A cellular automaton model to find the risk of developing autism through gut-mediated effects

Gut Microbiota Ecology and Inferred Functions in Children With ASD Compared to Neurotypical Subjects

Autism spectrum disorders (ASDs) is a multifactorial neurodevelopmental disorder. The communication between the gastrointestinal (GI) tract and the central nervous system seems driven by gut microbiota (GM). Herein, we provide GM profiling, considering GI functional symptoms, neurological impairment, and dietary habits. Forty-one and 35 fecal samples collected from ASD and neurotypical children (CTRLs), respectively, (age range, 3–15 years) were analyzed by 16S targeted-metagenomics (the V3–V4 region) and inflammation and permeability markers (i.e., sIgA, zonulin lysozyme), and then correlated with subjects’ metadata. Our ASD cohort was characterized as follows: 30/41 (73%) with GI functional symptoms; 24/41 (58%) picky eaters (PEs), with one or more dietary needs, including 10/41 (24%) with food selectivity (FS); 36/41 (88%) presenting high and medium autism severity symptoms (HMASSs). Among the cohort with GI symptoms, 28/30 (93%) showed HMASSs, 17/30 (57%) were picky eaters and only 8/30 (27%) with food selectivity. The remaining 11/41 (27%) ASDs without GI symptoms that were characterized by HMASS for 8/11 (72%) and 7/11 (63%) were picky eaters. GM ecology was investigated for the overall ASD cohort versus CTRLs; ASDs with GI and without GI, respectively, versus CTRLs; ASD with GI versus ASD without GI; ASDs with HMASS versus low ASSs; PEs versus no-PEs; and FS versus absence of FS. In particular, the GM of ASDs, compared to CTRLs, was characterized by the increase of Proteobacteria, Bacteroidetes, Rikenellaceae, Pasteurellaceae, Klebsiella, Bacteroides, Roseburia, Lactobacillus, Prevotella, Sutterella, Staphylococcus, and Haemophilus. Moreover, Sutterella, Roseburia and Fusobacterium were associated to ASD with GI symptoms compared to CTRLs. Interestingly, ASD with GI symptoms showed higher value of zonulin and lower levels of lysozyme, which were also characterized by differentially expressed predicted functional pathways. Multiple machine learning models classified correctly 80% overall ASDs, compared with CTRLs, based on Bacteroides, Lactobacillus, Prevotella, Staphylococcus, Sutterella, and Haemophilus features. In conclusion, in our patient cohort, regardless of the evaluation of many factors potentially modulating the GM profile, the major phenotypic determinant affecting the GM was represented by GI hallmarks and patients’ age.

The use of biomarkers associated with leaky gut as a diagnostic tool for early intervention in autism spectrum disorder: a systematic review

Background

Innovative research highlighted the probable connection between autism spectrum disorder (ASD) and gut microbiota as many autistic individuals have gastrointestinal problems as co-morbidities. This review emphasizes the role of altered gut microbiota observed frequently in autistic patients, and the mechanisms through which such alterations may trigger leaky gut.

Main body

Different bacterial metabolite levels in the blood and urine of autistic children, such as short-chain fatty acids, lipopolysaccharides, beta-cresol, and bacterial toxins, were reviewed. Moreover, the importance of selected proteins, among which are calprotectin, zonulin, and lysozyme, were discussed as biomarkers for the early detection of leaky gut as an etiological mechanism of ASD through the less integrative gut–blood–brain barriers. Disrupted gut–blood–brain barriers can explain the leakage of bacterial metabolites in these patients.

Conclusion

Although the cause-to-effect relationship between ASD and altered gut microbiota is not yet well understood, this review shows that with the consumption of specific diets, definite probiotics may represent a noninvasive tool to reestablish healthy gut microbiota and stimulate gut health. The diagnostic and therapeutic value of intestinal proteins and bacterial-derived compounds as new possible biomarkers, as well as potential therapeutic targets, are discussed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13099-021-00448-y.

Optimize the spatial distribution of crop water consumption based on a cellular automata model: A case study of the middle Heihe River basin, China

Globally, agriculture is by far the largest water consuming sector and in areas where water is scarce, the spatial optimization of crop water consumption used to improve irrigation benefits becomes critical for regional water management. The spatial heterogeneity of environmental parameters brings great challenge to spatial optimization. Therefore, cellular automaton (CA), crop suitability (CS), spatial distributed crop water consumption model and optimization model were integrated and applied on the middle reaches of Heihe River basin, northwest of China. The cellular automata based Water Consumption Optimization (CA-WCSO) model is not only a spatial dynamic optimization model for crop water consumption, but also a decision support tool that reflects the interaction between water consumption at field level and management regulations at regional level. Six optimization paths: i) forward progressive (FP), ii) forward interlacing (F-IL), iii) forward interpolation (F-IP), iv) reverse progressive (R-P), v) reverse interlacing (R-IL) and vi) reverse interpolation (R-IP) of crop water consumption for the baseline year and the planning year were applied on the study site. Results for baseline year (2015) demonstrate that the six optimization paths can slightly reduce the water consumption (>1.4%) but significantly improve the irrigation benefits of the region by 20.56%. Using CA-WCSO model, decision makers can modify model's constraints and select appropriate optimization path to get the optimized crop planting patterns and make future regional water allocation plans.

Evolution of the Individual Attitude in the Risk Decision of Waste Incinerator Construction: Cellular Automaton Model

In current work, the phenomenon of NIMBY (not in my back yard) for a municipal solid waste incinerator was recognized through an investigation for the evolution of individual risk attitude to group risk attitude (ItGRA). The cellular automaton model was employed to evaluate the risk attitude status with different frequencies of social interaction between residents. In the simulation case, the risk attitude of residents in the pseudo-rational state and non-pseudo-rational state was evaluated, which indicates the sheep-flock effect on the exaggeration of public NIMBY attitude. To the incinerator, the individual risk attitude evolved to supportive group risk attitude at a social interaction frequency 100 times higher than that in family or local neighborhoods, when the initial number of residents in opposition and support was equal. This was supported by the result of the model in the evaluation of resident risk attitude around the incinerator in Shanghai. On the contrary, for those in a non-pseudo-rational state, the ultimate group risk attitude depends on the probability that the residents have a supportive or opposing risk attitude as the concept of individuals was difficult to change. Accordingly, the decision strategy of incinerator construction should consider the influence of the sheep-flock effect, which can increase the attitude of residents in support and lead to the evolution of a group risk attitude to support attitude. Therefore, this study provides insight into the evolution of public attitude to NIMBY attitude and a promising evaluation method to quantify and guide the individual and group risk attitudes.