Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiresolution transient detection

Designs and studies the performance of a multiresolution-based transient detector. The transients the authors are interested in consist of wide-band, pulse-like, coherent structures in a turbulent flow. To take advantage of the fast pyramidal wavelet algorithm, an important point when processing large amounts of experimental data, the detector makes use of the discrete wavelet transform. The authors show how the lack of time-invariance drawback of the discrete transform can be efficiently overcome by using relevant analytic wavelets. They thus compare this detection technique with one based on a continuous wavelet transform, as well as with other standard methods and show that wavelets perform best when the transients are superimposed on a colored 1/f background noise. This description is very close to that of turbulence and relevant also in many other situations. <<ETX>>
Comment on “The PC Index Variations During 23/24 Solar Cycles: Relation to Solar Wind Parameters and Magnetic Disturbances” by Troschichev et al.

Examination of the contribution from O. A. Troshichev, S. Dolgacheva, N. A. Stepanov, and D. A. Sormakov: “The PC index variations during 23/24 solar cycles: relation to solar wind parameters and magnetic disturbances,” https://doi.org/10.1029/2020JA028491 has disclosed inconsistencies in the applied methods and errors in the calculated values. Some of the discrepancies reported in the present commentary affect directly the illustrations presented in their contribution while other possible errors may not be apparent since the use of relative values in their presentation makes thorough assessments difficult.
MMR vaccination induces a trained immunity program characterized by functional and metabolic reprogramming of γδ T cells

The measles, mumps and rubella (MMR) vaccine protects against all-cause mortality in children, but the immunological mechanisms mediating these effects are poorly known. We systematically investigated whether MMR can induce long-term functional changes in innate immune cells, a process termed trained immunity, that could at least partially mediate this heterologous protection. In a randomized placebo-controlled trial, 39 healthy adults received either the MMR vaccine or a placebo. By using single-cell RNA-sequencing, we found that MMR caused transcriptomic changes in CD14-positive monocytes and NK cells, but most profoundly in γδ T cells. Surprisingly, monocyte function was not altered by MMR vaccination. In contrast, the function of γδ T cells was significantly enhanced by MMR vaccination, with higher production of TNF and IFNγ, as well as upregulation of cellular metabolic pathways. In conclusion, we describe a new trained immunity program characterized by modulation of γδ T cell function induced by MMR vaccination. One-sentence summary MMR vaccination induces cellular and metabolic reprogramming in γδ T cells towards a more active phenotype.
Multi-omics analyses of single cell-derived colorectal cancer organoids reveal intratumor heterogeneity and immune response diversity

Colorectal cancer (CRC) organoids have similar genomic and functional characteristics to the original specimens. They have become a novel and promising tumor model. However, systematic research of heterogeneity and evolution based on tumor organoids and unbiased evaluation of CD4+ T cell-mediated tumor recognition is presently lacking. Here, we study the variability in genomic characterization and single-cell transcriptome between different tumor organoid clones derived from the same CRC patient. While clone-specific differences in driver gene mutations and cancer stem cell (CSC) subpopulations were observed, inter-clone heterogeneity was even more prevalent. Using single-cell RNA sequencing, we found that compared with bulk organoids, CRC organoid clones are mainly composed of cancer stem cells and transiently-amplifying cells (TACs), but no epithelial-mesenchymal transition (EMT)-like (S100A4+CDH1-) cells. In addition, we study the phenotypic characteristics and TCR properties of tumor-reactive CD4+ T cells by coupling the organoid-TILs co-culture model, single-cell profiling, and recognition testing of candidate TCRs. We found that the proliferating CD4+ T cells increased significantly from 7.1% to 35.4%, and the TCR clonotypes increased from 110 to 1043 after co-culture with organoids. Moreover, 4 of 10 (40%) candidate TCRs with a significant increase in clone abundance exhibited tumor recognition. These results provide evidence that the proliferating CD4+ T cells with clonal expansion after co-culture with tumor organoids are the potential tumor-reactive T cells. Collectively, we reconstructed intra-patient heterogeneity in single cell-derived organoid models and characterized the phenotypic characteristics and functional properties of tumor-reactive CD4+ T cells.
Fast actin disassembly and fimbrin mechanosensitivity support rapid turnover during clathrin-mediated endocytosis

