Estimating Carbon Sink Strength of Norway Spruce Forests Using Machine Learning

Forests sequester atmospheric carbon dioxide (CO2) which is important for climate mitigation. Net ecosystem production (NEP) varies significantly across forests in different regions depending on the dominant tree species, stand age, and environmental factors. Therefore, it is important to evaluate forest NEP and its potential changes under climate change in different regions to inform forestry policy making. Norway spruce (Picea abies) is the most prevalent species in conifer forests throughout Europe. Here, we focused on Norway spruce forests and used eddy covariance-based observations of CO2 fluxes and other variables from eight sites to build a XGBoost machine learning model for NEP estimation. The NEP values from the study sites varied between −296 (source) and 1253 (sink) g C m−2 yr−1. Overall, among the tested variables, air temperature was the most important factor driving NEP variations, followed by global radiation and stand age, while precipitation had a very limited contribution to the model. The model was used to predict the NEP of mature Norway spruce forests in different regions within Europe. The NEP median value was 494 g C m−2 yr−1 across the study areas, with higher NEP values, up to >800 g C m−2 yr−1, in lower latitude regions. Under the “middle-of-the-road” SSP2-4.5 scenario, the NEP values tended to be greater in almost all the studied regions by 2060 with the estimated median of NEP changes in 2041–2060 to be +45 g C m−2 yr−1. Our results indicate that Norway spruce forests show high productivity in a wide area of Europe with potentially future NEP enhancement. However, due to the limitations of the data, the potential decrease in NEP induced by temperature increases beyond the photosynthesis optima and frequent ecosystem disturbances (e.g., drought, bark beetle infestation, etc.) still needs to be evaluated.publishedVersio

Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond

Graph Neural Networks (GNNs) have emerged as one of the leading approaches for machine learning on graph-structured data. Despite their great success, critical computational challenges such as over-smoothing, over-squashing, and limited expressive power continue to impact the performance of GNNs. In this study, inspired from the time-reversal principle commonly utilized in classical and quantum physics, we reverse the time direction of the graph heat equation. The resulted reversing process yields a class of high pass filtering functions that enhance the sharpness of graph node features. Leveraging this concept, we introduce the Multi-Scaled Heat Kernel based GNN (MHKG) by amalgamating diverse filtering functions' effects on node features. To explore more flexible filtering conditions, we further generalize MHKG into a model termed G-MHKG and thoroughly show the roles of each element in controlling over-smoothing, over-squashing and expressive power. Notably, we illustrate that all aforementioned issues can be characterized and analyzed via the properties of the filtering functions, and uncover a trade-off between over-smoothing and over-squashing: enhancing node feature sharpness will make model suffer more from over-squashing, and vice versa. Furthermore, we manipulate the time again to show how G-MHKG can handle both two issues under mild conditions. Our conclusive experiments highlight the effectiveness of proposed models. It surpasses several GNN baseline models in performance across graph datasets characterized by both homophily and heterophily

Impact of a staggered scaffold structure on the mechanical properties and cell response in bone tissue engineering

The primary goal of bone tissue engineering is to fabricate scaffolds that can provide a microenvironment similar to that of natural bone. Therefore, various scaffolds have been designed to replicate the bone structure. Although most tissues exhibit complicated structures, their basic structural unit includes stiff platelets arranged in a staggered micro-array. Therefore, many researchers have designed scaffolds with staggered patterns. However, relatively few studies have comprehensively analyzed this type of scaffold. In this review, we have analyzed scientific research pertaining to staggered scaffold designs and summarized their effects on the physical and biological properties of scaffolds. Compression tests or finite element analysis are typically used to evaluate the mechanical properties of scaffolds, and most studies have performed experiments in cell cultures. Staggered scaffolds improve mechanical strength and are beneficial for cell attachment, proliferation, and differentiation in comparison with conventional designs. However, very few have been studied in vivo experiments. Additionally, studies on the effect of staggered structures on angiogenesis or bone regeneration in vivo, particularly in large animals, are required. Currently, with the prevalence of artificial intelligence (AI)-based technologies, highly optimized models can be developed, resulting in better discoveries. In the future, AI can be used to deepen our understanding on the staggered structure, promoting its use in clinical applications.publishedVersio

