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The Cosmic Microwave Background and H0

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arXiv 2023年

作者： Lemos, Pablo Shah, Paul Mila - Quebec Artificial Intelligence Institute Montréal 6666 Rue Saint-Urbain QCH2S 3H1 Canada Department of Physics Université de Montréal Montréal 1375 Avenue Thérèse-Lavoie-Roux QCH2V 0B3 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom

The cosmic microwave background (CMB) offers a unique window into the earlyuniverse, providing insights into cosmological parameters like the Hubbleconstant. Recent precise measurements of the CMB by experiments like Planckseem to point to a lower value for the Hubble constant compared to some othermeasurements like those from Type Ia supernovae. This discrepancy, known as theHubble tension, currently lacks a definitive explanation. In this chapter, weprovide an overview of how the Hubble constant is determined from detailedmeasurements of the CMB power spectrum. We explain the physics underlying keyfeatures of the CMB spectrum and their connection to cosmological *** then examine the consistency of Planck\'s Hubble constant determination, bothinternally within the data itself and externally with other astrophysicalprobes. While largely consistent, some anomalies like the lensing amplitudeparameter $A_L$ remain unresolved. We also explore various theoreticalextensions to the standard ${\\Lambda}$CDM cosmological model and assess theirpotential to resolve the Hubble tension. No clear resolution emerges,indicating significant tensions remain between early and late universe probeswithin simple extensions to ${\\Lambda}$CDM. Upcoming CMB experiments promiseimproved precision and should provide further insights into this cosmicconundrum. A coherent picture bridging measurements across cosmic time remainsan open challenge at the forefront of modern cosmology. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

The medical algorithmic audit (vol 4, pg e384, 2022)

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LANCET DIGITAL HEALTH 2022年第6期4卷 E405-E405页

作者： Liu, X. Glocker, B. McCradden, M. M. Ghassemi, M. Denniston, A. K. Oakden-Rayner, L. Academic Unit of Ophthalmology Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham UK Department of Ophthalmology University Hospitals Birmingham NHS Foundation Trust Birmingham UK Moorfields Eye Hospital NHS Foundation Trust London UK Health Data Research UK London UK Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham UK Biomedical Image Analysis Group Department of Computing Imperial College London London UK The Hospital for Sick Children Toronto ON Canada Dalla Lana School of Public Health Toronto ON Canada Institute for Medical Engineering and Science and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA National Institute of Health Research Biomedical Research Centre for Ophthalmology Moorfields Hospital London NHS Foundation Trust London UK University College London Institute of Ophthalmology London UK Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia. lauren.oakden-rayner@adelaide.edu.au

Artificial intelligence systems for health care, like any other medical device, have the potential to fail. However, specific qualities of artificial intelligence systems, such as the tendency to learn spurious correlates in training data, poor generalisability to new deployment settings, and a paucity of reliable explainability mechanisms, mean they can yield unpredictable errors that might be entirely missed without proactive investigation. We propose a medical algorithmic audit framework that guides the auditor through a process of considering potential algorithmic errors in the context of a clinical task, mapping the components that might contribute to the occurrence of errors, and anticipating their potential consequences. We suggest several approaches for testing algorithmic errors, including exploratory error analysis, subgroup testing, and adversarial testing, and provide examples from our own work and previous studies. The medical algorithmic audit is a tool that can be used to better understand the weaknesses of an artificial intelligence system and put in place mechanisms to mitigate their impact. We propose that safety monitoring and medical algorithmic auditing should be a joint responsibility between users and developers, and encourage the use of feedback mechanisms between these groups to promote learning and maintain safe deployment of artificial intelligence *** © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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Risk-based decision making: estimands for sequential prediction under interventions

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

HOW DOES THE BRAIN COMPUTE WITH PROBABILITIES?

