Numerical and semi-analytical models of sliding masses: application to the 1783 Scilla tsunamigenic landslide

The Scilla rock avalanche occurred on 6 February 1783 along the coast of the Calabria region (southern Italy), close to the Messina Strait. It was triggered by a mainshock of the Terremoto delle Calabrie seismic sequence, and it induced a tsunami wave responsible for more than 1500 casualties along the neighboring Marina Grande beach. The main goal of this work is the application of semi-analtycal and numerical models to simulate this event. The first one is a MATLAB code expressly created for this work that solves the equations of motion for sliding particles on a two-dimensional surface through a fourth-order Runge-Kutta method. The second one is a code developed by the Tsunami Research Team of the Department of Physics and Astronomy (DIFA) of the Bologna University that describes a slide as a chain of blocks able to interact while sliding down over a slope and adopts a Lagrangian point of view. A wide description of landslide phenomena and in particular of landslides induced by earthquakes and with tsunamigenic potential is proposed in the first part of the work. Subsequently, the physical and mathematical background is presented; in particular, a detailed study on derivatives discratization is provided. Later on, a description of the dynamics of a point-mass sliding on a surface is proposed together with several applications of numerical and analytical models over ideal topographies. In the last part, the dynamics of points sliding on a surface and interacting with each other is proposed. Similarly, different application on an ideal topography are shown. Finally, the applications on the 1783 Scilla event are shown and discussed.

A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study

When estimating the effect of treatment on HIV using data from observational studies, standard methods may produce biased estimates due to the presence of time-dependent confounders. Such confounding can be present when a covariate, affected by past exposure, is both a predictor of the future exposure and the outcome. One example is the CD4 cell count, being a marker for disease progression for HIV patients, but also a marker for treatment initiation and influenced by treatment. Fitting a marginal structural model (MSM) using inverse probability weights is one way to give appropriate adjustment for this type of confounding. In this paper we study a simple and intuitive approach to estimate similar treatment effects, using observational data to mimic several randomized controlled trials. Each 'trial' is constructed based on individuals starting treatment in a certain time interval. An overall effect estimate for all such trials is found using composite likelihood inference. The method offers an alternative to the use of inverse probability of treatment weights, which is unstable in certain situations. The estimated parameter is not identical to the one of an MSM, it is conditioned on covariate values at the start of each mimicked trial. This allows the study of questions that are not that easily addressed fitting an MSM. The analysis can be performed as a stratified weighted Cox analysis on the joint data set of all the constructed trials, where each trial is one stratum. The model is applied to data from the Swiss HIV cohort study.

Improved antiretroviral treatment outcome in a rural African setting is associated with cART initiation at higher CD4 cell counts and better general health condition

Background Data on combination antiretroviral therapy (cART) in remote rural African regions is increasing. Methods We assessed prospectively initial cART in HIV-infected adults treated from 2005 to 2008 at St. Francis Designated District Hospital, Ifakara, Tanzania. Adherence was assisted by personal adherence supporters. We estimated risk factors of death or loss to follow-up by Cox regression during the first 12 months of cART. Results Overall, 1,463 individuals initiated cART, which was nevirapine-based in 84.6%. The median age was 40 years (IQR 34-47), 35.4% were males, 7.6% had proven tuberculosis. Median CD4 cell count was 131 cells/μl and 24.8% had WHO stage 4. Median CD4 cell count increased by 61 and 130 cells/μl after 6 and 12 months, respectively. 215 (14.7%) patients modified their treatment, mostly due to toxicity (56%), in particular polyneuropathy and anemia. Overall, 129 patients died (8.8%) and 189 (12.9%) were lost to follow-up. In a multivariate analysis, low CD4 cells at starting cART were associated with poorer survival and loss to follow-up (HR 1.77, 95% CI 1.15-2.75, p = 0.009; for CD4 <50 compared to >100 cells/μl). Higher weight was strongly associated with better survival (HR 0.63, 95% CI 0.51-0.76, p < 0.001 per 10 kg increase). Conclusions cART initiation at higher CD4 cell counts and better general health condition reduces HIV related mortality in a rural African setting. Efforts must be made to promote earlier HIV diagnosis to start cART timely. More research is needed to evaluate effective strategies to follow cART at a peripheral level with limited technical possibilities.

The selective Cox-2 inhibitor Celecoxib suppresses angiogenesis and growth of secondary bone tumors: an intravital microscopy study in mice

BACKGROUND: The inhibition of angiogenesis is a promising strategy for the treatment of malignant primary and secondary tumors in addition to established therapies such as surgery, chemotherapy, and radiation. There is strong experimental evidence in primary tumors that Cyclooxygenase-2 (Cox-2) inhibition is a potent mechanism to reduce angiogenesis. For bone metastases which occur in up to 85% of the most frequent malignant primary tumors, the effects of Cox-2 inhibition on angiogenesis and tumor growth remain still unclear. Therefore, the aim of this study was to investigate the effects of Celecoxib, a selective Cox-2 inhibitor, on angiogenesis, microcirculation and growth of secondary bone tumors. METHODS: In 10 male severe combined immunodeficient (SCID) mice, pieces of A549 lung carcinomas were implanted into a newly developed cranial window preparation where the calvaria serves as the site for orthotopic implantation of the tumors. From day 8 after tumor implantation, five animals (Celecoxib) were treated daily with Celecoxib (30 mg/kg body weight, s.c.), and five animals (Control) with the equivalent amount of the CMC-based vehicle. Angiogenesis, microcirculation, and growth of A549 tumors were analyzed by means of intravital microscopy. Apoptosis was quantified using the TUNEL assay. RESULTS: Treatment with Celecoxib reduced both microvessel density and tumor growth. TUNEL reaction showed an increase in apoptotic cell death of tumor cells after treatment with Celecoxib as compared to Controls. CONCLUSION: Celecoxib is a potent inhibitor of tumor growth of secondary bone tumors in vivo which can be explained by its anti-angiogenic and pro-apoptotic effects. The results indicate that a combination of established therapy regimes with Cox-2 inhibition represents a possible application for the treatment of bone metastases.

A Cox Model for Biostatistics of the Future

Professor Sir David R. Cox (DRC) is widely acknowledged as among the most important scientists of the second half of the twentieth century. He inherited the mantle of statistical science from Pearson and Fisher, advanced their ideas, and translated statistical theory into practice so as to forever change the application of statistics in many fields, but especially biology and medicine. The logistic and proportional hazards models he substantially developed, are arguably among the most influential biostatistical methods in current practice. This paper looks forward over the period from DRC's 80th to 90th birthdays, to speculate about the future of biostatistics, drawing lessons from DRC's contributions along the way. We consider "Cox's model" of biostatistics, an approach to statistical science that: formulates scientific questions or quantities in terms of parameters gamma in probability models f(y; gamma) that represent in a parsimonious fashion, the underlying scientific mechanisms (Cox, 1997); partition the parameters gamma = theta, eta into a subset of interest theta and other "nuisance parameters" eta necessary to complete the probability distribution (Cox and Hinkley, 1974); develops methods of inference about the scientific quantities that depend as little as possible upon the nuisance parameters (Barndorff-Nielsen and Cox, 1989); and thinks critically about the appropriate conditional distribution on which to base infrences. We briefly review exciting biomedical and public health challenges that are capable of driving statistical developments in the next decade. We discuss the statistical models and model-based inferences central to the CM approach, contrasting them with computationally-intensive strategies for prediction and inference advocated by Breiman and others (e.g. Breiman, 2001) and to more traditional design-based methods of inference (Fisher, 1935). We discuss the hierarchical (multi-level) model as an example of the future challanges and opportunities for model-based inference. We then consider the role of conditional inference, a second key element of the CM. Recent examples from genetics are used to illustrate these ideas. Finally, the paper examines causal inference and statistical computing, two other topics we believe will be central to biostatistics research and practice in the coming decade. Throughout the paper, we attempt to indicate how DRC's work and the "Cox Model" have set a standard of excellence to which all can aspire in the future.