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Current biology : CB 2025年第5期35卷 R173-R174页

作者： Maaike J Bierenbroodspot Tatyana Darienko Sophie de Vries Thomas Pröschold Iker Irisarri Jan de Vries University of Goettingen Institute for Microbiology and Genetics Department of Applied Bioinformatics Goettingen Germany. University of Goettingen Institute for Microbiology and Genetics Department of Applied Bioinformatics Goettingen Germany University of Goettingen Albrecht-von-Haller-Institute for Plant Sciences Department of Experimental Phycology and Culture Collection of Algae Goettingen Germany. Research Department for Limnology University of Innsbruck Mondsee Austria. Department of Biodiversity and Evolutionary Biology Museo Nacional de Ciencias Naturales Madrid Spain Campus Institute Data Science University of Goettingen Goettingen Germany. Electronic address: Iker.Irisarri@mncn.csic.es. Campus Institute Data Science University of Goettingen Goettingen Germany Goettingen Center for Molecular Biosciences (GZMB) Department of Applied Bioinformatics University of Goettingen Goettingen Germany. Electronic address: devries.jan@uni-goettingen.de.

The more we learn about the algal relatives of embryophytes (land plants), the more complex and fascinating the evolution of key streptophyte traits becomes. With the sequencing of the Klebsormidium nitens genome, a range of molecular traits shared with land plants were found - despite ca. 800 million years of divergence. A few years ago, Streptofilum was described, superficially resembling members of Klebsormidiophyceae but showing divergence in traits. In our recent study, we reconstructed a phylogenomic backbone for Klebsormidiophyceae, and Streptofilum was nested within, although we pointed out the difficulties in placing Streptofilum. Now, Žárský and Eliáš have commented on this placement, building on a different approach to analyze the data.

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RHC: A dataset for In-Room and Out-Room Human Counting

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Procedia Computer Science 2021年 179卷 33-39页

作者： Bens Pardamean Hery Harjono Muljo Faizal Abid Herman Albert Susanto Tjeng Wawan Cenggoro Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Accounting Information Systems Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In recent years, research in human counting from CCTV (Closed Circuit Television) images have found an increasing demand to be deployed in real-world applications. The applications have been implemented in various settings, both indoor and outdoor. In the case of indoor setting, we found a type of room setting that conveys a problem to human counting model if we need to count only humans inside a room. With this respect, we present RHC (Room Human Counting) dataset, which images are captured in the aforementioned setting. The dataset can be used to develop a robust model that can differentiate between humans inside and outside a room. The dataset is publicly available at https://***/datasets/vt5c8h6kmh/1.

关键词： dataset human counting crowd counting in-room out-room

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Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

arXiv

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arXiv 2022年

作者： Ouyang, David Theurer, John Stein, Nathan R. Hughes, J. Weston Elias, Pierre He, Bryan Yuan, Neal Duffy, Grant Sandhu, Roopinder K. Ebinger, Joseph Botting, Patrick Jujjavarapu, Melvin Claggett, Brian Tooley, James E. Poterucha, Tim Chen, Jonathan H. Nurok, Michael Perez, Marco Perotte, Adler Zou, James Y. Cook, Nancy R. Chugh, Sumeet S. Cheng, Susan Albert, Christine M. Department of Cardiology Smidt Heart Institute Cedars-Sinai Medical Center United States Division of Artificial Intelligence in Medicine Department of Medicine Cedars-Sinai Medical Center United States Department of Computer Science Stanford University United States Milstein Division of Cardiology Department of Medicine Columbia University Irving Medical Center United States Department of Biomedical Informatics Columbia University Irving Medical Center United States Division of Cardiovascular Medicine Department of Medicine Brigham and Women's Hospital United States Division of Cardiology Department of Medicine Stanford University United States Division of Bioinformatics Research Department of Medicine Stanford University United States Division of Anesthesia Department of Surgery Cedars-Sinai Medical Center United States Department of Biomedical Data Science Stanford University United States Division of Preventive Medicine Department of Medicine Brigham and Women's Hospital United States

