Dual-wavelength thulium fluoride fiber laser based on SMF-TMSIF-SMF interferometer as potential source for microwave generationin 100-GHz region

A dual-wavelength thulium-doped fluoride fiber (TDFF) laser is presented. The generation of the TDFF laser is achieved with the incorporation of a single modemultimode- single mode (SMS) interferometer in the laser cavity. The simple SMS interferometer is fabricated using the combination of two-mode step index fiber and single-mode fiber. With this proposed design, as many as eight stable laser lines are experimentally demonstrated. Moreover, when a tunable bandpass filter is inserted in the laser cavity, a dual-wavelength TDFF laser can be achieved in a 1.5-μm region. By heterodyning the dual-wavelength laser, simulation results suggest that the generated microwave signals can be tuned from 105.678 to 106.524 GHz with a constant step of �0.14 GHz. The presented photonics-based microwave generation method could provide alternative solution for 5G signal sources in 100-GHz region

Establishing an Internet Based Paediatric Cancer Registration and Communication System for the Hungarian Paediatric Oncology Network

Cancer registration has developed in Europe over the last 50 years, and in the last decade intensive joint activities between the European Cancer Registries, in response to the need of pan-European harmonization of registration practices, have taken place. The Hungarian Paediatric Cancer Registry has been functioning as the database of the Hungarian Paediatric Oncology Network since 1971, aiming to follow the incidence and the treatment efficacy of malignant diseases.The goals of this globally unique open source information system are the following: 1) to raise the quality of the registration system to the European level by developing an Internet-based registration and communication system, modernizing the database, establishing automatic statistical analyses and adding an Internet website, 2) to support clinical epidemiological studies that we conduct with international collaborators on detailed analyses of the characteristics of patients and their diseases, evaluation of new diagnostic and therapeutic methods, prevention programs, and long-term quality of life and side effects.The benefits of the development of the Internet-based registration and communication system are as follows: a) introduction of an Internet-based case reporting system, b) modernization of the registry database according to international recommendations, c) automatic statistical summaries, encrypted mail systems, document repository, d) application of data security and privacy standards, e) establishment of a website and compilation of educational materials.The overall objective of this scientific project is to contribute towards the improvement of cancer prevention and cancer care for the benefit of the public in general and of cancer patients in particular

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Barnes Hospital Bulletin

https://digitalcommons.wustl.edu/bjc_barnes_bulletin/1219/thumbnail.jp

Spartan Daily, February 9, 1994

Volume 102, Issue 9https://scholarworks.sjsu.edu/spartandaily/8509/thumbnail.jp

Classification Based Analysis on Cancer Datasets Using Predictor Measures

Cancer is a life-threatening disease. Probably the most effective way to reduce cancer deaths is to detect it earlier. Diagnosing the disease earlier needs an accurate and reliable procedure which could be used by physicians to distinguish between cancer from malignant ones without leaving for surgical biopsy. Data mining offers solution for such types of the problems where a large quantity of information about patients and their conditions are stored in clinical database. This paper focuses on prediction of some such diseases like Leukemia and Breast cancers. Naïve Bayes and SVM prediction models are built for the prediction and classification. The performance of the proposed models produced significant results of above 96% while compared with other models in terms of accuracy, computational time and convergence. Keywords: Prediction, Data Mining, Diagnosis, Cancer, Naïve Bayes, Supper Vector machine (SVM). DOI: 10.7176/CEIS/10-6-05 Publication date:July 31st 201

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership