Sea ice fluctuations in the Baffin Bay and the Labrador Sea during glacial abrupt climate changes.

Sea ice decline in the North Atlantic and Nordic Seas has been proposed to contribute to the repeated abrupt atmospheric warmings recorded in Greenland ice cores during the last glacial period, known as Dansgaard-Oeschger (D-O) events. However, the understanding of how sea ice changes were coupled with abrupt climate changes during D-O events has remained incomplete due to a lack of suitable high-resolution sea ice proxy records from northwestern North Atlantic regions. Here, we present a subdecadal-scale bromine enrichment (Brenr) record from the NEEM ice core (Northwest Greenland) and sediment core biomarker records to reconstruct the variability of seasonal sea ice in the Baffin Bay and Labrador Sea over a suite of D-O events between 34 and 42 ka. Our results reveal repeated shifts between stable, multiyear sea ice (MYSI) conditions during cold stadials and unstable, seasonal sea ice conditions during warmer interstadials. The shift from stadial to interstadial sea ice conditions occurred rapidly and synchronously with the atmospheric warming over Greenland, while the amplitude of high-frequency sea ice fluctuations increased through interstadials. Our findings suggest that the rapid replacement of widespread MYSI with seasonal sea ice amplified the abrupt climate warming over the course of D-O events and highlight the role of feedbacks associated with late-interstadial seasonal sea ice expansion in driving the North Atlantic ocean-climate system back to stadial conditions.

Source, timing and dynamics of ionic species mobility in the Svalbard annual snowpack.

Nearly all ice core archives from the Arctic and middle latitudes (such as the Alps), apart from some very high elevation sites in Greenland and the North Pacific, are strongly influenced by melting processes. The increases in the average Arctic temperature has enhanced surface snow melting even of higher elevation ice caps, especially on the Svalbard Archipelago. The increase of the frequency and altitude of winter "rain on snow" events as well as the increase of the length of the melting season have had a direct impact on the chemical composition of the seasonal and permanent snow layers due to different migration processes of water-soluble species, such as inorganic ions. This re-allocation along the snowpack of ionic species could significantly modify the original chemical signal present in the annual snow. This paper aims to give a picture of the evolution of the seasonal snow strata with a daily time resolution to better understand: a) the processes that can influence deposition b) the distribution of ions in annual snow c) the impact of the presence of liquid water on chemical re-distribution within the annual snow pack. Specifically, the chemical composition of the first 100 cm of seasonal snow on the Austre Brøggerbreen Glacier (Spitsbergen, Svalbard Islands, Norway) was monitored daily from the 27th of March to the 31st of May 2015. The experimental period covered almost the entire Arctic spring until the melting season. This unique dataset gives us a daily picture of the snow pack composition, and helps us to understand the behaviour of cations (K+, Ca2+, Na+, Mg2+) and anions (Br-, I-, SO42-, NO3-, Cl-, MSA) in the Svalbard snow pack. We demonstrate that biologically related depositions occur only at the end of the snow season and that rain and melting events have different impacts on the snowpack chemistry.

Pairwise likelihood estimation of latent autoregressive count models.

Latent autoregressive models are useful time series models for the analysis of infectious disease data. Evaluation of the likelihood function of latent autoregressive models is intractable and its approximation through simulation-based methods appears as a standard practice. Although simulation methods may make the inferential problem feasible, they are often computationally intensive and the quality of the numerical approximation may be difficult to assess. We consider instead a weighted pairwise likelihood approach and explore several computational and methodological aspects including estimation of robust standard errors and the role of numerical integration. The suggested approach is illustrated using monthly data on invasive meningococcal disease infection in Greece and Italy.

Whole-body low-dose CT recognizes two distinct patterns of lytic lesions in multiple myeloma patients with different disease metabolism at PET/MRI.

We evaluated differences in density and 18F-FDG PET/MRI features of lytic bone lesions (LBLs) identified by whole-body low-dose CT (WB-LDCT) in patients affected by newly diagnosed multiple myeloma (MM). In 18 MM patients, 135 unequivocal LBLs identified by WB-LDCT were characterized for inner density (negative or positive Hounsfield unit (HU)), where negative density (HU < 0) characterizes normal yellow marrow whereas positive HU correlates with tissue-like infiltrative pattern. The same LBLs were analyzed by 18F-FDG PET/DWI-MRI, registering DWI signal with ADC and SUV max values. According to HU, 35 lesions had a negative density (- 56.94 ± 31.87 HU) while 100 lesions presented positive density (44.87 ± 23.89 HU). In seven patients, only positive HU LBLs were demonstrated whereas in eight patients, both positive and negative HU LBLs were detected. Intriguingly, in three patients (16%), only negative HU LBLs were shown. At 18F-FDG PET/DWI-MRI analysis, negative HU LBLs presented low ADC values (360.69 ± 154.38 × 10-6 mm2/s) and low SUV max values (1.69 ± 0.56), consistent with fatty marrow, whereas positive HU LBLs showed an infiltrative pattern, characterized by higher ADC (mean 868.46 ± 207.67 × 10-6 mm2/s) and SUV max (mean 5.04 ± 1.94) values. Surprisingly, histology of negative HU LBLs documented infiltration by neoplastic plasma cells scattered among adipocytes. In conclusion, two different patterns of LBLs were detected by WB-LDCT in MM patients. Both types of lesions were indicative for active disease, although only positive HU LBL were captured by 18F-FDG PET/DWI-MRI imaging, indicating that WB-LDCT adds specific information.