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59±19 years, 55% women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57- 13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for postoperative mortality for RCRI >2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77 - 0.92), non-cardiac surgery with an AUC of 0.83 (0.79 - 0.88), and catherization/endoscopy suite procedures with an AUC of 0.76 (0.72 - 0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to preoperative ECGs, can improve discrimination of post-operative mortality. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Breast Cancer Immunohistochemical Image Generation: a Benchmark dataset and Challenge Review

arXiv

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arXiv 2023年

作者： Zhu, Chuang Liu, Shengjie Yu, Zekuan Xu, Feng Aggarwal, Arpit Corredor, Germán Madabhushi, Anant Qu, Qixun Fan, Hongwei Li, Fangda Li, Yueheng Guan, Xianchao Zhang, Yongbing Singh, Vivek Kumar Akram, Farhan Sarker, Md Mostafa Kamal Shi, Zhongyue Jin, Mulan The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Academy for Engineering and Technology Fudan University Shanghai China Beijing Chaoyang Hospital Capital Medical University Beijing China Biomedical Engineering Department Georgia Tech and Emory University United States The Atlanta Veterans Affairs Medical Center United States Biomedical Engineering Department Emory University United States Data Science Institute Imperial College London United Kingdom The School of Computer and Electrical Engineering Purdue University United States The School of Computer Science and Technology Harbin Institute of Technology Shenzhen China The Department of Computer Engineering and Mathematics Rovira I Virgili University Spain The Department of Pathology and Clinical Bioinformatics Erasmus Medical Center Netherlands The Institute of Biomedical Engineering University of Oxford Oxford United Kingdom

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from Hematoxylin and Eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Financial transaction database design for jamu small-medium enterprises

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AIP Conference Proceedings 2023年第1期2594卷

作者： R. Rahutomo A. Permatasari Y. Lie B. Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

As jamu small-medium enterprises (SMEs) concentrate on establishing online sales and computerized systems, Point of Sales (PoS) becomes a strategic solution to maintain financial ...

As jamu small-medium enterprises (SMEs) concentrate on establishing online sales and computerized systems, Point of Sales (PoS) becomes a strategic solution to maintain financial stability with its capabilities in recording electronic transactions accurately. As the initial step in technology empowerment for jamu SMEs, the research proposes a concept in facilitating jamu SMEs to adapt in online business. A combination of Connolly and Begg's database design principles and McCarthy's Marketing Mix 4P generates a conceptual design of PoS database targeted specifically for jamu SMEs. The database design manages online purchasing transactions for jamu products by involving not only jamu SMEs and consumers but also a system administrator who manages data verification and extraction.

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Prediction of Therapeutic Potential of CD19 CAR-T Cells for LBCL By Histone Mark Analyses of Core Epigenetic Programming

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Blood 2024年 144卷 2028-2028页

作者： Salvatore Fiorenza Yue Cao Ye Zheng Janaki Purushe Tamara Bock Erik L Kimble Derek Janssens Delaney Kirchmeier Alexandre V. Vinaud Hirayama Jordan Gauthier David G. Maloney Stanley R. Riddell Akira Nguyen-Shaw Steven Henikoff Jean Yang Cameron J. Turtle Faculty of Medicine and Health University of Sydney Balwyn North Australia Sydney Precision Data Science Centre University of Sydney Sydney Australia Bioinformatics and Computational Biology Department The University of Texas MD Anderson Cancer Center Houston TX Fred Hutchinson Seattle WA Faculty of Medicine and Health University of Sydney Camperdown Australia Fred Hutchinson Cancer Center Seattle WA Basic Sciences Division Van Andel Ins Seattle WA Clinical Research Division Fred Hutchinson Cancer Center Seattle WA Fred Hutch Cancer Center University of Washington Seattle WA Translational Science and Therapeutics Division Fred Hutchinson Cancer Center Seattle WA Basic Sciences Division Fred Hutchinson Cancer Center Seattle WA Sydney Precision Data Science Centre University of Sydney Camperdown Australia University of Sydney Sydney Australia

