Functional Types Research Articles

P105 RENAL DENERVATION WITH SPYRAL SIMPLICITY SYSTEM: A SINGLE CENTER EXPERIENCE IN COLOMBIA

Background and Objectives: Hypertension is the leading cause of death with 10,4 million deaths and 218 disability adjusted life years (DALYs) worldwide in 2017. Despite GDMT, about 10 to 20% of hypertensive subjects will be poorly controlled, meeting the definition for difficult to treat (DTTH) and refractory hypertension (RH). Renal denervation (RDN) with Spyral Simplicity SystemTM has become a promising nonpharmacological treatment for DTTH and RH, and it has shown to be an effective and safe procedure for RH patients in randomized clinical trials with up to 12 months follow-up. Methods: This is a single-center registry including 6 adults with DTTH and RH confirmed by ABPM and without secondary hypertension, underwent RDN in a tertiary hospital in Bucaramanga, Colombia. A 6-months follow-up was performed, recording clinical data (symptoms, NYHA functional class, number and type of antihypertensive medications, heart rate and ER visits) and paraclinical tests (ABPM, eGFR and echocardiogram). Results: Mean age was 73 ± 4,64 years-old with 50% females. Main comorbidities were dyslipidemia (100%), cardiovascular disease (100%), and type 2 diabetes (83,3%). Pre-procedural medication average was 5. All patients were in NYHA II, with preserved left ventricular EF and mild to moderate diastolic disfunction. At 6 months of follow-up, there was a mean reduction in systolic blood pressure (SBP) of 10 mmHg (145 to 135 mmHg) in ABPM and 26 mmHg (169 to 143 mmHg) in office. There were not significant changes between pre- and post-procedural mean HR (68 vs. 61 bpm), neither short- or long-term complications associated with the procedure. In average, one drug withdrawal was observed. All the subjects improved their NYHA functional class. There were no ER visits in the 6-month follow-up. Conclusions: RDN is effective and safe for patients with DTTH and RH, achieving significant reduction in ABPM and office SBP, reducing medication number, improving NYHA functional class and avoiding visits to ER in the mid-term follow-up.

Predicting plants in the wild: Mapping arctic and boreal plants with UAS-based visible and near infrared reflectance spectra

Biophysical changes in the Arctic and boreal zones drive shifts in vegetation, such as increasing shrub cover from warming soil or loss of living mat species due to fire. Understanding current and future responses to these factors requires mapping vegetation at a fine taxonomic resolution and landscape scale. Plants vary in size and spectral signatures, which hampers mapping of meaningful functional groups at coarse spatial resolution. Fine spatial grain of remotely sensed data (<10 cm pixels) is often necessary to resolve patches of many Arctic and boreal plant groups, such as bryophytes and lichens, which are significant components of terrestrial vegetation cover. Separation of co-occurring small vegetation patches in images also requires high spectral resolution. Our goal here was to test the capabilities of UAS-based imaging spectroscopy for mapping plant functional types (PFT) using high spatial and spectral resolution data over Arctic and boreal vegetation at four sites in central Alaska. We then tested several Machine and Deep learning models of PFT cover using the reflectance spectra. The best models were very simple, balancing both bias (overfitting caused by imbalance sample sizes) and variance (fit to the independent validation data), explaining > 50 % of the independent ground cover estimation and > 84 % accuracy in estimating validation pixels. We explored the impact of spectral resolution on PFT mapping by including vegetation indices and a gradient of narrow (5 nm) to wide (50 nm) band features in our classification models across. Vegetation indices were the most important predictors for classifying PFTs, while including band features improved models, with narrow and wide bandwidths having similar importance but models with wide bandwidths performing slightly better. We conclude that Arctic and boreal PFT reflectance can be pooled across sites for mapping with relatively few labeled pixels. Underfit, simple algorithms outperformed deep learning, at least with these small sample sizes, in classifying PFTs by balancing bias and variance. Future work should aim to increase the number of labeled pixels and the detail of labels to further improve mapping taxonomic precision.

