Monash University

About: Monash University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 35920 authors who have published 100681 publications receiving 3027002 citations.

Nanoparticle-Based Medicines: A Review of FDA-Approved Materials and Clinical Trials to Date.

Abstract: In this review we provide an up to date snapshot of nanomedicines either currently approved by the US FDA, or in the FDA clinical trials process. We define nanomedicines as therapeutic or imaging agents which comprise a nanoparticle in order to control the biodistribution, enhance the efficacy, or otherwise reduce toxicity of a drug or biologic. We identified 51 FDA-approved nanomedicines that met this definition and 77 products in clinical trials, with ~40% of trials listed in clinicaltrials.gov started in 2014 or 2015. While FDA approved materials are heavily weighted to polymeric, liposomal, and nanocrystal formulations, there is a trend towards the development of more complex materials comprising micelles, protein-based NPs, and also the emergence of a variety of inorganic and metallic particles in clinical trials. We then provide an overview of the different material categories represented in our search, highlighting nanomedicines that have either been recently approved, or are already in clinical trials. We conclude with some comments on future perspectives for nanomedicines, which we expect to include more actively-targeted materials, multi-functional materials (“theranostics”) and more complicated materials that blur the boundaries of traditional material categories. A key challenge for researchers, industry, and regulators is how to classify new materials and what additional testing (e.g. safety and toxicity) is required before products become available.

Deep learning for time series classification: a review

Abstract: Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

Natural Innate and Adaptive Immunity to Cancer

Abstract: The immune system can identify and destroy nascent tumor cells in a process termed cancer immunosurveillance, which functions as an important defense against cancer. Recently, data obtained from numerous investigations in mouse models of cancer and in humans with cancer offer compelling evidence that particular innate and adaptive immune cell types, effector molecules, and pathways can sometimes collectively function as extrinsic tumor-suppressor mechanisms. However, the immune system can also promote tumor progression. Together, the dual host-protective and tumor-promoting actions of immunity are referred to as cancer immunoediting. In this review, we discuss the current experimental and human clinical data supporting a cancer immunoediting process that provide the fundamental basis for further study of immunity to cancer and for the rational design of immunotherapies against cancer.

Distributed leadership as a unit of analysis

Abstract: This article proposes a new unit of analysis in the study of leadership. As an alternative to the current focus, which is primarily on the deeds of individual leaders, the article proposes distributed leadership. The article shows how conventional constructs of leadership have difficulty accommodating changes in the division of labor in the workplace, especially, new patterns of interdependence and coordination which have given rise to distributed practice. A number of forms of distributed leadership are then outlined, in particular, three varieties of concertive action in which a key defining criterion is conjoint agency. These forms provide the basis for a taxonomy of distributed leadership and a review of examples in the literature. The article concludes with a consideration of some implications of the adoption of a revised unit of analysis, particularly for recent work on levels of analysis and for future research into leadership as a process.