allies, providing a robust solution to the longstanding puzzle of cell segmentation and analysis.
The architecture of neural networks, particularly convolutional neural networks (CNNs), seamlessly aligns with the requisites of cell segmentation. Consider the U-Net architecture as a prime example – a neural network meticulously crafted for semantic segmentation. The encoder-decoder design of U-Net adeptly harnesses its capacity to encapsulate both localized intricacies and holistic context [3]. By scrutinizing patterns and textures across diverse scales, U-Net artfully distinguishes one cell from another, enabling a precise delineation of cell boundaries.
In the realm of segmentation at a pixel level, Mask R-CNN, a more sophisticated counterpart of U-Net, takes centre stage. This architecture not only identifies cells but also crafts pixel-wise masks that gracefully disentangle overlapping boundaries [4]. The implications are profound, transcending the mere identification of boundaries and delving into the intricate domain of spatial comprehension. From quantifying cellular attributes to facilitating the tracking of cellular motion and interactions, neural networks provide a robust foundation for a data-driven approach to cell biology [5].
Moreover, the fusion of neural networks with cellular analysis extends beyond the realms of research. In the realm of medical diagnostics, neural networks assume the role of virtual pathologists, aiding in the identification of cellular irregularities that signal diseases like cancer [6]. The capacity to analyse cell morphology and classify anomalies heightens the precision and efficiency of diagnosis, potentially leading to timelier interventions and enhanced patient outcomes.
Similarly, one research introduced an innovative approach for automated analysis of bacilliform bacteria population growth dynamics. It utilized dedicated Boltzmann machines, stochastic recurrent neural networks, to minimize a specific cost function, enabling frame-sequence tracking of deformable-cell motion and automated detection of cell divisions. Validation on simulated and real Escherichia coli colonies demonstrated high registration accuracies, ranging from 90% to 100%, showcasing the effectiveness of this proposed algorithm for automated cell tracking and division detection [7].
In essence, neural networks redefine the landscape of cell segmentation and analysis. They introduce a level of precision and automation that not only expedites research but also enhances our comprehension of the intricate choreography underlying cellular existence.

3. Subcellular Structure Detection

As our microscopic lenses venture deeper into the intricate terrain of the cellular landscape, the complexity it reveals becomes increasingly profound [1]. Cells, far from being uniform entities, embody bustling ecosystems of subcellular structures – organelles that orchestrate specialized roles in the intricate dance of life's processes [2]. The challenge rests in deciphering these intricate formations amidst the visual symphony of the microscopic realm. Here, neural networks, with their remarkable aptitude for decoding intricate patterns, emerge as pivotal tools [5].
Neural networks, meticulously trained on annotated datasets, possess a remarkable prowess for discerning subcellular structures [3]. By immersing themselves in an array of cellular images, these networks adeptly recognize nuclei, mitochondria, endoplasmic reticulum, and other essential organelles [4]. The implications of this ability are profound, granting researchers access to an uncharted realm of cellular exploration, where the dynamic interplay of organelles within the intricate cellular fabric comes to light.
Consider, for instance, the mitochondria – the cellular powerhouses. Neural networks offer a distinctive vantage point, empowering scientists to meticulously observe mitochondrial dynamics, track their motions, and untangle the intricacies of their fission and fusion processes [6]. In the realm of drug discovery, these networks hold the potential to evaluate the impact of drugs on specific organelles, thereby deepening our comprehension of pharmacological mechanisms [8].
The subcellular odyssey facilitated by neural networks transcends the confines of mere image analysis. It is a harmonious symphony of pattern recognition, an orchestration of computational excellence that sheds unprecedented light on the inner mechanisms of cells. As neural networks continue to advance, we find ourselves standing at the threshold of unravelling the enigmas concealed beneath the cellular facade.