Intensified inundation shifts a freshwater wetland from a CO2 sink to a source

Climate change has altered global precipitation patterns and has led to greater variation in hydrological conditions. Wetlands are important globally for their soil carbon storage. Given that wetland carbon processes are primarily driven by hydrology, a comprehensive understanding of the effect of inundation is needed. In this study, we evaluated the effect of water level (WL) and inundation duration (ID) on carbon dioxide (CO2) fluxes by analysing a 10‐year (2008–2017) eddy covari-ance dataset from a seasonally inundated freshwater marl prairie in the Everglades National Park. Both gross primary production (GPP) and ecosystem respiration (ER) rates showed declines under inundation. While GPP rates decreased almost lin-early as WL and ID increased, ER rates were less responsive to WL increase beyond 30 cm and extended inundation periods. The unequal responses between GPP and ER caused a weaker net ecosystem CO2 sink strength as inundation intensity in-creased. Eventually, the ecosystem tended to become a net CO2 source on a daily basis when either WL exceeded 46 cm or inundation lasted longer than 7 months. Particularly, with an extended period of high‐WLs in 2016 (i.e., WL remained \u3e40 cm for \u3e9 months), the ecosystem became a CO2 source, as opposed to being a sink or neutral for CO2 in other years. Furthermore, the extreme inundation in 2016 was followed by a 4‐month postinundation period with lower net ecosystem CO2 uptake compared to other years. Given that inundation plays a key role in controlling ecosys-tem CO2 balance, we suggest that a future with more intensive inundation caused by climate change or water management activities can weaken the CO2 sink strength of the Everglades freshwater marl prairies and similar wetlands globally, creating a posi-tive feedback to climate change

Practical Delegatable Attribute-Based Anonymous Credentials with Chainable Revocation

Delegatable Anonymous Credentials (DAC) are an enhanced Anonymous Credentials (AC) system that allows credential owners to use credentials anonymously, as well as anonymously delegate them to other users. In this work, we introduce a new concept called Delegatable Attribute-based Anonymous Credentials with Chainable Revocation (DAAC-CR), which extends the functionality of DAC by allowing 1) fine-grained attribute delegation, 2) issuers to restrict the delegation capabilities of the delegated users at a fine-grained level, including the depth of delegation and the sets of delegable attributes, and 3) chainable revocation, meaning if a credential within the delegation chain is revoked, all subsequent credentials derived from it are also invalid. We provide a practical DAAC-CR instance based on a novel primitive that we identify as structure-preserving signatures on equivalence classes on vector commitments (SPSEQ-VC). This primitive may be of independent interest, and we detail an efficient construction. Compared to traditional DAC systems that rely on non-interactive zero-knowledge (NIZK) proofs, the credential size in our DAAC-CR instance is constant, independent of the length of delegation chain and the number of attributes. We formally prove the security of our scheme in the generic group model and demonstrate its practicality through performance benchmarks

Aquaporin-3 Attenuates Oxidative Stress-Induced Nucleus Pulposus Cell Apoptosis Through Regulating the P38 MAPK Pathway

Background/Aims: Previous studies have shown that oxidative damage is a main contributor to disc nucleus pulposus (NP) cell apoptosis. Aquaporin-3 (AQP-3) facilitates reactive oxygen species (ROS) scavenging and thus alleviates oxidative injury in other cells. This study aims to investigate the role and mechanism of AQP-3 in regulating NP cell apoptosis under oxidative damage. Methods: Rat NP cells were treated with H2O2 for 48 hours, while control NP cells were free of H2O2. Recombinant AQP-3 lentiviral vectors were used to investigate the effect of enhanced AQP-3 expression levels in NP cells. NP cell apoptosis was assessed by flow cytometry, caspase-3 activity, gene expression of apoptosis-related molecules (Bax, Bcl-2 and caspase-3), and protein expression of cellular apoptosis markers (cleaved PARP and cleaved caspase-3). Additionally, intracellular ROS content and activity of the p38 MAPK pathway were evaluated. Results: Compared with the control NP cells, oxidative damage in the treatment cells significantly increased cell apoptosis ratios and caspase-3 activity, upregulated gene expression of Bax and caspase-3, downregulated gene expression of Bcl-2, and increased protein expression of cleaved PARP and cleaved caspase-3, as well as increased intracellular ROS content and activity of the p38 MAPK pathway. However, AQP-3 overexpression partly alleviated cell apoptosis, decreased intracellular ROS content, and inhibited the p38 MAPK pathway in NP cells under oxidative damage. Conclusion: Oxidative damage can significantly downregulate AQP-3 expression. Enhancing AQP-3 expression in NP cells partly attenuates cellular apoptosis through regulating the p38 MAPK pathway under oxidative damage

DNA methylation repels binding of hypoxia-inducible transcription factors to maintain tumor immunotolerance.

BACKGROUND: Hypoxia is pervasive in cancer and other diseases. Cells sense and adapt to hypoxia by activating hypoxia-inducible transcription factors (HIFs), but it is still an outstanding question why cell types differ in their transcriptional response to hypoxia. RESULTS: We report that HIFs fail to bind CpG dinucleotides that are methylated in their consensus binding sequence, both in in vitro biochemical binding assays and in vivo studies of differentially methylated isogenic cell lines. Based on in silico structural modeling, we show that 5-methylcytosine indeed causes steric hindrance in the HIF binding pocket. A model wherein cell-type-specific methylation landscapes, as laid down by the differential expression and binding of other transcription factors under normoxia, control cell-type-specific hypoxia responses is observed. We also discover ectopic HIF binding sites in repeat regions which are normally methylated. Genetic and pharmacological DNA demethylation, but also cancer-associated DNA hypomethylation, expose these binding sites, inducing HIF-dependent expression of cryptic transcripts. In line with such cryptic transcripts being more prone to cause double-stranded RNA and viral mimicry, we observe low DNA methylation and high cryptic transcript expression in tumors with high immune checkpoint expression, but not in tumors with low immune checkpoint expression, where they would compromise tumor immunotolerance. In a low-immunogenic tumor model, DNA demethylation upregulates cryptic transcript expression in a HIF-dependent manner, causing immune activation and reducing tumor growth. CONCLUSIONS: Our data elucidate the mechanism underlying cell-type-specific responses to hypoxia and suggest DNA methylation and hypoxia to underlie tumor immunotolerance

Methylprednisolone as Adjunct to Endovascular Thrombectomy for Large-Vessel Occlusion Stroke

Importance It is uncertain whether intravenous methylprednisolone improves outcomes for patients with acute ischemic stroke due to large-vessel occlusion (LVO) undergoing endovascular thrombectomy. Objective To assess the efficacy and adverse events of adjunctive intravenous low-dose methylprednisolone to endovascular thrombectomy for acute ischemic stroke secondary to LVO. Design, Setting, and Participants This investigator-initiated, randomized, double-blind, placebo-controlled trial was implemented at 82 hospitals in China, enrolling 1680 patients with stroke and proximal intracranial LVO presenting within 24 hours of time last known to be well. Recruitment took place between February 9, 2022, and June 30, 2023, with a final follow-up on September 30, 2023.InterventionsEligible patients were randomly assigned to intravenous methylprednisolone (n = 839) at 2 mg/kg/d or placebo (n = 841) for 3 days adjunctive to endovascular thrombectomy. Main Outcomes and Measures The primary efficacy outcome was disability level at 90 days as measured by the overall distribution of the modified Rankin Scale scores (range, 0 [no symptoms] to 6 [death]). The primary safety outcomes included mortality at 90 days and the incidence of symptomatic intracranial hemorrhage within 48 hours. Results Among 1680 patients randomized (median age, 69 years; 727 female [43.3%]), 1673 (99.6%) completed the trial. The median 90-day modified Rankin Scale score was 3 (IQR, 1-5) in the methylprednisolone group vs 3 (IQR, 1-6) in the placebo group (adjusted generalized odds ratio for a lower level of disability, 1.10 [95% CI, 0.96-1.25]; P = .17). In the methylprednisolone group, there was a lower mortality rate (23.2% vs 28.5%; adjusted risk ratio, 0.84 [95% CI, 0.71-0.98]; P = .03) and a lower rate of symptomatic intracranial hemorrhage (8.6% vs 11.7%; adjusted risk ratio, 0.74 [95% CI, 0.55-0.99]; P = .04) compared with placebo. Conclusions and Relevance Among patients with acute ischemic stroke due to LVO undergoing endovascular thrombectomy, adjunctive methylprednisolone added to endovascular thrombectomy did not significantly improve the degree of overall disability.Trial RegistrationChiCTR.org.cn Identifier: ChiCTR210005172

Gap-filling continuously-measured soil respiration data: A highlight of time-series-based methods

Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017–2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (<1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM resulted in errors between −3.7% and 5.8% given the budgets ranged from 463 to 1152 g C m−2 year−1, while ANN exhibited larger errors from −11.3 to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when environmental variables are unavailable.publishedVersio