Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of feature vectors (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space, whereas negative bags only contain negative instances. The classes in a MIL problem are therefore not treated in the...

Multiple-instance learning (MIL) deals with learning under ambiguity, in which patterns to be classified are described by bags of instances. There has been a growing interest in the design and use of MIL algorithms as it provides a natural framework to solve a wide variety of pattern recognition problems. In this paper, we address MIL from a view that transforms the problem into a standard supe...

BACKGROUND The mitral isthmus is a critical part of perimitral reentrant tachycardia, as well as an important substrate of persistent atrial fibrillation. Deployment of an endocardial mitral isthmus line (MIL) with the end point of bidirectional block may be challenging and often requires additional epicardial ablation within the coronary sinus. METHODS AND RESULTS The study population compri...

Pulmonary arterial hypertension (PAH) is a progressive and debilitating condition. Despite promoting vasodilation, current drugs have a therapeutic window within which they are limited by systemic side effects. Nanomedicine uses nanoparticles to improve drug delivery and/or reduce side effects. We hypothesize that this approach could be used to deliver PAH drugs avoiding the systemic circulatio...

We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks. This framework can be used to model the traditional MIL definition as well as more general MIL definitions. Different levels of ambiguity – the portion of positive instances in a bag – can be explored in weakly supervised data. To train these models, we propose a discriminative maxmargin learning a...

Multiple Instance Learning (MIL) is proposed as a variation of supervised learning for problems with incomplete knowledge about labels of training examples. In supervised learning, every training instance is assigned with a discrete or real-valued label. In comparison, in MIL the labels are only assigned to bags of instances. In the binary case, a bag is labeled positive if at least one instanc...

To determine whether IL-4 is therapeutic in treating established experimental arthritis, a recombinant adenovirus carrying the gene that encodes murine IL-4 (Ad-mIL-4) was used for periarticular injection into the ankle joints into mice with established collagen-induced arthritis (CIA). Periarticular injection of Ad-mIL-4 resulted in a reduction in the severity of arthritis and joint swelling c...

Having a sense of meaning in life (MIL) has been acknowledged as a catalyst to psychological flourishing. As such, understanding ways to promote MIL represents a worthy goal for those interested in bolstering positive outcomes. This study sought to replicate the findings of Heintzelman, Trent & King (2013 Psychol. Sci.24, 991-998 (doi:10.1177/0956797612465878)), who found that MIL could be infl...

We cloned the sequence encoding murine interleukin-4 (mIL-4), including the secretory signal, into the genome of CVB3/0, an artificially attenuated strain of coxsackievirus B3, at the junction of the capsid protein 1D and the viral protease 2Apro. Two strains of chimeric CVB3 were constructed using, in one case, identical sequences to encode 2Apro cleavage sites (CVB3/0-mIL4/47) on either side ...

Effective eradication of established tumors and generation of a lasting systemic immune response is an important goal for cancer gene immunotherapy. The method of gene delivery may also be critical for the generation of an effective antitumor response. We compared the level of transgene expression and antitumor activity of two different interleukin (IL)-12 DNA preparations (naked DNA versus DNA...