Reliable off-resonance correction in high-field cardiac MRI using autonomous cardiac B0 segmentation with dual-modality deep neural networks

B0 field inhomogeneity is a long-lasting issue for Cardiac MRI (CMR) in high-field (3T and above) scanners. The inhomogeneous B0 fields can lead to corrupted image quality, prolonged scan time, and false diagnosis. B0 shimming is the most straightforward way to improve the B0 homogeneity. However, today’s standard cardiac shimming protocol requires manual selection of a shim volume, which often falsely includes regions with large B0 deviation (e.g., liver, fat, and chest wall). The flawed shim field compromises the reliability of high-field CMR protocols, which significantly reduces the scan efficiency and hinders its wider clinical adoption. This study aims to develop a dual-channel deep learning model that can reliably contour the cardiac region for B0 shim without human interaction and under variable imaging protocols. By utilizing both the magnitude and phase information, the model achieved a high segmentation accuracy in the B0 field maps compared to the conventional single-channel methods (Dice score: 2D-mag = 0.866, 3D-mag = 0.907, and 3D-mag-phase = 0.938, all p < 0.05). Furthermore, it shows better generalizability against the common variations in MRI imaging parameters and enables significantly improved B0 shim compared to the standard method (SD(B0Shim): Proposed = 15 ± 11% vs. Standard = 6 ± 12%, p < 0.05). The proposed autonomous model can boost the reliability of cardiac shimming at 3T and serve as the foundation for more reliable and efficient high-field CMR imaging in clinical routines

Chilling requirements and dormancy evolution in grapevine buds.

Fluctuations in winter chilling availability impact bud dormancy and budburst. The objective of this work was to determine chilling requirements to induce and overcome endodormancy (dormancy controlled by chilling) of buds in different grape cultivars. "Chardonnay", "Merlot" and "Cabernet Sauvignon" shoots were collected in Veranópolis-RS vineyards in 2010, and submitted to a constant 3 °C temperature or daily cycles of 3/15 °C for 12/12h or 18/6h, until reaching 1120 chilling hours (CH, sum of hours with temperature &#8804; 7.2 °C). Periodically, part of the samples in each treatment was transferred to 25 °C for budburst evaluation (green tip). Chilling requirements to induce and overcome endodormancy vary among cultivars, reaching a total of 136 CH for "Chardonnay", 298 CH for "Merlot" and 392 CH for "Cabernet Sauvignon". Of these, approximately 39, 53 and 91 CH are required for induction of endodormancy in the three cultivars, respectively. The thermal regimes tested (constant or alternating) do not influence the response pattern of each cultivar to cold, with 15 °C being inert in the CH accumulation process. In addition, time required to start budburst reduces with the increase in CH, at a rate of one day per 62 CH, without significant impacts on budburst uniformity. Index terms: Chilling hours; endodormancy; budburst; Vitis vinifera

New Dynamical Behaviour of the Coronavirus (2019-Ncov) Infection System with Non-Local Operator from Reservoirs to People

The mathematical accepts while analysing the evolution of real word problems magnetizes the attention of many scholars. In this connection, we analysed and find the solution for nonlinear system exemplifying the most dangerous and deadly virus called coronavirus. The six ordinary differential equations of fractional order nurtured the projected mathematical model and they are analysed using q-homotopy analysis transform method (q-HATM). Further, most considered fractional operator is applied to study and capture the more corresponding consequences of the system, known as Caputo operator. For different fractional order, the natures of the achieved results are illustrated in plots. Lastly, the present investigation may aid us analyse the distinct and diverse classes of models exemplifying real-world problems and helps to envisage their corresponding nature with parameters associated with the models

Sequential modeling to understand and predict differentiated flowering time responses to warming in apple tree in contrasting climatic regions

Long-term series of temperature and apple flowering date were set up for seven climate-contrasting locations in Western Europe. A statistical analysis and a sequential modeling approach has been used to understand how global warming impacted dormancy release and flowering time in recent past and will impact in the future. Selected models explained up to 90% of the flowering date variability in Europe. Both the statistical analysis and the modeling of flowering dates supported that flowering advances in European temperate climates were clearly linked to faster fulfillments of heat requirements due to marked spring warming since the end of the 1980s. Delays of dormancy release, linked to chill temperature declines, were likely to also occur especially in the French Mediterranean region, which can explain a stationarity of the flowering date series since the beginning of the 2010s. Predicted changes until the end of the 21st century in Mediterranean region would confirm both a stationarity of flowering time and increasing delays of dormancy release. In addition, historical series of temperature and flowering dates were set up for three mild climate locations (one in Northern Morocco and two in Southern Brazil). While a flowering advance and a spring warming were statistically supported in Morocco, both flowering date and temperature series have generally remained stationary in the Brazilian locations. Finally, differentiated responses of apple tree flowering time were highlighted at the world scale in relation with differentiated warming contexts. Moreover, both sequential models selected from European data and models selected from Moroccan or Brazilian data appeared inadequate to simulate the observed flowering dates in the Moroccan and Brazilian locations. This cast a doubt on the sequential modeling relevance to predict dormancy release and flowering changes in the future warming context of European regions

Sequential modeling to understand and predict differentiated flowering time responses to warming in apple tree in contrasting climatic regions

National audienceLong-term series of temperature and apple flowering date were set up for seven climate-contrasting locations in Western Europe. A statistical analysis and a sequential modeling approach has been used to understand how global warming impacted dormancy release and flowering time in recent past and will impact in the future. Selected models explained up to 90% of the flowering date variability in Europe. Both the statistical analysis and the modeling of flowering dates supported that flowering advances in European temperate climates were clearly linked to faster fulfillments of heat requirements due to marked spring warming since the end of the 1980s. Delays of dormancy release, linked to chill temperature declines, were likely to also occur especially in the French Mediterranean region, which can explain a stationarity of the flowering date series since the beginning of the 2010s. Predicted changes until the end of the 21st century in Mediterranean region would confirm both a stationarity of flowering time and increasing delays of dormancy release. In addition, historical series of temperature and flowering dates were set up for three mild climate locations (one in Northern Morocco and two in Southern Brazil). While a flowering advance and a spring warming were statistically supported in Morocco, both flowering date and temperature series have generally remained stationary in the Brazilian locations. Finally, differentiated responses of apple tree flowering time were highlighted at the world scale in relation with differentiated warming contexts. Moreover, both sequential models selected from European data and models selected from Moroccan or Brazilian data appeared inadequate to simulate the observed flowering dates in the Moroccan and Brazilian locations. This cast a doubt on the sequential modeling relevance to predict dormancy release and flowering changes in the future warming context of European regions