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A theoretic study of a distance-based regression model

science China Mathematics 2019年第5期62卷 979-998页

作者： Jialu Li Wei Zhang Sanguo Zhang Qizhai Li School of Mathematics and Statistics Beijing Institute of Technology Biostatistics and Bioinformatics Branch National Institute of Child Health and Human Development School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences LSC NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences

The distance-based regression model has many applications in analysis of multivariate response regression in various ?elds, such as ecology, genomics, genetics, human microbiomics, and neuroimaging. It yields a pseudo F test statistic that assesses the relation between the distance(dissimilarity) of the subjects and the predictors of interest. Despite its popularity in recent decades, the statistical properties of the pseudo F test statistic have not been revealed to our knowledge. This study derives the asymptotic properties of the pseudo F test statistic using spectral decomposition under the matrix normal assumption, when the utilized dissimilarity measure is the Euclidean or Mahalanobis distance. The pseudo F test statistic with the Euclidean distance has the same distribution as the quotient of two Chi-squared-type mixtures. The denominator and numerator of the quotient are approximated using a random variable of the form ξχ_d^2+ η, and the approximate error bound is given. The pseudo F test statistic with the Mahalanobis distance follows an F *** simulation studies, the approximated distribution well matched the "exact" distribution obtained by the permutation procedure. The obtained distribution was further validated on H1N1 in?uenza data, aging human brain data, and embryonic imprint data.

关键词： distance-based regression Euclidean pseudo F test statistic Mahalanobis

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Transcriptome analysis reveals a de novo DNA element that may interact with chromatin-associated proteins in Plasmodium berghei during erythrocytic development

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Scientific reports 2025年第1期15卷 18621页

作者： Adaobi Okafor Yagoub Adam Benedikt Brors Ezekiel Adebiyi Applied Bioinformatics German Cancer Research Center (DKFZ) Im Neuenheimer Feld 280 69120 Heidelberg Germany. Faculty of Biosciences Heidelberg University Im Neuenheimer Feld 234 69120 Heidelberg Germany. Covenant University Bioinformatics Research (CUBRe) Covenant University Ota 112233 Ogun State Nigeria. Applied Bioinformatics German Cancer Research Center (DKFZ) Im Neuenheimer Feld 280 69120 Heidelberg Germany. e.adebiyi@dkfz-heidelberg.de. Covenant University Bioinformatics Research (CUBRe) Covenant University Ota 112233 Ogun State Nigeria. e.adebiyi@dkfz-heidelberg.de. African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University 10218 Kampala Uganda. e.adebiyi@dkfz-heidelberg.de. Institute of Infectious Diseases (IDI) Makerere University 10218 Kampala Uganda. e.adebiyi@dkfz-heidelberg.de.

The life cycle of Plasmodium parasites involves intricate, multistage processes that are tightly regulated by stage-specific transcription factors. These factors bind to regulatory regions within gene promoters, enabling the precise expression of genes required for each developmental stage. Despite the importance of these transcriptional mechanisms, our understanding remains limited, particularly in the rodent model organism P. berghei. To address this, we conducted a genome-wide analysis of RNA-Seq data from different developmental stages of P. berghei by initially integrating data from human malaria parasites P. falciparum and P. vivax. We identified unique transcriptional signatures across Plasmodium species. Our analysis of P. berghei revealed stage-specific gene sets clustered by expression profiles and predicted regulatory motifs involved in their control. We interpreted these motifs using known binding sites for eukaryotic transcription factors including ApiAP2 proteins. Additionally, we expanded the annotation of the AGGTAA motif which resembles a de novo motif linked to erythrocytic development in P. falciparum, and identified its potential interacting proteins including members of the PfMORC and GCN5 complexes. This study enhances our understanding of gene regulation in P. berghei and provides new insights into the transcriptional dynamics underlying Plasmodium development.

