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Background: Ultra‐high field 7T MRI can provide excellent tissue contrast and anatomical details, but is often cost prohibitive, and is not widely accessible in clinical practice. Purpose: To generate synthetic 7T images from widely acquired 3T images with deep learning and to evaluate the feasibility of this approach for brain imaging. Study Type: Prospective. Population: 33 healthy volunteers and 89 patients with brain diseases, divided into training, and evaluation datasets in the ratio 4:1. Sequence and Field Strength: T1‐weighted nonenhanced or contrast‐enhanced magnetization‐prepared rapid acquisition gradient‐echo sequence at both 3T and 7T. Assessment: A generative adversarial network (SynGAN) was developed to produce synthetic 7T images from 3T images as input. SynGAN training and evaluation were performed separately for nonenhanced and contrast‐enhanced paired acquisitions. Qualitative image quality of acquired 3T and 7T images and of synthesized 7T images was evaluated by three radiologists in terms of overall image quality, artifacts, sharpness, contrast, and visualization of vessel using 5‐point Likert scales. Statistical Tests: Wilcoxon signed rank tests to compare synthetic 7T images with acquired 7T and 3T images and intraclass correlation coefficients to evaluate interobserver variability. P < 0.05 was considered significant. Results: Of the 122 paired 3T and 7T MRI scans, 66 were acquired without contrast agent and 56 with contrast agent. The average time to generate synthetic images was ~11.4 msec per slice (2.95 sec per participant). The synthetic 7T images achieved significantly improved tissue contrast and sharpness in comparison to 3T images in both nonenhanced and contrast‐enhanced subgroups. Meanwhile, there was no significant difference between acquired 7T and synthetic 7T images in terms of all the evaluation criteria for both nonenhanced and contrast‐enhanced subgroups (P ≥ 0.180). Data Conclusion: The deep learning model has potential to generate synthetic 7T images with similar image quality to acquired 7T images. Level of Evidence: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Background. Activating vestibular afferents via galvanic vestibular stimulation (GVS) has been recently shown to have a number of complex motor effects in Parkinson's disease (PD), but the basis of these improvements is unclear. The evaluation of network-level connectivity changes may provide us with greater insights into the mechanisms of GVS efficacy. Objective. To test the effects of different GVS stimuli on brain subnetwork interactions in both health control (HC) and PD groups using fMRI. Methods. FMRI data were collected for all participants at baseline (resting state) and under noisy, 1 Hz sinusoidal, and 70-200 Hz multisine GVS. All stimuli were given below sensory threshold, blinding subjects to stimulation. The subnetworks of 15 healthy controls and 27 PD subjects (on medication) were identified in their native space, and their subnetwork interactions were estimated by nonnegative canonical correlation analysis. We then determined if the inferred subnetwork interaction changes were affected by disease and stimulus type and if the stimulus-dependent GVS effects were influenced by demographic features. Results. At baseline, interactions with the visual-cerebellar network were significantly decreased in the PD group. Sinusoidal and multisine GVS improved (i.e., made values approaching those seen in HC) subnetwork interactions more effectively than noisy GVS stimuli overall. Worsening disease severity, apathy, depression, impaired cognitive function, and increasing age all limited the beneficial effects of GVS. Conclusions. Vestibular stimulation has widespread system-level brain influences and can improve subnetwork interactions in PD in a stimulus-dependent manner, with the magnitude of such effects associating with demographics and disease status. [ABSTRACT FROM AUTHOR]

There has been a growing interest in food image recognition for a wide range of applications. Among existing methods, mid-level image part-based approaches show promising performances due to their suitability for modelling deformable food parts (FPs). However, the achievable accuracy is limited by the FP representations based on low-level features. Benefiting from the capacity to learn powerful features with labelled data, deep learning approaches achieved state-of-the-art performances in several food image recognition problems. Both mid-level-based approaches and deep convolutional neural networks (DCNNs) approaches clearly have their respective advantages, but perhaps most importantly these two approaches can be considered complementary. As such, the authors propose a novel framework to better utilise DCNN features for food images by jointly exploring the advantages of both the mid-level-based approaches and the DCNN approaches. Furthermore, they tackle the challenge of training a DCNN model with the unlabelled mid-level parts data. They accomplish this by designing a clustering-based FP label mining scheme to generate part-level labels from unlabelled data. They test on three benchmark food image datasets, and the numerical results demonstrate that the proposed approach achieves competitive performance when compared with existing food image recognition approaches. [ABSTRACT FROM AUTHOR]

