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Polygalacturonase-inhibiting proteins as an exogenously applied natural solution for prevention of postharvest fungal infections

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Synthetic and Systems Biotechnology 2024年第3期9卷 481-493页

作者： Tiffany Chiu Yanran Li Graduate Program in Genetics Genomicsand Bioinformatics1140 Batchelor HallUniversity of California RiversideCalifornia92521USA Program of Chemical Engineering Department of NanoengineeringUniversity of CaliforniaSan DiegoCA92521USA

Polygalacturonase inhibiting proteins(PGIPs)are plant proteins involved in the inhibition of polygalacturonases(PGs),cell-wall degrading enzymes often secreted by phytopathogenic ***,we confirmed that PGIP2 from Phaseolus vulgaris(PvPGIP2)can inhibit the growth of Aspergillus niger and Botrytis cinerea on agar *** this study,we further validated the feasibility of using PGIP as an environmental and ecological friendly agent to prevent fungal infection *** found that application of either purified PGIP(full length PvPGIP2 or truncated tPvPGIP2_5-8),or PGIP-secreting Saccharomyces cerevisiae strains can effectively inhibit fungal growth and necrotic lesions on tobacco *** also examined the effective amount and thermostability of PGIP when applied on plants.A concentration of 0.75 mg/mL or higher can significantly reduce the area of *** *** activity of full-length PvPGIPs is not affected after incubation at various temperatures ranging from􀀀20 to 42◦C for 24 h,while truncated tPvPGIP2_5-8 lost some efficacy after incubation at 42◦***,we have also examined the efficacy of PGIP on tomato *** the purified PvPGIP2 proteins were applied to tomato fruit inoculated with *** at a concentration of roughly 1.0 mg/mL,disease inci-dence and area of disease had reduced by more than half compared to the controls without PGIP *** study explores the potential of PGIPs as exogenously applied,eco-friendly fungal control agents on fruit and vegetables post-harvest.

关键词： PGIP Antifungal agent Pest control Postharvest treatment

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Oryza CLIMtools:A genome–environment association resource reveals adaptive roles for heterotrimeric G proteins in the regulation of rice agronomic traits

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Plant Communications 2024年第4期5卷 145-165页

作者： Angel Ferrero-Serrano David Chakravorty Kobie J.Kirven Sarah M.Assmann Biology Department Pennsylvania State University208 Mueller LaboratoryUniversity ParkPA 16802USA Intercollege Graduate Degree Program in Bioinformatics and Genomics Pennsylvania State University208 Mueller LaboratoryUniversity ParkPA 16802USA

Modern crop varieties display a degree of mismatch between their current distributions and the suitability of the local climate for their *** address this issue,we present Oryza CLIMtools(https://***/CLIMtools/oryza_v1.0/),thefirst resource for pan-genome prediction of climate-associated genetic variants in a crop *** CLIMtools consists of interactive web-based databases that enable the user to(1)explore the local environments of traditional rice varieties(landraces)in South-East Asia and(2)investigate the environment by genome associations for 658 Indica and 283 Japonica rice landrace accessions collected from georeferenced local environments and included in the 3K Rice Ge-nomes *** demonstrate the value of these resources by identifying an interplay betweenflowering time and temperature in the local environment that is facilitated by adaptive natural variation in OsHD2 and disrupted by a natural variant in *** quantitative trait locus analysis has suggested the impor-tance of heterotrimeric G proteins in the control of agronomic ***,we analyzed the climate associations of natural variants in the different heterotrimeric G protein *** identified a coordi-nated role of G proteins in adaptation to the prevailing potential evapotranspiration gradient and revealed their regulation of key agronomic traits,including plant height and seed and panicle *** conclude by highlighting the prospect of targeting heterotrimeric G proteins to produce climate-resilient crops.

关键词： Oryza sativa rice G by E adaptation flowering time GWAS OsHD2 OsSOC1 heterotrimeric G pro-tein OsRGA1 d1 OsGS3 OsDEP1 OsGGC2

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Variational quantum neural network with optimized ansatz for predicting malignant ventricular arrhythmias 9th

Variational quantum neural network with optimized ansatz for...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Dominic, Nicholas Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Preventive strategies should be the utmost priority when dealing with diverse patients suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death (SCD). Electrocardiogram (ECG) data is commonly used as a predictor for MVA predictive models. In this study, all ECG signals from MIT-BIH databases were fragmented into five-minute durations with a frequency sampling of 128 Hz. To solve the absence of hybrid optimizations in Machine Learning (ML) models, a novel Variational Quantum Neural Network (VQNN) was invented. Empowered by deep learning capabilities and optimized quantum circuits design, VQNN achieved remarkable performances designated by an accuracy of up to 95.1%, a perfect 100% recall, and a 95.2% score of the area under the Receiver Operating Characteristic curve (AUC ROC) with Conjugate Gradient as an optimizer and EfficientSU2 as a quantum ansatz. Despite the susceptibility to quantum noise, this research settles a new trajectory of utilizing quantum variational algorithms to predict and expand its applicability for MVA cases. © 2024 The Authors.

