The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading

In network models of propagation processes, the individual, microscopic level perspective is the norm, with aggregations studied as possible outcomes. On the contrary, we adopted a mesoscale perspective with groups as the core element and in this sense we present a novel agent-group dynamic model of propagation in networks. In particular, we focus on ephemeral groups that dynamically form, create new links, and dissolve. The experiments simulated 160 model configurations and produced results describing cases of consecutive and non-consecutive dynamic grouping, bounded or unbounded in the number of repetitions. Results revealed the existence of complex dynamics and multiple behaviors. An efficiency metric is introduced to compare the different cases. A Null Model analysis disclosed a pattern in the difference between the group and random models, varying with the size of groups. Our findings indicate that a mesoscopic construct like the ephemeral group, based on assumptions about social behavior and absent any microscopic level change, could produce and describe complex propagation dynamics. A conclusion is that agent-group dynamic models may represent a powerful approach for modelers and a promising new direction for future research in models of coevolution between propagation and behavior in society.

Prediction of Protein pKa with Representation Learning

The behavior of proteins is closely related to the protonation states of the residues. Therefore, prediction and measurement of pKa are essential to understand the basic functions of proteins. In this work, we develop a new empirical scheme for protein pKa prediction that is based on deep representation learning. It combines machine learning with atomic environment vector (AEV) and learned quantum mechanical representation from ANI-2x neural network potential (J. Chem. Theory Comput. 2020, 16, 4192). The scheme requires only the coordinate information of a protein as the input and separately estimates the pKa for all five titratable amino acid types. The accuracy of the approach was analyzed with both cross-validation and an external test set of proteins. Obtained results were compared with the widely used empirical approach PROPKA. The new empirical model provides accuracy with MAEs below 0.5 for all amino acid types. It surpasses the accuracy of PROPKA and performs significantly better than the null model. Our model is also sensitive to the local conformational changes and molecular interactions.

“A Bias Recognized Is a Bias Sterilized”: The Effects of a Bias in Forecast Evaluation

Are traditional tests of forecast evaluation well behaved when the competing (nested) model is biased? No, they are not. In this paper, we show analytically and via simulations that, under the null hypothesis of no encompassing, a bias in the nested model may severely distort the size properties of traditional out-of-sample tests in economic forecasting. Not surprisingly, these size distortions depend on the magnitude of the bias and the persistency of the additional predictors. We consider two different cases: (i) There is both in-sample and out-of-sample bias in the nested model. (ii) The bias is present exclusively out-of-sample. To address the former case, we propose a modified encompassing test (MENC-NEW) robust to a bias in the null model. Akin to the ENC-NEW statistic, the asymptotic distribution of our test is a functional of stochastic integrals of quadratic Brownian motions. While this distribution is not pivotal, we can easily estimate the nuisance parameters. To address the second case, we derive the new asymptotic distribution of the ENC-NEW, showing that critical values may differ remarkably. Our Monte Carlo simulations reveal that the MENC-NEW (and the ENC-NEW with adjusted critical values) is reasonably well-sized even when the ENC-NEW (with standard critical values) exhibits rejections rates three times higher than the nominal size.

Feasibility of delivering parent-implemented NDBI interventions in low-resource regions: a pilot randomized controlled study

Background This implementation feasibility study was conducted to determine whether an evidence-based parent-implemented distance-learning intervention model for young children at high likelihood of having ASD could be implemented at fidelity by Part C community providers and by parents in low-resource communities. Methods The study used a community-academic partnership model to adapt an evidence-based intervention tested in the current pilot trial involving randomization by agency in four states and enrollment of 35 coaches and 34 parent-family dyads. After baseline data were gathered, providers in the experimental group received 12–15 h of training while control providers received six webinars on early development. Providers delivered 6 months of intervention with children-families, concluding with data collection. Regression analyses were used to model outcomes of the coach behaviors, the parent fidelity ratings, and child outcomes. Results A block design model-building approach was used to test the null model followed by the inclusion of group as a predictor, and finally the inclusion of the planned covariates. Model fit was examined using changes in R2 and F-statistic. As hypothesized, results demonstrated significant gains in (1) experimental provider fidelity of coaching implementation compared to the control group; and (2) experimental parent fidelity of implementation compared to the control group. There were no significant differences between groups on child developmental scores. Conclusions Even though the experimental parent group averaged less than 30 min of intervention weekly with providers in the 6 months, both providers and parents demonstrated statistically significant gains on the fidelity of implementation scores with moderate effect sizes compared to control groups. Since child changes in parent-mediated models are dependent upon the parents’ ability to deliver the intervention, and since parent delivery is dependent upon providers who are coaching the parents, these results demonstrated that two of these three links of the chain were positively affected by the experimental implementation model. However, a lack of significant differences in child group gains suggests that further work is needed on this model. Factors to consider include the amount of contact with the provider, the amount of practice children experience, the amount of contact both providers and parents spend on training materials, and motivational strategies for parents, among others. Trial registration Registry of Efficacy and Effectiveness Studies: #4360, registered 1xx, October, 2020 – Retrospectively registered, https://sreereg.icpsr.umich.edu/sreereg/

Geographical variation and associated factors of vitamin A supplementation among 6–59-month children in Ethiopia

Introduction Vitamin A has been one of the most important micronutrients which are necessary for the health of the children. In developing countries, the supplementation of vitamins under a regular schedule had different constraints. Awareness, access, and resource limitations were usually the problem. In the current study, we analyzed the data from the demographic health survey (EDHS) 2016 to uncover the spatial distribution, predictors, and to provide additional information for policymaking and interventions. Methods In this analysis, we applied intra-community correlation to measure the random effect; global Moran’s I to test the nature of variance in the null model; proportional change in variance to check the variance of null and neighborhood in subsequent models. We used STATA 15 for prediction; ArcGIS 10.7 for the spatial distribution of vitamin A supplementation; SaTscan 9.6.1 to specify location of clustering were the applied soft wares. After confirming that the traditional logistic regression cannot explore the variances, we applied multilevel logistic regression to examine predictors where p-value <0.25 was used to include variables into the model and p-value<0.05 was used to declare associations. We presented the result using means, standard deviations, numbers, and proportions or percent, and AOR with 95% CI. Result The vitamin A coverage was 4,029.22 (44.90%) in Ethiopia in 2016. The distribution followed some spatial geo-locations where Afar, Somali were severely affected (RR = 1.46, P-value < 0.001), some pockets of Addis Ababa (RR = 1.47, p-value <0.001), and the poor distribution also affected all other regions partially. Place of delivery 1.2(1–1.34), primary and secondary education 1.3 (1–1.6), media exposure 1.2(1.1–1.4), having work 1.4(1.2–1.5), and all visits of ANC were positively influenced the distribution. Conclusion The distribution of vitamin A coverage was not random as per the EDHS 2016 data. Regions like Afar, Somali, and some pocket areas in Addis inquires immediate interventions. Pastoralist, agrarian, and city administrations were all involved from severe to the lesser coverage in order. Since factors like Place of delivery, education, ANC, media exposure, and having work were showed positive associations, interventions considering awareness, access, and availability of service need more attention than ever.

Dynamic and Assembly of Benthic Bacterial Community in an Industrial-Scale In-Pond Raceway Recirculating Culture System

To reduce water utilization, limit environmental pollution, and guarantee aquatic production and quality, the in-pond raceway recirculating culture system (IPRS) has been developed and is widely used. The effectiveness and sustainability of IPRSs rely on a good understanding of the ecological processes related to bacterial communities in the purification area. In this study, we investigated the dynamics and assembly mechanisms of benthic bacterial communities in the purification area of an industrial-scale IRPS. We found significant temporal and spatial variations in the sediment characteristics and benthic bacterial communities of the IPRS, although correlation analyses revealed a very limited relationship between them. Among the different culture stages, we identified numerous benthic bacteria with different abundances. Abundances of the phyla Bacteroidota and Desulfobacterota decreased whereas those of Myxococcota and Gemmatimonadota increased as the culture cycle progressed. Co-occurrence networks revealed that the bacterial community was less complex but more stable in the IPRS at the final stage compared with the initial stage. The neutral community model (NCM) showed that stochastic processes were the dominant ecological processes shaping the assembly of the benthic bacterial community. The null model suggested that homogenizing dispersal was more powerful than dispersal limitation and drift in regulating the assembly of the community. These findings indicate that the benthic microbial communities in purification areas of the IPRS may not be affected by the deposited wastes, and a more stable benthic microbial communities were formed and mainly driven by stochastic processes. However, the benthic microbial communities in the purification area at the end of the culturing stage was characterized by potentially inhibited organic matter degradation and carbon and sulfur cycling abilities, which was not corresponding to the purification area’s function. From this point on, the IPRS, especially the purification area was needed to be further optimized and improved.