The actin cytoskeleton is central to force production in numerous cellular processes in eukaryotic cells. During clathrin-mediated endocytosis (CME), a dynamic actin meshwork is required to deform the membrane against high membrane tension or turgor pressure. Previous experimental work from our lab showed that several endocytic proteins, including actin and actin-interacting proteins, turn over several times during the formation of a vesicle during CME in yeast, and their dwell time distributions were reminiscent of Gamma distributions with a peak around 1 s (Lacy et al., 2019). However, the distribution for the filament crosslinking protein fimbrin contains a second peak around 0.5 s. To better understand the nature of these dwell time distributions, we developed a stochastic model for the dynamics of actin and its binding partners. Our model demonstrates that very fast actin filament disassembly is necessary to reproduce experimental dwell time distributions. Our model also predicts that actin-binding proteins bind rapidly to nascent filaments and filaments are fully decorated. Last, our model predicts that fimbrin detachment from actin endocytic structures is mechanosensitive to explain the extra peak observed in the dwell time distribution.
Thermodynamic Parameters Contributions of Single Internal Mismatches In RNA/DNA Hybrid Duplexes

Nucleic acid is a fundamental component of life. The thermodynamic is a basic physical quantity for nucleic acid and has been adopted in varieties of biotechnological applications. However, the parameters predicting thermodynamic parameters of RNA/DNA hybrid duplexes is incomplete and thus lead to the inability to proper predicting the properties. To address this problem, we measured for 99 RNA/DNA hybrid duplexes with 11 nucleotide length in a 1 M NaCl buffer using UV absorbance thermal denaturation. Thermodynamic parameters were obtained by fitting absorbance versus temperature profiles using SciPy non-linear curve fit. A dataset of 228 duplexes were constructed combining with literature experimental thermodynamic parameters for fitting a full set of all 113 possible Watson-Crick and single internal mismatch involving nearest-neighbor parameters using singular value decomposition. The new nearest-neighbor parameters predict thermodynamic parameters of RNA/DNA hybrid duplexes within an average error of 0.26 kcal/mol (3.55%) in free energy at 37°C (ΔG°37), 5.05 kcal/mol (6.70%) in enthalpy (ΔH°), 16.3 cal/mol·K (7.50%) in entropy (ΔS°) and 1.14°C (2.91%) in melting temperature (Tm). The highest accuracy of all parameters reported. We observed a trend of increased stability for mismatch involving pairs of rG/dT >> rU/dG > rG/dA > rG/dG ≈ rA/dC ≈ rA/dG > rC/dA ≈ rC/dT > rU/dT ≈ rU/dC >> rA/dA >> rC/dC, which can be explained by different hydrogen bond conditions in mismatch pairs. We further compared the stability with RNA/RNA and DNA/DNA duplexes and found a backbone structure depended pattern in these three kinds of nucleic acid duplexes. This nearest-neighbor parameter set could enhance thermodynamical understanding of RNA/DNA combination and have potential usage in predicting on- and off-target RNA-DNA binding activity when applied to biotechnological applications such as CRISPR/Cas systems, RNAi and qPCR.
Falcon gut microbiome is shaped by diet and enriched in Salmonella

The gut microbiome is increasingly being appreciated as a master regulator of animal health. However, most avian gut microbiome studies have focused on birds of economic importance while the gut microbiomes of raptors remain underexplored. Here we examine the gut microbiota of 29 samples from four Falco species including hybrid birds— raptors of historic importance —in the context of avian evolution by sequencing the 16S rDNA V4 region. Our results reveal that evolutionary histories and diet are significantly associated with avian gut microbiota in general, whereas diet plays a major role in shaping the falcon gut microbiota. Multiple analyses revealed that gut microbial diversity, composition, and abundance of key diet-discriminating bacterial genera in the falcon gut closely resemble those of carnivorous raptors rather than those of their closest phylogenetic relatives. Furthermore, the falcon microbiota is dominated by Firmicutes and consists of Salmonella at appreciable levels. Salmonella presence may potentially alter the functional capacity of the falcon gut microbiota as its abundance is associated with depletion of multiple predicted metabolic pathways involved in protein mass buildup, muscle maintenance, and enrichment of antimicrobial compound degradation, thus increasing the pathogenic potential of the falcon gut and presents a potential risk to human health. Author Summary in Arabic
Metabolic Consequences of Polyphosphate Synthesis and Imminent Phosphate Limitation

Cells must strike a delicate balance between the high demand of inorganic phosphate (Pi) for synthesizing nucleic acids and phospholipids and its detrimental bioenergetic effects by reducing the free energy of nucleotide hydrolysis. The latter may stall metabolism. ABSTRACT Cells stabilize intracellular inorganic phosphate (Pi) to compromise between large biosynthetic needs and detrimental bioenergetic effects of Pi. Pi homeostasis in eukaryotes uses Syg1/Pho81/Xpr1 (SPX) domains, which are receptors for inositol pyrophosphates. We explored how polymerization and storage of Pi in acidocalcisome-like vacuoles supports Saccharomyces cerevisiae metabolism and how these cells recognize Pi scarcity. Whereas Pi starvation affects numerous metabolic pathways, beginning Pi scarcity affects few metabolites. These include inositol pyrophosphates and ATP, a low-affinity substrate for inositol pyrophosphate-synthesizing kinases. Declining ATP and inositol pyrophosphates may thus be indicators of impending Pi limitation. Actual Pi starvation triggers accumulation of the purine synthesis intermediate 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), which activates Pi-dependent transcription factors. Cells lacking inorganic polyphosphate show Pi starvation features already under Pi-replete conditions, suggesting that vacuolar polyphosphate supplies Pi for metabolism even when Pi is abundant. However, polyphosphate deficiency also generates unique metabolic changes that are not observed in starving wild-type cells. Polyphosphate in acidocalcisome-like vacuoles may hence be more than a global phosphate reserve and channel Pi to preferred cellular processes. IMPORTANCE Cells must strike a delicate balance between the high demand of inorganic phosphate (Pi) for synthesizing nucleic acids and phospholipids and its detrimental bioenergetic effects by reducing the free energy of nucleotide hydrolysis. The latter may stall metabolism. Therefore, microorganisms manage the import and export of phosphate, its conversion into osmotically inactive inorganic polyphosphates, and their storage in dedicated organelles (acidocalcisomes). Here, we provide novel insights into metabolic changes that yeast cells may use to signal declining phosphate availability in the cytosol and differentiate it from actual phosphate starvation. We also analyze the role of acidocalcisome-like organelles in phosphate homeostasis. This study uncovers an unexpected role of the polyphosphate pool in these organelles under phosphate-rich conditions, indicating that its metabolic roles go beyond that of a phosphate reserve for surviving starvation.
PTHrP buffers Wnt/β-catenin activity through a negative feedback loop to maintain articular cartilage homeostasis

Osteoarthritis (OA) is the most common joint disease worldwide and a leading cause of disability. The Wnt/β-catenin cascade is essential in articular cartilage development and homeostasis. It has proved that both overexpression and loss of β-catenin lead to cartilage degeneration and OA symptoms. However, the mechanism of Wnt/β-catenin balance in healthy cartilage remains unclear. In the present work, we confirmed that the Wnt/β-catenin activation and PTHrP suppression in cartilage during the post-traumatic OA process. Then, we demonstrated that Wnt/β-catenin upregulated PTHrP expression through binding to its promoter (P2), and induce mRNA (AT6) transcript expression, while PTHrP repressed Wnt/β-catenin activity, and formed a Wnt/β-catenin-PTHrP negative feedback loop in the very primary chondrocytes to maintain cartilage homeostasis. However, this negative feedback loop vanished in dedifferentiated chondrocytes, hypertrophic chondrocytes, and IL-1β treated very primary chondrocytes. We further found that miR-106b-5p was increased in these “aberrant” chondrocytes and directly targeted PTHrP mRNA to abolish the feedback loop. PKC-ζ was activated by PTHrP through phosphorylation at Thr410/403, and subsequently induced β-catenin phosphorylation and ubiquitination. Finally, we disclosed that exogenous PTHrP attenuated OA progression exogenous PTHrP attenuated OA progression. Together, these findings reveal that PTHrP is a vital mediator to keep Wnt/β-catenin activity homeostasis in healthy cartilage through a negative feedback loop, and PTHrP might be a therapeutic target for OA and cartilage regeneration.
Spatial scale influences taxon conservation in the eukaryotic virome of a mosquito

Our knowledge on the diversity of eukaryotic viruses has recently undergone a massive expansion. This diversity could influence host physiology through yet unknown phenomena of potential interest to the fields of health and food production. However, the assembly processes in eukaryotic viromes of terrestrial animals remain elusive. This situation hinders hypothesis-driven tests of virome influence on host physiology. Here, we explore virome assembly at different spatial scales in the eukaryotic virome of the mosquito Culex pipiens. This mosquito is a vector of human pathogens worldwide. Several new aspects of virome assembly were unveiled through a sampling involving five countries in Africa and Europe and large sample sizes. A group of viruses was conserved in C. pipiens populations in all countries. This core group represented a relatively large and diverse fraction of the virome. However, core viruses were not shared by all host individuals in a given country, and their infection rates fluctuated between countries and years. Moreover, the distribution of co-infections in individual mosquitoes suggested random co-occurrence of certain core viruses. We also observed differences in the virome depending on geography, with viromes tending to cluster depending on the continent. Thus, our results unveil that taxon conservation in a eukaryotic virome changes with spatial scale. Thus, predictions on virome assembly seem possible at a large geographical scale in C. pipiens. IMPORTANCE The study of the eukaryotic virome of mosquitoes is an emerging research field. Beyond its fundamental interest, this field could lead to the development of control tools against the transmission of mosquito-borne human pathogens. However, we yet know little on the assembly patterns in the eukaryotic viromes of mosquitoes, as well as of terrestrial animals in general. This situation hampers the design of hypothesis-driven studies on the influence of the virome on pathogen transmission. Here, we have analyzed virome assembly in the mosquito vector Culex pipiens within and between countries in Africa and Europe. Our results show that integrating contrasted spatial scales allows to identify deterministic patterns in virome assembly. Such patterns can guide future studies of virome influence on mosquito physiology.
Dynamic Interaction Learning and Multimodal Representation for Drug Response Prediction

Mining multimodal pharmaceutical data is crucial for in-silico drug candidate screening and discovery. A daunting challenge of integrating multimodal data is to enable dynamic feature modeling generalizable for real-world applications. Unlike conventional approaches using a simple concatenation with fixed parameters, in this paper, we develop a dynamic interaction learning network to adaptively integrate drug and different reactants on multimodal tasks towards robust drug response prediction. The primary objective of dynamic learning falls into two key aspects: at micro-level, we aim to dynamically search specific relational patterns on the whole reactant range for each drug-reactant pair; at macro-level, drug features can be used to adaptively correlate with different reactants. Extensive experiments demonstrate the validity of our approach in both drug protein interaction (DPI) and cancer drug response (CDR) tasks. Our approach achieves superior performance on both DPI (AUC = 0.967) and CDR (AUC = 0.932) tasks, outperforming competitive baselines from four real-world, drug-outcome datasets. In addition, the performance on the challenging blind subsets is remarkably improved, where AUC value increases from 0.843 to 0.937 on blind protein set of DPI task, and Pearson’s correlation value increases from 0.516 to 0.566 on blind drug set of CDR task. A series of case studies highlight the potential generalization and interpretability of dynamic learning in the in-silico drug response assessment.
Brief Temporal Perturbations in Somatosensory Reafference Disrupt Perceptual and Neural Attenuation and Increase Supplementary Motor Area–Cerebellar Connectivity

