The role of the pre-Alpine polycrystalline basement in the paleogeographic configuration of multiple Neotethyan oceanic basins

The study provides a deeper understanding of the early Mesozoic paleogeogeographic spatial-temporal relationship by studying the two Adria-Europe intervening basement blocks. The Drina-Ivanjica and Pelagonian crustal fragments play important role in the internal early Alpine oceanic constitution further controlling the late Jurassic emplacement of Tethyan Dinaric-Hellenic ophiolites. The proposed paleogeographic reassessment is driven by the new paleocontinental inheritance data associated with the Variscan – pre-Variscan basement terranes. The recently published data suggest an Avalonian-type inheritance of the Pelagonian basement block which indicates a different pre-Variscan plate-tectonic journey, including separate spatial arrangement during Variscan amalgamation. In turn, Cadomian-type basement inheritance has been documented within the sliced Adria microplate. Thus, the Avalonian inheritance place the Pelagonian block away from the Apulia/Adria (Dinarides). In the investigated context of Paleozoic-Mesozoic paleogeographic transition, the Pelagonian block may represent a segment of the Cimmerian ribbon continent or southernmost segment of the Variscan Europe. With regards the nearby Adria microplate, a Triassic-Jurassic oceanic opening led to the decoupling (spreading away from the main Adria microplate) of the Drina-Ivanjica block. The rifting is in line with the simultaneous yet opposite or westward-directed drift of the Pelagonides. The breakup of south European Variscan configuration eventually result in the spatial alignment of the two basement fragments referred to as the “Drina–Pelagonide continental splinter”. By linking the paleogeographic pre-Jurassic–Jurassic relationship between these continental units, the two landlocked Neotethyan Vardar s.l. basins are extrapolated, “Dinaric Tethys” / Inner Dinaric-(Mirdita-Pindos) and the main Vardar Ocean (Western Vardar Zone).

Slice-direction geometric distortion evaluation and correction with reversed slice-select gradient acquisitions

Accurate spatial alignment of MRI data acquired across multiple contrasts in the same subject is often crucial for data analysis and interpretation, but can be challenging in the presence of geometric distortions that differ between acquisitions. It is well known that single-shot echo-planar imaging (EPI) acquisitions suffer from distortion in the phase-encoding direction due to B0 field inhomogeneities arising from tissue magnetic susceptibility differences and other sources, however there can be distortion in other encoding directions as well in the presence of strong field homogeneities. High-resolution ultrahigh-field MRI typically uses low bandwidth in the slice-encoding direction to acquire thin slices and, when combined with the pronounced B0 inhomogeneities, is prone to an additional geometric distortion in the slice direction as well. Here we demonstrate a presence of this slice distortion in high-resolution 7T EPI acquired with a novel pulse sequence allowing for the reversal of the slice-encoding gradient polarity that enables the acquisition of pairs of images with equal magnitudes of distortion in the slice direction but with opposing polarities. We also show that the slice-direction distortion can be corrected using gradient reversal-based method applying the same software used for conventional corrections of phase-encoding direction distortion.

Manipulating spatial alignment of donor and acceptor in host-guest MOF for TADF

Thermally activated delayed fluorescence (TADF) was achieved when electron-rich triphenylene (Tpl) donors (D) were confined to a cage-based porous MOF host (NKU-111) composed of electron-deficient 2,4,6-tri(pyridin-4-yl)-1,3,5-triazine (Tpt) acceptor (A) as the ligand. The spatially-separated D and A molecules in a face-to-face stacking pattern generated strong through-space charge transfer (CT) interactions with a small singlet-triplet excited states energy splitting (∼0.1 eV), which enabled TADF. The resulting Tpl@NKU-111 exhibited an uncommon enhanced emission intensity as the temperature increased. Extensive steady-state and time-resolved spectroscopic measurements and first-principles simulations revealed the chemical and electronic structure of this compound in both the ground and low-lying excited states. A double-channel (T1, T2) intersystem crossing mechanism with S1 was found and explained as single-directional CT from the degenerate HOMO-1/HOMO of the guest donor to the LUMO + 1 of one of the nearest acceptors. The rigid skeleton of the compound and effective through-space CT enhanced the photoluminescence quantum yield (PLQY). A maximum PLQY of 57.36% was achieved by optimizing the Tpl loading ratio in the host framework. These results indicate the potential of the MOFs for the targeted construction and optimization of TADF materials.

A Python toolbox for Automatic Face Alignment (AFA)

We describe AFA, an open-source Python package for automating the most common step in the preparation of facial stimuli for behavioral and neuro-imaging experiments – spatial alignment of faces (https://github.com/SourCherries/auto-face-align ). Face alignment is also important in the analysis of image statistics, and as a preprocessing step for machine learning. Automation of face alignment via AFA provides a reliable and efficient alternative to the very common practice of manual image-editing in graphics editors like Photoshop. As an open-source Python package, AFA encourages a clear and transparent specification of experimental method. AFA is based on facial landmark detection that is powered by the reliable and open-source DLIB library; and critical alignment code based on Generalized Procrustes Analysis (GPA) has been extensively unit-tested. AFA documentation and modularity provides opportunity for the modification and extensibility of AFA by the scientific community. As examples, we include functions for automatically generating image apertures that conceal areas outside the inner face; for image morphing between facial identities; and for shape-based averaging of facial identity. All of these are examples of stimulus preparation that have previously required manual landmark selection.

It is the Locus Coeruleus! Or… is it? : A proposition for analyses and reporting standards for structural and functional magnetic resonance imaging of the noradrenergic Locus Coeruleus

The noradrenergic locus coeruleus (LC) in the brainstem shows early signs of protein pathologies in neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. As the LC’s small size (approximately 2.5 mm in width) presents a challenge for molecular imaging, the past decade has seen a steep rise in structural and functional Magnetic Resonance (MR) studies aiming to characterise the LC’s changes in ageing and neurodegeneration. However, given its position in the brainstem and small volume, great care must be taken to yield methodologically reliable MR results as spatial deviations in transformations can greatly reduce the statistical power of the analyses at the group level. Here, we suggest a spatial transformation procedure and a set of quality assessment methods which allow LC researchers to achieve the spatial precision necessary for investigating this small but potentially impactful brain structure.Using a combination of available toolboxes (SPM12, ANTs, FSL, FreeSurfer), individual structural and functional 3T LC scans are transformed into MNI space via a study-specific anatomical template. Following this, the precision of spatial alignment in individual MNI-transformed images is quantified using in-plane distance measures based on slice-specific centroids of structural LC segmentations and based on landmarks of salient anatomical features in mean functional images, respectively.Median in-plane distance of all landmarks on the transformed structural as well as functional LC imaging data were below 2 mm, thereby falling below the typical LC width of 2.5 mm suggested by post-mortem data.With the set of spatial post-processing steps outlined in this paper and available for download, we hope to give readers interested in LC imaging a starting point for a reliable analysis of structural and functional MR data of the LC and to have also taken a first step towards establishing reporting standards of LC imaging data.

An Optical Flow Based Left-Invariant Metric for Natural Gradient Descent in Affine Image Registration

Accurate spatial alignment is essential for any population neuroimaging study, and affine (12 parameter linear/translation) or rigid (6 parameter rotation/translation) alignments play a major role. Here we consider intensity based alignment of neuroimages using gradient based optimization, which is a problem that continues to be important in many other areas of medical imaging and computer vision in general. A key challenge is robustness. Optimization often fails when transformations have components with different characteristic scales, such as linear versus translation parameters. Hand tuning or other scaling approaches have been used, but efficient automatic methods are essential for generalizing to new imaging modalities, to specimens of different sizes, and to big datasets where manual approaches are not feasible. To address this we develop a left invariant metric on these two matrix groups, based on the norm squared of optical flow induced on a template image. This metric is used in a natural gradient descent algorithm, where gradients (covectors) are converted to perturbations (vectors) by applying the inverse of the metric to define a search direction in which to update parameters. Using a publicly available magnetic resonance neuroimage database, we show that this approach outperforms several other gradient descent optimization strategies. Due to left invariance, our metric needs to only be computed once during optimization, and can therefore be implemented with negligible computation time.

Time and Action Co-Training in Reinforcement Learning Agents

In formation control, a robot (or an agent) learns to align itself in a particular spatial alignment. However, in a few scenarios, it is also vital to learn temporal alignment along with spatial alignment. An effective control system encompasses flexibility, precision, and timeliness. Existing reinforcement learning algorithms excel at learning to select an action given a state. However, executing an optimal action at an appropriate time remains challenging. Building a reinforcement learning agent which can learn an optimal time to act along with an optimal action can address this challenge. Neural networks in which timing relies on dynamic changes in the activity of population neurons have been shown to be a more effective representation of time. In this work, we trained a reinforcement learning agent to create its representation of time using a neural network with a population of recurrently connected nonlinear firing rate neurons. Trained using a reward-based recursive least square algorithm, the agent learned to produce a neural trajectory that peaks at the “time-to-act”; thus, it learns “when” to act. A few control system applications also require the agent to temporally scale its action. We trained the agent so that it could temporally scale its action for different speed inputs. Furthermore, given one state, the agent could learn to plan multiple future actions, that is, multiple times to act without needing to observe a new state.