Tapering practices of strongman athletes: Test-retest reliability study

BACKGROUND: Little is currently known about the tapering practices of strongman athletes. We have developed an Internet-based comprehensive self-report questionnaire examining the training and tapering practices of strongman athletes. OBJECTIVE: The objective of this study was to document the test-retest reliability of questions associated with the Internet-based comprehensive self-report questionnaire on the tapering practices of strongman athletes. The information will provide insight on the reliability and usefulness of the online questionnaire for use with strongman athletes. METHODS: Invitations to complete an Internet questionnaire were sent via Facebook Messenger to identified strongman athletes. The survey consisted of four main areas of inquiry, including demographics and background information, training practices, tapering, and tapering practices. Of the 454 athletes that completed the survey over the 8-week period, 130 athletes responded on Facebook Messenger indicating that they intended to complete, or had completed, the survey. These participants were asked if they could complete the online questionnaire a second time for a test-retest reliability analysis. Sixty-four athletes (mean age 33.3 years, standard deviation [SD] 7.7; mean height 178.2 cm, SD 11.0; mean body mass 103.7 kg, SD 24.8) accepted this invitation and completed the survey for the second time after a minimum 7-day period from the date of their first completion. Agreement between athlete responses was measured using intraclass correlation coefficients (ICCs) and kappa statistics. Confidence intervals (at 95%) were reported for all measures and significance was set at P<.05. RESULTS: Test-retest reliability for demographic and training practices items were significant (P<.001) and showed excellent (ICC range=.84 to .98) and fair to almost perfect agreement (κ range=.37-.85). Moderate to excellent agreements (ICC range=.56-.84; P<.01) were observed for all tapering practice measures except for the number of days athletes started their usual taper before a strongman competition (ICC=.30). When the number of days were categorized with additional analyses, moderate reliability was observed (κ=.43; <.001). Fair to substantial agreement was observed for the majority of tapering practices measures (κrange=.38-.73; P<.001) except for how training frequency (κ=.26) and the percentage and type of resistance training performed, which changed in the taper (κ=.20). Good to excellent agreement (ICC=.62-.93; P<.05) was observed for items relating to strongman events and traditional exercises performed during the taper. Only the time at which the Farmer's Walk was last performed before competition showed poor reliability (ICC=.27). CONCLUSIONS: We have developed a low cost, self-reported, online retrospective questionnaire, which provided stable and reliable answers for most of the demographic, training, and tapering practice questions. The results of this study support the inferences drawn from the Tapering Practices of Strongman Athletes Stud

Future-proofing the state: managing risks, responding to crises and building resilience

Summary: This book focuses on the challenges facing governments and communities in preparing for and responding to major crises — especially the hard to predict yet unavoidable natural disasters ranging from earthquakes and tsunamis to floods and bushfires, as well as pandemics and global economic crises. Future-proofing the state and our societies involves decision-makers developing&nbsp;capacities to learn from recent ‘disaster’ experiences in order to be better placed&nbsp;to anticipate and prepare for foreseeable challenges. To undertake such futureproofing&nbsp;means taking long-term (and often recurring) problems seriously, managing&nbsp;risks appropriately, investing in preparedness, prevention and mitigation, reducing&nbsp;future vulnerability, building resilience in communities and institutions, and cultivating&nbsp;astute leadership. In the past we have often heard calls for ‘better future-proofing’&nbsp;in the aftermath of disasters, but then neglected the imperatives of the message. Future-Proofing the State is organised around four key themes: how can we better&nbsp;predict and manage the future; how can we transform the short-term thinking&nbsp;shaped by our political cycles into more effective long-term planning; how can we&nbsp;build learning into our preparations for future policies and management; and how&nbsp;can we successfully build trust and community resilience to meet future challenges&nbsp;more adequately

Development of Automotive Handheld Tool

In the automotive repair industry doors are being stripped every day to be repainted. Insurance appraisers will pay for up to one hour for removal and installing door moldings. The process includes removing leftover adhesive tape and re-taping for installation. The current industry practice is timely and aggravating for technicians. The goal of this project is to develop a tool that removes double-sided adhesive tape from door moldings in a timely fashion. An objective was to model a device to fit the constraints of the tools operating path. A second objective was to explore different angles for our blade to see its effects on adhesive removal. An analysis of the design and its several iterations, along with fabrication details are discussed in this report

Systems level characterizations of single and combination drug mechanisms of action in vitro and in vivo

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references.Small molecule characterization is a critical limiting step in cancer drug development. At the present time, high throughput screens of natural products and combinatorial synthesis libraries generate more pharmaceutical leads than can be characterized in detail. Lead optimization further generates many derivatives of these cytotoxic hits in an attempt to generate optimized compounds with better physical or chemical properties. This leaves many promising agents stranded in drug development and poorly characterized. In addition, most small molecules interact biochemically with a diverse set of proteins. While characterizing the diversity of biochemical interactions that can occur is important to understanding function, only a subset are likely to be necessary or sufficient for therapeutic efficacy. In light of this diversity, the functional characterization of the mechanisms of cell death by cytotoxic agents should improve drug discovery by allowing for the early prioritization of cytotoxic leads, derivatized compounds, and targeted inhibitors on the basis of the mechanisms by which they cause death in intact cells. Using RNAi mediated suppression of key mediators of apoptosis; we found that we could predict the functional mechanisms of drug action in lymphoma cells across many categories of cytotoxic therapeutics with as few as 8 shRNAs. Beyond single drug mechanisms, most drugs used in cancer are used as drug combinations. These combinations were largely formulated on two principles: compounds must have a unique mechanism of action so that more cumulative drug can be dosed with non-overlapping toxicity, and they must have statistically independent mechanisms of drug resistance. However, beyond clinical efficacy, the basic mechanisms of combination therapy have never been examined. Thus, in light of the central role of apoptosis in guiding mammalian cell death to cancer therapy, we sought to examine the functional signatures of cell death in the face of combination therapy. Surprisingly we find that RNAi mediated suppression of cell death mediators in response to common cytotoxic regimens, averages both sensitivity and resistance to therapy and neutralizes the effects of genetic variation. This suggests that common cytotoxic regimens are intrinsically depersonalized and difficult to genetically stratify.by Justin Pritchard.Ph.D

Understanding resistance to combination chemotherapy

available in PMC 2014 April 04The current clinical application of combination chemotherapy is guided by a historically successful set of practices that were developed by basic and clinical researchers 50–60 years ago. Thus, in order to understand how emerging approaches to drug development might aid the creation of new therapeutic combinations, it is critical to understand the defining principles underlying classic combination therapy and the original experimental rationales behind them. One such principle is that the use of combination therapies with independent mechanisms of action can minimize the evolution of drug resistance. Another is that in order to kill sufficient cancer cells to cure a patient, multiple drugs must be delivered at their maximum tolerated dose – a condition that allows for enhanced cancer cell killing with manageable toxicity. In light of these models, we aim to explore recent genomic evidence underlying the mechanisms of resistance to the combination regimens constructed on these principles. Interestingly, we find that emerging genomic evidence contradicts some of the rationales of early practitioners in developing commonly used drug regimens. However, we also find that the addition of recent targeted therapies has yet to change the current principles underlying the construction of anti-cancer combinatorial regimens, nor have they made substantial inroads into the treatment of most cancers. We suggest that emerging systems/network biology approaches have an immense opportunity to impact the rational development of successful drug regimens. Specifically, by examining drug combinations in multivariate ways, next generation combination therapies can be constructed with a clear understanding of how mechanisms of resistance to multi-drug regimens differ from single agent resistance.Massachusetts Institute of Technology (Eisen and Chang Career Development Associate Professor of Biology)National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program (ICBP), #U54-CA112967-06)National Institutes of Health (U.S.) (NIH RO1-CA128803-04

Addressing Genetic Tumor Heterogeneity through Computationally Predictive Combination Therapy

