Mass spectrometry imaging (MSI) is a powerful scientific tool for understanding the spatial distribution of biochemical compounds in tissue structures. MSI data analysis presents problems due to the large file sizes and computational resource requirements and also due to the complexity of interpreting the raw spectral data. Dimensionality reduction techniques that address the first issue do not necessarily result in readily interpretable features. In this paper, we present non-negative matrix factorization (NMF) as a dimensionality reduction algorithm that reduces the size of MSI datasets by three orders of magnitude with limited loss of information, yielding spatial and spectral components with meaningful correlation to tissue structure. This analysis is demonstrated on an MSI dataset for an animal model of comorbid visceral pain hypersensitivity (CPH). The significant findings are: 1) High-dimensional MSI data (~ 100,000 ions per pixel) was reduced to 20 spectral NMF components with < 20% loss in reconstruction accuracy. 2) Spatial NMF components are reproducible and correlate well with H&E-stained tissue images. 3) Spatial NMF components may be used to provide images with enhanced specificity for different tissue types. 4) Small patches of NMF data (i.e., 20 spatial NMF components over 15 x 15 pixels) provide an accuracy of ~87% in classifying CPH vs naive control subjects. This paper presents novel methodologies for data augmentation to support classification, ranking of features according to their contribution to classification, and image registration to support tissue-specific imaging.

Demands of world's whole population for insubstantial products has thus prompted the need of effective utilization of materials and assets. As huge changes in society have been impacted for good by the advancement in Information and Communication Technology, their use has a negative effect on the environment and human wellbeing. The reason being, our society is in a deep need of a greener future where there is judicial use of resources along with minimal use of non-renewable resources. This will eventually decrease the pollution & promote lesser power consumption. Green Internet of Things takes one of the main parts while in transit to make a sustainable & green spot for living. We investigate and talk about how the different empowering advancements, (for example, the Internet, smart objects, sensors, and so forth) can be effectively implemented to accomplish a green IoT. Moreover, we likewise audit different IoT applications, standardization and projects which are at present under way. At last, we recognize a portion of the challenges which should be tended to later on to empower a green IoT.

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