Novel readthrough agent suppresses nonsense mutations and restores functional type VII collagen and laminin 332 in epidermolysis bullosa

Recessive Dystrophic Epidermolysis Bullosa (RDEB) and Junctional Epidermolysis Bullosa (JEB) are lethal blistering skin disorders resulting from mutations in genes coding for type VII collagen (COL7A1) and laminin 332 (LAMA3, LAMB3, or LAMC2), respectively. In RDEB, 25% of patients harbor nonsense mutations causing premature termination codons (PTCs). In JEB, a majority of mutations in LAMB3 are nonsense mutations (80%). ELX-02, an aminoglycoside analog, has demonstrated superior PTC readthrough activity and lower toxicity compared to gentamicin in various genetic disorders. This study investigated ELX-02's ability to suppress PTCs and promote the expression of C7 and laminin 332 in primary RDEB keratinocytes/fibroblasts and primary JEB keratinocytes harboring nonsense mutations. ELX-02 induced a dose-dependent production of C7 or laminin β3 that surpassed the results achieved with gentamicin. ELX-02 reversed RDEB and JEB cellular hypermotility and improved poor cell-substratum adhesion in JEB cells. Importantly, ELX-02-induced C7 and laminin 332 localized to the dermal-epidermal junction. This is the first study demonstrating that ELX-02 can induce PTC readthrough and restore functional C7 and laminin 332 in RDEB and JEB caused by nonsense mutations. Therefore, ELX-02 may offer a novel and safe therapy for RDEB, JEB, and other inherited skin diseases caused by nonsense mutations.

제주지역 노인의 지역사회 계속거주에 미치는 영향요인 연구

This study examined the factors influencing Aging-in-Place(AIP) in older adults in the Jeju region. Also, the study tried to find supporting ways of AIP for older adults. The study used data from the 2020 Ministry of Health and Welfare Survey on Korea Older Adults and analyzed 375 cases of older adults in Jeju. The influencing factors of AIP consisted of functional, structural, phenomenological, and ecological factors. The main results are as follows: First, 98.2% of older adults in a healthy situation chose AIP, but this number drops to 59.5% when their health becomes weak and unhealthy. Second, the factors influencing the AIP of older adults in the Jeju area were age as a functional factor, household type and economic satisfaction as a structural factor, housing and residential environment satisfaction variables with a phenomenological factor, and satisfaction with neighborhood relationships as an ecological factor. Based on the above results, this study suggested the policy to support older adults in the Jeju area as they lead their daily lives and face old age. In particular, the willingness to AIP decreases significantly when the health of older people in the Jeju region deteriorates; the study emphasized and discussed the importance of creating a residential environment.

Microbial nitrogen transformations in tundra soil depend on interactive effects of seasonality and plant functional types

Microbial nitrogen transformations in tundra soil depend on interactive effects of seasonality and plant functional types

Gut bacterial type III secretion systems aggravate colitis in mice and serve as biomarkers of Crohn’s disease

Mesenteric adipose tissue (mAT) hyperplasia, known as creeping fat, is a pathologic characteristic of Crohn's disease (CD). In our previously reported cohort, we observed that Achromobacter pulmonis was the most abundant and prevalent bacteria cultivated from creeping fat. A whole genomic sequencing and identification of T3SS orthologs of mAT-derived A. pulmonis were used. A functional type III secretion system (T3SS) mediated the pathogenic potential of A. pulmonis invitro and in mouse colitis model. Furthermore, a T3SS Finder pipeline was introduced to evaluate gut bacterial T3SS orthologs in the feces of CD patients, ulcerative colitis and colorectal cancer patients. Here, we reveal that mAT-derived A. pulmonis possesses a functional T3SS, aggravates colitis in mice via T3SS, and exhibits T3SS-dependent cytotoxicity via a caspase-independent mechanism in macrophages and epithelial cells, which demonstrated the pathogenic potential of the T3SS-harboring A. pulmonis. Metagenomic analyses demonstrate an increased abundance of Achromobacter in the fecal of Crohn's disease patients compared to healthy controls. A comprehensive comparison of total microbial vT3SS abundance in various intestine diseases demonstrated that the specific enrichment of vT3SS genes was shown in fecal samples of CD, neither ulcerative colitis nor colorectal cancer patients, and ten T3SS gene-based biomarkers for CD were discovered and validated in a newly recruited CD cohort. Furthermore, treatment with exclusive enteral nutrition (EEN), an intervention that improves CD patient symptomatology, was found associated with a significant reduction in the prevalence of T3SS genes in fecal samples. These findings highlight the pathogenic significance of T3SSs in the context of CD and identify specific T3SS genes that could potentially function as biomarkers for diagnosing and monitoring the clinical status of CD patients. This work is supported by the National Key Research and Development Program of China (2020YFA0907800), the China Postdoctoral Science Foundation (2023M744089), the National Natural Science Foundation of China (32000096), the Shenzhen Science and Technology Programs (KQTD20200820145822023, RCIC20231211085944057, and ZDSYS20220606100803007), National Key Clinical Discipline, Guangdong Provincial Clinical Research Center for Digestive Diseases (2020B1111170004), Qingfeng Scientific Research Fund of the China Crohn's & Colitis Foundation (CCCF) (CCCF-QF-2022B71-1), and the Sixth Affiliated Hospital, Sun Yat-sen University Clinical Research 1010 Program 1010CG(2023)-08. These funding provided well support for this research work, which involved data collection, analysis, interpretation, patient recruitment and so on.

