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

Mesures in-situ par incandescence induite par Laser des tailles de nano-particules lors de la synthèse de nanotubes de carbone par dépôt chimique en phase vapeur

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Review of dynamic modelling, system identification and control scheme in solvent-based post-combustion carbon capture process

Solvent-based post-combustion carbon capture (PCC) process is widely viewed as the most viable option for reducing CO 2 emission. This technology has been deployed globally and many researches have been conducted in this area. In this paper, current status of dynamic modelling, system identification and control scheme of solvent-based PCC process is reviewed. Different research directions of these areas are discussed to conclude the existing challenges. Based on this, this paper is also trying to provide potential solutions as possible pathways for flexible and economical operation

Molecular basis of methyl-salicylate-mediated plant airborne defence

Aphids transmit viruses and are destructive crop pests1. Plants that have been attacked by aphids release volatile compounds to elicit airborne defence (AD) in neighbouring plants2,3,4,5. However, the mechanism underlying AD is unclear. Here we reveal that methyl-salicylate (MeSA), salicylic acid-binding protein-2 (SABP2), the transcription factor NAC2 and salicylic acid-carboxylmethyltransferase-1 (SAMT1) form a signalling circuit to mediate AD against aphids and viruses. Airborne MeSA is perceived and converted into salicylic acid by SABP2 in neighbouring plants. Salicylic acid then causes a signal transduction cascade to activate the NAC2–SAMT1 module for MeSA biosynthesis to induce plant anti-aphid immunity and reduce virus transmission. To counteract this, some aphid-transmitted viruses encode helicase-containing proteins to suppress AD by interacting with NAC2 to subcellularly relocalize and destabilize NAC2. As a consequence, plants become less repellent to aphids, and more suitable for aphid survival, infestation and viral transmission. Our findings uncover the mechanistic basis of AD and an aphid–virus co-evolutionary mutualism, demonstrating AD as a potential bioinspired strategy to control aphids and viruses

Foxtail Mosaic Virus-induced Flowering Assays in Monocot Crops

Virus-induced flowering (VIF) exploits RNA or DNA viruses to express flowering time genes to induce flowering in plants. Such plant virus-based tools have recently attracted widespread attention for their fundamental and applied uses in flowering physiology and in accelerating breeding in dicotyledonous crops and woody fruit-trees. We now extend this technology to a monocot grass and a cereal crop. Using the Foxtail mosaic virus-based VIF system, dubbed FoMViF, we showed that expression of florigenic Flowering Locus T (FT) genes can promote early flowering and spikelet development in proso millet, a C4 grass species with potential for nutritional food and biofuel resources, and in non-vermalized C3 wheat, a major food crop worldwide. Floral and spikelet/grain induction in the two monocot plants was caused by the virally expressed untagged or FLAG-tagged FT orthologues, and the florigenic activity of rice Hd3a was more pronounced than its dicotyledonous counterparts in proso millet. The FoMViF system is easy to perform and its efficacy to induce flowering and early spikelet/grain production is high. In addition to proso millet and wheat, we envisage that FoMViF will be also applicable to many economically important monocotyledonous food and biofuel crops

Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison

Abstract This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.</jats:p

Community shift of ammonia-oxidizing bacteria along an anthropogenic pollution gradient from the Pearl River Delta to the South China Sea

The phylogenetic diversity and abundance of ammonia-oxidizing beta-proteobacteria (beta-AOB) was analyzed along an anthropogenic pollution gradient from the coastal Pearl River Delta to the South China Sea using the ammonia monooxygenase subunit A (amoA) gene. Along the gradient from coastal to the open ocean, the phylogenetic diversity of the dominant genus changed from Nitrosomonas to Nitrosospira, indicating the niche specificity by these two genera as both salinity and anthropogenic influence were major factors involved. The diversity of bacterial amoA gene was also variable along the gradient, with the highest in the deep-sea sediments, followed by the marshes sediments and the lowest in the coastal areas. Within the Nitrosomonas-related clade, four distinct lineages were identified including a putative new one (A5-16) from the different sites over the large geographical area. In the Nitrosospira-related clade, the habitat-specific lineages to the deep-sea and coastal sediments were identified. This study also provides strong support that Nitrosomonas genus, especially Nitrosomonas oligotropha lineage (6a) could be a potential bio-indicator species for pollution or freshwater/wastewater input into coastal environments. A suite of statistical analyses used showed that water depth and temperature were major factors shaping the community structure of beta-AOB in this study area

Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison

This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance