Nitrogen (N) fertilizer sustains modern crop production, yet less than half of applied reactive N is recovered in harvested products, with the remainder lost through volatilization, denitrification, leaching, and runoff. Although hundreds of genes regulating N uptake, assimilation, and remobilization have been identified, their translation into stable field-level improvements remains limited. This gap reflects the fact that nitrogen use efficiency (NUE) is not a single-gene trait, but an emergent property of the integrated plant–soil–microbe system. Here, we organize the genetic determinants of NUE into four functional layers: hardware, comprising transporters and assimilatory enzymes that execute N fluxes; software, encompassing signaling and transcriptional networks that regulate these components; balance, referring to carbon-nitrogen coordination that supplies the energy and carbon skeletons required for assimilation; and the plant-associated microbiome as a second genome that expands plant nitrogen-acquisition capacity. Unlike previous NUE reviews organized by gene family or by physiological process, we tier each target by evidence strength, the supporting endpoint (yield at a given N rate or grain N recovery at maintained yield), and whether its benefit depends on N supply, and we retain failure cases and model-only paradigms alongside successes. Across these layers, effective NUE engineering should prioritize repair of steps shown to constrain flux and mitigation of associated trade-offs rather than indiscriminate enhancement of N flux. Key strategies include spatially constrained expression of high-activity modules, release of transcriptional brakes, carbon–nitrogen coordination through regulatory hubs, and breeding of stable host–microbe partnerships. We further propose a translational roadmap centered on cis-regulatory engineering and combinatorial testing of mechanistically complementary alleles. Treating NUE as an engineering problem of the integrated plant–soil–microbe system is essential for bridging the laboratory-to-field translation gap in nitrogen-efficient crop breeding.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The results show that the embedded industry has been able to apply agile methods in its development processes and that the appreciation of the agile methods and their individual practices appears to increase once adopted and applied in practice.
O. Salo, P. Abrahamsson· IET Software· 238 citations· ⚡9
Consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts are found.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· Journal of Systems and Softw...· 236 citations· ⚡13
The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed, which helped develop an agile development approach for mobile application development.
P. Abrahamsson, Antti Hanhineva, H. Hulkko et al.· Conference on Object-Oriente...· 225 citations· ⚡18
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
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