Intrinsic delays in sensory feedback can be detrimental for motor control. As a compensation strategy, the brain predicts the sensory consequences of movement via a forward model on the basis of a copy of the motor command. Using these predictions, the brain attenuates somatosensory reafference to facilitate the processing of exafferent information. Theoretically, this predictive attenuation is disrupted by (even minimal) temporal errors between the predicted and actual reafference; however, direct evidence of such disruption is lacking as previous neuroimaging studies contrasted nondelayed reafferent input with exafferent input. Here, we combined psychophysics with functional magnetic resonance imaging to test whether subtle perturbations in the timing of somatosensory reafference disrupt its predictive processing. Twenty-eight participants (14 women) generated touches on their left index finger by tapping a sensor with their right index finger. The touches on the left index finger were delivered close to the time of contact of the two fingers or with a temporal perturbation (i.e., 153 ms delay). We found that such a brief temporal perturbation disrupted the attenuation of the somatosensory reafference at both the perceptual and neural levels, leading to greater somatosensory and cerebellar responses and weaker somatosensory connectivity with the cerebellum, proportional to the perceptual changes. We interpret these effects as the failure of the forward model to predictively attenuate the perturbed somatosensory reafference. Moreover, we observed increased connectivity of the supplementary motor area with the cerebellum during the perturbations, which could indicate the communication of the temporal prediction error back to the motor centers. SIGNIFICANCE STATEMENT Our brain receives somatosensory feedback from our movements with a delay. To counteract these delays, motor control theories postulate that the brain predicts the timing of somatosensory consequences of our movements and attenuates sensations received at that time. Thus, a self-generated touch feels weaker than an identical external touch. However, how subtle temporal errors between the predicted and actual somatosensory feedback perturb this predictive attenuation remains unknown. We show that such errors make the otherwise attenuated touch feel stronger, elicit stronger somatosensory responses, weaken cerebellar connectivity with somatosensory areas, and increase this connectivity with motor areas. These findings show that motor and cerebellar areas are fundamental in forming temporal predictions about the sensory consequences of our movements.
Use of Generalised Linear Mixed Models for the Analysis of Clustered Survival Data

Data from a litter matched tumorigenesis experiment are analysed using a generalised linear mixed model (GLMM) approach to the analysis of clustered survival data in which there is a dependence of failure time observations within the same litter. Maximum likelihood (ML) and residual maximum likelihood (REML) estimates of risk variable parameters, variance component parameters and the prediction of random effects are given. Estimation of treatment effect parameter (carcinogen effect) has good agreement with previous analyses obtained in the literature though the dependence structure within a litter is modelled in different ways. The variance component estimation provides the estimated dispersion of the random effects. The prediction of random effects, is useful, for instance, in identifying high risk litters and individuals. The present analysis illustrates its wider application to detecting increased risk of occurrence of disease in particular families of a study population.
Real Estate Market Analysis For Investment and Buying Cycles Prediction

The traditional methods used to estimate the Real Estate prices are sometimes too subjective and lack accuracy. The most common approaches to calculate a property’s price are the Market Approach, the Income Approach, and the Cost Approach. Artificial Intelligence is applied to house price prediction, and Machine Learning models are developed and tested to research the best algorithm to achieve better accuracy results to overcome the subjectivity carried by these methods. This project provides some background on how the Real Estate market functions and how some State-of-the-Art solutions address the industry’s requirement of Artificial Intelligence. It also describes a few experiments on several algorithms to understand how adequate they are in the scope of the problem, either by trying to achieve a precise duplicate detection model, or by aiming to develop a model capable of computing Real Estate values. In those experiments it was found that it is of value to separate the dataset by location, creating subsets of data. Also, from the several algorithms tested, the majority of subsets achieved better results with Random Forest and Gradient Boosting.