关键词： Plasmodium berghei Plasmodium falciparum Plasmodium vivax ApiAP2 proteins RNA-Seq Regulatory motifs

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Micro-Chimerism Detected By Highly Sensitive AlloHeme TM Solution Is Associated with Treatment Response and Relapse after Allogeneic Stem Cell Transplantation

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Transplantation and Cellular Therapy 2024年第2期30卷 S403-S404页

作者： Kothari, Ashish S, MD, MS Dwivedi, Nishant, MD, PhD Chattopadhyay, Shrikanta, MD Wang, Yun, PhD Zhou, Mingming Vaskova, Evgeniya, PhD Viard, Thierry, PhD Wong, Lily, MS Gulbahce, Natali, PhD Qu, Kunbin, PhD Grskovic, Marica, PhD Kongtim, Piyanuch, MD, PhD Ciurea, Stefan O., MD Clinical Development CareDx Brisbane CA SVP Medical TScan Therapeutics Waltham MA TScan Therapeutics Waltham MA Biostatistics and Data Science CareDx Inc Brisbane CA R&D CareDx Brisbane CA CareDx Inc Brisbane CA Bioinformatics CareDx Brisbane CA Data Science and Biostatistics CareDx Inc Brisbane CA Cell Transplant Therapy CareDx Inc Brisbane CA Division of Hematology/Oncology Department of Medicine Hematopoietic Stem Cell Transplantation and Cellular Therapy Program University of California Irvine Orange CA

Post-transplant chimerism status is associated with graft survival and potentially disease relapse after allogeneic stem cell transplantation (allo-HCT). However, detection of low-level micro-chimerism (MC) following allo-HCT using the current standard with short tandem repeats (STR) PCR method has been challenging due to STR's poor limit of detection (LOD) of 1-5%. A more sensitive method of chimerism detection could help inform early treatment decisions. We present preliminary results from two multi-center trials using a next-generation sequencing (NGS)-based MC detection method, AlloHeme TM (AH), with a higher sensitivity (LOD 0.035-0.043%) (Table 1), as a translational biomarker of treatment response and early relapse, including minimal residual disease (MRD), after allo-HCT for acute leukemias and MDS. Trial 1: NCT04635384 (ACROBAT, CareDx Inc.) is a prospective multi-center observational cohort study. AlloHeme is the study's primary biomarker. ACROBAT has fully enrolled 289 subjects, with a median follow-up of 150 days. Trial 2: NCT05473910 (TSCAN-001, TScan Inc.) is a multi-arm non-randomized controlled Phase 1 study evaluating the safety and efficacy of TScan's TSC100 and TSC101 - allogeneic T-cell receptor-engineered T cells that target haematopoietically restricted antigens HA-1 and HA-2 respectively, both presented on HLA-A*02:01, in mismatched reduced-intensity conditioning (RIC)-haplo donor HCT recipients. AlloHeme is used as a translational biomarker. Complete chimerism (CC) was defined as >99.9% recipient chimerism. Early review of the ongoing ACROBAT study data with CD33 CC (N=73; median follow-up 270 days, range 72-608 days) revealed three relapses (4%), which compares favorably with 18-20% 6-month relapse rates in the CIBMTR data, suggestive of prognostic potential. This data is corroborated by the preliminary findings of Trial 2. Increased MC (iMC) was defined as ≥1% increase in recipient chimerism between two consecutive AH results. Patients with

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The Sensorium competition on predicting large-scale mouse primary visual cortex activity

arXiv

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arXiv 2022年

作者： Willeke, Konstantin F. Fahey, Paul G. Bashiri, Mohammad Pede, Laura Burg, Max F. Blessing, Christoph Cadena, Santiago A. Ding, Zhiwei Lurz, Konstantin-Klemens Ponder, Kayla Muhammad, Taliah Patel, Saumil S. Ecker, Alexander S. Tolias, Andreas S. Sinz, Fabian H. International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Institute for Theoretical Physics University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Electrical and Computer Engineering Rice University Houston United States

The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the SENSORIUM benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (SENSORIUM) or stimulus plus behavior (SENSORIUM+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond. © 2022, CC BY-NC-ND.

关键词： Neural networks

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Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Erick Firmansyah Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia 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

The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (AI)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

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Provoking Tweets by Indonesia Media Twitter in the Initial Month of Coronavirus Disease Hit

Provoking Tweets by Indonesia Media Twitter in the Initial M...