Recent tumor sequencing data suggest an urgent need to develop a methodology to directly address intratumoral heterogeneity in the design of anticancer treatment regimens. We use RNA interference to model heterogeneous tumors, and demonstrate successful validation of computational predictions for how optimized drug combinations can yield superior effects on these tumors both in vitro and in vivo. Importantly, we discover here that for many such tumors knowledge of the predominant subpopulation is insufficient for determining the best drug combination. Surprisingly, in some cases, the optimal drug combination does not include drugs that would treat any particular subpopulation most effectively, challenging straightforward intuition. We confirm examples of such a case with survival studies in a murine preclinical lymphoma model. Altogether, our approach provides new insights about design principles for combination therapy in the context of intratumoral diversity, data that should inform the development of drug regimens superior for complex tumors.National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program (ICBP), Grant U54-CA112967-06)National Institutes of Health (U.S.) (NIH/National Institute of General Medical Sciences (NIGMS) Interdepartmental Biotechnology Training Program, 5T32GM008334)National Cancer Institute (U.S.) (Koch Institute Support (core) Grant P30-CA14051

Predicting cancer drug mechanisms of action using molecular network signatures

Molecular signatures are a powerful approach to characterize novel small molecules and derivatized small molecule libraries. While new experimental techniques are being developed in diverse model systems, informatics approaches lag behind these exciting advances. We propose an analysis pipeline for signature based drug annotation. We develop an integrated strategy, utilizing supervised and unsupervised learning methodologies that are bridged by network based statistics. Using this approach we can: 1, predict new examples of drug mechanisms that we trained our model upon; 2, identify “New” mechanisms of action that do not belong to drug categories that our model was trained upon; and 3, update our training sets with these “New” mechanisms and accurately predict entirely distinct examples from these new categories. Thus, not only does our strategy provide statistical generalization but it also offers biological generalization. Additionally, we show that our approach is applicable to diverse types of data, and that distinct biological mechanisms characterize its resolution of categories across different data types. As particular examples, we find that our predictive resolution of drug mechanisms from mRNA expression studies relies upon the analog measurement of a cell stress-related transcriptional rheostat along with a transcriptional representation of cell cycle state; whereas, in contrast, drug mechanism resolution from functional RNAi studies rely upon more dichotomous (e.g., either enhances or inhibits) association with cell death states. We believe that our approach can facilitate molecular signature-based drug mechanism understanding from different technology platforms and across diverse biological phenomena.National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program grant U54-CA112967

Disruption of Mycobacterium avium subsp. paratuberculosis-specific genes impairs in vivo fitness

Background: Mycobacterium avium subsp. paratuberculosis (MAP) is an obligate intracellular pathogen that infects many ruminant species. The acquisition of foreign genes via horizontal gene transfer has been postulated to contribute to its pathogenesis, as these genetic elements are absent from its putative ancestor, M. avium subsp. hominissuis (MAH), an environmental organism with lesser pathogenicity. In this study, high-throughput sequencing of MAP transposon libraries were analyzed to qualitatively and quantitatively determine the contribution of individual genes to bacterial survival during infection. Results: Out of 52384 TA dinucleotides present in the MAP K-10 genome, 12607 had a MycoMarT7 transposon in the input pool, interrupting 2443 of the 4350 genes in the MAP genome (56%). Of 96 genes situated in MAP-specific genomic islands, 82 were disrupted in the input pool, indicating that MAP-specific genomic regions are dispensable for in vitro growth (odds ratio = 0.21). Following 5 independent in vivo infections with this pool of mutants, the correlation between output pools was high for 4 of 5 (R = 0.49 to 0.61) enabling us to define genes whose disruption reproducibly reduced bacterial fitness in vivo. At three different thresholds for reduced fitness in vivo, MAP-specific genes were over-represented in the list of predicted essential genes. We also identified additional genes that were severely depleted after infection, and several of them have orthologues that are essential genes in M. tuberculosis. Conclusions: This work indicates that the genetic elements required for the in vivo survival of MAP represent a combination of conserved mycobacterial virulence genes and MAP-specific genes acquired via horizontal gene transfer. In addition, the in vitro and in vivo essential genes identified in this study may be further characterized to offer a better understanding of MAP pathogenesis, and potentially contribute to the discovery of novel therapeutic and vaccine targets. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-415) contains supplementary material, which is available to authorized users