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Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

RETRACTED ARTICLE: An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain

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IETE Journal of Research 2023年第11期69卷 CVII-CXX页

作者： Abu Buker Siddique Rafaqat Kazmi Habib Ullah Khan Sikandar Ali Ali Samad Gulraiz Javaid 1 Department of Software Engineering The Islamia University of Bahawalpur Bahawalpur63100 Pakistan 2 Department of Accounting and Information Systems College of Business and Economics Qatar University Doha 2713 Qatar 3 Department of Information Technology The University of Haripur Haripur22620 Khyber Pakhtunkhwa Pakistan 4 Faculty of Computing Department of Computer Sciences The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain systems and Industrial Internet of Things”:Abu Buker Siddique, Rafaqat Kazmi, Habib Ullah Khan, Sikandar Ali, Ali Samad & Gulraiz Javaid (2022) An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain, IETE Journal of Research, DOI:10.1080/03772063.2022.2052984Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

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SHIPBOARD DAMAGED STABILITY ASSESSMENT - THE FLOODING CASUALTY CONTROL software

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NAVAL ENGINEERS JOURNAL 1993年第3期105卷 152-166页

作者： ZAHN, PB ROSBOROUGH, J CARLSTROM, R Peter B. Zahn:is a section chief in the Naval Architecture Department at Advanced Marine Enterprises. He is responsible for software development and special trials support. He has led the Flooding Casualty Control Software (FCCS) development effort since joining AME in 1989. He is also responsible for the material handling strikedown and stowage (MHS&S) software development program as well as special trials support for the ACVLAP and T-AGOS 19 programs. Prior to joining AME Mr. Zahn spent nine years with various companies of the ARCTEC Group. His experience includes field trials in venues from the Caribbean to the Bering Sea as well as model tests and engineering design and development efforts. Mr. Zahn received his B.S. in naval architecture and marine engineering from Webb Institute in 1980 and has published various papers on pollution prevention offshore systems and icebreaker design and performance. He is a member of ASNE and SNAME. John Rosborough:has been a naval architect for the Naval Sea Systems Command (NavSea) for the past 15 years. He is currently assigned to the Hydrodynamics Division with responsibility for aircraft carrier stability evaluations and as a task leader for computer-aided ship design development. In the Stability Division he was previously responsible for stability analyses on various amphibious ships SWATHs and foreign ships. He manages the upgrade and augmentation of SHCP at NavSea and is the Navy's technical point of contact for evaluating shipboard stability software and transitioning technology to the fleet. Mr. Rosborough received his B.S. in naval architecture and marine engineering from the University of Michigan in 1977. He is a member of USNI and the Association of Scientists and Engineers at NavSea. Richard Carlstrom:is a naval architect with Advanced Marine Enterprises. He has been responsible for software development in support of a variety of major programs including FCCS and SHCP and is a specialist in stability evaluations for complex ships such as CVs CVNs

The Flooding Casualty Control software (FCCS) was developed under the auspices of the Naval Sea systems Command (NavSea) and is currently being deployed on a variety of ships in the neets of both the U.S. Navy and the U.S. Coast Guard. The primary objective of FCCS is to enable damage control personnel to identify critical stability conditions, especially when related to the loss of reserve buoyancy due to battle damage and the destabilizing effects of large quantities of firefighting water, in a timely manner. FCCS was initially deploved in 1990. It utilizes the standard algorithms of the Ship Hull Characteristics Program (SHCP). The user interface was designed to allow quick familiarity for shipboard users, primarily the damage control assistant (DCA) and his staff. Intact stability evaluations include the effects of topside icing, high winds, personnel crowding, heavy lifts over the side, high speed turns, and towing. FCCS also supports ballasting analysis for amphibious ships as well as providing bottom reaction and beached stability data for grounding incidents. Bv providing a tool for the ''fuel king'' and DCA to generate the required daily updates on the current ship load and liquids status, FCCS is assured of an accurate baseline in the event of damage. The design allows the evaluation of the ultimate ship stability status for a damage event using simple compartmentation and flooding status inputs. Evaluation of the adequacy of resulting stabilitv, as well as identification of such critical stability parameters as off center loading, margin line immersion, and negative GM, are accomplished by the program. Guidance is provided for the user to initiate appropriate flooding related damage control activities. Initially fitted on USS Oliver Hazard Perry Class frigates, FCCS databases have been for the USCG Hamilton class high endurance cutters, USS Arleigh Burke class Aegis destroyers, and a variety of other U.S. Navy and U.S. Coast Guard ship classes. The progra

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Quantum capacitance-limited MoS2 biosensors enable remote label-free enzyme measurements

