Installation Effect of Controlled Modulus Columns on Nearby Existing Structures

© 2016 ASCE. Controlled modulus columns (CMC) ground improvement technique is a novel approach to reduce ground settlement. To install CMC, a rotary displacement auger is used to form a vertical cylindrical cavity, by displacing the surrounding soils laterally, followed by grout injection. While the method reduces spoil generation, excessive lateral soil displacement may damage the adjacent structures and freshly-grouted CMCs. Although there has been growing interest in quantifying such effects, only a handful of studies have been attempted. This paper presents results of a numerical investigation on the CMC installation effect on an existing bridge pile using the three-dimensional finite difference software package FLAC3D. The bridge pile response to the lateral soil movement induced by the CMC installation are presented and discussed

The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study.

Purpose: To validate a new 5-tier prognostic classification system to better discriminate cancer specific mortality in men diagnosed with primary non-metastatic prostate cancer. Patients and Methods: We applied a recently described 5 strata model (Cambridge Prognostic Groups-CPG) in 2 international cohorts and tested prognostic performance against the current standard 3 strata classification of low, intermediate or high-risk disease. Diagnostic clinico-pathological data of men from Prostate Cancer Data Base Sweden (PCBaSe) and the Singapore Health Study were used. The main outcome measure was prostate cancer mortality (PCM) stratified by age group and treatment modality. Results: The PCBaSe cohort included 72,337 men, of whom 7,162 died of prostate cancer. The CPG model successfully classified men with different risks of PCM with competing risk-regression confirming significant intergroup distinction (p<0.0001). The CPGs were significantly better at stratified prediction of PCM compared to the current 3-tier system (C-Index 0.81 vs. 0.77, p<0.0001). This superiority was maintained for every age group division (p<0.0001). Also in the ethnically different Singapore cohort of 2,550 men with 142 prostate cancer deaths, the CPG model outperformed the 3 strata categories (C-Index 0.79 vs. 0.76, p<0.0001). The model also retained superior prognostic discrimination in treatment sub-groups - Radical prostatectomy (n=20,586): C-Index 0.77 vs. 074, radiotherapy (n=11,872): C-Index 0.73 vs. 0.68, and conservative management (n=14,950): C-Index 0.74 vs. 0.73. The CPG groups that sub-divided the old intermediate (CPG2 vs. CPG3) and high-risk categories (CPG4 vs.CPG5) significantly discriminated PCM outcomes after radical therapy or conservative management (p<0.0001). Conclusion: This validation study of nearly 75,000 men, confirms that the CPG 5-tiered prognostic model has superior discrimination in predicting prostate cancer death over the 3-tier model across different age and treatment groups. Crucially, it identifies distinct sub-groups of men within the old intermediate-risk and high-risk criteria who have very different prognostic outcomes We therefore propose adoption of the CPG model as a simple to use but more accurate prognostic stratification tool to help guide management for men with newly diagnosed prostate cancer

Novel Genetic Loci Underlying Human Intracranial Volume Identified through Genome-Wide Association

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five novel loci for intracranial volume and confirmed two known signals. Four of the loci are also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic=0.748), which indicated a similar genetic background and allowed for the identification of four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, Parkinson’s disease, and enriched near genes involved in growth pathways including PI3K–AKT signaling. These findings identify biological underpinnings of intracranial volume and provide genetic support for theories on brain reserve and brain overgrowth

Patient safety in dentistry: development of a candidate 'never event' list for primary care

Introduction The 'never event' concept is often used in secondary care and refers to an agreed list of patient safety incidents that 'should not happen if the necessary preventative measures are in place'. Such an intervention may raise awareness of patient safety issues and inform team learning and system improvements in primary care dentistry. Objective To identify and develop a candidate never event list for primary care dentistry. Methods A literature review, eight workshops with dental practitioners and a modified Delphi with 'expert' groups were used to identify and agree candidate never events. Results Two-hundred and fifty dental practitioners suggested 507 never events, reduced to 27 distinct possibilities grouped across seven themes. Most frequently occurring themes were: 'checking medical history and prescribing' (119, 23.5%) and 'infection control and decontamination' (71, 14%). 'Experts' endorsed nine candidate never event statements with one graded as 'extreme risk' (failure to check past medical history) and four as 'high risk' (for example, extracting wrong tooth). Conclusion Consensus on a preliminary list of never events was developed. This is the first known attempt to develop this approach and an important step in determining its value to patient safety. Further work is necessary to develop the utility of this method