Generalized Partially Functional Linear Model with Interaction between Functional Predictors

This paper proposes a generalized partially functional linear model with interaction terms. It is suitable for cases where the response variable is scalar, and the predictor variables include a mix of functional and scalar types, while considering the correlations among functional predictor variables. The model uses principal component analysis for dimensionality reduction, employs maximum likelihood estimation to obtain parameter values, proves the asymptotic properties of the estimates, and validates the model’s accuracy through data simulation experiments. Finally, the proposed model was applied to investigate the influence of air quality, climate factors, and medical and social indicators, along with their interactions, on cancer incidence, which is a binary response.

Plant functional types and tissue stoichiometry explain nutrient transfer in common arbuscular mycorrhizal networks of temperate grasslands

Abstract Plants and mycorrhizal fungi form mutualistic relationships that affect how resources flow between organisms and within ecosystems. Common mycorrhizal networks (CMNs) could facilitate preferential transfer of carbon and limiting nutrients, but this remains difficult to predict. Do CMNs favour fungal resource acquisition at the expense of plant resource demands (a fungi‐centric view), or are they passive channels through which plants regulate resource fluxes (a plant‐centric view)? We used stable isotope tracers (13CO2 and 15NH3), plant traits, and mycorrhizal DNA to quantify above‐ and below‐ground carbon and nitrogen transfer between 18 plant species along a 520‐km latitudinal gradient in the Pacific Northwest, USA. Plant functional type and tissue stoichiometry were the most important predictors of interspecific resource transfer. Of ‘donor’ plants, 98% were 13C‐enriched, but we detected transfer in only 2% of ‘receiver’ plants. However, all donors were 15N‐enriched and we detected transfer in 81% of receivers. Nitrogen was preferentially transferred to annuals (0.26 ± 0.50 mg N per g leaf mass) compared with perennials (0.13 ± 0.30 mg N per g leaf mass). This corresponded with tissue stoichiometry differences. Synthesis Our findings suggest that plants and fungi that are located closer together in space and with stronger demand for resources over time are more likely to receive larger amounts of those limiting resources. Read the free Plain Language Summary for this article on the Journal blog.

Helicobacter pylori induces the expression of Lgr5 and stem cell properties in gastric target cells.

Helicobacter pylori infection predisposes carriers to a high risk of developing gastric cancer. The cell-of-origin of antral gastric cancer is the Lgr5+ stem cell. Here, we show that infection of antrum-derived gastric organoid cells with H. pylori increases the expression of the stem cell marker Lgr5 as determined by immunofluorescence microscopy, qRT-PCR, and Western blotting, both when cells are grown and infected as monolayers and when cells are exposed to H. pylori in 3D structures. H. pylori exposure increases stemness properties as determined by spheroid formation assay. Lgr5 expression and the acquisition of stemness depend on a functional type IV secretion system (T4SS) and at least partly on the T4SS effector CagA. The pharmacological inhibition or genetic ablation of NF-κB reverses the increase in Lgr5 and spheroid formation. Constitutively active Wnt/β-catenin signaling because of Apc inactivation exacerbates H. pylori-induced Lgr5 expression and stemness, both of which persist even after eradication of the infection. The combined data indicate that H. pylori has stemness-inducing properties that depend on its ability to activate NF-κB signaling.

Tropospheric ozone pollution increases the sensitivity of plant production to vapor pressure deficit across diverse ecosystems in the Northern Hemisphere

Tropospheric ozone (O3) pollution often accompanies droughts and heatwaves, which could collectively reduce plant productivity. Previous research suggested that O3 pollution can alter plant responses to drought by interfering with stomatal closure while drought can reduce stomatal conductance and provide protection against O3 stress. However, the interactions between O3 pollution and drought stress remain poorly understood at ecosystem scales with diverse plant functional types. To address this research gap, we used 10-year (2012−2021) satellite near-infrared reflectance of vegetation (NIRv) observations, reanalysis data of vapor pressure deficit (VPD), soil moisture (SM), and air temperature (Ta), along with O3 measurements and reanalysis data across the Northern Hemisphere to statistically disentangle the interconnections between NIRv, VPD, SM, and Ta under varying O3 levels. We found that high O3 concentrations significantly exacerbate the sensitivity of NIRv to VPD while have no notable impacts on the sensitivity of NIRv to Ta or SM for all plant functional types, indicating an enhanced combined impact of VPD and O3 on plants. Specifically, the sensitivity of NIRv to VPD increased by >75 % when O3 anomalies increased from the lowest 10 to the highest 10 percentiles across diverse plant functional types. This is likely because long-term exposure to high O3 concentrations can inhibit stomatal closure and photosynthetic enzyme activities, resulting in reduced water use efficiency and photosynthetic efficiency. This study highlights the need to consider O3 in understanding plant responses to climate factors and that O3 can alter plant responses to VPD independently of Ta and SM.

Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy

Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures.

StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features

Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive. Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. The peptide sequences are encoded using split amino acid composition (SAAC), segmented position specific scoring matrix (SegPSSM), histogram of oriented gradients-based PSSM (HOGPSSM) and feature extraction based graphical and statistical (FEGS) descriptors. Next, principal component analysis (PCA) is used to select the best subset of attributes. After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. The proposed StackDPPred method improves the overall accuracy by 13.41% and 7.62% compared to existing DPs predictors iDPF-PseRAAC and iDEF-PseRAAC, respectively on validation test. Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. We believe, StackDPPred could serve as a valuable tool accelerating the screening of large-scale DPs and peptide-based drug discovery process.

Interrelationships of biological spectra, life-form, landform and functional vegetation type in the riparian forests of a tropical river

Interrelationships of biological spectra, life-form, landform and functional vegetation type in the riparian forests of a tropical river

Nonlinear models based on leaf architecture traits explain the variability of mesophyll conductance across plant species.

Mesophyll conductance ( ) describes the efficiency with which moves from substomatal cavities to chloroplasts. Despite the stipulated importance of leaf architecture in affecting , there remains a considerable ambiguity about how and whether leaf anatomy influences . Here, we employed nonlinear machine-learning models to assess the relationship between 10 leaf architecture traits and . These models used leaf architecture traits as predictors and achieved excellent predictability of . Dissection of the importance of leaf architecture traits in the models indicated that cell wall thickness and chloroplast area exposed to internal airspace have a large impact on interspecific variation in . Additionally, other leaf architecture traits, such asleaf thickness, leaf densityand chloroplast thickness, emerged as important predictors of . We also found significant differences in the predictability between models trained on different plant functional types. Therefore, by moving beyond simple linear and exponential models, our analyses demonstrated that a larger suite of leaf architecture traits drive differences in than has been previously acknowledged. These findings pave the way for modulating by strategies that modify its leaf architecture determinants.

Technological Evaluation of Fiber Effects in Wheat-Based Dough and Bread.

Dietary fiber incorporation in bread offers potential health benefits but poses challenges due to its impact on dough rheology and bread quality. This study evaluated the effects of pea, cocoa, and apple fiber on wheat-based dough and bread properties using rheological methods (farinograph, alveograph, pasting, and proofing) and baking trials. Substituting flour with fiber at 1%, 5%, or 10% increased water absorption and affected dough development, stability, and extensibility, particularly at high fiber concentrations. Pasting properties showed varying gelatinization behaviors influenced by fiber type and concentration. Principal component analysis (PCA) highlighted the clustering of dough and bread characteristics based on fiber concentration and type. At low fiber concentrations (up to 5% of flour replacement), negative effects were minimal, suggesting no need for comprehensive compositional analysis. However, high fiber concentrations (10%) introduced significant variability and complexity in dough properties. New farinographic parameters (FU4, FU6, FU8, FU10, and FU12) improved the explanatory power of PCA, enhancing the understanding of fiber-rich dough dynamics. The significant alterations in moisture content and texture underscore the intricate relationship between type of fiber, concentration, and dough functionality. Optimizing rheological parameters for fiber-enriched flour is crucial for adapting the bread-making process to produce high-quality bread with desired characteristics and enhanced nutritional benefits.

Comprehensive analysis of smart grids functionalities virtualization

The implementation of advanced digital technologies in the conventional electric grid has triggered a transformation towards an intelligent network, known as Smart Grid. The associated benefits are diverse, ranging from more efficient energy management and demand response to the distributed integration of renewable energy sources. Ultimately, this transition promotes a more reliable, sustainable, and cost-effective energy supply. In this context, there is increasing recognition of the advantages of employing intelligent at edge to provide redundancy, virtualize functions that were previously in different proprietary hardware in the same device, or introduce new functionalities into the electric grid. This study focuses on conducting a comprehensive analysis on the key aspects to consider when implementing virtualized solutions in substations. Strategies have been sought to ensure the optimal deployment of virtualized nodes within the electrical sector, taking into account factors such as functional requirements, facility types, virtualization methodologies, and node specifications, among others. Furthermore, throughout the study, several virtualization tools have been analysed to determine their feasibility and the advantages they offer when integrated into the Smart Grid.

Leaf functional traits predict timing of nutrient resorption and carbon depletion in deciduous subarctic plants

Abstract Resorption of key elements promotes their conservation in plants in nutrient‐poor ecosystems. In seasonal environments, the timing of resorption is expected to influence resorption efficiency and plant fitness due to the trade‐off between maximizing photosynthetic carbon gain by late resorption and minimizing frost risks and nutrient loss by early resorption. Here, we hypothesize that (1) these alternative strategies with respect to the timing of element resorption both occur among summergreen species; (2) deciduous woody plants favour delayed resorption while herbaceous species benefit from gradual, early onset resorption; (3) this pattern is part of a more general relationship in which species with conservative resource economic traits have more delayed resorption. We measured nitrogen (N), phosphorus (P) and carbon (C) contents of mature, senescing and senesced leaves of 22 predominant plant species across four types of ecosystems in a subarctic region. We then calculated timing of resorption as the Julian calendar day of 50% of element resorption (T50), and examined its relationship with plant functional types and leaf resource economic traits (leaf mass per area [LMA], leaf C, N and P contents). The timing of N resorption in subarctic plants ranged from day 213 to 254, while the timing of P resorption ranged from day 211 to 261 and of C from day 214 to 260 across species. On average, the resorption of N and P and depletion of C were 13, 12 and 19 days respectively earlier in herbaceous plants than in woody ones. For all the three elements, T50 of plants decreased significantly with increasing acquisitive economic traits. Synthesis: As hypothesized, we found that (1) both “steady‐and‐slow” and “late‐and‐fast” strategies of resorption timing co‐occurred in a subarctic flora; consistent with these two strategies, herbaceous plants resorbed nutrients and carbon earlier than deciduous woody species; (2) relevant functional traits representing resource conservation were positively and linearly related to the timing of nutrient resorption and carbon depletion. These findings for element resorption timing have important implications for functional changes in the vegetation composition of seasonal regions in response to temperature fluctuations in a changing climate.

The applicability of a SIF-based mechanistic model for estimating GPP at the canopy scale