Observing the states of a linear system has been an extensively studied topic over the past few decades. Different from full-state observers and reduced-order observers which are used to reconstruct the entire state space and the unmeasured states, respectively, functional observers are developed for applications in which only a linear combination of the states is required. For linear time-invariant (LTI) systems with known and/or unknown inputs and assuming availability of the derivatives of the input and the output signals, we study the existence of a novel stable functional observer. Considering the output to be the extended vector of the output and its derivatives, we derive the existence conditions for a functional observer by making the estimation error asymptotically approach zero. The derived existence conditions are applicable to multi-input multi-output (MIMO) systems ( e.g., with either known or unknown inputs, and with or without the derivatives of the signals being available). We also provide an estimate of the required observer order for the reconstruction of a desired functional and present how to design this functional observer by modifying certain current techniques. As an application, we apply the designed functional observer to model the stage of information comprehension in the user of a shared/human controlled system. [ABSTRACT FROM AUTHOR]

The authors suggest a framework for human-automation interaction in safety-critical continuous systems under shared control and consider continuous-time linear time-invariant (LTI) dynamics to formalise our physical models mathematically. Their goal is to determine whether or not a given user-interface provides the information required for a certain task, under the assumption that the user does not have access to any information beyond what is provided in the display. They identify observability-based conditions under which a user-interface provides the user with necessary information to accomplish a given task, formulated as a subset of the state space. They, therefore formulate the novel delay-incorporating user-observable subspace, and the delay-incorporating user-predictable subspace and compare them with the space spanned by the combination of the states which create the task. They assume the user is a special type of observer, with capabilities corresponding to different levels of knowledge regarding the current user's input and its derivatives. In addition, they consider that state reconstruction and prediction incorporate a processing delay. [ABSTRACT FROM AUTHOR]

Hohe Selektivitäten und ausgezeichnete Kontrolle des Reaktionsverlaufs regen Chemiker dazu an, in der organischen Synthese Biokatalysatoren einzusetzen. Viele nützliche Reaktionen sind auf diese Weise jedoch nicht zugänglich, weil sie im Repertoire der Natur nicht vorkommen. In diesem Aufsatz werden wir einen evolutiven Ansatz zur Entwicklung von Enzymen beschreiben, die solche nicht ‐ natürlichen Reaktionen katalysieren. Wir beginnen mit Beispielen dafür, wie die Natur neue katalytische Funktionen entdeckt hat und wie solche evolutiven Fortschritte im Labor nachvollzogen wurden, indem man von existierenden Enzymen ausging. Wir untersuchen dann nicht ‐ natürliche Enzymaktivitäten, die für die chemische Synthese entdeckt und verwendet wurden, wobei wir uns auf Reaktionen ohne natürliches Pendant konzentrieren. Wir zeigen Beispiele, wie nicht ‐ natürliche Aktivitäten durch gerichtete Evolution verbessert wurden, indem der Prozess nachgeahmt wurde, mit dem die Natur neue Katalysatoren erzeugt. Zum Schluss beschreiben wir noch Entdeckungen nicht ‐ natürlicher katalytischer Funktionen, die künftig Chancen auf eine Ausdehnung des enzymatischen Universums bieten können. Die Entwicklung von Enzymen, die in der Natur unbekannte Reaktionen katalysieren, erweitert das Feld von Transformationen für die Biokatalyse. Diese Aufsatz beschreibt einen allgemeinen Weg, nach dem neue Enzymaktivitäten in der Natur entstehen, und zeigt an Beispielen, wie ein ähnlicher Ansatz zur Erzeugung von Enzymen für nicht ‐ natürliche Reaktionen dienen kann. [ABSTRACT FROM AUTHOR]

High selectivity and exquisite control over the outcome of reactions entice chemists to use biocatalysts in organic synthesis. However, many useful reactions are not accessible because they are not in nature's known repertoire. In this Review, we outline an evolutionary approach to engineering enzymes to catalyze reactions not found in nature. We begin with examples of how nature has discovered new catalytic functions and how such evolutionary progression has been recapitulated in the laboratory starting from extant enzymes. We then examine non-native enzyme activities that have been exploited for chemical synthesis, with an emphasis on reactions that do not have natural counterparts. Non-natural activities can be improved by directed evolution, thus mimicking the process used by nature to create new catalysts. Finally, we describe the discovery of non-native catalytic functions that may provide future opportunities for the expansion of the enzyme universe. [ABSTRACT FROM AUTHOR]

Engineering enzymes capable of modes of activation unprecedented in nature will increase the range of industrially important molecules that can be synthesized through biocatalysis. However, low activity for a new function is often a limitation in adopting enzymes for preparative-scale synthesis, reaction with demanding substrates, or when a natural substrate is also present. By mutating the proximal ligand and other key active-site residues of the cytochrome P450 enzyme from Bacillus megaterium (P450-BM3), a highly active Hisligated variant of P450-BM3 that can be employed for the enantioselective synthesis of the levomilnacipran core was engineered. This enzyme, BM3-Hstar, catalyzes the cyclopropanation of N,N-diethyl-2-phenylacrylamide with an estimated initial rate of over 1000 turnovers per minute and can be used under aerobic conditions. Cyclopropanation activity is highly dependent on the electronic properties of the P450 proximal ligand, which can be used to tune this non-natural enzyme activity. [ABSTRACT FROM AUTHOR]

We consider continuous-time LTI systems with either unknown-input or with lack of information about the input and output derivatives. We compute the unknown-input observability subspace and the observability subspace with unknown derivatives of input and output. We first formulate the unknown-input observability subspace via projection matrices, then show that through having the unknown-input observability subspace, one can easily evaluate the effect of known input and output signals but unknown derivatives on the observability subspace. Our method is demonstrated on the dynamics of a longitudinal aircraft in steady-state flight. [ABSTRACT FROM AUTHOR]

Nitrogen activation: Though P450 enzymes are masters of oxygen activation and insertion into CH bonds, their ability to use nitrogen for the same purpose has so far not been explored. Engineered variants of cytochrome P450BM3 have now been found to catalyze intramolecular CH aminations in azide substrates. Mutations to two highly conserved residues significantly increased this activity. [ABSTRACT FROM AUTHOR]

Corticomuscular activity modeling based on multiple data sets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. In this paper, we propose modeling corticomuscular activity by combining partial least squares (PLS) and canonical correlation analysis (CCA). The proposedmethod takes advantage of both PLS and CCA to ensure that the extracted components are maximally correlated across two data sets and meanwhile can well explain the information within each data set. This complementary combination generalizes the statistical assumptions beyond both PLS and CCA methods. Simulations were performed to illustrate the performance of the proposed method. We also applied the proposedmethod to concurrent EEG and EMG data collected in a Parkinson's disease (PD) study. The results reveal several highly correlated temporal patterns between EEG and EMG signals and indicate meaningful corresponding spatial activation patterns. In PD subjects, enhanced connections between occipital region and other regions are noted, which is consistent with previous medical knowledge. The proposed framework is a promising technique for performing multisubject and bimodal data analysis. [ABSTRACT FROM AUTHOR]

An enantioselective cyclopropanation by using a mutant of P450 enzyme and the proposed formal synthesis of levomilnacipran affording compounds (I) and (II), respectively, are reported. [ABSTRACT FROM AUTHOR]

The reaction is conducted under environmentally benign conditions. [ABSTRACT FROM AUTHOR]

Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity. Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accurate error-control of the inferred edges. The PCfdr algorithm, which was developed by Li and Wang, was to provide a computationally efficient means to control the false discovery rate (FDR) of computed edges asymptotically. The original PCfdr algorithm was unable to accommodate a priori information about connectivity and was designed to infer connectivity from a single subject rather than a group of subjects. Here we extend the original PCfdr algorithm and propose a multisubject, error-rate-controlled brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity. In simulations, we show that the two proposed extensions can still control the FDR around or below a specified threshold. When the proposed approach is applied to fMRI data in a Parkinson's disease study, we find robust group evidence of the disease-related changes, the compensatory changes, and the normalizing effect of L-dopa medication. The proposed method provides a robust, accurate, and practical method for the assessment of brain connectivity patterns from functional neuroimaging data. [ABSTRACT FROM AUTHOR]