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Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran’s index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms.

Pregnancy induces neuroanatomical changes in the human brain. Previous studies detected both traces of motherhood decades after childbirth and adaptations in fathers. It is unclear which effects can be attributed to persisting traces of pregnancy and which are effects of parenthood. We investigated effects of past birth and of pregnancy loss in women, and effects of fatherhood in men, using univariate and machine learning analyses on 205 regional brain volumes. A group of mothers and an age‐matched sample of nulliparous women (N = 4357 per group, mean age 63 years) from the UK Biobank, with no past pregnancy losses, showed significant volumetric group differences in 14 regions at Bonferroni‐adjusted α = 0.05. Likewise, we identified 18 significant group differences between age‐matched samples of fathers and non‐fathers of the same size (mean age 63.4), with 9 regions overlapping between sexes. Brain‐wide association statistics for past live birth in mothers and those for fatherhood correlated (r = 0.55). XGBoost machine learning models trained to classify parenthood status separately in both datasets showed performance that was low, but significantly above chance (10‐fold cross validation: AUC = 0.56, p < 1e‐5 Motherhood classifier, AUC = 0.54, p < 1e‐5, Fatherhood classifier, 10 k permutations). We tested the motherhood classification model on an independent test sample comprising four age‐matched groups: 1. women who have never been pregnant, 2. women with past pregnancy loss but no live births, 3. women with live births but no pregnancy loss, and 4. women who experienced both. Class probability was significantly associated with live births, but not past loss. These findings may suggest that neuroanatomical patterns of past childbirth partly also reflect traces of parenthood and not solely persisting traces of past pregnancy, although a more detailed characterization of pregnancy loss data would be needed for full confirmation of this interpretation. Therefore, further research is needed to quantify the extent and understand the nature of these changes, particularly considering the known vulnerability for mental disorders associated with reproductive events. We cross‐sectionally investigated effects of past birth and of pregnancy loss in women, and effects of fatherhood in men, using univariate and machine learning analyses on 205 regional brain volumes from the UK Biobank. While we could identify effects on parenthood showing similarity between sexes, we found no associations with pregnancy loss, concluding that neuroanatomical patterns of past childbirth partly also reflect traces of parenthood and not solely persisting traces of past pregnancy.

Background Gene regulatory network (GRN) models that are formulated as ordinary differential equations (ODEs) can accurately explain temporal gene expression patterns and promise to yield new insights into important cellular processes, disease progression, and intervention design. Learning such gene regulatory ODEs is challenging, since we want to predict the evolution of gene expression in a way that accurately encodes the underlying GRN governing the dynamics and the nonlinear functional relationships between genes. Most widely used ODE estimation methods either impose too many parametric restrictions or are not guided by meaningful biological insights, both of which impede either scalability, explainability, or both. Results We developed PHOENIX, a modeling framework based on neural ordinary differential equations (NeuralODEs) and Hill-Langmuir kinetics, that overcomes limitations of other methods by flexibly incorporating prior domain knowledge and biological constraints to promote sparse, biologically interpretable representations of GRN ODEs. We tested the accuracy of PHOENIX in a series of in silico experiments, benchmarking it against several currently used tools. We demonstrated PHOENIX’s flexibility by modeling regulation of oscillating expression profiles obtained from synchronized yeast cells. We also assessed the scalability of PHOENIX by modeling genome-scale GRNs for breast cancer samples ordered in pseudotime and for B cells treated with Rituximab. Conclusions PHOENIX uses a combination of user-defined prior knowledge and functional forms from systems biology to encode biological “first principles” as soft constraints on the GRN allowing us to predict subsequent gene expression patterns in a biologically explainable manner.

Background: A major source of noise pollution is traffic. In Germany, the SARS-CoV-2 lockdown caused a substantial decrease in mobility, possibly affecting noise levels. The aim is to analyze the effects of the lockdown measures on noise levels in the densely populated Ruhr Area. We focus on the analysis of noise levels before and during lockdown considering different land use types, weekdays, and time of day. Methods: We used data from 22 automatic sound devices of the SALVE (Acoustic Quality and Health in Urban Environments) project, running since 2019 in Bochum, Germany. We performed a pre/during lockdown comparison of A-weighted equivalent continuous sound pressure levels. The study period includes five weeks before and five weeks during the SARS-CoV-2 induced administrative lockdown measures starting on 16 March 2020. We stratified our data by land use category (LUC), days of the week, and daytime. Results: We observed highest noise levels pre-lockdown in the ‘main street’ and ‘commercial areas’ (68.4 ± 6.7 dB resp. 61.0 ± 8.0 dB), while in ‘urban forests’ they were lowest (50.9 ± 6.6 dB). A distinct mean overall noise reduction of 5.1 dB took place, with noise reductions occurring in each LUC. However, the magnitude of noise levels differed considerably between the categories. Weakest noise reductions were found in the ‘main street’ (3.9 dB), and strongest in the ‘urban forest’, ‘green space’, and ‘residential area’ (5.9 dB each). Conclusions: Our results are in line with studies from European cities. Strikingly, all studies report noise reductions of about 5 dB. Aiming at a transformation to a health-promoting urban mobility can be a promising approach to mitigating health risks of noise in cities. Overall, the experiences currently generated by the pandemic offer data for best practices and policies for the development of healthy urban transportation—the effects of a lower traffic and more tranquil world were experienced firsthand by people during this time.

