Acute effects of nicotine on visual search tasks in young adult smokers

Rationale Nicotine is known to improve performance on tests involving sustained attention and recent research suggests that nicotine may also improve performance on tests involving the strategic allocation of attention and working memory. Objectives We used measures of accuracy and response latency combined with eye-tracking techniques to examine the effects of nicotine on visual search tasks. Methods In experiment 1 smokers and non-smokers performed pop-out and serial search tasks. In experiment 2, we used a within-subject design and a more demanding search task for multiple targets. In both studies, 2-h abstinent smokers were asked to smoke one of their own cigarettes between baseline and tests. Results In experiment 1, pop-out search times were faster after nicotine, without a loss in accuracy. Similar effects were observed for serial searches, but these were significant only at a trend level. In experiment 2, nicotine facilitated a strategic change in eye movements resulting in a higher proportion of fixations on target letters. If the cigarette was smoked on the first trial (when the task was novel), nicotine additionally reduced the total number of fixations and refixations on all letters in the display. Conclusions Nicotine improves visual search performance by speeding up search time and enabling a better focus of attention on task relevant items. This appears to reflect more efficient inhibition of eye movements towards task irrelevant stimuli, and better active maintenance of task goals. When the task is novel, and therefore more difficult, nicotine lessens the need to refixate previously seen letters, suggesting an improvement in working memory

Convergent and parallel evolution in life habit of the scallops (Bivalvia: Pectinidae)

<p>Abstract</p> <p>Background</p> <p>We employed a phylogenetic framework to identify patterns of life habit evolution in the marine bivalve family Pectinidae. Specifically, we examined the number of independent origins of each life habit and distinguished between convergent and parallel trajectories of life habit evolution using ancestral state estimation. We also investigated whether ancestral character states influence the frequency or type of evolutionary trajectories.</p> <p>Results</p> <p>We determined that temporary attachment to substrata by byssal threads is the most likely ancestral condition for the Pectinidae, with subsequent transitions to the five remaining habit types. Nearly all transitions between life habit classes were repeated in our phylogeny and the majority of these transitions were the result of parallel evolution from byssate ancestors. Convergent evolution also occurred within the Pectinidae and produced two additional gliding clades and two recessing lineages. Furthermore, our analysis indicates that byssal attaching gave rise to significantly more of the transitions than any other life habit and that the cementing and nestling classes are only represented as evolutionary outcomes in our phylogeny, never as progenitor states.</p> <p>Conclusions</p> <p>Collectively, our results illustrate that both convergence and parallelism generated repeated life habit states in the scallops. Bias in the types of habit transitions observed may indicate constraints due to physical or ontogenetic limitations of particular phenotypes.</p

The Evolutionary Basis of Naturally Diverse Rice Leaves Anatomy

Rice contains genetically and ecologically diverse wild and cultivated species that show a wide variation in plant and leaf architecture. A systematic characterization of leaf anatomy is essential in understanding the dynamics behind such diversity. Therefore, leaf anatomies of 24 Oryza species spanning 11 genetically diverse rice genomes were studied in both lateral and longitudinal directions and possible evolutionary trends were examined. A significant inter-species variation in mesophyll cells, bundle sheath cells, and vein structure was observed, suggesting precise genetic control over these major rice leaf anatomical traits. Cellular dimensions, measured along three growth axes, were further combined proportionately to construct three-dimensional (3D) leaf anatomy models to compare the relative size and orientation of the major cell types present in a fully expanded leaf. A reconstruction of the ancestral leaf state revealed that the following are the major characteristics of recently evolved rice species: fewer veins, larger and laterally elongated mesophyll cells, with an increase in total mesophyll area and in bundle sheath cell number. A huge diversity in leaf anatomy within wild and domesticated rice species has been portrayed in this study, on an evolutionary context, predicting a two-pronged evolutionary pathway leading to the ‘sativa leaf type’ that we see today in domesticated species

Comparing rates of introgression in parasitic feather lice with differing dispersal capabilities

Organisms vary in their dispersal abilities, and these differences can have important biological consequences, such as impacting the likelihood of hybridization events. However, there is still much to learn about the factors influencing hybridization, and specifically how dispersal ability affects the opportunities for hybridization. Here, using the ecological replicate system of dove wing and body lice (Insecta: Phthiraptera), we show that species with higher dispersal abilities exhibited increased genomic signatures of introgression. Specifically, we found a higher proportion of introgressed genomic reads and more reticulated phylogenetic networks in wing lice, the louse group with higher dispersal abilities. Our results are consistent with the hypothesis that differences in dispersal ability might drive the extent of introgression through hybridization.National Science Foundation (NSF) DEB-1239788 DEB-1342604 DEB-1926919 DEB-1925487European Commision H2020-MSCA-IF-2019 INTROSYM: 88653

An update to the HIV-TRePS system: the development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype

Objectives: Optimizing antiretroviral drug combination on an individual basis in resource-limited settings is challenging because of the limited availability of drugs and genotypic resistance testing. Here, we describe our latest computational models to predict treatment responses, with or without a genotype, and compare the potential utility of global and local models as a treatment tool for South Africa. Methods: Global random forest models were trained to predict the probability of virological response to therapy following virological failure using 29 574 treatment change episodes (TCEs) without a genotype, 3179 of which were from South Africa and were used to develop local models. In addition, 15 130 TCEs including genotypes were used to develop another set of models. The 'no-genotype' models were tested with an independent global test set (n = 1700) plus a subset from South Africa (n = 222). The genotype models were tested with 750 independent cases. Results: The global no-genotype models achieved area under the receiver-operating characteristic curve (AUC) values of 0.82 and 0.79 with the global and South African tests sets, respectively, and the South African models achieved AUCs of 0.70 and 0.79. The genotype models achieved an AUC of 0.84. The global no-genotype models identified more alternative, locally available regimens that were predicted to be effective for cases that failed their new regimen in the South African clinics than the local models. Both sets of models were significantly more accurate predictors of outcomes than genotyping with rules-based interpretation. Conclusions: These latest global models predict treatment responses accurately even without a genotype, out-performed the local South African models and have the potential to help optimize therapy, particularly in resource-limited settings.</p

The development of artificial neural networks to predict virological response to combination HIV therapy.

Introduction: When used in combination, antiretroviral drugs are highly effective for suppressing HIV replication. Nevertheless, treatment failure commonly occurs and is generally associated with viral drug resistance. The choice of an alternative regimen may be guided by a drug-resistance test. However, interpretation of resistance from genotypic data poses a major challenge. Methods: As an alternative to current interpretation systems, we have developed artificial neural network (ANN) models to predict virological response to combination therapy from HIV genotype and other clinical information. Results: ANN models trained with genotype, baseline viral load and time to follow-up viral load (1,154 treatment change episodes from multiple clinics), produced predictions of virological response that were highly significantly correlated with actual responses (r2=0.53; P<0.00001) using independent test data from clinics that contributed training data. Augmented models, trained with the additional variables of baseline CD4+ T-cell count and four treatment history variables, were more accurate, explaining 69% of the variance in virological response. Models trained with the full input dataset, but only those data involving highly active antiretroviral therapy (three or more full-dose antiretroviral drugs in combination), performed at an intermediate level, explaining 61% of the variance. The augmented models performed less well when tested with data from unfamiliar clinics that had not contributed data to the training dataset, explaining 46% of the variance in response. Conclusion: These data indicate that ANN models can be quite accurate predictors of virological response to HIV therapy even for patients from unfamiliar clinics. ANN models therefore warrant further development as a potential tool to aid treatment selection