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arXiv 2018年

作者： Le, Son T. Guros, Nicholas B. Bruce, Robert C. Cardone, Antonio Amin, Niranjana D. Zhang, Siyuan Klauda, Jeffery B. Pant, Harish C. Richter, Curt A. Balijepalli, Arvind Nanoscale Device Characterization Division National Institute of Standards and Technology GaithersburgMD20899 United States Theiss Research San DiegoCA92037 United States Microsystems and Nanotechnology Division National Institute of Standards and Technology GaithersburgMD20899 United States Department of Chemical and Biomolecular Engineering University of Maryland College ParkMD20742 United States Software and Systems Division National Institute of Standards and Technology GaithersburgMD20899 United States University of Maryland Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States National Institute of Neurological Disorders and Stroke National Institutes of Health BethesdaMD20892 United States

We have demonstrated atomically thin, quantum capacitance-limited, field-effect transistors (FETs) that enable the detection of pH changes with»75-fold higher sensitivity (4.4 V/pH) over the Nernst value of 59 mV/pH at room temperature when used as a biosensor. The transistors, which are fabricated from a monolayer of MoS2 with a room temperature ionic liquid (RTIL) in place of a conventional oxide gate dielectric, exhibit very low intrinsic noise resulting in a pH limit of detection (LOD) of 92´10-6 at 10 Hz. This high device performance, which is a function of the structure of our device, is achieved by remotely connecting the gate to a pH sensing element allowing the FETs to be reused. Because pH measurements are fundamentally important in biotechnology, the low limit of detection demonstrated here will benefit numerous applications ranging from pharmaceutical manufacturing to clinical diagnostics. As an example, we experimentally quantified the function of the kinase Cdk5, an enzyme implicated in Alzheimer's disease, at concentrations that are 5-fold lower than physiological values, and with sufficient time-resolution to allow the estimation of both steady-state and kinetic parameters in a single experiment. The high sensitivity, low LOD and fast turnaround time of the measurements will allow the development of early diagnostic tools and novel therapeutics to detect and treat neurological conditions years before currently possible. Copyright © 2018, The Authors. All rights reserved.

关键词： Enzyme activity

Rapid, quantitative therapeutic screening for alzheimer's enzymes enabled by optimal signal transduction with transistors

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arXiv 2019年

作者： Le, Son T. Morris, Michelle A. Cardone, Antonio Guros, Nicholas B. Klauda, Jeffery B. Sperling, Brent A. Richter, Curt A. Pant, Harish C. Balijepalli, Arvind Alternative Computing Group Nanoscale Device Characterization Division National Institute of Standards and Technology GaithersburgMD20899 United States Theiss Research La JollaCA92037 United States Biophysics Group Microsystems and Nanotechnology Division National Institute of Standards and Technology GaithersburgMD20899 United States Information Systems Group Software and Systems Division National Institute of Standards and Technology GaithersburgMD20899 United States University of Maryland Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Department of Chemical and Biomolecular Engineering University of Maryland College ParkMD20742 United States Chemical Process and Nuclear Measurements Group Chemical Sciences Division National Institute of Standards and Technology GaithersburgMD20899 United States National Institute of Neurological Disorders and Stroke National Institutes of Health BethesdaMD20892 United States

We show that simple, commercially sourced n-channel silicon field-effect transistors (nFETs) operating under closed loop control exhibit an;«3-fold improvement in pH readout resolution to (7.2±0.3)×10-3at a bandwidth of 10 Hz when compared with the open loop operation commonly employed by integrated ion-sensitive field-effect transistors (ISFETs). We leveraged the improved nFET performance to measure the change in solution pH arising from the activity of a pathological form of the kinase Cdk5, an enzyme implicated in Alzheimer's disease, and showed quantitative agreement with previous measurements. The improved pH resolution was realized while the devices were operated in a remote sensing configuration with the pH sensing element off-chip and connected electrically to the FET gate terminal. We compared these results with those measured by using a custom-built dual-gate 2D field-effect transistor (dg2DFET) fabricated with 2D semi-conducting MoS2 channels and a moderate device gain, a=8. Under identical solution conditions the pH resolution of the nFETs was only 2-fold worse than the dg2DFETs pH resolution of (3.9±0.7)×10-3. Finally, using the nFETs, we demonstrated the effectiveness of a custom polypeptide, p5, as a therapeutic agent in restoring the function of Cdk5. We expect that the straight-forward modifications to commercially sourced nFETs demonstrated here will lower the barrier to widespread adoption of these remote-gate devices and enable sensitive bioanalytical measurements for high throughput screening in drug discovery and precision medicine applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Metals

Predicting Churn: How Multilayer Perceptron Method Can Help with Customer Retention in Telecom Industry

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IOP Conference Series: Materials Science and engineering 2020年第1期864卷

作者： NNA Sjarif NF Azmi HM Sarkan SM Sam MZ Osman Advanced Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra 54100 Kuala Lumpur Malaysia Soft Computing Research & Intelligent System Group (SPINT) Faculty of Systems Computer & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Pahang Malaysia

Customer churn prediction has been used widely in various kind of domain especially subscription-basis industries. With the rapid growth of telecommunication industry over the last decade, this industry not only focuses on providing numerous products, but also satisfying the customers as it is one of the key solutions to remain competitive. This research proposed MultiLayer Perceptron Method for churn prediction. The evaluation is compared with three classifiers which includes are Support Vector Machine, Naïve Bayes and Decision Tree in term of several aspects. In preprocessing phase, we employed Principal Component Analysis and normalization to find the correlation among all the variables. For the postprocessing, InfoGainAttribute is used to identify the highest factor attribute that leads to customer retention. It is found that MultiLayer Perceptron outperforms other classifiers and international plan plays important role to retain customer from leaving organization.

