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Talc pleurodesis, a frequently performed procedure for refractory pneumothorax or pleural effusion, induces chronic granulomatous inflammation. It can present years later with pleural thickening and markedly increased uptake on positron emission tomography/computed tomography (PET/CT), mimicking the presentation of malignancies. We present the case of a 63-year-old female with positive 18F-fluorodeoxyglucose PET/CT 20 years after talc pleurodesis. Malignancy such as mesothelioma could not initially be ruled out. CT-guided biopsy confirmed an extensive foreign-body giant-cell reaction consistent with talc-related inflammatory change. This case highlights the need for the consideration of talcoma in the differential diagnosis of patients who undergo talc pleurodesis, and is unique in the significant timespan of 20 years between pleurodesis and positive imaging findings.

Machine learning (ML) models are widely used in life sciences and medicine; however, they are scattered across various platforms and there are several challenges that hinder their accessibility, reproducibility and reuse. In this manuscript, we present the formalisation and pilot implementation of community protocol to enable FAIReR (Findable, Accessible, Interoperable, Reusable, and Reproducible) sharing of ML models. The protocol consists of eight steps, including sharing model training code, dataset information, reproduced figures, model evaluation metrics, trained models, Dockerfiles, model metadata, and FAIR dissemination. Applying these measures we aim to build and share a comprehensive public collection of FAIR ML models in the BioModels repository through incentivized community curation. In a pilot implementation, we curated diverse ML models to demonstrate the feasibility of our approach and we discussed the current challenges. Building a FAIReR collection of ML models will directly enhance the reproducibility and reusability of ML models, minimising the effort needed to reimplement models, maximising the impact on the application and significantly accelerating the advancement in the field of life science and medicine.

Smart structural health monitoring (SHM) for large-scale infrastructure is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1-DCNN-LSTM, featuring two algorithms—convolutional neural network (CNN) and long-short term memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1-DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic data sets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful 2-D CNN, but with a lower time and memory complexity, making it suitable for real-time SHM. Note to Practitioners —This article aims to develop a practical data-driven method for automatically monitoring the operational state of structures. In order to achieve consistently and highly accurate results in performing different tasks for diverse structures, we combine underlying features in both time and frequency domains extracted from measured signal vibration data. Three popular data featuring methods are combined to achieve the diversity gain which would not be possible with each individual method. As the vibration is usually measured by long time-series signals, the most efficient deep learning architecture for time-series signal, namely long-short term memory (LSTM), is considered for this work. Besides, each structure has its own dynamic properties, i.e., eigenfrequencies, around which the most relevant information is in the frequency domain, thus convolutional neural network specifically designed for capturing local information is used in combination with LSTM, forming a hybrid deep learning architecture. The applicability and effectiveness of the proposed approach are supported by three case studies with different types of structures, showing highly accurate damage detection with reduced resource requirements. These advantages can be valuable for developing a model for live monitoring of structural health in the future life-line infrastructures.

The conjugated molecules based on pyrene and dithienopyrrole derivatives including 4-(2-ethylhexyl)-2-(pyren-1-yl)-4H-dithieno[3,2-b:2’,3’-d]pyrrole (EP4HP) and 4-(2-ethylhexyl)-2,6-di(pyren-1-yl)-4H-dithieno[3,2-b:2’,3’-d]pyrrole (EDP4HP) have been successfully synthesized via C-H direct arylation reaction. The structures of these conjugated molecules were determined via nuclear magnetic resonance and Fourier-transform infrared spectroscopy (FTIR). The optical properties of conjugated molecules were evaluated via ultraviolet visible (UV-Vis) and fluorescence spectroscopies. The conjugated molecules exhibited the efficient fluorescence quenching toward herbicide mesotrione as nitroaromatic pesticides that could be promising candidates as the chemosensor for tracing of nitroaromatic pesticides.

In some modules of digital systems, such as Fast Fourier Transform (FFT), Discrete Fourier transform (DFT), IQ (in-phase and quadrature components) modulation/ demodulation, the outputs use the complex data formed , and the calculation of its magnitude value √ are required. In software digital signal processing platform, the multiplication and square root operations are executed by using its math library; however, in Application specific integrated circuit (ASIC) digital system design, the implementation of those operators via Coordinate Rotation Digital Computer (CORDIC) algorithm requires the numerous resources and delays. So, in this paper, we present a fast approximation method for above problem which takes a small delay but acceptable accuracy for AISC digital system design. Keywords—ASIC, Digital system design, FFT, DFT, Fast amplitude approximation, Max-Min approximation.

The paper gives the demonstrates of quantum super dense protocol and its related. Firstly, Qiskit's simulator is used to set-up and run the proposed quantum circuit, and then IBM quantum computer will be used to run the circuit for evaluation. In addition, we also introduce the two types of attacks which are possible on communication system and its simulation on IBM quantum computer via using quantum super dense protocol. The main findings are discussed in the wider setting of quantum cyber-security, where ways of hacking including the role of insider threats are discussed. Keywords—Quantum information, Quantum communication protocol, Quantum super dense coding, Quantum simulator, Quantum IBM Computer, Qiskit’s simulator

A major challenge for the dissemination, replication, and reuse of epidemiological forecasting studies during COVID-19 pandemics is the lack of clear guidelines and platforms to exchange models in a Findable, Accessible, Interoperable, and Reusable (FAIR) manner, facilitating reproducibility of research outcomes. During the beginning of pandemics, models were developed in diverse tools that were not interoperable, opaque without traceability and semantics, and scattered across various platforms - making them hard to locate, infer and reuse. In this work, we demonstrate that implementing the standards developed by the systems biology community to encode and share COVID-19 epidemiological models can serve as a roadmap to implement models as a tool in medical informatics, in general. As a proof-of-concept, we encoded and shared 24 epidemiological models using the standard format for model exchange in systems biology, annotated them with cross-references to data resources, packed up all associated files in COMBINE archives for easy sharing, and finally, disseminated the models through BioModels repository to significantly enhance their reproducibility and repurposing potential. We recommend the use of systems biology standards to encode and share models of epidemic and pandemic forecasts to improve their findability, accessibility, interoperability, reusability, and reproducibility.

This paper will discuss one of the most important problems in touch screen systems named palm rejection. Various solutions for palm rejection are discussed through the paper, which are based on hardware modules, software modules, and hybrid software-hardware modules. We hope that our proposed technical research with various solutions will be a great reference for engineers and scientists working on touch screen system design.