Asymmetry drives the cumulative impacts of multiple stressors on freshwater ecosystems under a warming climate

Climate warming is an important stressor in freshwater ecosystems, yet its interactive effects with other environmental changes are poorly understood. We address this challenge by testing the ability of three contrasting null models to predict the joint impacts of warming and a second stressor using a new database of 296 experimental combinations. Despite concerns that stressors will interact to cause synergisms, we found that net impacts were best explained by the effect of the worst stressor (the dominance null model). When this stressor’s impact was at least 50% greater than that of the second, the dominance model was most accurate in 62% of responses. Prediction accuracy depended on the identity of the stressors and declined at higher levels of biological organisation. Together these findings suggest we can often effectively forecast impacts of multiple stressors by focusing on the degree of asymmetry that exists among their independent impacts.

Distribution and Assembly Processes of Soil Fungal Communities along an Altitudinal Gradient in Tibetan Plateau

In soil ecosystems, fungi exhibit diverse biodiversity and play an essential role in soil biogeochemical cycling. Fungal diversity and assembly processes across soil strata along altitudinal gradients are still unclear. In this study, we investigated the structure and abundance of soil fungal communities among soil strata and elevational gradients on the Tibetan Plateau using Illumina MiSeq sequencing of internal transcribed spacer1 (ITS1). The contribution of neutral and niche ecological processes were quantified using a neutral community model and a null model-based methodology. Our results showed that fungal gene abundance increased along altitudinal gradients, while decreasing across soil strata. Along with altitudinal gradients, fungal α-diversity (richness) decreased from surface to deeper soil layers, while β-diversity showed weak correlations with elevations. The neutral community model showed an excellent fit for neutral processes and the lowest migration rate (R2 = 0.75). The null model showed that stochastic processes dominate in all samples (95.55%), dispersal limitations were dominated at the surface layer and decreased significantly with soil strata, while undominated processes (ecological drift) show a contrary trend. The log-normal model and the null model (βNTI) correlation analysis also neglect the role of niche-based processes. We conclude that stochastic dispersal limitations, together with ecological drifts, drive fungal communities.

Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates

Near-term, ecological forecasting with iterative model refitting and uncertainty partitioning has great promise for improving our understanding of ecological processes and the predictive skill of ecological models, but to date has been infrequently applied to predict biogeochemical fluxes. Bubble fluxes of methane (CH4) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CH4 ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH4 ebullition rates, including: development of models that can be applied across time horizons and ecosystems, identification of the timescales for which predictions can provide useful information, and quantification of uncertainty in predictions. To assess the capacity of near-term, iterative forecasting workflows to improve ebullition rate predictions, we developed and tested a near-term, iterative forecasting workflow of CH4 ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH4 ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH4 forecasts from our workflow to both alternative forecasts generated without iterative model refitting and a persistence null model. Our forecasts with iterative model refitting estimated CH4 ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 loge(mg CH4 m−2 d−1) at 2-week ahead horizons]. Forecasts with iterative model refitting outperformed forecasts without refitting and the persistence null model at both 1- and 2-week forecast horizons. Driver uncertainty and model process uncertainty contributed the most to total forecast uncertainty, suggesting that future workflow improvements should focus on improved mechanistic understanding of CH4 models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH4 ebullition predictions, provides insight into predictability of ebullition rates into the future, and identifies which sources of uncertainty are the most important contributors to the total uncertainty in CH4 ebullition predictions.