Defects in the IFT-B component IFT172 cause Jeune and Mainzer-Saldino syndromes in humans

Intraflagellar transport (IFT) depends on two evolutionarily conserved modules, subcomplexes A (IFT-A) and B (IFT-B), to drive ciliary assembly and maintenance. All six IFT-A components and their motor protein, DYNC2H1, have been linked to human skeletal ciliopathies, including asphyxiating thoracic dystrophy (ATD; also known as Jeune syndrome), Sensenbrenner syndrome, and Mainzer-Saldino syndrome (MZSDS). Conversely, the 14 subunits in the IFT-B module, with the exception of IFT80, have unknown roles in human disease. To identify additional IFT-B components defective in ciliopathies, we independently performed different mutation analyses: candidate-based sequencing of all IFT-B-encoding genes in 1,467 individuals with a nephronophthisis-related ciliopathy or whole-exome resequencing in 63 individuals with ATD. We thereby detected biallelic mutations in the IFT-B-encoding gene IFT172 in 12 families. All affected individuals displayed abnormalities of the thorax and/or long bones, as well as renal, hepatic, or retinal involvement, consistent with the diagnosis of ATD or MZSDS. Additionally, cerebellar aplasia or hypoplasia characteristic of Joubert syndrome was present in 2 out of 12 families. Fibroblasts from affected individuals showed disturbed ciliary composition, suggesting alteration of ciliary transport and signaling. Knockdown of ift172 in zebrafish recapitulated the human phenotype and demonstrated a genetic interaction between ift172 and ift80. In summary, we have identified defects in IFT172 as a cause of complex ATD and MZSDS. Our findings link the group of skeletal ciliopathies to an additional IFT-B component, IFT172, similar to what has been shown for IFT-A.

Canadian Stroke Best Practice Recommendations: Mood, Cognition and Fatigue Following Stroke practice guidelines, update 2015

Every year, approximately 62 000 people with stroke and transient ischemic attack are treated in Canadian hospitals, and the evidence suggests one-third or more will experience vascular-cognitive impairment, and/or intractable fatigue, either alone or in combination. The 2015 update of the Canadian Stroke Best Practice Recommendations: Mood, Cognition and Fatigue Module guideline is a comprehensive summary of current evidence-based recommendations for clinicians in a range of settings, who provide care to patients following stroke. The three consequences of stroke that are the focus of the this guideline (poststroke depression, vascular cognitive impairment, and fatigue) have high incidence rates and significant impact on the lives of people who have had a stroke, impede recovery, and result in worse long-term outcomes. Significant practice variations and gaps in the research evidence have been reported for initial screening and in-depth assessment of stroke patients for these conditions. Also of concern, an increased number of family members and informal caregivers may also experience depressive symptoms in the poststroke recovery phase which further impact patient recovery. These factors emphasize the need for a system of care that ensures screening occurs as a standard and consistent component of clinical practice across settings as stroke patients transition from acute care to active rehabilitation and reintegration into their community. Additionally, building system capacity to ensure access to appropriate specialists for treatment and ongoing management of stroke survivors with these conditions is another great challenge.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.