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Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

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arXiv 2023年

作者： Jha, Debesh Sharma, Vanshali Banik, Debapriya Bhattacharya, Debayan Roy, Kaushiki Hicks, Steven A. Tomar, Nikhil Kumar Thambawita, Vajira Krenzer, Adrian Ji, Ge-Peng Poudel, Sahadev Batchkala, George Alam, Saruar Ahmed, Awadelrahman M.A. Trinh, Quoc-Huy Khan, Zeshan Nguyen, Tien-Phat Shrestha, Shruti Nathan, Sabari Gwak, Jeonghwan Jha, Ritika K. Zhang, Zheyuan Schlaefer, Alexander Bhattacharjee, Debotosh Bhuyan, M.K. Das, Pradip K. Fan, Deng-Ping Parasa, Sravanthi Ali, Sharib Riegler, Michael A. Halvorsen, Pål de Lange, Thomas Bagci, Ulas Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States SimulaMet Oslo Norway Oslo Metropolitan University Oslo Norway Jadavpur University Kolkata India Indian Institute of Technology Guwahati India Institute of Medical Technology and Intelligent Systems Technische Universität Hamburg Germany University of Oslo Norway Julius-Maximilian University of Würzburg Germany Faculty of Information Technology University of Science VNU-HCM Viet Nam Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of School of Computing University of Leeds LeedsLS2 9JT United Kingdom College of Engineering Australian National University Canberra Australia Department of Engineering Science University of Oxford Oxford United Kingdom National University of Computer and Emerging Sciences Karachi Campus Pakistan Swedish Medical Center Seattle United States Kathmandu Nepal Couger Inc Tokyo Japan Department of Software Korea National University of Transportation Chungju Korea Republic of University of Bergen Bergen Norway Department of Medicine and Emergencies Mölndal Sahlgrenska University Hospital Region Västra Götaland Sweden Department of Molecular and Clinical Medicin Sahlgrenska Academy University of Gothenburg Sweden ETH Zurich Zurich Switzerland

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a

关键词： computer aided diagnosis

[Retracted] Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

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Security and Communication Networks 2023年第1期2023卷

作者： Mohammed Naif Alatawi Najah Alsubaie Habib Ullah Khan Tariq Sadad Hathal Salamah Alwageed Shaukat Ali Islam Zada Information Technology Department Faculty of Computing and Information Technology University of Tabuk Tabuk Saudi Arabiaut.edu.sa Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabiapnu.edu.sa Department of Accounting and Information Systems College of Business and Economics Qatar University Doha Qatarqu.edu.qa Department of Computer Science University Engineering and Technology Mardan Khyber Pakhtunkhwa Pakistan College of Computer and Information Sciences Jouf University Sakakah Saudi Arabiaju.edu.sa Department of Computer Science University of Peshawar Peshawar Khyber Pakhtunkhwa Pakistanuop.edu.pk Department of Software Engineering Faculty of Computing & Information Technology International Islamic University Islamabad Pakistaniiu.edu.pk

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.

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Construction and analysis of mathematical models of granular aggregates

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AIP Conference Proceedings 2023年第1期2758卷

作者： Lilia H. Zagorodnyuk Vladislav D. Ryzhikh Gleb A. Syitov Dmitry A. Sumskoy Department of Building materials science Products and structures Belgorod State Technological University named after V.G. Shukhov Kostyukov St. 46 Belgorod 308012 Russia Department of Software engineering for computers and computer-based systems Belgorod State Technological University named after V.G. Shukhov Kostyukov St. 46 Belgorod 308012 Russia

The task of developing effective granular aggregates, as well as the creation of a mathematical model and management of this process, is the most promising and relevant. The paper deals with the construction of mathematical models based on the results of experimental studies of granular aggregates (GA). We present the processing of the results of experimental studies on the production of granular aggregates using the classical method with the use of the high-level general-purpose Python programming language. The processing of research results is fully object-oriented. This approach to the generation of the obtained results allowed us to automate the processing of the array of data for the planning of the experiment. The results of mathematical modeling and nomograms reflecting the variability of the developed compositions, which allowed to obtain samples with the required technological, physical-mechanical and operational characteristics are presented. The paper calculates the values of reproducibility dispersions: for sand fraction ≤0.16 mm - 0.889; for sand fraction ≤0.315 mm - 0.967; for sand fraction ≤0.63 mm - 0.981. The analysis of nomograms has shown that the greatest strength values have the compositions with the sand fraction of 0.16 mm and the minimum content of sand (as an aggregate). For mixtures with sand fractions ≤0.315 mm it is established that the strongest will be the mixture with the minimum content of aggregate. It is revealed that when using sand fraction ≤0.63 mm the strength is reduced.

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