Miracle-dz/UniFAR-FTUs huggingface dataset:
In vivo fate of systemically administered encapsulin protein nanocages and implications for their use in targeted drug delivery

Encapsulins, self-assembling protein nanocages derived from prokaryotes, are promising nanoparticle-based drug delivery systems (NDDS). However, the in vivo behavior and fate of encapsulins are poorly understood. In this pre-clinical study, we probe the interactions between the model encapsulin from Thermotoga maritima (TmEnc) and key biological barriers encountered by NDDS. Here, a purified TmEnc formulation that exhibited colloidal stability, storability, and blood compatibility was intravenously injected into BALB/c mice. TmEnc had an excellent nanosafety profile, with no abnormal weight loss or gross pathology observed, and only temporary alterations in toxicity biomarkers detected. Notably, TmEnc demonstrated immunogenic properties, inducing the generation of nanocage-specific IgM and IgG antibodies, but without any prolonged pro-inflammatory effects. An absence of antibody cross-reactivity also suggested immune-orthogonality among encapsulins systems. Moreover, TmEnc formed a serum-derived protein corona on its surface which changed dynamically and appeared to play a role in immune recognition. TmEnc’s biodistribution profile further revealed its sequestration from the blood circulation by the liver and then biodegraded within Kupffer cells, thus indicating clearance via the mononuclear phagocyte system. Collectively, these findings provide critical insights into how encapsulins behave in vivo, thereby informing their future design, modification, and application in targeted drug delivery.
Brain aromatase dynamics reflect parental experience and behaviour in male mice

In most mammals, providing paternal care is not automatic. In house mice, experience with pups governs the extent and quality of paternal care. First-time fathers undergo a dramatic transition from ignoring or killing pups to caring for pups. The behavioural shift occurs together with changes in brain estrogen signalling as indicated by changes in estrogen receptor presence and distribution in multiple areas regulating olfaction, emotion, and motivation. Here, we report estrogen dynamics by altered local estrogen synthesis via changes in aromatase, a key enzyme catalysing the conversion of testosterone to estrogen. The amount of paternal experience (5 or 27 days) was associated with increased numbers of immunocytochemically-identified aromatase expressing cells in the medial and cortical amygdala, piriform cortex, and ventromedial hypothalamus. In the lateral septum, and to some extent in the medial preoptic area, parental experience increased aromatase only in fathers with 27 days of experience, and only in the right brain hemisphere, a new case of brain-functional lateralisation with experience. Nuclei/areas associated with maternal care (medial preoptic area, bed nucleus of stria terminalis, nucleus accumbens) exhibited a left-hemisphere advantage in aromatase expressing cells. This newly found lateralisation may contribute to the left-hemisphere dominant processing and perception of pup calls to release parental behaviour.
An autophagy program that promotes T cell egress from the lymph node controls responses to immune checkpoint blockade

Lymphatic endothelial cells (LECs) lining the lymphatic vessels of the lymph node (LN) parenchyma orchestrate leukocyte trafficking and peripheral T cell dynamics. T cell responses to immunotherapy largely rely on peripheral T cell recruitment in tumors. Yet, a systematic and molecular understanding of how LECs within the LNs control T cell dynamics under steady state and tumor-bearing conditions is lacking. Using intravital and high-resolution imaging combined with immune phenotyping, we show that LEC-specific deletion of the essential autophagy gene Atg5 alters intranodal positioning of lymphocytes and accrues their persistence in the LNs, by increasing the availability of the main egress signal S1P. Single-cell RNA-sequencing of tumor-draining LNs from WT and ATG5LEC-KO mice unveils that loss of ATG5 remodels niche-specific LEC phenotypes, involved in molecular pathways regulating lymphocyte trafficking and LEC-T cell interactions. Functionally, loss of LEC-autophagy prevents recruitment of tumor-infiltrating T cells and NK cells and abrogates tumor regression in response to anti-PD-1 or anti-CTLA4-based immunotherapy. Thus, a unique LEC-autophagy program boosts immune-checkpoint responses by guiding systemic T cell dynamics. Graphical Abstract
Measuring Connectivity in Linear Multivariate Processes with Penalized Regression Techniques

The evaluation of time and frequency domain measures of coupling and causality relies on the parametric representation of linear multivariate processes. The study of temporal dependencies among time series is based on the identification of a Vector Autoregressive model. This procedure is pursued through the definition of a regression problem solved by means of Ordinary Least Squares (OLS) estimator. However, its accuracy is strongly influenced by the lack of data points and a stable solution is not always guaranteed. To overcome this issue, it is possible to use penalized regression techniques. The aim of this work is to compare the behavior of OLS with different penalized regression methods used for a connectivity analysis in different experimental conditions. Bias, accuracy in the reconstruction of network structure and computational time were used for this purpose. Different penalized regressions were tested by means of simulated data implementing different ground-truth networks under different amounts of data samples available. Then, the approaches were applied to real electroencephalographic signals (EEG) recorded from a healthy volunteer performing a motor imagery task. Penalized regressions outperform OLS in simulation settings when few data samples are available. The application on real EEG data showed how it is possible to use features extracted from brain networks for discriminating between two tasks even in conditions of data paucity. Penalized regression techniques can be used for brain connectivity estimation and can be exploited for the computation of all the connectivity estimators based on linearity assumption overcoming the limitations imposed by the classical OLS.
Tar patties are hotspots of hydrocarbon turnover and nitrogen fixation during a nearshore pollution event in the oligotrophic southeastern Mediterranean Sea

Weathered oil, that is, tar, forms hotspots of hydrocarbon degradation by complex biota in marine environment. Here, we used marker gene sequencing and metagenomics to characterize the communities of bacteria, archaea and eukaryotes that colonized tar patties and control samples (wood, plastic), collected in the littoral following an offshore spill in the warm, oligotrophic southeastern Mediterranean Sea (SEMS). We show aerobic and anaerobic hydrocarbon catabolism niches on tar interior and exterior, linking carbon, sulfur and nitrogen cycles. Alongside aromatics and larger alkanes, short-chain alkanes appear to fuel dominant populations, both the aerobic clade UBA5335 (Macondimonas), anaerobic Syntropharchaeales, and facultative Mycobacteriales. Most key organisms, including the hydrocarbon degraders and cyanobacteria, have the potential to fix dinitrogen, potentially alleviating the nitrogen limitation of hydrocarbon degradation in the SEMS. We highlight the complexity of these tar-associated communities, where bacteria, archaea and eukaryotes co-exist, exchanging metabolites and competing for resources and space.
TULIP — a Transformer based Unsupervised Language model for Interacting Peptides and T-cell receptors that generalizes to unseen epitopes

The accurate prediction of binding between T-cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a novel method, TULIP, that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.
Energetics of tidally generated internal waves for nonuniform stratification

Abstract The generation of internal waves from barotropic tides can be quantified in terms of the conversion rates. These have often been obtained by applying the WKB approximation, which yields an expression for the conversion rates which is proportional to the seabed buoyancy frequency NB. For small values of NB or strong variations of the buoyancy profile N(z), this gives unreliable results. Using homogenization theory it is here shown that the conversion rate instead depends on the value of N(z) averaged over a vertical region at the bottom of the same magnitude as the vertical length-scale of the internal wave, which for the lowest modes is of the same order as the entire ocean depth. This gives a substantially larger conversion rate.
Rapid mode switching facilitates the growth of Trichodesmium: A model analysis

Trichodesmium is one of the dominant dinitrogen(N2)-fixers in the ocean, influencing global carbon and nitrogen cycles through its biochemical reactions. Although the photosynthetic activity of Trichodesmium fluctuates rapidly at the cellular level, the physiological or ecological advantage of this characteristic is not clear. Here we develop a metabolic model of Trichodesmium that can perform dinitrogen (N2) fixation during the daytime. We examined (1) the effect of the duration of switches between photosynthetic and non-photosynthetic cellular states and (2) the effect of the presence and absence of N2 fixation in photosynthetic states. Our results show that a rapid switch between photosynthetic and non-photosynthetic states increases Trichodesmium growth rates by improving metabolic efficiencies due to optimized C and N metabolism. Our results show the possibility and advantage of the rapid switch, providing a strategy for previous observations that all Trichodesmium cells can contain nitrogenase, which was previously considered to be a paradox. This study reveals the importance of fluctuating photosynthetic activity and provides a mechanism for daytime N2 fixation that allows Trichodesmium to fix N2 aerobically in the global ocean.
Recurrent composite markers of cell types and states

Determining concise sets of genomic markers that identify cell types and states within tissue ecosystems remains challenging. To address this challenge, we developed Recurrent Composite Markers for Biological Identities with Neighborhood Enrichment (RECOMBINE). Validations of RECOMBINE with simulation and transcriptomics data in bulk, single-cell and spatial resolutions demonstrated the method’s ability for unbiased selection of composite markers that characterize biological subpopulations. RECOMBINE captured markers of mouse visual cortex from single-cell RNA sequencing data and provided a gene panel for targeted spatial transcriptomics profiling. RECOMBINE identified composite markers of CD8 T cell states including GZMK+HAVCR2- effector memory cells associated with anti-PD1 therapy response. The method outperformed differential gene expression analysis in characterizing a rare cell subpopulation within mouse intestine. Using RECOMBINE, we uncovered hierarchical gene programs of inter- and intra-tumoral heterogeneity in breast and skin tumors. In conclusion, RECOMBINE offers a data-driven approach for unbiased selection of composite markers, resulting in improved interpretation, discovery, and validation of cell types and states.
Nonlinear feedback modulation contributes to the optimization of flexible decision-making

Neural activity in the primate brain correlates with both sensory evaluation and action selection aspects of decision-making. However, the intricate interaction between these distinct neural processes and their impact on decision behaviors remains unexplored. Here, we examined the interplay of these decision processes in posterior parietal cortex (PPC) when monkeys performed a flexible decision task. We found that the PPC activity related to monkeys’ abstract decisions about visual stimuli was nonlinearly modulated by monkeys’ following saccade choices directing outside each neuron’s response field. Recurrent neural network modeling indicated that the feedback connections, matching the learned stimuli-response associations during the task, mediated such feedback modulation. Further analysis on network dynamics revealed that selectivity-specific feedback connectivity intensified the attractor basins of population activity underlying saccade choices, thereby increasing the consistency of flexible decisions. These results highlight an iterative computation between different decision processes, mediated primarily by precise feedback connectivity, contributing to the optimization of flexible decision-making.
The Effect of Gradual Ovarian Failure on Dynamic Muscle Function and the Role of High Intensity Interval Training on Mitigating Impairments

Skeletal muscle function is impaired in menopause and exercise may mitigate tshis decline. We used the VCD model of menopause to investigate the effects of gradual ovarian failure on skeletal muscle contractile function and whether high intensity interval training (HIIT) can mitigate impairments. Sexually mature female CD-1 mice were assigned to one of three groups: control (n=5), VCD-sedentary (n=5), or VCD-training (n=5). Following ovarian failure, the VCD-training group underwent 8 weeks of uphill HIIT. Mice were sacrificed 8 weeks after ovarian failure, representing late menopause. Single muscle fibres from the soleus (SOL) and extensor digitorum longus (EDL) muscles were dissected, chemically permeabilized, and mechanically tested. Single muscle fibres were maximally activated (pCa 4.5) then isotonic load clamps were performed to calculate force-velocity-power curves. Absolute force and peak power were 31% and 32% lower in VCD-sedentary fibres compared to control fibres, respectively, in both SOL and EDL muscles. Despite reductions in absolute force and therefore lighter relative loads imposed during the isotonic contractions in VCD-sedentary fibres, there were no concomitant increases in contractile velocity. HIIT was partially effective at mitigating power loss (22% higher peak power in VCD-training compared to VCD-sedentary), but only in fast-type SOL fibres. These findings indicate that ovarian failure impairs dynamic contractile function – likely through a combination of lower force-generating capacity and slower shortening velocity – and that HIIT may be insufficient to completely counteract the deleterious effects of menopause at the cellular level. New & Noteworthy Reductions in circulating ovarian hormones impair static muscle contractile performance, but less is known about dynamic properties like power. Typically, rodent models of menopause completely remove the ovaries and fail to mimic the prolonged and complex hormonal transition that includes a retention in ovarian androgen production. Using an ovary intact VCD model of ovarian failure, we found that single fibre power was impaired compared with controls in both SOL and EDL fibres. Our uphill high intensity interval training program was partially sufficient to reverse power loss, but only in fast-type SOL fibres. Impairments in muscle power following ovarian failure are likely driven by a combination of decreased muscle size and force-generating capacity.
iPSC-Astrocyte morphology reflects patient clinical markers

Human iPSCs provide powerful cellular models of Alzheimer’s disease (AD) and offer many advantages over non-human models, including the potential to reflect variation in individual-specific pathophysiology and clinical symptoms Previous studies have demonstrated that iPSC-neurons from individuals with Alzheimer’s disease (AD) reflect clinical markers, including β-amyloid (Aβ) levels and synaptic vulnerability. However, despite neuronal loss being a key hallmark of AD pathology, many risk genes are predominantly expressed in glia, highlighting them as potential therapeutic targets. In this work iPSC-derived astrocytes were generated from a cohort of individuals with high versus low levels of the inflammatory marker YKL-40, in their cerebrospinal fluid (CSF). iPSC-derived astrocytes were treated with exogenous Aβ oligomers and high content imaging demonstrated a correlation between astrocytes that underwent the greatest morphology change from patients with low levels of CSF-YKL-40 and more protective APOE genotypes. This finding was subsequently verified using similarity learning as an unbiased approach. This study shows that iPSC-derived astrocytes from AD patients reflect key aspects of the pathophysiological phenotype of those same patients, thereby offering a novel means of modelling AD, stratifying AD patients and conducting therapeutic screens. Graphical Abstract
Improvement on Quasi-Variational Coupled Cluster(QVCC) method

The thesis starts from mathematically introducing the ab-initio theoretical chemistry, beginning with the energy and variation of a Slater determinant, then the Hartree-Fock and Coupled Cluster theory. In the second chapter the methodological background of the project is introduced, starting from the detailed description of Traditional Coupled Cluster method and the Variational Coupled Cluster one, then introducing the early methods developed by my group: Linked Pair Functional method, Orbital-Optimized Quasi-Variational Coupled Cluster method and possible improvement. The rest of the thesis gives the detail of my research that is improvement made to the current Quasi-Variational Coupled Cluster method.
A class of weighted log-rank test statistics for interval-censored data

Comparison of two or more samples when data are censored is one of the main topics in survival studies. The complexity of the censoring mechanism has lead to the development of new statistical methods. While many tests have been proposed when data are right-censored, research for interval-censored data is still ongoing. Peto and Peto [1] were among the first authors to propose testing methods for interval-censored data. They extend the Wilcoxon test and the log-rank test to interval-censored data and use a permutation procedure to avoid the difficulty of finding the distribution of the corresponding test statistics. Finkelstein [2] derives the log-rank test as a score statistic of a proportional hazards model. This author assumes grouped continuous data and uses the observed Fisher information matrix, instead of the permutation distribution, to obtain the asymptotic distribution of the test statistic. In this paper, we extend the G ‰ and G ‰;‚ families of tests first proposed by Fleming and Harrington [3] and widely used in survival analysis applications with right-censored data. We derive the class of tests G ‰ as a family of score statistics in a general regression model for discrete data. Following ideas of Fay [4], [5] and Fay and Shih [6], we obtain two equivalent expressions for the G ‰ family: as a weighted log-rank form and as a linear form with a term for each individual. We show how to compute the covariance matrix by using either the observed Fisher information or a permutation procedure. The permutation approach is less trouble to implement than the likelihood approach and it applies to discrete and continuous data. Finally, we define the G ‰;‚ family for interval-censored data as a natural generalization of the G ‰ family.
Pullback attractors of FitzHugh-Nagumo system on the time-varying domains

The existence and uniqueness of solutions satisfying energy equality is proved for non-autonomous FitzHugh-Nagumo system on a special time-varying domain which is a (possibly non-smooth) domain expanding with time. By constructing a suitable penalty function for the two cases respectively, we establish the existence of a pullback attractor for non-autonomous FitzHugh-Nagumo system on a special time-varying domain.
Gas accretion from the cosmic web feeding disk galaxies

Abstract Disk galaxies in cosmological numerical simulations grow by accreting gas from the cosmic web. This gas reaches the external disk, and then spirals in dragged along by tidal forces and/or disk instabilities. The importance of gas infall is as clear from numerical simulations as it is obscure to observations. Extremely metal poor (XMP) galaxies seem to be the best example we have of the gas accretion process at work. They have large off-center starbursts which show significant metallicity drop compared with the host galaxy. This observation is naturally explained as a gas accretion event caught in the act. We present preliminary results of the kinematical properties of the metal poor starbursts in XMPs, which suggest that the starbursts are kinematically decoupled entities within the host galaxy.
Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes

Squirrel-cage induction motors (SCIMs) are key machines in many industrial applications. In this regard, the monitoring of their operating condition aiming at avoiding damage and reducing economical losses has become a demanding task for industry. In the literature, several techniques and methodologies to detect faults that affect the integrity and performance of SCIMs have been proposed. However, they have only been focused on analyzing either the start-up transient or the steady-state operation regimes, two common operating scenarios in real practice. In this work, a novel methodology for broken rotor bar (BRB) detection in SCIMs during both start-up and steady-state operation regimes is proposed. It consists of two main steps. In the first one, the analysis of three-axis vibration signals using fractal dimension (FD) theory is carried out. Since different FD-based algorithms can give different results, three algorithms named Katz’ FD, Higuchi’s FD, and box dimension, are tested. In the second step, a fuzzy logic system for each regime is presented for automatic diagnosis. To validate the proposal, a motor with different damage levels has been tested: one with a partially BRB, a second with one fully BRB, and the third with two BRBs. The obtained results demonstrate the proposed effectiveness.
On a Local Stability of the Jensen Functional Equation

The problem of s tab i l i t y of Cauohy's femotional equation on a r e s t r i c t domain D in R has a positive answer [ 5 ] . In this paper -we shal l show that CauOhy's functional equation and Jensen's funotional equation on a res t r io t domain D being some subset of ft" are stable . Moreover, we shal l use a suoh type result to give a positive answer to a problem of K. Nikodem ( [ 3 ] , [4]) . Let (X, ||*||) be a real Banach space and let D be a subset of X. We say that a function f i D — X i s ¿-addit ive (t3>0 i s f ixed) in D i f f