Physico-chemical characteristics and methanogen communities in swine and dairy manure storage tanks: Spatio-temporal variations and impact on methanogenic activity

Greenhouse gas emissions represent a major environmental problem associated with the management of manure from the livestock industry. Methane is the primary GHG emitted during manure outdoor storage. In this paper, the variability of two swine and two dairy manure storage tanks was surveyed, in terms of physico-chemical and microbiological parameters. The impact of the inter-tank and spatio-temporal variations of these parameters on the methanogenic activity of manure was ascertained. A Partial Least Square regression was carried out, which demonstrated that physico-chemical as well as microbiological parameters had a major influence on the methanogenic activity. Among the 19 parameters included in the regression, the concentrations of VFAs had the strongest negative influence on the methane emission rate of manure, resulting from their well-known inhibitory effect. The relative abundance of two amplicons in archaeal fingerprints was found to positively influence the methanogenic activity, suggesting that Methanoculleus spp. and possibly Methanosarcina spp. are major contributors to methanogenesis in storage tanks. This work gave insights into the mechanisms, which drive methanogenesis in swine and dairy manure storage tanks

Structural Equation Models to estimate Dynamic Effective Connectivity Networks in Resting fMRI. A comparison between individuals with Down syndrome and controls

Emerging evidence suggests that an effective or functional connectivity network does not use a static process over time but incorporates dynamic connectivity that shows changes in neuronal activity patterns. Using structural equation models (SEMs), we estimated a dynamic component of the effective network through the effects (recursive and nonrecursive) between regions of interest (ROIs), taking into account the lag 1 effect. The aim of the paper was to find the best structural equation model (SEM) to represent dynamic effective connectivity in people with Down syndrome (DS) in comparison with healthy controls. Twenty-two people with DS were registered in a functional magnetic resonance imaging (fMRI) resting-state paradigm for a period of six minutes. In addition, 22 controls, matched by age and sex, were analyzed with the same statistical approach. In both groups, we found the best global model, which included 6 ROIs within the default mode network (DMN). Connectivity patterns appeared to be different in both groups, and networks in people with DS showed more complexity and had more significant effects than networks in control participants. However, both groups had synchronous and dynamic effects associated with ROIs 3 and 4 related to the upper parietal areas in both brain hemispheres as axes of association and functional integration. It is evident that the correct classification of these groups, especially in cognitive competence, is a good initial step to propose a biomarker in network complexity studies

Complexity Analysis of the Default Mode Network Using Resting-State fMRI in Down Syndrome: Relationships Highlighted by A Neuropsychological Assessment

Background: Studies on complexity indicators in the field of functional connectivity derived from resting-state fMRI (rs-fMRI) in Down syndrome (DS) samples and their possible relationship with cognitive functioning variables are rare. We analyze how some complexity indicators estimated in the subareas that constitute the default mode network (DMN) might be predictors of the neuropsychological outcomes evaluating Intelligence Quotient (IQ) and cognitive performance in persons with DS. Methods: Twenty-two DS people were assessed with the Kaufman Brief Test of Intelligence (KBIT) and Frontal Assessment Battery (FAB) tests, and fMRI signals were recorded in a resting state over a six-minute period. In addition, 22 controls, matched by age and sex, were evaluated with the same rs-fMRI procedure. Results: There was a significant difference in complexity indicators between groups: the control group showed less complexity than the DS group. Moreover, the DS group showed more variance in the complexity indicator distributions than the control group. In the DS group, significant and negative relationships were found between some of the complexity indicators in some of the DMN networks and the cognitive performance scores. Conclusions: The DS group is characterized by more complex DMN networks and exhibits an inverse relationship between complexity and cognitive performance based on the negative parameter estimates

Resting-state default mode network connectivity in young individuals with Down syndrome

Background: Down syndrome (DS) is a chromosomal disorder that causes intellectual disability. Few studies have been conducted on functional connectivity using restingstate fMRI (functional magnetic resonance imaging) signals or more specifically, on the relevant structure and density of the default mode network (DMN). Although data on this issue have been reported in adult DS individuals (age: >45 years), the DMN properties in young DS individuals have not been studied. The aim of this study was to describe the density and structure of the DMN network from fMRI signals in young DS (age: <36 years). Method: A sample of 22 young people with DS between the ages of 16 and 35 (M = 25.5 and SD = 5.1) was recruited in various centers for people with intellectual disability (ID). In addition to sociodemographic data, a six-minute fMRI session was recorded with a 3. T Philips Ingenia scanner. A control group of 22 young people, matched by age and gender, was obtained from the Human Connectome Project (to compare the networks properties between groups). Results: The values of the 48 ROIs that configured the DMN were obtained, and the connectivity graphs for each subject, the average connectivity graph for each group, the clustering and degree values for each ROI, and the average functional connectivity network were estimated. Conclusions: A higher density of overactivation was identified in DS group in the ventral, sensorimotor, and visual DMN networks, although within a framework of a wide variability of connectivity patterns in comparison with the control group network. These results extend our understanding of the functional connectivity networks pattern and intrasubject variability in DS

Impact of Organic Loading Rate on Psychrophilic Anaerobic Digestion of Solid Dairy Manure

Increasing the feed total solids to anaerobic digester improves the process economics and decreases the volume of liquid effluent from current wet anaerobic digestion. The objective of this study was to develop a novel psychrophilic (20 °C) anaerobic digestion technology of undiluted cow feces (total solids of 11%–16%). Two sets of duplicate laboratory-scale sequence batch bioreactors have been operated at organic loading rates (OLR) of 6.0 to 8.0 g total chemical oxygen demand (TCOD) kg−1 inoculum day−1 (d−1) during 210 days. The results demonstrated that the process is feasible at treatment cycle length (TCL) of 21 days; however, the quality of cow feces rather than the OLR had a direct influence on the specific methane yield (SMY). The SMY ranged between 124.5 ± 1.4 and 227.9 ± 4.8 normalized liter (NL) CH4 kg−1 volatile solids (VS) fed d−1. Substrate-to-inoculum mass ratio (SIR) was 0.63 ± 0.05, 0.90 ± 0.09, and 1.06 ± 0.07 at OLR of 6.0, 7.0, and 8.0 g TCOD kg−1 inoculum d−1, respectively. No volatile fatty acids (VFAs) accumulation has been observed which indicated that hydrolysis was the rate limiting step and VFAs have been consumed immediately. Bioreactors performance consistency in terms of the level of SMYs, VFAs concentrations at end of the TCL, pH stability and volatile solids reduction indicates a stable and reproducible process during the entire operation

Effects of Adding Corn Dried Distiller Grains with Solubles (DDGS) to the Dairy Cow Diet and Effects of Bedding in Dairy Cow Slurry on Fugitive Methane Emissions

The specific objectives of this experiment were to investigate the effects of adding 10% or 30% corn dried distillers grains with solubles (DDGS) to the dairy cow diet and the effects of bedding type (wood shavings, straw or peat moss) in dairy slurry on fugitive CH4 emissions. The addition of DDGS10 to the dairy cow diet significantly increased (29%) the daily amount of fat excreted in slurry compared to the control diet. The inclusion of DDGS30 in the diet increased the daily amounts of excreted DM, volatile solids (VS), fat, neutral detergent fiber (NDF), acid detergent fiber (ADF) and hemicellulose by 18%, 18%, 70%, 30%, 15% and 53%, respectively, compared to the control diet. During the storage experiment, daily fugitive CH4 emissions showed a significant increase of 15% (p &lt; 0.05) for the slurry resulting from the corn DDGS30 diet. The addition of wood shavings and straw did not have a significant effect on daily fugitive CH4 emissions relative to the control diet, whereas the addition of peat moss caused a significant increase of 27% (p &lt; 0.05) in fugitive CH4 emissions