Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging

Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snapshot mosaic sensors offer a promising approach due to their fast acquisition speed and compact size. However, a demosaicking algorithm is required to fully recover the spatial and spectral information of the snapshot images. Most state-of-the-art demosaicking algorithms require ground-truth training data with paired snapshot and high-resolution hyperspectral images, but such imagery pairs with the exact same scene are physically impossible to acquire in intraoperative settings. In this work, we present a fully unsupervised hyperspectral image demosaicking algorithm which only requires exemplar snapshot images for training purposes. We regard hyperspectral demosaicking as an ill-posed linear inverse problem which we solve using a deep neural network. We take advantage of the spectral correlation occurring in natural scenes to design a novel inter spectral band regularisation term based on spatial gradient consistency. By combining our proposed term with standard regularisation techniques and exploiting a standard data fidelity term, we obtain an unsupervised loss function for training deep neural networks, which allows us to achieve real-time hyperspectral image demosaicking. Quantitative results on hyperspetral image datasets show that our unsupervised demosaicking approach can achieve similar performance to its supervised counter-part, and significantly outperform linear demosaicking. A qualitative user study on real snapshot hyperspectral surgical images confirms the results from the quantitative analysis. Our results suggest that the proposed unsupervised algorithm can achieve promising hyperspectral demosaicking in real-time thus advancing the suitability of the modality for intraoperative use

Cloning, chromosome mapping and expression pattern of porcine PLIN and M6PRBP1 genes

The PAT proteins, named after the three PLIN/ADRP/TIP47 (PAT) proteins, PLIN for perilipin, ADRP for adipose differentiation-related protein and TIP47 for tail-interacting protein of 47 kDa, now officially named M6PRBP1 for mannose-6-phosphate receptor binding protein 1, is a set of intracellular lipid droplet binding proteins. They are localized in the outer membrane monolayer enveloping lipid droplets and are involved in the metabolism of intracellular lipid. This work describes the cloning and sequencing of porcine PLIN and M6PRBP1 cDNAs, the chromosome mapping of these two genes, as well as the expression pattern of porcine PAT genes. Sequence analysis shows that the porcine PLIN cDNA contains an open reading frame of 1551 bp encoding 516 amino acids and that the porcine M6PRBP1 cDNA contains a coding region of 1320 bp encoding 439 amino acids. Comparison of PLIN and M6PRBP1 amino-acid sequences among various species reveals that porcine and bovine proteins are the most conserved. Porcine PLIN and M6PRBP1 genes have been mapped to pig chromosomes 7 and 2, respectively, by radiation hybrid analysis using the IMpRH panel. Expression analyses in pig showed a high expression of PLIN mRNA in adipose tissue, M6PRBP1 mRNA in small intestine, kidney and spleen and ADRP mRNA in adipose tissue, lung and spleen

Exploration and prediction evaluation on causative factors of water inrush from separation layers of mining overburden in coal mines

The occurrence of mine water inrushes is common and poses significant hazards in various mining areas throughout China. However, the existing regulations lack of specific engineering geological and hydrogeological exploration guidelines tailored to the water inrush from separation layers (WISL). Exploring the methods of engineering geological and hydrogeological exploration and assessment for the WISL can contribute to further enhancing the prevention and control of mine water disasters in China. This paper begins by examining the mechanism behind WISL in coal mines. It analyzes the geological conditions governing the progression of WISL from its inception to full-scale occurrence and categorizes three prevalent types of WISL in China, i.e., dynamic water inrush, hydrostatic water inrush, and mud and sand-carrying water inrush, all originating from separation layers. Subsequently, it identifies “water source”“channel”“force source”and “material source” as pivotal concealed factors dictating the nature and severity of WISL. Then, the concept of a “inrush separation zone” referring to composite stratigraphic layers situated above traditional water-conducting fractured zones is introduced, where the WISL may transpire during mining activities. Furthermore, it presents a method for delineating “inrush separation zone” in coal mines, outlining exploration stages and specifying crucial investigative focal points. The exploration of water damage in upper strata of coal mine should include two stages: first, the exploration of basic engineering geology and hydrogeological conditions of overlying rock should be carried out to evaluate the possibility of water damage in the exploration area and determine the horizon of potential mining overlying rock ; second, regarding the “inrush separation zone” and “source” layer as the exploration target layer, the special investigation of the hidden disaster factors of the upper layer water damage should be conducted to assess the type and intensity of the water damage in the upper layer. The hydrodynamic conditions and the evolution of overburden fractures are investigated during the mining period. Lastly, a comprehensive forecast evaluation model is proposed and constructed on the coordinated evolutionary mechanisms arising from the interaction of causative factors like “water source”“channel”“force source”and “material source”. This model predicts and evaluates the types, locations, and inflows of water inrush at mining faces before operations commence