scholarly journals The InterPro protein families and domains database: 20 years on

2020 ◽  
Vol 49 (D1) ◽  
pp. D344-D354 ◽  
Author(s):  
Matthias Blum ◽  
Hsin-Yu Chang ◽  
Sara Chuguransky ◽  
Tiago Grego ◽  
Swaathi Kandasaamy ◽  
...  
Keyword(s):  
Protein Families ◽  
Performance Improvements ◽  
Additional Information ◽  
The Status ◽  
And Performance ◽  
Rest Api ◽  
Database Content ◽  
Go Terms ◽  
Interpro Database

Abstract The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. InterProScan is the underlying software that allows protein and nucleic acid sequences to be searched against InterPro's signatures. Signatures are predictive models which describe protein families, domains or sites, and are provided by multiple databases. InterPro combines signatures representing equivalent families, domains or sites, and provides additional information such as descriptions, literature references and Gene Ontology (GO) terms, to produce a comprehensive resource for protein classification. Founded in 1999, InterPro has become one of the most widely used resources for protein family annotation. Here, we report the status of InterPro (version 81.0) in its 20th year of operation, and its associated software, including updates to database content, the release of a new website and REST API, and performance improvements in InterProScan.

HBTs FOR MICROWAVE POWER APPLICATIONS

1994 ◽  
Vol 05 (03) ◽  
pp. 275-348
Author(s):  
BURHAN BAYRAKTAROGLU ◽  
J. AIDEN HIGGINS
Keyword(s):  
Electronic Properties ◽  
Microwave Power ◽  
High Performance ◽  
Microwave Devices ◽  
Rapid Progress ◽  
Performance Improvements ◽  
The Status ◽  
Military Systems ◽  
And Performance ◽  
Made In

The status of the microwave power HBT is reviewed. The rapid progress made in this technology is fueled by the need for high performance transmitter amplifiers, advanced military systems and supported by the favorable electronic properties of the device. A survey of the device design and fabrication techniques indicates that the technology has not yet reached its limit; further advances are possible and performance improvements will continue. The power and efficiency of GaAs HBTs are now competitive with the most advanced microwave devices in the 1–20 GHz range.

Pathways ahead for New Zealand hill country farming

2016 ◽  
Vol 78 ◽  
pp. 73-82 ◽  
Author(s):  
F.G. Scrimgeour
Keyword(s):  
New Zealand ◽  
Rural Communities ◽  
New Technologies ◽  
Service Providers ◽  
Farming Systems ◽  
Hill Country ◽  
The Status ◽  
And Performance ◽  
Hill Farming ◽  
The Hill

This paper provides a stocktake of the status of hill country farming in New Zealand and addresses the challenges which will determine its future state and performance. It arises out of the Hill Country Symposium, held in Rotorua, New Zealand, 12-13 April 2016. This paper surveys people, policy, business and change, farming systems for hill country, soil nutrients and the environment, plants for hill country, animals, animal feeding and productivity, and strategies for achieving sustainable outcomes in the hill country. This paper concludes by identifying approaches to: support current and future hill country farmers and service providers, to effectively and efficiently deal with change; link hill farming businesses to effective value chains and new markets to achieve sufficient and stable profitability; reward farmers for the careful management of natural resources on their farm; ensure that new technologies which improve the efficient use of input resources are developed; and strategies to achieve vibrant rural communities which strengthen hill country farming businesses and their service providers. Keywords: farming systems, hill country, people, policy, productivity, profitability, sustainability

Materials Issues and Characterization of Low-k Dielectric Materials

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 49-54 ◽  
Author(s):  
E. Todd Ryan ◽  
Andrew J. McKerrow ◽  
Jihperng Leu ◽  
Paul S. Ho
Keyword(s):  
Process Integration ◽  
Dielectric Materials ◽  
Propagation Delay ◽  
Thermomechanical Properties ◽  
General Criterion ◽  
Crosstalk Noise ◽  
Total Delay ◽  
Interlayer Dielectrics ◽  
Performance Improvements ◽  
And Performance

