Inversion of temperature and species concentration distributions from radiometric measurements involves solving nonlinear, ill-posed and high-dimensional problems. Machine Learning approaches allow solving such highly nonlinear problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. In this study, we present a machine learning approach for retrieving temperatures and species concentrations from spectral infrared emission measurements in combustion systems. The training spectra for the machine learning model were synthesized through calculations from HITEMP 2010 for gas mixtures of CO2, H2O, and CO. The method was tested for different line-of-sight temperature and concentration distributions, different gas path lengths and different spectral intervals. Experimental validation was carried out by measuring spectral emission from a Hencken flat flame burner with a Fourier-transform infrared spectrometer with different spectral resolutions. The temperature fields above the burner for combustion with equivalence ratios of ϕ = 1, ϕ = 0.8, and ϕ = 1.4 were retrieved and were in excellent agreement with temperatures deduced from Rayleigh scattering thermometry.

Three-dimensional physical interactions within chromosomes dynamically regulate gene expression in a tissue-specific manner. However, the 3D organization of chromosomes during human brain development and its role in regulating gene networks dysregulated in neurodevelopmental disorders, such as autism or schizophrenia, are unknown. Here we generate high-resolution 3D maps of chromatin contacts during human corticogenesis, permitting large-scale annotation of previously uncharacterized regulatory relationships relevant to the evolution of human cognition and disease. Our analyses identify hundreds of genes that physically interact with enhancers gained on the human lineage, many of which are under purifying selection and associated with human cognitive function. We integrate chromatin contacts with non-coding variants identified in schizophrenia genome-wide association studies (GWAS), highlighting multiple candidate schizophrenia risk genes and pathways, including transcription factors involved in neurogenesis, and cholinergic signalling molecules, several of which are supported by independent expression quantitative trait loci and gene expression analyses. Genome editing in human neural progenitors suggests that one of these distal schizophrenia GWAS loci regulates FOXG1 expression, supporting its potential role as a schizophrenia risk gene. This work provides a framework for understanding the effect of non-coding regulatory elements on human brain development and the evolution of cognition, and highlights novel mechanisms underlying neuropsychiatric disorders.