117,616 research outputs found
Oligosaccharides related to tumor-associate antigens. Part 3. Synthesis of the propyl glycosides of the trisaccharide β-D-Galp-(1→3)-β-D-GalpNAc-(1→3)-α-D-Galp and of the Tetrasaccharide α-L-Fucp-(1→2)-β-D-Galp-(1→3)-β-D-GalpNAc-(1→3)-α-D-Galp, components of a tumor antigen recognized by the antibody MBr1
No full textThe synthesis of the trisaccharide β-D-Galp-(1 → 3)-β-D-GalpNAc-(1 → 3)-α-D-Galp-1-OPr (2) and of the tetrasaccharide α-L-Fucp-(1 → 2)-β-D-Galp-(1 → 3)-β-D-GalpNAc-(1 → 3)-α-D-Galp-1-OPr (3), starting from the disaccharidic dihydrooxazole donor 5, is described. Glycosylation of 5 with 6 in the presence of Me3SiOTf gave the trisaccharide 7 which was deprotected with standard methods to give, via 8, compound 2. Alternatively, protection of 8 as the 4',6'-O-benzylidene derivative 9 followed by glycosylation with 10 and by standard deprotection afforded the tetrasaccharide 3. Biological testing showed that trisaccharide 2 is unable to inhibit the binding of the monoclonal antibody MBr1 to the target tumor cells MCF7, while tetrasaccharide 3 inhibits the binding in ca. 7-fold extent with respect to the previously tested trisaccharide α-L-Fucp-(1 → 2)-β-D-Galp-(1 → 3)-β-D-GalpNAc-1-OPr. These results indicate that the galactose corresponding to the unit D of compound 1 plays an important role in defining the MBr1-recognized epitope and confirm the essential role of fucose for MAb recognition
An auto-encoder based approach to unsupervised learning of subword units
No full textIn this paper we propose an autoencoder-based method for the unsupervised identification of subword units. We experiment with different types and architectures of autoencoders to asses what autoencoder properties are most important for this task. We first show that the encoded representation of speech produced by standard autencoders is more effective than Gaussian posteriorgrams in a spoken query classification task. Finally we evaluate the subword inventories produced by the proposed method both in terms of classification accuracy in a word classification task (with lexicon size up to 263 words) and in terms of consistency between subword transcription of different word examples of a same word type. The evaluation is carried out on Italian and American English datasets. © 2014 IEEE
A new Italian dataset of parallel acoustic and articulatory data
No full textIn this paper we introduce a new Italian dataset consisting of simultaneous recordings of continuous speech and trajectories of important vocal tract articulators (i.e. tongue, lips, incisors) tracked by Electromagnetic Articulography (EMA). It includes more than 500 sentences uttered in citation condition by three speakers, one male (cnz) and two females (lls, olm), for approximately 2 hours of speech material. Such dataset has been designed to be large enough and phonetically balanced so as to be used in speech applications (e.g. speech recognition systems). We then test our speaker-dependent articulatory Deep- Neural-Network Hidden-Markov-Model (DNN-HMM) phone recognizer on the set of data recorded from the cnz speaker. We show that phone recognition results are comparable to the ones that we previously obtained using two well-known British-English datasets with EMA data of equivalent vocal tract articulators. That suggests that the new set of data is a equally useful and coherent resource. The dataset is the session 1 of a larger Italian corpus, called Multi-SPeaKing-style-Articulatory (MSPKA) corpus, including parallel audio and articulatory data in diverse speaking styles (e.g. read, hyperarticulated and hypoarticulated speech). It is freely available at http://www.mspkacorpus.it for research purposes. In the immediate future the whole corpus will be released
Synthesis, modeling and binding affinity of an ester analogue of the terminal trisaccharide of the tumor-associated antigen globo-H-1
No full textThe synthesis of the trisaccharide α-L-Fucp-(1→2)-β-D-Galp-(1→3)- β-D-Galp2AcOPr (3), mimicking the globo-H terminal trisaccharide unit (2), is described. The conformational properties of 3 were investigated with the aid of molecular mechanics energy minimizations, molecular dynamics simulations, and 1H-NMR spectroscopic analysis and resulted in strict analogy with those of 2. Nevertheless, analysis of MBr1 binding to these compounds indicate that substitution of the acetamido group at C-2 with the ester group lowers the affinity, thus suggesting that the amide hydrogen atom of 2 is involved in intermolecular interactions with the MBr1 antibody
Deep-level acoustic-to-articulatory mapping for DBN-HMM based phone recognition
No full textIn this paper we experiment with methods based on Deep Belief Networks (DBNs) to recover measured articulatory data from speech acoustics. Our acoustic-to-articulatory mapping (AAM) processes go through multi-layered and hierarchical (i.e., deep) representations of the acoustic and the articulatory domains obtained through unsupervised learning of DBNs. The unsupervised learning of DBNs can serve two purposes: (i) pre-training of the Multi-layer Perceptrons that perform AAM; (ii) transformation of the articulatory domain that is recovered from acoustics through AAM. The recovered articulatory features are combined with MFCCs to compute phone posteriors for phone recognition. Tested on the MOCHA-TIMIT corpus, the recovered articulatory features, when combined with MFCCs, lead to up to a remarkable 16.6% relative phone error reduction w.r.t. a phone recognizer that only use
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