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International Conference on Information Management and Technology (ICIMTech)

作者： Ika Nurlaila Reza Rahutomo Kartika Purwandari Bens Pardamean BINUS Online Learning Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

After months in denial, on 2 nd March 2020 the first case of COVID-19 in Indonesia was announced and soon was declared as a national pandemic. COVID-19 was since being the headlines of the news offline and online. This trigger either positive or negative responses from the readers, which in turn affect at least the depth of understanding and furthermore the effectiveness of any enforced intervention taken with the purpose to control the transmission of the COVID-19. In order to assess how likely the COVID-19-containing news being forwarded to reach more readers and how the pertinent news bridge further virtual interaction, we crawled data from Twitter as the major online microblogging platform to analyze how likely COVID-19-containing tweets were echoed and what typical tweet about COVID-19 that gained attentions. This is critical to evaluate how people virtually responded against the tweets. Our analyzed data is visualized using Matplotlib by Python and Graph Prism8 accordingly. We figured out that timing and number of followers are not determinative for the tweets being retweeted. Instead, provoking headline add likelihood for the tweets to be moving forward. Also appeared in our analysis that the top two media (Detik and Kompas) shared the same proportion on their tweet type fractions on where information dominated the fraction of their tweets.

关键词： Twitter Media Corona Tagging Diseases data visualization

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Effects of sickle trait on maternal and perinatal outcomes among pregnant women

Blood Vessels, Thrombosis & Hemostasis

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Blood Vessels, Thrombosis & Hemostasis 2025年第2期2卷 100049-100049页

作者： Sarah Sewaralthahab Jiling Chou Stephen Fernandez Nawar Shara Hedy P. Smith Department of Medicine College of Medicine King Saud University Riyadh Saudi Arabia Division of Hematology Oncology Center King Saud University Medical City Riyadh Saudi Arabia Department of Biostatistics Bioinformatics and Biomathematics Center of Biostatistics Informatics and Data Science Medstar Research Institute Hyattsville MD Depatment of Internal Medicine Medstar Washington Hospital Center Washington DC

Abstract The risk of multiple adverse pregnancy outcomes and perinatal outcomes among pregnant women with sickle cell trait (SCT) is not known. Our objective was to compare differences in adverse outcomes, specifically pregnancy-related hypertensive disease (PRHD), pyelonephritis/urinary tract infection (UTI), and low birth weight (LBW), between pregnant women with SCT and healthy controls. This was a retrospective cohort study of women who delivered between 2015 and 2020. We included all women with SCT, that is, hemoglobin electrophoresis AS. Women with SCT were matched in a 1:2 to women without SCT, controlling for age, gravidity, and parity. Our primary outcomes were PRHD, pyelonephritis/UTI, and LBW baby. Multivariable logistic regression modeling examined the associations between patients’ characteristics and the primary *** were 162 women with SCT, and 324 healthy control women were enrolled. Bivariate analysis revealed that women with SCT had a higher proportion of PRHD (38.9% vs 34.9%; P = .39), pyelonephritis/UTI (11.7% vs 7.1%; P = .09), but a lower proportion of LBW (10.5% vs 16.0%; P = .1). In multivariable analysis, after controlling for confounders, SCT was not an independent predictor of PRHD. However, SCT was an independent predictor of pyelonephritis/UTI (adjusted odds ratio [aOR], 1.98; 95% confidence interval [CI], 1.02-3.85) and of a lower risk of having a LBW baby (aOR, 0.48; 95% CI, 0.25-0.94). SCT is not associated with an increased risk of PRHD. However, SCT is associated with pregnancy outcomes, including higher risk of pyelonephritis/UTI but a lower risk of LBW babies.

