Congratulations to all finalists for their outstanding achievements and contributions. Preprints.org is delighted to announce the 2023 Most Popular Preprints Award winners. This esteemed recognition has been conferred upon the following 16 preprints, acknowledged for accruing the highest votes in their respective subject categories:

Biology and Life Sciences

1st Prize Article

Biology and Life SciencesImmunology and Microbiology

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A Recombinant Protein XBB.1.5 RBD/Alum/CpG Vaccine Elicits High Neutralizing Antibody Titers against Omicron Subvariants of SARS-CoV-2

Syamala Rani Thimmiraju

Abstract

(1) Background: We previously reported the development of a recombinant protein SARS-CoV-2 vaccine, consisting of the Receptor-Binding Domain (RBD) of the SARS-CoV-2 spike protein, adjuvanted with aluminum hydroxide (alum) and CpG oligonucleotides. In mice and non-human primates, our wild-type (WT) RBD vaccine induced high neutralizing antibody titers against the WT isolate of the virus, and, with partners in India and Indonesia it was later developed into two closely resembling human vaccines, Corbevax and Indovac. Here, we describe the development and characterization of a next-generation vaccine adapted to the recently emerging XBB variants of SARS-CoV-2. (2) Methods: We conducted preclinical studies in mice using a novel yeast-produced SARS-CoV-2 XBB.1.5 RBD subunit vaccine candidate formulated with alum and CpG. We examined the neutralization profile of sera obtained from mice vaccinated twice intramuscularly at a 21-day interval with the XBB.1.5-based RBD vaccine, against WT, Beta, Delta, BA.4, BQ.1.1, BA.2.75.2, XBB.1.16, XBB.1.5 and EG.5.1 SARS-CoV-2 pseudoviruses. (3) Results: The XBB.1.5 RBD/CpG/alum vaccine elicited a robust antibody response in mice. Furthermore, serum from vaccinated mice demonstrated potent neutralization against the XBB.1.5 pseudovirus as well as several other Omicron pseudoviruses. However, regardless of high antibody cross-reactivity by ELISA, the anti-XBB.1.5 RBD antigen serum showed low neutralizing titers against the WT and Delta virus variants. (4) Conclusions: Whereas we observed modest cross-neutralization against Omicron subvariants by sera from mice vaccinated with the WT RBD/CpG/Alum vaccine or with the BA.4/5-based vaccine, sera raised against the XBB.1.5 RBD showed robust cross-neutralization. These findings underscore the imminent opportunity for an updated vaccine formulation utilizing the XBB.1.5 RBD antigen.

Keywords

Immune escape; Vaccine efficacy; COVID-19; SARS-CoV-2

30 Sep 2023

2nd Prize Article

Biology and Life SciencesEcology, Evolution, Behavior and Systematics

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Cross-species Transcriptomics Analysis Highlights Conserved Molecular Responses to Per- and Polyfluoroalkyl Substances (PFAS)

Livia Beccacece

Filippo Costa

Jennifer Paola Pascali

Federico Manuel Giorgi

28 Jun 2023

Finalist

Chemistry and Materials Science

1st Prize Article

Chemistry and Materials ScienceAnalytical Chemistry

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Metabolome Mining of Curcuma longa L. Using HPLC-MS/MS and Molecular Networking

Rabin Budhathoki

Arjun Prasad Timilsina

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27 Jun 2023

2nd Prize Review

Chemistry and Materials ScienceMedicinal Chemistry

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Polymers in 3D Printing of External Maxillofacial Prostheses and Their Retention Systems

29 Aug 2023

Finalist

Computer Science and Mathematics

1st Prize Article

Computer Science and MathematicsAlgebra and Number Theory

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A Proof of the Riemann Hypothesis Based on a New Expression of the Completed Zeta Function

Weicun Zhang

30 Oct 2023

2nd Prize Article

Computer Science and MathematicsArtificial Intelligence and Machine Learning

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GPT-4 Can't Reason

Konstantine Arkoudas

07 Aug 2023

Finalist

Engineering

1st Prize Article

EngineeringOther

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An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization

Oguzhan Katar

and

Ozal Yildirim

Abstract

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model's multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.

