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Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
Free base 5,10,15,20-tetrakis(4-carboxylatophenyl)porphyrin stands for the class of powerful porphyrin photosensitizers for singlet oxygen generation and light-harvesting. The atomic level selectivity of dynamic UV pump - N K-edge probe X-ray absorption spectroscopy in combination with time-dependent density functional theory (TD-DFT) gives direct access to the crucial excited molecular states within the unusual relaxation pathway.
The efficient intersystem crossing, that is El-Sayed forbidden and not facilitated by a heavy atom is confirmed to be the result of the long singlet excited state lifetime (Q(x) 4.9 ns) and thermal effects.
Overall, the interplay of stabilization by conservation of angular momenta and vibronic relaxation drive the de-excitation in these chromophores.
Fragmentation of peptides leaves characteristic patterns in mass spectrometry data, which can be used to identify protein sequences, but this method is challenging for mutated or modified sequences for which limited information exist. Altenburg et al. use an ad hoc learning approach to learn relevant patterns directly from unannotated fragmentation spectra.
Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins.
Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge.
Here, to elevate unrestricted learning from spectra, we introduce 'ad hoc learning of fragmentation' (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments.
We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task.
Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%.
In the past decade, sediment connectivity has become a widely recognized characteristic of a geomorphic system. However, the quantification of functional connectivity (i.e. connectivity which arises due to the actual occurrence of sediment transport processes) and its variation over space and time is still a challenge. In this context, this study assesses the effects of expected future phenomena in the context of climate change (i.e. glacier retreat, permafrost degradation or meteorological extreme events) on sediment transport dynamics in a glacierised Alpine basin. The study area is the Sulden river basin (drainage area 130 km(2)) in the Italian Alps, which is composed of two geomorphologically diverse sub-basins. Based on graph theory, we evaluated the spatio-temporal variations in functional connectivity in these two sub-basins. The graph-object, obtained by manually mapping sediment transport processes between landforms, was adapted to 6 different hydro-meteorological scenarios, which derive from combining base, heatwave and rainstorm conditions with snowmelt and glacier-melt periods. For each scenario and each sub-basin, the sediment transport network and related catchment characteristics were analysed. To compare the effects of the scenarios on functional connectivity, we introduced a connectivity degree, calculated based on the area of the landforms involved in sediment cascades. Results indicate that the area of the basin connected to its outlet in terms of sediment transport might feature a six-fold increase in case of rainstorm conditions compared to "average " meteorological conditions assumed for the base scenario. Furthermore, markedly different effects of climate change on sediment connectivity are expected between the two sub-catchments due to their contrasting morphological and lithological characteristics, in terms of relative importance of rainfall triggered colluvial processes vs temperature-driven proglacial fluvial dynamics.
EC 22536-5304
(2021)
Helium-burning hot subdwarf stars of spectral types O and B (sdO/B) are thought to be produced through various types of binary interactions. The helium-rich hot subdwarf star EC 22536-5304 was recently found to be extremely enriched in lead. Here, we show that EC 22536-5304 is a binary star with a metal-poor subdwarf F-type (sdF) companion. We performed a detailed analysis of high-resolution SALT/HRS and VLT/UVES spectra, deriving metal abundances for the hot subdwarf, as well as atmospheric parameters for both components. Because we consider the contribution of the sdF star, the derived lead abundance for the sdOB, + 6.3 +/- 0.3 dex relative to solar, is even higher than previously thought. We derive T-eff = 6210 +/- 70 K, log g = 4.64 +/- 0.10, [FE/H] = - 1.95 +/- 0.04, and [alpha/Fe] = + 0.40 +/- 0.04 for the sdF component. Radial velocity variations, although poorly sampled at present, indicate that the binary system has a long orbital period of about 457 days. This suggests that the system was likely formed through stable Roche lobe overflow (RLOF). A kinematic analysis shows that EC 22536-5304 is on an eccentric orbit around the Galactic centre. This, as well as the low metallicity and strong alpha enhancement of the sdF-type companion, indicate that EC 22536-5304 is part of the Galactic halo or metal-weak thick disc. As the first long-period hot subdwarf binary at [FE/H] less than or similar to- 1, EC 22536-5304 may help to constrain the RLOF mechanism for mass transfer from low-mass, low-metallicity red giant branch (RGB) stars to main-sequence companions.
Reducing greenhouse gas emissions in food systems is becoming more challenging as food is increasingly consumed away from producer regions, highlighting the need to consider emissions embodied in trade in agricultural emissions accounting.
To address this, our study explores recent trends in trade-adjusted agricultural emissions of food items at the global, regional, and national levels.
