Showing papers by "Institut national de la recherche agronomique published in 2021"

Widespread deoxygenation of temperate lakes

Abstract: The concentration of dissolved oxygen in aquatic systems helps to regulate biodiversity1,2, nutrient biogeochemistry3, greenhouse gas emissions4, and the quality of drinking water5. The long-term declines in dissolved oxygen concentrations in coastal and ocean waters have been linked to climate warming and human activity6,7, but little is known about the changes in dissolved oxygen concentrations in lakes. Although the solubility of dissolved oxygen decreases with increasing water temperatures, long-term lake trajectories are difficult to predict. Oxygen losses in warming lakes may be amplified by enhanced decomposition and stronger thermal stratification8,9 or oxygen may increase as a result of enhanced primary production10. Here we analyse a combined total of 45,148 dissolved oxygen and temperature profiles and calculate trends for 393 temperate lakes that span 1941 to 2017. We find that a decline in dissolved oxygen is widespread in surface and deep-water habitats. The decline in surface waters is primarily associated with reduced solubility under warmer water temperatures, although dissolved oxygen in surface waters increased in a subset of highly productive warming lakes, probably owing to increasing production of phytoplankton. By contrast, the decline in deep waters is associated with stronger thermal stratification and loss of water clarity, but not with changes in gas solubility. Our results suggest that climate change and declining water clarity have altered the physical and chemical environment of lakes. Declines in dissolved oxygen in freshwater are 2.75 to 9.3 times greater than observed in the world’s oceans6,7 and could threaten essential lake ecosystem services2,3,5,11. Analysis of temperate lakes finds a widespread decline in dissolved oxygen concentrations in surface and deep waters, which is associated with reduced solubility at warmer surface water temperatures and increased stratification at depth.

Six modes of co-production for sustainability

Abstract: The promise of co-production to address complex sustainability challenges is compelling. Yet, co-production, the collaborative weaving of research and practice, encompasses diverse aims, terminologies and practices, with poor clarity over their implications. To explore this diversity, we systematically mapped differences in how 32 initiatives from 6 continents co-produce diverse outcomes for the sustainable development of ecosystems at local to global scales. We found variation in their purpose for utilizing co-production, understanding of power, approach to politics and pathways to impact. A cluster analysis identified six modes of co-production: (1) researching solutions; (2) empowering voices; (3) brokering power; (4) reframing power; (5) navigating differences and (6) reframing agency. No mode is ideal; each holds unique potential to achieve particular outcomes, but also poses unique challenges and risks. Our analysis provides a heuristic tool for researchers and societal actors to critically explore this diversity and effectively navigate trade-offs when co-producing sustainability. Co-production includes diverse aims, terminologies and practices. This study explores such diversity by mapping differences in how 32 initiatives from 6 continents co-produce diverse outcomes for the sustainable development of ecosystems at local to global scales.

Ecosystems monitoring powered by environmental genomics: a review of current strategies with an implementation roadmap

Abstract: A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Uncovering the structure of clinical EEG signals with self-supervised learning.

Abstract: Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based approaches. However, in most situations, unlabeled data is available in abundance. By extracting information from this unlabeled data, it might be possible to reach competitive performance with deep neural networks despite limited access to labels. Approach. We investigated self-supervised learning (SSL), a promising technique for discovering structure in unlabeled data, to learn representations of EEG signals. Specifically, we explored two tasks based on temporal context prediction as well as contrastive predictive coding on two clinically-relevant problems: EEG-based sleep staging and pathology detection. We conducted experiments on two large public datasets with thousands of recordings and performed baseline comparisons with purely supervised and hand-engineered approaches. Main results. Linear classifiers trained on SSL-learned features consistently outperformed purely supervised deep neural networks in low-labeled data regimes while reaching competitive performance when all labels were available. Additionally, the embeddings learned with each method revealed clear latent structures related to physiological and clinical phenomena, such as age effects. Significance. We demonstrate the benefit of SSL approaches on EEG data. Our results suggest that self-supervision may pave the way to a wider use of deep learning models on EEG data.

The JOREK non-linear extended MHD code and applications to large-scale instabilities and their control in magnetically confined fusion plasmas

Abstract: JOREK is a massively parallel fully implicit non-linear extended magneto-hydrodynamic (MHD) code for realistic tokamak X-point plasmas. It has become a widely used versatile simulation code for studying large-scale plasma instabilities and their control and is continuously developed in an international community with strong involvements in the European fusion research programme and ITER organization. This article gives a comprehensive overview of the physics models implemented, numerical methods applied for solving the equations and physics studies performed with the code. A dedicated section highlights some of the verification work done for the code. A hierarchy of different physics models is available including a free boundary and resistive wall extension and hybrid kinetic-fluid models. The code allows for flux-surface aligned iso-parametric finite element grids in single and double X-point plasmas which can be extended to the true physical walls and uses a robust fully implicit time stepping. Particular focus is laid on plasma edge and scrape-off layer (SOL) physics as well as disruption related phenomena. Among the key results obtained with JOREK regarding plasma edge and SOL, are deep insights into the dynamics of edge localized modes (ELMs), ELM cycles, and ELM control by resonant magnetic perturbations, pellet injection, as well as by vertical magnetic kicks. Also ELM free regimes, detachment physics, the generation and transport of impurities during an ELM, and electrostatic turbulence in the pedestal region are investigated. Regarding disruptions, the focus is on the dynamics of the thermal quench (TQ) and current quench triggered by massive gas injection and shattered pellet injection, runaway electron (RE) dynamics as well as the RE interaction with MHD modes, and vertical displacement events. Also the seeding and suppression of tearing modes (TMs), the dynamics of naturally occurring TQs triggered by locked modes, and radiative collapses are being studied.

Secreted pectin monooxygenases drive plant infection by pathogenic oomycetes

Abstract: The oomycete Phytophthora infestans is a damaging crop pathogen and a model organism to study plant-pathogen interactions. We report the discovery of a family of copper-dependent lytic polysaccharide monooxygenases (LPMOs) in plant pathogenic oomycetes and its role in plant infection by P. infestans We show that LPMO-encoding genes are up-regulated early during infection and that the secreted enzymes oxidatively cleave the backbone of pectin, a charged polysaccharide in the plant cell wall. The crystal structure of the most abundant of these LPMOs sheds light on its ability to recognize and degrade pectin, and silencing the encoding gene in P. infestans inhibits infection of potato, indicating a role in host penetration. The identification of LPMOs as virulence factors in pathogenic oomycetes opens up opportunities in crop protection and food security.

Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects

Abstract: Plant phenomics is a new avenue for linking plant genomics and environmental studies, thereby improving plant breeding and management. Remote sensing techniques have improved high-throughput plant phenotyping. However, the accuracy, efficiency, and applicability of three-dimensional (3D) phenotyping are still challenging, especially in field environments. Light detection and ranging (lidar) provides a powerful new tool for 3D phenotyping with the rapid development of facilities and algorithms. Numerous efforts have been devoted to studying static and dynamic changes of structural and functional phenotypes using lidar in agriculture. These progresses also improve 3D plant modeling across different spatial–temporal scales and disciplines, providing easier and less expensive association with genes and analysis of environmental practices and affords new insights into breeding and management. Beyond agriculture phenotyping, lidar shows great potential in forestry, horticultural, and grass phenotyping. Although lidar has resulted in remarkable improvements in plant phenotyping and modeling, the synthetization of lidar-based phenotyping for breeding and management has not been fully explored. We identify three main challenges in lidar-based phenotyping development: 1) developing low cost, high spatial–temporal, and hyperspectral lidar facilities, 2) moving into multi-dimensional phenotyping with an endeavor to generate new algorithms and models, and 3) embracing open source and big data.

Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

Abstract: We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Global transpiration data from sap flow measurements: the SAPFLUXNET database

Abstract: . Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/ , last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository ( https://doi.org/10.5281/zenodo.3971689 ; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.

Biological control of invasive stink bugs: review of global state and future prospects

Abstract: Invasive stink bugs (Hemiptera: Pentatomidae) are responsible for high economic losses to agricul-ture on a global scale. The most important species, dating from recent to old invasions, includeBagrada hilaris (Burmeister), Halyomorpha halys (Stal), Piezodorus guildinii (Westwood), Nezara vir-idula (L.), and Murgantia histrionica (Hahn). Bagrada hilaris, H. halys,andN. viridula are nowalmost globally distributed. Biological control of these pests faces a complex set of challenges thatmust be addressed to maintain pest populations below the economic injury level. Several case studiesof classical and conservation biological control of invasive stink bugs are reported here. The mostcommon parasitoids in their geographical area of origin are egg parasitoids (Hymenoptera: Scelion-idae, Encyrtidae, and Eupelmidae). Additionally, native parasitoids of adult stink bugs (Diptera:Tachinidae) have in some cases adapted to the novel hosts in the invaded area and native predatorsare known to prey on the various instars. Improving the efficacy of biocontrol agents is possiblethrough conservation biological control techniques and exploitation of their chemical ecology.Moreover, integration of biological control with other techniques, such as behavioural manipulationof adult stink bugs and plant resistance, may be a sustainable pest control method within organicfarming and integrated pest management programs. However, additional field studies are needed toverify the efficacy of these novel methods and transfer them from research to application.

Microalgae n-3 PUFAs Production and Use in Food and Feed Industries.

Abstract: N-3 polyunsaturated fatty acids (n-3 PUFAs), and especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are essential compounds for human health. They have been proven to act positively on a panel of diseases and have interesting anti-oxidative, anti-inflammatory or anti-cancer properties. For these reasons, they are receiving more and more attention in recent years, especially future food or feed development. EPA and DHA come mainly from marine sources like fish or seaweed. Unfortunately, due to global warming, these compounds are becoming scarce for humans because of overfishing and stock reduction. Although increasing in recent years, aquaculture appears insufficient to meet the increasing requirements of these healthy molecules for humans. One alternative resides in the cultivation of microalgae, the initial producers of EPA and DHA. They are also rich in biochemicals with interesting properties. After defining macro and microalgae, this review synthesizes the current knowledge on n-3 PUFAs regarding health benefits and the challenges surrounding their supply within the environmental context. Microalgae n-3 PUFA production is examined and its synthesis pathways are discussed. Finally, the use of EPA and DHA in food and feed is investigated. This work aims to define better the issues surrounding n-3 PUFA production and supply and the potential of microalgae as a sustainable source of compounds to enhance the food and feed of the future.

Multi-Label Learning from Single Positive Labels

Abstract: Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate training data for multi-label classification. When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image. Furthermore, in some settings detection is intrinsically difficult e.g. finding small object instances in high resolution images. As a result, multi-label training data is often plagued by false negatives. We consider the hardest version of this problem, where annotators provide only one relevant label for each image. As a result, training sets will have only one positive label per image and no confirmed negatives. We explore this special case of learning from missing labels across four different multi-label image classification datasets for both linear classifiers and end-to-end fine-tuned deep networks. We extend existing multi-label losses to this setting and propose novel variants that constrain the number of expected positive labels during training. Surprisingly, we show that in some cases it is possible to approach the performance of fully labeled classifiers despite training with significantly fewer confirmed labels.

A Systematic Review of Biosynthesized Metallic Nanoparticles as a Promising Anti-Cancer-Strategy.

Abstract: Cancer is one of the foremost causes of death worldwide. Cancer develops because of mutation in genes that regulate normal cell cycle and cell division, thereby resulting in uncontrolled division and proliferation of cells. Various drugs have been used to treat cancer thus far; however, conventional chemotherapeutic drugs have lower bioavailability, rapid renal clearance, unequal delivery, and severe side effects. In the recent years, nanotechnology has flourished rapidly and has a multitude of applications in the biomedical field. Bio-mediated nanoparticles (NPs) are cost effective, safe, and biocompatible and have got substantial attention from researchers around the globe. Due to their safe profile and fewer side effects, these nanoscale materials offer a promising cure for cancer. Currently, various metallic NPs have been designed to cure or diagnose cancer; among these, silver (Ag), gold (Au), zinc (Zn) and copper (Cu) are the leading anti-cancer NPs. The anticancer potential of these NPs is attributed to the production of reactive oxygen species (ROS) in cellular compartments that eventually leads to activation of autophagic, apoptotic and necrotic death pathways. In this review, we summarized the recent advancements in the biosynthesis of Ag, Au, Zn and Cu NPs with emphasis on their mechanism of action. Moreover, nanotoxicity, as well as the future prospects and opportunities of nano-therapeutics, are also highlighted.

Analysis of Environmental Total Factor Productivity Evolution in European Agricultural Sector

Abstract: Productivity analysis has been an important avenue for economic research. Therefore, medleys of quantitative techniques have been proposed to operationalize productivity analysis. In this paper, we propose an extended by-production model which ensures a link between the production and the pollution-generating sub-technologies. The corresponding dual formulations are provided to interpret the economic role of pollution-generating inputs in the sub-technologies. Finally, we integrate the proposed model with the environmental Luenberger-Hicks-Moorsteen productivity indicator based upon input and output directional distance functions. The proposed model is applied to measure the green economic growth of agricultural sectors of the selected European countries.

Association Between Initial Treatment Strategy and Long-term Survival in Pulmonary Arterial Hypertension

Abstract: Rationale: The relationship between the initial treatment strategy and survival in pulmonary arterial hypertension (PAH) remains uncertain. Objectives: To evaluate the long-term survival of patient...

Integrin alpha5 in human breast cancer is a mediator of bone metastasis and a therapeutic target for the treatment of osteolytic lesions.

Abstract: Bone metastasis remains a major cause of mortality and morbidity in breast cancer. Therefore, there is an urgent need to better select high-risk patients in order to adapt patient’s treatment and prevent bone recurrence. Here, we found that integrin alpha5 (ITGA5) was highly expressed in bone metastases, compared to lung, liver, or brain metastases. High ITGA5 expression in primary tumors correlated with the presence of disseminated tumor cells in bone marrow aspirates from early stage breast cancer patients (n = 268; p = 0.039). ITGA5 was also predictive of poor bone metastasis-free survival in two separate clinical data sets (n = 855, HR = 1.36, p = 0.018 and n = 427, HR = 1.62, p = 0.024). This prognostic value remained significant in multivariate analysis (p = 0.028). Experimentally, ITGA5 silencing impaired tumor cell adhesion to fibronectin, migration, and survival. ITGA5 silencing also reduced tumor cell colonization of the bone marrow and formation of osteolytic lesions in vivo. Conversely, ITGA5 overexpression promoted bone metastasis. Pharmacological inhibition of ITGA5 with humanized monoclonal antibody M200 (volociximab) recapitulated inhibitory effects of ITGA5 silencing on tumor cell functions in vitro and tumor cell colonization of the bone marrow in vivo. M200 also markedly reduced tumor outgrowth in experimental models of bone metastasis or tumorigenesis, and blunted cancer-associated bone destruction. ITGA5 was not only expressed by tumor cells but also osteoclasts. In this respect, M200 decreased human osteoclast-mediated bone resorption in vitro. Overall, this study identifies ITGA5 as a mediator of breast-to-bone metastasis and raises the possibility that volociximab/M200 could be repurposed for the treatment of ITGA5-positive breast cancer patients with bone metastases.

Genomics accelerated isolation of a new stem rust avirulence gene-wheat resistance gene pair.

Abstract: Stem rust caused by the fungus Puccinia graminis f. sp. tritici (Pgt) is a devastating disease of the global staple crop wheat. Although this disease was largely controlled in the latter half of the twentieth century, new virulent strains of Pgt, such as Ug99, have recently evolved1,2. These strains have caused notable losses worldwide and their continued spread threatens global wheat production. Breeding for disease resistance provides the most cost-effective control of wheat rust diseases3. A number of rust resistance genes have been characterized in wheat and most encode immune receptors of the nucleotide-binding leucine-rich repeat (NLR) class4, which recognize pathogen effector proteins known as avirulence (Avr) proteins5. However, only two Avr genes have been identified in Pgt so far, AvrSr35 and AvrSr50 (refs. 6,7), and none in other cereal rusts8,9. The Sr27 resistance gene was first identified in a wheat line carrying an introgression of the 3R chromosome from Imperial rye10. Although not deployed widely in wheat, Sr27 is widespread in the artificial crop species Triticosecale (triticale), which is a wheat-rye hybrid and is a host for Pgt11,12. Sr27 is effective against Ug99 (ref. 13) and other recent Pgt strains14,15. Here, we identify both the Sr27 gene in wheat and the corresponding AvrSr27 gene in Pgt and show that virulence to Sr27 can arise experimentally and in the field through deletion mutations, copy number variation and expression level polymorphisms at the AvrSr27 locus.

Pollinator Monitoring More than Pays for Itself

Abstract: 1. Resilient pollination services depend on sufficient abundance of pollinating insects over time. Currently, however, most knowledge about the status and trends of pollinators is based on changes in pollinator species richness and distribution only. 2. Systematic, long-term monitoring of pollinators is urgently needed to provide baseline information on their status, to identify the drivers of declines and to inform suitable response measures. 3. Power analysis was used to determine the number of sites required to detect a 30% change in pollinator populations over 10 years. We then evaluated the full economic costs of implementing four national monitoring schemes in the UK: 1) professional pollinator monitoring, 2) professional pollination service monitoring, 3) volunteer collected pan-traps and 4) volunteer focal floral observations. These costs were compared to: i) the costs of implementing separate, expert-designed research and monitoring networks and ii) the economic benefits of pollination services threatened by pollinator loss. 4. Estimated scheme costs ranged from £6,159/year for a 75 site volunteer focal flower observation scheme to £2.7M/year for an 800 site professional pollination service monitoring network. The estimated research costs saved by using the site network as research infrastructure range from £1.46-4.17M/year. The economic value of UK crop yield lost following a 30% decline in pollinators was estimated at ~£188M/year. 5. Synthesis and applications. We evaluated the full costs of running pollinator monitoring schemes against the economic benefits to research and society they provide. The annual costs of monitoring are <0.02% of the economic value of pollination services that would be lost after a 30% decline in pollination services. Furthermore, by providing high quality scientific data, monitoring schemes would save at least £1.5 on data collection per £1 spent. Our findings demonstrate that long-term systematic monitoring can be a cost-effective tool for both answering key research questions and setting action points for policy-makers. Careful consideration must be given to scheme design, the logistics of national-scale implementation and resulting data quality when selecting the most appropriate combination of surveyors, methods and site networks to deliver a successful scheme.

Cryptic Patterns of Speciation in Cryptic Primates: Microendemic Mouse Lemurs and the Multispecies Coalescent

Abstract: Mouse lemurs (Microcebus) are a radiation of morphologically cryptic primates distributed throughout Madagascar for which the number of recognized species has exploded in the past two decades. This taxonomic revision has prompted understandable concern that there has been substantial oversplitting in the mouse lemur clade. Here, we investigate mouse lemur diversity in a region in northeastern Madagascar with high levels of microendemism and predicted habitat loss. We analyzed RADseq data with multispecies coalescent (MSC) species delimitation methods for two pairs of sister lineages that include three named species and an undescribed lineage previously identified to have divergent mtDNA. Marked differences in effective population sizes, levels of gene flow, patterns of isolation-by-distance, and species delimitation results were found among the two pairs of lineages. Whereas all tests support the recognition of the presently undescribed lineage as a separate species, the species-level distinction of two previously described species, M. mittermeieri and M. lehilahytsara is not supported-a result that is particularly striking when using the genealogical discordance index (gdi). Nonsister lineages occur sympatrically in two of the localities sampled for this study, despite an estimated divergence time of less than 1 Ma. This suggests rapid evolution of reproductive isolation in the focal lineages and in the mouse lemur clade generally. The divergence time estimates reported here are based on the MSC calibrated with pedigree-based mutation rates and are considerably more recent than previously published fossil-calibrated relaxed-clock estimates. We discuss the possible explanations for this discrepancy, noting that there are theoretical justifications for preferring the MSC estimates in this case. [Cryptic species; effective population size; microendemism; multispecies coalescent; speciation; species delimitation.].

VLT/SPHERE imaging survey of the largest main-belt asteroids: Final results and synthesis

Abstract: Context. Until recently, the 3D shape, and therefore density (when combining the volume estimate with available mass estimates), and surface topography of the vast majority of the largest (D ≥ 100 km) main-belt asteroids have remained poorly constrained. The improved capabilities of the SPHERE/ZIMPOL instrument have opened new doors into ground-based asteroid exploration.Aims. To constrain the formation and evolution of a representative sample of large asteroids, we conducted a high-angular-resolution imaging survey of 42 large main-belt asteroids with VLT/SPHERE/ZIMPOL. Our asteroid sample comprises 39 bodies with D ≥ 100 km and in particular most D ≥ 200 km main-belt asteroids (20/23). Furthermore, it nicely reflects the compositional diversity present in the main belt as the sampled bodies belong to the following taxonomic classes: A, B, C, Ch/Cgh, E/M/X, K, P/T, S, and V.Methods. The SPHERE/ZIMPOL images were first used to reconstruct the 3D shape of all targets with both the ADAM and MPCD reconstruction methods. We subsequently performed a detailed shape analysis and constrained the density of each target using available mass estimates including our own mass estimates in the case of multiple systems.Results. The analysis of the reconstructed shapes allowed us to identify two families of objects as a function of their diameters, namely “spherical” and “elongated” bodies. A difference in rotation period appears to be the main origin of this bimodality. In addition, all but one object (216 Kleopatra) are located along the Maclaurin sequence with large volatile-rich bodies being the closest to the latter. Our results further reveal that the primaries of most multiple systems possess a rotation period of shorter than 6 h and an elongated shape (c∕a ≤ 0.65). Densities in our sample range from ~1.3 g cm−3 (87 Sylvia) to ~4.3 g cm−3 (22 Kalliope). Furthermore, the density distribution appears to be strongly bimodal with volatile-poor (ρ ≥ 2.7 g cm−3) and volatile-rich (ρ ≤ 2.2 g cm−3) bodies. Finally, our survey along with previous observations provides evidence in support of the possibility that some C-complex bodies could be intrinsically related to IDP-like P- and D-type asteroids, representing different layers of a same body (C: core; P/D: outer shell). We therefore propose that P/ D-types and some C-types may have the same origin in the primordial trans-Neptunian disk.

Plant-Based Synthesis of Zinc Oxide Nanoparticles (ZnO-NPs) Using Aqueous Leaf Extract of Aquilegia pubiflora: Their Antiproliferative Activity against HepG2 Cells Inducing Reactive Oxygen Species and Other In Vitro Properties.

Abstract: The anti-cancer, anti-aging, anti-inflammatory, antioxidant, and anti-diabetic effects of zinc oxide nanoparticles (ZnO-NPs) produced from aqueous leaf extract of Aquilegia pubiflora were evaluated in this study. Several methods were used to characterize ZnO-NPs, including SEM, FTIR, XRD, DLS, PL, Raman, and HPLC. The nanoparticles that had a size of 34.23 nm as well as a strong aqueous dispersion potential were highly pure, spherical or elliptical in form, and had a mean size of 34.23 nm. According to FTIR and HPLC studies, the flavonoids and hydroxycinnamic acid derivatives were successfully capped. Synthesized ZnO-NPs in water have a zeta potential of -18.4 mV, showing that they are stable solutions. The ZnO-NPs proved to be highly toxic for the HepG2 cell line and showed a reduced cell viability of 23.68 ± 2.1% after 24 hours of ZnO-NP treatment. ZnO-NPs also showed excellent inhibitory potential against the enzymes acetylcholinesterase (IC50: 102 μg/mL) and butyrylcholinesterase (IC50: 125 μg/mL) which are involved in Alzheimer's disease. Overall, the enzymes involved in aging, diabetes, and inflammation showed a moderate inhibitory response to ZnO-NPs. Given these findings, these biosynthesized ZnO-NPs could be a good option for the cure of deadly diseases such as cancer, diabetes, Alzheimer's, and other inflammatory diseases due to their strong anticancer potential and efficient antioxidant properties.

Field evaluation of biocontrol agents against black-foot and Petri diseases of grapevine

Abstract: Background Black-foot and Petri diseases are the main fungal diseases associated with young grapevine decline. Two field experiments were established to evaluate the preventive effect of two potential biocontrol agents (BCAs), that is Streptomyces sp. E1 + R4 and Pythium oligandrum Po37, and three BCA-commercial products containing Trichoderma atroviride SC1, Trichoderma koningii TK7 and Pseudomonas fluorescens + Bacillus atrophaeus on fungal infection in grafted plants and plant growth parameters. Results The effectiveness of some BCA in reducing the incidence and severity of both diseases was dependent on the plant part analyzed and the plant age. No single BCA application was able to control both diseases. Streptomyces sp. E1 + R4 were able to reduce significantly the infection of the most prevalent black-foot disease fungi while P. oligandrum Po37 and Trichoderma spp. were able to reduce significantly Phaeomoniella chlamydospora and Phaeoacremonium minimum (Petri disease) infection. BCA treatments had no effect on the shoot weight, and root weight was significantly lower in all BCA treatments with respect to the control. Conclusions The combination of the disease-suppressive activity of two or more beneficial microbes in a biocontrol preparation is required to prevent infection by black-foot and Petri disease fungi in vineyards.

Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber

Abstract: The rumen harbors a complex microbial mixture of archaea, bacteria, protozoa, and fungi that efficiently breakdown plant biomass and its complex dietary carbohydrates into soluble sugars that can be fermented and subsequently converted into metabolites and nutrients utilized by the host animal. While rumen bacterial populations have been well documented, only a fraction of the rumen eukarya are taxonomically and functionally characterized, despite the recognition that they contribute to the cellulolytic phenotype of the rumen microbiota. To investigate how anaerobic fungi actively engage in digestion of recalcitrant fiber that is resistant to degradation, we resolved genome-centric metaproteome and metatranscriptome datasets generated from switchgrass samples incubated for 48 h in nylon bags within the rumen of cannulated dairy cows. Across a gene catalog covering anaerobic rumen bacteria, fungi and viruses, a significant portion of the detected proteins originated from fungal populations. Intriguingly, the carbohydrate-active enzyme (CAZyme) profile suggested a domain-specific functional specialization, with bacterial populations primarily engaged in the degradation of hemicelluloses, whereas fungi were inferred to target recalcitrant cellulose structures via the detection of a number of endo- and exo-acting enzymes belonging to the glycoside hydrolase (GH) family 5, 6, 8, and 48. Notably, members of the GH48 family were amongst the highest abundant CAZymes and detected representatives from this family also included dockerin domains that are associated with fungal cellulosomes. A eukaryote-selected metatranscriptome further reinforced the contribution of uncultured fungi in the ruminal degradation of recalcitrant fibers. These findings elucidate the intricate networks of in situ recalcitrant fiber deconstruction, and importantly, suggest that the anaerobic rumen fungi contribute a specific set of CAZymes that complement the enzyme repertoire provided by the specialized plant cell wall degrading rumen bacteria.

Cascading extinctions as a hidden driver of insect decline

Abstract: 1. The decline in insect abundance and diversity observed in many ecosystems is of major concern because of the long-term consequences for ecosystem function and stability. 2. Species in ecological communities are connected through interactions forming complex networks. Therefore, initial extinctions can cause further species losses through co-extinctions and extinction cascades, where single extinctions can lead to waves of secondary extinctions. Such knock-on effects can multiply the initial impact of disturbances, thereby largely adding to the erosion of biodiversity. However, our knowledge of their importance for the current insect decline is hampered because secondary extinctions are challenging to both detect and predict. 3. In this review, we bring together theory and knowledge about secondary extinctions in the light of the main drivers of insect decline. We evaluate potential and evidence for cascading extinction for the different drivers and identify major pathways. By providing selected examples we discuss how habitat loss, pollution, species invasions, climate change and overexploitation can cause cascading extinctions. We argue that habitat loss and pollution in particular have the largest potential for such extinctions by changing community structure, the physical environment, and community robustness. 4. Overall, cascading extinction are part of an ecosystems' response to anthropogenic drivers but are so far not explicitly measured in their contribution when evaluating biodiversity loss. This knowledge is necessary to predict biodiversity loss and find strategies to buffer against the devastating long-term impact of habitat loss, pollution, species invasions, and climate change.

Mitochondrial metabolism supports resistance to IDH mutant inhibitors in acute myeloid leukemia.

Abstract: Mutations in IDH induce epigenetic and transcriptional reprogramming, differentiation bias, and susceptibility to mitochondrial inhibitors in cancer cells. Here, we first show that cell lines, PDXs, and patients with acute myeloid leukemia (AML) harboring an IDH mutation displayed an enhanced mitochondrial oxidative metabolism. Along with an increase in TCA cycle intermediates, this AML-specific metabolic behavior mechanistically occurred through the increase in electron transport chain complex I activity, mitochondrial respiration, and methylation-driven CEBPα-induced fatty acid β-oxidation of IDH1 mutant cells. While IDH1 mutant inhibitor reduced 2-HG oncometabolite and CEBPα methylation, it failed to reverse FAO and OxPHOS. These mitochondrial activities were maintained through the inhibition of Akt and enhanced activation of peroxisome proliferator-activated receptor-γ coactivator-1 PGC1α upon IDH1 mutant inhibitor. Accordingly, OxPHOS inhibitors improved anti-AML efficacy of IDH mutant inhibitors in vivo. This work provides a scientific rationale for combinatory mitochondrial-targeted therapies to treat IDH mutant AML patients, especially those unresponsive to or relapsing from IDH mutant inhibitors.

Coordination of stem and leaf traits define different strategies to regulate water loss and tolerance ranges to aridity.

Abstract: Adaptation to drought involves complex interactions of traits that vary within and among species. To date, few data are available to quantify within-species variation in functional traits and they are rarely integrated into mechanistic models to improve predictions of species response to climate change. We quantified intraspecific variation in functional traits of two Hakea species growing along an aridity gradient in southeastern Australia. Measured traits were later used to parameterise the model SurEau to simulate a transplantation experiment to identify the limits of drought tolerance. Embolism resistance varied between species but not across populations. Instead, populations adjusted to drier conditions via contrasting sets of trait trade-offs that facilitated homeostasis of plant water status. The species from relatively mesic climate, Hakea dactyloides, relied on tight stomatal control whereas the species from xeric climate, Hakea leucoptera dramatically increased Huber value and leaf mass per area, while leaf area index (LAI) and epidermal conductance (gmin ) decreased. With trait variability, SurEau predicts the plasticity of LAI and gmin buffers the impact of increasing aridity on population persistence. Knowledge of within-species variability in multiple drought tolerance traits will be crucial to accurately predict species distributional limits.