Tantalate-based Perovskite for Solar Energy Applications

To realize a sustainable society in the near future, the development of clean, renewable, cheap and sustainable resources and the remediation of environmental pollutions using solar energy as the driving force would be important. During the past few decades, plenty of efforts have been focused on this area to develop solar light active materials to meet the increased energy and environmental crisis. Owning to the unique perovskite-type structure, tantalate-based semiconductors with unable chemical composition show high activities toward the conversion of solar radiation into chemical energy. Moreover, various engineering strategies, including crystal structure engineering, electronic structure engineering, surface/interface engineering, co-catalyst engineering and so on, have been developed in order to modulate the charge separation and transfer efficiency, optical absorption, band gap position and photochemical and photophysical stability, which would open a realm of new possibilities for exploring novel materials for solar energy applications

Оцінка впливу замісної гормоно-терапії гіпотиреозу на стан вагітності та виношування плоду

Гіпотиреоз – захворювання щитовидної залози, при якому знижується її продуктивність, тиреоїдних гормонів виробляється менше чим необхідно організму для нормальної життєдіяльності. За результатами популяційних досліджень, поширеність гіпотиреозу серед вагітних становить 2-3 %. Серед них близько двох третин мають субклінічний та 0,5 % − маніфестний гіпотиреоз. За даними багатьох дослідників, тільки 20-30% жінок з гіпотиреозом мають клінічні прояви гіпотиреозу, у інших, як правило, захворювання протікає без симптомів. Патологія ЩЗ негативно впливає на перебіг вагітності, розвиток плода й адаптацію новонародженого. Тиреоїдна дисфункція загрожує викиднями, передчасними пологами, відшаруванням плаценти, прееклампсією, післяпологовим тиреоїдитом у матері, а також зниженням інтелектуального потенціалу народжених дітей

Seasonal predictability of Kiremt rainfall in coupled general circulation models

The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June–September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985–2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.publishedVersio

Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic

There is a growing demand for skillful prediction systems in the Arctic. Using the Norwegian Climate Prediction Model (NorCPM) that combines the fully-coupled Norwegian Earth System Model and the Ensemble Kalman filter, we present a system that performs both, weakly-coupled data assimilation (wCDA) when assimilating ocean hydrogaphy (by updating the ocean alone) and strongly-coupled data assimilation (sCDA) when assimilating sea ice concentration (SIC) (by jointly updating the sea ice and ocean). We assess the seasonal prediction skill of this version of NorCPM, the first climate prediction system using sCDA, by performing retrospective predictions (hindcasts) for the period 1985 to 2010. To better understand origins of the prediction skill of Arctic sea ice, we compare this version with a version that solely performs wCDA of ocean hydrography. The reanalysis that assimilates just ocean data, exhibits a skillful hydrography in the upper Arctic ocean, and features an improved sea ice state, such as improved summer SIC in the Barents Sea, or reduced biases in sea ice thickness. Skillful prediction of SIE up to 10-12 lead months are only found during winter in regions of a relatively deep ocean mixed layer outside the Arctic basin. Additional DA of SIC data notably further corrects the initial sea ice state, confirming the applicability of the results of Kimmritz et al. (2018) in a historical setting. The resulting prediction skill of SIE is widely enhanced compared to predictions initialised through wCDA of only ocean data. Particularly high skill is found for July-initialised autumn SIE predictions.publishedVersio

Better synoptic and subseasonal sea ice thickness predictions are urgently required: a lesson learned from the YOPP data validation

In the context of global warming, Arctic sea ice has declined substantially during the satellite era (Kwok 2018). The retreating and thinning of Arctic sea ice provide opportunities for human activities in the Arctic, such as tourism, fisheries, shipping, natural resource exploitation, and wildlife management; however, new risks emerge. To ensure the safety and emergency management of human activities in the Arctic, reliable Arctic sea ice prediction is essential

Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF

This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)—a fully-coupled forecasting system—to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6- and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño–Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content.publishedVersio

Virus-induced gene complementation reveals a transcription factor network in modulation of tomato fruit ripening

Plant virus technology, in particular virus-induced gene silencing, is a widely used reverse- and forward-genetics tool in plant functional genomics. However the potential of virus technology to express genes to induce phenotypes or to complement mutants in order to understand the function of plant genes is not well documented. Here we exploit Potato virus X as a tool for virus-induced gene complementation (VIGC). Using VIGC in tomato, we demonstrated that ectopic viral expression of LeMADS-RIN, which encodes a MADS-box transcription factor (TF), resulted in functional complementation of the non-ripening rin mutant phenotype and caused fruits to ripen. Comparative gene expression analysis indicated that LeMADS-RIN up-regulated expression of the SBP-box (SQUAMOSA promoter binding protein-like) gene LeSPL-CNR, but down-regulated the expression of LeHB-1, an HD-Zip homeobox TF gene. Our data support the hypothesis that a transcriptional network may exist among key TFs in the modulation of fruit ripening in tomato