Miracle-dz/UniFAR-FTUs huggingface dataset:
Entwicklung eines Fusionsassays basierend auf porenüberspannenden Membranen

Fusion of biological membranes is a central requirement for many cellular processes. In recent years a great variety of fusion assays based on artificial membrane systems has been developed. Such reductionist approaches mainly account for our current knowledge on fusion processes. However, there are still a number of drawbacks associated with these assays. The aim of this thesis was the establishment of a new vesicle-planar membrane fusion assay to be able to gain insight into protein-mediated fusion processes. To achieve this goal, membranes suspending the pores of a highly ordered porous material were established, which have the advantage that they are very robust, mechanically stable and the membranes are accessible from both sides. The results clearly demonstrate that large unilamellar vesicles can successfully be fused with pore-spanning membranes. Micro-black lipid membranes (micro-BLMs) were prepared by the painting-technique resulting in a hybrid lipid bilayer doped with Oregon Green DHPE. The addition of large unilamellar vesicles (600 nm in average diameter), doped with Texas Red DHPE, to the pore-suspending membrane allowed for the observation of single fusion events by means of fluorescence microscopy. Lipid mixing was followed by the distribution of the Texas Red fluorescence in the plane of the pore-suspending membrane while simultaneously, quenching of the Oregon Green fluorescence due to Forster resonance energy transfer (FRET) between the Oregon Green and Texas Red dye was monitored. For the Texas Red distribution a diffusion coefficient of D = (9 ± 5) µm²/s was determined. This value is in very good agreement with fluorescence recovery after photobleaching analyis. By means of scanning ion conductance microsopy (SICM) a detailed elucidation of membrane topology before and after fusion was possible. It was shown that the pore-spanning membranes bent deeper into the pore due to the excess of additional lipid material after fusion of a vesicle. The analysis of two different molecular recognition systems based on solvent-free pore-spanning membranes revealed the importance of membrane destabilization by insertion of a transmembrane anchor for the initiation of the fusion process. The interaction between two membranes doped with head-goup modified lipids mediated by the peptide H6WGC yielded only low fusion rates. In contrast, the insertion of PNA-sequences coupled to the native SNARE-transmembrane domain resulted in very efficient fusion. The PNA sequences, identical in both membranes or complementary in vesicle and pore-spanning membrane, barely influenced the fusion efficiency. Destabilization of the vesicle appears to be the key to cause fusion with peptide-free pore spanning membranes.
Enantiomerentrennung regioisomerer Methylendioxyamphetamine mittels chiral modifizierter HPLC

Enantiomerentrennungen regioisomerer Methylendioxyamphetamine an chiralen stationaren Phasen (CSP) mittels Hochleistungsflussigchromatographie (HPLC) werden vorgestellt. Insbesondere b-Cyclodextrin und einige Derivate sind als CSP zur Untersuchung gekommen. Der Einfluss von Puffern, Temperatur und Modifiern bei der Trennung der Enantiomere ist systematisch untersucht worden. Ein Vergleich mit einer alternativen Enantiomerentrennung durch Diastereomerenbildung wird diskutiert. Die Detektion der mit der HPLC getrennten Verbindungen erfolgt mittels Dioden-Array- und Fluoreszenz- Detektor. Messungen des Cirkulardichroismus als weiteren HPLC-Detektor werden vorgestellt. Die Diastereomerentrennung nach Bildung der S-(-)-N-(Trifluoroacetyl)-L-prolyl - Derivate erfolgt gaschromatograpisch mit anschliesender massenselektiver Detektion. Mit Hilfe von 1H-NMR Experimenten werden enantioselektive Interaktionen der Analyten mit Cyclodextrin beschrieben. Aufgrund dieser Untersuchungen konnen Vorhersagen fur erfolgreiche Trennergebnisse in der HPLC getroffen werden.
Role‐based attention in deep reinforcement learning for games

