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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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Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort

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Genomics and Informatics 2025年第1期23卷 1-11页

作者： Ndagijimana, Frank Aimee Rodrigue Park, Taesung Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Department of Statistics Seoul National University Seoul South Korea

Background: Understanding the progression and recovery process of COVID-19 is crucial for guiding public health strategies and developing targeted interventions. This longitudinal cohort study aims to elucidate the dynamics of COVID-19 severity progression and evaluate the impact of underlying health conditions on these transitions, providing critical insights for more effective disease management. Methods: Data from 4549 COVID-19 patients admitted to Seoul National University Boramae Medical Center between February 5th, 2020, and October 30th, 2021, were analyzed using a 5-state continuous-time Markov multistate model. The model estimated instantaneous transition rates between different levels of COVID-19 severity, predicted probabilities of state transitions, and determined hazard ratios associated with underlying comorbidities. Results: The analysis revealed that most patients stabilized in their initial state, with 72.2% of patients with moderate symptoms remaining moderate. Patients with hypertension had a 67.6% higher risk of progressing from moderate to severe, while those with diabetes had an 89.9% higher risk of deteriorating from severe to critical. Although transition rates to death were low early in hospitalization, these comorbidities significantly increased the likelihood of worsening conditions. Conclusion: This study highlights the utility of continuous-time Markov multistate models in assessing COVID-19 severity progression among hospitalized patients. The findings indicate that patients are more likely to recover than to experience worsening conditions. However, hypertension and diabetes significantly increase the risk of severe outcomes, underscoring the importance of managing these conditions in COVID-19 patients. © The Author(s) 2025.

关键词： Comorbidity COVID-19 progression Markov multistate model Severity state Transition intensity

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Physicochemical and structural properties of starches from non-traditional sources in China

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Food Science and Human Wellness 2023年第2期12卷 416-423页

作者： Jingyi Yang Chagam Koteswara Reddy Zhili Fan Baojun Xu Food Science and Technology Program BNU-HKBU United International CollegeZhuhai 519087China Department of Biochemistry and Bioinformatics Institute of ScienceGITAM(Deemed to be University)Visakhapatnam 530045India

In this study,we isolated starches from non-traditional sources,including quinoa,lentil,arrowhead,gorgon fruit,sorghum,chickpea,proso millet,and purple potato and investigated their morphology,physicochemical,and functional *** differences in starch particle morphology,swelling power,solubility,syneresis,crystalline pattern,and pasting viscosity were observed among the starches from these nontraditional ***,all these isolated starches had unique properties because of their characteristic distinct granules when seen under scanning electron microscopy(SEM).The amylose content of the isolated starches shown significant difference(P<0.05),and the values ranged between 11.46%and 37.61%.Results demonstrated that the isolated starches contained between 79.82%to 86.56%starch,indicating that the isolated starches had high purity.X-ray diffraction(XRD)patterns of starches isolated from sorghum,proso millet,quinoa,purple potato,and gorgon fruit presented A-type diffraction pattern;while lentil seeds,arrowhead,and chickpea starches presented C-type diffraction ***,these results will promote the development of products based on starch isolated from non-traditional starches.

关键词： Starch Viscosity Morphology Quinoa Sorghum Proso millet

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Histological demonstration of gonad development in the banana shrimp,Fenneropenaeus merguiensis

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Aquaculture and Fisheries 2025年第1期10卷 94-104页

作者： Jirakanit Chimnual Jirawat Saetan Wilaiwan Chotigeat Division of Biological Science Molecular Biotechnology and Bioinformatics ProgramFaculty of SciencePrince of Songkla UniversitySongkhla90110Thailand Division of Health and Applied Sciences Faculty of SciencePrince of Songkla UniversitySongkhla90110Thailand Center for Genomics and Bioinformatics Research Faculty of SciencePrince of Songkla UniversitySongkhla90110Thailand

The genetic diversity of the banana shrimp Fenneropenaeus merguiensis is at risk because of over-harvesting,which consequently reduces food *** endangerment is exacerbated because this species is not commonly cultivated by ***,these factors necessitate conservation of this shrimp species across its natural *** on the migration of primordial germ cells(PGCs)to form a gonad is essential for shrimp preservation techniques such as broodstock preparation,sex differentiation,and germ cell *** this study,histological analysis and in situ hybridization of vasa expression(from embryo to testis development)were used to demonstrate the movement of *** and Eosin staining and in situ hybridization with the VASA probe revealed that the PGCs migrated retrogradely along the midgut,colonizing the area between the hepatopancreas and heart,a region that becomes the genital ridge in the postlarval *** sexual organs appeared at approximately 4 months of *** real-time PCR,the expression of the vasa gene was detected early on postlarval day 7,whereas its abundant expression was detected in the ovaries and testes of adult *** study could help with the identification and monitoring of PGCs or spermatogonia in banana shrimp and facilitate the implementation of other germ cell-relevant techniques in the future.

关键词： Fenneropenaeus merguiensis Primordial germ cell Spermatogonia vasa

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Uncertainty Quantification and Statistical Inference for Biologically Informed Neural Networks

Uncertainty Quantification and Statistical Inference for Bio...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Lee, Chanhee Shin, Sangyeon Kim, Haeyoung Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

In precision medicine, deep learning models play an important role in identifying relevant biomarkers and making predictions for the early diagnosis and prognosis of diseases. While successful in developing accurate prediction models for prognosis and diagnosis, the 'black box' nature of deep learning poses significant challenges, which makes it difficult to have a meaningful biological interpretation. Recently, several biologically informed neural networks (BINNs), such as the Visible Neural Network and P-NET, have been proposed to incorporate biological knowledge for improved interpretation. These models assess node importance using quantitative measures such as SHAP and DeepLIFT. However, these approaches have limitations because they typically provide a single estimate of node importance without any information on the uncertainty of these estimates. This study aims to assess uncertainty of node importance through a novel statistical framework. Bootstrapping is used to calculate confidence interval of node importance. Our findings validate that the proposed statistical framework quantifies the uncertainty of nodes in BINNs. By providing robust confidence intervals of pathways and genes, this framework enhances the interpretability and reliability of BINNs. © 2024 IEEE.

关键词： Neural network models

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Confidence Interval Estimation for Machine Learning Models in Forecasting Infectious Diseases

Confidence Interval Estimation for Machine Learning Models i...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Han, Kyulhee Song, Hanbyul Park, Jiwon Liu, Zhe Oh, Jooha Jose, Sayooj Aby Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

Forecasting models have been instrumental in managing the COVID-19 pandemic by facilitating efficient resource distribution and proper interventions. Although various forecasting models - such as mathematical models, statistical models, and machine learning models - are available, a few models account for the uncertainty in their predictions. The lack of uncertainty information constrains the reliability of forecasting results, making them less applicable for use in decision-making. Confidence Intervals (CIs) are widely used in statistical inference to provide uncertainty information. In this study, we introduce a framework that provides bootstrap-based CIs readily applicable to various forecasting models. The key concept of this framework is repeated training on bootstrap data based on resampled residuals from the forecasting model. After fitting the forecasting model, bootstrap datasets are generated considering time series characteristics to retrain the forecasting models. Then, CIs are calculated based on the distribution of prediction values. This framework was applied to forecasting COVID-19 outcomes - such as daily confirmed COVID-19 cases, deaths, and ICU patients - in South Korea. Our results demonstrate that bootstrap-based CIs can be successfully applied to provide additional information on the reliability of prediction. In conclusion, our framework can provide uncertainty information without requiring complex assumptions, regardless of the forecasting model or response variable type. Therefore, this approach can support public health policymakers in gaining a deeper understanding of epidemic trends, thereby offering valuable insights for decision-making. © 2024 IEEE.

关键词： COVID-19

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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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Noise response in monomolecular closed systems

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Journal of the Chinese Chemical Society 2023年第3期70卷 747-758页

作者： Fajiculay, Erickson Hsu, Chao-Ping Institute of Chemistry Academia Sinica Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Bioinformatics and Structure Biology National Tsinghua University Hsinchu City Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan

One of the primary interests in understanding biological systems is the interaction between noise and cellular regulation. Noise levels can be altered by rate constant perturbations, which may result in an imbalance of important biological functions. Although the theory of noise localization can estimate noise perturbation response, it is currently limited to open systems, where the majority of biological networks belong. However, during their lifetime, some components of biological systems participate in reactions that can be categorized under a closed system. Therefore, a counterpart theory for closed systems is desirable. In this work, steady-state perturbations of monomolecular closed systems and ways to estimate the response of noise as (co)variances are explored. We extend the structural sensitivity analysis for analyzing noise response patterns by using additional clues derived from multinomial assumptions. We identified scenarios in which this combination might fail in noise prediction, which we call noise flip, arising from the nonlinear dependence of variance upon composition. Nevertheless, theoretical intuition and simulation show that noise flip diminishes with increasing system size. This work provides an efficient and scalable means to estimate noise responses with potential applications in model discrimination, network-wide response scanning, and reconstruction. © 2023 The Chemical Society Located in Taipei & Wiley-VCH GmbH.

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SeqCP: A sequence-based algorithm for searching circularly permuted proteins

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Computational and Structural Biotechnology Journal 2023年 21卷 185-201页

作者： Chen, Chi-Chun Huang, Yu-Wei Huang, Hsuan-Cheng Lo, Wei-Cheng Lyu, Ping-Chiang Bioinformatics Program Institute of Information Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu300 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei112 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Center for Bioinformatics Research National Yang Ming Chiao Tung University Hsinchu Taiwan

Circular permutation (CP) is a protein sequence rearrangement in which the amino- and carboxyl-termini of a protein can be created in different positions along the imaginary circularized sequence. Circularly permutated proteins usually exhibit conserved three-dimensional structures and functions. By comparing the structures of circular permutants (CPMs), protein research and bioengineering applications can be approached in ways that are difficult to achieve by traditional mutagenesis. Most current CP detection algorithms depend on structural information. Because there is a vast number of proteins with unknown structures, many CP pairs may remain unidentified. An efficient sequence-based CP detector will help identify more CP pairs and advance many protein studies. For instance, some hypothetical proteins may have CPMs with known functions and structures that are informative for functional annotation, but existing structure-based CP search methods cannot be applied when those hypothetical proteins lack structural information. Despite the considerable potential for applications, sequence-based CP search methods have not been well developed. We present a sequence-based method, SeqCP, which analyzes normal and duplicated sequence alignments to identify CPMs and determine candidate CP sites for proteins. SeqCP was trained by data obtained from the Circular Permutation Database and tested with nonredundant datasets from the Protein Data Bank. It shows high reliability in CP identification and achieves an AUC of 0.9. SeqCP has been implemented into a web server available at: http://***/SeqCP/. © 2022 The Authors

关键词： Proteins

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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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