Impact of chemotherapy on cancer-related fatigue and cytokines in 1312 patients: A systematic review of quantitative studies

Purpose of review: Cancer-related fatigue (CRF) is the most common psychosomatic distress experienced by cancer patients before, during and after chemotherapy. Its impact on functional status and Health Related Quality of Life is a great concern among patients, healthcare professionals and researchers. The primary objective of this systematic review is to determine whether the different chemotherapies affect the association of CRF with individual pro- and anti-inflammatory cytokines. The PRISMA statement guideline has been followed to systematically search and screen article from PubMed and Embase. Recent findings: This review has examined 14 studies which included a total of 1312 patients. These studies assayed 20 different kinds of cytokines. The cytokines interleukin-6, interleukin-1RA, TGF-[beta] and sTNF-R2 were associated with CRF in patients receiving anthracycline-based chemotherapy. However, only interleukin-13 was identified in the taxane-based chemotherapy. Similarly, different sets of cytokines were linked with CRF in patients with chemotherapy regimens containing platinum, cyclophosphamides, topotecan or bleomycin. Summary: This review has identified that cytokines are differentially linked with CRF according to the various types of chemotherapy regimens.

In-training assessment - Its potential in enhancing clinical teaching

Context In-training assessment (ITA) has established its place alongside formative and summative assessment at both the undergraduate and postgraduate level. In this paper the authors aimed to identify those characteristics of ITA that could enhance clinical teaching. Methods A literature review and discussions by an expert working group at the Ninth Cambridge Conference identified the aspects of ITA that could enhance clinical teaching. Results The features of ITA identified included defining the specific benefits to the learner, teacher and institution, and highlighting the patient as the context for ITA and clinical teaching. The ‘mapping’ of a learner’s progress towards the clinical teaching objectives by using multiple assessments over time, by multiple observers in both a systematic and opportunistic way correlates with the incremental nature of reaching clinical competence. Conclusions The importance of ITA based on both direct and indirect evidence of what the learner actually does in the real clinical setting is emphasized. Particular attention is given to addressing concerns in the more controversial areas of assessor training, ratings and documentation for ITA. Areas for future research are also identified.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.

Mapping sociocultural controversies across digital media platforms: One week of #gamergate on Twitter, YouTube, and Tumblr

Social media play a prominent role in mediating issues of public concern, not only providing the stage on which public debates play out but also shaping their topics and dynamics. Building on and extending existing approaches to both issue mapping and social media analysis, this article explores ways of accounting for popular media practices and the special case of ‘born digital’ sociocultural controversies. We present a case study of the GamerGate controversy with a particular focus on a spike in activity associated with a 2015 Law and Order: SVU episode about gender-based violence and harassment in games culture that was widely interpreted as being based on events associated with GamerGate. The case highlights the importance and challenges of accounting for the cultural dynamics of digital media within and across platforms.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

The selection, optimization, and compensation model in the work context: A systematic review and meta-analysis of two decades of research

Over the past two decades, the selection, optimization, and compensation (SOC) model has been applied in the work context to investigate antecedents and outcomes of employees' use of action regulation strategies. We systematically review, meta-analyze, and critically discuss the literature on SOC strategy use at work and outline directions for future research and practice. The systematic review illustrates the breadth of constructs that have been studied in relation to SOC strategy use, and that SOC strategy use can mediate and moderate relationships of person and contextual antecedents with work outcomes. Results of the meta-analysis show that SOC strategy use is positively related to age (rc = .04), job autonomy (rc = .17), self-reported job performance (rc = .23), non-self-reported job performance (rc = .21), job satisfaction (rc = .25), and job engagement (rc = .38), whereas SOC strategy use is not significantly related to job tenure, job demands, and job strain. Overall, our findings underline the importance of the SOC model for the work context, and they also suggest that its measurement and reporting standards need to be improved to become a reliable guide for future research and organizational practice.

Mixed pixel analysis and assessment for flood mapping

The most difficult operation in flood inundation mapping using optical flood images is to map the ‘wet’ areas where trees and houses are partly covered by water. This can be referred to as a typical problem of the presence of mixed pixels in the images. A number of automatic information extracting image classification algorithms have been developed over the years for flood mapping using optical remote sensing images, with most labelling a pixel as a particular class. However, they often fail to generate reliable flood inundation mapping because of the presence of mixed pixels in the images. To solve this problem, spectral unmixing methods have been developed. In this thesis, methods for selecting endmembers and the method to model the primary classes for unmixing, the two most important issues in spectral unmixing, are investigated. We conduct comparative studies of three typical spectral unmixing algorithms, Partial Constrained Linear Spectral unmixing, Multiple Endmember Selection Mixture Analysis and spectral unmixing using the Extended Support Vector Machine method. They are analysed and assessed by error analysis in flood mapping using MODIS, Landsat and World View-2 images. The Conventional Root Mean Square Error Assessment is applied to obtain errors for estimated fractions of each primary class. Moreover, a newly developed Fuzzy Error Matrix is used to obtain a clear picture of error distributions at the pixel level. This thesis shows that the Extended Support Vector Machine method is able to provide a more reliable estimation of fractional abundances and allows the use of a complete set of training samples to model a defined pure class. Furthermore, it can be applied to analysis of both pure and mixed pixels to provide integrated hard-soft classification results. Our research also identifies and explores a serious drawback in relation to endmember selections in current spectral unmixing methods which apply fixed sets of endmember classes or pure classes for mixture analysis of every pixel in an entire image. However, as it is not accurate to assume that every pixel in an image must contain all endmember classes, these methods usually cause an over-estimation of the fractional abundances in a particular pixel. In this thesis, a subset of adaptive endmembers in every pixel is derived using the proposed methods to form an endmember index matrix. The experimental results show that using the pixel-dependent endmembers in unmixing significantly improves performance.