Biology subjects

Manning, J.

Publications and source records attributed to Manning, J..

3 recordsLinked to original sources

bioRxiv2018

The chemistry and pharmacology of putative synthetic cannabinoid receptor agonist (SCRA) new psychoactive substances (NPS) 5F-PY-PICA, 5F-PY-PINACA, and their analogues

The structural diversity of synthetic cannabinoid receptor agonist (SCRA) new psychoactive substances (NPS) has increased since the first examples were reported a decade ago. 5F-PY-PICA and 5F-PY-PINACA were identified in 2015 as putative SCRA NPS, although nothing is known of their pharmacology. 5F-PY-PICA, 5F-PY-PINACA, and analogues intended to explore structure-activity relationships within this class of SCRAs were synthesized and characterized by nuclear magnetic resonance spectroscopy and liquid chromatography-quadrupole time-of-flight-mass spectrometry. Using competitive binding experiments and fluorescence-based plate reader membrane potential assays, the affinities and activities of all analogues at cannabinoid type 1 and type 2 receptors (CB1 and CB2) were evaluated. All ligands showed minimal affinity for CB1 (pKi < 5), although several demonstrated moderate CB2 binding (pKi = 5.45-6.99). At 10 M none of the compounds produced an effect > 50% of CP55,950 at CB1, while several compounds showed a slightly higher relative efficacy at CB2. Unlike other SCRA NPS, 5F-PYPICA and 5F-PY-PINACA did not produce cannabimimetic effects in mice at doses up to 10 mg/kg.

pharmacology and toxicology↗

bioRxiv2018

On simulating cold stunned turtle strandings on Cape Cod

Kemps ridley turtles were on the verge of extinction in the 1960s. While they have slowly recovered, they are still endangered. In the last few years, the number of strandings on Cape Cod Massachusetts beaches has increased by nearly an order of magnitude relative to preceding decades. This study uses a combination of ocean observations and a well-respected ocean model to investigate the causes and transport of cold-stunned animals in Cape Cod Bay. After validating the model using satellite-tracked drifters and local temperature moorings, ocean currents were examined in the Cape Cod Bay in an attempt to explain stranding locations as observed by volunteers and, for some years, backtracking was conducted to examine the potential source regions. The general finding, as expected, is that sub 10.5{degrees}C water temperatures in combination with persistent strong wind stress (>0.4Pa) will result in increased strandings along particular sections of the coast dependent on the wind direction. However, it is still uncertain where in the water column the majority of cold stunned turtles reside and, if many of them are on the surface, considerable more work will need to be done to incorporate the direct effects of wind and waves on the advective processes.

ecology↗

bioRxiv2017

A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

Recent research shows that the covariance structure of functional magnetic resonance imaging (fMRI) data - commonly described as functional connectivity - can change as a function of the participants cognitive state (for review see [35]). Here we present a Bayesian hierarchical matrix factorization model, termed hierarchical topographic factor analysis (HTFA), for efficiently discovering full-brain networks in large multi-subject neuroimaging datasets. HTFA approximates each subjects network by first re-representing each brain image in terms of the activities of a set of localized nodes, and then computing the covariance of the activity time series of these nodes. The number of nodes, along with their locations, sizes, and activities (over time) are learned from the data. Because the number of nodes is typically substantially smaller than the number of fMRI voxels, HTFA can be orders of magnitude more efficient than traditional voxel-based functional connectivity approaches. In one case study, we show that HTFA recovers the known connectivity patterns underlying a collection of synthetic datasets. In a second case study, we illustrate how HTFA may be used to discover dynamic full-brain activity and connectivity patterns in real fMRI data, collected as participants listened to a story. In a third case study, we carried out a similar series of analyses on fMRI data collected as participants viewed an episode of a television show. In these latter case studies, we found that the HTFA-derived activity and connectivity patterns can be used to reliably decode which moments in the story or show the participants were experiencing. Further, we found that these two classes of patterns contained partially non-overlapping information, such that decoders trained on combinations of activity-based and dynamic connectivity-based features performed better than decoders trained on activity or connectivity patterns alone. We replicated this latter result with two additional (previously developed) methods for efficiently characterizing full-brain activity and connectivity patterns.

neuroscience↗