Neurophysiological processing of emotion and parenting interact to predict inhibited behavior: an affective-motivational framework

Although inhibited behavior problems are prevalent in childhood, relatively little is known about the intrinsic and extrinsic factors that predict a child\u27s ability to regulate inhibited behavior during fear- and anxiety-provoking tasks. Inhibited behavior may be linked to both disruptions in avoidance-related processing of aversive stimuli and in approach-related processing of appetitive stimuli, but previous findings are contradictory and rarely integrate consideration of the socialization context. The current exploratory study used a novel combination of neurophysiological and observation-based methods to examine whether a neurophysiological measure sensitive to approach- and avoidance-oriented emotional processing, the late positive potential (LPP), interacted with observed approach- (promotion) and avoidance- (prevention) oriented parenting practices to predict children\u27s observed inhibited behavior. Participants were 5- to 7-year-old (N = 32) typically-developing children (M = 75.72 months, SD = 6.01). Electroencephalography was continuously recorded while children viewed aversive, appetitive, or neutral images, and the LPP was generated to each picture type separately. Promotion and prevention parenting were observed during an emotional challenge with the child. Child inhibited behavior was observed during a fear and a social evaluation task. As predicted, larger LPPs to aversive images predicted more inhibited behavior during both tasks, but only when parents demonstrated low promotion. In contrast, larger LPPs to appetitive images predicted less inhibited behavior during the social evaluative task, but only when parents demonstrated high promotion; children of high promotion parents showing smaller LPPs to appetitive images showed the greatest inhibition. Parent-child goodness-of-fit and the LPP as a neural biomarker for emotional processes related to inhibited behavior are discussed

Reproducibility Starts at the Source: R, Python, and Julia Packages for Retrieving USGS Hydrologic Data

Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval represent a critical component in reproducible scientific workflows. Earth science is no different in this regard. To fulfill that basic need, we developed R, Python, and Julia packages providing programmatic access to the U.S. Geological Survey’s National Water Information System database and the multi-agency Water Quality Portal. Together, these packages create a common interface for retrieving hydrologic data in the Jupyter ecosystem, which is widely used in water research, operations, and teaching. Source code, documentation, and tutorials for the packages are available on GitHub. Users can go there to learn, raise issues, or contribute improvements within a single platform, which helps foster better engagement and collaboration between data providers and their users

Systemic leukotriene B₄ receptor antagonism lowers arterial blood pressure and improves autonomic function in the spontaneously hypertensive rat

KEY POINTS: Evidence indicates an association between hypertension and chronic systemic inflammation in both human hypertension and experimental animal models. Previous studies in the spontaneously hypertensive rat (SHR) support a role for leukotriene B(4) (LTB(4)), a potent chemoattractant involved in the inflammatory response, but its mode of action is poorly understood. In the SHR, we observed an increase in T cells and macrophages in the brainstem; in addition, gene expression profiling data showed that LTB(4) production, degradation and downstream signalling in the brainstem of the SHR are dynamically regulated during hypertension. When LTB(4) receptor 1 (BLT1) receptors were blocked with CP‐105,696, arterial pressure was reduced in the SHR compared to the normotensive control and this reduction was associated with a significant decrease in systolic blood pressure (BP) indicators. These data provide new evidence for the role of LTB(4) as an important neuro‐immune pathway in the development of hypertension and therefore may serve as a novel therapeutic target for the treatment of neurogenic hypertension. ABSTRACT: Accumulating evidence indicates an association between hypertension and chronic systemic inflammation in both human hypertension and experimental animal models. Previous studies in the spontaneously hypertensive rat (SHR) support a role for leukotriene B(4) (LTB(4)), a potent chemoattractant involved in the inflammatory response. However, the mechanism for LTB(4)‐mediated inflammation in hypertension is poorly understood. Here we report in the SHR, increased brainstem infiltration of T cells and macrophages plus gene expression profiling data showing that LTB(4) production, degradation and downstream signalling in the brainstem of the SHR are dynamically regulated during hypertension. Chronic blockade of the LTB(4) receptor 1 (BLT1) receptor with CP‐105,696, reduced arterial pressure in the SHR compared to the normotensive control and this reduction was associated with a significant decrease in low and high frequency spectra of systolic blood pressure, and an increase in spontaneous baroreceptor reflex gain (sBRG). These data provide new evidence for the role of LTB(4) as an important neuro‐immune pathway in the development of hypertension and therefore may serve as a novel therapeutic target for the treatment of neurogenic hypertension

Natural Products Chemistry and Taxonomy of the Marine Cyanobacterium Blennothrix cantharidosmum

A Papua New Guinea field collection of the marine cyanobacterium Blennothrix cantharidosmum was investigated for its cytotoxic constituents. Bioassay-guided isolation defined the cytotoxic components as the known compounds lyngbyastatins 1 and 3. However, six new acyl proline derivatives, tumonoic acids D−I, plus the known tumonoic acid A were also isolated. Their planar structures were defined from NMR and MS data, while their stereostructures followed from a series of chiral chromatographies, degradation sequences, and synthetic approaches. The new compounds were tested in an array of assays, but showed only modest antimalarial and inhibition of quorum sensing activities. Nevertheless, these are the first natural products to be reported from this genus, and this inspired a detailed morphologic and 16S rDNA-based phylogenetic analysis of the producing organism

Stem cell‐derived enteroid cultures as a tool for dissecting host‐parasite interactions in the small intestinal epithelium.

Toxoplasma gondii and Cryptosporidium spp. can cause devastating pathological effects in humans and livestock, and in particular to young or immunocompromised individuals. The current treatment plans for these enteric parasites are limited due to long drug courses, severe side effects, or simply a lack of efficacy. The study of the early interactions between the parasites and the site of infection in the small intestinal epithelium has been thwarted by the lack of accessible, physiologically relevant, and species-specific models. Increasingly, 3D stem cell-derived enteroid models are being refined and developed into sophisticated models of infectious disease. In this review we shall illustrate the use of enteroids to spearhead research into enteric parasitic infections, bridging the gap between cell line cultures and in vivo experiments

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation