Recently published papers: An ancient debate, novel monitors and post ICU outcome in the elderly

Tracheostomies have been around for close to 3000 years, so one would hope that the controversies might have been thrashed out by now, but apparently not. Judging by some recent publications it would appear that we still do not know when or how to insert them. Monitoring is fundamental to critical care; two papers describe novel/modified techniques for assessing traumatic brain injury and cardiac output. The intensive care unit imposes a heavy treatment burden, particularly on the elderly. What impact does this have on the lives of the survivors

Improved variable selection with Forward-Lasso adaptive shrinkage

Recently, considerable interest has focused on variable selection methods in regression situations where the number of predictors, $p$, is large relative to the number of observations, $n$. Two commonly applied variable selection approaches are the Lasso, which computes highly shrunk regression coefficients, and Forward Selection, which uses no shrinkage. We propose a new approach, "Forward-Lasso Adaptive SHrinkage" (FLASH), which includes the Lasso and Forward Selection as special cases, and can be used in both the linear regression and the Generalized Linear Model domains. As with the Lasso and Forward Selection, FLASH iteratively adds one variable to the model in a hierarchical fashion but, unlike these methods, at each step adjusts the level of shrinkage so as to optimize the selection of the next variable. We first present FLASH in the linear regression setting and show that it can be fitted using a variant of the computationally efficient LARS algorithm. Then, we extend FLASH to the GLM domain and demonstrate, through numerous simulations and real world data sets, as well as some theoretical analysis, that FLASH generally outperforms many competing approaches.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS375 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Can the whole life cost of railway track be reduced through the effective management of tangential wheel-rail loading?

Imperial Users onl

Functional linear regression that's interpretable

Regression models to relate a scalar $Y$ to a functional predictor $X(t)$ are becoming increasingly common. Work in this area has concentrated on estimating a coefficient function, $\beta(t)$, with $Y$ related to $X(t)$ through $\int\beta(t)X(t) dt$. Regions where $\beta(t)\ne0$ correspond to places where there is a relationship between $X(t)$ and $Y$. Alternatively, points where $\beta(t)=0$ indicate no relationship. Hence, for interpretation purposes, it is desirable for a regression procedure to be capable of producing estimates of $\beta(t)$ that are exactly zero over regions with no apparent relationship and have simple structures over the remaining regions. Unfortunately, most fitting procedures result in an estimate for $\beta(t)$ that is rarely exactly zero and has unnatural wiggles making the curve hard to interpret. In this article we introduce a new approach which uses variable selection ideas, applied to various derivatives of $\beta(t)$, to produce estimates that are both interpretable, flexible and accurate. We call our method "Functional Linear Regression That's Interpretable" (FLiRTI) and demonstrate it on simulated and real-world data sets. In addition, non-asymptotic theoretical bounds on the estimation error are presented. The bounds provide strong theoretical motivation for our approach.Comment: Published in at http://dx.doi.org/10.1214/08-AOS641 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

MAZI Deliverable Report D2.5: – Design, progress and evaluation of the Deptford CreekNet pilot (version 2)

In this deliverable, the second in a series of three, we report on progress in the Creeknet pilot. We describe progress towards tasks identified in the Description of Work (DoW) for Task 2.2, focusing on activities in Year 2 (2017: months 13-24) and look forward to Year 3. The Creeknet pilot consists of four phases. This year, our focus has been on consolidating initial contacts made in Year 1 (Phase 1), and continuing community engagement activities alongside carrying out an initial deployment of the MAZI toolkit with a number of engaged community groups and individuals (Phase 2). In the second half of the year, as the toolkit was developed and an integrated set of tool established these groups and others were invited to engage in further trials, and feedback was gathered to further inform onward development (Phase 3). We have continued with our efforts to build upon existing relationships in Deptford Creek and further afield to help us explore the different ways in which DIY networking in the broadest sense and the use of the MAZI toolkit in particular might help address local challenges. We have reassessed some of our foci through seeking out new opportunities for engagement and trialling the MAZI toolkit. A major activity was planning and running the two day MAZI London Cross-fertilisation symposium. This created the opportunity for Creeknet participants to share their experiences and engage with the other MAZI pilots, bringing together existing community contacts in Deptford Creek, and MAZI partners, and attracted new contributors. Through our activities, working with the emerging MAZI toolkit that evolved through several iterations during the year, we have better understood local circumstances and the complexity involved in the conceptualisation of ‘DIY networking’ - it cannot be assumed to be a single notion. We have identified that both social and technological concerns can restrict its uptake, and consider routes to overcoming these challenges. We provide analysis of work carried out so far, and look towards the future activities

Sparse regulatory networks

In many organisms the expression levels of each gene are controlled by the activation levels of known "Transcription Factors" (TF). A problem of considerable interest is that of estimating the "Transcription Regulation Networks" (TRN) relating the TFs and genes. While the expression levels of genes can be observed, the activation levels of the corresponding TFs are usually unknown, greatly increasing the difficulty of the problem. Based on previous experimental work, it is often the case that partial information about the TRN is available. For example, certain TFs may be known to regulate a given gene or in other cases a connection may be predicted with a certain probability. In general, the biology of the problem indicates there will be very few connections between TFs and genes. Several methods have been proposed for estimating TRNs. However, they all suffer from problems such as unrealistic assumptions about prior knowledge of the network structure or computational limitations. We propose a new approach that can directly utilize prior information about the network structure in conjunction with observed gene expression data to estimate the TRN. Our approach uses $L_1$ penalties on the network to ensure a sparse structure. This has the advantage of being computationally efficient as well as making many fewer assumptions about the network structure. We use our methodology to construct the TRN for E. coli and show that the estimate is biologically sensible and compares favorably with previous estimates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS350 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multimedia retrieval in MultiMatch: The impact of speech transcript errors on search behaviour

This study discusses the findings of an evaluation study on the performance of a multimedia multimodal information access sub-system (MIAS), incorporating automatic speech recognition technology (ASR) to automatically transcribe the speech content of video soundtracks. The study’s results indicate that an information-rich but minimalist graphical interface is preferred. It was also discovered that users tend to have a misplaced confidence in the accuracy of ASR-generated speech transcripts, thus they are not inclined to conduct a systematic auditory inspection (their usual search behaviour) of a video’s soundtrack if the query term does not appear in the transcript. In order to alert the user to the possibility that a search term may be incorrectly recognised as some other word, a matching algorithm is proposed that searches for word sequences of similar phonemic structure to the query term