Keywords

Vision Transformers; white blood cells; explainable AI models; deep learning; Score-CAM

15 Jun 2023

2nd Prize Review

EngineeringBioengineering

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Glycosylation Shapes the Efficacy and Safety of Diverse Protein, Gene and Cell Therapies

03 Mar 2023

Finalist

Environmental and Earth Sciences

1st Prize Article

Environmental and Earth SciencesGeophysics and Geology

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Understanding Landslide Expression in SAR Backscatter Data: A Global Study

Erin Lindsay

^* ,

Alexandra Jarna Ganerød

Abstract

During disaster response, clouds or darkness can prevent the use of optical images for detecting consequences of natural disasters, including landslides. In these situations, radar images can be used to detect changes more rapidly. However, Synthetic Aperture Radar (SAR) backscatter intensity images are underutilized for landslide detection. Unfortunately, there remains a lack of understanding about how to interpret landslide signatures in SAR imagery. In this study, we investigate how the morphometric features and material properties of landslides, and preexisting land cover, control their expression in SAR backscatter intensity change images. Trends in the spatial and temporal signatures of over 1000 landslides in 30 diverse case studies are investigated, using multi-temporal composites and dense time-series of Sentinel-1 C-band SAR backscatter intensity data. The results show that the orientation of landslide surfaces relative to the sensor, pre-existing land cover, and the roughness of the landslide surface, determine whether landslides will produce an increase or decrease in backscatter intensity values. In certain cases, we can identify morphometric features of landslides (e.g. scarps, transit zone, deposits, ponding) and material properties. Generally, we see that landslides appear most clearly with a strong increase in intensity when they occur in herbaceous vegetation or non-vegetated ground surfaces, due to an increase in surface roughness. While in forested or densely vegetated areas, landslides produce a more complex signature with both decreases due to radar shadow and vegetation removal, and an adjacent edge of increased intensity due to double bounce and direct return from vertical tree trunks and convex edges. In most cases, rough deposits produce an increase in intensity, while smooth deposits (e.g. from mudslides) exhibit specular reflection, and thus show decreased values. Landslides are less visible in cases with pre-event very rough ground, or mixed vegetation conditions. The conceptual model developed can aid interpretation of landslides in SAR imagery, and provide domain knowledge needed to train models for automatic landslide detection.

Keywords

change detection; time-series; landslide detection; land cover; Sentinel-1; backscatter; Google Earth Engine;

22 Feb 2023

2nd Prize Article

Environmental and Earth SciencesRemote Sensing

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Production of High-Resolution Map of Biomass Carbon over Forests in Malaysia Estimated using Aboveground Carbon Density Indicator (ACDI) and a Collection of 12 Years Inventory Data

