Methane delayed ileal peristaltic conduction velocity by augmenting contractility. Hydrogen shortened colonic transit, and that effect was more prominent in the proximal colon than distal colon.

The pathogenesis of nutcracker oesophagus (NE) and ineffective oesophageal motility (IEM) is unclear. Damage to the enteric nervous system or smooth muscle can cause oesophageal dysmotility. We tested the hypothesis that NE and IEM are associated with abnormal muscular or neural constituents of the oesophageal wall. Oesophageal manometry was performed in patients prior to total gastrectomy for gastric cancer. The oesophageal manometries were categorized as normal (n = 7), NE (n = 13), or IEM (n = 5). Histologic examination of oesophageal tissue obtained during surgery was performed after haematoxylin and eosin (H&E) and trichrome staining. Oesophageal innervation was examined after immunostaining for protein gene product-9.5 (PGP-9.5), choline acetyltransferase (ChAT) and neuronal nitric oxide synthase (nNOS). There were no significant differences in inner circular smooth muscle thickness or degree of fibrosis among the three groups. Severe muscle fibre loss was found in four of five patients with IEM. The density of PGP-9.5-reactive neural structures was not different among the three groups. The density of ChAT immunostaining in the myenteric plexus (MP) was significantly greater in patients with NE (P < 0.05) and the density of nNOS immunostaining in the circular muscle (CM) was significantly greater in IEM patients (P < 0.05). The ChAT/nNOS ratio in both MP and CM was significantly greater in NE patients. NE may result from an imbalance between the excitatory and inhibitory innervation of the oesophagus, because more than normal numbers of ChAT-positive myenteric neurones are seen in NE. Myopathy and/or increased number of nNOS neurones may contribute to the hypocontractile motor activity of IEM.

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We propose an end-to-end unpaired learning approach to screen-shot image demoiréing based on cyclic moiré learning. The proposed cyclic moiré learning algorithm consists of the moiréing network and the demoiréing network. The moiréing network generates moiré images to construct a pseudo-paired set of moiré and clean images. Then, the demoiréing network is trained in a supervised manner using the generated pseudo-paired dataset to remove moiré artifacts. In the moiréing network, the moiré generation is separately learned as global pixel intensity degradation and moiré pattern generation for more realistic moiré artifact generation. Furthermore, the moiréing network and the demoiréing network are integrated together to be trained in an end-to-end manner. Experimental results on different datasets demonstrate that the proposed algorithm significantly outperforms state-of-the-art unsupervised demoiréing algorithms as well as image restoration algorithms.