Resolution enhancement of NMR by decoupling with the low-rank Hankel model.

Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine. Although J-coupling carries essential structural information it may also limit the spectral resolution. Homonuclear decoupling remains a challenging problem. In this work, we introduce a new approach that uses a specific coupling value as prior knowledge, and the Hankel property of the exponential NMR signal to achieve broadband heteronuclear decoupling using the low-rank method. Our results on synthetic and realistic HMQC spectra demonstrate that the proposed method not only effectively enhances resolution by decoupling, but also maintains sensitivity and suppresses spectral artefacts. The approach can be combined with non-uniform sampling, which means that the resolution can be further improved without any extra acquisition time.

NMR spectrum reconstruction as a pattern recognition problem.

A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra. When trained at a fixed non-uniform sampling (NUS) schedule, the WNN benefits from pattern recognition of the corresponding point spread function (PSF) pattern produced by each spectral peak resulting in the highest quality and robust reconstruction of the NUS spectra as demonstrated in simulations and exemplified in this work on 2D 1H-15N correlation spectra of three representative globular proteins with different sizes: Ubiquitin (8.6 kDa), Azurin (14 kDa), and Malt1 (44 kDa). The pattern recognition by WNN is also demonstrated for successful virtual homo-decoupling in a 2D methyl 1H-13C - HMQC spectrum of MALT1. We demonstrate using WNN that prior knowledge about the NUS schedule, which so far was not been fully exploited, can be used for designing new powerful NMR processing techniques that surpass the existing algorithmic methods.

Live imaging and biophysical modeling support a button-based mechanism of somatic homolog pairing in Drosophila.

Three-dimensional eukaryotic genome organization provides the structural basis for gene regulation. In Drosophila melanogaster, genome folding is characterized by somatic homolog pairing, where homologous chromosomes are intimately paired from end to end; however, how homologs identify one another and pair has remained mysterious. Recently, this process has been proposed to be driven by specifically interacting 'buttons' encoded along chromosomes. Here, we turned this hypothesis into a quantitative biophysical model to demonstrate that a button-based mechanism can lead to chromosome-wide pairing. We tested our model using live-imaging measurements of chromosomal loci tagged with the MS2 and PP7 nascent RNA labeling systems. We show solid agreement between model predictions and experiments in the pairing dynamics of individual homologous loci. Our results strongly support a button-based mechanism of somatic homolog pairing in Drosophila and provide a theoretical framework for revealing the molecular identity and regulation of buttons.

Correction to: The Relative Contribution of High-Gamma Linguistic Processing Stages of Word Production, and Motor Imagery of Articulation in Class Separability of Covert Speech Tasks in EEG Data.

The author regrets that the acknowledgment was left out from the original publication. The acknowledgement is written below.

Leukemia inhibitory factor (LIF) modulates the development of dendritic cells in a dual manner.

Objective: Dendritic cells (DCs) are professional antigen presenting cells majorly modulated by various environmental factors. Leukemia inhibitory factor (LIF) is a pleiotropic cytokine from interleukin-6 family. Previous studies demonstrate that LIF is associated with several tolerogenic events; yet the exact effect of this cytokine on the generation and function of DCs was not explicitly identified. Materials and methods: To clarify the role of LIF in DCs development, immature DCs were differentiated from mouse bone marrow (BM) in a GM-CSF and IL-4 containing medium with or without LIF. Afterwards, in maturation process, the differentiated DCs were exposed to TNF-α in the presence or absence of LIF. Results: Immature DCs differentiated in the presence of LIF, proved a significant enhancement in the expression of MHCII, CD40, or CD86 molecules and in the antigen uptake function. LIF treatment of normal DCs while stimulating for maturation, caused a significant decrement in the expression of phenotypic markers as well as an increment in the antigen uptake function in comparison with TNF-α-only stimulated cells; however, the reduced ability for induction of allogenic T-cell proliferation proved no statistical significance. Conclusions: Our results can reflect a role for LIF in the generation and particularly maturation of DCs. It can be assumed that LIF rather modulates the maturation level, leading to the development of semi-mature and tolerogenic DCs. According to the high levels of LIF in immune-privileged sites like brain and uterine, it seems that the cytokine may account for the formation of local DCs that help the establishment of immunosuppressive environments.

A Novel EEG-Based Four-Class Linguistic BCI.

