T1 and T2 relaxation times and iron concentrations were measured in 24 specimens of gray matter from fresh human and monkey brains at magnetic fields from 0.05 to 1.5 Tesla. Three different effects were found that correlate with iron content: a T1-shortening that falls off somewhat at high fields, a T2-shortening that is field-independent and thus important at low fields, and a contribution to 1/T2 that increases linearly with field strength. This linear field dependence has been seen only in ferritin and other ferric oxyhydroxide particles. Our results are in agreement with in vivo MRI studies and are generally consistent with values for ferritin solution, except for differences such as clustering of ferritin in tissue. A cerebral cavernous hemangioma specimen showed similar T2-shortening, but with a 2.7 times larger magnitude, attributed to larger clusters of hemosiderin in macrophages. The dependence on interecho time 2 tau was measured in three brains; 1/T2 increased significantly for tau up to 32 ms, as expected from the size of the ferritin clusters. These findings support the theory that ferritin iron is the primary determinant of MRI contrast in normal gray matter.

In recent years, a deep learning model called convolutional neural network with an ability of extracting features of high-level abstraction from minimum preprocessing data has been widely used. In this research, we proposed a new approach in classifying DNA sequences using the convolutional neural network while considering these sequences as text data. We used one-hot vectors to represent sequences as input to the model; therefore, it conserves the essential position information of each nucleotide in sequences. Using 12 DNA sequence datasets, we evaluated our proposed model and achieved significant improvements in all of these datasets. This result has shown a potential of using convolutional neural network for DNA sequence to solve other sequence problems in bioinformatics.

T2 was measured in samples of human blood and monkey brain over a field range of 0.02-1.5 Tesla, with variable interecho times, and was compared with previous data on ferritin solutions (taken with the same apparatus). 1/T2 in deoxygenated blood increased quadratically with field strength, as noted previously, but in brain gray matter the increase was linear, as also was the case in ferritin solution. In both deoxygenated blood and gray matter, 1/T2 increased with interecho time, but appeared to level off at times around 50 msec, as expected from the theory of diffusion through magnetic gradients. Diffusion times estimated by using the chemical exchange approximation were 3.4 msec for deoxygenated blood and 5.7 msec for the globus pallidus. The quadratic field dependence in blood is consistent with this same theory, but the linear dependence in brain tissue and in ferritin solutions remains unexplained.

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We developed a ground observation system for solid precipitation using two-dimensional video disdrometer (2DVD). Among 16,010 particles observed by the system, around 10% of them were randomly sampled and manually classified into five classes which are snowflake, snowflake-like, intermediate, graupel-like, and graupel. At first, each particle was represented as a vector of 72 features containing fractal dimension and box-count to represent the complexity of particle shape. Feature analysis on the dataset clarified the importance of fractal dimension and box-count features for characterizing particles varying from snowflakes to graupels. On the other hand, performance evaluation of two-class classification by Support Vector Machine (SVM) was conducted. The experimental results revealed that, by selecting only 10 features out of 72, the average accuracy of classifying particles into snowflakes and graupels could reach around 95.4%, which had not been achieved by previous studies.

BackgroundEpitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms.ResultsWe have developed a novel epitope prediction method named EpicCapo and its variants, EpicCapo+ and EpicCapo+REF. Nonapeptides were encoded numerically using a novel peptide-encoding scheme for machine learning algorithms by utilizing 40 amino acid pairwise contact potentials (referred to as AAPPs throughout this paper). The predictive performances of EpicCapo+ and EpicCapo+REF outperformed other state-of-the-art methods without losing interpretability. Interestingly, the most informative AAPPs estimated by our study were those developed by Micheletti and Simons while previous studies utilized two AAPPs developed by Miyazawa & Jernigan and Betancourt & Thirumalai. In addition, we found that all amino acid positions in nonapeptides could effect on performances of the predictive models including non-anchor positions. Finally, EpicCapo+REF was applied to identify candidates of promiscuous epitopes. As a result, 67.1% of the predicted nonapeptides epitopes were consistent with preceding studies based on immunological experiments.ConclusionsOur method achieved high performance in testing with benchmark datasets. In addition, our study identified a number of candidates of promiscuous CTL epitopes consistent with previously reported immunological experiments. We speculate that our techniques may be useful in the development of new vaccines. The R implementation of EpicCapo+REF is available at http://pirun.ku.ac.th/~fsciiok/EpicCapoREF.zip. Datasets are available at http://pirun.ku.ac.th/~fsciiok/Datasets.zip.

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