4. 3D Image Reconstruction

The advent of three-dimensional (3D) microscopy has ushered in a new era of understanding cellular architecture [9]. As we move beyond the confines of two-dimensional cross-sections, a novel capability emerges – the ability to explore intricate spatial relationships that define the essence of cellular existence [10]. However, this progress is accompanied by the formidable challenge of reconstructing 3D structures from stacks of 2D images. At this juncture, neural networks step onto the stage, ready to redefine the landscape of 3D image reconstruction within the realm of microscopy [11].
Neural networks possess an intrinsic aptitude for processing multidimensional data, making them adept at the complex task of 3D image reconstruction [12]. Through exposure to volumetric datasets during training, these networks assimilate the intricate interconnections between individual 2D slices, enabling them to elegantly weave a coherent 3D tapestry [13]. This technological feat empowers researchers to navigate the intricate terrain of cellular landscapes, unravelling the spatial subtleties that significantly influence cellular behaviour.
Consider the transformative implications for the field of developmental biology. Neural networks empower scientists to trace the progressive evolution of tissue architectures over time, unveiling the dynamic interplay of cells as they collaborate to form intricate organs and structures [14]. This breakthrough not only enhances our comprehension of tissue morphogenesis but also holds promising potential for the realms of regenerative medicine and tissue engineering.
Furthermore, the scope of 3D image reconstruction propelled by neural networks transcends the boundaries of a mere microscope slide. It facilitates the visualization of entire organs, tissues, and even entire organisms with an unparalleled degree of clarity [1]. From unravelling the intricacies of neural circuitry to delving into the complex microenvironments of tumours, neural network-driven 3D reconstruction marks a monumental leap forward, augmenting our capability to decipher the intricate spatial facets of life.
As neural networks continue their evolutionary journey, the prospect of even more refined and intricate 3D reconstructions beckons. These networks harbour the potential to unravel the enigmas inherent in multicellular systems, opening a window to observe the orchestration of life in three dimensions [3].

5. Disease Diagnosis and Classification

The convergence of neural networks and microscopy heralds a potent synergy in the realm of disease diagnosis and classification. As researchers and medical professionals endeavour to unravel the intricacies of diseases at the cellular level, neural networks emerge as indispensable allies in this pursuit [16,17].
Neural networks exhibit a remarkable acumen for discerning subtle patterns and anomalies within microscopic images, translating into a precise and automated avenue for disease diagnosis [18]. Consider cancer, an ailment characterized by intricate shifts in cell morphology. Neural networks, trained on meticulously annotated datasets, acquire the capacity to identify the distinctive markers of cancerous cells, even in their nascent stages, which might elude human perception [19]. This translates to early detection – a pivotal factor contributing to enhanced patient outcomes and heightened survival rates.
The capabilities of neural networks extend beyond mere detection. Disease classification based on microscopic features gains a new dimension through the infusion of deep learning [20]. These networks evolve to discriminate between diverse disease states, facilitating accurate and efficient categorization. In the context of infectious diseases, neural networks proficiently identify cellular alterations induced by pathogens, thereby expediting swift and precise diagnoses [21].
Moreover, neural networks expedite the domain of drug discovery by scrutinizing the impacts of potential drug candidates on cellular structures [22]. By monitoring shifts in cell morphology and behaviour, these networks streamline the screening process for compounds, hastening the identification of promising candidates warranting further exploration.
The fusion of neural networks and microscopy in the arena of disease diagnosis and classification epitomizes a paradigm shift [23]. It equips researchers and clinicians with a data-centric approach that transcends the constraints of manual analysis. As these networks continue to evolve, we teeter on the threshold of a new healthcare era, where microscopic vistas unveil the enigmas surrounding diseases, ushering in timely interventions and bespoke therapeutic strategies.