Hamdan Omar

^* ,

Muhamad Afizzul Misman

Valeria Linggok

Suhaini Haron

Ahmad Ashrin Mohd Bohari

Mohammad Nor Firdaus Haji Sariee

Abstract

The accurate estimation of biomass carbon in forests is of paramount importance for effective forest management and mitigating climate change. This study presents a novel approach to produce a high-resolution map of biomass carbon over forests in Malaysia using the Aboveground Carbon Density Indicator (ACDI) and a comprehensive collection of 12 years of inventory data, i.e., from 2012 to 2023. The ACDI was derived based on several vegetation indices (VIs) that were produced from the original Landsat images to indicate the level of aboveground biomass carbon (AGC) stock in the forested areas. The VIs includes Normalised Difference Vegetation Index (NDVI), Normalised Burn Ratio (NBR), Shadow Index (SI), Soil-Adjusted Vegetation Index (SAVI), Iron Oxide Index (IO), Modified Normalised Difference Water Index (NDWI), and Enhanced Vegetation Index (EVI). The ACDI was then integrated with ground-based measurements, and serves as a robust indicator for estimating AGC. This calculation was conducted on Google Earth Engine (GEE) platform to match the date of field observation with the satellite imagery datasets. The production of seamless mosaic of the latest date of Landsat imagery and the forest type classification were also performed on GEE. The forested areas were classified into three major types, which are dry inland forest, mangrove forest, and peat swamp forest. Results indicated significant spatial variations in AGC across Malaysia's forests. The derived AGC prediction models based on the ACDI varied among the forest types. Based on the estimates, a 30-metre resolution, wall-to-wall map of AGC across the entire forested region of Malaysia has been created. The ACDI was calibrated and validated using a separate validation plots dataset to ensure the accuracy of the AGC estimates. The total AGC in all types of forests in Malaysia was estimated at 3.0 billion Mg C with an attainable accuracy of about 80%. These estimates were also divided into categories and reported to the AGC at the state level. This high-resolution map provides essential information for various stakeholders, with critical implications for carbon sequestration efforts, conservation priorities, and sustainable forest management. The presented methodology not only showcases the value of combining advanced remote sensing techniques with long-term inventory data but also underscores the potential for similar approaches in other tropical forest regions globally. Ultimately, this study contributes to the understanding of carbon dynamics in Malaysian forests and promotes effective strategies for mitigating climate change through better-informed forest conservation and management practices.

Keywords

Aboveground carbon stock; tropical forest; Landsat; Malaysia ecosystem; spectral indicators

31 Oct 2023

Finalist

Physical Sciences

1st Prize Article

Physical SciencesTheoretical Physics

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Euclidean Relativity Solves 15 Mysteries of Physics

Markolf H. Niemz

Abstract

Special and general relativity (SR/GR) describe nature subjectively. Mathematically, they are correct. Here I show: (1) Physically, SR/GR have an issue. Both theories describe nature from the perspective of just one observer each (one group of observers, to be exact). After a transformation, there is again just one active perspective. Because of this constraint, there is no holistic view of nature. The issue manifests itself in the fact that SR/GR have not solved several mysteries, such as the Hubble tension, dark energy, and non-locality. Still, SR/GR work well for each observer because the Lorentz factor and gravitational time dilation are correct. (2) Euclidean relativity (ER) describes nature objectively. Any (!) object’s proper space d₁, d₂, d₃ and its proper time τ span “natural spacetime”, which is 4D Euclidean space (ES) if we interpret cτ as d₄. All energy moves through ES at the speed of light c. Each observer’s reality is created by projecting ES orthogonally to his proper space and to his proper time. These two concepts are reassembled in SR/GR to a non-Euclidean spacetime. Since information is lost in each projection, the performance of SR/GR is limited. However, the SO(4) symmetry of ES is not compatible with waves. This is fine because ER tells us that wave and particle are subjective concepts: What I deem wave, deems itself particle at rest. We must distinguish between the master reality ES (without waves) and an observer’s reality (with waves). I conclude: A holistic view of nature is a necessary requirement for solving the Hubble tension, dark energy, and non-locality.

Keywords

spacetime; cosmology; Hubble constant; Hubble diagram; quantum mechanics

29 Nov 2023

2nd Prize Article

Physical SciencesTheoretical Physics

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The Geometrization of Maxwell’s Equations and the Emergence of Gravity

Raymond Beach

15 May 2023

Finalist

Medicine and Pharmacology & Public Health and Healthcare

1st Prize Review

Medicine and PharmacologyCardiac and Cardiovascular Systems

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Autopsy Proven Fatal COVID-19 Vaccine-Induced Myocarditis

