Synaptically activated burst-generating conductances may underlie a group-pacemaker mechanism for respiratory rhythm generation in mammals

Breathing, chewing, and walking are critical life-sustaining behaviors in mammals that consist essentially of simple rhythmic movements. Breathing movements in particular involve the diaphragm, thorax, and airways but emanate from a network in the lower brain stem. This network can be studied in reduced preparations in vitro and using simplified mathematical models that make testable predictions. An iterative approach that employs both in vitro and in silico models argues against canonical mechanisms for respiratory rhythm in neonatal rodents that involve reciprocal inhibition and pacemaker properties. We present an alternative model in which emergent network properties play a rhythmogenic role. Specifically, we show evidence that synaptically activated burst-generating conductances-which are only available in the context of network activity-engender robust periodic bursts in respiratory neurons. Because the cellular burst-generating mechanism is linked to network synaptic drive we dub this type of system a group pacemaker. © 2010 Elsevier B.V

Yeast IME2 Functions Early in Meiosis Upstream of Cell Cycle-Regulated SBF and MBF Targets

BACKGROUND: In Saccharomyces cerevisiae, the G1 cyclin/cyclin-dependent kinase (CDK) complexes Cln1,-2,-3/Cdk1 promote S phase entry during the mitotic cell cycle but do not function during meiosis. It has been proposed that the meiosis-specific protein kinase Ime2, which is required for normal timing of pre-meiotic DNA replication, is equivalent to Cln1,-2/Cdk1. These two CDK complexes directly catalyze phosphorylation of the B-type cyclin/CDK inhibitor Sic1 during the cell cycle to enable its destruction. As a result, Clb5,-6/Cdk1 become activated and facilitate initiation of DNA replication. While Ime2 is required for Sic1 destruction during meiosis, evidence now suggests that Ime2 does not directly catalyze Sic1 phosphorylation to target it for destabilization as Cln1,-2/Cdk1 do during the cell cycle. METHODOLOGY/PRINCIPAL FINDINGS: We demonstrated that Sic1 is eventually degraded in meiotic cells lacking the IME2 gene (ime2Δ), supporting an indirect role of Ime2 in Sic1 destruction. We further examined global RNA expression comparing wild type and ime2Δ cells. Analysis of these expression data has provided evidence that Ime2 is required early in meiosis for normal transcription of many genes that are also periodically expressed during late G1 of the cell cycle. CONCLUSIONS/SIGNIFICANCE: Our results place Ime2 at a position in the early meiotic pathway that lies upstream of the position occupied by Cln1,-2/Cdk1 in the analogous cell cycle pathway. Thus, Ime2 may functionally resemble Cln3/Cdk1 in promoting S phase entry, or it could play a role even further upstream in the corresponding meiotic cascade

The sociological significance of domestic violence: Tensions, paradoxes and implications

Sociology and sociological theory has been effective in analyzing societal and institutional conflict and violence, but less so the specifics of interpersonal violence. This article examines the sociological significance of domestic violence. This relationship, or sometimes its neglect, is underlain by several tensions and paradoxes, which in turn have broader implications for sociology and sociological theory. These matters are examined through: the possible paradox of violence and intimacy in the phenomenon of domestic violence; the importance of the naming and framing of such violence; explanation, responsibility and agency; and gender, hegemony and discourse in men’s violence to known women, as part of a multifaceted power approach

Natural language processing of radiology reports to detect complications of ischemic stroke

Background Abstraction of critical data from unstructured radiologic reports using natural language processing (NLP) is a powerful tool to automate the detection of important clinical features and enhance research efforts. We present a set of NLP approaches to identify critical findings in patients with acute ischemic stroke from radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI). Methods We trained machine learning classifiers to identify categorical outcomes of edema, midline shift (MLS), hemorrhagic transformation, and parenchymal hematoma, as well as rule-based systems (RBS) to identify intraventricular hemorrhage (IVH) and continuous MLS measurements within CT/MRI reports. Using a derivation cohort of 2289 reports from 550 individuals with acute middle cerebral artery territory ischemic strokes, we externally validated our models on reports from a separate institution as well as from patients with ischemic strokes in any vascular territory. Results In all data sets, a deep neural network with pretrained biomedical word embeddings (BioClinicalBERT) achieved the highest discrimination performance for binary prediction of edema (area under precision recall curve [AUPRC] > 0.94), MLS (AUPRC > 0.98), hemorrhagic conversion (AUPRC > 0.89), and parenchymal hematoma (AUPRC > 0.76). BioClinicalBERT outperformed lasso regression (p  Conclusions Our study demonstrates robust performance and external validity of a core NLP tool kit for identifying both categorical and continuous outcomes of ischemic stroke from unstructured radiographic text data. Medically tailored NLP methods have multiple important big data applications, including scalable electronic phenotyping, augmentation of clinical risk prediction models, and facilitation of automatic alert systems in the hospital setting