Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term stimulation of collective cell migration in tissues reprograms long-term supracellular dynamics

The ability to program collective cell migration can allow us to control critical multicellular processes in development, regenerative medicine, and invasive disease. However, while various technologies exist to make individual cells migrate, translating these tools to control myriad, collectively interacting cells within a single tissue poses many challenges. For instance, do cells within the same tissue interpret a global migration ‘command’ differently based on where they are in the tissue? Similarly, since no stimulus is permanent, what are the long-term effects of transient commands on collective cell dynamics? We investigate these questions by bioelectrically programming large epithelial tissues to globally migrate ‘rightward’ via electrotaxis. Tissues clearly developed distinct rear, middle, side, and front responses to a single global migration stimulus. Furthermore, at no point post-stimulation did tissues return to their pre-stimulation behavior, instead equilibrating to a third, new migratory state. These unique dynamics suggested that programmed migration resets tissue mechanical state, which was confirmed by transient chemical disruption of cell-cell junctions, analysis of strain wave propagation patterns, and quantification of cellular crowd dynamics. Overall, this work demonstrates how externally driving the collective migration of a tissue can reprogram baseline cell-cell interactions and collective dynamics, even well beyond the end of the global migratory cue, and emphasizes the importance of considering the supracellular context of tissues and other collectives when attempting to program crowd behaviors.
T helper cell-licensed mast cells promote inflammatory Th17 cells

CD4+ T helper cells (Th) infiltrate sites of inflammation and orchestrate the immune response by instructing local leukocytes. Mast cells (MCs) are tissue sentinel cells particularly abundant in skin and mucosa. Here, we analyzed the interplay between human MCs and Th cells and, through the application of RNAseq and functional assays, showed that Th cells induced a specific transcriptomic program in helped MCs (named here MCTH) driving them toward an inflammatory phenotype. The gene signature of MCTH indicated that MCs helped by Th cell acquired in turn the capacity to regulate effector T cell response through wide-range of soluble and membrane ligands. Accordingly, we showed that MCTH promoted Th17 cells and notably an inflammatory subset of Th17, producing both IFN-γ and GM-CSF, through a PGE2 and IL-1β axis. Our findings demonstrate that activated effector/memory CD4+ T cells activate and instruct resting MCs toward a specific differentiated pro-inflammatory phenotype endowed with the capacity to speak back to effector T cells and to mold their functions.
Unravelling the effects of disease-associated mutations in TDP-43 protein via molecular dynamics simulation and machine learning

Over the last two decades, the pathogenic aggregation of TAR DNA-binding protein 43 (TDP-43) is found to be strongly associated with several fatal neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTD), etc. While the mutations and truncation in TDP-43 protein have been suggested to be responsible for TDP-43 pathogenesis by accelerating the aggregation process, the effects of these mutations on the bio-mechanism of pathologic TDP-43 protein remained poorly understood. Investigating this at the molecular level, we formulized an integrated workflow of molecular dynamic simulation and machine learning models (MD-ML). By performing an extensive structural analysis of three disease-related mutations (i.e. I168A, D169G, and I168A-D169G) in the conserved RNA recognition motifs (RRMs) of TDP-43 and we observed that the I168A-D169G double mutant delineates the highest packing of the protein inner core as compared to the other mutations, which may indicate more stability and higher chances of pathogenesis. Moreover, through our MD-ML workflow, we identified the biological descriptors of TDP-43 which includes the interacting residue pairs and individual protein residues that influence the stability of the protein and could be experimentally evaluated to develop potential therapeutic strategies.
Heritable changes in division speed accompany the diversification of single T cell fate

