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Abstract Due to their simple structure (two bond species randomly arranged on a cubic lattice), the zincblende A1−xBxC semiconductor alloys (zb-SCA) set a benchmark to explore how physical properties are impacted by disorder. A longstanding controversy was whether the lattice dynamics (phonons), governed by the bond force constant, i.e., a local physical property, is blind to the alloy disorder or actually sees it. Over the past two decades, we introduced the percolation model (PM) that distinguishes between like bonds depending on whether they vibrate in same (homo) or alien (hetero) environments (1-bond → 2-mode scheme). The PM seems to apply universally among zb-SCA, and hence would solve the controversy in favor of the second scenario. Here our aim is to take one step forward and complete in the main lines a PM-based taxonomy of high-pressure vibration spectra of zb-SCA. This might clarify how a disordered atomic alloy, seen by each bond species in terms of a homo/hetero composite (i.e., at the unusual mesoscopic scale) from the angle of the PM, behaves when the lattice shrinks under hydrostatic pressure. We focus on Cd1−xZnxTe as the last sensitive pending case. This tidying-up exercise is attractive at the fundamental level and useful for projecting phonon-based devices involving zb-SCA.

Obesity, the fifth leading cause of death globally and linked to chronic low-grade inflammation and development of numerous severe pathologies, is a major public health problem. Fermented foods, probiotics, and postbiotics emerge as promising avenues for combating obesity and inflammation. The aim of our study was to develop and characterize phyto-postbiotics corresponding to prebiotic compounds fermented by gut bacteria, which could act on obesity and related-inflammation. Chicory extract fermented by Akkermansia muciniphila (C-Akm) was selected as the most antioxidant of 20 fermented extracts. The identification of metabolites derived from C-Akm extract has enabled us to detect mostly amino acids, acids, and some polyphenols (daidzein and genistein). The anti-inflammatory and anti-obesity activities of C-Akm extract were studied by testing the extract (50 μg/mL) on the polarization of THP-1 into macrophages, the secretion of pro-inflammatory cytokines in LPS-stimulated PBMCs, and the secretion of leptin and adiponectin in adipospheroids derived from human adipose stem cells. Finally, the extract was examined in 3D co-culture model mimicking inflamed obese adipose tissue. We found that C-Akm extract decreased ROS generation, TNF-α and Il-6 gene expression in polarized macrophages, INFγ and IL-17A secretion in LPS-stimulated PBMCs stimulated with LPS. It also decreased leptin expression while increasing adiponectin and HSL expression levels in both adipocytes and co-cultures. In addition, C-Akm extract stimulated adiponectin secretion in the co-culture model. Finally, our in vitro investigations demonstrated the potential benefits of C-Akm extract in the prevention and treatment of obesity-related inflammation.

The Upper Rhine Graben (URG), located in France and Germany, is bordered by north–south-trending faults, some of which are considered active, posing a potential threat to the dense population and infrastructures on the Alsace plain. The largest historical earthquake in the region was the M6.5±0.5 Basel earthquake in 1356. Current seismicity (M>2.5 since 1960) is mostly diffuse and located within the graben. We build upon previous seismic hazard studies of the URG by exploring uncertainties in greater detail and revisiting a number of assumptions. We first take into account the limited evidence of neotectonic activity and then explore tectonic scenarios that have not been taken into account previously, exploring uncertainties for Mmax, its recurrence time, the b value, and the moment released aseismically or through aftershocks. Uncertainties in faults' moment deficit rates, on the observed seismic events' magnitude–frequency distribution and on the moment–area scaling law of earthquakes, are also explored. Assuming a purely dip-slip normal faulting mechanism associated with a simplified model with three main faults, Mmax maximum probability is estimated at Mw 6.1. Considering this scenario, there would be a 99 % probability that Mmax is less than 7.3. In contrast, with a strike-slip assumption associated with a four-main-fault model, consistent with recent paleoseismological studies and the present-day stress field, Mmax is estimated at Mw 6.8. Based on this scenario, there would be a 99 % probability that Mmax is less than 7.6.

Since 1999, Environment and Climate Change Canada (ECCC) has been coordinating a multi-laboratory comparison of measurements of long-lived greenhouse gases in whole air samples collected at the Global Atmosphere Watch (GAW) Alert Observatory located in the Canadian High Arctic (82∘28′ N, 62∘30′ W). In this paper, we evaluate the measurement agreement of atmospheric CO2, CH4, N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) between leading laboratories from seven independent international institutions. The measure of success is linked to target goals for network compatibility outlined by the World Meteorological Organization's (WMO) GAW greenhouse gas measurement community. Overall, based on ∼ 8000 discrete flask samples, we find that the co-located atmospheric CO2 and CH4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, and ECCC versus NOAA (the designated reference laboratory) are generally consistent with the WMO compatibility goals of ± 0.1 ppm CO2 and ± 2 ppb CH4 over the 17-year period (1999–2016), although there are periods where differences exceed target levels and persist as systematic bias for months or years. Consistency with the WMO goals for N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) has not been demonstrated. Additional analysis of co-located comparison measurements between CSIRO and SIO versus NOAA or INSTAAR (for the isotopes of CO2) at other geographical sites suggests that the findings at Alert for CO2, CH4, N2O, and δ13C–CO2 could be extended across the CSIRO, SIO, and NOAA observing networks. The primary approach to estimate an overall measurement agreement level was carried out by pooling the differences of all individual laboratories versus the designated reference laboratory and determining the 95th percentile range of these data points. Using this approach over the entire data record, our best estimate of the measurement agreement range is −0.51 to +0.53 ppm for CO2, −0.09 ‰ to +0.07 ‰ for δ13C, −0.50 ‰ to +0.58 ‰ for δ18O, −4.86 to +6.16 ppb for CH4, −0.75 to +1.20 ppb for N2O, and −0.14 to +0.09 ppt for SF6. A secondary approach of using the average of 2 standard deviations of the means for all flask samples taken in each individual sampling episode provided similar results. These upper and lower limits represent our best estimate of the measurement agreement at the 95 % confidence level for these individual laboratories, providing more confidence for using these datasets in various scientific applications (e.g., long-term trend analysis).

Abstract The emerging CdTe–BeTe semiconductor alloy that exhibits a dramatic mismatch in bond covalency and bond stiffness clarifying its vibrational-mechanical properties is used as a benchmark to test the limits of the percolation model (PM) worked out to explain the complex Raman spectra of the related but less contrasted Zn1−xBex-chalcogenides. The test is done by way of experiment ( $$x\le 0.11$$ x ≤ 0.11 ), combining Raman scattering with X-ray diffraction at high pressure, and ab initio calculations ( $$x$$ x ~ 0–0.5; $$x$$ x ~1). The (macroscopic) bulk modulus $${B}_{0}$$ B 0 drops below the CdTe value on minor Be incorporation, at variance with a linear $${B}_{0}$$ B 0 versus $$x$$ x increase predicted ab initio, thus hinting at large anharmonic effects in the real crystal. Yet, no anomaly occurs at the (microscopic) bond scale as the regular bimodal PM-type Raman signal predicted ab initio for Be–Te in minority ( $$x$$ x ~0, 0.5) is barely detected experimentally. At large Be content ( $$x$$ x ~1), the same bimodal signal relaxes all the way down to inversion, an unprecedented case. However, specific pressure dependencies of the regular ( $$x$$ x ~0, 0.5) and inverted ( $$x$$ x ~1) Be–Te Raman doublets are in line with the predictions of the PM. Hence, the PM applies as such to Cd1−xBexTe without further refinement, albeit in a “relaxed” form. This enhances the model’s validity as a generic descriptor of phonons in alloys.

We have constructed an atmospheric inversion framework based on TM5-4DVAR to jointly assimilate measurements of methane and δ13C of methane in order to estimate source-specific methane emissions. Here we present global emission estimates from this framework for the period 1999–2016. We assimilate a newly constructed, multi-agency database of CH4 and δ13C measurements. We find that traditional CH4-only atmospheric inversions are unlikely to estimate emissions consistent with atmospheric δ13C data, and assimilating δ13C data is necessary to derive emissions consistent with both measurements. Our framework attributes ca. 85 % of the post-2007 growth in atmospheric methane to microbial sources, with about half of that coming from the tropics between 23.5∘ N and 23.5∘ S. This contradicts the attribution of the recent growth in the methane budget of the Global Carbon Project (GCP). We find that the GCP attribution is only consistent with our top-down estimate in the absence of δ13C data. We find that at global and continental scales, δ13C data can separate microbial from fossil methane emissions much better than CH4 data alone, and at smaller scales this ability is limited by the current δ13C measurement coverage. Finally, we find that the largest uncertainty in using δ13C data to separate different methane source types comes from our knowledge of atmospheric chemistry, specifically the distribution of tropospheric chlorine and the isotopic discrimination of the methane sink.

Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection.

Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is useful for improving our understanding of their behaviour. For doing so, several statistical methods exist, among which principal component regression is one of the most widely used in paleoclimatology. Here, we provide the software ClimIndRec to the climate community; it is based on four regression methods (principal component regression, PCR; partial least squares, PLS; elastic net, Enet; random forest, RF) and cross-validation (CV) algorithms, and enables the systematic reconstruction of a given climate index. A prerequisite is that there are proxy records in the database that overlap in time with its observed variations. The relative efficiency of the methods can vary, according to the statistical properties of the mode and the proxy records used. Here, we assess the sensitivity to the reconstruction technique. ClimIndRec is modular as it allows different inputs like the proxy database or the regression method. As an example, it is here applied to the reconstruction of the North Atlantic Oscillation by using the PAGES 2k database. In order to identify the most reliable reconstruction among those given by the different methods, we use the modularity of ClimIndRec to investigate the sensitivity of the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the targeted reconstruction period. We obtain the best reconstruction of the North Atlantic Oscillation (NAO) using the random forest approach. It shows significant correlation with former reconstructions, but exhibits higher validation scores.

Instruments on satellites for Earth observation on polar orbits usually employ a two-point calibration technique, in which deep space and an onboard calibration target provide two reference flux levels. As the direction of the deep-space view is in general close to the celestial equator, the Moon sometimes moves through the field of view and introduces an unwelcome additional signal. One can take advantage of this intrusion, however, by using the Moon as a third flux standard, and this has actually been done for checking the lifetime stability of sensors operating at visible wavelengths. As the disk-integrated thermal emission of the Moon is less well known than its reflected sunlight, this concept can in the microwave range only be used for stability checks and intercalibration. An estimate of the frequency of appearances of the Moon in the deep-space view, a description of the limiting factors of the measurement accuracy and models of the Moon's brightness, and a discussion of the benefits from complementing the naturally occurring appearances of the Moon with dedicated spacecraft maneuvers show that it would be possible to detect photometric lifetime drifts of a few percent with just two measurements. The pointing accuracy is the most crucial factor for the value of this method. Planning such observations in advance would be particularly beneficial, because it allows observing the Moon at well-defined phase angles and putting it at the center of the field of view. A constant phase angle eliminates the need for a model of the Moon's brightness when checking the stability of an instrument. With increasing spatial resolution of future microwave sensors another question arises, viz. to what extent foreground emission from objects other than the Moon will contaminate the flux entering the deep-space view, which is supposed to originate exclusively in the cosmic microwave background. We conclude that even the brightest discreet sources have flux densities below the detection limit of microwave sensors in a single scan.

Abstract Background Most research that includes Red River Métis tends to be pan-Indigenous. Grouping Métis with First Nations and Inuit can diminish their unique and diverse experiences, as well as distinctions-based approaches. Taking a step toward addressing this problem, the Manitoba Métis Federation (MMF; the national government of the Red River Métis) invited researchers within the Canadian network Translating Emergency Knowledge for Kids to partner in this research, which focuses on understanding engagement strategies that can help expose Red River Métis parents to child health research opportunities and build trust and transparency amongst research partners and participants. Methods A qualitative descriptive approach underpinned by a participatory paradigm guided this study. Semi-structured interviews were conducted with 19 Red River Métis parents and Elders via Zoom or telephone. An inductive thematic analysis approach was used to explore patterns and themes across the data. Results Analysis generated four themes: (a) show me why you are here; (b) make it easier for me to take part; (c) create welcoming spaces; and (d) it should be a give and take experience. Parents emphasized the importance of kinship and personal connections, and said they want to hear about child health study opportunities from trusted organizations. They want researchers to be transparent about their research motivations and to offer multiple ways to participate to help them fit research into their busy lives. Relational approaches within the research process (e.g., creating informal/welcoming environments, gathering over food, fostering shared learning/creative opportunities) are prominent in the findings. Conclusion There is a dearth of research focused on the distinct experiences of Red River Métis families. Engaging Red River Métis parents early in the research process will help incorporate their preferences and perspectives into the design of a larger project to adapt child health resources. More training is needed to help researchers improve their abilities to engage in self-reflection, listen, recognize power relationships, and demonstrate trust and transparency in each research interaction. It is only through respectful and successful engagement that child health research can be relevant, applicable and positively impact Red River Métis children, families and communities.