Chimeric antigen receptor-T cell (CAR-T) therapy has produced durable responses in a subset of patients with otherwise incurable B-lineage malignancies. Efforts are underway to identify genetic and epigenetic programs in CAR-T cells that influence product quality, thereby enabling development of next generation CAR-T products. The subset-of-origin (SOO) for CAR-T cell manufacturing and the effects of serial stimulation are key factors that might influence CAR-T quality. We hypothesized that serial stimulation might impart different effects on the epigenetic programming of CAR-T cells manufactured from distinct subsets; and that these epigenetic programs might associate with outcomes of clinical CAR-T therapy. We previously showed that sequencing of DNA bound to transcriptionally permissive (H3K4me2) and repressive (H3K27me3) histone methylation marks identifies numerous important differences in CAR-T cells from healthy donors (HD) and patients that were not apparent by RNA-seq. Here, we applied novel histone mark analyses to identify epigenetic programs that impact outcomes of large B cell lymphoma (LBCL) patients receiving CAR-T cell therapy. We first validated a novel in vitro model generating a roadmap of epigenetic and transcriptional programs during longitudinal serial stimulation of CAR T-cells manufactured from distinct SOO. H3K27me3 and H3K4me2 analyses and RNA-seq were performed on ex vivo isolated CD8+ Naïve (N), Central Memory (CM) and Effector Memory (EM) subsets from HD, on each SOO-derived CAR-T cell product after manufacturing, and prior to cycles of serial stimulation with a CD19+ lymphoma cell line. We first derived principal component analysis (PCA) plots from the 3 datasets (H3K4me2, H3K27me3, RNA-seq), each comprising 15 samples (3 SOO, 5 timepoints) from 7 HD. In PCA plots of histone mark data, we identified two epigenetic programmatic axes. One axis distinguished the SOO of CAR-T manufacturing. A second axis recapitulated the stimulation histor

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Increased Donor-Derived Cell-Free DNA as a Predictor for the Early Detection of Antibody-Mediated Rejection Following Heart Transplantation

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Clinical transplantation 2025年第6期39卷 e70209页

作者： Dana Dlouha Sarka Chytilova Jevgenija Vymetalova Eva Rohlova Marianna Lukasova Sarka Novakova Jaroslav Alois Hubacek Experimental Medicine Center Institute of Clinical and Experimental Medicine Prague Czech Republic. Department of Data Science Institute of Clinical and Experimental Medicine Prague Czech Republic. Cardio Center Institute of Clinical and Experimental Medicine Prague Czech Republic. Laboratory of Genomics and Bioinformatics Institute of Molecular Genetics of the Czech Academy of Sciences Prague Czech Republic. Third Department of Internal Medicine First Faculty of Medicine Charles University Prague Czech Republic.

Plasma circulating donor-derived cell-free DNA (ddcfDNA) can be used to noninvasively monitor acute rejection of heart transplants (HTx). This study utilized digital droplet PCR to analyze ddcfDNA concentrations (measured in copies per milliliter) and the fractional abundance (%ddcfDNA) to differentiate between donor and recipient DNA on the basis of single nucleotide polymorphism (SNP) homozygosity. Seventy-seven patients participated in a study, providing 300 plasma samples. Both markers, mean ddcfDNA (cp/mL) and %ddcfDNA, showed similar decreasing trends following the HTx, (R < 0.2; p < 0.001). Significantly higher levels of ddcfDNA (cp/mL) and %ddcfDNA were observed during episodes of acute rejection (AR) compared to non-rejection samples (p < 0.001). Additionally, antibody-mediated rejection (AMR) was associated with increased %ddcfDNA levels compared to non-rejection and to acute cellular rejection samples (p < 0.001 and p < 0.01). A logistic regression model identified %ddcfDNA as an early predictor of AMR risk 10-19 days post-heart transplant (odds ratio 158, p < 0.02). Performance analysis established an optimal %ddcfDNA threshold of 0.125% for AMR detection, correctly identifying all patients without subsequent AMR. These findings suggest that early %ddcfDNA measurements post-HTx can accurately identify individuals unlikely to develop AMR during the first posttransplant year.

关键词： SNP biomarker cfDNA rejection transplantation

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Racial disparities in lung function by pulmonary function testing among lung transplant candidates and race-specific reference equations

JHLT Open

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JHLT Open 2025年 8卷 100252-100252页

作者： Daniel M. Guidot Mackenzie Wood Emily Poehlein Scott Palmer Lisa McElroy Geriatrics Research Education and Care Center Durham VA Medical Center Durham North Carolina Division of Pulmonary Allergy and Critical Care Duke University Medical Center Durham North Carolina Department of Biostatistics and Data Science Wake Forest School of Medicine Winston-Salem North Carolina Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham North Carolina Duke Clinical Research Institute Duke University Medical Center Durham North Carolina Division of Abdominal Transplant Department of Surgery Duke University Medical Center Durham North Carolina Department of Population Health Sciences Duke University Medical Center Durham North Carolina

