Magnetic resonance imaging (MRI) benefits significantly from parallel imaging and low-rank matrix completion approaches to reconstruct accelerated multidimensional acquisitions. As one such algorithm, low-rank matrix modelling of local k-space neighbourhoods (LORAKS) provides image reconstruction by exploiting sparsity in undersampled multichannel data acquisitions. Besides using spatial and multichannel redundancies, joint-reconstruction of multi-contrast data provides higher-quality reconstruction and enables higher acceleration factors. However, the computational demands of LORAKS limit its applicability for joint-reconstruction of modern multichannel multi-contrast high-resolution datasets, but methods to speed up the acquisition and reconstruction process are highly desired. In this work, we present PyLORAKS, a graphics processing unit (GPU) accelerated implementation of LORAKS built using the PyTorch framework. The framework employs efficient parallelisation, GPU compute strategies, and randomised singular value decomposition methods for increased computational efficiency in dealing with huge LORAKS matrix sizes encountered in modern high-resolution MRI. The computation speedup enabled parameter optimisation via Bayesian methods, while automated differentiable tracing of the LORAKS formulation was used for data-driven subsampling optimisation. In benchmark experiments, reduction in memory overhead was achieved, paired with approximately 5–6× speedup over the previous (MATLAB) LORAKS implementations using CPU computation, and a ~30-fold improvement using PyLORAKS on GPUs. While ensuring similar reconstruction performance, the computational efficiency allows for reconstruction of larger data matrices previously posing challenges to memory or computation time demands. Automated parameter optimisation of LORAKS parameters $\lambda$ and $r$ yielded better results than manual selection, avoiding inter-contrast leakage artefacts. Additionally, the highest reconstruction quality was achieved by using the maximal number of available contrasts for joint-reconstruction, exemplarily resulting in an increased structural similarity (SSIM) from 0.9468 to 0.9688 when doubling the number of echoes in joint-reconstruction of a 4-fold undersampled multi-echo acquisition. Furthermore, complementary sampling across echoes was found beneficial as reconstruction of the same sampling per echo achieved an SSIM of 0.955, whereas interleaving phase encode lines per echo increased the SSIM to 0.972 for the same acquisition and otherwise optimal parameter combinations. We further demonstrate superior performance of complementary sampling using an optimal sampling pattern obtained by backpropagation through the PyLORAKS algorithm. Overall, PyLORAKS enables fast and efficient reconstruction of multichannel multi-contrast high-resolution MRI data. It enables parameter and data-driven reconstruction and acquisition optimisation and provides a platform for physics-informed or artificial intelligence-augmented development. The framework is openly available, including containerised environments for large-scale deployment.

Quantitative T-2 mapping offers a unique contrast for detailed brain imaging. At ultra-high field strengths (7 T), the higher signal-to-noise ratio (SNR) enables higher spatial resolution and the delineation of smaller structures. The translation of multi-echo spin-echo-based acquisitions to higher field strength, however, is complicated by inhomogeneities in the radio frequency (RF) transmit field resulting in stronger stimulated echoes and multi-echo refocusing pathways. The T-2 decay will thus depend on the specific sequence details and other experimental properties. The signal can be modeled by Bloch equation simulations to create a dictionary of possible signal patterns to fit the experimental data and estimate T-2. Particularly at smaller voxel sizes and shorter T-2 times, noise will affect the dictionary matching of the data by the introduction of a bias in the acquired signal magnitude dependent on the SNR. This study aims to develop a robust, accurate, and fast T-2 mapping approach at 7 T, addressing RF inhomogeneity and noise bias. We employed a 2D multi-echo spin-echo sequence combined with a Bloch equation simulation-aided dictionary matching technique. The method incorporated a pre-measured B-1(+) map for regularization of the dictionary fit and applied a patch-based PCA denoising algorithm with magnitude bias correction to mitigate noise-induced errors. The method was tested in simulations, phantom validations, and in five human participants. In vivo, (0.7 mm)(3) isotropic high-resolution T-2 maps showed detailed contrast within cortical and subcortical areas. Notably, regions with high iron content, such as the substantia nigra or nucleus ruber, were distinctly visible. The proposed method provided consistent T-2 values across different brain regions that aligned well with the literature where available. Simulations and experiments demonstrated the importance of the noise correction to achieve high-quality maps. The proposed method can significantly contribute to studies on brain microstructure and pathology, since it produces reliable T-2 maps at high resolution.

Abstract Summary Sophisticated approaches for the in silico prediction of toxicity are required to support the risk assessment of chemicals. The number of chemicals on the global chemical market and the speed of chemical innovation stand in massive contrast to the capacity for regularizing chemical use. We recently proved our ready-to-use application deepFPlearn as a suitable approach for this task. Here, we present its extension deepFPlearn+ incorporating (i) a graph neural network to feed our AI with a more sophisticated molecular structure representation and (ii) alternative train-test splitting strategies that involve scaffold structures and the molecular weights of chemicals. We show that the GNNs outperform the previous model substantially and that our models can generalize on unseen data even with a more robust and challenging test set. Therefore, we highly recommend the application of deepFPlearn+ on the chemical inventory to prioritize chemicals for experimental testing or any chemical subset of interest in monitoring studies. Availability and implementation The software is compatible with python 3.6 or higher, and the source code can be found on our GitHub repository: https://github.com/yigbt/deepFPlearn. The data underlying this article are available in Zenodo, and can be accessed with the link below: https://zenodo.org/record/8146252. Detailed installation guides via Docker, Singularity, and Conda are provided within the repository for operability across all operating systems.

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

Transforming growth factor beta 3 (TGFβ3) promotes tenogenic differentiation and may enhance tendon regeneration in vivo. This study aimed to apply TGFβ3 absorbed in decellularized equine superficial digital flexor tendon scaffolds, and to investigate the bioactivity of scaffold-associated TGFβ3 in an in vitro model. TGFβ3 could effectively be loaded onto tendon scaffolds so that at least 88% of the applied TGFβ3 were not detected in the rinsing fluid of the TGFβ3-loaded scaffolds. Equine adipose tissue-derived multipotent mesenchymal stromal cells (MSC) were then seeded on scaffolds loaded with 300 ng TGFβ3 to assess its bioactivity. Both scaffold-associated TGFβ3 and TGFβ3 dissolved in the cell culture medium, the latter serving as control group, promoted elongation of cell shapes and scaffold contraction (p < 0.05). Furthermore, scaffold-associated and dissolved TGFβ3 affected MSC musculoskeletal gene expression in a similar manner, with an upregulation of tenascin c and downregulation of other matrix molecules, most markedly decorin (p < 0.05). These results demonstrate that the bioactivity of scaffold-associated TGFβ3 is preserved, thus TGFβ3 application via absorption in decellularized tendon scaffolds is a feasible approach.

Age-related degenerative changes in tendon tissue represent a common cause for acute tendon pathologies. Although the regenerative potential of multipotent mesenchymal stromal cells (MSC) was reported to restore functionality in injured tendon tissue, cellular mechanisms of action remain partly unclear. Potential tenogenic differentiation of applied MSC is affected by various intrinsic and extrinsic factors. The current study presents an in vitro model to evaluate the combined extrinsic effects of decellularized equine tendon matrix, transforming growth factor beta 3 (TGFβ3) and bone morphogenetic protein 12 (BMP12) on the tenogenic fate of equine adipose tissue-derived MSC. Monolayer MSC cultures supplemented with TGFβ3 and BMP12 as well as MSC cultured on tendon matrix scaffolds preloaded with the growth factors were incubated for 3 and 5 days. Histological evaluation and real time reverse transcription polymerase chain reaction (RT-PCR) revealed that growth factor-mediated tenogenic induction of MSC was modified by the conditions of the surrounding microenvironment. While the gene expression pattern in monolayer cultures supplemented with TGFβ3 or TGFβ3 and BMP12 revealed an upregulation for collagen 1A2, collagen 3A1, tenascin c, scleraxis and mohawk ( p < 0.05 ), the presence of tendon matrix led to an upregulation of decorin and osteopontin as well as to a downregulation of smad8 ( p < 0.05). Preloading of scaffolds with either TGFβ3, or with TGFβ3 and BMP12 promoted a tenocyte-like phenotype and improved cell alignment. Furthermore, gene expression in scaffold culture was modulated by TGFβ3 and/or BMP12, with downregulation of collagen 1A2, collagen 3A1, decorin, scleraxis, smad8 and osteopontin, whereas gene expression of tenascin c was increased. This study shows that growth factor-induced tenogenic differentiation of equine MSC is markedly altered by topographical constraints of decellularized tendon tissue in vitro. While TGFβ3 represents an effective mediator for tenogenic induction, the role of BMP12 in tenogenesis may be of modulatory character and needs further evaluation.

