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\"As protector of the universe, Captain Mar-Vell has triumphed over foes large and small. But when the Kree hero is diagnosed with cancer, he finds himself face-to-face with a foe even his vast might can't defeat -- and both he, and the universe that loves him, must rally together to accept the inevitable. Plus: Experience the classic adventures that introduced Mar-Vell and the seminal encounter with Nitro that began his descent into illness. And -- in the midst of a battle against Titan's planet-sized computer system, corrupted by Thanos' madness -- witness the budding romance between Mar-Vell and his true love, Elysius! Guest starring the Avengers, the X-Men, the Fantastic Four, and more!\" -- Back cover.

Reliable projections of wildfire and associated socioeconomic risks are crucial for the development of efficient and effective adaptation and mitigation strategies. The lack of or limited observational constraints for modeling outputs impairs the credibility of wildfire projections. Here, we present a machine learning framework to constrain the future fire carbon emissions simulated by 13 Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6), using historical, observed joint states of fire-relevant variables. During the twenty-first century, the observation-constrained ensemble indicates a weaker increase in global fire carbon emissions but higher increase in global wildfire exposure in population, gross domestic production, and agricultural area, compared with the default ensemble. Such elevated socioeconomic risks are primarily caused by the compound regional enhancement of future wildfire activity and socioeconomic development in the western and central African countries, necessitating an emergent strategic preparedness to wildfires in these countries. A new study develops a machine learning framework to observationally constrain CMIP6-simulated fire carbon emissions, finding a weaker increase in 21st-century global fires but higher increase in their socioeconomic risks than previously thought.

David Haller is no ordinary mutant. Son of Charles Xavier, founder of the X-Men, David's incredible mental powers fractured his mind and now, each of his personalities controls a different ability! And they're not all friendly, as Xavier and the New Mutants find out the hard way! But as Legion struggles to control the chaos in his head, he attracts the attention of one of Xavier's oldest and most malevolent foes: Amahl Farouk, the Shadow King, who's secretly been stalking and manipulating the X-Men and their allies. When the Shadow King sinks his hooks deep into David's mind, will two teams of X-Men be enough to defeat him -- or will David be the key to the villain's ultimate victory? Includes the Muir Island Saga storyline.

59 I. 59 II. 62 III. 69 IV. 73 73 References 73 SUMMARY: There is wide breadth of root function within ecosystems that should be considered when modeling the terrestrial biosphere. Root structure and function are closely associated with control of plant water and nutrient uptake from the soil, plant carbon (C) assimilation, partitioning and release to the soils, and control of biogeochemical cycles through interactions within the rhizosphere. Root function is extremely dynamic and dependent on internal plant signals, root traits and morphology, and the physical, chemical and biotic soil environment. While plant roots have significant structural and functional plasticity to changing environmental conditions, their dynamics are noticeably absent from the land component of process‐based Earth system models used to simulate global biogeochemical cycling. Their dynamic representation in large‐scale models should improve model veracity. Here, we describe current root inclusion in models across scales, ranging from mechanistic processes of single roots to parameterized root processes operating at the landscape scale. With this foundation we discuss how existing and future root functional knowledge, new data compilation efforts, and novel modeling platforms can be leveraged to enhance root functionality in large‐scale terrestrial biosphere models by improving parameterization within models, and introducing new components such as dynamic root distribution and root functional traits linked to resource extraction.

Terrestrial biosphere models (TBMs) are highly sensitive to model representation of photosynthesis, in particular the parameters maximum carboxylation rate and maximum electron transport rate at 25°C (V c,max.25 and J max.25, respectively). Many TBMs do not include representation of Arctic plants, and those that do rely on understanding and parameterization from temperate species. We measured photosynthetic CO2 response curves and leaf nitrogen (N) content in species representing the dominant vascular plant functional types found on the coastal tundra near Barrow, Alaska. The activation energies associated with the temperature response functions of V c,max and J max were 17% lower than commonly used values. When scaled to 25°C, V c,max.25 and J max.25 were two- to five-fold higher than the values used to parameterize current TBMs. This high photosynthetic capacity was attributable to a high leaf N content and the high fraction of N invested in Rubisco. Leaf-level modeling demonstrated that current parameterization of TBMs resulted in a two-fold underestimation of the capacity for leaf-level CO2 assimilation in Arctic vegetation. This study highlights the poor representation of Arctic photosynthesis in TBMs, and provides the critical data necessary to improve our ability to project the response of the Arctic to global environmental change.