Reinforcement learning method that learns while interacting with the environment, relies heavily on the concept of state as the input to the policy and value function. In the task, the view of agent contains a lot of information, and it is difficult for the agent to learn to ignore the irrelevant information and focus on the key information. Inspired by recent work in attention models for computer vision, we present a role‐based attention model for reinforcement learning. The proposed model uses convolutional neural networks to generate soft attention maps, adding crucial role information in the task, forcing the agent to focus on important features and distinguish task‐related information. To validate the performance in complex problems, the proposed approach is evaluated in a challenging scenario, Football Academy in Google Research Football Environment, a newly released reinforcement learning environment with physics‐based three‐dimensional simulator. The experimental results demonstrate that agents using role‐based attention mechanism can perform better in football games.
The truncate soft-shell clam, Mya truncata, as a biomonitor of municipal wastewater exposure and historical anthropogenic impacts in the Canadian Arctic

Municipal wastewater is a large source of pollution to Canadian waters, yet its effects on Arctic marine ecosystems remains relatively unknown. We characterized the impacts of municipal wastewater from a growing northern community, Iqaluit, Nunavut on the Arctic truncate soft-shell clam, Mya truncata. Clams were sampled from six locations that varied in proximity to the wastewater treatment plant and shell biogeochemical analysis revealed that clams nearest the wastewater treatment plant had slower growth rates, lower carbon and oxygen stable isotope ratios, and elevated concentrations of copper and lead. A parallel analysis on mRNA expression profiles characterized M. truncata’s physiological response to wastewater effluent. Clams nearest the wastewater treatment plant had significantly lower mRNA expression of genes associated with metabolism, antioxidants, molecular chaperones, and phase I and II detoxification, but had heightened mRNA expression in genes coding for enzymes that bind and remove contaminants. These results demonstrated a biological response to Iqaluit’s wastewater effluent and highlight M. truncata’s potential to act as a biomonitor of municipal wastewater along Canadian Arctic coastlines.
Non-parametric synergy modeling with Gaussian processes

Background Understanding the synergetic and antagonistic effects of combinations of drugs and toxins is vital for many applications, including treatment of multifactorial diseases and ecotoxicological monitoring. Synergy is usually assessed by comparing the response of drug combinations to a predicted non-interactive response from reference (null) models. Possible choices of null models are Loewe additivity, Bliss independence and the recently rediscovered Hand model. A different approach is taken by the MuSyC model, which directly fits a generalization of the Hill model to the data. All of these models, however, fit the dose-response relationship with a parametric model. Results We propose the Hand-GP model, a non-parametric model based on the combination of the Hand model with Gaussian processes. We introduce a new logarithmic squared exponential kernel for the Gaussian process which captures the logarithmic dependence of response on dose. From the monotherapeutic response and the Hand principle, we construct a null reference response and synergy is assessed from the difference between this null reference and the Gaussian process fitted response. We evaluated performance of our model on a simulated data from Greco, two simulated data sets of our own design and two benchmark data sets from Chou and Talalay. We compare the Hand-GP model to standard synergy models and show that our model performs better than these standards. We also compare our model to the MuSyC model as example of a recent method which also fits a complete dose-response surface. Also in this case, the Hand-GP model performs better. Conclusion The Hand-GP model is a flexible model to capture synergy. Its non-parametric natures allows it to model a wide variety of response patterns.
Ionothermal versus hydrothermal synthesis at the example of the CdO-MO-As2O5-(H2O/IL)-system

