Publisher Correction: Yield gap decomposition: quantifying factors limiting soybean yield in Southern Africa
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Yield gap decomposition: quantifying factors limiting soybean yield in Southern Africa
Soybean production in Sub-Saharan Africa (SSA) is increasing as its demand for food, feed, cash, and soil fertility improvement soars. Yet, the difference between the smallholder farmers’ yield and either the attainable or the potential is large. Here, we assessed the contribution of various crop management practices to yield gap, and the major factors limiting soybean yield increase per unit area. This study showed that besides soil nutrients and plant nutrition, soybean variety is the most limiting factor in Malawi and Zambia, whereas, in Mozambique, seed rate is significant. Overall, in the Southern Africa region (Malawi, Zambia, and Mozambique) the major soybean yield gap contributors are: variety (63.9%), seed rate (49.7%), and disease damage (36.3%), especially soybean rust. An indication that through yield gap decomposition, interventions could be prioritized to target the most yield-limiting factors with the minimum resources available to smallholder farmers and immensely narrow the yield gap.
The genomic landscape of gene-level structural variations in Japanese and global soybean Glycine max cultivars
Japanese soybeans are traditionally bred to produce soy foods such as tofu, miso and boiled soybeans. Here, to investigate their distinctive genomic features, including genomic structural variations (SVs), we constructed 11 nanopore-based genome references for Japanese and other soybean lines. Our assembly-based comparative method, designated ‘Asm2sv’, identified gene-level SVs comprehensively, enabling pangenome analysis of 462 worldwide cultivars and varieties. Based on these, we identified selective sweeps between Japanese and US soybeans, one of which was the pod-shattering resistance gene PDH1. Genome-wide association studies further identified several quantitative trait loci that accounted for large-seed phenotypes of Japanese soybean lines, some of which were also close to regions of the selective sweeps, including PDH1. Notably, specific combinations of alleles, including SVs, were found to increase the seed size of some Japanese landraces. In addition to the differences in cultivation environments, distinct food processing usages might result in changes in Japanese soybean genomes.
A scalable synergy-first backbone decomposition of higher-order structures in complex systems
In the last decade, there has been an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order interactions. These “synergistic” dependencies reflect information that is in the “whole” but not any of the “parts.” Arguably the most successful framework for exploring synergies is the partial information decomposition (PID). Despite its considerable power, the PID has a number of limitations that restrict its general applicability. Subsequently, other heuristic measures, such as the O-information, have been introduced, although these measures typically only provide a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. We define synergy as that information encoded in the joint state of a set of elements that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered “backbone” of partial synergy atoms that sweeps the system’s scale. This approach applies to the entropy, the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information (thus recovering a “backbone” PID). Finally, we show that this approach can be used to decompose higher-order interactions beyond information theory by showing how synergistic combinations of edges in a graph support global integration via communicability. We conclude by discussing how this perspective on synergistic structure can deepen our understanding of part-whole relationships in complex systems.
Development of quality indicators for hypertension management at the primary health care level in South Africa
Despite many quality initiatives at the primary health care (PHC) level, little is known about the actual quality of care of patients diagnosed with hypertension in South Africa. This study aimed to develop quality indicators for hypertension management at the PHC level to improve the quality of care and patient outcomes. The RAND/UCLA Appropriateness Method, comprising two rounds, was used to develop clear, appropriate, and feasible evidence-based quality indicators for hypertension. In Round 1, a 9-point scale was used by a panel of 11 members to rate clarity and appropriateness of 102 hypertension quality indicator statements, grouped under 9 dimensions of quality hypertension management, using an online MS Excel® spreadsheet. In Round 2, 9 of the same panellists discussed all indicators and rated their appropriateness and feasibility during a remote online, interactive face-to-face MS Teams® meeting. Statements rated ≥7–9 with agreement were defined as either appropriate or feasible. The panel rated 46 hypertension quality indicator statements ≥7–9 with agreement for the appropriate and feasible measurement of the management of hypertension: monitoring (n = 16), review (n = 5), lifestyle advice (n = 9), tests (n = 7), intermediate outcomes (n = 6), referrals (n = 2) and practice/facility structures (n = 1). No indicator statements were rated both appropriate and feasible for measuring blood pressure levels and treatment. If applied, these indicators would improve monitoring and management of patients with hypertension, patient outcomes, and data quality in South Africa and result in more efficient use of scarce resources. This study can be replicable for improving care of other non-communicable diseases across Africa.
Effects of nitrogen vacancy sites of oxynitride support on the catalytic activity for ammonia decomposition
Nitrogen-containing compounds such as imides and amides have been reported as efficient materials that promote ammonia decomposition over nonnoble metal catalysts. However, these compounds decompose in an air atmosphere and become inactive, which leads to difficulty in handling. Here, we focused on perovskite oxynitrides as air-stable and efficient supports for ammonia decomposition catalysts. Ni-loaded oxynitrides exhibited 2.5–18 times greater catalytic activity than did the corresponding oxide-supported Ni catalysts, even without noticeable differences in the Ni particle size and surface area of the supports. The catalytic performance of the Ni-loaded oxynitrides is well correlated with the nitrogen desorption temperature during N2 temperature-programmed desorption, which suggests that the lattice nitrogen in the oxynitride support rather than the Ni surface is the active site for ammonia decomposition. Furthermore, NH3 temperature-programmed surface reactions and density functional theory (DFT) calculations revealed that NH3 molecules are preferentially adsorbed on the nitrogen vacancy sites on the support surface rather than on the Ni surface. Thus, the ammonia decomposition reaction is facilitated by a vacancy-mediated reaction mechanism.
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