For over three decades, a mainstay and goal of clinical oncology has been the development of therapies promoting the effective elimination of cancer cells by apoptosis. This programmed cell death process is mediated by several signalling pathways (referred to as intrinsic and extrinsic) triggered by multiple factors, including cellular stress, DNA damage and immune surveillance. The interaction of apoptosis pathways with other signalling mechanisms can also affect cell death. The clinical translation of effective pro-apoptotic agents involves drug discovery studies (addressing the bioavailability, stability, tumour penetration, toxicity profile in non-malignant tissues, drug interactions and off-target effects) as well as an understanding of tumour biology (including heterogeneity and evolution of resistant clones). While tumour cell death can result in response to therapy, the selection, growth and dissemination of resistant cells can ultimately be fatal. In this Review, we present the main apoptosis pathways and other signalling pathways that interact with them, and discuss actionable molecular targets, therapeutic agents in clinical translation and known mechanisms of resistance to these agents.

Targeting apoptosis in cancer therapy

This article reviews and presents various solved and open problems in the development, analysis, and control of epidemic models. The proper modeling and analysis of spreading processes has been a long-standing area of research among many different fields, including mathematical biology, physics, computer science, engineering, economics, and the social sciences. One of the earliest epidemic models conceived was by Daniel Bernoulli in 1760, which was motivated by studying the spread of smallpox [1]. In addition to Bernoulli, there were many different researchers also working on mathematical epidemic models around this time [2]. These initial models were quite simplistic, and the further development and study of such models dates back to the 1900s [3]-[6], where still-simple models were studied to provide insight into how various diseases can spread through a population. In recent years, there has been a resurgence of interest in these problems as the concept of "networks" becomes increasingly prevalent in modeling many different aspects of the world today. A more comprehensive review of the history of mathematical epidemiology can be found in [7] and [8].

Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks

Salicylic acid (SA) and acetylsalicylic acid (ASA), previously shown to inhibit proteinase inhibitor synthesis induced by wounding, oligouronides (H.M. Doherty, R.R. Selvendran, D.J. Bowles [1988] Physiol Mol Plant Pathol 33: 377-384), and linolenic acid (H. Pena-Cortes, T. Albrecht, S. Prat, E.W. Weiler, L. Willmitzer [1993] Planta 191: 123-128), are shown here to be potent inhibitors of systemin-induced and jasmonic acid (JA)-induced synthesis of proteinase inhibitor mRNAs and proteins. The inhibition by SA and ASA of proteinase inhibitor synthesis induced by systemin and JA, as well as by wounding and oligosaccharide elicitors, provides further evidence that both oligosaccharide and polypeptide inducer molecules utilize the octadecanoid pathway to signal the activation of proteinase inhibitor genes. Tomato (Lycopersicon esculentum) leaves were pulse labeled with [35S]methionine, followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and the inhibitory effects of SA are shown to be specific for the synthesis of a small number of JA-inducible proteins that includes the proteinase inhibitors. Previous results have shown that SA inhibits the conversion of 13S-hydroperoxy linolenic acid to 12-oxo-phytodienoic acid, thereby inhibiting the signaling pathway by blocking synthesis of JA. Here we report that the inhibition of synthesis of proteinase inhibitor proteins and mRNAs by SA in both light and darkness also occurs at a step in the signal transduction pathway, after JA synthesis but preceding transcription of the inhibitor genes.

Salicylic acid inhibits synthesis of proteinase inhibitors in tomato leaves induced by systemin and jasmonic acid

Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

The literature on programming sensor networks has focused so far on providing higher-level abstractions for expressing local node behavior. Kairos is a natural next step in sensor network programming in that it allows the programmer to express, in a centralized fashion, the desired global behavior of a distributed computation on the entire sensor network. Kairos’ compile-time and runtime subsystems expose a small set of programming primitives, while hiding from the programmer the details of distributed-code generation and instantiation, remote data access and management, and inter-node program flow coordination. In this paper, we describe Kairos’ programming model, and demonstrate its suitability, through actual implementation, for a variety of distributed programs—both infrastructure services and signal processing tasks—typically encountered in sensor network literature: routing tree construction, localization, and object tracking. Our experimental results suggest that Kairos does not adversely affect the performance or accuracy of distributed programs, while our implementation experiences suggest that it greatly raises the level of abstraction presented to the programmer.