Im Rahmen von systematischen Untersuchungen von mineralahnlichen Arsenaten in dem System CdO-MO-As2O5-(H2O/IL) wurden zwei Polymorphe von (C6H11N2)2[CdBr4], alpha-(C6H11N2)2[CdBr4] (1) und beta-(C6H11N2)2[CdBr4] (2) und ein Zink-Arsenat, Zn9(AsO4)6(H2O)4 (3), unter ionothermalen Bedingungen mit der ionischen Flussigkeit (IF) 1-Ethyl-3-Methylimidazolium Bromid, C6H11BrN2, synthetisiert. Weiters wurde ein Ni-haltiges, mineralahnliches Cd-Arsenat, Cd4.65Ni0. 35(H2O)4(AsO4)2(HAsO4)2 (4), unter hydrothermalen Bedingungen synthesisiert. Die unterschiedlichen Ansatze der Synthese von mineralahnlichen Arsenaten, die Temperaturbehandlung und die Rolle der unterschiedlichen Losungsmittel wurden auch behandelt. Alle Verbindungen wurden mithilfe von Einkristalldiffrakometrie, SEM/EDS und Schwingungsspektroskopie charakterisiert: 1 monoklin, P21/c, a = 13,975(3) A, b = 10,343(2) A, c = 14,895(3) A, beta = 94,35(3)°, V = 2146,8(8) A3, Z = 4, R1 = 0,039; 2 tetragonal, I41 /a, a = 14,863(2) A, c = 20,600(2) A, V = 4550,9(16) A3, Z = 22, R1 = 0,042; 3 triklin, P1 , a = 6,6983(13) A, b = 9,1885(18) A, c = 10,146(2) A, alpha = 69,44(3)°, beta = 94,35(3)°, gamma = 75,78(3)° V = 560,6(2) A3, Z = 1, R1 = 0,0287; 4 monoclinic C2/c, a = 18,375(4) A, b = 9,5395(19) A, c = 9,977(2) A, beta = 96,19(3)°, V = 1738,6(6) A3, Z = 4, R1 = 0,0296. Zn9(AsO4)6(H2O)4 (3) ist eine bekannte Verbindung, wurde aber das erste Mal unter ionothermalen Bedingungen synthetisiert und die Positionen der Wasserstoffatome wurde bestimmt und verfeinert. Cd4.65Ni0. 35(H2O)4(AsO4)2(HAsO4)2 (4) ist ein neues Ni-haltiges Cd-Arsenat mit einer mineralahnlichen Struktur.
Callosal Fiber Length Scales with Brain Size According to Functional Lateralization, Evolution, and Development

Brain size significantly impacts the organization of white matter fibers. Fiber length scaling, the degree to which fiber length varies according to brain size, was overlooked. We investigated how fiber lengths within the corpus callosum, the most prominent white matter tract, vary according to brain size. The results showed substantial variation in length scaling among callosal fibers, replicated in two large healthy cohorts (∼2000 human subjects, including both sexes). The underscaled callosal fibers mainly connected the precentral gyrus and parietal cortices, whereas the overscaled callosal fibers mainly connected the prefrontal cortices. The variation in such length scaling was biologically meaningful: larger scaling corresponded to larger neurite density index but smaller fractional anisotropy values; cortical regions connected by the callosal fibers with larger scaling were more lateralized functionally as well as phylogenetically and ontogenetically more recent than their counterparts. These findings highlight an interaction between interhemispheric communication and organizational and adaptive principles underlying brain development and evolution. SIGNIFICANCE STATEMENT Brain size varies across evolution, development, and individuals. Relative to small brains, the neural fiber length in large brains is inevitably increased, but the degree of such increase may differ between fiber tracts. Such a difference, if it exists, is valuable for understanding adaptive neural principles in large versus small brains during evolution and development. The present study showed a substantial difference in the length increase between the callosal fibers that connect the two hemispheres, replicated in two large healthy cohorts. Together, our study demonstrates that reorganization of interhemispheric fibers length according to brain size is intrinsically related to fiber composition, functional lateralization, cortical myelin content, and evolutionary and developmental expansion.
Auxin-responsive (phospho)proteome analysis reveals regulation of cell cycle and ethylene signaling during rice crown root development

