MonolithNet: Training monolithic deep neural networks via a partitioned training strategy

In this study, we explore the training of monolithic deep neural net-works in an effective manner. One of the biggest challenges withtraining such networks to the desired level of accuracy is the dif-ficulty in converging to a good solution using iterative optimizationmethods such as stochastic gradient descent due to the enormousnumber of parameters that need to be learned. To achieve this,we introduce a partitioned training strategy, where proxy layersare connected to different partitions of a deep neural network toenable isolated training of a much smaller number of parametersto convergence. To illustrate the efficacy of this training strategy,we introduce MonolithNet, a massive residual deep neural networkconsisting of 437 million parameters. The trained MonolithNet wasable to achieve a top-1 accuracy of 97% on the CIFAR10 imageclassification dataset, which demonstrates the feasibility of the pro-posed training strategy for training monolithic deep neural networksto high accuracies

Frontline over ivory tower: key competencies in community-based curricula

Background: The Royal College of Physicians and Surgeons of Canada mandates that community experiences be incorporated into medicine-based specialties.  Presently there is wide variability in community endocrine experiences across Canadian training programs.  This is complicated by the paucity of literature providing guidance on what constitutes a ‘community’ rotation.Method: A modified Delphi technique was used to determine the CanMEDS competencies best taught in a community endocrinology curriculum. The Delphi technique is a qualitative-research method that uses a series of questionnaires sent to a group of experts with controlled feedback provided by the researchers after each survey round.  The experts in this study included endocrinology program directors, community endocrinologists, endocrinology residents and recent endocrinology graduates.Results: Thirty four out of 44 competencies rated by the panel were deemed suitable for a community curriculum.  The experts considered the “Manager” role best taught in the community, while they considered the community least suitable to learn the “Medical Expert” competency.Conclusions: To our knowledge, this is the first time the content of a community-based subspecialty curriculum was determined using the Delphi process in Canada.  These findings suggest that community settings have potential to fill in gaps in residency training in regards to the CanMEDS Manager role.  The results will aid program directors in designing competency-based community endocrinology rotations and competency-based community rotations in other medical subspecialty programs

Loss of Striatonigral GABAergic Presynaptic Inhibition Enables Motor Sensitization in Parkinsonian Mice

SummaryDegeneration of dopamine (DA) neurons in Parkinson’s disease (PD) causes hypokinesia, but DA replacement therapy can elicit exaggerated voluntary and involuntary behaviors that have been attributed to enhanced DA receptor sensitivity in striatal projection neurons. Here we reveal that in hemiparkinsonian mice, striatal D1 receptor-expressing medium spiny neurons (MSNs) directly projecting to the substantia nigra reticulata (SNr) lose tonic presynaptic inhibition by GABAB receptors. The absence of presynaptic GABAB response potentiates evoked GABA release from MSN efferents to the SNr and drives motor sensitization. This alternative mechanism of sensitization suggests a synaptic target for PD pharmacotherapy

Cache valley virus in a patient diagnosed with aseptic meningitis

Cache Valley virus was initially isolated from mosquitoes and had been linked to central nervous system-associated diseases. A case of Cache Valley virus infection is described. The virus was cultured from a patient's cerebrospinal fluid and identified with real-time reverse transcription-PCR and sequencing, which also yielded the complete viral coding sequences

Global State Measures of the Dentate Gyrus Gene Expression System Predict Antidepressant-Sensitive Behaviors

Background Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common form of medication treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance. Methods We take a novel approach to this problem by demonstrating that the gene expression system of the dentate gyrus responds to fluoxetine (FLX), a commonly used antidepressant medication, in a stereotyped-manner involving changes in the expression levels of thousands of genes. The aggregate behavior of this large-scale systemic response was quantified with principal components analysis (PCA) yielding a single quantitative measure of the global gene expression system state. Results Quantitative measures of system state were highly correlated with variability in levels of antidepressant-sensitive behaviors in a mouse model of depression treated with fluoxetine. Analysis of dorsal and ventral dentate samples in the same mice indicated that system state co-varied across these regions despite their reported functional differences. Aggregate measures of gene expression system state were very robust and remained unchanged when different microarray data processing algorithms were used and even when completely different sets of gene expression levels were used for their calculation. Conclusions System state measures provide a robust method to quantify and relate global gene expression system state variability to behavior and treatment. State variability also suggests that the diversity of reported changes in gene expression levels in response to treatments such as fluoxetine may represent different perspectives on unified but noisy global gene expression system state level responses. Studying regulation of gene expression systems at the state level may be useful in guiding new approaches to augmentation of traditional antidepressant treatments