Miracle-dz/UniFAR-FTUs huggingface dataset:
A new immunodeficient Duchenne muscular dystrophy rat model to evaluate engraftment after human cell transplantation

Duchenne muscular dystrophy (DMD) is an X-linked fatal muscular disease, affecting one in 3,500 live male births worldwide. Currently, there is no cure for this disease, except for steroid-based treatment to attenuate disease progression. Cell transplantation therapy is a promising therapeutic approach, however, there is a lack of appropriate animal models to conduct large-scale preclinical studies using human cells, including biochemical and functional tests. Here, we established an immunodeficient DMD rat model and performed exhaustive pathological analysis and transplantation efficiency evaluation to assess its suitability to study DMD. Our DMD rat model exhibited histopathological characteristics similar to those observed in human patients with DMD. Human myoblasts demonstrated successful engraftment following transplantation into these rats. Therefore, this immunodeficient DMD rat model would be useful in preclinical studies to develop cellular transplantation therapies for DMD.
The human P2X7 receptor alters microglial morphology and cytokine secretion following immunomodulation

Introduction: In recent years, purinergic signaling via the P2X7 receptor (P2X7R) on microglia has repeatedly been implicated in depression genesis. However, it remains unclear which role the human P2X7R (hP2X7R) plays in regulating both microglia morphology and cytokine secretion upon different environmental and immune stimuli, respectively. Methods: For this purpose, we used primary microglial cultures derived from a humanized microglia-specific conditional P2X7R knockout mouse line to emulate different gene-environment interactions between microglial hP2X7R and molecular proxies of psychosocial and pathogen-derived immune stimuli. Microglial cultures were subjected to treatments with the agonists 2′(3′)-O-(4-benzoylbenzoyl)-ATP (BzATP) and lipopolysaccharides (LPS) combined with specific P2X7R antagonists (JNJ-47965567, A-804598). Results: Morphotyping revealed overall high baseline activation due to the in vitro conditions. Both BzATP and LPS + BzATP treatment increased round/ameboid microglia and decreased polarized and ramified morphotypes. This effect was stronger in hP2X7R-proficient (CTRL) compared to knockout (KO) microglia. Aptly, we found antagonism with JNJ-4796556 and A-804598 to reduce round/ameboid microglia and increase complex morphologies only in CTRL but not KO microglia. Single cell shape descriptor analysis confirmed the morphotyping results. Compared to KO microglia, hP2X7R-targeted stimulation in CTRLs led to a more pronounced increase in microglial roundness and circularity along with an overall higher decrease in aspect ratio and shape complexity. JNJ-4796556 and A-804598, on the other hand, led to opposite dynamics. In KO microglia, similar trends were observed, yet the magnitude of responses was much smaller. Parallel assessment of 10 cytokines demonstrated the proinflammatory properties of hP2X7R. Following LPS + BzATP stimulation, IL-1β, IL-6, and TNFα levels were found to be higher and IL-4 levels lower in CTRL than in KO cultures. Vice versa, hP2X7R antagonists reduced proinflammatory cytokine levels and increased IL-4 secretion. Discussion: Taken together, our results help disentangle the complex function of microglial hP2X7R downstream of various immune stimuli. In addition, this is the first study in a humanized, microglia-specific in vitro model identifying a so far unknown potential link between microglial hP2X7R function and IL-27 levels.
Comparative molecular dynamics simulations of pathogenic and non-pathogenic huntingtin protein monomers and dimers

Polyglutamine expansion at the N-terminus of the huntingtin protein exon 1 (Htt-ex1) is closely associated with a number of neurodegenerative diseases, which result from the aggregation of the increased polyQ repeat. However, the underlying structures and aggregation mechanism are still poorly understood. We performed microsecond-long all-atom molecular dynamics simulations to study the folding and dimerization of Htt-ex1 (about 100 residues) with non-pathogenic and pathogenic polyQ lengths, and uncovered substantial differences. The non-pathogenic monomer adopts a long α-helix that includes most of the polyQ residues, which forms the interaction interface for dimerization, and a PPII-turn-PPII motif in the proline-rich region. In the pathogenic monomer, the polyQ region is disordered, leading to compact structures with many intra-protein interactions and the formation of short β-sheets. Dimerization can proceed via different modes, where those involving the N-terminal headpiece bury more hydrophobic residues and are thus more stable. Moreover, in the pathogenic Htt-ex1 dimers the proline-rich region interacts with the polyQ region, which slows the formation of β-sheets.
Size Statistics of Cumulus Cloud Populations in Large-Eddy Simulations

Abstract Cloud size distributions of shallow cumulus cloud populations are calculated using the large-eddy simulation (LES) approach. A range of different cases is simulated, and the results are compared to observations of real cloud populations. Accordingly, the same algorithm is applied as in observational studies using high-altitude photography or remote sensing. The cloud size density of the simulated cloud populations is described well by a power law at the smaller sizes. This scaling covers roughly one order of magnitude of cloud sizes, with a power-law exponent of −1.70, which is comparable to exponents found in observational studies. A sensitivity test for the resolution suggests that the scaling continues at sizes smaller than the standard grid spacing. In contrast, on the other end, the scaling region is bounded by a distinct scale break. When the cloud size is nondimensionalized by the scale break size, the cloud size densities of all cases collapse. This corroborates the idea of a universal de.
Predictions of DNA mechanical properties at a genomic scale reveal potentially new functional roles of DNA flexibility

Mechanical properties of DNA have been implied to influence many its biological functions. Recently, a new high-throughput method, called loop-seq, that allows measuring the intrinsic bendability of DNA fragments, has been developed. Using loop-seq data, we created a deep learning model to explore the biological significance of local DNA flexibility in a range of different species from different kingdoms. Consistently, we observed a characteristic and largely nucleotide-composition-driven change of local flexibility near transcription start sites. No evidence of a generally present region of lowered flexibility upstream of transcription start sites to facilitate transcription factor binding was found. Yet, depending on the actual transcription factor investigated, flanking-sequence-dependent DNA flexibility was identified as a potential factor influencing binding. Compared to randomized genomic sequences, depending on species and taxa, actual genomic sequences were observed both with increased and lowered flexibility. Furthermore, in Arabidopsis thaliana, crossing-over and mutation rates, both de novo and fixed, were found to be linked to rigid sequence regions. Our study presents a range of significant correlations between characteristic DNA mechanical properties and genomic features, the significance of which with regard to detailed molecular relevance awaits further experimental and theoretical exploration.
Effect of vessel compression on blood flow in microvascular networks and its implications for tumour tissue hypoxia

Vessel compression is reported as a cause of tumour tissue hypoxia, in turn related to reduced treatment efficacy and increased metastases. The authors investigate computationally how vessel compression affects blood flow in microvascular networks, detecting a reduced haematocrit value upon compression of the vessels and an increased haematocrit heterogeneity, postulating a causal relationship with the presence of tumour hypoxia. The tumour microenvironment is abnormal and one of its consequences is that blood vessels are compressed. Vessel compression correlates with reduced survival rates, while decompression of vessels improves tissue oxygenation as well as increases survival rates. Vessel compression contributes, at a single vascular bifurcation, to the increase of heterogeneity of red blood cell (RBC) transport. However, the effect that vessel compression has at a network level is unknown. This work numerically investigates the effect of vessel compression on RBC transport in microvascular networks. The key findings are that vessel compression both reduces the average haematocrit, and increases haematocrit heterogeneity, in vessels in the network. The mechanisms for these changes in haematocrit distribution are unravelled, and a parameter sweep shows that networks with lower inlet haematocrits are more susceptible to haemodilution from vessel compression over a wide range of compressed fraction of a network. These findings provide a theoretical underpinning for the link between vessel compression and tumour tissue hypoxia.
Non-Hebbian plasticity produces long-lasting associative memories

Hebbian plasticity, widely regarded as the cellular mechanism for learning and memory, posits that the formation of a long-lasting memory requires a tight temporal co-activation of pre-and postsynaptic inputs encoding that memory. Here we demonstrate that forms of synaptic manipulation deviating from Hebbian rules can produce a long-lasting memory. To this end, we optogenetically manipulate independently two inputs to the lateral amygdala (LA), a region known to mediate fear memories. We focused on the association of one input to the LA with a foot shock, which does not form a detectable memory. This experience can be converted to a long-lasting memory by induction of synaptic potentiation of the input alone, delivered minutes before, minutes after, or even 24 hours later. Notably, a similar conversion to a long-lasting memory is achieved by potentiating an independent input to the LA delivered minutes, but not 24 hours after the experience. Surprisingly, in a non-associative conditioning paradigm, strong stimulation of an independent input uncovers the aversive memory of the shock. Our results indicate that different forms of plasticity can proactively as well as retroactively impact the persistence of memories, an effect with computational and behavioral implications.
Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses

Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson’s disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlight the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.
Loss of myeloid cannabinoid CB1 receptor confers atheroprotection by reducing macrophage proliferation and immunometabolic reprogramming

Although the cannabinoid CB1 receptor has been implicated in atherosclerosis, its cell-specific effects in this disease are not well understood. Here, we report that male mice with myeloid-specific Cnr1 deficiency on atherogenic background developed smaller lesions and necrotic cores than controls, while only minor genotype differences were observed in females. Male Cnr1 deficient mice showed reduced arterial monocyte recruitment and macrophage proliferation with less inflammatory phenotype. The sex-specific differences were reproducible in bone marrow derived macrophages and blunted by estradiol. Kinase activity profiling revealed a CB1-dependent regulation of p53 and cyclin-dependent kinases. Transcriptomic profiling further unveiled chromatin modifications, mRNA processing and mitochondrial respiration among the key processes affected by CB1 signaling, which was supported by metabolic flux assays. Chronic administration of the peripherally-restricted CB1 antagonist JD5037 inhibited plaque progression and macrophage proliferation, but only in male mice. Finally, CNR1 expression was detectable in human carotid endarterectomy plaques and inversely correlated with proliferation, oxidative metabolism and inflammatory markers, hinting to a possible implication of CB1-dependent regulation in human pathophysiology. In conclusion, impaired CB1 signaling in macrophages is atheroprotective by limiting their arterial recruitment, proliferation and inflammatory reprogramming. The importance of macrophage CB1 signaling seems to be more pronounced in male mice. Graphical summary (created with BioRender.com)
Membrane curvature sensing by model biomolecular condensates

Biomolecular condensates (BCs) are fluid droplets that form in biological cells by liquid-liquid phase separation. Their major components are intrinsically disordered proteins. Vast attention has been given in recent years to BCs inside the cytosol and nucleus. BCs at the cell membrane have not been studied to the same extent so far. However, recent studies provide increasingly more examples of interfaces between BCs and membranes which function as platforms for diverse biomolecular processes. Galectin-3, for example, is known to mediate clathrin-independent endocytosis and has been recently shown to undergo liquid-liquid phase separation, but the function of BCs of galectin-3 in endocytic pit formation is unknown. Here, we use dissipative particle dynamics simulations to study a generic coarse-grained model for BCs interacting with lipid membranes. In analogy to galectin-3, we consider polymers comprising two segments – one of them mediates multivalent attractive interactions between the polymers, and the other one has affinity for association with specific lipid head groups. When these polymers are brought into contact with a multi-component membrane, they spontaneously assemble into droplets and, simultaneously, induce lateral separation of lipids within the membrane. Interestingly, we find that if the membrane is bent, the polymer droplets localize at membrane regions curved inward. Although the polymers have no particular shape or intrinsic curvature, they appear to sense membrane curvature when clustered at the membrane. Our results indicate toward a generic mechanism of membrane curvature sensing by BCs involved in such processes as endocytosis.
Fermeture intégrale et changement de base

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Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

Significance Are age-related changes in brain functional network connectivity (FNC) involved in schizophrenia (SCZ) risk? We measured FNC from childhood to adulthood across multiple fMRI acquisitions in 7,431 neurotypical individuals (NC), 201 first-degree relatives of patients with SCZ, of which 156 siblings (SIB), 195 patients with SCZ, and 1,409 individuals with subthreshold psychotic symptoms (PSY). Young SIB are characterized by early FNC alterations in prefrontal–sensorimotor and cerebellar–occipitoparietal circuits; these FNC patterns are associated with polygenic risk for SCZ. Two of these alterations were found in SCZ patients, one showed inverse patterns. Young PSY show the same patterns. Risk-related FNC patterns always resemble those of older healthy persons, whereas older SIB patterns approach the NC distribution once their age-evolving patterns reach maturity.
Divertor stray light analysis in JET-ILW and implications for the H-α diagnostic in ITER

We report on the first results for the spectrum of divertor stray light (DSL) and the signal-to-background ratio for D-α light emitted from the far SOL and divertor in JET in the recent ITER-like wall (ILW) campaign. The results support the expectation of a strong impact of DSL upon the H-alpha (and Visible Light) Spectroscopy Diagnostic in ITER.
Detection of microservice‐based software anomalies based on OpenTracing in cloud

Today, the noticeable tendency of the software industry to break large software projects into loosely coupled modules through a microservice‐based architecture is more than ever. This is because of advantages such as scalability, independence, smaller and faster deployments, improved fault isolation, and flexibility. On the other hand, it should be noted that with the growth of microservice architecture, new complexities have emerged. We need to have a mature DevOps team to handle the complexity involved in maintaining and supporting systems, namely functional and non‐functional monitoring (anomaly monitoring and detection). This challenge can lead to a lot of software development time being spent monitoring and identifying anomalies. Existing approaches are not accurate enough to identify anomalies, and if they are able to identify them, they are unable to identify the category of the anomaly. Our approach in this research is to use distributed tracing with the help of machine learning algorithms to identify performance anomalies, the exact location of each anomaly, and predict its category. In this research, we implemented a software based on microservice architecture and then created a variety of anomalies over time (e.g., physical resources, virtual resources, database, application) to be able to evaluate the proposed model. The resulting dataset is publicly available. Our simulation results show that the proposed model is able to accurately identify the anomalies with 98% accuracy and their category with 99% accuracy.
Li–Yorke chaos in linear dynamics

We obtain new characterizations of Li–Yorke chaos for linear operators on Banach and Fréchet spaces. We also offer conditions under which an operator admits a dense set or linear manifold of irregular vectors. Some of our general results are applied to composition operators and adjoint multipliers on spaces of holomorphic functions.
Analysis of Factors Affecting Customer Satisfaction on PLN Mobile Users at PLN Tanjungpandan Customer Service Unit

The PLN Mobile application is an application that can be used by smartphone devices that aim to provide services for PLN customers through digital media. The presence of the PLN mobile application is expected to increase customer satisfaction. This study aims to determine customer satisfaction factors, especially for PLN Mobile Application users. This study uses five variables, namely application reliability, interface display, complaints menu, and service response speed as independent variables, and customer satisfaction as the dependent variable, using multiple linear regression analysis. The method used in the study is using multiple linear regression. The results showed that the four independent variables could explain 93.4% of the dependent variable. In contrast, the remaining 6.6% was explained by other variables not included in this study. The independent variables X1 and X3 partially affect the dependent variable Y. In contrast, the independent variables X2 and X4 do not partially affect the dependent variable Y. The four independent variables together influence customer satisfaction variables. The empirical results of this study are higher level of application reliability and complaint menu, the higher the costumer satisfaction of PLN Mobile users.
Algebraic solvability tests for linear matrix inequalities

Discusses algebraic tests for the solvability of the indefinite linear matrix inequality (LMI) (A*P+PA+Q/B*P+S* PB+S/R)/spl ges/0 which arises in the general LQ problem and in H/sub /spl infin//-control. The author presents a new geometric algorithm which allows one to directly reduce the LMI to a certain algebraic Riccati inequality (ARI). Under a mild regularity assumption the author describes how to further reduce the Riccati inequality to an indefinite Lyapunov inequality with a matrix whose eigenvalues are located at the imaginary axis. Finally, the author derives new general necessary conditions for the solvability of such Lyapunov inequalities and discusses cases under which these conditions are also sufficient. <<ETX>>
Remote Sensing of Atmospheric Water Vapor and Liquid Water with the Nimbus 5 Microwave Spectrometer

Abstract The passive microwave spectrometer on the Nimbus 5 satellite has two channels that measure atmospheric water vapor and liquid water abundances over ocean. Observed water vapor abundances range up to 6 g cm−2 and differ from nearby radiosondes by ∼0.4 g cm−2. Average liquid water abundances over a 300 km observation zone range from −0.01 to 0.2 g cm−2, and have an rms error estimated to be ∼0.01 g cm−2 for most circumstances. These quantitative measurements can be used to construct global maps or to accumulate global statistics.
ChatGPT for Future Medical and Dental Research

ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI and it first became available to the public in November 2022. ChatGPT can assist in finding academic papers on the web and summarizing them. This chatbot has the potential to be applied in scientific writing, it has the ability to generate automated drafts, summarize articles, and translate content from several languages. This in turn can make academic writing faster and less challenging. However, due to ethical considerations, its use in scientific writing should be regulated and carefully monitored. Few papers have discussed the use of ChatGPT in scientific research writing. This review aims to discuss all the relevant published papers that discuss the use of ChatGPT in medical and dental research.