Assessing the success of soil reclamation programs can be costly and time-consuming due to the cost of traditional soil analytical techniques. One cost-effective tool that has been successfully used to efficiently analyze a range of soil parameters is reflectance spectroscopy. We used reflectance data to analyze natural and reclaimed soils in the field, examining three key soil parameters: soil organic carbon (SOC), total nitrogen (TN), and soil pH. Continuous wavelet transforms combined with machine learning models were used to predict these parameters. Based on the root mean square error (RMSE), R 2 value, and the ratio of performance to deviation (RPD), the Cubist model produced the most accurate models for SOC, TN, and pH. The RMSE, R 2 , and RPD values for SOC were 0.60%, 0.80, and 2.2, respectively. The TN model results were 0.05%, 0.81 and 2.5, and pH model results were 0.44, 0.69 and 1.8. Overall, the optimal model can be used to predict SOC and TN accurately, and improvements in the pH model are needed as pH values less than 6.5 were consistently overpredicted.Key words: reflectance spectroscopy, reclamation, carbon, nitrogen.Résumé : Le coût des techniques classiques d'analyse du sol rend parfois l'efficacité des programmes de restauration du sol aussi onéreuse que laborieuse à évaluer. La spectroscopie par réflectance est une technique rentable dont on s'est servi pour analyser efficacement un éventail de paramètres du sol. Les auteurs ont recouru à la réflectance pour analyser des sols naturels et restaurés sur le terrain, notamment les trois grands paramètres que sont la teneur en carbone organique, la concentration d'azote total et le pH. Ils ont prévu ces paramètres en combinant les transformées d'ondelettes continues et des modèles d'apprentissage machine. D'après l'écart-type, le coefficient R 2 et le ratio performance/écart (RPD), le modèle cubiste est celui qui donne les résultats les plus précis pour le carbone organique du sol, la concentration totale d'azote et le pH. L'écart-type, le coefficient R 2 et le RPD du carbone organique du sol s'établissent respectivement à 0,60 %, 0,80 et 2,2. Les résultats du modèle de l'azote total se chiffrent à 0,05 %, 0,81 et 2,5, tandis que ceux du modèle du pH sont de 0,44, 0,69 et 1,8. En règle générale, on peut se servir du modèle optimal pour prévoir avec précision la concentration de carbone organique et celle d'azote total dans le sol; il faudrait améliorer le modèle du pH, car il y a toujours surestimation des prévisions quand le pH est inférieur à 6,5. [Traduit par la Rédaction]

Influenza A viruses contain eight single-stranded, negative-sense RNA segments as viral genomes in the form of viral ribonucleoproteins (vRNPs). During genome replication in the nucleus, positive-sense complementary RNPs (cRNPs) are produced as replicative intermediates, which are not incorporated into progeny virions. To analyze the mechanism of selective vRNP incorporation into progeny virions, we quantified vRNPs and cRNPs in the nuclear and cytosolic fractions of infected cells, using a strand-specific qRT-PCR. Unexpectedly, we found that cRNPs were also exported to the cytoplasm. This export was chromosome region maintenance 1 (CRM1)-independent unlike that of vRNPs. Although both vRNPs and cRNPs were present in the cytosol, viral matrix (M1) protein, a key regulator for viral assembly, preferentially bound vRNPs over cRNPs. These results indicate that influenza A viruses selectively uptake cytosolic vRNPs through a specific interaction with M1 during viral assembly.

The resource extraction that powers global economies is often manifested in Indigenous Peoples’ territories. Indigenous Peoples living on the land are careful observers of resulting biodiversity changes, and Indigenous-led research can provide evidence to inform conservation decisions. In the Nearctic western boreal forest, landscape change from forest harvesting and petroleum extraction is intensive and extensive. A First Nations community in the Canadian oil sands co-created camera-trap research to explore observations of presumptive species declines, seeking to identify the relative contributions of different industrial sectors to changes in mammal distributions. Camera data were analyzed via generalized linear models in a model-selection approach. Multiple forestry and petroleum extraction features positively and negatively affected boreal mammal species. Pipelines had the greatest negative effect size (for wolves), whereas well sites had a large positive effect size for multiple species, suggesting the energy sector as a target for co-management. Co-created research reveals spatial relationships of disturbance, prey, and predators on Indigenous traditional territories. It provides hypotheses, tests, and interpretations unique to outside perspectives; Indigenous participation in conservation management of their territories scales up to benefit global biodiversity conservation.

No abstract

No abstract