Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the Integrated Nested Laplace Approximation methodology to make inference and obtain the posterior estimates. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence-absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model's versatility, we compute absolute probability maps of landslide occurrences and check its predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far for landslide susceptibility. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model

Max-infinitely divisible models and inference for spatial extremes

For many environmental processes, recent studies have shown that the dependence strength is decreasing when quantile levels increase. This implies that the popular max-stable models are inadequate to capture the rate of joint tail decay, and to estimate joint extremal probabilities beyond observed levels. We here develop a more flexible modeling framework based on the class of max-infinitely divisible processes, which extend max-stable processes while retaining dependence properties that are natural for maxima. We propose two parametric constructions for max-infinitely divisible models, which relax the max-stability property but remain close to some popular max-stable models obtained as special cases. The first model considers maxima over a finite, random number of independent observations, while the second model generalizes the spectral representation of max-stable processes. Inference is performed using a pairwise likelihood. We illustrate the benefits of our new modeling framework on Dutch wind gust maxima calculated over different time units. Results strongly suggest that our proposed models outperform other natural models, such as the Student-t copula process and its max-stable limit, even for large block sizes

Управління трудовим потенціалом при створенні інноваційної продукції

Super-resolution microscopy (SRM) bypasses the diffraction limit, a physical barrier that restricts the optical resolution to roughly 250 nm and was previously thought to be impenetrable. SRM techniques allow the visualization of subcellular organization with unprecedented detail, but also confront biologists with the challenge of selecting the best-suited approach for their particular research question. Here, we provide guidance on how to use SRM techniques advantageously for investigating cellular structures and dynamics to promote new discoveries

Targeting the CXCR4 pathway using a novel anti-CXCR4 IgG1 antibody (PF-06747143) in chronic lymphocytic leukemia.

BackgroundThe CXCR4-CXCL12 axis plays an important role in the chronic lymphocytic leukemia (CLL)-microenvironment interaction. Overexpression of CXCR4 has been reported in different hematological malignancies including CLL. Binding of the pro-survival chemokine CXCL12 with its cognate receptor CXCR4 induces cell migration. CXCL12/CXCR4 signaling axis promotes cell survival and proliferation and may contribute to the tropism of leukemia cells towards lymphoid tissues and bone marrow. Therefore, we hypothesized that targeting CXCR4 with an IgG1 antibody, PF-06747143, may constitute an effective therapeutic approach for CLL.MethodsPatient-derived primary CLL-B cells were assessed for cytotoxicity in an in vitro model of CLL microenvironment. PF-06747143 was analyzed for cell death induction and for its potential to interfere with the chemokine CXCL12-induced mechanisms, including migration and F-actin polymerization. PF-06747143 in vivo efficacy was determined in a CLL murine xenograft tumor model.ResultsPF-06747143, a novel-humanized IgG1 CXCR4 antagonist antibody, induced cell death of patient-derived primary CLL-B cells, in presence or absence of stromal cells. Moreover, cell death induction by the antibody was independent of CLL high-risk prognostic markers. The cell death mechanism was dependent on CXCR4 expression, required antibody bivalency, involved reactive oxygen species production, and did not require caspase activation, all characteristics reminiscent of programmed cell death (PCD). PF-06747143 also induced potent B-CLL cytotoxicity via Fc-driven antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity activity (CDC). PF-06747143 had significant combinatorial effect with standard of care (SOC) agents in B-CLL treatment, including rituximab, fludarabine (F-ara-A), ibrutinib, and bendamustine. In a CLL xenograft model, PF-06747143 decreased tumor burden and improved survival as a monotherapy, and in combination with bendamustine.ConclusionsWe show evidence that PF-06747143 has biological activity in CLL primary cells, supporting a rationale for evaluation of PF-06747143 for the treatment of CLL patients

Ecological resilience in lakes and the conjunction fallacy

There is a pressing need to apply stability and resilience theory to environmental management to restore degraded ecosystems effectively and to mitigate the effects of impending environmental change. Lakes represent excellent model case studies in this respect and have been used widely to demonstrate theories of ecological stability and resilience that are needed to underpin preventative management approaches. However, we argue that this approach is not yet fully developed because the pursuit of empirical evidence to underpin such theoretically grounded management continues in the absence of an objective probability framework. This has blurred the lines between intuitive logic (based on the elementary principles of probability) and extensional logic (based on assumption and belief) in this field

Modeling of spatial extremes in environmental data science: Time to move away from max-stable processes

Environmental data science for spatial extremes has traditionally relied heavily on max-stable processes. Even though the popularity of these models has perhaps peaked with statisticians, they are still perceived and considered as the `state-of-the-art' in many applied fields. However, while the asymptotic theory supporting the use of max-stable processes is mathematically rigorous and comprehensive, we think that it has also been overused, if not misused, in environmental applications, to the detriment of more purposeful and meticulously validated models. In this paper, we review the main limitations of max-stable process models, and strongly argue against their systematic use in environmental studies. Alternative solutions based on more flexible frameworks using the exceedances of variables above appropriately chosen high thresholds are discussed, and an outlook on future research is given, highlighting recommendations moving forward and the opportunities offered by hybridizing machine learning with extreme-value statistics

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering (CARS)

Coherent Raman imaging techniques have seen a dramatic increase in activity over the past decade due to their promise to enable label-free optical imaging with high molecular specificity 1. The sensitivity of these techniques, however, is many orders of magnitude weaker than fluorescence, requiring milli-molar molecular concentrations 1,2. Here, we describe a technique that can enable the detection of weak or low concentrations of Raman-active molecules by amplifying their signal with that obtained from strong or abundant Raman scatterers. The interaction of short pulsed lasers in a biological sample generates a variety of coherent Raman scattering signals, each of which carry unique chemical information about the sample. Typically, only one of these signals, e.g. Coherent Anti-stokes Raman scattering (CARS), is used to generate an image while the others are discarded. However, when these other signals, including 3-color CARS and four-wave mixing (FWM), are collected and compared to the CARS signal, otherwise difficult to detect information can be extracted 3. For example, doubly-resonant CARS (DR-CARS) is the result of the constructive interference between two resonant signals 4. We demonstrate how tuning of the three lasers required to produce DR-CARS signals to the 2845 cm-1 CH stretch vibration in lipids and the 2120 cm-1 CD stretching vibration of a deuterated molecule (e.g. deuterated sugars, fatty acids, etc.) can be utilized to probe both Raman resonances simultaneously. Under these conditions, in addition to CARS signals from each resonance, a combined DR-CARS signal probing both is also generated. We demonstrate how detecting the difference between the DR-CARS signal and the amplifying signal from an abundant molecule's vibration can be used to enhance the sensitivity for the weaker signal. We further demonstrate that this approach even extends to applications where both signals are generated from different molecules, such that e.g. using the strong Raman signal of a solvent can enhance the weak Raman signal of a dilute solute

The Relationship between Fenestrations, Sieve Plates and Rafts in Liver Sinusoidal Endothelial Cells

Fenestrations are transcellular pores in endothelial cells that facilitate transfer of substrates between blood and the extravascular compartment. In order to understand the regulation and formation of fenestrations, the relationship between membrane rafts and fenestrations was investigated in liver sinusoidal endothelial cells where fenestrations are grouped into sieve plates. Three dimensional structured illumination microscopy, scanning electron microscopy, internal reflectance fluorescence microscopy and two-photon fluorescence microscopy were used to study liver sinusoidal endothelial cells isolated from mice. There was an inverse distribution between sieve plates and membrane rafts visualized by structured illumination microscopy and the fluorescent raft stain, Bodipy FL C5 ganglioside GM1. 7-ketocholesterol and/or cytochalasin D increased both fenestrations and lipid-disordered membrane, while Triton X-100 decreased both fenestrations and lipid-disordered membrane. The effects of cytochalasin D on fenestrations were abrogated by co-administration of Triton X-100, suggesting that actin disruption increases fenestrations by its effects on membrane rafts. Vascular endothelial growth factor (VEGF) depleted lipid-ordered membrane and increased fenestrations. The results are consistent with a sieve-raft interaction, where fenestrations form in non-raft lipid-disordered regions of endothelial cells once the membrane-stabilizing effects of actin cytoskeleton and membrane rafts are diminished.Full Tex