The impact of boundary conditions on CO2 capacity estimation in aquifers

The boundary conditions of an aquifer determine the extent to which fluids (including formation water and CO2) and pressure can be transferred into adjacent geological formations, either laterally or vertically. Aquifer boundaries can be faults, lithological boundaries, formation pinch-outs, salt walls, or outcrop. In many cases compliance with regulations preventing CO2 storage influencing areas outside artificial boundaries defined by non-geological criteria (international boundaries; license limits) may be necessary. A bounded aquifer is not necessarily a closed aquifer. The identification of an aquifer’s boundary conditions determines how CO2 storage capacity is estimated in the earliest screening and characterization stages. There are different static capacity estimation methods in use for closed systems and open systems. The method used has a significant impact on the final capacity estimate. The recent EU Directive (2009/31/EC) stated that where more than one storage site within a single “hydraulic unit” (bounded aquifer volume) is being considered, the characterization process should account for potential pressure interactions. The pressure interplay of multiple sites (or even the pressure footprint of just one site) is heavily influenced by boundary conditions

Indication of electron neutrino appearance from an accelerator-produced off-axis muon neutrino beam

The T2K experiment observes indications of nu(mu) -> nu(mu) e appearance in data accumulated with 1.43 x 10(20) protons on target. Six events pass all selection criteria at the far detector. In a three-flavor neutrino oscillation scenario with |Delta m(23)(2)| = 2.4 x 10(-3) eV(2), sin(2)2 theta(23) = 1 and sin(2)2 theta(13) = 0, the expected number of such events is 1.5 +/- 0.3(syst). Under this hypothesis, the probability to observe six or more candidate events is 7 x 10(-3), equivalent to 2.5 sigma significance. At 90% C.L., the data are consistent with 0.03(0.04) < sin(2)2 theta(13) < 0.28(0.34) for delta(CP) = 0 and a normal (inverted) hierarchy

Multiple Domain Associations within the Arabidopsis Immune Receptor RPP1 Regulate the Activation of Programmed Cell Death

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Upon recognition of pathogen virulence effectors, plant nucleotide-binding leucine-rich repeat (NLR) proteins induce defense responses including localized host cell death. In an effort to understand the molecular mechanisms leading to this response, we examined the Arabidopsis thaliana NLR protein RECOGNITION OF PERONOSPORA PARASITICA1 (RPP1), which recognizes the Hyaloperonospora arabidopsidis effector ARABIDOPSIS THALIANA RECOGNIZED1 (ATR1). Expression of the N-terminus of RPP1, including the Toll/interleukin-1 receptor (TIR) domain (“N-TIR”), elicited an effector-independent cell death response, and we used allelic variation in TIR domain sequences to define the key residues that contribute to this phenotype. Further biochemical characterization indicated that cell death induction was correlated with N-TIR domain self-association. In addition, we demonstrated that the nucleotide-binding (NB)-ARC1 region of RPP1 self-associates and plays a critical role in cell death activation, likely by facilitating TIR:TIR interactions. Structural homology modeling of the NB subdomain allowed us to identify a putative oligomerization interface that was shown to influence NB-ARC1 self-association. Significantly, full-length RPP1 exhibited effector-dependent oligomerization and, although mutations at the NB-ARC1 oligomerization interface eliminated cell death induction, RPP1 self-association was unaffected, suggesting that additional regions contribute to oligomerization. Indeed, the leucine-rich repeat domain of RPP1 also self-associates, indicating that multiple interaction interfaces exist within activated RPP1 oligomers. Finally, we observed numerous intramolecular interactions that likely function to negatively regulate RPP1, and present a model describing the transition to an active NLR protein

Liking but devaluing animals: emotional and deliberative paths to speciesism

We explore whether priming emotion versus deliberation affects speciesism—the tendency to prioritize certain individuals over others on the basis of their species-membership (three main and two supplementary studies; four pre-registered; N = 3,288). We find that the tendency to prioritize humans over animals (anthropocentric speciesism) decreases when participants were asked to think emotionally compared to deliberately. In contrast, the tendency to prioritize dogs over other animals (pet speciesism) increases when participants were asked to think emotionally compared to deliberately. We hypothesize that, emotionally, people like animals in general, and dogs in particular; however, deliberatively, people attribute higher moral status to humans than animals, and roughly equal status to dogs, chimpanzees, elephants and pigs. In support of this explanation, participants tended to discriminate between animals based on likability when thinking emotionally and based on moral status when thinking deliberately. These findings shed light on the psychological underpinnings of speciesism

On-chip electrically controlled routing of photons from a single quantum dot

Electrical control of on-chip routing of photons emitted by a single InAs/GaAs self-assembled quantum dot (SAQD) is demonstrated in a photonic crystal cavity-waveguide system. The SAQD is located inside an H1 cavity, which is coupled to two photonic crystal waveguides. The SAQD emission wavelength is electrically tunable by the quantum-confined Stark effect. When the SAQD emission is brought into resonance with one of two H1 cavity modes, it is preferentially routed to the waveguide to which that mode is selectively coupled. This proof of concept provides the basis for scalable, low-power, high-speed operation of single-photon routers for use in integrated quantum photonic circuits

Scattering beneath Western Pacific subduction zones: Evidence for oceanic crust in the mid-mantle

Small-scale heterogeneities in the mantle can give important insight into the dynamics and composition of the Earth's interior. Here, we analyse seismic energy found as precursors to PP, which is scattered offsmall-scale heterogeneities related to subduction zones in the upper and mid-mantle. We use data from shallow earthquakes (less than 100 km depth) in the epicentral distance range of 90°-110° and use array methods to study a 100 s window prior to the PP arrival. Our analysis focuses on energy arriving offthe great circle path between source and receiver. We select coherent arrivals automatically, based on a semblance weighted beampower spectrum, maximizing the selection of weak amplitude arrivals. Assuming single P-to-P scattering and using the directivity information from array processing, we locate the scattering origin by ray tracing through a 1-D velocity model. Using data from the small-aperture Eielson Array (ILAR) in Alaska, we are able to image structure related to heterogeneities in western Pacific subduction zones. We find evidence for ~300 small-scale heterogeneities in the region around the present-day Japan, Izu-Bonin, Mariana and West Philippine subduction zones. Most of the detected heterogeneities are located in the crust and upper mantle, but 6 per cent of scatterers are located deeper than 600 km. Scatterers in the transition zone correlate well with edges of fast features in tomographic images and subducted slab contours derived from slab seismicity. We locate deeper scatterers beneath the Izu-Bonin/Mariana subduction zones, which outline a steeply dipping pseudo-planar feature to 1480 km depth, and beneath the ancient (84-144 Ma) Indonesian subduction trench down to 1880 km depth. We image the remnants of subducted crustal material, likely the underside reflection of the subducted Moho. The presence of deep scatterers related to past and present subduction provides evidence that the subducted crust does descend into the lower mantle at least for these steeply dipping subduction zones. Applying the same technique to other source-receiver paths will increase our knowledge of the small-scale structure of the mantle and will provide further constraints on geodynamic models.

Oral Tau Aggregation Inhibitor for Alzheimer’s Disease : Design, Progress and Basis for Selection of the 16 mg/day Dose in a Phase 3, Randomized, Placebo-Controlled Trial of Hydromethylthionine Mesylate

Funding Information: We gratefully acknowledge the contribution of the scientific advisory board, study investigators, and the generosity of study participants. The authors thank EVERSANA™ for providing medical writing support, which was funded by TauRx Therapeutics in accordance with Good Publication Practice (GPP3) guidelines ( http://www.ismpp.org/gpp3 ). Publisher Copyright: © 2022, The Author(s).Peer reviewedPublisher PD

Shelterbelts: a row of trees or the next best thing to mitigating GHGs on prairie landscapes

Non-Peer Reviewe

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.PT and YS were supported by EPSRC grant no EP/L000296/1 “Personalized Medicine through Learning in the Model Space.” This work was supported by grants to ZK from the Biotechnology and Biological Sciences Research Council (H012508), the Leverhulme Trust (RF-2011-378), and the (European Community's) Seventh Framework Programme (FP7/2007-2013) under agreement PITN-GA-2011-290011

Geographical and socioeconomic inequalities in female breast cancer incidence and mortality in Iran: A Bayesian spatial analysis of registry data

Background In Iran, trends in breast cancer incidence and mortality have generally been monitored at national level. The purpose of this study is to examine province-level disparities in age-standardised breast cancer incidence versus mortality from 2000 to 2010 and their association with socioeconomic status. Methods In this study, data from Iran’s national cancer and death registry systems, and covariates from census and household expenditure surveys were used. We estimated the age-standardised incidence and mortality rates in women aged more than 30 years for all 31 provinces in the consecutive time intervals 2000–2003, 2004–2007 and 2008–2010 using a Bayesian spatial model. Results Mean age-standardised breast cancer incidence across provinces increased over time from 15.0 per 100,000 people (95% credible interval 12.0,18.3) in 2000–2003 to 39.6 (34.5,45.1) in 2008–2010. The mean breast cancer mortality rate declined from 10.9 (8.3,13.8) to 9.9 (7.5,12.5) deaths per 100,000 people in the same period. When grouped by wealth index quintiles, provinces in the highest quintile had higher levels of incidence and mortality. In the wealthiest quintile, reductions in mortality over time were larger than those observed among provinces in the poorest quintile. Relative breast cancer mortality decreased by 16.7% in the highest quintile compared to 10.8% in the lowest quintile. Conclusions Breast cancer incidence has increased over time, with lower incidence in the poorest provinces likely driven by underdiagnoses or late-stage diagnosis. Although the reported mortality rate is still higher in wealthier provinces, the larger decline over time in these provinces indicates a possible future reversal, with the most deprived provinces having higher mortality rates. Ongoing analysis of incidence and mortality at sub-national level is crucial in addressing inequalities in healthcare systems and public health both in Iran and elsewhere