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Repurposing transfer learning strategy of computer vision for owl sound classification

Procedia Computer Science 2023年 216卷 424-430页

作者： Kevin William Gunawan Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Accurately predicting an owl species based on its sound can be helpful for owl conservation. To build an accurate model for owl sound classification, deep learning is currently the most preferred algorithm, due to its excellent performance for modeling audio data. However, deep learning is generally underperformed for a small dataset, which is the case for recognizing scops owl sound. To overcome the issue, we proposed a transfer learning strategy, which is common for computer vision tasks, that can alleviate overfitting in a deep learning model for the owl sound classification. In our approach, we propose a neural network architecture consisting of the backbone of a EfficientNet model pre-trained on the massive ImageNet database. The model takes the sound input that has been converted as two image representations: Spectrogram and Mel Frequency Cepstral Coefficients. Our strategy enables the use of a relatively small size of pre-trained image classification model, which is widely available, for transfer learning in owl sound classification. Deploying the lightweight model in an automatic sound classifier provides a fast and accurate tool for various owl conservation purposes.

关键词： acoustics deep learning bird sound species detection transfer learning

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Ornithine aspartate effects on bacterial composition and metabolic pathways in a rat model of steatotic liver disease

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World Journal of Hepatology 2024年第5期16卷 832-842页

作者： Elisa Carolina Lange Pabulo Henrique Rampelotto Larisse Longo Laura Bainy Rodrigues de Freitas Carolina Uribe-Cruz Mario Reis Alvares-da-Silva Experimental Laboratory of Hepatology and Gastroenterology Hospital de Clínicas de Porto AlegrePorto Alegre 90035-007Brazil Graduate Program in Gastroenterology and Hepatology Universidade Federal do Rio Grande do SulPorto Alegre 90035-007Brazil Bioinformatics and Biostatistics Core Facility Universidade Federal do Rio Grande do SulPorto Alegre 91501-970Brazil Graduate Program in Biological Sciences:Pharmacology and Therapeutics Universidade Federal do Rio Grande do SulPorto Alegre 90035-007Brazil Facultad de Ciencias de la Salud Universidad Católica de las MisionesPosadasMisiones 3300Argentina Division of Gastroenterology Hospital de Clínicas de Porto AlegrePorto Alegre 90035-007RSBrazil Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq)researcher Brasília 71605-001Brazil

BACKGROUND Metabolic-dysfunction associated steatotic liver disease(MASLD)is a hepatic manifestation of metabolic *** suggest ornithine aspartate(LOLA)as drug *** To analyze the influence of LOLA intake on gut microbiota using a nutritional model of *** Adult male Sprague Dawley rats were randomized into three groups:Control(10 rats fed with a standard diet),MASLD(10 rats fed with a high-fat and choline-deficient diet),and LOLA(10 rats receiving 200 mg/kg/d LOLA,after the 16th week receiving high-fat and choline-deficient diet).After 28 wk of the experiment,animals were euthanized,and feces present in the intestine were *** fecal DNA extraction,the V4 region of the 16S rRNA gene was amplified followed by sequencing in an Ion S5™*** Alpha and beta diversity metrics were comparable between MASLD and LOLA.3 OTUs were differentially abundant between MASLD and LOLA,which belong to the species Helicobacter rodentium,Parabacteroides goldsteinii,and Parabacteroides *** functional prediction provided two different metabolic profiles between MASLD and *** 9 pathways differentially abundant in MASLD are related to a change in energy source,adenosine/purine nucleotides degradation as well as guanosine and adenosine deoxyribonucleotides *** 14 pathways differentially abundant in LOLA are associated with four major metabolic functions primarily influenced by L-aspartate,including tricarboxylic acid cycle pathways,purine/guanosine nucleotides biosynthesis,pyrimidine ribonucleotides biosynthesis and salvage as well as lipid IVA *** Although LOLA had no influence on alpha and beta diversity in this nutritional model of MASLD,it was associated with changes in specific gut microbes and their related metabolic pathways.

