Cortical-subcortical interactions in goal-directed behavior.

Flexibly selecting appropriate actions in response to complex, ever-changing environments requires both cortical and subcortical regions, which are typically described as participating in a strict hierarchy. In this traditional view, highly specialized subcortical circuits allow for efficient responses to salient stimuli, at the cost of adaptability and context specificity, which are attributed to the neocortex. Their interactions are often described as the cortex providing top-down command signals for subcortical structures to implement; however, as available technologies develop, studies increasingly demonstrate that behavior is represented by brainwide activity and that even subcortical structures contain early signals of choice, suggesting that behavioral functions emerge as a result of different regions interacting as truly collaborative networks. In this review, we discuss the field's evolving understanding of how cortical and subcortical regions in placental mammals interact cooperatively, not only via top-down cortical-subcortical inputs but through bottom-up interactions, especially via the thalamus. We describe our current understanding of the circuitry of both the cortex and two exemplar subcortical structures, the superior colliculus and striatum, to identify which information is prioritized by which regions. We then describe the functional circuits these regions form with one another, and the thalamus, to create parallel loops and complex networks for brainwide information flow. Finally, we challenge the classic view that functional modules are contained within specific brain regions; instead, we propose that certain regions prioritize specific types of information over others, but the subnetworks they form, defined by their anatomical connections and functional dynamics, are the basis of true specialization.

Adaptive response of Pseudomonas aeruginosa under serial ciprofloxacin exposure.

Understanding the evolution of antibiotic resistance is important for combating drug-resistant bacteria. In this work, we investigated the adaptive response of Pseudomonas aeruginosa to ciprofloxacin. Ciprofloxacin-susceptible P. aeruginosa ATCC 9027, CIP-E1 (P. aeruginosa ATCC 9027 exposed to ciprofloxacin for 14 days) and CIP-E2 (CIP-E1 cultured in antibiotic-free broth for 10 days) were compared. Phenotypic responses including cell morphology, antibiotic susceptibility, and production of pyoverdine, pyocyanin and rhamnolipid were assessed. Proteomic responses were evaluated using comparative iTRAQ labelling LC-MS/MS to identify differentially expressed proteins (DEPs). Expression of associated genes coding for notable DEPs and their related regulatory genes were checked using quantitative reverse transcriptase PCR. CIP-E1 displayed a heterogeneous morphology, featuring both filamentous cells and cells with reduced length and width. By contrast, although filaments were not present, CIP-E2 still exhibited size reduction. Considering the MIC values, ciprofloxacin-exposed strains developed resistance to fluoroquinolone antibiotics but maintained susceptibility to other antibiotic classes, except for carbapenems. Pyoverdine and pyocyanin production showed insignificant decreases, whereas there was a significant decrease in rhamnolipid production. A total of 1039 proteins were identified, of which approximately 25 % were DEPs. In general, there were more downregulated proteins than upregulated proteins. Noted changes included decreased OprD and PilP, and increased MexEF-OprN, MvaT and Vfr, as well as proteins of ribosome machinery and metabolism clusters. Gene expression analysis confirmed the proteomic data and indicated the downregulation of rpoB and rpoS. In summary, the response to CIP involved approximately a quarter of the proteome, primarily associated with ribosome machinery and metabolic processes. Potential targets for bacterial interference encompassed outer membrane proteins and global regulators, such as MvaT.

Mixtures of strategies underlie rodent behavior during reversal learning.

In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions.

Orthodontic care in suburban Cuyahoga County, Ohio: who provides treatment and whom do they treat?

It has been reported that orthodontic services are being provided to a larger segment of the population by an increasing number of providers. The present study surveyed the dental and orthodontic experiences of 10th grade students attending 16 public and two parochial high schools in suburban Cuyahoga County, Ohio, as well as two schools from the city of Cleveland. Questionnaires were distributed in the classroom and data obtained for 2808 students. Approximately 50% of the sample were girls with an average age of 15.5 +/- 0.8 years. Results of the survey revealed that 84% (2371/2808) had seen a dentist within the past year, and 37% (1047/2808) of the students had received orthodontic treatment from 171 different providers. Of those treated, 87.2% (913/1047) were treated by a specialist in orthodontics, 10.8% (114/1047) by a general dentist, and 0.7% (7/1047) by a pediatric dentist, with 1.3% missing or unknown (13/1047). Patients who had seen a dentist within the past year were more likely to have had orthodontic treatment. Only 7% of the untreated students were told by a dental professional that they needed braces compared with 71% of the treated group. Therefore, we conclude that orthodontic specialists provide most of the orthodontic services in the suburbs of Cuyahoga County, and visiting a general dentist positively influences the utilization of orthodontic services.