Rapid clonal expansion of antigen specific T cells is a fundamental feature of adaptive immune responses. It enables the outgrowth of an individual T cell into thousands of clonal descendants that diversify into short-lived effectors and long-lived memory cells. Clonal expansion is thought to be programmed upon priming of a single naïve T cell and then executed by homogenously fast divisions of all of its descendants. However, the actual speed of cell divisions in such an emerging ‘T cell family’ has never been measured with single-cell resolution. Here, we utilize continuous live-cell imaging in vitro to track the division speed and genealogical connections of all descendants derived from a single naïve CD8+ T cell throughout up to ten divisions of activation-induced proliferation. This comprehensive mapping of T cell family trees identifies a short burst phase, in which division speed is homogenously fast and maintained independent of external cytokine availability or continued T cell receptor stimulation. Thereafter, however, division speed diversifies and model-based computational analysis using a novel Bayesian inference framework for tree-structured data reveals a segregation into heritably fast and slow dividing branches. This diversification of division speed is preceded already during the burst phase by variable expression of the interleukin-2 receptor alpha chain. Later it is accompanied by selective expression of memory marker CD62L in slower dividing branches. Taken together, these data demonstrate that T cell clonal expansion is structured into subsequent burst and diversification phases the latter of which coincides with specification of memory vs. effector fate. Significance Rapid clonal expansion of antigen-specific T cells is a fundamental feature of adaptive immune responses. Here, we utilize continuous live-cell imaging in vitro to track the division speed and genealogical connections of all descendants derived from a single naïve CD8+ T cell throughout up to ten divisions of activation-induced proliferation. Bayesian inference of tree-structured data reveals that clonal expansion is divided into a homogenously fast burst phase encompassing two to three divisions and a subsequent diversification phase during which T cells segregate into quickly dividing effector T cells and more slowly cycling memory precursors. Our work highlights cell cycle speed as a major heritable property that is regulated in parallel to key lineage decisions of activated T cells.
Where and how does d-amphetamine act to reveal antipsychotic-induced dopamine supersensitivity in rats?

Antipsychotic treatment can produce a dopamine supersensitive state. In both schizophrenia patients and rodents, this is linked to antipsychotic treatment failure. In rodents, dopamine supersensitivity is often confirmed by an exaggerated behavioural response to the indirect monoamine agonist, d-amphetamine, after discontinuation of antipsychotic treatment. Here we investigated where and how d-amphetamine acts to trigger behavioural expression of dopamine supersensitivity, as this could uncover pathophysiological mechanisms underlying this supersensitivity. First, we examined the contributions of a central increase in dopamine/monoamine activity. Haloperidol-treated rats showed a potentiated psychomotor response to systemic d-amphetamine, confirming dopamine supersensitivity. However, they showed a normal psychomotor response to an increase in ventral midbrain dopamine impulse flow or to intracerebroventricular injection of d-amphetamine. This suggests that d-amphetamine’s peripheral effects are required for a supersensitive response. Second, we determined the specific contributions of dopamine neurotransmission. The D2 agonist quinpirole, but not the D1 agonist SKF38393 or the dopamine reuptake blocker GBR12783 produced a supersensitive psychomotor response in haloperidol-treated rats. In these rats, the D1 antagonist SCH39166 decreased d-amphetamine-induced psychomotor activity, whereas the D2 antagonist sulpiride enhanced it. Thus, when d-amphetamine triggers a supersensitive response, this involves both D1- and D2-mediated transmission. Finally, we measured d-amphetamine-induced changes in D1- and D2-mediated intracellular signalling pathways in the striatum. In haloperidol-treated rats, a supersensitive response to d-amphetamine was linked to enhanced GSK3β activity and suppressed ERK1/2 activity in the nucleus accumbens, suggesting increased D2-mediated signalling. These findings provide new insights into the neurobiology of antipsychotic-evoked dopamine supersensitivity.
Synaptic Release Potentiation at Aging Auditory Ribbon Synapses

