Surface Instability of Icicles

Quantitatively-unexplained stationary waves or ridges often encircle icicles. Such waves form when roughly 0.1 mm-thick layers of water flow down the icicle. These waves typically have a wavelength of 1cm approximately independent of external temperature, icicle thickness, and the volumetric rate of water flow. In this paper we show that these waves can not be obtained by naive Mullins-Sekerka instability, but are caused by a quite new surface instability related to the thermal diffusion and hydrodynamic effect of thin water flow.Comment: 11 pages, 5 figures, Late

An unbiased proteomic screen reveals caspase cleavage is positively and negatively regulated by substrate phosphorylation

Post-translational modifications of proteins regulate diverse cellular functions, with mounting evidence suggesting that hierarchical cross-talk between distinct modifications may fine-tune cellular responses. For example, in apoptosis, caspases promote cell death via cleavage of key structural and enzymatic proteins that in some instances is inhibited by phosphorylation near the scissile bond. In this study, we systematically investigated how protein phosphorylation affects susceptibility to caspase cleavage using an N-terminomic strategy, namely, a modified terminal amino isotopic labeling of substrates (TAILS) workflow, to identify proteins for which caspase-catalyzed cleavage is modulated by phosphatase treatment. We validated the effects of phosphorylation on three of the identified proteins and found that Yap1 and Golgin-160 exhibit decreased cleavage when phosphorylated, whereas cleavage of MST3 was promoted by phosphorylation. Furthermore, using synthetic peptides we systematically examined the influence of phosphoserine throughout the entirety of caspase-3, -7, and -8 recognition motifs and observed a general inhibitory effect of phosphorylation even at residues considered outside the classical consensus motif. Overall, our work demonstrates a role for phosphorylation in controlling caspase-mediated cleavage and shows that N-terminomic strategies can be tailored to study cross-talk between phosphorylation and proteolysis. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc

Kob-Andersen model: a non-standard mechanism for the glassy transition

We present new results reflecting the analogies between the Kob-Andersen model and other glassy systems. Studying the stability of the blocked configurations above and below the transition we also give arguments that supports their relevance for the glassy behaviour of the model. However we find, surprisingly, that the organization of the phase space of the system is different from the well known organization of other mean-field spin glasses and structural glasses.Comment: New reference added and one update

ipmr: flexible implementation of integral projection models in R

1. Integral projection models (IPMs) are an important tool for studying the dynamics of populations structured by one or more continuous traits (e.g. size, height, body mass). Researchers use IPMs to investigate questions ranging from linking drivers to population dynamics, planning conservation and management strategies, and quantifying selective pressures in natural populations. The popularity of stage-structured population models has been supported by R scripts and packages (e.g. IPMpack, popbio, popdemo, lefko3) aimed at ecologists, which have introduced a broad repertoire of functionality and outputs. However, pressing ecological, evolutionary and conservation biology topics require developing more complex IPMs, and considerably more expertise to implement them. Here, we introduce ipmr, a flexible R package for building, analysing and interpreting IPMs. 2. The ipmr framework relies on the mathematical notation of the models to express them in code format. Additionally, this package decouples the model parameterization step from the model implementation step. The latter point substantially increases ipmr's flexibility to model complex life cycles and demographic processes. 3. ipmr can handle a wide variety of models, including those that incorporate density dependence, discretely and continuously varying stochastic environments, and multiple continuous and/or discrete traits. ipmr can accommodate models with individuals cross-classified by age and size. Furthermore, the package provides methods for demographic analyses (e.g. asymptotic and stochastic growth rates) and visualization (e.g. kernel plotting). 4. ipmr is a flexible R package for integral projection models. The package substantially reduces the amount of time required to implement general IPMs. We also provide extensive documentation with six vignettes and help files, accessible from an R session and online

Making use of transcription factor enrichment to identify functional microRNA-regulons

microRNAs (miRNAs) are important modulators of messenger RNA stability and translation, controlling wide gene networks. Albeit generally modest on individual targets, the regulatory effect of miRNAs translates into meaningful pathway modulation through concurrent targeting of regulons with functional convergence. Identification of miRNA-regulons is therefore essential to understand the function of miRNAs and to help realise their therapeutic potential, but it remains challenging due to the large number of false positive target sites predicted per miRNA. In the current work, we investigated whether genes regulated by a given miRNA were under the transcriptional control of a predominant transcription factor (TF). Strikingly we found that for ~50% of the miRNAs analysed, their targets were significantly enriched in at least one common TF. We leveraged such miRNA-TF co-regulatory networks to identify pathways under miRNA control, and demonstrated that filtering predicted miRNA-target interactions (MTIs) relying on such pathways significantly enriched the proportion of predicted true MTIs. To our knowledge, this is the first description of an in- silico pipeline facilitating the identification of miRNA-regulons, to help understand miRNA function.Pacôme B. Prompsy, John Toubia, Linden J. Gearing, Randle L. Knight, Samuel C. Forster, Cameron P. Bracken, Michael P. Gantie

CANELC: constructing an e-language corpus

This paper reports on the construction of CANELC: the Cambridge and Nottingham e-language Corpus.3 CANELC is a one million word corpus of digital communication in English, taken from online discussion boards, blogs, tweets, emails and SMS messages. The paper outlines the approaches used when planning the corpus: obtaining consent; collecting the data and compiling the corpus database. This is followed by a detailed analysis of some of the patterns of language used in the corpus. The analysis includes a discussion of the key words and phrases used as well as the common themes and semantic associations connected with the data. These discussions form the basis of an investigation of how e-language operates in both similar and different ways to spoken and written records of communication (as evidenced by the BNC - British National Corpus). 3 CANELC stands for Cambridge and Nottingham e-language Corpus. This corpus has been built as part of a collaborative project between The University of Nottingham and Cambridge University Press with whom sole copyright of the annotated corpus resides. CANELC comprises one-million words of digital English taken from SMS messages, blogs, tweets, discussion board content and private/business emails. Plans to extend the corpus are under discussion. The legal dimension to corpus ‘ownership’ of some forms of unannotated data is a complex one and is under constant review. At the present time the annotated corpus is only available to authors and researchers working for CUP and is not more generally available

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores

The change in productivity of Chinese state enterprises, 1983–1987

This study estimates the change in productivity of Chinese state enterprises during 1983–1987 using a panel data set of 403 firms. A new approach to productivity measurement is used. Under this approach, the production functions can differ arbitrarily across firms, important given the heterogeneity of the sample. The resulting coefficients estimate the marginal products of each factor as well as overall productivity growth. The results suggest Chinese productivity increased by 4.6% per year, with about half of this growth due to the rapidly improving education of the labor force.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47564/1/11123_2005_Article_BF01073492.pd

Protocol for a nested case-control study design for omics investigations in the Environmental Determinants of Islet Autoimmunity cohort

Background: The Environmental Determinants of Islet Autoimmunity (ENDIA) pregnancy-birth cohort investigates the developmental origins of type 1 diabetes (T1D), with recruitment between 2013 and 2019. ENDIA is the first study in the world with comprehensive data and biospecimen collection during pregnancy, at birth and through childhood from at-risk children who have a first-degree relative with T1D. Environmental exposures are thought to drive the progression to clinical T1D, with pancreatic islet autoimmunity (IA) developing in genetically susceptible individuals. The exposures and key molecular mechanisms driving this progression are unknown. Persistent IA is the primary outcome of ENDIA; defined as a positive antibody for at least one of IAA, GAD, ZnT8 or IA2 on two consecutive occasions and signifies high risk of clinical T1D.Method: A nested case-control (NCC) study design with 54 cases and 161 matched controls aims to investigate associations between persistent IA and longitudinal omics exposures in ENDIA. The NCC study will analyse samples obtained from ENDIA children who have either developed persistent IA or progressed to clinical T1D (cases) and matched control children at risk of developing persistent IA. Control children were matched on sex and age, with all four autoantibodies absent within a defined window of the case's onset date. Cases seroconverted at a median of 1.37 years (IQR 0.95, 2.56). Longitudinal omics data generated from approximately 16,000 samples of different biospecimen types, will enable evaluation of changes from pregnancy through childhood.Conclusions: This paper describes the ENDIA NCC study, omics platform design considerations and planned univariate and multivariate analyses for its longitudinal data. Methodologies for multivariate omics analysis with longitudinal data are discovery-focused and data driven. There is currently no single multivariate method tailored specifically for the longitudinal omics data that the ENDIA NCC study will generate and therefore omics analysis results will require either cross validation or independent validation.KEY MESSAGESThe ENDIA nested case-control study will utilize longitudinal omics data on approximately 16,000 samples from 190 unique children at risk of type 1 diabetes (T1D), including 54 who have developed islet autoimmunity (IA), followed during pregnancy, at birth and during early childhood, enabling the developmental origins of T1D to be explored.Helena Oakey ... Lynne C. Giles ... Rebecca L. Thomson ... Pat Ashwood ... Emma J. Knight ... Simon C. Barry ... Kelly McGorm ... Jennifer J. Couper ... Megan A. S. Penno ... the ENDIA Study Group ... et al