The rice root system, which primarily consists of adventitious/crown roots (AR/CR) developed from the coleoptile base, is an excellent model system for studying shoot-to-root trans-differentiation process. We reveal global changes in protein and metabolite abundance, and protein phosphorylation in response to an auxin stimulus during CR development. Global proteome and metabolome analyses of developing crown root primordia (CRP) and emerged CRs uncovered that the biological processes associated with chromatin conformational change, gene expression, and cell cycle were translationally regulated by auxin signaling. Spatial gene expression pattern analysis of differentially abundant proteins disclosed their stage-specific dynamic expression pattern during CRP development. Further, our tempo-spatial gene expression and functional analyses revealed that auxin creates a regulatory feedback module during CRP development and activates ethylene biosynthesis exclusively during CRP initiation. Ethylene signaling promotes CR formation by repressing the cytokinin response regulator, OsRR2. Additionally, the (phospho)proteome analysis identified differential phosphorylation of the Cyclin-dependent kinase G-2 (OsCDKG;2), and cell wall proteins, in response to auxin signaling, suggesting that auxin-dependent phosphorylation may be required for cell cycle activation, and cell wall synthesis during root organogenesis. Thus, our study provides evidence for the translational and post-translational regulation during CRP trans-differentiation downstream of the auxin signaling pathway. Highlight Global (phospho)proteome and metabolic profiling of rice CRP and CRs uncover differential proteins and metabolites associated with gene expression, cell cycle, ethylene signaling and cell wall synthesis during CR development.
Effect of telomerization on the growth patterns of adult cutaneous diploid fibroblasts

The article describes effects of telomerization on proliferative activity in cultured cutaneous diploid fibroblasts obtained from an adult men aged 57 years. It is shown that the cells in the control culture initially divide quite actively, but starting from the 27th to the 40th passages, the rate of their division decreases and the time to reach the monolayer increases by about 2 times, and starting from the 42nd passage, the time to reach the monolayer increases by 4-10 times (up to 16-40 days). In a single case, an increase in the doubling time of control non-thelomerized cells was recorded up to 30 times (136 days). The introduction of the telomerase gene allowed cells maintain a high rate of cell division to about 45-47 passages. At the same time, the culture with the telomerase gene exceeds the control culture by 10 passages. Rate of growth the cells with telomerase gene after 48 and up to 57 passages slowed down by 10-18 times (up to 40-73 days). At the 47th passage in the control culture and at the 57th passage in the experimental culture, the cells practically stopped dividing and their number did not increase. The results show that the inclusion of the telomerase gene in adult fibroblasts does not always cancel the limit of cell divisions.
A General Framework to Learn Tertiary Structure for Protein Sequence Characterization

During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.
Co-infection of chickens with H9N2 and H7N9 avian influenza viruses leads to emergence of reassortant H9N9 virus with increased fitness for poultry and enhanced zoonotic potential

An H7N9 low pathogenicity avian influenza virus (LPAIV) emerged through genetic reassortment between H9N2 and other LPAIVs circulating in birds in China. This virus causes inapparent clinical disease in chickens, but zoonotic transmission results in severe and fatal disease in humans. We evaluated the consequences of reassortment between the H7N9 and the contemporary H9N2 viruses of G1 lineage that are enzootic in poultry across the Indian sub-continent and the Middle East. Co-infection of chickens with these viruses resulted in emergence of novel reassortant H9N9 viruses carrying genes derived from both H9N2 and H7N9 viruses. These reassortant H9N9 viruses showed significantly increased replication fitness, enhanced pathogenicity in chicken embryos and the potential to transmit via contact among ferrets. Our study highlights that the co-circulation of H7N9 and H9N2 viruses could represent a threat for the generation of novel reassortant viruses with greater virulence in poultry and an increased zoonotic potential. Graphical Abstract In Brief H9N2 viruses have a high propensity to reassort with other avian influenza viruses. We found that co-infection of chickens with H9N2 and H7N9 led to the emergence of reassortant viruses including the H9N9 subtype. Some reassortant H9N9 viruses exhibited increased replication fitness, increased pathogenicity in the chicken embryo, greater avidity for human and avian cell receptors, lower pH fusion and contact-transmission to ferrets. This study demonstrated the ability of viruses that already exist in nature to exchange genetic material, highlighting the potential emergence of viruses from these subtypes with increased zoonotic potential. There are nine H9 influenza A subtypes carrying different neuraminidase (NA) genes, including H9N9 viruses, while they are not common they do exist in nature as wildtypes (CDC). Highlights Co-infection of chickens with H7N9 and H9N2 led to emergence of reassortant H9N9 viruses Reassortant H9N9 viruses had an increased replication rate in avian and human cells Reassortant H9N9 viruses had a lower pH fusion and significantly higher receptor binding to α 2,3 sialoglycans Reassortant H9N9 replicated in ferrets at similar levels compared to H7N9 and transmitted via direct contact Ferrets exposed to reassortant H9N9 by aerosol contact were also found to be seropositive Experimental simulation of events that may occur naturally with circulating viruses has demonstrated the risk of emergence of viruses with increased zoonotic potential.
NeuriteNet: A Convolutional Neural Network for determining morphological differences in neurite growth