Age-related hidden hearing loss is often described as a cochlear synaptopathy that results from a progressive degeneration of the inner hair cell (IHC) ribbon synapses. The functional changes occurring at these synapses during aging are not fully understood. Here, we characterized this aging process in IHCs of C57BL/6J mice, a strain which is known to carry a cadherin23 mutation and experiences early hearing loss with age. These mice, while displaying a large increase in auditory brainstem thresholds due to 50 % loss of IHC synaptic ribbons at middle age (postnatal day 365), paradoxically showed enhanced acoustic startle reflex suggesting a hyperacusis-like response. The auditory defect was associated with a large shrinkage of the IHCs’ cell body and a drastic enlargement of their remaining presynaptic ribbons which were facing enlarged postsynaptic AMPAR clusters. Presynaptic Ca2+ microdomains and the capacity of IHCs to sustain high rates of exocytosis were largely increased, while on the contrary the expression of the fast repolarizing BK channels, known to negatively control transmitter release, was decreased. This age-related synaptic plasticity in IHCs suggested a functional potentiation of synaptic transmission at the surviving synapses, a process that could partially compensate the decrease in synapse number and underlie hyperacusis.
Cognitive, synaptic and neuropathological changes in Alzheimer’s brain-inoculated mice

Alzheimer’s disease is characterized by lesions including extracellular amyloid-β plaques, intracellular tau accumulations, activated microglia around amyloid plaques and synaptic alterations that lead to cognitive impairments. Tau lesions occur in the form of tau-positive aggregates surrounding amyloid-β deposits leading to neuritic plaques, neuropil threads, and neurofibrillary tangles. The interactions between these lesions and their contribution to cognitive impairments are still debated.In this study, through the intrahippocampal inoculation of human Alzheimer-brain extracts into an amyloid-β plaque-bearing mouse model, we induced amyloid plaques, Alzheimer-like tau- positive neuritic plaques, neuropil threads and neurofibrillary tangles that spread through the brain, microgliosis as well as synaptic and cognitive impairments in some animals.Neuritic plaques, but not other tau-positive lesions, were detected in both non-inoculated and control-brain-inoculated amyloid-β plaque-bearing mice. Alzheimer-brain extracts inoculation further increased tau pathology within neuritic plaques. As opposed to the control-brain extract, Alzheimer-brain extracts induced neuropil threads and neurofibrillary tangles next to the inoculation site. These lesions also spread to connected brain regions such as the perirhinal/entorhinal cortex.Different levels of synaptic loss and cognitive impairments were induced by inoculating two types of Alzheimer-brain extracts originating from slowly (clAD) or rapidly evolving forms of Alzheimer’s disease (rpAD), although no difference in amyloid-β deposition, tau pathology and microgliosis was identified between clAD- and rpAD-inoculated animals.A complementary analysis investigated relationships between synaptic or cognitive impairments and Alzheimer pathology. Synaptic defects were associated with the severity of tau lesions and with lower microglial load. Lower cognitive scores correlated with synaptic defects as well as with amyloid and tau pathologies in the hippocampus, and with tau lesions in the perirhinal/entorhinal cortex.Taken together, this study shows that amyloid-β deposits are sufficient to induce tau pathology within neuritic plaques in Aβ plaque–bearing mice that do not overexpress tau. Alzheimer-brain extract inoculation however increases tau pathology within neuritic plaques, and induces neuropil threads and neurofibrillary tangles that spread in the brain. Inoculation of different human Alzheimer-brain extracts leads to different levels of synaptic loss and cognitive impairments. Synaptic loss and cognitive impairments are associated with multiple factors such as the severity of tau lesions and lower microglial activity, as well as amyloid deposition for cognitive changes. These results highlight that microglial activity may protect against synaptic loss.
Robust image-based risk predictions from the deep learning of lung tumors in motion

Deep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e. degenerated object appearance or blur) on DL model accuracy remains unclear. Here we examine the impact of tumor motion on an image-based DL framework that predicts local failure risk for patients with lung cancer receiving stereotactic body radiotherapy. We show that an image-based DL risk score derived from a series of four-dimensional CT images varies in a deterministic, sinusoidal trajectory in phase with the respiratory cycle. Critically, the mean of the scores derived from time series of images and the score obtained from free breathing scans (average tumor position) were highly associated (Pearson r = 0.99). These results indicate that deep learning models of tumors in motion can be robust to fluctuations in object appearance due to movement.
COVID-19 outcomes in hospitalized puerperal, pregnant, and neither pregnant nor puerperal women

