Microbiome-gut-brain axis and toll-like receptors in parkinson\u2019s disease

Parkinson’s disease (PD) is a progressively debilitating neurodegenerative disease characterized by α-synucleinopathy, which involves all districts of the brain-gut axis, including the central, autonomic and enteric nervous systems. The highly bidirectional communication between the brain and the gut is markedly influenced by the microbiome through integrated immunological, neuroendocrine and neurological processes. The gut microbiota and its relevant metabolites interact with the host via a series of biochemical and functional inputs, thereby affecting host homeostasis and health. Indeed, a dysregulated microbiota-gut-brain axis in PD might lie at the basis of gastrointestinal dysfunctions which predominantly emerge many years prior to the diagnosis, corroborating the theory that the pathological process is spread from the gut to the brain. Toll-like receptors (TLRs) play a crucial role in innate immunity by recognizing conserved motifs primarily found in microorganisms and a dysregulation in their signaling may be implicated in α-synucleinopathy, such as PD. An overstimulation of the innate immune system due to gut dysbiosis and/or small intestinal bacterial overgrowth, together with higher intestinal barrier permeability, may provoke local and systemic inflammation as well as enteric neuroglial activation, ultimately triggering the development of alpha-synuclein pathology. In this review, we provide the current knowledge regarding the relationship between the microbiota-gut–brain axis and TLRs in PD. A better understanding of the dialogue sustained by the microbiota-gut-brain axis and innate immunity via TLR signaling should bring interesting insights in the pathophysiology of PD and provide novel dietary and/or therapeutic measures aimed at shaping the gut microbiota composition, improving the intestinal epithelial barrier function and balancing the innate immune response in PD patients, in order to influence the early phases of the following neurodegenerative cascade

Bayesian inference for the half-normal and half-t distributions

In this article we consider approaches to Bayesian inference for the half-normal and half-t distributions. We show that a generalized version of the normal-gamma distribution is conjugate to the half-normal likelihood and give the moments of this new distribution. The bias and coverage of the Bayesian posterior mean estimator of the halfnormal location parameter are compared with those of maximum likelihood based estimators. Inference for the half-t distribution is performed using Gibbs sampling and model comparison is carried out using Bayes factors. A real data example is presented which demonstrates the fitting of the half-normal and half-t models

Post-Wildfire Effects on a Headwater Stream in the San Bernardino National Forest

Southern California has experienced prolonged drought conditions that have supported frequent wildfires that adversely impact ecosystems, natural resources, and human development. A primary consequence of these events is the impact on water quality and quantity. Of equal concern is evaluating how diverse land use configurations within a watershed can alter the physio-chemical properties of headwater reaches where drought and wildfire conditions are prevalent. To better understand the extent to which wildfires impact water quality and quantity across a headwater watershed, this study investigates wildfire impacts from the 2021 South Fire to Lytle Creek, a headwater stream of the Santa Ana River Basin located within the San Bernardino National Forest in Southern California. In situ parameter analysis in this study includes dissolved oxygen (D.O.), temperature, flow rate, and conductivity. Additional laboratory testing includes turbidity, pH, nitrate (NO3-), ammonium (NH4+), E. coli, total coliform, and enterococci. Lytle Creek was monitored during the dry and wet seasons and in burned and unburned sampling sites. This study found that there were no significant differences between the water quality of the sampling sites located within the burned stream (LC2) compared to the sampling site of the adjacent stream (LC1). However, it should be noted that although there was a fire within the LC2 sampling site and multiple parameters had sampling events that exceeded the standards and objectives, impacts did not persist across the study period, indicating that the hydrological system was able to recover from the wildfire. This can be best described because of the South Fire’s low burn severity to moderate-low burn severity and the natural landscape where the South Fire occurred

BAYESIAN INFERENCE FOR THE HALF-NORMAL AND HALF-T DISTRIBUTIONS

In this article we consider approaches to Bayesian inference for the half-normal and half-t distributions. We show that a generalized version of the normal-gamma distribution is conjugate to the half-normal likelihood and give the moments of this new distribution. The bias and coverage of the Bayesian posterior mean estimator of the halfnormal location parameter are compared with those of maximum likelihood based estimators. Inference for the half-t distribution is performed using Gibbs sampling and model comparison is carried out using Bayes factors. A real data example is presented which demonstrates the fitting of the half-normal and half-t models.