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

Echocardiographic Guidance During Neonatal and Pediatric Jugular Cannulation for ECMO

Background Internal jugular vein extracorporeal membrane oxygenation (ECMO) cannula position is traditionally confirmed via plain film. Misplaced cannulae can result in need for repositioning and increased morbidity. Echocardiography (ECHO) may be used during cannulation as a more accurate means of guiding cannula position. This study reviews the effect of a protocol encouraging the use of ECHO at cannulation. Methods and materials Single institution retrospective review of patients who received ECMO support using jugular venous cannulation. We compared those who underwent ECHO (ECHO+) at the time of cannulation with those who did not (ECHO−). Results Eighty-nine patients were included: 26 ECHO+, 63 ECHO−. Most ECHO+ patients underwent dual-lumen veno-venous (VV) cannulation (65%); 32% of ECHO− patients had VV support (P = 0.003). There was no difference in the rate of cannula repositioning between the two groups: 8% ECHO+ and 10% ECHO−, P = 0.78. In the VV ECMO subgroup, ECHO+ patients required no repositioning (0/17), while 20% (4/20) of ECHO− VV patients did (P = 0.10). After cannulation, there were 0.58 ECHO studies per patient to verify cannula position in the ECHO+ group compared with 0.22 in the ECHO− group (P = 0.02). Each group had a major mechanical complication: atrial perforation from a guidewire during cannulation in ECHO+ and late atrial perforation from a loose cannula in ECHO−, and there was no difference in minor complications. Conclusions ECHO guidance during neonatal and pediatric jugular cannulation for ECMO did not decrease morbidity or reduce the need for cannula repositioning. ECHO may still be a useful adjunct for precise placement of a dual-lumen VV cannula and during difficult cannulations

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

The key role of nitric oxide in hypoxia: hypoxic vasodilation and energy supply-demand matching

Significance: a mismatch between energy supply and demand induces tissue hypoxia with the potential to cause cell death and organ failure. Whenever arterial oxygen concentration is reduced, increases in blood flow - 'hypoxic vasodilation' - occur in an attempt to restore oxygen supply. Nitric oxide is a major signalling and effector molecule mediating the body's response to hypoxia, given its unique characteristics of vasodilation (improving blood flow and oxygen supply) and modulation of energetic metabolism (reducing oxygen consumption and promoting utilization of alternative pathways). Recent advances: this review covers the role of oxygen in metabolism and responses to hypoxia, the hemodynamic and metabolic effects of nitric oxide, and mechanisms underlying the involvement of nitric oxide in hypoxic vasodilation. Recent insights into nitric oxide metabolism will be discussed, including the role for dietary intake of nitrate, endogenous nitrite reductases, and release of nitric oxide from storage pools. The processes through which nitric oxide levels are elevated during hypoxia are presented, namely (i) increased synthesis from nitric oxide synthases, increased reduction of nitrite to nitric oxide by heme- or pterin-based enzymes and increased release from nitric oxide stores, and (ii) reduced deactivation by mitochondrial cytochrome c oxidase. Critical issues: several reviews covered modulation of energetic metabolism by nitric oxide, while here we highlight the crucial role NO plays in achieving cardiocirculatory homeostasis during acute hypoxia through both vasodilation and metabolic suppression Future directions: we identify a key position for nitric oxide in the body's adaptation to an acute energy supply-demand mismatc

Unilateral versus bilateral thyroarytenoid Botulinum toxin injections in adductor spasmodic dysphonia: a prospective study

OBJECTIVES: In this preliminary prospective study, we compared unilateral and bilateral thyroarytenoid muscle injections of Botulinum toxin (Dysport) in 31 patients with adductor spasmodic dysphonia, who had undergone more than 5 consecutive Dysport injections (either unilateral or bilateral) and had completed 5 concomitant self-rated efficacy and complication scores questionnaires related to the previous injections. We also developed a Neurophysiological Scoring (NPS) system which has utility in the treatment administration. METHOD AND MATERIALS: Data were gathered prospectively on voice improvement (self-rated 6 point scale), length of response and duration of complications (breathiness, cough, dysphagia and total voice loss). Injections were performed under electromyography (EMG) guidance. NPS scale was used to describe the EMG response. Dose and unilateral/bilateral injections were determined by clinical judgment based on previous response. Time intervals between injections were patient driven. RESULTS: Low dose unilateral Dysport injection was associated with no significant difference in the patient's outcome in terms of duration of action, voice score (VS) and complication rate when compared to bilateral injections. Unilateral injections were not associated with any post treatment total voice loss unlike the bilateral injections. CONCLUSION: Unilateral low dose Dysport injections are recommended in the treatment of adductor spasmodic dysphonia

Low-Rank Subspace Override for Unsupervised Domain Adaptation

Current supervised learning models cannot generalize well across domain boundaries, which is a known problem in many applications, such as robotics or visual classification. Domain adaptation methods are used to improve these generalization properties. However, these techniques suffer either from being restricted to a particular task, such as visual adaptation, require a lot of computational time and data, which is not always guaranteed, have complex parameterization, or expensive optimization procedures. In this work, we present an approach that requires only a well-chosen snapshot of data to find a single domain invariant subspace. The subspace is calculated in closed form and overrides domain structures, which makes it fast and stable in parameterization. By employing low-rank techniques, we emphasize on descriptive characteristics of data. The presented idea is evaluated on various domain adaptation tasks such as text and image classification against state of the art domain adaptation approaches and achieves remarkable performance across all tasks

A primer for the student joining the general thoracic surgery service tomorrow: Primer 2 of 7.

This medical student primer discusses general thoracic surgery, which involves treating pathologies involving all structures in the thorax except the heart, thoracic aorta, great vessels, and spine

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