Background During development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due to the complex arborization and heterogeneous patterns of neurite growth in vitro. New Method NeuriteNet is a Convolutional Neural Network (CNN) sorting model that uses a novel adaptation of the XRAI saliency map overlay, which is a region-based attribution method. NeuriteNet compares neuronal populations based on differences in neurite growth patterns, sorts them into respective groups, and overlays a saliency map indicating which areas differentiated the image for the sorting procedure. Results In this study, we demonstrate that NeuriteNet effectively sorts images corresponding to dissociated neurons into control and treatment groups according to known morphological differences. Furthermore, the saliency map overlay highlights the distinguishing features of the neuron when sorting the images into treatment groups. NeuriteNet also identifies novel morphological differences in neurites of neurons cultured from control and genetically modified mouse strains. Comparison with Existing Methods Unlike other neurite analysis platforms, NeuriteNet does not require manual manipulations, such as segmentation of neurites prior to analysis, and is more accurate than experienced researchers for categorizing neurons according to their pattern of neurite growth. Conclusions NeuriteNet can be used to effectively screen for morphological differences in a heterogeneous group of neurons and to provide feedback on the key features distinguishing those groups via the saliency map overlay. Highlights NeuriteNet is a machine learning model developed to identify differences in control and experimental groups of cultured neurons based on morphological criteria. NeuriteNet’s saliency map highlights the features of the image that associate the neuron with a particular group. NeuriteNet outperforms trained researchers in assigning neurons to control or experimental groups with a known morphological difference as well as those with no previously described difference.
Deep Generative Analysis for Task-Based Functional MRI Experiments

While functional magnetic resonance imaging (fMRI) remains one of the most widespread and important methods in basic and clinical neuroscience, the data it produces—time series of brain volumes—continue to pose daunting analysis challenges. The current standard (“mass univariate”) approach involves constructing a matrix of task regressors, fitting a separate general linear model at each volume pixel (“voxel”), computing test statistics for each model, and correcting for false positives post hoc using bootstrap or other resampling methods. Despite its simplicity, this approach has enjoyed great success over the last two decades due to: 1) its ability to produce effect maps highlighting brain regions whose activity significantly correlates with a given variable of interest; and 2) its modeling of experimental effects as separable and thus easily interpretable. However, this approach suffers from several well-known drawbacks, namely: inaccurate assumptions of linearity and noise Gaussianity; a limited ability to capture individual effects and variability; and difficulties in performing proper statistical testing secondary to independently fitting voxels. In this work, we adopt a different approach, modeling entire volumes directly in a manner that increases model flexibility while preserving interpretability. Specifically, we use a generalized additive model (GAM) in which the effects of each regressor remain separable, the product of a spatial map produced by a variational autoencoder and a (potentially nonlinear) gain modeled by a covariate-specific Gaussian Process. The result is a model that yields group-level effect maps comparable or superior to the ones obtained with standard fMRI analysis software while also producing single-subject effect maps capturing individual differences. This suggests that generative models with a decomposable structure might offer a more flexible alternative for the analysis of task-based fMRI data.