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Agroforestry has potential for strengthening the climate change resilience of smallholder farmers in Southeast Asia. In Indonesia, the food security challenges faced by smallholders will likely worsen due to climate change impacts. Agroforestry provides and option for strengthening climate change resilience, while contributing to food access, income, health, and environmental stability. To evaluate the evidence for such benefits, this systematic review identifies 22 peer-reviewed articles published between 2000 and 2019 which assess agroforestry’s contributions to food security in Indonesia, mostly in Java or Sumatra. Analysis of the studies indicate that traditional and commercial agroforestry contribute to food security in diverse ways: for example, traditional homegardens offer 20% more dietary diversity than commercial counterparts, while commercial homegardens may contribute up to five times more income. Agri-silviculture contributions fall along a timber versus non-timber forest product continuum that displays a similar tradeoff between diversity and income. Those systems with a commercial focus may receive 54% of their income from a single commodity crop such as coffee, while traditional systems allow greater access to plants with medicinal benefits. Nearly all agroforestry systems offered indirect benefits for food security, such as allowing more off-farm work than traditional agriculture and contributing to environmental stability: users of agroforestry were found by one study to collect 83% less fuelwood from natural forests. One study highlighted that agroforestry options have up to 98% greater net present value (for periods over 30 years) compared to slash and burn style agriculture. However, very few studies of Indonesian agroforestry focused explicitly on financial analysis and food security, indicating the need for further research. Given the similar situations faced by many Southeast Asia countries, our findings contribute to emerging trends throughout the region regarding the relationship between agroforestry and food security.

Mitral regurgitation (MR) and tricuspid regurgitation (TR) commonly coexist in patients with heart failure (HF). Their concomitant occurrence carries a much poorer prognosis than isolated valve disease. Transcatheter edge-to-edge repair (TEER) of MR and TR is safe and effective, but there is limited data on combined MR/TR TEER. The study evaluates the safety and efficacy of combined TEER for MR and TR in a real-world cohort. This retrospective safety and efficacy analysis included the first 40 patients treated with combined MR/TR TEER between 2019 and 2021 at our single tertiary care referral centre. Combined procedural success (MR reduction ≥2° and TR reduction ≥1°) was achieved in 80% of the cases. Simultaneous TEER was safe, with no intraprocedural death, myocardial infarction (MI), stroke, or major bleeding. At 1-year follow-up, the median New York Heart Association functional (NYHA) class improved by one grade; twelve patients (30%) died, and fourteen patients (35%) were hospitalized for HF. Procedural success and postprocedural residual MR ≤ 1° were associated with reduced 1-year mortality rates but not HF hospitalizations. Combined MR/TR TEER is safe and reduces MR and TR in most patients, conferring a potential benefit regarding symptoms and prognosis. Randomized controlled trials (RCTs) are needed to rigorously evaluate combination therapy in this setting.

The scientific community that includes meteorologists, physical scientists, engineers, medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for years because of its effects on, directly or indirectly, the daily life of human beings. The total economic losses associated with the impact of the presence of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles including the word ``fog'' in Journals of American Meteorological Society alone was found to be about 4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog processes over various time and space scales. Fog processes involve droplet microphysics, aerosol chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past achievements related to the understanding of fog formation, development and decay, and in this respect, the analysis of observations and the development of forecasting models and remote sensing methods are discussed in detail. Finally, future perspectives for fog-related research are highlighted.[PUBLICATION ABSTRACT]

Soil is the most important natural resource for life on Earth after water. Given its fundamental role in sustaining the human population, both the availability and quality of soil must be managed sustainably and protected. To ensure sustainable management we need to understand the intrinsic functional capacity of different soils across Europe and how it changes over time. Soil monitoring is needed to support evidence-based policies to incentivise sustainable soil management. To this aim, we assessed which soil attributes can be used as potential indicators of five soil functions; (1) primary production, (2) water purification and regulation, (3) carbon sequestration and climate regulation, (4) soil biodiversity and habitat provisioning and (5) recycling of nutrients. We compared this list of attributes to existing national (regional) and EU-wide soil monitoring networks. The overall picture highlighted a clearly unbalanced dataset, in which predominantly chemical soil parameters were included, and soil biological and physical attributes were severely under represented. Methods applied across countries for indicators also varied. At a European scale, the LUCAS-soil survey was evaluated and again confirmed a lack of important soil biological parameters, such as C mineralisation rate, microbial biomass and earthworm community, and soil physical measures such as bulk density. In summary, no current national or European monitoring system exists which has the capacity to quantify the five soil functions and therefore evaluate multi-functional capacity of a soil and in many countries no data exists at all. This paper calls for the addition of soil biological and some physical parameters within the LUCAS-soil survey at European scale and for further development of national soil monitoring schemes.

We use the Polar Wind Outflow Model (PWOM) to study the geomagnetically quiet conditions in the polar cap during solar maximum. The PWOM solves the gyrotropic transport equations for O+, H+, and He+ along several magnetic field lines in the polar region in order to reconstruct the full 3D solution. We directly compare our simulation results to the data based empirical model of Kitamura et al. (2011) of electron density which is based on 63 months of Akebono satellite observations. The modeled ion and electron temperatures are also compared with a statistical compilation of quiet time data obtained by the EISCAT Svalbard Radar (ESR) and Intercosmos Satellites. The data and model agree reasonably well, albeit with some differences. This study shows that photoelectrons play an important role in explaining the differences between sunlit and dark results of electron density, ion composition, as well as ion and electron temperatures of the quiet time polar wind solution. Moreover, these results provide an initial validation of the PWOM's ability to model the quiet time “background” solution. Key Points Photoelectrons play a role in the difference between sunlit and dark polar wind O+ is the main polar wind species up to at least 8000 km under sunlit conditions We present an initial validation of the quiet time PWOM solution