We find that emissions are largely dependent on a country’s consumption patterns and their agricultural emission intensities relative to their trading partners’.
The absolute differences between the production-based and trade-adjusted emissions accounting approaches are especially apparent for major agricultural exporters and importers and where large shares of emission-intensive items such as ruminant meat, milk products and rice are involved.
In relative terms, some low-income and emerging and developing economies with consumption of high emission intensity food products show large differences between approaches.
Similar trends are also found under various specifications that account for trade and re-exports differently.
These findings could serve as an important element towards constructing national emissions reduction targets that consider trading partners, leading to more effective emissions reductions overall.
Kollaborative, partizipative Instrumente zur Krisenbekämpfung haben in den letzten Jahren zunehmend an Aufmerksamkeit gewonnen. Ein Beispiel hierfür ist der #WirVsVirus-Hackathon, der als Reaktion auf die COVID-19-Pandemie durchgeführt wurde und über 28.000 Teilnehmer:innen erreichte. Bislang wurden die Auswirkungen solch groß angelegter, kollaborativer Ansätze zur Krisenbewältigung auf staatliches Krisenmanagement nur selten untersucht. Diese Studie analysiert den Hackathon und die daraus entstandenen Projekte aus der Perspektive des Open Governance-Paradigmas. Auf Grundlage von neun Experteninterviews untersuchen wir, wie sich digitale Open Governance auf die Regierungsfähigkeit und Legitimität in Krisenzeiten auswirkt. Unsere Analyse zeigt, dass digitale Open Governance zur Leistungsfähigkeit und Legitimität staatlichen Handelns in Krisenzeiten beitragen kann, da solche Projekte eine breite und diverse Teilnehmerschaft mobilisieren und in kurzer Zeit bürgerzentrierte, nutzbare Lösungen für krisenbezogene Probleme entwickeln können. Dem stehen allerdings Zweifel an der langfristigen Beständigkeit der Projekte, ihrer Skalierbarkeit, sowie Risiken hinsichtlich der Legitimität und Rechenschaftspflicht entgegen.
The Lena Delta in Siberia is the largest delta in the Arctic and as a snow-dominated ecosystem particularly vulnerable to climate change.
Using the two decades of MODerate resolution Imaging Spectroradiometer satellite acquisitions, this study investigates interannual and spatial variability of snow-cover duration and summer vegetation vitality in the Lena Delta.
We approximated snow by the application of the normalized difference snow index and vegetation greenness by the normalized difference vegetation index (NDVI). We consolidated the analyses by integrating reanalysis products on air temperature from 2001 to 2021, and air temperature, ground temperature, and the date of snow-melt from time-lapse camera (TLC) observations from the Samoylov observatory located in the central delta.
We extracted spring snow-cover duration determined by a latitudinal gradient. The 'regular year' snow-melt is transgressing from mid-May to late May within a time window of 10 days across the delta.
We calculated yearly deviations per grid cell for two defined regions, one for the delta, and one focusing on the central delta. We identified an ensemble of early snow-melt years from 2012 to 2014, with snow-melt already starting in early May, and two late snow-melt years in 2004 and 2017, with snow-melt starting in June. In the times of TLC recording, the years of early and late snow-melt were confirmed.
In the three summers after early snow-melt, summer vegetation greenness showed neither positive nor negative deviations. Whereas, vegetation greenness was reduced in 2004 after late snow-melt together with the lowest June monthly air temperature of the time series record. Since 2005, vegetation greenness is rising, with maxima in 2018 and 2021.
The NDVI rise since 2018 is preceded by up to 4 degrees C warmer than average June air temperature. The ongoing operation of satellite missions allows to monitor a wide range of land surface properties and processes that will provide urgently needed data in times when logistical challenges lead to data gaps in land-based observations in the rapidly changing Arctic.
The concepts of CO2 emission, global warming, climate change, and their environmental impacts are of utmost importance for the understanding and protection of the ecosystems.
Among the natural sources of gases into the atmosphere, the contribution of geogenic sources plays a crucial role. However, while subaerial emissions are widely studied, submarine outgassing is not yet well understood.
In this study, we review and catalog 122 literature and unpublished data of submarine emissions distributed in ten coastal areas of the Aegean Sea. This catalog includes descriptions of the degassing vents through in situ observations, their chemical and isotopic compositions, and flux estimations.
Temperatures and pH data of surface seawaters in four areas affected by submarine degassing are also presented.
This overview provides useful information to researchers studying the impact of enhanced seawater CO2 concentrations related either to increasing CO2 levels in the atmosphere or leaking carbon capture and storage systems.