Adaptation Algorithm and Theory Based on Generalized Discrepancy

We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm previously shown to outperform a number of algorithms for this task. Unlike many previous algorithms for domain adaptation, our algorithm does not consist of a fixed reweighting of the losses over the training sample. We show that our algorithm benefits from a solid theoretical foundation and more favorable learning bounds than discrepancy minimization. We present a detailed description of our algorithm and give several efficient solutions for solving its optimization problem. We also report the results of several experiments showing that it outperforms discrepancy minimization

Risks and Rewards of Advanced Practice Providers in Cardiothoracic Surgery Training: National Survey

Background Changes in healthcare have led to increasing utilization of Advanced Practice Providers (APPs), but their role in Cardiothoracic Surgery (CTS) education remains undefined. This study aimed to analyze the extent of APP utilization on the CTS team, their role within the hierarchy of clinical care, and the impact of PEs on CTS training from the resident perspective. Methods CTS residents’ responses to the 2017 Thoracic Surgery Residents Association (TSRA)/Thoracic Surgery Directors Association (TSDA) In-Service Training Examination (ITE) survey regarding the role of APPs in specific clinical scenarios, and perception of APP contribution to residents’ educational environment were analyzed. Statistical analysis of categorical variables was performed in SPSS using a Fisher’s exact test and Pearson Chi-Square with statistical significance set at p<0.05. Results Response rate was 82.1% (280/341). The median number of employed APPs was 16-20 and 50.4% (n=141) reported 11-25 PEs at their institution. The median forAPPs in the operating room, floor, and intensive care unit was 3, 3, and 2 respectively. Overall impression of APPs was positive in 87.5% (n=245) of respondents, with 47.7% (n=133) being “very positive” and 40.1% being “positive” (n=112). In general, residents reported greater resident involvement in post-operative issues and operative consults and greater APP involvement in floor issues. 72.5% of residents had not missed a surgical opportunity due to APPs while, 9.6% missed an opportunity due to a APP despite being at an appropriate level of training. Of those that reported missed opportunities, 44% were I-6 residents. There were no significant differences in APPs’ operative role based on resident seniority. Conclusions The overall impression of APPs among CTS residents is favorable, and they more commonly are involved assisting on the floor or the operating room. Occasionally, residents report missing a surgical opportunity due to APPs. There is further opportunity to optimize and standardize their role within programs, in order to improve clinical outcomes and enhance the CTS educational experience for residents

Orbital dynamics of "smart dust" devices with solar radiation pressure and drag

This paper investigates how perturbations due to asymmetric solar radiation pressure, in the presence of Earth shadow, and atmospheric drag can be balanced to obtain long-lived Earth centred orbits for swarms of micro-scale 'smart dust' devices, without the use of active control. The secular variation of Keplerian elements is expressed analytically through an averaging technique. Families of solutions are then identified where Sun-synchronous apse-line precession is achieved passively to maintain asymmetric solar radiation pressure. The long-term orbit evolution is characterized by librational motion, progressively decaying due to the non-conservative effect of atmospheric drag. Long-lived orbits can then be designed through the interaction of energy gain from asymmetric solar radiation pressure and energy dissipation due to drag. In this way, the usual short drag lifetime of such high area-to-mass spacecraft can be greatly extended (and indeed selected). In addition, the effect of atmospheric drag can be exploited to ensure the rapid end-of-life decay of such devices, thus preventing long-lived orbit debris

Disabled-2 (Dab2) inhibits Wnt/β-catenin signalling by binding LRP6 and promoting its internalization through clathrin

Wnt signalling requires caveolin-dependent endocytic uptake of the Fz/LRP6 receptor complex. The tumour suppressor Disabled-2 inhibits Wnt signalling by sequestering CK2-phosphorylated LRP6 into an alternative clathrin-dependent endocytic pathway

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

<p>Abstract</p> <p>Background</p> <p>The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published <it>q</it>-Norm MKL algorithm.</p> <p>Results</p> <p>We demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities<it> ab initio</it> along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against.</p> <p>Conclusions</p> <p>We present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.</p

A critical role for endocytosis in Wnt signaling

BACKGROUND: The Wnt signaling pathway regulates many processes during embryonic development, including axis specification, organogenesis, angiogenesis, and stem cell proliferation. Wnt signaling has also been implicated in a number of cancers, bone density maintenance, and neurological conditions during adulthood. While numerous Wnts, their cognate receptors of the Frizzled and Arrow/LRP5/6 families and downstream pathway components have been identified, little is known about the initial events occurring directly after receptor activation. RESULTS: We show here that Wnt proteins are rapidly endocytosed by a clathrin- and dynamin-mediated process. While endocytosis has traditionally been considered a principal mechanism for receptor down-regulation and termination of signaling pathways, we demonstrate that interfering with clathrin-mediated endocytosis actually blocks Wnt signaling at the level of β-catenin accumulation and target gene expression. CONCLUSION: A necessary component of Wnt signaling occurs in a subcellular compartment distinct from the plasma membrane. Moreover, as internalized Wnts transit partially through the transferrin recycling pathway, it is possible that a "signaling endosome" serves as a nexus for activated Wnt pathway components