The murine interleukin 5 receptor (mIL-5R) is composed of two distinct subunits, alpha and beta. The alpha subunit (mIL-5R alpha) specifically binds IL-5 with low affinity. The beta subunit (mIL-5R beta) does not bind IL-5 by itself, but forms the high-affinity receptor with mIL-5R alpha. mIL-5R beta has been revealed to be the mIL-3R-like protein, AIC2B which is shared with receptors for IL-3 ...

Millepachine (MIL) was a novel chalcone that was separated from Millettia pachycarpa Benth (Leguminosae). We found MIL induced apoptosis through activating NF-κB pathway both in SK-OV-3 and A2780S cells. Western blot showed that MIL increased the levels of IKKα, p-IKKα/β, p-IκBα and NF-κB (p65) proteins, and decreased the expression of IκBα protein. Immunohistochemistry analysis indicated that ...

Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-based image retrieval and text categorization can be viewed as MIL problems. In this paper, we propose a new graph-based semi-supervised learning approach for multiple instance learning. By defining an instance-level g...

VMEbus Systems / August 2005 MIL-STD-1553 is a serial, time-division multiplex data bus that has been used as the primary command and control data interconnect in military aircraft for the past three decades. MIL-STD1553’s robust performance, high level of interoperability, large installed base, and well established infrastructure of vendors has made it the network of choice for military avioni...

The effect of synthesis pH and H2O/EtOH molar ratio on the textural properties of different aluminium trimesate metal organic frameworks (MOFs) prepared in the presence of the well-known cationic surfactant cetyltrimethylammonium bromide (CTAB) at 120 °C was studied with the purpose of obtaining a MOF with hierarchical pore structure. Depending on the pH and the solvent used, different topologi...

Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator tha...

We extend a previous work on a multithreaded typed assembly language (MIL) targeted at shared memory multiprocessors, and describe the design of a type-preserving compiler from the π-calculus into MIL. The language enforces a policy on lock usage through a typing system that also ensures race-freedom for typable programs, while allowing for typing various important concurrency patterns. Our tra...

In this paper, the effectiveness of a corrective learning algorithm MIL (Mirror Image Learning) [1] [2] is comparatively studied with that of ALSM (Average Learning Subspace Method) [3]. Both MIL and ALSM were proposed to improve the learning effectiveness of class conditional distributions. While the ALSM modifies the basis vectors of a subspace by subtracting the autocorrelation matrix for co...

We extend a previous work on a multithreaded typed assembly language (MIL) targeted at shared memory multiprocessors, and describe the design of a type-preserving compiler from the π-calculus into MIL. The language enforces a policy on lock usage through a typing system that also ensures race-freedom for typable programs, while allowing for typing various important concurrency patterns. Our tra...

In the supervised learning setting termed Multiple-Instance Learning (MIL), the examples are bags of instances, and the bag label is a function of the labels of its instances. Typically, this function is the Boolean OR. The learner observes a sample of bags and the bag labels, but not the instance labels that determine the bag labels. The learner is then required to emit a classification rule f...