The COVID-19 pandemic led to widespread mandates requiring the wearing of face masks, which led to debates on their benefits and possible adverse effects. To that end, the physiological effects at the systemic and at the brain level are of interest. We have investigated the effect of commonly available face masks (FFP2 and surgical) on cerebral hemodynamics and oxygenation, particularly microvascular cerebral blood flow (CBF) and blood/tissue oxygen saturation (StO₂), measured by transcranial hybrid near-infrared spectroscopies and on systemic physiology in 13 healthy adults (ages: 23 to 33 y). The results indicate small but significant changes in cerebral hemodynamics while wearing a mask. However, these changes are comparable to those of daily life activities. This platform and the protocol provides the basis for large or targeted studies of the effects of mask wearing in different populations and while performing critical tasks.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

The nuclear pore complex (NPC) is the central portal for macromolecular exchange between the nucleus and cytoplasm. In all eukaryotes, NPCs assemble into an intact nuclear envelope (NE) during interphase, but the process of NPC biogenesis remains poorly characterized. Furthermore, little is known about how NPC assembly leads to the fusion of the outer and inner NE, and no factors have been identified that could trigger this event. Here, we characterize the transmembrane protein Brl1 as an NPC assembly factor required for NE fusion in budding yeast. Brl1 preferentially associates with NPC assembly intermediates and its depletion halts NPC biogenesis, leading to NE herniations that contain inner and outer ring nucleoporins but lack the cytoplasmic export platform. Furthermore, we identify an essential amphipathic helix in the luminal domain of Brl1 that mediates interactions with lipid bilayers. Mutations in this amphipathic helix lead to NPC assembly defects, and cryo-electron tomography analyses reveal multilayered herniations of the inner nuclear membrane with NPC-like structures at the neck, indicating a failure in NE fusion. Taken together, our results identify a role for Brl1 in NPC assembly and suggest a function of its amphipathic helix in mediating the fusion of the inner and outer nuclear membranes.

Intracranial pressure (ICP) is an important parameter to monitor in several neuropathologies. However, because current clinically accepted methods are invasive, its monitoring is limited to patients in critical conditions. On the other hand, there are other less critical conditions for which ICP monitoring could still be useful; therefore, there is a need to develop non-invasive methods. We propose a new method to estimate ICP based on the analysis of the non-invasive measurement of pulsatile, microvascular cerebral blood flow with diffuse correlation spectroscopy. This is achieved by training a recurrent neural network using only the cerebral blood flow as the input. The method is validated using a 50% split sample method using the data from a proof-of-concept study. The study involved a population of infants (n = 6) with external hydrocephalus (initially diagnosed as benign enlargement of subarachnoid spaces) as well as a population of adults (n = 6) with traumatic brain injury. The algorithm was applied to each cohort individually to obtain a model and an ICP estimate. In both diverse cohorts, the non-invasive estimation of ICP was achieved with an accuracy of <4 mm Hg and a negligible small bias. Further, we have achieved a good correlation (Pearson's correlation coefficient >0.9) and good concordance (Lin's concordance correlation coefficient >0.9) in comparison with standard clinical, invasive ICP monitoring. This preliminary work paves the way for further investigations of this tool for the non-invasive, bedside assessment of ICP.

Antibiotic-Refractory Lyme Arthritis (ARLA) involves a complex interplay of T cell responses targeting Borrelia burgdorferi antigens succeeding towards autoantigens by epitope spreading. However, the precise molecular mechanisms driving the pathogenic T cell response in ARLA remain unclear. Our aim was to elucidate the molecular program of disease-specific Th cells. Using flow cytometry, high-throughput T cell receptor (TCR) sequencing and scRNA-seq of CD4+ Th cells isolated from the joints of European ARLA patients, we aimed at inferring antigen specificity through unbiased analysis of TCR repertoire patterns, identifying surrogate markers for disease-specific TCRs and connecting TCR specificity to transcriptional patterns. PD-1hiHLA-DR+CD4+ effector T cells were clonally expanded within the inflamed joints and persisted throughout disease course. Among these cells, we identified a distinct TCRβ motif restricted to HLA-DRB1*11 or *13 alleles. These alleles, being underrepresented in North American ARLA patients, were unexpectedly prevalent in our European cohort. The identified TCRβ motif served as surrogate marker for a convergent TCR response specific to ARLA, distinguishing it from other rheumatic diseases. In the scRNA-seq dataset, the TCRβ motif particularly mapped to peripheral T helper (TPH) cells displaying signs of sustained proliferation, continuous TCR signaling, and expressing CXCL13 and IFN-γ. By inferring disease-specific TCRs from synovial T cells we identified a convergent TCR response in the joints of ARLA patients that continuously fueled the expansion of TPH cells expressing a pathogenic cytokine effector program. The identified TCRs will aid in uncovering the major antigen targets of the maladaptive immune response. Supported by the German Research Foundation (DFG) MO 2160/4-1; the Federal Ministry of Education and Research (BMBF; Advanced Clinician Scientist-Program INTERACT; 01EO2108) embedded in the Interdisciplinary Center for Clinical Research (IZKF) of the University Hospital Würzburg; the German Center for Infection Research (DZIF; Clinical Leave Program; TI07.001_007) and the Interdisciplinary Center for Clinical Research (IZKF) Würzburg (Clinician Scientist Program, Z-2/CSP-30).