Feedback mechanisms between snow and atmospheric mercury: Results and observations from field campaigns on the Antarctic plateau.

The Antarctic Plateau snowpack is an important environment for the mercury geochemical cycle. We have extensively characterized and compared the changes in surface snow and atmospheric mercury concentrations that occur at Dome C. Three summer sampling campaigns were conducted between 2013 and 2016. The three campaigns had different meteorological conditions that significantly affected mercury deposition processes and its abundance in surface snow. In the absence of snow deposition events, the surface mercury concentration remained stable with narrow oscillations, while an increase in precipitation results in a higher mercury variability. The Hg concentrations detected confirm that snowfall can act as a mercury atmospheric scavenger. A high temporal resolution sampling experiment showed that surface concentration changes are connected with the diurnal solar radiation cycle. Mercury in surface snow is highly dynamic and it could decrease by up to 90% within 4/6 h. A negative relationship between surface snow mercury and atmospheric concentrations has been detected suggesting a mutual dynamic exchange between these two environments. Mercury concentrations were also compared with the Br concentrations in surface and deeper snow, results suggest that Br could have an active role in Hg deposition, particularly when air masses are from coastal areas. This research presents new information on the presence of Hg in surface and deeper snow layers, improving our understanding of atmospheric Hg deposition to the snow surface and the possible role of re-emission on the atmospheric Hg concentration.

Random-effects meta-analysis: the number of studies matters.

This paper investigates the impact of the number of studies on meta-analysis and meta-regression within the random-effects model framework. It is frequently neglected that inference in random-effects models requires a substantial number of studies included in meta-analysis to guarantee reliable conclusions. Several authors warn about the risk of inaccurate results of the traditional DerSimonian and Laird approach especially in the common case of meta-analysis involving a limited number of studies. This paper presents a selection of likelihood and non-likelihood methods for inference in meta-analysis proposed to overcome the limitations of the DerSimonian and Laird procedure, with a focus on the effect of the number of studies. The applicability and the performance of the methods are investigated in terms of Type I error rates and empirical power to detect effects, according to scenarios of practical interest. Simulation studies and applications to real meta-analyses highlight that it is not possible to identify an approach uniformly superior to alternatives. The overall recommendation is to avoid the DerSimonian and Laird method when the number of meta-analysis studies is modest and prefer a more comprehensive procedure that compares alternative inferential approaches. R code for meta-analysis according to all of the inferential methods examined in the paper is provided.

Statistical modelling of citation exchange between statistics journals.

Rankings of scholarly journals based on citation data are often met with scepticism by the scientific community. Part of the scepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of researchers. The paper focuses on analysis of the table of cross-citations among a selection of statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care to avoid potential overinterpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's research assessment exercise shows strong correlation at aggregate level between assessed research quality and journal citation 'export scores' within the discipline of statistics.

Hybrid pairwise likelihood analysis of animal behavior experiments.

The study of the determinants of fights between animals is an important issue in understanding animal behavior. For this purpose, tournament experiments among a set of animals are often used by zoologists. The results of these tournament experiments are naturally analyzed by paired comparison models. Proper statistical analysis of these models is complicated by the presence of dependence between the outcomes of fights because the same animal is involved in different contests. This paper discusses two different model specifications to account for between-fights dependence. Models are fitted through the hybrid pairwise likelihood method that iterates between optimal estimating equations for the regression parameters and pairwise likelihood inference for the association parameters. This approach requires the specification of means and covariances only. For this reason, the method can be applied also when the computation of the joint distribution is difficult or inconvenient. The proposed methodology is investigated by simulation studies and applied to real data about adult male Cape Dwarf Chameleons.

A mixed autoregressive probit model for ordinal longitudinal data.

Longitudinal data with binary and ordinal outcomes routinely appear in medical applications. Existing methods are typically designed to deal with short measurement series. In contrast, modern longitudinal data can result in large numbers of subject-specific serial observations. In this framework, we consider multivariate probit models with random effects to capture heterogeneity and autoregressive terms for describing the serial dependence. Since likelihood inference for the proposed class of models is computationally burdensome because of high-dimensional intractable integrals, a pseudolikelihood approach is followed. The methodology is motivated by the analysis of a large longitudinal study on the determinants of migraine severity.