关键词： Quantum computers

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Measurement of renal blood flow in a rat model using diffuse correlation spectroscopy

Measurement of renal blood flow in a rat model using diffuse...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Nakabayashi, Mikie Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Japan

ISBN: (纸本)9781957171340

关键词： Detectors Laser sources Lasers Near infrared Photon counting Spectroscopy

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Multiplexed Nanoscopy via Buffer Exchange

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ACS Nano 2024年第34期18卷 23445-23456页

作者： Chang, Ting-Jui Ben Yang, T. Tony Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Department of Physics National Taiwan University Taipei10617 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Taiwan University Taipei10617 Taiwan

Understanding cellular functions, particularly in their intricate complexity, can greatly benefit from the spatial mapping of diverse molecules through multitarget single-molecule localization microscopy (SMLM). Existing methodologies, primarily restricting the encoding dimensions to color and lifetime or requiring cyclic staining, often involve broad chromatic detection, specialized optical configurations, or sophisticated labeling techniques. Here, we propose a simple approach called buffer-exchange stochastic optical reconstruction microscopy (beSTORM), which introduces an additional dimension to differentiate between single molecules irrespective of their spectral properties. This method leverages the distinguishable photoblinking responses to distinct buffer conditions, offering a straightforward yet effective means of fluorophore discrimination. Through buffer exchanges, beSTORM achieves multitarget SMLM imaging with minimal crosstalk. Direct integration with expansion microscopy (ExM) demonstrates its capability to resolve up to six proteins at the molecular level within a single emission color without chromatic aberration. Overall, beSTORM presents a highly compatible imaging platform, promising significant advancements in highly multiplexed nanoscopy for exploring multiple targets in biological systems with nanoscale precision. © 2024 The Authors. Published by American Chemical Society.

关键词： Stochastic systems

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Systematic Literature Review on Metaheuristic Algorithms in SNP Analysis for Diseases 6

Systematic Literature Review on Metaheuristic Algorithms in ...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting to embrace metaheuristic methods, a review was done by gathering papers from the Google Scholar database from 2018 to 2023, which resulted in 20 papers after title, abstract, and content filtering. The findings show that the Genetic Algorithm and the Harmony Search Algorithm have become popular approaches in SNP analysis, particularly in studies on breast cancer, age-related macular degeneration, and colorectal cancer. However, the review shows that while researchers have proven most methods effective in finding disease-related SNPs, a more measurable study in SNP analysis is needed, due to the lack of elaboration on measurement metrics in the found studies. © 2024 IEEE.

关键词： metaheuristic algorithms single nucleotide polymorphisms

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Evaluating the Effectiveness of Transformer Models for Molecular Property Prediction: A Review 9

Evaluating the Effectiveness of Transformer Models for Molec...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Imani, Alyssa Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Transformer models, originally successful in natural language processing, are now being applied to chemical and biological studies, excelling in areas such as molecular property prediction, material science, and drug discovery. BERT, a Transformer-based model, has become foundational in cheminformatics, particularly for QSAR (Quantitative Structure-Activity Relationship) modeling and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluations in drug discovery. However, achieving higher accuracy often requires designing more complex models, which can compromise their interpretability. This posing a challenge for researchers who need to understand the reasoning behind the predictions. The trade-off between accuracy and interpretability presents a critical challenge in applying black box models to real-world problems in cheminformatics. This work compares Transformer-based models with traditional machine learning and deep learning approaches, focusing on both interpretability and performance. The goal is to highlight the strengths and limitations of each method, offering insights into their optimal use in drug discovery and material science. © 2024 IEEE.

关键词： Prediction models

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Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework 9th

Exploring EfficientNet Variants for Image Encoding in Auxili...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework. © 2024 The Authors.

关键词： Medical imaging

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Semi-automated meningioma segmentation with bounding boxes 9th

Semi-automated meningioma segmentation with bounding boxes

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Heryanto, Nur Adhianti Isnan, Mahmud Henry, Matthew Martianus Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Segmentation is manually performed by physicians, which takes considerable time and may be subject to observers. Automating this task can increase efficiency and consistency. Existing studies on meningioma segmentation used data from limited study centers, indicating the need for research on multi-center data to assess generalizability. In this work, two semi-automated methods with bounding box priors, LiteMedSAM and BBU-Net, are evaluated on the brain tumor segmentation (BraTS) 2023 meningioma dataset collected from five study-centers. Preprocessing included exclusion of small tumors, z-score normalization, and extraction of slices that contain tumors, generating 25,602 2D axial magnetic resonance imaging (MRI) scans. A fine-tuning strategy is adopted for LiteMedSAM while BBU-Net is trained from scratch. The models are evaluated using a five-fold cross-validation, with data split at the case level. Results show that while U-Net models can achieve performance close to LiteMedSAM, the foundation model has overall better performance, with more than 90% in all evaluation scores. © 2024 The Authors.

关键词： Magnetic resonance imaging

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LDA Topic Modeling for bioinformatics Terms in arXiv Documents 9th

LDA Topic Modeling for Bioinformatics Terms in arXiv Documen...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Karnyoto, Andrea Stevens Henry, Matthew Martianus Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

A wide variety of disciplines contribute to bioinformatics research, including computer science, biology, chemistry, mathematics, and physics. This study determines the number of research articles published on arXiv classified as bioinformatics topics and the most frequently used bioinformatics terms using topic modeling, Latent Dirichlet Allocation (LDA). An algorithm based on LDA is used to discover topics hidden within large collections of documents through the use of statistical analysis. Our research examined 226453 articles on arXiv between January 2023 and January 2024. As a result, there are more than 10521 articles categorized into bioinformatics topics. Most commonly, 6352 documents are in the "Mathematical Physics" category. The second most popular category is "Computer Science," with 2950 documents. Accordingly, the terms 'RNA,' 'sequence,' 'tree,' and 'homology' are the three most commonly used terms in bioinformatics. The study of RNA plays a vital role in molecular biology;thus, the study of RNA is prevalent in bioinformatics. Sequential data refer to the order in which nucleotides or amino acids can be found in a DNA molecule or a protein. © 2024 The Authors.

关键词： arXiv bioinformatics Latent Dirichlet Allocation

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