关键词： Animal model Gut microbiota Metabolic-associated steatotic liver disease Metabolic prediction Ornithine aspartate

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Exploring transposable element-based markers to identify allelic variations underlying agronomic traits in rice

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Plant Communications 2022年第3期3卷 59-71页

作者： Haidong Yan David C.Haak Song Li Linkai Huang Aureliano Bombarely School of Plant and Environmental Sciences Virginia TechBlacksburgVA 24061USA Graduate Program in Genetics Bioinformatics and Computational Biology(GBCB)Virginia TechBlacksburgVA 24061USA Department of Grassland Science Animal Science and Technology CollegeSichuan Agricultural UniversityChengdu 611130China Department of Bioscience Universita degli Studi di Milano(UNIMI)20133 MilanoItaly Instituto de Biologıa Molecular y Celular de Plantas(IBMCP) UPV-CSIC46022 ValenciaSpain

Transposable elements(TEs)are a major force in the production of new alleles during domestication;nevertheless,their use in association studies has been limited because of their *** have developed a TE genotyping pipeline(TEmarker)and applied it to whole-genome genome-wide association study(GWAS)data from 176 Oryza sativa *** accessions to identify genetic elements associated with specific agronomic *** markers recovered a large proportion(69%)of single-nucleotide polymorphism(SNP)-based GWAS peaks,and these TE peaks retained ca.25%of the *** use of TEs in GWASs may reduce false positives associated with linkage disequilibrium(LD)among SNP markers.A genome scan revealed positive selection on TEs associated with agronomic *** found several cases of insertion and deletion variants that potentially resulted from the direct action of TEs,including an allele of LOC_Os11g08410 associated with plant height and panicle length ***,these findings reveal the utility of TE markers for connecting genotype to phenotype and suggest a potential role for TEs in influencing phenotypic variations in rice that impact agronomic traits.

关键词： transposable element marker agronomic trait rice GWAS

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Influence of conformational change of chain unit on the intrinsic negative thermal expansion of polymers

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Chinese Chemical Letters 2021年第4期32卷 1515-1518页

作者： Qiangsheng Sun Ke Jin Yuhui Huang Jia Guo Thanyada Rungrotmongkol Phornphimon Maitarad Changchun Wang State Key Laboratory of Molecular Engineering of Polymers and Department of Macromolecular Science Fudan UniversityShanghai 200433.China Biocatalyst and Environmental Biotechnology Research Unit Department of BiochemistryFaculty of Science and Program in Bioinformatics and Computational BiologyGraduate SchoolChulalongkom UniversityBangkok 10330Thailand Research Center of Nano Science and Technology Shanghai UniversityShanghai 200444China

Negative thermal expansion(NTE) behavior has roused wide interest for the control of thermomechanical properties of functional *** NTE behaviors have been found in kinds of compounds,it remains challenging for polymers to achieve intrinsic NTE *** this work,we systematically studied the conformational change of dibenzocyclooctadiene(DBCOD) derivatives between chair(C) and twist-boat(TB) forms based on density-functional theo ry(DFT) calculations,and found clear evidence of the relationship between the structure of DBCOD units and the thermal contraction behavior of the related *** order to obtain the polymer with NTE property,two conditions should be met for the thermal contracting DBCOD related units as follows:(i) the TB conformation can turn into C conformation as the temperature increases,and(ii) the volume of C conformation is smaller than that of TB *** rule should offer a guidance to exploration of the new intrinsic NTE polymers in the future.

关键词： Negative thermal expansion Conformational change Polyarylamides Density-functional theory Dibenzocyclooctadiene

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Data Mining and bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Joko Pebrianto Trinugroho Alam Ahmad Hidayat Rudi Nirwantono Faisal Asadi Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment.

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data

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Procedia Computer Science 2023年 227卷 1062-1069页

作者： Alam Ahmad Hidayat Arif Budiarto Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters.

关键词： deep learning forecasting sleep quality time series wearable devices

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Air-liquid microfluidics-integrated surface-enhanced Raman spectroscopy for selective molecular adsorption and detection to achieve bacterial discrimination

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Biosensors and Bioelectronics 2025年 285卷 117576页

作者： Ku, Chi-Yao Chiang, Yu-Wei Hsu, Huai-Yuan Cheng, Ho-Wen Chen, Ko-Lun Han, Yin-Yi Wang, Juen-Kai Wang, Yuh-Lin Huang, Nien-Tsu Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Atomic and Molecular Sciences Academia Sinica Taipei Taiwan Molecular Science and Technology Taiwan International Graduate Program Academia Sinica Taipei Taiwan International Graduate Program of Molecular Science and Technology National Taiwan University Taipei Taiwan Department of Anesthesiology National Taiwan University Hospital Taipei100229 Taiwan Department of Trauma National Taiwan University Hospital Taipei100229 Taiwan Center for Condensed Matter Sciences National Taiwan University Taipei Taiwan Center for Atomic Initiative for New Materials National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Bacterial discrimination is crucial for accurate microbiological diagnosis and timely antibiotic treatment. Surface-enhanced Raman spectroscopy (SERS) is an ideal technique due to its non-invasive, label-free molecular sensing capabilities. By analyzing bacterial supernatants, containing various purine derivatives, SERS can differentiate bacterial species based on their unique spectral distributions. However, the same bacterial species with different antibiotic resistance may secrete similar purine derivatives, differing only slightly in composition. Furthermore, each purine derivative may have a different molecular affinity to the SERS substrate, making it difficult to distinguish the exact molecular ratio. To improve SERS-based bacterial discrimination, we propose an air-liquid microfluidics-integrated SERS system (ALM-SERS) that selectively adsorbs and detects bacterial secretions. By taking features of precise microdroplet manipulation and contact area from microfluidics and features of selective molecular adsorption and fingerprints characterization from the SERS technique, we successfully perform a "sequential molecular adsorption" strategy to address the signal interference of complicated molecular mixtures in existing SERS methods. As a proof of concept, we first evaluate the molecular affinity of purine derivatives and then demonstrate the competitive analyte adsorption using adenine/cytosine and hypoxanthine/uracil sample mixtures. Finally, we tested six bacterial supernatants, including two Gram types and four strains with identical taxonomy but differing antibiotic resistance. Another six clinically isolated bacterial samples with different antibiotic resistance were also applied. Our results showed that a successful bacterial discrimination between similar species was not possible using a single SERS spectrum. In summary, the ALM-SERS system offers a powerful approach for bacterial discrimination, even when spectral differences are subtle. Beyond microb

关键词： Raman spectroscopy

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Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

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7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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High-Resolution Imaging of Human Cancer Proteins Using Microprocessor Materials

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ChemBioChem 2022年第17期23卷 e202200310-e202200310页

作者： Solares, Maria J. Jonaid, G.M. Luqiu, William Y. Berry, Samantha Khadela, Janki Liang, Yanping Evans, Madison C. Pridham, Kevin J. Dearnaley, William J. Sheng, Zhi Kelly, Deborah F. Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Department of Biomedical Engineering Pennsylvania State University University Park 16802 PA United States Center for Structural Oncology Pennsylvania State University University Park 16802 PA United States Department of Electrical and Computer Engineering Duke University Durham 27708 NC United States Fralin Biomedical Research Institute at VTC Virginia Tech Roanoke 24016 VA United States

Mutations in tumor suppressor genes, such as Tumor Protein 53 (TP53), are heavily implicated in aggressive cancers giving rise to gain- and loss-of-function phenotypes. While individual domains of the p53 protein have been studied extensively, structural information for full-length p53 remains incomplete. Functionalized microprocessor chips (microchips) with properties amenable to electron microscopy permitted us to visualize complete p53 assemblies for the first time. The new structures revealed p53 in an inactive dimeric state independent of DNA binding. Residues located at the protein-protein interface corresponded with modification sites in cancer-related hot spots. Changes in these regions may amplify the toxic effects of clinical mutations. Taken together, these results contribute advances in technology and imaging approaches to decode native protein models in different states of activation. © 2022 Wiley-VCH GmbH.

关键词： cancer electron microscopy microchips p53, molecular modeling

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Systematic Literature Review: An Intelligent Pulmonary TB Detection from Chest X-Rays 1

Systematic Literature Review: An Intelligent Pulmonary TB De...

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1st International Conference on Computer Science and Artificial Intelligence, ICCSAI 2021

作者： Jimmy Cenggoro, Tjeng Wawan Pardamean, Bens Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Tuberculosis (TB) is one of the top ten reasons for death from an infectious agent. Although TB is curable and preventable, delay in diagnosis and treatment can lead the patient to death. Advancements in computer-Aided diagnosis (CAD), particularly in medical images classification, significantly contribute to early TB detection. The current state-of-Art CAD for medical images classification applications using a method base on deep learning techniques. The problem faced in this deep learning technique is that, in general, it only uses a single modal for the model. In contrast, in medical practice, the data used for TB analysis not only focuses on images but also includes clinical data such as demographics, patient assessments, and lab test results. This systematic literature review describes different deep learning methods using single modal or multimodal techniques that combined images with other clinical data. We conducted a systematic search on Springer, PubMed, ResearchGate, and google scholar for original research leveraging deep learning for Pulmonary TB detection. © 2021 IEEE.

关键词： Computer aided diagnosis

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