TREEasy: An automated workflow to infer gene trees,species trees,and phylogenetic networks from multilocus data

Multilocus genomic data sets can be used to infer a rich set of information about the evolutionary history of a lineage, including gene trees, species trees, and phylogenetic networks. However, user‐friendly tools to run such integrated analyses are lacking, and workflows often require tedious reformatting and handling time to shepherd data through a series of individual programs. Here, we present a tool written in Python—TREEasy—that performs automated sequence alignment (with MAFFT), gene tree inference (with IQ‐Tree), species inference from concatenated data (with IQ‐Tree and RaxML‐NG), species tree inference from gene trees (with ASTRAL, MP‐EST, and STELLS2), and phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires FASTA files and nine parameters as inputs. The tool can be run as command line or through a Graphical User Interface (GUI). As examples, we reproduced a recent analysis of staghorn coral evolution, and performed a new analysis on the evolution of the “WGD clade” of yeast. The latter revealed novel patterns that were not identified by previous analyses. TREEasy represents a reliable and simple tool to accelerate research in systematic biology ( https://github.com/MaoYafei/TREEasy ).     

Genomic and phenomic analysis of island ant community assembly

Island biodiversity has long fascinated biologists as it typically presents tractable systems for unpicking the eco‐evolutionary processes driving community assembly. In general, two recurring themes are of central theoretical interest. First, immigration, diversification, and extinction typically depend on island geographical properties (e.g., area, isolation, and age). Second, predictable ecological and evolutionary trajectories readily occur after colonization, such as the evolution of adaptive trait syndromes, trends toward specialization, adaptive radiation, and eventual ecological decline. Hypotheses such as the taxon cycle draw on several of these themes to posit particular constraints on colonization and subsequent eco‐evolutionary dynamics. However, it has been challenging to examine these integrated dynamics with traditional methods. Here, we combine phylogenomics, population genomics and phenomics, to unravel community assembly dynamics among Pheidole (Hymenoptera, Formicidae) ants in the isolated Fijian archipelago. We uphold basic island biogeographic predictions that isolated islands accumulate diversity primarily through in situ evolution rather than dispersal, and population genomic support for taxon cycle predictions that endemic species have decreased dispersal ability and demography relative to regionally widespread taxa. However, rather than trending toward island syndromes, ecomorphological diversification in Fiji was intense, filling much of the genus‐level global morphospace. Furthermore, while most endemic species exhibit demographic decline and reduced dispersal, we show that the archipelago is not an evolutionary dead‐end. Rather, several endemic species show signatures of population and range expansion, including a successful colonization to the Cook islands. These results shed light on the processes shaping island biotas and refine our understanding of island biogeographic theory.     

Protocol of a prospective study on the diagnostic value of transcranial duplex scanning of the substantia nigra in patients with parkinsonian symptoms

Background  

Parkinson's disease (PD) is the second most common neurodegenerative disorder. As there is no definitive diagnostic test, its diagnosis is based on clinical criteria. Recently transcranial duplex scanning (TCD) of the substantia nigra in the brainstem has been proposed as an instrument to diagnose PD. We and others have found that TCD scanning of substantia nigra duplex is a relatively accurate diagnostic instrument in patients with parkinsonian symptoms. However, all studies on TCD so far have involved well-defined, later-stage PD patients, which will obviously lead to an overestimate of the diagnostic accuracy of TCD.     

Membrane properties and the balance between excitation and inhibition control gamma-frequency oscillations arising from feedback inhibition

Computational studies as well as in vivo and in vitro results have shown that many cortical neurons fire in a highly irregular manner and at low average firing rates. These patterns seem to persist even when highly rhythmic signals are recorded by local field potential electrodes or other methods that quantify the summed behavior of a local population. Models of the 30-80 Hz gamma rhythm in which network oscillations arise through 'stochastic synchrony' capture the variability observed in the spike output of single cells while preserving network-level organization. We extend upon these results by constructing model networks constrained by experimental measurements and using them to probe the effect of biophysical parameters on network-level activity. We find in simulations that gamma-frequency oscillations are enabled by a high level of incoherent synaptic conductance input, similar to the barrage of noisy synaptic input that cortical neurons have been shown to receive in vivo. This incoherent synaptic input increases the emergent network frequency by shortening the time scale of the membrane in excitatory neurons and by reducing the temporal separation between excitation and inhibition due to decreased spike latency in inhibitory neurons. These mechanisms are demonstrated in simulations and in vitro current-clamp and dynamic-clamp experiments. Simulation results further indicate that the membrane potential noise amplitude has a large impact on network frequency and that the balance between excitatory and inhibitory currents controls network stability and sensitivity to external inputs.