Inhibition of the glucocorticoid receptor results in an enhanced miR-99a/100-mediated radiation response in stem-like cells from human prostate cancers

Radiation therapy is a major primary treatment option for both localized early stage prostate cancer, and for advanced, regionally un-resectable, cancer. However, around 30% of patients still experience biochemical recurrence after radiation therapy within 10 years. Thus, identification of better biomarkers and new targets are urgently required to improve current therapeutic strategies. The miR-99 family has been shown to play an important role in the regulation of the DNA damage response, via targeting of the SWI/SNF chromatin remodeling factors, SMARCA5 and SMARCD1 in cell line models. In the present study, we have demonstrated that low expression of miR-99a and miR-100 is present in cell populations which are relatively radiation insensitive, for example in prostate cancer stem cells and in castration-resistant prostate cancer. Additionally, treatment of cells with the synthetic glucocorticoid, Dexamethasone resulted in decreased miR-99a and 100 expression, suggesting a new mechanism of miR-99a and 100 regulation in androgen-independent prostate cells. Strikingly, treatment of prostate cells with the glucocorticoid receptor inhibitor, Mifepristone was found to sensitize prostate cells to radiation by increasing the levels of miR-99a and miR-100. These results qualify the miR99 family as markers of radiation sensitivity and as potential therapeutic targets to improve efficiency of radiotherapy

The pros and cons of getting engaged in an online social community embedded within digital cognitive behavioral therapy for insomnia: survey among users

Background: Sleepio is a proven digital sleep improvement programme based on cognitive behavioural therapy (CBT) techniques. Users have the option to join an online community that includes weekly expert discussions, peer-to-peer discussion forums and personal message walls. Objective: The aims of this study were to (1) explore the reasons for deciding to engage with the Sleepio online community, (2) explore the potential benefits arising from engagement with the online community, (3) identify and describe any problematic issues related to use of the online community. Methods: In total, 100 respondents (70% female; mean age 51, range 26-82 years) completed the online survey. Most respondents had started Sleepio with chronic sleep problems (59% to to 10 years, 35% >10years), and had actively engaged with the online community (85% had made a discussion or wall post). At the time of the survey, respondents had used Sleepio for a median of 12 weeks (range from 3 weeks to 2 years). Results: Responses to the open-ended questions were analysed using thematic analysis. This analysis revealed five initial drivers for engagement including: 1) the desire to connect with people facing similar issues; 2) seeking personalised advice; 3) curiosity; 4) being invited by other members; and 5) wanting to use all available sleep improvement tools. Advantages of engagement included: access to continuous support, reduced sense of isolation, being part of a non-judgmental community, personalised advice, positive comparisons with others, encouragement to keep going, and altruism. Five potential disadvantages were: design and navigation issues, uncertain quality of user-generated content, negative comparisons with others, excessive time commitments, and data privacy concerns. Participants related their community experiences to engagement with the Sleepio programme with the many stating it had supported their efforts to achieve sleep improvement, as well as helping with adherence and commitment to the programme. Conclusions: Despite some concerns, members regarded the Sleepio community as a valuable resource. Online communities may be a useful means through which to support long-term engagement with online therapy for insomnia

Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging

Identification of genes associated with brain aging should markedly improve our understanding of the biological processes that govern normal age-related decline. However, challenges to identifying genes that facilitate successful brain aging are considerable, including a lack of established phenotypes and difficulties in modeling the effects of aging per se, rather than genes that influence the underlying trait. In a large cohort of randomly selected pedigrees (n = 1,129 subjects), we documented profound aging effects from young adulthood to old age (18-83 y) on neurocognitive ability and diffusion-based white-matter measures. Despite significant phenotypic correlation between white-matter integrity and tests of processing speed, working memory, declarative memory, and intelligence, no evidence for pleiotropy between these classes of phenotypes was observed. Applying an advanced quantitative gene-by-environment interaction analysis where age is treated as an environmental factor, we demonstrate a heritable basis for neurocognitive deterioration as a function of age. Furthermore, by decomposing gene-by-aging (G × A) interactions, we infer that different genes influence some neurocognitive traits as a function of age, whereas other neurocognitive traits are influenced by the same genes, but to differential levels, from young adulthood to old age. In contrast, increasing white-matter incoherence with age appears to be nongenetic. These results clearly demonstrate that traits sensitive to the genetic influences on brain aging can be identified, a critical first step in delineating the biological mechanisms of successful aging

Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. Here we show that no single saliency map can perform well under all metrics. Instead, we propose a principled approach to solve the benchmarking problem by separating the notions of saliency models, maps and metrics. Inspired by Bayesian decision theory, we define a saliency model to be a probabilistic model of fixation density prediction and a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric given the model density. We derive these optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision. We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics. Our method allows researchers to have their model compete on many different metrics with state-of-the-art in those metrics: "good" models will perform well in all metrics.Comment: published at ECCV 201

Ferruccio Ritossa’s scientific legacy 50 years after his discovery of the heat shock response: a new view of biology, a new society, and a new journal

The pioneering discovery of the heat shock response by the Italian scientist Ferruccio Ritossa reached maturity this year, 2012. It was 50 years ago that Professor Ritossa, through an extraordinary combination of serendipity, curiosity, knowledge and inspiration, published the first observation that cells could mount very strong transcriptional activity when exposed to elevated temperatures, which was coined the heat shock response. This discovery led to the identification of heat shock proteins, which impact many areas of current biology and medicine, and has created a new avenue for more exciting discoveries. In recognition of the discovery of the heat shock response, Cell Stress Society International (CSSI) awarded Professor Ritossa with the CSSI medallion in October 2010 in Dozza, Italy. This article is based on a session of the Fifth CSSI Congress held in Québec commemorating Professor Ritossa and his discovery

Diabetes-specific genetic effects on obesity traits in American Indian populations: the Strong Heart Family Study

<p>Abstract</p> <p>Background</p> <p>Body fat mass distribution and deposition are determined by multiple environmental and genetic factors. Obesity is associated with insulin resistance, hyperinsulinemia, and type 2 diabetes. We previously identified evidence for genotype-by-diabetes interaction on obesity traits in Strong Heart Family Study (SHFS) participants. To localize these genetic effects, we conducted genome-wide linkage scans of obesity traits in individuals with and without type 2 diabetes, and in the combined sample while modeling interaction with diabetes using maximum likelihood methods (SOLAR 2.1.4).</p> <p>Methods</p> <p>SHFS recruited American Indians from Arizona, North and South Dakota, and Oklahoma. Anthropometric measures and diabetes status were obtained during a clinic visit. Marker allele frequencies were derived using maximum likelihood methods estimated from all individuals and multipoint identity by descent sharing was estimated using Loki. We used variance component linkage analysis to localize quantitative trait loci (QTLs) influencing obesity traits. We tested for evidence of additive and QTL-specific genotype-by-diabetes interactions using the regions identified in the diabetes-stratified analyses.</p> <p>Results</p> <p>Among 245 diabetic and 704 non-diabetic American Indian individuals, we detected significant additive gene-by-diabetes interaction for weight and BMI (<it>P </it>< 0.02). In analysis accounting for QTL-specific interaction (<it>P </it>< 0.001), we detected a QTL for weight on chromosome 1 at 242 cM (LOD = 3.7). This chromosome region harbors the adiponectin receptor 1 gene, which has been previously associated with obesity.</p> <p>Conclusion</p> <p>These results suggest distinct genetic effects on body mass in individuals with diabetes compared to those without diabetes, and a possible role for one or more genes on chromosome 1 in the pathogenesis of obesity.</p