In the vast field of Quantum Optimization, Quantum Iterative Power Algorithms (QIPA) has been introduced recently with a promise of exponential speedup over an already established and well-known method, the variational Quantum Imaginary Time Evolution (varQITE) algorithm. Since the convergence and error of varQITE are known, the promise of QIPA also implied certain collapses in the complexity hierarchy — such as NP ⊆ BQP, as we show in our study. However the original article of QIPA explicitly states the algorithm does not cause any collapses. In this study we prove that these collapses indeed do not occur, and with that, prove that the promised exponential separation is practically unachievable. We do so by introducing criteria for the exponential separation between QIPA 2 that uses a double exponential function and varQITE, and then showing how these criteria require certain properties in problem instances. After that we introduce a preprocessing step that enforces problems to satisfy these criteria, and then we show that the algorithmic error blows up exponentially for these instances, as there is an inverse polynomial term between speedup and the error. Despite the theoretical results, we also show that practically relevant polynomial enhancement is still possible, and show experimental results on a small problem instance, where we used our preprocessing step to achieve the improvement.

Patients with stable coronary artery disease were randomly assigned to fractional flow reserve–guided PCI or medical therapy. At 5 years, the composite of death, myocardial infarction, or urgent revascularization was significantly less frequent in the PCI group.

Although the satellite cell (SC) is a key regulator of muscle growth during development and muscle adaptation following exercise, the regulation of human muscle SC function remains largely unexplored. STAT3 signalling mediated via interleukin-6 (IL-6) has recently come to the forefront as a potential regulator of SC proliferation. The early response of the SC population in human muscle to muscle-lengthening contractions (MLC) as mediated by STAT3 has not been studied. Twelve male subjects (21±2 y; 83±12 kg) performed 300 maximal MLC of the quadriceps femoris at 180°•s(-1) over a 55° range of motion with muscle samples (vastus lateralis) and blood samples (antecubital vein) taken prior to exercise (PRE), 1 hour (T1), 3 hours (T3) and 24 hours (T24) post-exercise. Cytoplasmic and nuclear fractions of muscle biopsies were purified and analyzed for total and phosphorylated STAT3 (p-STAT3) by western blot. p-STAT3 was detected in cytoplasmic fractions across the time course peaking at T24 (p<0.01 vs. PRE). Nuclear total and p-STAT3 were not detected at appreciable levels. However, immunohistochemical analysis revealed a progressive increase in the proportion of SCs expressing p-STAT3 with ∼60% of all SCs positive for p-STAT3 at T24 (p<0.001 vs. PRE). Additionally, cMyc, a STAT3 downstream gene, was significantly up-regulated in SCs at T24 versus PRE (p<0.05). Whole muscle mRNA analysis revealed induction of the STAT3 target genes IL-6, SOCS3, cMyc (peaking at T3, p<0.05), IL-6Rα and GP130 (peaking at T24, p<0.05). In addition, Myf5 mRNA was up-regulated at T24 (p<0.05) with no appreciable change in MRF4 mRNA. We demonstrate that IL-6 induction of STAT3 signaling occurred exclusively in the nuclei of SCs in response to MLC. An increase in the number of cMyc+ SCs indicated that human SCs were induced to proliferate under the control of STAT3 signaling.

The restoration and management of shallow, pond-like systems are hindered by limitations in the applicability of the well-known models describing the relationship between nutrients and lake phytoplankton biomass in higher ranges of nutrient concentration. Trophic models for naturally eutrophic small, shallow, endorheic lakes have not yet been developed, even though these are the most frequent standing waters in continental lowlands. The aim of this study was to identify variables that can be considered as main drivers of phytoplankton biomass and to build a predictive model. The influence of potential drivers of phytoplankton biomass (nutrients, other chemical variables, land use, lake use and lake depth) from 24 shallow eutrophic lakes was tested using data in the Pannonian ecoregion (Hungary and Romania). By incorporating lake depth, TP, TN and lake use as independent and Chl- a as dependent variables into different models (multiple regression model, GLM and multilayer perception model) predictive models were built. These models explained >50% of the variance. Although phytoplankton biomass in small, shallow, enriched lakes is strongly influenced by stochastic effects, our results suggest that phytoplankton biomass can be predicted by applying a multiple stressor approach, and that the model results can be used for management purposes.

Some space weather models, such as the Space Weather Modeling Framework (SWMF) used in this study, use solar wind propagated from the first Lagrange point (L1) to the bow shock nose (BSN) to forecast geomagnetic storms. The SWMF is a highly coupled framework of space weather models that include multiple facets of the Geospace environment, such as the magnetosphere and ionosphere. The propagated solar wind measurements are used as a boundary condition for SWMF. The solar wind propagation method is a timeshift based on the calculated phase front normal (PFN) which leads to some uncertainties. For example, the propagated solar wind could have evolved during this timeshift. We use a data set of 123 geomagnetic storms between 2010 and 2019 run by the SWMF Geospace configuration to analyze the impact solar wind propagation and solar wind driving has on the geomagnetic indices. We look at the probability distributions of errors in SYM‐H, cross polar cap potential (CPCP), and auroral electrojet indices AL and AU. Through studying the median errors (MdE), standard deviations and standardized regression coefficients, we find that the errors depend on the propagation parameters. Among the results, we show that the accuracy of the simulated SYM‐H depends on the spacecraft distance from the Sun‐Earth line. We also quantify the dependence of the standard deviation in SYM‐H errors on the PFN and solar wind pressure. These statistics provide an insight into how the propagation method affects the final product of the simulation, which are the geomagnetic indices.