In this work, we present a novel EEG-based Linguistic BCI, which uses the four phonemic structures "BA", "FO", "LE", and "RY" as covert speech task classes. Six neurologically healthy volunteers with the age range of 19-37 participated in this experiment. Participants were asked to covertly speak a phonemic structure when they heard an auditory cue. EEG was recorded with 64 electrodes at 2048 samples/s. The duration of each trial is 312ms starting with the cue. The BCI was trained using a mixed randomized recording run containing 15 trials per class. The BCI is tested by playing a simple game of "Wack a mole" containing 5 trials per class presented in random order. The average classification accuracy for the 6 users is 82.5%. The most valuable features emerge after Auditory cue recognition (~100ms post onset), and within the 70-128 Hz frequency range. The most significant identified brain regions were the Prefrontal Cortex (linked to stimulus driven executive control), Wernicke's area (linked to Phonological code retrieval), the right IFG, and Broca's area (linked to syllabification). In this work, we have only scratched the surface of using Linguistic tasks for BCIs and the potential for creating much more capable systems in the future using this approach exists.

The Relative Contribution of High-Gamma Linguistic Processing Stages of Word Production, and Motor Imagery of Articulation in Class Separability of Covert Speech Tasks in EEG Data.

Word production begins with high-Gamma automatic linguistic processing functions followed by speech motor planning and articulation. Phonetic properties are processed in both linguistic and motor stages of word production. Four phonetically dissimilar phonemic structures "BA", "FO", "LE", and "RY" were chosen as covert speech tasks. Ten neurologically healthy volunteers with the age range of 21-33 participated in this experiment. Participants were asked to covertly speak a phonemic structure when they heard an auditory cue. EEG was recorded with 64 electrodes at 2048 samples/s. Initially, one-second trials were used, which contained linguistic and motor imagery activities. The four-class true positive rate was calculated. In the next stage, 312 ms trials were used to exclude covert articulation from analysis. By eliminating the covert articulation stage, the four-class grand average classification accuracy dropped from 96.4% to 94.5%. The most valuable features emerge after Auditory cue recognition (~100 ms post onset), and within the 70-128 Hz frequency range. The most significant identified brain regions were the Prefrontal Cortex (linked to stimulus driven executive control), Wernicke's area (linked to Phonological code retrieval), the right IFG, and Broca's area (linked to syllabification). Alpha and Beta band oscillations associated with motor imagery do not contain enough information to fully reflect the complexity of speech movements. Over 90% of the most class-dependent features were in the 30-128 Hz range, even during the covert articulation stage. As a result, compared to linguistic functions, the contribution of motor imagery of articulation in class separability of covert speech tasks from EEG data is negligible.

Covert Speech vs. Motor Imagery: a comparative study of class separability in identical environments.

In this study a single experimental protocol and analysis pipeline is used: once for MI tasks, and once for covert speech tasks. The goal of this study is not to maximizing classification accuracy; rather the main objective is to provide an identical environment for both paradigms, while identifying the most important activities related to the most class dependent features. Four volunteers participated in this experiment. With four classes, the average classification accuracy for covert speech tasks is 82.5%, and for motor imagery is 77.2%. The average performance is significantly higher than chance level for both paradigms, suggesting that the results are meaningful, despite being imperfect. For motor imagery tasks the most important activities are the execution of imagined movements, and goal driven executive control for suppression of overt movements, which also occur for covert speech tasks. However, the most important activity for covert speech tasks is the linguistic processing stages of word production prior to articulation, which does not occur in motor imagery. These high-Gamma linguistic processes are extremely class dependent, which contribute to the higher performance of covert speech tasks, compared to motor imagery in an otherwise identical environment.

The contribution of different frequency bands in class separability of covert speech tasks for BCIs.

Several recent studies demonstrate the possibility of using user initiated covert speech mental tasks in brain computer interfaces with varying degrees of success, but details of the best frequency features had not been investigated. In this work, ten volunteers in the age range of 22-70 years participated in the experiment. Eight of them were neurologically healthy, one user was dyslexic, and another was autistic. The four words "back", "forward", "left", and "right" were shortened into "BA", "FO", "LE", and "RY", which are phonetically dissimilar and cognitively relevant directional commands. Participants were asked to covertly speak each as soon as the letters appeared on a screen. Volunteers completed five recording runs. During each run the four words were presented in random succession to avoid sequence bias. The recorded EEG data from the ten users were analysed to discover the best features within a Gabor Transform of the signals, i.e., those yielding the highest word-pair classification accuracy for this specific type of linguistic mental activity. Using this BCI, suitable class separability of covert speech tasks is confirmed for all, including disabled users, with consistently high classification accuracy from 72% to 88% in all cases. Like motor imagery tasks, Alpha and Beta band activity were found to contain 12% and 31% of the most important features respectively. Gamma band activity, which indicates high mental functions, contains 57% of the most important features in this study.