Continuing improvement in device density and performance has significantly affected the dimensions and complexity of the wiring structure for on-chip interconnects. These enhancements have led to a reduction in the wiring pitch and an increase in the number of wiring levels to fulfill demands for density and performance improvements. As device dimensions shrink to less than 0.25 μm, the propagation delay, crosstalk noise, and power dissipation due to resistance-capacitance (RC) coupling become significant. Accordingly the interconnect delay now constitutes a major fraction of the total delay limiting the overall chip performance. Equally important is the processing complexity due to an increase in the number of wiring levels. This inevitably drives cost up by lowering the manufacturing yield due to an increase in defects and processing complexity.To address these problems, new materials for use as metal lines and interlayer dielectrics (ILDs) and alternative architectures have surfaced to replace the current Al(Cu)/SiO2 interconnect technology. These alternative architectures will require the introduction of low-dielectric-constant k materials as the interlayer dielectrics and/or low-resistivity conductors such as copper. The electrical and thermomechanical properties of SiO2 are ideal for ILD applications, and a change to material with different properties has important process-integration implications. To facilitate the choice of an alternative ILD, it is necessary to establish general criterion for evaluating thin-film properties of candidate low-k materials, which can be later correlated with process-integration problems.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Sa1343 CLASSIFICATION OF AUTOIMMUNE GASTRITIS BASED ON THE STATUS OF POLYGLANDULAR POLYGLANDULAR AUTOIMMUNE SYNDROME

2020 ◽  
Vol 158 (6) ◽  
pp. S-324-S-325
Author(s):  
Takahisa Furuta ◽  
Takuma Kagami ◽  
Mihoko Yamade ◽  
Takahiro Suzuki ◽  
Tomohiro Higuchi ◽  
...  
Keyword(s):  
Autoimmune Gastritis ◽  
Polyglandular Autoimmune Syndrome ◽  
Autoimmune Syndrome ◽  
The Status

Study on Common Ground Testing Platform Technology for Telemetry Core Equipment

2012 ◽  
Vol 220-223 ◽  
pp. 1472-1475
Author(s):  
Qiu Lin Tan ◽  
Xiang Dong Pei ◽  
Si Min Zhu ◽  
Ji Jun Xiong
Keyword(s):  
Data Exchange ◽  
Common Ground ◽  
Digital Signal ◽  
Test System ◽  
Automatic Test System ◽  
Module Design ◽  
The Status ◽  
Output Module ◽  
And Performance ◽  
Hardware Circuit

On the basis of automatic test system of the status in domestic and foreign, by analysis of the various functions and performance of the integrated test system, a design of the integrated test system is proposed, FPGA as the core logic controller of the hardware circuit. The system of the hardware design include: digital signal source output modules, analog output module and PCM codec module. Design of hardware circuit are mainly described. In addition, a detailed analysis of some key technologies in the design process was given. Overall, its data exchange with host computer is through the PCI card, data link and bandwidth can be expanded in accordance with the actual needs. The entire system designed in the modular principle, which has a strong scalability.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

Remote Thermal Performance Monitoring and Diagnostics: Turning Data Into Knowledge

2013 ◽  
Author(s):  
Xiaomo Jiang ◽  
Craig Foster
Keyword(s):  
Anomaly Detection ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Integrated System ◽  
Site Specific ◽  
Performance Improvements ◽  
Maintenance Schedule ◽  
Effectiveness Monitoring ◽  
And Performance

Gas turbine simple or combined cycle plants are built and operated with higher availability, reliability, and performance in order to provide the customer with sufficient operating revenues and reduced fuel costs meanwhile enhancing customer dispatch competitiveness. A tremendous amount of operational data is usually collected from the everyday operation of a power plant. It has become an increasingly important but challenging issue about how to turn this data into knowledge and further solutions via developing advanced state-of-the-art analytics. This paper presents an integrated system and methodology to pursue this purpose by automating multi-level, multi-paradigm, multi-facet performance monitoring and anomaly detection for heavy duty gas turbines. The system provides an intelligent platform to drive site-specific performance improvements, mitigate outage risk, rationalize operational pattern, and enhance maintenance schedule and service offerings via taking appropriate proactive actions. In addition, the paper also presents the components in the system, including data sensing, hardware, and operational anomaly detection, expertise proactive act of company, site specific degradation assessment, and water wash effectiveness monitoring and analytics. As demonstrated in two examples, this remote performance monitoring aims to improve equipment efficiency by converting data into knowledge and solutions in order to drive value for customers including lowering operating fuel cost and increasing customer power sales and life cycle value.

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