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arXiv 2024年

作者： Haefner, Ralf M. Beck, Jeff Savin, Cristina Salmasi, Mehrdad Pitkow, Xaq Department of Brain and Cognitive Sciences University of Rochester RochesterNY United States Department of Neurobiology Duke University DurhamNC United States Departments of Neural Science and Data Science New York University New YorkNY United States Gatsby Computational Neuroscience Unit Max Planck UCL Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Neuroscience Institute Department of Machine Learning Carnegie Mellon University PittsburghPA United States Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Electrical and Computer Engineering Department of Computer Science Rice University HoustonTX United States

This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question 'How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations - Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) - and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and *** Codes 92 Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

PSXV-6 Influence of GDF5 Gene Polymorphism on the Concentration of Essential and Toxic Elements in Blood Serum and Milk Productivity of the Holstein Cows

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Journal of Animal Science 2022年第SUPPLEMENT_3期100卷 212–212页

作者： Zubac, Isidora Sycheva, Irina Komarchev, Alexey Nikitin, Petr Orishev, Aleksandr Vasilyeva, Irina Krasavina, Vera University of Belgrade Russian State Agrarian University–Moscow Timiryazev Agricultural Academy Federal Scientific Center «All-Russian Research and Technological Institute of Poultry» of Russian Academy of Sciences Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)

The aim of this study was to evaluate the effect of GDF5 (growth differentiation factor 5) gene polymorphism on metabolism of chemical elements and milk production of cows. The studies were performed on Holstein cows (n=100). To determine the single nucleotide polymorphism of GDF5 (T586C in exon 1), blood samples were taken. DNA samples were isolated from whole blood using the IsoGeneLab reagent kit. The thermal cycler "MyCycler" was used for DNA amplification. The elemental composition of blood serum was estimated according to 13 chemical elements (Al, As, Pb, Sn, Sr, Co, Cr, Cu, Fe, I, Mn, Se, Zn) by ICP AES and ICP MS. The frequency of occurrence of polymorphic groups in the GDF5 gene was established, which was for homozygous alleles: TT (n=41), CC (n=10), heterozygous (TS)–(n=49). The analysis of the data obtained showed that blood serum of cows with the CC genotype, relative to animals with the TT and TC genotypes had a greater concentration of I - by 19.3 (P£0.001) and 10.4% (P£0.01), Se - by 18.3 (Р£0.001) and 11.7% (Р£0.01), Zn – by 15.8 (Р£0.01) and 13.2% (Р£0.01), while relatively low concentrations of Al were registered - by 10.8 (Р£0.001) and 21.2% (Р£0.01), Pb - by 16.4 (Р£0.01) and 23.7% (Р£0.001) respectively. In terms of milk fat yield (kg/day), animals with CC genotype advanced over animals with the TT and TC genotypes by 4.8 (P£0.05) and 5.2% (P£0.01), protein by 1.2 and 1.4% (Р£0.05), milk yield - by 4.8 (Р£0.05) and 5.3% (Р£0.01). Thus, we can conclude that the metabolism of chemical elements and the milk productivity of cows are significantly determined by the polymorphism of the GDF5 gene. In order to select cows of the Holstein breed according to the GDF5 gene polymorphism, it is advisable to select homozygous animals for the CC genotype.

关键词： blood serum cattle essential and toxic elements Holstein breed genotype GDF5

PSXI-17 Evaluation of Influence of the Total Accumulation of Toxic Elements in Blood Serum on the Indicators of Milk Productivity of the Holstein Cows

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Journal of Animal Science 2022年第SUPPLEMENT_3期100卷 221–221页

作者： Zubac, Isidora Sycheva, Irina Komarchev, Alexey Nikitin, Petr Vasilev, Vladimir Vasilyeva, Irina Stepantsevich, Marina University of Belgrade Russian State Agrarian University–Moscow Timiryazev Agricultural Academy Federal Scientific Center «All-Russian Research and Technological Institute of Poultry» of Russian Academy of Sciences Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Regional Research and Clinical Institute "MONIKI" Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)

The main objective was to study the milk productivity of lactating cows depending on the level of total accumulation of toxic metals in the blood serum. The experimental part of work was carried out on clinically healthy Holstein cows (n=45; lactation stage - 20-40 days after calving) bred in a single farm. The elemental composition of blood serum was studied for 4 chemical elements (Al, Pb, Sn, Sr) by ICP AES and ICP MS. The indicators of milk productivity of cows were evaluated by the yield of milk fat, protein, lactose, dry matter, SOMO and the average daily milk yield. To assess the magnitude of the toxic load on the body of cows, the coefficient of the total toxic load Ktox (µg/ml) was calculated. To calculate the coefficient, the sum of the concentrations of some toxic elements in the blood serum was used: Ktox=Al+Pb+Sn+Sr. Based on the data on the value of the total toxic load coefficient Ktox in blood serum, three groups were formed: group I included cows with a minimum value of the toxic load coefficient (Ktox=0.128 (0.088-0.142) μg/ml relative to the study sample; group II with an average (Ktox=0.229 (0.206-0.276) µg/ml; Group III included cows with the maximum coefficient (Ktox=0.395 (0.306-0.453) µg/ml. Comparative analysis showed that cows with the minimum Ktox value (group I) outperformed individuals from I and group II in terms of milk fat yield by 15.4 (P£0.001) and 19.7% (P£0.001), protein by 5.6 and 12.5% (P£0.05), lactose -10.9 (P£0.01) and 18.4% (P£0.001), dry matter by -7.6 and 12.8% (P£0.05) and average daily milk yield by 9.4 and 17.5% (P£0.01), respectively. Thus, in the course of the study, it was registered that with an increase in the total accumulation of heavy metals in the body of animals, the milk productivity of cows decreases.

关键词： blood serum cattle milk productivity toxic elements

Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers machine learning Random Forest

Big data: Prospects and Challenges

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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

Sketch-based Medical Image Retrieval

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arXiv 2023年

作者： Kobayashi, Kazuma Gu, Lin Hataya, Ryuichiro Mizuno, Takaaki Miyake, Mototaka Watanabe, Hirokazu Takahashi, Masamichi Takamizawa, Yasuyuki Yoshida, Yukihiro Nakamura, Satoshi Kouno, Nobuji Bolatkan, Amina Kurose, Yusuke Harada, Tatsuya Hamamoto, Ryuji Division of Medical AI Research and Development National Cancer Center Research Institute 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Cancer Translational Research Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Machine Intelligence for Medical Engineering Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan Medical Data Deep Learning Team Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Experimental Therapeutics National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Diagnostic Radiology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Neurosurgery and Neuro-Oncology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Colorectal Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Thoracic Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Radiation Safety and Quality Assurance Division National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Division of Research and Development for Boron Neutron Capture Therapy National Cancer Center Exploratory Oncology Research & Clinical Trial Center 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Medical Physics Laboratory Division of Health Science Graduate School of Medicine Osaka University Yamadaoka 1-7 Osaka Suita-shi565-0871 Japan Department of Surgery Kyoto University Graduate School of Medicine 54 Shogoin Kawahara-cho Sakyo-ku Kyoto606-8507 Japan

The amount of medical images stored in hospitals is increasing faster than ever;however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIR) systems usually require example images to construct query vectors;nevertheless, example images cannot always be prepared. Besides, there can be images with rare characteristics that make it difficult to find similar example images, which we call isolated samples. Here, we introduce a novel sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without example images. The key idea lies in feature decomposition of medical images, whereby the entire feature of a medical image can be decomposed into and reconstructed from normal and abnormal features. By extending this idea, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. Subsequently, it integrates the two kinds of input to construct a query vector and retrieves reference images with the closest reference vectors. Using two datasets, ten healthcare professionals with various clinical backgrounds participated in the user test for evaluation. As a result, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples. Our SBMIR system achieves flexible medical image retrieval on demand, thereby expanding the utility of medical image databases. Copyright © 2023, The Authors. All rights reserved.

关键词： Search engines