Stress response function of a two-dimensional ordered packing of frictional beads

We study the stress profile of an ordered two-dimensional packing of beads in response to the application of a vertical overload localized at its top surface. Disorder is introduced through the Coulombic friction between the grains which gives some indeterminacy and allows the choice of one constrained random number per grain in the calculation of the contact forces. The so-called `multi-agent' technique we use, lets us deal with systems as large as $1000\times1000$ grains. We show that the average response profile has a double peaked structure. At large depth $z$, the position of these peaks grows with $cz$, while their widths scales like $\sqrt{Dz}$. $c$ and $D$ are analogous to `propagation' and `diffusion' coefficients. Their values depend on that of the friction coefficient $\mu$. At small $\mu$, we get $c_0-c \propto \mu$ and $D \propto \mu^\beta$, with $\beta \sim 2.5$, which means that the peaks get closer and wider as the disorder gets larger. This behavior is qualitatively what was predicted in a model where a stochastic relation between the stress components is assumed.Comment: 7 pages, 7 figures, accepted version to Europhys. Let

Carcinoembryonic antigen-related cell adhesion molecule 1 is the 85-kilodalton pronase-resistant biliary glycoprotein in the cholesterol crystallization promoting low density protein-lipid complex

A pronase resistant 85-kd glycoprotein in the Concanavalin A-binding fraction (CABF) of biliary glycoproteins has been reported to act as a promotor of cholesterol crystallization. De Bruijn et al. (Gastroenterology 1996;110:1936-1944) found this protein in a low-density protein-lipid complex (LDP) with potent cholesterol crystallization promoting activity. This study identifies and characterizes this protein. An LDP was prepared from CABF by discontinuous gradient ultracentrifugation. Proteins were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), blotting and immunochemical staining with anti-carcinoembryonic antigen, CEA-related adhesion molecule 1 (CEACAM1) cross-reacting antibodies. Biliary concentrations of CEA cross-reacting proteins were determined in patients with and without gallstones. Two isoforms of CEACAM1 (85- and 115-kd bands), CEA and 2 CEA cross-reacting protein bands of 40 and 50 kd were found in human bile. All bands were also present in CABF, but only a subfraction of the 85-kd band found in the LDP was resistant to digestion with pronase. CEACAM1-85 exhibited potent cholesterol crystallization promoting activity in vitro and accounted for most of the activity in CABF. Total CEA cross-reacting protein concentrations were the same in gallbladder biles from patients with cholesterol and pigment gallstones but only half of those in biles from nongallstone subjects. In conclusion, we have identified the protein component of the cholesterol crystallization promoting LDP to be CEACAM1-8

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks