Miracle-dz/UniFAR-FTUs huggingface dataset:
Envelopes and vertical amplitudes of disc-crossing orbits

We recently found that regular orbits in axially symmetric galactic disks have their envelopes $Z(R)$ accurately described by the relation $Z(R)\propto[\Sigma_I(R)]^{-1/3}$, if their amplitudes are comparable to the disk thickness, where $\Sigma_I$ is the surface density of the disk (integrated over its whole vertical range). Moreover, the usual adiabatic approximation gives a good description of the orbits' envelopes for low vertical amplitudes. However, these two approaches are apparently disconnected, since their expressions differ qualitatively. Our purpose in this paper is to fill this gap by extending these previous formulae to regular orbits with arbitrary vertical amplitudes inside the disk. We compare existing $Z(R)$ estimates: the razor-thin disk case, the adiabatic approximation (low-amplitude orbits in three-dimensional disks), and the integrated surface-density estimate (high-amplitude orbits in three-dimensional disks) in order to establish a connection between them. The formula presented here links the aforementioned results in an elegant and continuous way, being valid for vertical amplitudes throughout the whole vertical extension of the disk and with an expression which has the same form for all regimes. The advantage of the present formalism is the dependence of $Z(R)$ only on observable quantities, namely the disk's vertically integrated surface density, without the need to obtain the gravitational potential for the system.
Optimizing Bioink Composition for Human Chondrocyte Expression of Lubricin

The surface zone of articular cartilage is the first area impacted by cartilage defects, commonly resulting in osteoarthritis. Chondrocytes in the surface zone of articular cartilage synthesize and secrete lubricin, a proteoglycan that functions as a lubricant protecting the deeper layers from shear stress. 3D bioprinting is a tissue engineering technique that uses cells encapsulated in biomaterials to fabricate 3D constructs. Gelatin methacrylate (GelMA) is a frequently used biomaterial for 3D bioprinting cartilage. Oxidized methacrylated alginate (OMA) is a chemically modified alginate designed for its tunable degradation rate and mechanical properties. To determine an optimal combination of GelMA and OMA for lubricin expression, we used our novel high-throughput human articular chondrocyte reporter system. Primary human chondrocytes were transduced with PRG4 (lubricin) promoter-driven Gaussia luciferase, allowing for temporal assessment of lubricin expression. A lubricin expression driven Design of Experiment screen and subsequent validation identified 14% GelMA/2% OMA for further study. Therefore, 14% GelMA/2% OMA, 14% GelMA and 16% GelMA were 3D bioprinted. The combination of lubricin protein expression and shape retention over the 22 days in culture, determined the 14% GelMA/2%OMA to be the optimal formulation for lubricin secretion.
Forgetting dynamics for items of different categories

How the dynamic evolution of forgetting changes for different materials is unexplored. By using a common experimental paradigm with stimuli of different types, we were able to directly cross-examine the emerging dynamics and we found that even though the presentation sets differ minimally by design, the obtained curves appear to fall on a discrete spectrum. Furthermore, we have previously proposed a model of forgetting based on the notion of retrograde interference with a single integer parameter. All measured curves were compatible with the model with different values of the parameter, hinting to a potential common underlying mechanism of forgetting.
LATTICE DYNAMICS OF BATIO3, PBTIO3, AND PBZRO3 : A COMPARATIVE FIRST-PRINCIPLES STUDY

The full phonon dispersion relations of lead titanate and lead zirconate in the cubic perovskite structure are computed using first-principles variational density-functional perturbation theory, with ab initio pseudopotentials and a plane-wave basis set. Comparison with the results previously obtained for barium titanate shows that the change of a single constituent (Ba to Pb, Ti to Zr) has profound effects on the character and dispersion of unstable modes, with significant implications for the nature of the phase transitions and the dielectric and piezoelectric responses of the compounds. Examination of the interatomic force constants in real space, obtained by a transformation which correctly treats the long-range dipolar contribution, shows that most are strikingly similar, while it is the differences in a few key interactions which produce the observed changes in the phonon dispersions. These trends suggest the possibility of the transferability of force constants to predict the lattice dynamics of perovskite solid solutions.
Highly Realistic Whole Transcriptome Synthesis through Generative Adversarial Networks

The transcriptome is the most extensive and standardized among all biological data, but its lack of inherent structure impedes the application of deep learning tools. This study resolves the neighborhood relationship of protein-coding genes through uniform manifold approximation and projection (UMAP) of high-quality gene expression data. The resultant transcriptome image is conducive to classification tasks and generative learning. Convolutional neural networks (CNNs) trained with full or partial transcriptome images differentiate normal versus lung squamous cell carcinoma (LUSC) and LUSC versus lung adenocarcinoma (LUAD) with over 96% accuracy, comparable to XGBoost. Meanwhile, the generative adversarial network (GAN) model trained with 93 TcgaTargetGtex transcriptome classes synthesizes highly realistic and diverse tissue/cancer-specific transcriptome images. Comparative analysis of GAN-synthesized LUSC and LUAD transcriptome images show selective retention and enhancement of epithelial identity gene expression in the LUSC transcriptome. Further analyses of synthetic LUSC transcriptomes identify a novel role for mitochondria electron transport complex I expression in LUSC stratification and prognosis. In summary, this study provides an intuitive transcriptome embedding compatible with generative deep learning and realistic transcriptome synthesis. Significance Statement Deep learning is most successful when the subject is structured. This study provides a novel way of converting unstructured gene expression lists to 2D-structured transcriptome portraits that are intuitive and compatible with a generative adversarial network (GAN)-based deep learning. The StyleGAN generator trained with transcriptome portrait libraries synthesizes tissue- and disease-specific transcriptomes with significant diversity. Detailed analyses of the synthetic transcriptomes reveal selective enhancement of clinically significant features not apparent in the original transcriptome. Therefore, transcriptome-image-based generative learning may become a significant source of de novo insight generation.
Cortical reactivations predict future sensory responses

Many theories of offline memory consolidation posit that the pattern of neurons activated during a salient sensory experience will be faithfully reactivated, thereby stabilizing the pattern^ 1 , 2 . However, sensory-evoked patterns are not stable but, instead, drift across repeated experiences^ 3 – 6 . Here, to investigate the relationship between reactivations and the drift of sensory representations, we imaged the calcium activity of thousands of excitatory neurons in the mouse lateral visual cortex. During the minute after a visual stimulus, we observed transient, stimulus-specific reactivations, often coupled with hippocampal sharp-wave ripples. Stimulus-specific reactivations were abolished by local cortical silencing during the preceding stimulus. Reactivations early in a session systematically differed from the pattern evoked by the previous stimulus—they were more similar to future stimulus response patterns, thereby predicting both within-day and across-day representational drift. In particular, neurons that participated proportionally more or less in early stimulus reactivations than in stimulus response patterns gradually increased or decreased their future stimulus responses, respectively. Indeed, we could accurately predict future changes in stimulus responses and the separation of responses to distinct stimuli using only the rate and content of reactivations. Thus, reactivations may contribute to a gradual drift and separation in sensory cortical response patterns, thereby enhancing sensory discrimination^ 7 . Offline cortical reactivations predict the gradual drift and separation in sensory cortical response patterns and may enhance sensory discrimination.
Legionella pneumophila modulates host cytoskeleton by an effector of transglutaminase activity

The bacterial pathogen Legionella pneumophila delivers more than 330 effector proteins into host cells through its Dot/Icm type IV secretion system (T4SS) to facilitate its intracellular replication. A number of these effectors modulate organelle trafficking pathways to create a membrane-bound niche called the Legionella containing vacuole (LCV). In this study, we found that L. pneumophila induces F-actin accumulation in host cell cortex by its Dot/Icm substrate RavJ (Lpg0944). RavJ harbors an C101H138D170 motif associated with human tissue transglutaminases (TGs). We showed that RavJ catalyzes a covalent linkage between actin and the Motin family proteins Angiomotin (AMOT) and Angiomotin-like 1 (AMOTL1), proteins known to regulate tube formation and cell migration. Further study revealed that RavJ-induced crosslink between actin and AMOT occurs on its Gln354 residue. Crosslink between actin and AMOT significantly reduces the binding between actin and its binding partner cofilin, suggesting that RavJ inhibits actin depolymerization. We also demonstrated that the metaeffector LegL1 directly interacts with RavJ to antagonize its transglutaminase activity, leading to reduced crosslink between actin and Motin proteins. Our results reveal a novel mechanism of modulating the host actin cytoskeleton by L. pneumophila.
Firing rate adaptation in continuous attractor neural networks accounts for theta phase shift of hippocampal place cells

1 Hippocampal place cells of freely moving animals display ‘theta phase precession’, whereby 2 spikes are fired at successively earlier phases of the 6-10 Hz local field potential (LFP) theta 3 rhythm. In some cases, this phase precession is interleaved with periods of ‘phase procession’, 4 in which spikes are fired at successively later phases of the theta rhythm. Here we propose a 5 continuous attractor neural network (CANN) with firing rate adaptation to understand both kinds 6 of phase shift in place cell firing. We show that firing rate adaptation induces intrinsic mobility of 7 the location represented by the ‘bump’ of population activity, which competes with the extrinsic 8 mobility arising from external sensory inputs. The interplay between these two factors causes the 9 activity bump to oscillate around the external input, resembling the forward and backward sweeps 10 of decoded position during locomotion. Analysis of single cell dynamics reveals that these forward 11 and backward sweeps naturally account for theta phase precession and procession of individual 12 neurons, respectively. Furthermore, by tuning the adaptation strength, we explain the difference 13 between bimodal and unimodal cells, with the former having interleaved phase precession and 14 procession, and the latter having only the predominant phase precession. Our model reproduces 15 other experimental findings, including the constant cycling of theta sweeps along different arms in a 16 T-maze environment, the speed modulation of place cells’ firing frequency, and the continued phase 17 shift after transient silencing of the hippocampus. Our results show how the intrinsic dynamics of 18 a population of place cells affords both types of theta phase shift. We hope that they will aid an 19 understanding of the neural mechanisms supporting theta phase coding in the brain. 20
Electrical Stimulation of Human Mesenchymal Stem Cells on Conductive Chitosan-Polyaniline Substrates Promotes Neural Priming

Electrical stimulation (ES) within conductive polymer substrates has been suggested to promote the differentiation of stem cells toward a neuronal phenotype. The use of conductive scaffolds in tissue regeneration provides a unique and attractive new option to control the amount and location of ES delivery. Scaffold stiffness has also been shown to be an important regulator of stem cells’ behavior and fate. Therefore, to improve stem cell-based regenerative therapies, it is essential to characterize the simultaneous effects of electroconductive substrate stiffness and electric field stimuli on stem cell fate processes. In this study, biodegradable electroconductive substrates based on chitosan-polyaniline (CS-g-PANI) were fabricated with different stiffnesses. Human mesenchymal stem cells (hMSCs) seeded on these scaffolds were electrically stimulated for 14 days with 100 mV/ cm (20 min every day). For hMSCs cultured on soft conductive scaffolds, a morphological change with significant filopodial elongation was observed after 2 weeks of electrically stimulated culture. Compared with stiff conductive CS-g-PANI scaffolds and non-conductive CS scaffolds, for soft conductive CS-g-PANI scaffolds microtubule-associated protein 2 (MAP2) and neurofilament (NF-H) expression increased after application of ES. At the same time, there was a decrease in the expression of the glial markers glial fibrillary acidic protein (GFAP) and vimentin after ES. Furthermore, the elevation of intracellular calcium [Ca2+] during spontaneous, cell-generated Ca2+ transients further suggested that electric field stimulation of hMSCs cultured on conductive CS-g-PANI substrates can promote a neural-like phenotype. Our findings propose that the combination of the soft conductive CS-g-PANI substrate and ES is a promising new tool for enhancing nerve tissue engineering outcomes.
Non-parametric GWAS: Another View on Genome-wide Association Study

Genome-wide association study (GWAS) is a fundamental step for understanding the genetic link to traits (phenotypes) of interest, such as disease, BMI and height. Typically, GWAS estimates the effect of SNP on the phenotype using a linear model by coding SNP as working code, {0, 1, 2}, according to the minor allele frequency. Looking inside the linear model, we find that the coding strategy of SNP plays a key role in detecting SNPs contributed to the phenotype. Specifically, a partial mismatch between the order of the working code and that of the underlying true code will lead to false negatives, which has been ignored for a long time. Motivated by this phenomenon, we propose an indicator of possible false negatives and several non-parametric GWAS methods independent of coding strategy. Results from both simulations and real data analysis show the advantages of new methods in identifying significant loci, indicating their important complementary role in GWAS.
Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions

Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behaviors of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it outperforms existing machine learning methods in the prediction of experimentally measured species abundance and metabolite concentrations. Further, we present an experimental design algorithm to select a set of experimental conditions that simultaneously maximize the expected gain in information and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions. 1 Author summary The functions performed by microbiomes hold tremendous promise to address grand challenges facing society ranging from improving human health to promoting plant growth. To design their properties, flexible computational models that can predict the temporally changing behaviors of microbiomes in response to key environmental parameters are needed. When considering bottom-up design of microbiomes, the number of possible communities grows exponentially with the number of organisms and environmental factors, which makes it challenging to navigate the microbiome function landscape. To overcome these challenges, we present a physically constrained machine learning model for microbiomes and a Bayesian experimental design framework to efficiently navigate the space of possible communities and environmental factors.
LXR-inducible host E3 ligase IDOL targets a human cytomegalovirus reactivation determinant

Liver X receptor (LXR) signaling broadly restricts virus replication; however, the mechanisms of restriction are poorly defined. Here, we demonstrate that the LXR-inducible cellular E3 ligase IDOL (inducible degrader of low-density lipoprotein receptor, LDLR) targets the human cytomegalovirus (HMCV) UL136p33 protein for turnover. UL136 encodes multiple proteins that differentially impact latency and reactivation. UL136p33 is a determinant of reactivation. UL136p33 is targeted for rapid turnover by the proteasome and its stabilization by mutation of lysine residues to arginine results in a failure to quiet replication for latency. We show that IDOL targets UL136p33 for turnover, but not the stabilized variant. IDOL is highly expressed in undifferentiated hematopoietic cells where HCMV establishes latency, but is sharply downregulated upon differentiation, a stimulus for reactivation. We hypothesize that IDOL maintains low levels of UL136p33 for the establishment of latency. Consistent with this, knockdown of IDOL impacts viral gene expression in WT HCMV infection, but not in infection where UL136p33 has been stabilized. Further, induction of LXR signaling restricts WT HCMV reactivation from latency, but does not affect replication of a recombinant virus expressing a stabilized variant of UL136p33. This work establishes the UL136p33-IDOL interaction as a key regulator of the bistable switch between latency and reactivation. It further suggests a model whereby a key viral determinant of HCMV reactivation is regulated by a host E3 ligase and acts as a sensor at the tipping point between the decision to maintain the latent state or exit latency for reactivation. Importance Herpesviruses establish life-long latent infections, which pose an important risk for disease particularly in the immunocompromised. Our work is focused on the beta-herpesvirus, human cytomegalovirus (HCMV) that latently infects the majority of the population worldwide. Defining the mechanisms by which HCMV establishes latency or reactivates from latency is important to controlling viral disease. Here, we demonstrate that the cellular inducible degrader of low-density lipoprotein receptor, IDOL, targets a HCMV determinant of reactivation for degradation. The instability of this determinant is important for the establishment of latency. This work defines a pivotal virus-host interaction that allows HCMV to sense changes in host biology to navigate decisions to establish latency or replicate.
Nonlinear balance and gravity-inertial wave saturation in a simple atmospheric model

Numerical experiments with a highly truncated, forced-dissipative version of the shallow water equations suggest that for realistic atmospheric parameters, high-frequency inertial-gravity waves are almost always present in the solution, even in the limit of small Rossby numbers and long times. The waves are intermittent in character and appear to be generated naturally by the nonlinear interactions of the system. This behaviour is at variance with the notion that under normal atmospheric scaling, there is an attracting invariant manifold embedded in the full phase space which is completely free of fast oscillations (i.e., corresponding to “super quasigeostrophic” motion). The high-frequency component leads to systematic differences between actual flow and high-order balanced states, suggesting that there is an inherent limitation to the amount of information about the divergent part of the flow that can be gleaned from the rotational motion. The information is expected to be a rapidly decreasing function of Rossby number. DOI: 10.1111/j.1600-0870.1986. tb00416.x
AhR-mediated activation of innate lymphocytes restrains tissue-resident memory-like CD8+ T cell responses during contact hypersensitivity

Allergic contact dermatitis (ACD) and the mouse model of hapten-induced contact hypersensitivity (CHS) are inflammatory skin responses triggered by the repeated exposure to exogenous allergens and haptens. ACD and CHS effector responses have been extensively studied, but the regulatory mechanisms that control inflammation and determine the kinetics of its resolution are still incompletely understood. In addition, although CHS can be mediated by both innate and adaptive effector cells in a non-redundant manner, leading to distinct skin pathologies, their interplay during the course of inflammation remains so far unaddressed. Here, we show that NKp46+ innate lymphoid cells (ILCs) limit the extent of CHS inflammation by modulating the CD8+ TRM immune compartment. This regulatory effect of ILCs depends on the expression of the ligand-induced transcription factor aryl-hydrocarbon receptor (AhR). AhR-deficiency in NKp46+ ILCs did not affect the memory response to hapten, but led to spatial propagation and amplification of inflammatory response in the skin. This phenotype correlated with increased numbers of Ifng-producing CD8+ TRM-like cells and neutrophilic infiltration in the skin. Our study thereby demonstrates a novel AhR-driven innate-adaptive immune interplay in regulating skin inflammation.
Photospheric Sources and Brightening of the Internetwork Chromosphere

We analyze a unique 9 hr sequence of near-simultaneous, high-resolution and high-cadence G-band and K-line solar filtergrams, together with magnetograms of lower cadence and resolution. Our focus is on the phenomena surrounding discrete photospheric darkening "events" in internetwork G-band intensities. 72% of the darkenings are followed after 2 minutes by K-line brightenings. In the remaining cases, the darkenings are instead preceded by K-line brightenings 2 minutes earlier. Equivalent results are found when reference is shifted to K-line brightening events, although these two sets overlap by no more than 15%. The timing and coupling of the photospheric darkenings and chromospheric brightenings appear to be regulated by a preexisting 4 minute oscillation of the solar atmosphere. Other oscillations with periods in the range 1-8 minutes also are present, and in general the wave power is doubled at the time of an event. Our results favor an acoustic source for enhanced amplitudes of K-line intensity oscillations. The magnetic field acts as a passive tracer of horizontal photospheric flows that converge on the photospheric darkening events and then rebound.