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Smart IoT CPO Storage Tank

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Rengga Arnalis Renjani Hermantoro Prima Okta Adi Nugraha Kartika Purwandari Teddy Suparyanto Bens Pardamean Department of Agricultural Engineering Institute of Agriculture STIPER 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 Bina Nusantara University Jakarta Indonesia 11480

Indonesia's crude palm oil (CPO) production from year to year continues to increase, at the end of 2020 it reached 17.35 million tons, up 3.6% from the previous year. Increasing production will result in more CPO stock and require good storage. The storage process that occurs is to maintain the temperature of the CPO so that the quality is not damaged. This temperature regulation is still done manually and raises the risk of work accidents. The purpose of this research is to create a temperature control system and automatic volume measurement that can be monitored from a smartphone. The manufacture of a control system used ESP8266 NodeMCu microcontroller, temperature sensor, proximity sensor, and 1000-Watt heater. Programming used the Arduino IDE and C++. The result of this study was an IoT CPO Storage Tank design equipped with sensors and microcontrollers. The temperature was measured with the DS18B20 sensor had a data accuracy of 99.19% while the volume measured with the HC-SR04 sensor had an accuracy of 99.78%. data obtained from the sensor could be seen through the Thingspeak application from a computer or smartphone.

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Antibiotic Treatment Drives the Diversification of the Human Gut Resistome

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Genomics, Proteomics & bioinformatics 2019年第1期17卷 39-51页

作者： Jun Li Elizabeth A.Rettedal Eric van der Helm Mostafa Ellabaan Gianni Panagiotou Morten O.A.Sommer Department of Infectious Diseases and Public Health Colleague of Veterinary Medicine and Life Sciences City Univerity of Hong Kong School of Data Science City Univerity of Hong Kong Novo Nordisk Foundation Center for Biosustainability Systems Biology and Bioinformatics Unit Leibniz Institute for Natural Product Research and Infection Biology – Hans Kn?ll Institute Systems Biology and Bioinformatics Group School of Biological Sciences Faculty of Sciences The University of Hong Kong Department of Microbiology Li Ka Shing Faculty of Medicine The University of Hong Kong

Despite the documented antibiotic-induced disruption of the gut microbiota, the impact of antibiotic intake on strain-level dynamics, evolution of resistance genes, and factors influencing resistance dissemination potential remains poorly understood. To address this gap we analyzed public metagenomic datasets from 24 antibiotic treated subjects and controls, combined with an in-depth prospective functional study with two subjects investigating the bacterial community dynamics based on cultivation-dependent and independent methods. We observed that shortterm antibiotic treatment shifted and diversified the resistome composition, increased the average copy number of antibiotic resistance genes, and altered the dominant strain genotypes in an individual-specific manner. More than 30% of the resistance genes underwent strong differentiation at the single nucleotide level during antibiotic treatment. We found that the increased potential for horizontal gene transfer, due to antibiotic administration, was ～3-fold stronger in the differentiated resistance genes than the non-differentiated ones. This study highlights how antibiotic treatment has individualized impacts on the resistome and strain level composition, and drives the adaptive evolution of the gut microbiota.

关键词： Antibiotics Resistome Gut microbiome Strain Evolution Horizontal gene transfer

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A roadmap to fair and trustworthy prediction model validation in healthcare

arXiv

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arXiv 2023年

作者： Ning, Yilin Volovici, Victor Ong, Marcus Eng Hock Goldstein, Benjamin Alan Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore169857 Singapore Department of Neurosurgery Erasmus MC University Medical Center Rotterdam3015 GD Netherlands Department of Public Health Erasmus MC Rotterdam3015 GD Netherlands Programme in Health Services and Systems Research Duke-NUS Medical School Singapore169857 Singapore Health Services Research Centre Singapore Health Services Singapore169856 Singapore Department of Emergency Medicine Singapore General Hospital Singapore169608 Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC27710 United States Institute of Data Science National University of Singapore Singapore117602 Singapore

A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear. In practice, prediction models are externally validated using data from very different settings, including populations from other health systems or countries, with predictably poor results. This may not be a fair reflection of the performance of the model which was designed for a specific target population or setting, and may be stretching the expected model generalizability. To address this, we suggest to externally validate a model using new data from the target population to ensure clear implications of validation performance on model reliability, whereas model generalizability to broader settings should be carefully investigated during model development instead of explored post-hoc. Based on this perspective, we propose a roadmap that facilitates the development and application of reliable, fair, and trustworthy artificial intelligence prediction models. © 2023, CC BY.

关键词： Population statistics

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