DNA protein binding recognition based on lifelong learning.

In recent years, research in the field of bioinformatics has focused on predicting the raw sequences of proteins, and some scholars consider DNA-binding protein prediction as a classification task. Many statistical and machine learning-based methods have been widely used in DNA-binding proteins research. The aforementioned methods are indeed more efficient than those based on manual classification, but there is still room for improvement in terms of prediction accuracy and speed. In this study, researchers used Average Blocks, Discrete Cosine Transform, Discrete Wavelet Transform, Global encoding, Normalized Moreau-Broto Autocorrelation and Pseudo position-specific scoring matrix to extract evolutionary features. A dynamic deep network based on lifelong learning architecture was then proposed in order to fuse six features and thus allow for more efficient classification of DNA-binding proteins. The multi-feature fusion allows for a more accurate description of the desired protein information than single features. This model offers a fresh perspective on the dichotomous classification problem in bioinformatics and broadens the application field of lifelong learning. The researchers ran trials on three datasets and contrasted them with other classification techniques to show the model's effectiveness in this study. The findings demonstrated that the model used in this research was superior to other approaches in terms of single-sample specificity (81.0%, 83.0%) and single-sample sensitivity (82.4%, 90.7%), and achieves high accuracy on the benchmark dataset (88.4%, 80.0%, and 76.6%).

A survey on image enhancement for Low-light images.

In real scenes, due to the problems of low light and unsuitable views, the images often exhibit a variety of degradations, such as low contrast, color distortion, and noise. These degradations affect not only visual effects but also computer vision tasks. This paper focuses on the combination of traditional algorithms and machine learning algorithms in the field of image enhancement. The traditional methods, including their principles and improvements, are introduced from three categories: gray level transformation, histogram equalization, and Retinex methods. Machine learning based algorithms are not only divided into end-to-end learning and unpaired learning, but also concluded to decomposition-based learning and fusion based learning based on the applied image processing strategies. Finally, the involved methods are comprehensively compared by multiple image quality assessment methods, including mean square error, natural image quality evaluator, structural similarity, peak signal to noise ratio, etc.

Ultrasound phase rotation beamforming on multi-core DSP.

Phase rotation beamforming (PRBF) is a commonly-used digital receive beamforming technique. However, due to its high computational requirement, it has traditionally been supported by hardwired architectures, e.g., application-specific integrated circuits (ASICs) or more recently field-programmable gate arrays (FPGAs). In this study, we investigated the feasibility of supporting software-based PRBF on a multi-core DSP. To alleviate the high computing requirement, the analog front-end (AFE) chips integrating quadrature demodulation in addition to analog-to-digital conversion were defined and used. With these new AFE chips, only delay alignment and phase rotation need to be performed by DSP, substantially reducing the computational load. We implemented the delay alignment and phase rotation modules on a Texas Instruments C6678 DSP with 8 cores. We found it takes 200 µs to beamform 2048 samples from 64 channels using 2 cores. With 4 cores, 20 million samples can be beamformed in one second. Therefore, ADC frequencies up to 40 MHz with 2:1 decimation in AFE chips or up to 20 MHz with no decimation can be supported as long as the ADC-to-DSP I/O requirement can be met. The remaining 4 cores can work on back-end processing tasks and applications, e.g., color Doppler or ultrasound elastography. One DSP being able to handle both beamforming and back-end processing could lead to low-power and low-cost ultrasound machines, benefiting ultrasound imaging in general, particularly portable ultrasound machines.

Approximate single-diode photovoltaic model for efficient I-V characteristics estimation.

Precise photovoltaic (PV) behavior models are normally described by nonlinear analytical equations. To solve such equations, it is necessary to use iterative procedures. Aiming to make the computation easier, this paper proposes an approximate single-diode PV model that enables high-speed predictions for the electrical characteristics of commercial PV modules. Based on the experimental data, statistical analysis is conducted to validate the approximate model. Simulation results show that the calculated current-voltage (I-V) characteristics fit the measured data with high accuracy. Furthermore, compared with the existing modeling methods, the proposed model reduces the simulation time by approximately 30% in this work.

Body mass index and mortality from ischaemic heart disease in a lean population: 10 year prospective study of 220,000 adult men.

BACKGROUND: Increased body mass index (BMI) is known to be related to ischaemic heart disease (IHD) in populations where many are overweight (BMI>or=25 kg/m2) or obese (BMI>or=30). Substantial uncertainty remains, however, about the relationship between BMI and IHD in populations with lower BMI levels. METHODS: We examined the data from a population-based, prospective cohort study of 222,000 Chinese men aged 40-79. Relative and absolute risks of death from IHD by baseline BMI were calculated, standardized for age, smoking, and other potential confounding factors. RESULTS: The mean baseline BMI was 21.7 kg/m2, and 1942 IHD deaths were recorded during 10 years of follow-up (6.5% of all such deaths). Among men without prior vascular diseases at baseline, there was a J-shaped association between BMI and IHD mortality. Above 20 kg/m2 there was a positive association of BMI with risk, with each 2 kg/m2 higher in usual BMI associated with 12% (95% CI 6-19%, 2P=0.0001) higher IHD mortality. Below this BMI range, however, the association appeared to be reversed, with risk ratios of 1.00, 1.09, and 1.15, respectively, for men with BMI 20-21.9, 18-19.9, and <18 kg/m2. The excess IHD risk observed at low BMI levels persisted after restricting analysis to never smokers or excluding the first 3 years of follow-up, and became about twice as great after allowing for blood pressure. CONCLUSIONS: Lower BMI is associated with lower IHD risk among people in the so-called normal range of BMI values (20-25 kg/m2), but below that range the association may well be reversed.