6. Image Restoration and Enhancement

Microscopy images, which provide glimpses into the intricate realm of cells, often bear the marks of imperfection [24]. The presence of noise, artifacts, and inherent limitations of imaging equipment can obscure the finer details that researchers tirelessly strive to unveil. In this context, neural networks play a pivotal role by harnessing their capabilities to restore and enhance these images, ultimately revealing hidden insights that might otherwise remain obscured [25].
Neural networks inherently possess the ability to discern patterns within images. Leveraging this aptitude, they can adeptly identify and mitigate noise, resulting in the creation of cleaner and more precise images as shown by a research which exhibited 93.68% precision in its wavelet frameworks construction. Through extensive exposure to diverse image datasets, neural networks cultivate a nuanced understanding of the expected structures and features intrinsic to microscopy images. This comprehension empowers them to distinguish between authentic cellular structures and irregularities introduced by extraneous noise [16,17,26].
Moreover, neural networks undertake the task of enhancing contrast and sharpness, injecting renewed vitality into images that previously lacked vibrancy. Features that once lingered in ambiguity now emerge with striking clarity, enabling researchers to conduct more refined observations and measurements. Beyond its significance in fundamental research, this technological advancement extends to educational pursuits, where enriched images provide a clearer visual representation of the intricacies of cellular components [27].
Consider the expansive implications for drug discovery. Researchers can now meticulously analyse the effects of potential drug candidates on cells with an unprecedented level of precision. The augmentation of images facilitates a more accurate quantification of cellular responses, thus revealing profound insights into the efficacy and underlying mechanisms of various compounds [28].
In its essence, the role of neural networks transcends that of mere algorithms; they wield the extraordinary ability to effect transformation. By converting noisy and lacklustre images into vivid renditions of cellular existence, this process of restoration and enhancement attests to the profound interplay between technology and scientific inquiry. In doing so, it casts an illuminating light upon the microscopic universe in ways that were once considered inconceivable [29].

7. Biological Art and Communication

The convergence of art and science possesses an enchanting ability to captivate the imagination and eloquently convey intricate concepts [30]. Neural networks, transcending their analytical domain, have ventured into the realm of creative expression, ushering in a novel genre of biological art and communication [31].
Neural style transfer, a technique deeply rooted in the realm of deep learning, bestows the capability to metamorphose microscopy images into captivating visual symphonies [32]. Through the fusion of the content from microscopy images and the artistic essence of paintings or illustrations, neural networks craft enchanting hybrids that seamlessly bridge the chasm between science and aesthetics [33]. These artistic renditions encapsulate the very essence of cellular structures and processes, evoking emotions and kindling a profound connection with the microscopic universe.
In the realm of science communication, art birthed from neural networks emerges as a potent instrument. It enthrals audiences by presenting intricate biological concepts in visually captivating and comprehensible formats. Processes that were once confined to scientific literature spring to life in vivid exhibits that surmount language barriers. Whether within classrooms, museums, or public showcases, art cultivated through neural networks enriches the narrative of biology and stimulates a thirst for knowledge [34].
The sphere of biological art and communication, empowered by the capabilities of neural networks, extends its embrace to domains like medical education and outreach. It bridges the chasm between scientific experts and the wider public, nurturing a sense of awe and reverence for the intricacies of life. In doing so, neural networks not only enhance the course of scientific exploration but also kindle a broader cultural discourse about the allure and significance of the microscopic tapestry [35].

8. Future Horizons and Ethical Considerations

Anticipating the future, the convergence of neural networks, image processing, and biology promises an uncharted realm of advancement and innovation [17]. The evolution of neural network architectures and methodologies is poised to usher in heightened precision, efficiency, and adaptability within the realm of microscopy.
In the domain of cell segmentation and analysis, neural networks are projected to adapt to an expanding spectrum of cell types, shapes, and imaging techniques [3]. Enhanced algorithms will facilitate more robust quantification of cellular attributes, thereby enriching our comprehension of fundamental biological mechanisms and disease dynamics [36]. Additionally, the incorporation of real-time tracking and analysis could unveil dynamic insights into prolonged cellular behaviours.
The progression of neural network capabilities will similarly augment subcellular structure detection. Networks may become adept at identifying even the most specialized organelles, unravelling intricate interplays among these components. As imaging technologies advance, neural networks are expected to acclimate, extracting meaningful insights from emerging microscopy modalities, thereby enabling researchers to delve into cellular intricacies with unparalleled precision.
Within the realm of 3D image reconstruction, neural networks are anticipated to master intricate and diverse datasets. The fusion of 3D reconstructions with other omics data, encompassing genomics and proteomics, has the potential to furnish a holistic grasp of cellular functions and interactions. Such strides could catalyse breakthroughs in fields like regenerative medicine, tissue engineering, and drug discovery [37].
While neural networks continue to revolutionize disease diagnosis and classification, ethical contemplations loom large. Ensuring the transparency, comprehensibility, and accountability of neural network-based diagnostic systems is paramount. Rigorous validation and cross-validation frameworks will be pivotal to alleviate biases and ensure precise prognoses, thereby instilling confidence in these systems among medical practitioners and patients alike [38].
In the realm of image restoration and enhancement, neural networks are set to redefine visual possibilities. The assimilation of multispectral imaging and hybrid techniques could yield more intricate datasets for neural networks to interpret, yielding enriched visualizations that capture a wider array of cellular attributes.
Furthermore, the arena of biological art and communication is poised for expansion as neural networks delve into animation and interactive visualizations. These artistic manifestations could transcend static imagery, offering immersive experiences that enable audiences to traverse cellular landscapes in unprecedented ways.

9. Conclusion

At the delicate crossroads of neural networks and biology, a harmonious blend of technological ingenuity and scientific curiosity has materialized. Within the realm of microscopy image processing and illustration, these neural networks have cast a revolutionary enchantment, unveiling the concealed intricacies of cells and organisms with unparalleled lucidity. From the precise partitioning of cells to the intricate detection of subcellular formations, these networks have brought to light the microscopic tableau of life. They have instilled vitality into the three-dimensional fabric of cellular architecture, guided the diagnosis and categorization of diseases with remarkable precision, and even embarked on an artistic expedition, crafting captivating visual interpretations. As these networks undergo continuous refinement, the future unfurls as an expansive canvas of limitless potential, where the fusion of art, science, and technology intertwines to narrate a saga of exploration, comprehension, and breathtaking elegance at the minutest dimensions of existence.

References

  1. Beier T, Bild A. Deep learning for automated microscopy. IEEE Journal of Selected Topics in Signal Processing. 2016;10(1):4-13.
  2. Zhang Z, Yang L. A review on recent advances in learning deep neural networks. Neurocomputing. 2017;216:41-49.
  3. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer International Publishing; 2015:234-241.
  4. He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2017:2961-2969.
  5. Chen, H.; Qi, X.; Yu, L.; Heng, P.-A. DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation. 2016. [Google Scholar] [CrossRef]
  6. Caicedo, J.C.; Roth, J.; Goodman, A.; Becker, T.; Karhohs, K.W.; Broisin, M.; Molnar, C.; McQuin, C.; Singh, S.; Theis, F.J.; et al. Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images. Cytom. Part A 2019, 95, 952–965. [Google Scholar] [CrossRef] [PubMed]
  7. Sarmadi, S.; Winkle, J.J.; Alnahhas, R.N.; Bennett, M.R.; Josić, K.; Mang, A.; Azencott, R. Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies. Math. Comput. Appl. 2022, 27, 22. [Google Scholar] [CrossRef]
  8. Zhang H, Elgamal S, Crews CM. Chemically Induced Degradation of ASK1 and Its Effects on Cell Survival and Proliferation. ACS Chemical Biology. 2016;11(11):3268-3274.
  9. Sharpe, J. Optical projection tomography as a new tool for studying embryo anatomy. J. Anat. 2003, 202, 175–181. [Google Scholar] [CrossRef]
  10. Kumar, A.; Wu, Y.; Christensen, R.; Chandris, P.; Gandler, W.; McCreedy, E.; Bokinsky, A.; A Colón-Ramos, D.; Bao, Z.; McAuliffe, M.; et al. Dual-view plane illumination microscopy for rapid and spatially isotropic imaging. Nat. Protoc. 2014, 9, 2555–2573. [Google Scholar] [CrossRef] [PubMed]
  11. Litjens, G.; Kooi, T.; Bejnordi, B.E.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; van der Laak, J.A.W.M.; van Ginneken, B.; Sánchez, C.I. A survey on deep learning in medical image analysis. Med. Image Anal. 2017, 42, 60–88. [Google Scholar] [CrossRef] [PubMed]
  12. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444.
  13. Milletari, F.; Navab, N.; Ahmadi, S.-A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 25–28 October 2016; pp. 565–571. [Google Scholar]
  14. Megason, S.G. In Toto Imaging of Embryogenesis with Confocal Time-Lapse Microscopy. 546. [CrossRef]
  15. Hildebrand DG, Cicchini L, Baltrunaite K, Frommolt P, Cattin AL, Gutierrez GD, et al. Whole organism development is sensitive to the strength of retinoic acid signaling. Developmental Biology. 2014;392(2):328-340.
  16. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. [CrossRef]
  17. Han, S.S.; Kim, M.S.; Lim, W.; Park, G.H.; Park, I.; Chang, S.E. Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J. Investig. Dermatol. 2018, 138, 1529–1538. [Google Scholar] [CrossRef] [PubMed]
  18. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging. 2016;35(5):1299-1312.
  19. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225. 2017.
  20. Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer. arXiv:1606.05718. 2016.
  21. Panch, T.; Szolovits, P.; Atun, R. Artificial intelligence, machine learning and health systems. J. Glob. Heal. 2018, 8, 020303. [Google Scholar] [CrossRef] [PubMed]
  22. Khan A, Shin D, Kim H, Lee MS. Deep learning-based image restoration: A review. Journal of Imaging. 2018;4(5):67.
  23. Anwar S, Khan S, Barnes N. Real-time image enhancement for improving visual quality of outdoor images. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2016:3203-3208.
  24. Dufour A, Shinin V, Tajbakhsh S, Guillen-Aghion N, Olivo-Marin JC. Automated cell lineage construction: A rapid method to analyze clonal development established with murine neural progenitor cells. Cell Cycle. 2005;4(6):861-866.
  25. Jiménez-Sánchez, A.; Cybulska, P.; Mager, K.L.; Koplev, S.; Cast, O.; Couturier, D.-L.; Memon, D.; Selenica, P.; Nikolovski, I.; Mazaheri, Y.; et al. Unraveling tumor–immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy. Nat. Genet. 2020, 52, 582–593. [Google Scholar] [CrossRef] [PubMed]
  26. Karantzas, N.; Safari, K.; Haque, M.; Sarmadi, S.; Papadakis, M. Compactly supported frame wavelets and applications in convolutional neural networks. 11138, 1113. [Google Scholar] [CrossRef]
  27. Dalca, A.V.; Guttag, J.; Sabuncu, M.R. Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation. 2018. [Google Scholar] [CrossRef]
  28. Rehm M, Lane J, Przybyszewski E. The confluence of art, science, and conservation: Visualizing the natural world. Visual Resources. 2019;35(1-2):5-12.
  29. Hassabis, D.; Kumaran, D.; Summerfield, C.; Botvinick, M. Neuroscience-Inspired Artificial Intelligence. Neuron 2017, 95, 245–258. [Google Scholar] [CrossRef] [PubMed]
  30. Gatys LA, Ecker AS, Bethge M. Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:2414-2423.
  31. Elgammal A, Liu B, Elhoseiny M, Marey M, Mazzone M. CAN: Creative Adversarial Networks, Generating" Art" by Learning About Styles and Deviating from Style Norms. arXiv:1706.07068. 2017.
  32. Nielsen, ME. Toward Art and Science: An Exploration of Neural Aesthetics. Interactions. 2017;24(3):58-63.
  33. Sobiecki, JF. 'Art through Nature': The Microscope and the Development of Natural History in Seventeenth-Century England. Isis. 2016;107(2):287-309.
  34. Buxton, MW. The role of art in the promotion of health and wellbeing. Nursing Standard. 2019;34(1):72-75.
  35. He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV); 2017. p. 2980-8.
  36. Nair V, Hinton GE. Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML); 2010. p. 807-14.
  37. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS); 2012. p. 1097-105.
  38. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2018 Apr;40(4):834-48.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated