Explore the vast range of titles available.

Vegetation biodiversity in mountainous regions is controlled by altitudinal gradients and their corresponding microclimate. Higher temperatures, shorter snow cover periods, and high variability in the precipitation regime might lead to changes in vegetation distribution in mountains all over the world. In this study, we evaluate vegetation distribution along an altitudinal gradient (1334–1667 m.a.s.l.) in the Zao Mountains, northeastern Japan, by means of alpha diversity indices, including species richness, the Shannon index, and the Simpson index. In order to assess vegetation species and their characteristics along the mountain slope selected, fourteen 50 m × 50 m plots were selected at different altitudes and scanned with RGB cameras attached to Unmanned Aerial Vehicles (UAVs). Image analysis revealed the presence of 12 dominant tree and shrub species of which the number of individuals and heights were validated with fieldwork ground truth data. The results showed a significant variability in species richness along the altitudinal gradient. Species richness ranged from 7 to 11 out of a total of 12 species. Notably, species such as Fagus crenata, despite their low individual numbers, dominated the canopy area. In contrast, shrub species like Quercus crispula and Acer tschonoskii had high individual numbers but covered smaller canopy areas. Tree height correlated well with canopy areas, both representing tree size, which has a strong relationship with species diversity indices. Species such as F. crenata, Q. crispula, Cornus controversa, and others have an established range of altitudinal distribution. At high altitudes (1524–1653 m), the average shrubs’ height is less than 4 m, and the presence of Abies mariesii is negligible because of high mortality rates caused by a severe bark beetle attack. These results highlight the complex interactions between species abundance, canopy area, and altitude, providing valuable insights into vegetation distribution in mountainous regions. However, species diversity indices vary slightly and show some unusually low values without a clear pattern. Overall, these indices are higher at lower altitudes, peak at mid-elevations, and decrease at higher elevations in the study area. Vegetation diversity indices did not show a clear downward trend with altitude but depicted a vegetation composition at different altitudes as controlled by their surrounding environment. Finally, UAVs showed their significant potential for conducting large-scale vegetation surveys reliably and in a short time, with low costs and low manpower.

Biodiversity is a characteristic of ecosystems that plays a crucial role in the study of their evolution, and to estimate it, the species of all plants need to be determined. In this study, we used Unmanned Aerial Vehicles to gather RGB images of mid-to-high-altitude ecosystems in the Zao mountains (Japan). All the data-collection missions took place in autumn so the plants present distinctive seasonal coloration. Patches from single trees and bushes were manually extracted from the collected orthomosaics. Subsequently, Deep Learning image-classification networks were used to automatically determine the species of each tree or bush and estimate biodiversity. Both Convolutional Neural Networks (CNNs) and Transformer-based models were considered (ResNet, RegNet, ConvNeXt, and SwinTransformer). To measure and estimate biodiversity, we relied on the Gini–Simpson Index, the Shannon–Wiener Index, and Species Richness. We present two separate scenarios for evaluating the readiness of the technology for practical use: the first scenario uses a subset of the data with five species and a testing set that has a very similar percentage of each species to those present in the training set. The models studied reach very high performances with over 99 Accuracy and 98 F1 Score (the harmonic mean of Precision and Recall) for image classification and biodiversity estimates under 1% error. The second scenario uses the full dataset with nine species and large variations in class balance between the training and testing datasets, which is often the case in practical use situations. The results in this case remained fairly high for Accuracy at 90.64% but dropped to 51.77% for F1 Score. The relatively low F1 Score value is partly due to a small number of misclassifications having a disproportionate impact in the final measure, but still, the large difference between the Accuracy and F1 Score highlights the complexity of finely evaluating the classification results of Deep Learning Networks. Even in this very challenging scenario, the biodiversity estimation remained with relatively small (6–14%) errors for the most detailed indices, showcasing the readiness of the technology for practical use.

Although coronaviruses (CoVs) have long been predicted to cause zoonotic diseases and pandemics with high probability, the lack of effective anti-pan-CoVs drugs rapidly usable against the emerging SARS-CoV-2 actually prevented a promptly therapeutic intervention for COVID-19. Development of host-targeting antivirals could be an alternative strategy for the control of emerging CoVs infections, as they could be quickly repositioned from one pandemic event to another. To contribute to these pandemic preparedness efforts, here we report on the broad-spectrum CoVs antiviral activity of MEDS433, a new inhibitor of the human dihydroorotate dehydrogenase (hDHODH), a key cellular enzyme of the de novo pyrimidine biosynthesis pathway. MEDS433 inhibited the in vitro replication of hCoV-OC43 and hCoV-229E, as well as of SARS-CoV-2, at low nanomolar range. Notably, the anti-SARS-CoV-2 activity of MEDS433 against SARS-CoV-2 was also observed in kidney organoids generated from human embryonic stem cells. Then, the antiviral activity of MEDS433 was reversed by the addition of exogenous uridine or the product of hDHODH, the orotate, thus confirming hDHODH as the specific target of MEDS433 in hCoVs-infected cells. Taken together, these findings suggest MEDS433 as a potential candidate to develop novel drugs for COVID-19, as well as broad-spectrum antiviral agents exploitable for future CoVs threats.

Abstract Identification and development of effective drugs active against SARS-CoV-2 are urgently needed. Here, we report on the anti-SARS-CoV-2 activity of MEDS433, a novel inhibitor of human dihydroorotate dehydrogenase (hDHODH), a key cellular enzyme of the de novo pyrimidines biosynthesis. MEDS433 inhibits in vitro virus replication in the low nanomolar range, and through a mechanism that stems from its ability to block hDHODH activity. MEDS433 thus represents an attractive candidate to develop novel anti-SARS-CoV-2 agents. Competing Interest Statement The authors have declared no competing interest.