Objective To compare hospitalized reproductive age women with COVID-19 who were pregnant, puerperal, or neither one nor the other in terms of demographic and clinical characteristics and disease progression using Brazilian epidemiological data. Methods A retrospective analysis of the records of the Information System of the Epidemiological Surveillance of Influenza of the Health Ministry of Brazil was performed. It included the data of female patients aged 10 to 49 years hospitalized because of severe COVID-19 disease (RT-PCR+ for SARS-CoV-2), from February 17, 2020, to January 02, 2021. They were separated into 3 groups: pregnant, puerperal, and neither pregnant nor puerperal. General comparisons and then adjustments for confounding variables (propensity score matching [PSM]) were made, using demographic and clinical characteristics, disease progression (admission to the intensive care unit [ICU] and invasive or noninvasive ventilatory support), and outcome (cure or death). Deaths were analyzed in each group according to comorbidities, invasive or noninvasive ventilatory support, and admission to the ICU. Results As many as 40,640 reproductive age women hospitalized for COVID-19 were identified: 3,372 were pregnant, 794 were puerperal, and 36,474 were neither pregnant nor puerperal. Groups were significantly different in terms of demographic data and comorbidities. Pregnant and puerperal women were less likely to be symptomatic than the women who were neither one nor the other. Pregnant women, however, had a higher frequency of cough, anosmia, and ageusia. Puerperal women had a worse prognosis than pregnant women with respect to admission to the ICU, invasive ventilatory support, and death. Conclusion Puerperal women were at a higher risk for serious outcomes (need for the ICU, need for invasive and noninvasive ventilatory support, and death) than pregnant women.
Computation and Storage Efficient Sparse MART Algorithm for 2-D, 3-D Reconstruction from Fan Beam, Cone-Beam Projection Data

ABSTRACT Algebraic reconstruction algorithms are a better choice compared to transform-based algorithms whenever projection data is limited in nature. High computational cost and huge memory requirements are two major downsides of iterative reconstruction methods. Among all algebraic techniques, the Multiplicative Algebraic Reconstruction Technique (MART) is most popular because it maximizes the entropy (of the image) in the limiting case. In the present work, our ultimate goal is to reduce computational complexity and cope with the huge storage scenario of the MART algorithm. We propose a new sparse MART algorithm (Sp-MART) and test it with two-dimensional and three-dimensional (2D/3D) numerical data. A more accurate and efficient geometrical formula for calculating intersection length is also presented. Experimental projection data of human tooth and drip irrigation pipe is processed for further validation of the Sp-MART algorithm. Reconstructions of real specimens are also done using the FDK algorithm. The difference between two algorithms are investigated by calculating the structural similarity index (SSIM) and the L2 error of the results.
Centering Racially Minoritized Student Voices in High-Impact Practices

Robert M. Gonyea is Associate Director, Center for Postsecondary Research, Indiana University Bloomington, where he coordinates research and reporting for the National Survey of Student Engagement and related projects. Alexander C. McCormick is Associate Professor of Educational Leadership and Policy Studies and Director of the National Survey of Student Engagement, Indiana University Bloomington.
Effect of Rosemary (Rosmarinus officinalis) Extract on the Antioxidant Status and Proximate Composition of Prawn (Macrobrachium rosenbergii) Meat

ABSTRACT Considering the potential of rosemary as a metabolic modulator, the use of this plant as a supplement in the diet of freshwater prawns can contribute to improving the physiological and metabolic conditions of prawns. This study aimed to evaluate the effects of rosemary extract supplementation on the physiology and body composition of Macrobrachium rosenbergii prawn. Four formulations were made containing 0%, 2%, 5%, and 15% of rosemary extract; the prawns received supplemented diet for 90 days. The zootechnical parameters, the antioxidant status, aspartate aminotransferase (AST) and alanine aminotransferase (ALT), glutathione reductase (GR), glutathione S-transferase (GST) activities, reduced glutathione (GSH) levels and thiobarbituric acid reactive substances (TBARS), and the proximate composition of meat were evaluated. Reduced glutathione levels and increased GR and GST activities were observed for all rosemary extract concentrations studied. In addition, the supplementation with 15% of rosemary extract significantly increased the lipid peroxidation in prawn liver. Regarding the proximate composition of meat, the rosemary extract led to significant reductions in lipid content without affecting the other parameters. The addition of rosemary extract had a pro-oxidant potential and dose-dependent effect, leading to significant physiological changes reflected in the composition of prawn meat.
Mapping 30 m Fractional Forest Cover over China's Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods

The accurate monitoring of forest cover and its changes are essential for environmental change research, but current satellite products for forest coverage carry many uncertainties. This study used 30-m Landsat-8 data, and aggregated 1-m GaoFen-2 (GF-2) satellite images to construct the training samples and used multiple machine learning algorithms (MLAs) to estimate the fractional forest cover (FFC) in China’s Three North Region (TNR). In this study, multiple MLAs were merged to construct stacked generalization (SG) models based on the idea of SG, and the performances of the MLAs in the FFC estimation were evaluated. The results of the 10-fold cross-validation showed that all non-linear algorithms had a good performance, with an R2 value of greater than 0.8 and a root-mean square error (RMSE) of less than 0.05. In the bagging ensemble, the random forest (RF) (R2 = 0.993, RMSE = 0.020) model performed the best and in the boosting ensemble, the light gradient boosted machine (LGBM) (R2 = 0.992, RMSE = 0.022) performed the best. Although the evaluation index of the RF is slightly better than that of the LGBM, the independent validation results show that the two models have similar performances. The model evaluation results of the independent datasets showed that, in the SG model, the performance of the SG(LGBM) (R2 = 0.991, RMSE = 0.034) was better than that of the single or non-ensemble model. Comparing the FFC estimates of our model with those of existing datasets showed that our model exhibited more forest spatial distribution details and higher accuracy in complex landscapes. Overall, in this study, the method of using high-resolution remote sensing (RS) images to extract samples for FFC estimation is feasible. Our results demonstrate the potential of the ensemble MLAs to map the FFC. The research results also show that among many MALs, the RF algorithm is the most suitable algorithm for estimating FFC, which provides a reference for future research.
Integration of Satellite InSAR with a Wireless Network of Geotechnical Sensors for Slope Monitoring in Urban Areas: The Pariana Landslide Case (Massa, Italy)

Slow to extremely slow landslides in urban areas may cause severe damage to buildings and infrastructure that can lead to the evacuation of local populations in case of slope accelerations. Monitoring the spatial and temporal evolution of this type of natural hazard represents a major concern for the public authorities in charge of risk management. Pariana, a village with 400 residents located in the Apuan Alps (Massa, Tuscany, Italy), is an example of urban settlement where the population has long been forced to live with considerable slope instability. In the last 30 years, due to the slope movements associated with a slow-moving landslide that has affected a significant portion of the built-up area, several buildings have been damaged, including a school and the provincial road crossing the unstable area, leading to the need for an installation of a slope monitoring system with early warning capabilities, in parallel with the implementation of mitigation works. In this paper, we show how satellite multi-temporal interferometric synthetic aperture radar (MT-InSAR) data can be effectively used when coupled with a wireless sensor network made of several bar extensometers and a borehole inclinometer. In fact, thanks to their wide area coverage and opportunistic nature, satellite InSAR data allow one to clearly identify the spatial distribution of surface movements and their long-term temporal evolution. On the other hand, geotechnical sensors installed on specific elements at risk (e.g., private buildings, retaining walls, etc.), and collected through Wi-Fi dataloggers, provide near real-time data that can be used to identify sudden accelerations in slope movements, subsequently triggering alarms. The integration of those two-monitoring systems has been tested and assessed in Pariana. Results show how a hybrid slope monitoring program based on the two different technologies can be used to effectively monitor slow-moving landslides and to identify sudden accelerations and activate a response plan.