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Macro-programming wireless sensor networks using Kairos

Single-user interactive computer applications are pervasive in our daily lives and work. Leveraging single-user applications for supporting multi-user collaboration has the potential to significantly increase the availability and improve the usability of collaborative applications. In this article, we report an innovative Transparent Adaptation (TA) approach and associated supporting techniques that can be used to convert existing and new single-user applications into collaborative ones, without changing the source code of the original application. The cornerstone of the TA approach is the operational transformation (OT) technique and the method of adapting the single-user application programming interface to the data and operation models of OT. This approach and supporting techniques were developed and tested in the process of transparently converting two commercial off-the-shelf single-user applications (Microsoft Word and PowerPoint) into real-time collaborative applications, called CoWord and CoPowerPoint, respectively. CoWord and CoPowerPoint not only retain the functionalities and “look-and-feel” of their single-user counterparts, but also provide advanced multi-user collaboration capabilities for supporting multiple interaction paradigms, ranging from concurrent and free interaction to sequential and synchronized interaction, and for supporting detailed workspace awareness, including multi-user telepointers and radar views. The TA approach and generic collaboration engine software component developed from this work are potentially applicable and reusable in adapting a wide range of single-user applications.

Transparent adaptation of single-user applications for multi-user real-time collaboration

Single-user interactive computer applications are pervasive in our daily lives and work. Leveraging single-user applications for multi-user collaboration has the potential to significantly increase the availability and improve the usability of collaborative applications. In this paper, we report an innovative transparent adaptation approach for this purpose. The basic idea is to adapt the single-user application programming interface to the data and operational models of the underlying collaboration supporting technique, namely Operational Transformation. Distinctive features of this approach include: (1) Application transparency: it does not require access to the source code of the single-user application; (2) Unconstrained collaboration: it supports concurrent and free interaction and collaboration among multiple users; and (3) Reusable collaborative software components: collaborative software components developed with this approach can be reused in adapting a wide range of single-user applications. This approach has been applied to transparently convert MS Word into a real-time collaborative word processor, called CoWord, which supports multiple users to view and edit any objects in the same Word document at the same time over the Internet. The generality of this approach has been tested by re-applying it to convert MS PowerPoint into CoPowerPoint.

/pdf/leveraging-single-user-applications-for-multi-user-18hfc77zqx.pdf

Leveraging single-user applications for multi-user collaboration: the coword approach

Operational Transformation (OT) is a technique originally invented for supporting consistency maintenance in collaborative text editors. Word processors have much richer data types and more comprehensive operations than plain text editors. Among others, the capability of updating attributes of any types of object is an essential feature of all word processors. In this paper, we report an extension of OT for supporting a generic Update operation, in addition to Insert and Delete operations, for collaborative word processing. We focus on technical issues and solutions involved in transforming Updates for both consistency maintenance and group undo. A novel technique, called Multi-Version Single-Display (MVSD), has been devised to resolve conflict between concurrent Updates, and integrated into the framework of OT. This work has been motivated by and conducted in the CoWord project, which aims to convert MS Word into a real-time collaborative word processor without changing its source code. This OT extension is relevant not only to word processors but also to a range of interactive applications that can be modelled as editors.

Operational transformation for collaborative word processing

Operational Transformation (OT) is a consistency maintenance technique for collaborative editing systems-a special class of distributed applications for supporting human-computer-human interaction and collaboration over communication networks. The theory of causality has been the foundation of all prior OT systems, but it is inadequate to meet essential OT requirements in functionality and correctness. In this paper, we analyze the limitation of the causality theory, propose a novel theory of operation context as the new foundation for OT systems, and present a new OT algorithm-Context-based OT (COT)-which provides uniform and efficient solutions to both consistency maintenance and undo problems. The COT algorithm has been implemented and used for supporting a range of novel collaborative applications. The context theory and context vectors are potentially applicable to other distributed computing applications.

Context-Based Operational Transformation in Distributed Collaborative Editing Systems

Stress causes and exacerbates many physiological and mental health problems. Routine and unobtrusive monitoring of stress would enable a variety of treatments, from break-taking to calming exercises. It may also be a valuable tool for assessing effects (frustration, difficulty) of using interfaces or applications. Custom sensing hardware is a poor option, because of the need to buy/wear/use it continuously, even before stress-related problems are evident. Here we explore stress measurement from common computer mouse operations. We use a simple model of arm-hand dynamics that captures muscle stiffness during mouse movement. We show that the within-subject mouse-derived stress measure is quite strong, even compared to concurrent physiological sensor measurements. While our study used fixed mouse tasks, the stress signal was still strong even when averaged across widely varying task geometries. We argue that mouse sensing "in the wild" may be feasible, by analyzing frequently-performed operations of particular geometries.

/pdf/moustress-detecting-stress-from-mouse-motion-54y8q03yis.pdf

David Sun

Papers

Transparent adaptation of single-user applications for multi-user real-time collaboration

Leveraging single-user applications for multi-user collaboration: the coword approach

Operational transformation for collaborative word processing

Context-Based Operational Transformation in Distributed Collaborative Editing Systems

MouStress: detecting stress from mouse motion