ABSTRACT
(1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring methods are too invasive, expensive, and bulky to be widely used. Compared with previous methods, bowel sound monitoring is the most commonly used non-invasive monitoring method for intestinal diseases and may even provide clinical support for doctors. (2) Methods: This paper proposes a method based on the features of bowel sound signals, which uses a BP classification neural network to predict bowel defecation and realizes a non-invasive collection of physiological signals. Firstly, according to the physiological function of human defecation, bowel sound signals were selected for monitoring and analysis before defecation, and a portable non-invasive bowel sound collection system was built. Then, the detector algorithm based on iterative kurtosis and the signal processing method based on Kalman filter was used to process the signal to remove the aliasing noise in the bowel sound signal, and feature extraction was carried out in the time domain, frequency domain, and time-frequency domain. Finally, BP neural network was selected to build a classification training method for the features of bowel sound signals. (3) Results: Experimental results based on real data sets show that the proposed method can converge to a stable state and achieve a prediction accuracy of 88.71% in 232 records, which is better than other classification methods. (4) Conclusions: The result indicates that the proposed method could provide a high-precision defecation prediction result for patients with fecal incontinence, so as to prepare for defecation in advance.
Subject(s)
Defecation , Neural Networks, Computer , Algorithms , Humans , Signal Processing, Computer-Assisted , SoundABSTRACT
The fasciclin-like arabinogalactan proteins (FLAs) play important roles in plant development and adaptation to the environment. FLAs contain both fasciclin domains and arabinogalactan protein (AGP) regions, which have been identified in several plants. The evolutionary history of this gene family in plants is still undiscovered. In this study, we identified the FLA gene family in 13 plant species covering major lineages of plants using bioinformatics methods. A total of 246 FLA genes are identified with gene copy numbers ranging from one (Chondrus crispus) to 49 (Populus trichocarpa). These FLAs are classified into seven groups, mainly based on the phylogenetic analysis of plant FLAs. All FLAs in land plants contain one or two fasciclin domains, while in algae, several FLAs contain four or six fasciclin domains. It has been proposed that there was a divergence event, represented by the reduced number of fasciclin domains from algae to land plants in evolutionary history. Furthermore, introns in FLA genes are lost during plant evolution, especially from green algae to land plants. Moreover, it is found that gene duplication events, including segmental and tandem duplications are essential for the expansion of FLA gene families. The duplicated gene pairs in FLA gene family mainly evolve under purifying selection. Our findings give insight into the origin and expansion of the FLA gene family and help us understand their functions during the process of evolution.