Transplantation of multipotent mesenchymal progenitor cells is a valuable option for treating tendon disease. Tenogenic differentiation leading to cell replacement and subsequent matrix modulation may contribute to the regenerative effects of these cells, but it is unclear whether this occurs in the inflammatory environment of acute tendon disease. Equine adipose-derived stromal cells (ASC) were cultured as monolayers or on decellularized tendon scaffolds in static or dynamic conditions, the latter represented by cyclic stretching. The impact of different inflammatory conditions, as represented by supplementation with interleukin-1β and/or tumor necrosis factor-α or by co-culture with allogeneic peripheral blood leukocytes, on ASC functional properties was investigated. High cytokine concentrations increased ASC proliferation and osteogenic differentiation, but decreased chondrogenic differentiation and ASC viability in scaffold culture, as well as tendon scaffold repopulation, and strongly influenced musculoskeletal gene expression. Effects regarding the latter differed between the monolayer and scaffold cultures. Leukocytes rather decreased ASC proliferation, but had similar effects on viability and musculoskeletal gene expression. This included decreased expression of the tenogenic transcription factor scleraxis by an inflammatory environment throughout culture conditions. The data demonstrate that ASC tenogenic properties are compromised in an inflammatory environment, with relevance to their possible mechanisms of action in acute tendon disease.

Finding the antiderivative of expressions is often challenging and requires advanced mathematical skills even for simple looking problems.Computer algebra systems (CAS) like Mathematica (Wolfram Research, Inc., Champaign, IL), Maple (Maplesoft, a division of Waterloo Maple Inc., Waterloo, Ontario), and Maxima (maxima.sourceforge.net)provide integrators to compute antiderivatives symbolically.However, these systems give no insight as to how an antiderivative is found or why it could not be computed.Also, they use advanced methods incomprehensible to humans that often result in huge antiderivatives unnecessarily involving special functions.

Abstract In human eyes, the maximum visual acuity correlates locally with the fovea, a shallow depression in the retina. Previous examinations have been reduced to simple geometrical fovea models derived from postmortem preparations and considering only a few superficial ray propagation aspects. In the current study, an extended and realistic analysis of ray-optical simulations for a comprehensive anatomical realistic eye model for the anterior part and realistic aspherical human foveal topographical profiles deduced from in vivo optical coherence tomography (OCT) are presented, and the refractive index step at the transition from vitreous to retinal tissue is taken into account. The optical effect of a commonly shaped (averaged) and an extraordinarily shaped foveal pit were both compared to the analysis of an assumed pure spherical boundary layer. The influence of the aperture size, wavelength, and incident angle on the spot size and shape, as well as the axial focal and lateral centroid position is investigated, and a lateral displacement of about 2 μm and an axial shift of the best focal position of less than 4 μm are found. These findings indicate only small optical effects that are laterally in the range of inter-receptor distances and axially less than the photoreceptor outer segment dimension.

Die konfokale Laser-Scanning-Mikroskopie ist ein vielseitig genutztes Werkzeug in der medizinischen Forschung und ermöglicht insbesondere die nichtinvasive In-vivo-Darstellung des subbasalen Nervenplexus (SNP) der Kornea. Die vorliegende Arbeit hat zum Ziel, eine strukturierte Übersicht zu Möglichkeiten der Detektion und Quantifizierung von Nervenfasern des subbasalen Nervenplexus in konfokalmikroskopischen Aufnahmen zu geben. Hierfür notwendige Schritte werden erläutert, und es werden mögliche Einflussfaktoren auf die Qualität der Ergebnisse aufgezeigt. Die aus der Struktur des SNP gewonnenen Informationen können potenziell als klinische Parameter im Rahmen von Diagnostik und Therapie der diabetischen Neuropathie Anwendung finden.

A novel method for the automated detection of the outer choroid boundary within spectral-domain optical coherence tomography image data, based on an image model within the space of functions of bounded variation and the application of quadratic measure filters, is presented. The same method is used for the segmentation of retinal layer boundaries and proves to be suitable even for data generated without special imaging modes and moderate line averaging. Based on the segmentations, an automated determination of the central fovea region and choroidal thickness measurements for this and two adjacent 1-mm regions are provided. The quality of the method is assessed by comparison with manual delineations performed by five trained graders. The study is based on data from 50 children of the ages 8 to 13 that were obtained in the framework of the LIFE Child study at Leipzig University.

The advance of optical coherence tomography (OCT) enables a detailed examination of the human retina in-vivo for clinical routine and experimental eye research. Only few investigations to date captured human foveal morphology in a large subject group on the basis of a detailed analysis employing mathematical models. However, even for important foveal characteristics unified terminology and clear definitions were not implemented so far. This might be a reason, why to this day the human fovea is considered to be a mostly symmetric and round structure. Therefore, the most important finding of this work is the detailed analysis of the asymmetric structure of the human fovea. We employed five clinically highly relevant foveal characteristics, which are derived from a previously published fovea model. For each, an accurate mathematical description is given. The presented properties include (1) mean retinal thickness inside a defined radius, (2) foveal bowl area, (3) a new, exact definition of foveal radius, (4) maximum foveal slope, and (5) the maximum height of the foveal rim. Furthermore, minimum retinal thickness was derived and analyzed. 220 strictly controlled healthy Caucasian subjects of European decent with an even distribution of age and gender were imaged with an Heidelberg Spectralis OCT. Detailed analysis demonstrated the following general results: (1) significant gender difference regarding the central foveal subfield thickness (CFST) but no significant differences for the minimum central retinal thickness, (2) a strong correlation between right and left eye of the same subject, and, as essential finding, (3) strong structural differences of the fovea form in the different anatomical directions (nasal, temporal, inferior and superior). In the analysis of the foveal asymmetry, it will be demonstrated that the foveal radius is larger in nasal and temporal direction compared to inferior and superior position. Furthermore, it will be shown that the circular fovea rather has an elliptic form with the larger axis along the nasal to temporal direction. Interestingly, the foveal slope shows a divergent behavior as the temporal direction has the smallest slope angle and both, inferior and superior angles are clearly larger than the others. The findings in this work can be used for an exact quantification of changes in early stages of various retinal diseases and as a marker for initial diagnosis.

Abstract Purpose: The corneal subbasal nerve plexus (SNP) offers high potential for early diagnosis of diabetic peripheral neuropathy. Changes in subbasal nerve fibers can be assessed in vivo by confocal laser scanning microscopy (CLSM) and quantified using specific parameters. While current study results agree regarding parameter tendency, there are considerable differences in terms of absolute values. The present study set out to identify factors that might account for this high parameter variability. Materials and methods: In three healthy subjects, we used a novel method of software-based large-scale reconstruction that provided SNP images of the central cornea, decomposed the image areas into all possible image sections corresponding to the size of a single conventional CLSM image (0.16 mm2), and calculated a set of parameters for each image section. In order to carry out a large number of virtual examinations within the reconstructed image areas, an extensive simulation procedure (10,000 runs per image) was implemented. Results: The three analyzed images ranged in size from 3.75 mm2 to 4.27 mm2. The spatial configuration of the subbasal nerve fiber networks varied greatly across the cornea and thus caused heavily location-dependent results as well as wide value ranges for the parameters assessed. Distributions of SNP parameter values varied greatly between the three images and showed significant differences between all images for every parameter calculated (p < 0.001 in each case). Conclusions: The relatively small size of the conventionally evaluated SNP area is a contributory factor in high SNP parameter variability. Averaging of parameter values based on multiple CLSM frames does not necessarily result in good approximations of the respective reference values of the whole image area. This illustrates the potential for examiner bias when selecting SNP images in the central corneal area.

A proof-of-principle study was accomplished assessing the descriptive potential of two simple geometric measures (shape descriptors) applied to sets of segmented glands within images of 125 prostate cancer tissue sections. Respective measures addressing glandular shapes were (i) inverse solidity and (ii) inverse compactness. Using a classifier based on logistic regression, Gleason grades 3 and 4/5 could be differentiated with an accuracy of approx. 95%. Results suggest not only good discriminatory properties, but also robustness against gland segmentation variations. False classifications in part were caused by inadvertent Gleason grade assignments, as a-posteriori re-inspections had turned out.

This paper deals with pattern formation in Langmuir monolayers of two sets of lipid mixtures that include (1) a fluorinated lipid for phase separation, (2) a polymerizable lipid for stabilization of the patterned structure, and (3) a unit for the presentation of biological recognition units. Differences in the distribution of these functionalities allow a polymerization of dispersed or continuous phase and a placement of the recognition units in crystalline or solid analogue phase. Also, a ternary mixture including a lipid modified with the tandem repeat domain of MUC1 plus a TN-antigen was studied. Based on the biphasic pattern obtained (starlike crystals of up to 50 μm with a fine structure of some micrometers), we also verified the potential of the laterally patterned monolayer to stimulate the immune system (quartz crystal microbalance). The second set of lipids combines a highly fluorinated itaconic ester (polymerizable unit) with the natural phospholipid 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine.

Drug addiction is a chronic, relapsing disease caused by neurochemical and molecular changes in the brain. In this human autopsy study qualitative and quantitative changes of glial fibrillary acidic protein (GFAP)-positive astrocytes in the hippocampus of 26 lethally intoxicated drug addicts and 35 matched controls are described. The morphological characterization of these cells reflected alterations representative for astrogliosis. But, neither quantification of GFAP-positive cells nor the Western blot analysis indicated statistical significant differences between drug fatalities versus controls. However, by semi-quantitative scoring a significant shift towards higher numbers of activated astrocytes in the drug group was detected. To assess morphological changes quantitatively, graph-based representations of astrocyte morphology were obtained from single cell images captured by confocal laser scanning microscopy. Their underlying structures were used to quantify changes in astroglial fibers in an automated fashion. This morphometric analysis yielded significant differences between the investigated groups for four different measures of fiber characteristics (Euclidean distance, graph distance, number of graph elements, fiber skeleton distance), indicating that, e.g., astrocytes in drug addicts on average exhibit significant elongation of fiber structures as well as two-fold increase in GFAP-positive fibers as compared with those in controls. In conclusion, the present data show characteristic differences in morphology of hippocampal astrocytes in drug addicts versus controls and further supports the involvement of astrocytes in human pathophysiology of drug addiction. The automated quantification of astrocyte morphologies provides a novel, testable way to assess the fiber structures in a quantitative manner as opposed to standard, qualitative descriptions.

Much effort has been directed towards the optimization of the capture of in vivo hepatocytes from their microenvironment. Some methods of capture include an ex vivo cellular model in a bioreactor based liver module, a micropatterned module, a microfluidic 3D chip, coated plates, and other innovative approaches for the functional maintenance of primary hepatocytes. However, none of the above methods meet US Food and Drug Administration (FDA) guidelines, which recommend and encourage that the duration of a toxicity assay of a drug should be a minimum of 14 days, to a maximum of 90 days for a general toxicity assay. Existing innovative reports have used undefined extracellular matrices like matrigel, rigid collagen, or serum supplementations, which are often problematic, unacceptable in preclinical and clinical applications, and can even interfere with experimental outcomes. We have overcome these challenges by using integrated nanostructured self-assembling peptides and a special combination of growth factors and cytokines to establish a proof of concept to mimic the in vivo hepatocyte microenvironment pattern in vitro for predicting the in vivo drug hepatotoxicity in a scalable bioartificial liver module. Hepatocyte functionality (albumin, urea) was measured at days 10, 30, 60, and 90 and we observed stable albumin secretion and urea function throughout the culture period. In parallel, drug metabolizing enzyme biomarkers such as ethoxyresorufin-O-deethylase, the methylthiazol tetrazolium test, and the lactate dehydrogenase test were carried out at days 10, 30, 60, and 90. We noticed excellent mitochondrial status and membrane stability at 90 days of culture. Since alpha glutathione S-transferase (GST) is highly sensitive and a specific marker of hepatocyte injury, we observed significantly low alpha GST levels on all measured days (10, 30, 60, and 90). Finally, we performed the image analysis of mitochondria-cultured hepatocytes at day 90 in different biophysical parameters using confocal microscopy. We applied an automatic algorithm-based method for 3D visualization to show the classic representation of the mitochondrial distribution in double hepatocytes. An automated morphological measurement was conducted on the mitochondrial distribution in the cultured hepatocytes. Our proof of concept of a scalable bioartificial liver modular device meets FDA guidelines and may function as an alternative model of animal experimentation for pharmacological and toxicological studies involving drug metabolism, enzyme induction, transplantation, viral hepatitis, hepatocyte regeneration, and can also be used in other existing bioreactor modules for long-term culture for up to 90 days or more.