^* ,

Abstract

Background: COVID-19 vaccines have been linked to myocarditis which in some circumstances can be fatal. This systematic review aims to investigate potential causal links between COVID-19 vaccines and death from myocarditis using post-mortem analysis. Methods: We performed a systematic review of all published autopsy reports involving COVID-19 vaccination-related myocarditis through July 3^rd, 2023. All autopsy studies that include COVID-19 vaccine-induced myocarditis as a possible cause of death were included, without imposing any additional restrictions. Causality in each case was determined by three independent reviewers with cardiac pathology experience and expertise. Results: We initially identified 1,691 studies and, after screening for our inclusion criteria, included 14 papers that contained 28 autopsy cases. The cardiovascular system was the only organ system affected in 26 cases. In 2 cases, myocarditis was characterized as a consequence from multisystem inflammatory syndrome (MIS). The mean and median number of days from last COVID-19 vaccination until death was 6.2 and 3 days, respectively. Most of the deaths occurred within a week from the last injection. We established that all 28 deaths were causally linked to COVID-19 vaccination by independent adjudication. Conclusions: The temporal relationship, internal and external consistency seen among cases in this review with known COVID-19 vaccine-induced myocarditis, its pathobiological mechanisms and related excess death, complemented with autopsy confirmation, independent adjudication, and application of the Bradford Hill criteria to the overall epidemiology of vaccine myocarditis, suggests there is a high likelihood of a causal link between COVID-19 vaccines and death from suspected myocarditis in cases where sudden, unexpected death has occurred in a vaccinated person. Urgent investigation is required for the purpose of risk stratification and mitigation in order to reduce the population occurrence of fatal COVID-19 vaccine-induced myocarditis.

Keywords

myocarditis; sudden death; chest pain; autopsy; necropsy; COVID-19; COVID-19 vaccines; mRNA; SARS-CoV-2 vaccination; death; excess mortality; spike protein; organ system

18 Jul 2023

2nd Prize Article

Medicine and PharmacologyOncology and Oncogenics

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Logic-Based Modeling and Drug Repurposing for the Prediction of Novel Therapeutic Targets and Combination Regimens against E2F1-Driven Melanoma Progression

Abstract

Skin melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Thus, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis identified two potent drugs (Cialis and Finasteride) that can efficiently inhibit AKT1 and MDM2 protein signatures respectively, and with better therapeutic properties. We proposed that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

Keywords

Melanoma; Core regulatory network; in silico perturbationSystems pharmacology; Boolean model; Small molecule inhibitors; Virtual screening; ADME; E2F1

12 Jun 2023

Finalist

1st Prize Article

Social SciencesEducation

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Bridging Digital Competence and Sustainability: Unveiling the Synergistic Potential of Design and Maker Education

Noble Po-kan Lo

Abstract

Purpose: This study investigates the implementation and impact of maker culture—viewed as a tool for developing green digital skills—in higher education institutions in Hong Kong. Maker culture, a collaborative educational approach, embraces students’ capacity for self-paced, autonomous learning and applies this knowledge to creative problem-solving and innovation, key aspects of sustainability education. Methods: An empirical study was conducted, focusing on the experiences of teachers in the higher education sector in Hong Kong. Eight individuals were interviewed to gain insights into their perceptions and experiences with maker education within sustainability contexts. The sample was limited to ensure cross-sectional comparability and direct weighting of teachers’ experiences within a singular, complementary educational setting. Results: The findings provide valuable insights into the benefits and challenges of integrating maker education into traditional educational systems to foster green digital skills. It became evident that adequate resources, effective teachers, and improved administrative systems play significant roles in the successful implementation of this approach. Conclusion: Maker education, as a tool for developing green digital skills, offers a promising alternative to traditional performance-based studies. It has the potential to lead to a future of education that is creative, innovative, and student-directed, fostering sustainability competences. Therefore, despite the challenges, with the right support and resources, the integration of maker culture into educational systems could significantly transform teaching and learning processes, advancing sustainability education.

Keywords

maker education; green digital skills; problem-solving; innovation; creativity; engagement; sustainable education

08 Dec 2023

2nd Prize Article

Business, Economics and ManagementBusiness and Management

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Lightning Network’s Evolution: Unraveling Its Present State and the Emergence of Disruptive Digital Business Models

Thomas Dasaklis

^* and

Vangelis Malamas

08 May 2023

Finalist