Slip statistics of dislocation avalanches under different loading modes

Slowly compressed microcrystals deform via intermittent slip events, observed as displacement jumps or stress drops. Experiments often use one of two loading modes: an increasing applied stress (stress driven, soft), or a constant strain rate (strain driven, hard). In this work we experimentally test the influence of the deformation loading conditions on the scaling behavior of slip events. It is found that these common deformation modes strongly affect time series properties, but not the scaling behavior of the slip statistics when analyzed with a mean-field model. With increasing plastic strain, the slip events are found to be smaller and more frequent when strain driven, and the slip-size distributions obtained for both drives collapse onto the same scaling function with the same exponents. The experimental results agree with the predictions of the used mean-field model, linking the slip behavior under different loading modes

Negative Selection during the Peripheral Immune Response to Antigen

Thymic selection depends on positive and negative selective mechanisms based on the avidity of T cell interaction with antigen–major histocompatibility complex complexes. However, peripheral mechanisms for the recruitment and clonal expansion of the responding T cell repertoire remain obscure. Here we provide evidence for an avidity-based model of peripheral T cell clonal expansion in response to antigenic challenge. We have used the encephalitogenic, H-2 Au-restricted, acetylated NH2-terminal nonameric peptide (Ac1-9) epitope from myelin basic protein as our model antigen. Peptide analogues were generated that varied in antigenic strength (as assessed by in vitro assay) based on differences in their binding affinity for Au. In vivo, these analogues elicited distinct repertoires of T cells that displayed marked differences in antigen sensitivity. Immunization with the weakest (wild-type) antigen expanded the high affinity T cells required to induce encephalomyelitis. In contrast, immunization with strongly antigenic analogues led to the elimination of T cells bearing high affinity T cell receptors by apoptosis, thereby preventing disease development. Moreover, the T cell repertoire was consistently tuned to respond to the immunizing antigen with the same activation threshold. This tuning mechanism provides a peripheral control against the expansion of autoreactive T cells and has implications for immunotherapy and vaccine design

Forecasts of non-Gaussian parameter spaces using Box-Cox transformations

Forecasts of statistical constraints on model parameters using the Fisher matrix abound in many fields of astrophysics. The Fisher matrix formalism involves the assumption of Gaussianity in parameter space and hence fails to predict complex features of posterior probability distributions. Combining the standard Fisher matrix with Box-Cox transformations, we propose a novel method that accurately predicts arbitrary posterior shapes. The Box-Cox transformations are applied to parameter space to render it approximately multivariate Gaussian, performing the Fisher matrix calculation on the transformed parameters. We demonstrate that, after the Box-Cox parameters have been determined from an initial likelihood evaluation, the method correctly predicts changes in the posterior when varying various parameters of the experimental setup and the data analysis, with marginally higher computational cost than a standard Fisher matrix calculation. We apply the Box-Cox-Fisher formalism to forecast cosmological parameter constraints by future weak gravitational lensing surveys. The characteristic non-linear degeneracy between matter density parameter and normalisation of matter density fluctuations is reproduced for several cases, and the capabilities of breaking this degeneracy by weak lensing three-point statistics is investigated. Possible applications of Box-Cox transformations of posterior distributions are discussed, including the prospects for performing statistical data analysis steps in the transformed Gaussianised parameter space.Comment: 14 pages, 7 figures; minor changes to match version published in MNRA

COVID-19 Pandemic Development in Jordan-Short-Term and Long-Term Forecasting

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves’ occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves' occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.Peer reviewe

Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan

Three simple approaches to forecast the COVID-19 epidemic in Jordan were previously proposed by Hussein, et al.: a short-term forecast (STF) based on a linear forecast model with a learning database on the reported cases in the previous 5–40 days, a long-term forecast (LTF) based on a mathematical formula that describes the COVID-19 pandemic situation, and a hybrid forecast (HF), which merges the STF and the LTF models. With the emergence of the OMICRON variant, the LTF failed to forecast the pandemic due to vital reasons related to the infection rate and the speed of the OMICRON variant, which is faster than the previous variants. However, the STF remained suitable for the sudden changes in epi curves because these simple models learn for the previous data of reported cases. In this study, we revisited these models by introducing a simple modification for the LTF and the HF model in order to better forecast the COVID-19 pandemic by considering the OMICRON variant. As another approach, we also tested a time-delay neural network (TDNN) to model the dataset. Interestingly, the new modification was to reuse the same function previously used in the LTF model after changing some parameters related to shift and time-lag. Surprisingly, the mathematical function type was still valid, suggesting this is the best one to be used for such pandemic situations of the same virus family. The TDNN was data-driven, and it was robust and successful in capturing the sudden change in +qPCR cases before and after of emergence of the OMICRON variant

Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan

Three simple approaches to forecast the COVID-19 epidemic in Jordan were previously proposed by Hussein, et al.: a short-term forecast (STF) based on a linear forecast model with a learning database on the reported cases in the previous 5–40 days, a long-term forecast (LTF) based on a mathematical formula that describes the COVID-19 pandemic situation, and a hybrid forecast (HF), which merges the STF and the LTF models. With the emergence of the OMICRON variant, the LTF failed to forecast the pandemic due to vital reasons related to the infection rate and the speed of the OMICRON variant, which is faster than the previous variants. However, the STF remained suitable for the sudden changes in epi curves because these simple models learn for the previous data of reported cases. In this study, we revisited these models by introducing a simple modification for the LTF and the HF model in order to better forecast the COVID-19 pandemic by considering the OMICRON variant. As another approach, we also tested a time-delay neural network (TDNN) to model the dataset. Interestingly, the new modification was to reuse the same function previously used in the LTF model after changing some parameters related to shift and time-lag. Surprisingly, the mathematical function type was still valid, suggesting this is the best one to be used for such pandemic situations of the same virus family. The TDNN was data-driven, and it was robust and successful in capturing the sudden change in +qPCR cases before and after of emergence of the OMICRON variant

Atherogenic Lipid Stress Induces Platelet Hyperactivity Through CD36-Mediated Hyposensitivity To Prostacyclin-; The Role Of Phosphodiesterase 3A

Prostacyclin (PGI2) controls platelet activation and thrombosis through a cyclic adenosine monophosphate (cAMP) signalling cascade. However, in patients with cardiovascular diseases this protective mechanism fails for reasons that are unclear. Using both pharmacological and genetic approaches we describe a mechanism by which oxidised low density lipoproteins (oxLDL) associated with dyslipidaemia promote platelet activation through impaired PGI2 sensitivity and diminished cAMP signalling. In functional assays using human platelets, oxLDL modulated the inhibitory effects of PGI2, but not a PDE-insensitive cAMP analogue, on platelet aggregation, granule secretion and in vitro thrombosis. Examination of the mechanism revealed that oxLDL promoted the hydrolysis of cAMP through the phosphorylation and activation of phosphodiesterase 3A (PDE3A), leading to diminished cAMP signalling. PDE3A activation by oxLDL required Src family kinases, Syk and protein kinase C. The effects of oxLDL on platelet function and cAMP signalling were blocked by pharmacological inhibition of CD36, mimicked by CD36-specific oxidised phospholipids and ablated in CD36-/- murine platelets. The injection of oxLDL into wild type mice strongly promoted FeCl3 induced carotid thrombosis in vivo, which was prevented by pharmacological inhibition of PDE3A. Furthermore, blood from dyslipidaemic mice was associated with increased oxidative lipid stress, reduced platelet sensitivity to PGI2 ex vivo and diminished PKA signalling. In contrast, platelet sensitivity to a PDE-resistant cAMP analogue remained normal. Genetic deletion of CD36, protected dyslipidaemic animals from PGI2 hyposensitivity and restored PKA signalling. These data suggest that CD36 can translate atherogenic lipid stress into platelet hyperactivity through modulation of inhibitory cAMP signalling.