CONTENTS: 34 I. 35 II. 35 III. 41 IV. 43 V. 49 VI. 50 VII. 51 VIII. 52 53 References 53 SUMMARY: Plant roots play a critical role in ecosystem function in arctic tundra, but root dynamics in these ecosystems are poorly understood. To address this knowledge gap, we synthesized available literature on tundra roots, including their distribution, dynamics and contribution to ecosystem carbon and nutrient fluxes, and highlighted key aspects of their representation in terrestrial biosphere models. Across all tundra ecosystems, belowground plant biomass exceeded aboveground biomass, with the exception of polar desert tundra. Roots were shallowly distributed in the thin layer of soil that thaws annually, and were often found in surface organic soil horizons. Root traits – including distribution, chemistry, anatomy and resource partitioning – play an important role in controlling plant species competition, and therefore ecosystem carbon and nutrient fluxes, under changing climatic conditions, but have only been quantified for a small fraction of tundra plants. Further, the annual production and mortality of fine roots are key components of ecosystem processes in tundra, but extant data are sparse. Tundra root traits and dynamics should be the focus of future research efforts. Better representation of the dynamics and characteristics of tundra roots will improve the utility of models for the evaluation of the responses of tundra ecosystems to changing environmental conditions.

Notch signaling is often aberrantly activated in solid and hematological cancers and regulates cell fate decisions and the maintenance of cancer stem cells. In addition, increased expression of Notch pathway components is clinically associated with poorer prognosis in several types of cancer. Targeting Notch may have chemopreventive and anti-cancer effects, leading to reduced disease incidence and improved survival. While therapeutic agents are currently in development to achieve this goal, several researchers have turned their attention to dietary and natural agents for targeting Notch signaling. Given their natural abundance from food sources, the use of diet-derived agents to target Notch signaling offers the potential advantage of low toxicity to normal tissue. In this review, we discuss several dietary agents including curcumin, EGCG, resveratrol, and isothiocyanates, which modulate Notch pathway components in a context-dependent manner. Dietary agents modulate Notch signaling in several types of cancer and concurrently decrease in vitro cell viability and in vivo tumor growth, suggesting a potential role for their clinical use to target Notch pathway components, either alone or in combination with current therapeutic agents.

Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. An important source of this uncertainty lies in the dependency of photosynthesis on the maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax). Understanding and making accurate prediction of C fluxes thus requires accurate characterization of these rates and their relationship with plant nutrient status over large geographic scales. Plant nutrient status is indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Correlations between Vcmax and Jmax and leaf nitrogen (N) are typically derived from local to global scales, while correlations with leaf phosphorus (P) and specific leaf area (SLA) have typically been derived at a local scale. Thus, there is no global‐scale relationship between Vcmax and Jmax and P or SLA limiting the ability of global‐scale carbon flux models do not account for P or SLA. We gathered published data from 24 studies to reveal global relationships of Vcmax and Jmax with leaf N, P, and SLA. Vcmax was strongly related to leaf N, and increasing leaf P substantially increased the sensitivity of Vcmax to leaf N. Jmax was strongly related to Vcmax, and neither leaf N, P, or SLA had a substantial impact on the relationship. Although more data are needed to expand the applicability of the relationship, we show leaf P is a globally important determinant of photosynthetic rates. In a model of photosynthesis, we showed that at high leaf N (3 gm−2), increasing leaf P from 0.05 to 0.22 gm−2 nearly doubled assimilation rates. Finally, we show that plants may employ a conservative strategy of Jmax to Vcmax coordination that restricts photoinhibition when carboxylation is limiting at the expense of maximizing photosynthetic rates when light is limiting. Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. To reduce this uncertainty we analysed data collected in the literature from across the globe on the maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) in relation to plant nutrient status indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Vcmax was strongly related to leaf N and increasing leaf P substantially increased the sensitivity of Vcmax to leaf N and in a model of photosynthesis we showed that at high leaf N (3 gm−2) increasing leaf P from 0.05 to 0.22 gm−2 nearly doubled assimilation rates. Finally we show that plants may employ a conservative strategy of Jmax to Vcmax co‐ordination that restricts photoinhibition when carboxylation is limiting at the expense of maximising photosynthetic rates when light is limiting.

Current knowledge of the spatiotemporal patterns of changes in soil moisture-based terrestrial aridity has considerable uncertainty. Using Standardized Soil Moisture Index (SSI) calculated from multi-source merged data sets, we find widespread drying in the global midlatitudes, and wetting in the northern subtropics and in spring between 45°N–65°N, during 1971–2016. Formal detection and attribution analysis shows that human forcings, especially greenhouse gases, contribute significantly to the changes in 0–10 cm SSI during August–November, and 0–100 cm during September–April. We further develop and apply an emergent constraint method on the future SSI’s signal-to-noise (S/N) ratios and trends under the Shared Socioeconomic Pathway 5-8.5. The results show continued significant presence of human forcings and more rapid drying in 0–10 cm than 0–100 cm. Our findings highlight the predominant human contributions to spatiotemporally heterogenous terrestrial aridification, providing a basis for drought and flood risk management. Historical latitudinal and seasonal trends in global soil moisture aridity are attributable to greenhouse gas emissions.

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk. Fire is an important component of many African ecosystems, but prediction of fire activity is challenging. Here, the authors use a statistical framework to assess the seasonal environmental drivers of African fire, which allow for a better prediction of fire activity.