General procedure to initialize the cyclic soil water balance by the Thornthwaite and Mather method

The original Thornthwaite and Mather method, proposed in 1955 to calculate a climatic monthly cyclic soil water balance, is frequently used as an iterative procedure due to its low input requirements and coherent estimates of water balance components. Using long term data sets to establish a characteristic water balance of a location, the initial soil water storage is generally assumed to be at field capacity at the end of the last month of the wet season, unless the climate is (semi-) arid when the soil water storage is lower than the soil water holding capacity. To close the water balance, several iterations might be necessary, which can be troublesome in many situations. For (semi-) arid climates with one dry season, Mendon a derived in 1958 an equation to quantify the soil water storage monthly at the end of the last month of the wet season, which avoids iteration procedures and closes the balance in one calculation. The cyclic daily water balance application is needed to obtain more accurate water balance output estimates. In this note, an equation to express the water storage for the case of the occurrence of more than one dry season per year is presented as a generalization of Mendon a's equation, also avoiding iteration procedures

Anaerobic membrane bioreactors: Are membranes really necessary?

Membranes themselves represent a significant cost for the full scale application of anaerobic membrane bioreactors (AnMBR). The possibility of operating an AnMBR with a self-forming dynamic membrane generated by the substances present in the reactor liquor would translate into an important saving. A self-forming dynamic membrane only requires a support material over which a cake layer is formed, which determines the rejection properties of the system. The present research studies the application of self-forming dynamic membranes in AnMBRs. An AnMBR was operated under thermophilic and mesophilic conditions, using woven and non woven materials as support for the dynamic membranes. Results showed that the formation of a cake layer over the support materials enables the retention of more than 99% of the solids present in the reactor. However, only low levels of flux were achieved, up to 3 L/m2 x h, and reactor operation was unstable, with sudden increases in filtration resistance, due to excessive cake layer formation. Further fine-tuning of the proposed technology involves looking for conditions that can control effectively cake layer formation

Determinação da lâmina média de irrigação em pivô central

With the purpose of improving the accuracy of the average irrigation depth calculation, an expression is presented assuming linear variation of the depth between two following collectors, for the determination of the average depth of center pivot irrigation systems. It is concluded that the proposed expression should be used for the determination of the mean weighed depth for being conceptually more correct, although, in practice the values calculated by the proposed methodology are very close to those obtained with the traditional calculation method.Com o objetivo de melhorar a exatidão do cálculo da lâmina média d'água, por faixa representativa do coletor, apresentou-se uma expressão, considerando a variação linear de lâmina entre dois coletores subseqüentes, para fins de avaliação do sistema de irrigação do tipo pivô central. Concluiu-se que a expressão proposta deve ser utilizada para determinação da lâmina média ponderada por estar conceitualmente mais correta, apesar de na prática os valores calculados pela metodologia proposta serem muito próximos daqueles obtidos pelo cálculo tradicional

Language motivation in a reconfigured Europe: access, identity, autonomy

In this paper, I propose that we need to develop an appropriate set of conceptual tools for examining motivational issues pertaining to linguistic diversity, mobility and social integration in a rapidly changing and expanding Europe. I begin by drawing on research that has begun to reframe the concept of integrative motivation in the context of theories of self and identity. Expanding the notion of identity, I discuss the contribution of the Council of Europe's European Language Portfolio in promoting a view of motivation as the development of a plurilingual European identity and the enabling of access and mobility across a multilingual Europe. Next, I critically examine the assumption that the individual pursuit of a plurilingual identity is unproblematic, by highlighting the social context in which motivation and identity are constructed and embedded. To illuminate the role of this social context, I explore three inter-related theoretical frameworks: poststructuralist perspectives on language motivation as 'investment'; sociocultural theory; and theories of autonomy in language education. I conclude with the key message that, as with autonomy, language motivation today has an inescapably political dimension of which we need to take greater account in our research and pedagogical practice

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T

At ultrahigh field strengths images of the body are hampered by B1-field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a “bias field” to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1-field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1-field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1-field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.</p

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T

At ultrahigh field strengths images of the body are hampered by B1-field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a “bias field” to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1-field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1-field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1-field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.</p

GATA2 haploinsufficient patients lack innate lymphoid cells that arise after hematopoietic cell transplantation.

Innate lymphoid cells (ILC) are important barrier tissue immune regulators. They play a pivotal role in early non-specific protection against infiltrating pathogens, regulation of epithelial integrity, suppression of pro-inflammatory immune responses and shaping the intestinal microbiota. GATA2 haploinsufficiency causes an immune disorder that is characterized by bone marrow failure and (near) absence of monocytes, dendritic cells, B cells and natural killer (NK) cells. T cells develop normally, albeit at lower numbers. Here, we describe the absence of ILCs and their progenitors in blood and bone marrow of two patients with GATA2 haploinsufficiency and show that all subsets of ILCs appear after allogeneic hematopoietic stem cell transplantation, irrespective of the preparative conditioning regimen. Our data indicate that GATA2 is involved in the development of hematopoietic precursor cells (HPC) towards the ILC lineage