Non-White patients with interstitial lung disease (ILD) experience racial disparities in lung transplant waitlist mortality. Race-specific equations for spirometry may contribute by underestimating restriction severity in non-White candidates. We analyzed US lung transplant candidates to assess for disparities in forced vital capacity (FVC) at listing, comparing absolute and adjusted values using race-specific and race-neutral equations. We identified 17,457 adults with ILD listed May 4, 2005 to September 31, 2023. At listing, mean absolute FVC was higher for White patients (2.03 ± 0.80 liters) than Black patients (1.61 ± 0.67 liters) and Asian patients (1.49 ± 0.86 liters). Differences were attenuated after applying race-specific equations (White patients 50.0 ± 17.5%, Black patients 47.7 ± 17.9%, Asian patients 46.2 ± 24.2%). Compared with race-neutral equations, race-specific equations had higher odds of classifying FVC as severe (≤40%) requiring listing in White patients (OR 1.37, 95% CI 1.28–1.40) but lower odds in Black patients (OR 0.82, 95% CI 0.74–0.90). Using race-neutral equations might help improve racial disparities for lung transplant candidates with ILD.

关键词： BMI Body mass index CI Confidence interval cm Centimeter FVC Forced vital capacity GLI Global lung initiative IPF Idiopathic pulmonary fibrosis ILD Interstitial lung disease kg/m 2 Kilogram per meter squared L Liter OR Odds ratio Sep September STAR Standard Transplant Analysis Research UNOS United Network of Organ Sharing interstitial lung disease lung transplantation forced vital capacity race-specific reference equations racial disparity referral United Network for Organ Sharing

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Statistical analysis and data visualization of Indonesia and Malaysia SARS Cov-2 metadata

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AIP Conference Proceedings 2023年第1期2594卷

作者： D. Sudigyo A. Budiarto B. Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

SARS CoV-2 is a fascinating topic to investigate, especially in Indonesia and Malaysia, which share similar racial demographics. However, statistical analysis of information on the SARS CoV-2 from a database, especially GISAID, does not contain specific customizations related to virus comparisons between selected countries. Therefore, the researchers conducted statistical analysis and data visualization using the Python programming language to describe and investigate SARS CoV-2 Indonesia and Malaysia from the GISAID database. SARS CoV-2 metadata from Indonesia (N=117) and Malaysia (N=250), which were gathered during 2020, were compared. This comparison was aimed to investigate the discrepancies of COVID-19 cases in closely related populations. Firstly, data visualization was conducted using the Python Matplotlib library to create bar charts for clades and mutation comparison. Additionally, a series of boxplots were generated to show age discrepancies stratified by gender. Furthermore, the statistical tests showed that only the dominant Malaysian (G and O) clades were found to be significantly different compared to Indonesian cases (p-value=0.016). The proportion of two major mutations (G614D and NSP12 P323L) were also significantly different in the two countries caused by the dominant clade differences (p-value=0.007). Lastly, the differences in the age distribution of COVID-19 cases between the two countries were significant only in the male group (p-value=0.017).

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The by-product of oil palm dosage application on Turnera subulata growth supports water supply

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AIP Conference Proceedings 2024年第1期3026卷

作者： Pauliz Budi Hastuti Gilang Tri Anggara Wiwin Dyah Ully Parwati Teddy Suparyanto Digdo Sudigyo Bens Pardamean Faculty of Agriculture STIPER Institute of Agriculture Yogyakarta Indonesia 55281 Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Plant growth with complex organic matter content can be enhanced by using oil palm by-products. These organic materials can maintain the water supply in the plant body. Therefore, in oil palm plantations with significant groundwater consumption, organic matter’s the ability to sustain water availability. Caterpillars that devour palm leaves are among the numerous pests that threaten oil palm plantations. Predators of leaf-eating caterpillars use the holly rose flower (Turnera subulata) in oil palm farms as a habitat. Therefore, the effect of the type and dose treatment of oil palm by-products on the growth of Turnera subulata was observed to support sustainable water management in oil palm plantations. The research was carried out using a factorial design arranged in a Completely Randomized Design (CRD) consisting of two components: the type of palm oil mill by-product (boiler ash, empty bunches, and solid) and the dose of a by-product (% volume) consisting of four dosages (0, 25, 33, and 50% volume). The research data were analyzed using variance at the 5% test level. A DMRT test at the 5% test level was conducted on the treatment that had a significant effect. The growth of Turnera subulata was most positively impacted by a solid application dose of 50% volume. Turnera subulata plants responded the least favorably to the application of empty bunches in any dose treatment.

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