Mechanistically linking gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) is an essential step to unleash the full potential of SIF for remote sensing-based predictions of GPP across biomes, climates, and spatiotemporal scales. The latest SIF-based mechanistic light response model that includes the fraction of open photosystem II reaction centers as key parameter (qMLR-SIF model), can accurately reproduce leaf-scale photosynthesis under various conditions. However, it remains unclear to what extent the qMLR-SIF model is suitable for estimating GPP at larger scales such as the canopy scale. Therefore, canopy-scale data of tower-based far-red SIF, GPP and key environmental variables from 10 study sites were collected to analyze the SIF-GPP relationship and to compare the qMLR-SIF model with the widely used Farquhar, von Caemmerer, Berry (FvCB) model and with a light use efficiency (LUE) model for different plant functional types (PFTs), photosynthetic pathways (C3 and C4), and temporal scales (hourly, daily and 4-day). Results showed that the nonlinear SIF-GPP relationship existed in all PFTs and the degree of linearity increased at larger temporal scales. The qMLR-SIF model exhibited wide applicability to quantify canopy GPP for different PFTs (R2 = 0.55–0.80, RMSE = 2.72–11.03 μmol CO2 m-2 s-1), photosynthetic pathways (R2 = 0.70–0.78, RMSE = 5.29–9.05 μmol CO2 m-2 s-1) and temporal scales (R2 = 0.82–0.97, RMSE = 3.42–8.32 μmol CO2 m-2s-1). Compared with the two other models, the qMLR-SIF model performed best overall, which is mainly due to its simpler model structure and the mechanistic link between SIF and photosynthesis. Particularly, the qMLR-SIF model could more accurately estimate GPP in C4 species, with higher R2 (0.78) and lower RMSE (8.46 μmol CO2 m-2s-1). These findings highlight the advantages of the qMLR-SIF model in GPP estimation at the canopy scale, showing its potential in applications at regional and global scales.

Development of a Bmi1+ Cardiac Mouse Progenitor Immortalized Model to Unravel the Relationship with Its Protective Vascular Endothelial Niche.

The adult mammalian heart has been demonstrated to be endowed with low but real turnover capacity, especially for cardiomyocytes, the key functional cell type. The source, however, of that turnover capacity remains controversial. In this regard, we have defined and characterized a resident multipotent cardiac mouse progenitor population, Bmi1+DR (for Bmi1+ Damage-Responsive cells). Bmi1+DR is one of the cell types with the lowest ROS (Reactive Oxygen Species) levels in the adult heart, being particularly characterized by their close relationship with cardiac vessels, most probably involved in the regulation of proliferation/maintenance of Bmi1+DR. This was proposed to work as their endothelial niche. Due to the scarcity of Bmi1+DR cells in the adult mouse heart, we have generated an immortalization/dis-immortalization model using Simian Vacuolating Virus 40-Large Antigen T (SV40-T) to facilitate their in vitro characterization. We have obtained a heterogeneous population of immortalized Bmi1+DR cells (Bmi1+DRIMM) that was validated attending to different criteria, also showing a comparable sensitivity to strong oxidative damage. Then, we concluded that the Bmi1-DRIMM population is an appropriate model for primary Bmi1+DR in vitro studies. The co-culture of Bmi1+DRIMM cells with endothelial cells protects them against oxidative damage, showing a moderate depletion in non-canonical autophagy and also contributing with a modest metabolic regulation.

Hyperspectral characterization of vegetation in hemiboreal, boreal and Arctic peatlands using a geographically extensive field dataset

Northern peatlands store up to 25% of global soil organic carbon and function as important hotspots for biodiversity. However, they are facing degradation from climate change driven by human activities as well as anthropogenic land use changes, up to the point of endangering the ecosystems' functioning and the storage of soil organic carbon entailed within them. The surface vegetation of northern peatlands is an important indicator of the ecosystem's functioning and ecohydrology, highlighting the importance of its large-scale, continuous monitoring. Approaches utilizing hyperspectral data for monitoring vegetation health and species composition can also be applied to peatland vegetation. To support the development of methods for interpreting hyperspectral satellite data from peatlands, we conducted a comprehensive in situ study of hemiboreal, boreal, sub-Arctic and Arctic peatland vegetation. We measured the reflectance spectra (350–2500 nm), soil moisture, and various vegetation-related attributes from a total of 446 vegetation plots in Estonia and Finland, from a 1500 km south-north interval. We then investigated (i) the spectral variation in surface vegetation of hemiboreal, boreal, sub-Arctic and Arctic peatlands and (ii) explored its connection to plant functional types (PFTs) and soil moisture, as well as evaluated the potential of hyperspectral data in estimating PFT cover using simple vegetation indices and partial least square (PLS) regression. Our results indicate that (i) the best spectral regions to retrieve information regarding the PFT vary greatly especially between vascular plants and bryophytes, (ii) the reflectance at an individual wavelength as well as simple vegetational index can, to some extent, predict the PFT, and that (iii) the PLS regression can predict the PFT with good accuracy. Overall, our findings demonstrate the potential of using hyperspectral data in monitoring PFTs in northern peatlands. The spectral library and the ancillary data from the peatland sites collected for this study are available as open data.