Selection of Spectral Data for Classification of Steels Using Laser-Induced Breakdown Spectroscopy

Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.

Refining biome labeling for large-scale microbial community samples:Leveraging neural networks and transfer learning

Microbiome research has generated an extensive amount of data,resulting in a wealth of publicly accessible samples.Accurate annotation of these samples is crucial for effectively utilizing microbiome data across scientific disciplines.However,a notable challenge arises from the lack of essential annotations,particularly regarding collection location and sample biome information,which significantly hinders environmental microbiome research.In this study,we introduce Meta-Sorter,a novel approach utilizing neural networks and transfer learning,to enhance biome labeling for thousands of microbiome samples in the MGnify database that have incomplete information.Our findings demonstrate that Meta-Sorter achieved a remarkable accuracy rate of 96.7%in classifying samples among the 16,507 lacking detailed biome annotations.Notably,Meta-Sorter provides precise classifications for representative environmental samples that were previously ambiguously labeled as“Marine”in MGnify,thereby elucidating their specific origins in benthic and water column environments.Moreover,Meta-Sorter effectively distinguishes samples derived from human-environment interactions,enabling clear differentiation between environmental and human-related studies.By improving the completeness of biome label information for numerous microbial community samples,our research facilitates more accurate knowledge discovery across diverse disciplines,with particular implications for environmental research.

An Efficient WRF Framework for Discovering Risk Genes and Abnormal Brain Regions in Parkinson's Disease Based on Imaging Genetics Data

As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primarily lie with detecting the association among different modal data and reducing data attributes.The data mining method after fusion and the overall analysis framework are neglected.In this study,we propose a weighted random forest(WRF)model as the feature screening classifier.The interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis method.We implement sample classification and optimal feature selection based on WRF,and construct a multimodal analysis framework for exploring the pathogenic factors of PD.The experimental results in Parkinson's Progression Markers Initiative(PPMI)database show that WRF performs better compared with some advanced methods,and the brain regions and genes related to PD are detected.The fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively,which provides a novel perspective for the diagnosis and research of PD.We also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.