Miracle-dz/UniFAR-FTUs huggingface dataset:
Methylglyoxal induces ambience for cancer promotion in HepG2 cells via Warburg effect and promotes glycation

Methylglyoxal (MGO) is a toxic, highly reactive metabolite derived mainly from glucose and amino acids degradation. MGO is also one of the prime precursors for advanced glycation end products formation. The present research was performed to check whether MGO has any role in the promotion of cancer in HepG2 cells. For this, cells were incubated with MGO (50 µM) for 24 h and subjected to various analyses. Aminoguanidine (200 µM) was positive control. The various biochemical and protein expression studies, relevant to the MGO detoxification system, oxidative stress, and glycolysis were performed. MGO caused the reduction of expression of GLO 1 (27%) and GLO 2 (11%) causing weakening of the innate detoxification system. This is followed by an increase of RAGE (95%), AGEs or methylglyoxal adducts. We also observed hypoxia via estimation of oxygen consumption rate and surplus reactive oxygen species (ROS) (24%). To investigate the off‐target effect of MGO we checked its effect on glucose transport, and its associated proteins. Glucose uptake was found to increase (15%) significantly with overexpression of GLUT 1 (35%). We also found a significant increase of glycolytic enzymes such as hexokinase II, phosphofructokinase 1, and lactate dehydrogenase along with lactate production. Observation of surplus ROS and enhanced glycolysis led us to check the expression of HIF 1α which is their downstream signaling pathway. Interestingly HIF 1α was found to increase significantly (35%). It is known that enhanced glycolysis and oxidative stress are catalysts for the overexpression of HIF 1α which in turn creates an ambience for the promotion of cancer. Aminoguanidine was able to prevent the adverse effect of MGO partially. This is the first study to show the potential of MGO for the promotion of cancer in the non‐tumorigenic HepG2 cells via the Warburg effect and glycation.
X{ray and optical monitoring of the peculiar source 4U 1700+24/V934 Her ?

We report on ASCA and BeppoSAX X{ray broad band observations of the galactic low-luminosity X{ray source 4U 1700+24 performed on 1995 and 1998, respectively, and on (quasi-)simultaneous ground observations of its optical counterpart, V934 Her, from the Loiano 1.5-meter telescope. In order to better understand the nature of the source we also analyze public archival ROSAT and RXTE data as well as the RXTE ASM light curve of 4U 1700+24; we also re{analyze a 1985 EXOSAT pointing. The optical spectra are typical of a M2 III star; this allows us to determine a revised distance to the object of400 pc. While these spectra do not show either any spectral change between the two epochs or any peculiar feature apart from those observed in normal red giants, the spectroscopic measurements carried out in X{rays reveal a complex and long-term variable spectrum, with a clear soft excess. The X{ray spectral properties of the source are best described by a thermal Comptonization spectrum plus a soft energy (<1 keV) excess, which can be modeled in the form of a blackbody emission with kTBB 1k eV; the latter component is not detected at the lowest source flux levels. The ratio between the two components varies substantially with the source flux. The X{ray emission from the object appears to become harder as its luminosity increases: indeed, the RXTE data acquired during an outburst occurred in October-November 1997 display a hard tail, detected up to 100 keV and modeled with a comptonizing cloud which is hotter and less opaque than that seen in the low intensity state. Apart from erratic shot-noise variability on timescales of tens to thousands of seconds, no signicant properties (such as pulsations or QPOs) are found from the timing analysis of the X{ray light curves extracted from the observations presented here. With the new distance determination, the 2{10 keV X{ray luminosity range spanned in the considered observations lies between210 32 and110 34 erg s 1 . A ll this information, combined with the ndings by other authors, allows us to suggest that the scenario which best describes the object consists of a wide binary system in which a neutron star accretes matter from the wind of a M-type giant star. Implications of such a model are discussed.
Symbolic Causal Inference via Operations on Probabilistic Circuits

Causal inference provides a means of translating a target causal query into a causal 1 formula, which is a function of the observational distribution, given some assump- 2 tions on the domain. With the advent of modern neural probabilistic models, this 3 opens up the possibility to perform accurate and tractable causal inference on realis- 4 tic, high-dimensional data distributions, a crucial component of reasoning systems. 5 However, for most model classes, the computation of the causal formula from the 6 observational model is intractable. In this work, we hypothesize that probabilistic 7 circuits, a general and expressive class of tractable probabilistic models, may be 8 more amenable for the computation of causal formulae. Unfortunately, we prove 9 that evaluating even simple causal formulae is still intractable for most types of 10 probabilistic circuits. Motivated by this, we devise a conceptual framework for an- 11 alyzing the tractability of causal formulae by decomposing them into compositions 12 of primitive operations, in order to identify tractable subclasses of circuits. This 13 allows us to derive, for a specific subclass of circuits, the first tractable algorithms 14 for computing the backdoor and frontdoor adjustment formulae.
Integrating T-cell receptor and transcriptome for large-scale single-cell immune profiling analysis

Recent advancements in single-cell immune profiling that enable the measurement of the transcriptome and T-cell receptor (TCR) sequences simultaneously have emerged as a promising approach to study immune responses at cellular resolution. Yet, combining these different types of information from multiple datasets into a joint representation is complicated by the unique characteristics of each modality and the technical effects between datasets. Here, we present mvTCR, a multimodal generative model to learn a unified representation across modalities and datasets for joint analysis of single-cell immune profiling data. We show that mvTCR allows the construction of large-scale and multimodal T-cell atlases by distilling modality-specific properties into a shared view, enabling unique and improved data analysis. Specifically, we demonstrated mvTCR’s potential by revealing and separating SARS-CoV-2-specific T-cell clusters from bystanders that would have been missed in individual unimodal data analysis. Finally, mvTCR can enable automated analysis of new datasets when combined with transfer-learning approaches. Overall, mvTCR provides a principled solution for standard analysis tasks such as multimodal integration, clustering, specificity analysis, and batch correction for single-cell immune profiling data.
Echolocating bats prefer a high risk-high gain foraging strategy to increase prey profitability

Most bats catch nocturnal prey during active flight guided by echolocation but some species depart from this ancestral behaviour to capture ground prey using passive listening. Here, we explore the costs and benefits of these hunting transitions by combining high-resolution biologging data and DNA metabarcoding to quantify the relative contributions of aerial and ground prey to the total food intake of wild greater mouse-eared bats. We show that these bats use both foraging strategies with similar average nightly captures of 25 small, aerial insects and 30 large, ground-dwelling insects per bat, but with higher capture success in air (78 % in air vs 30 % on ground). However, owing to the 3 to 20 times heavier ground prey, 85 % of the estimated nightly food acquisition comes from ground prey despite the 2.5 times higher failure rates. Further, we find that most bats use the same foraging strategy on a given night suggesting that bats adapt their hunting behaviour to weather and ground conditions. We conclude that prey switching matched to environmental dynamics plays a key role in covering the energy intake even in specialised predators.
Extremely fast construction and querying of compacted and colored de Bruijn graphs with GGCAT

Compacted de Bruijn graphs are one of the most fundamental data structures in computational genomics. Colored compacted graphs Bruijn graphs are a variant built on a collection of sequences, and associate to each k-mer the sequences in which it appears. We present GGCAT, a tool for constructing both types of graphs, based on a new approach merging the k-mer counting step with the unitig construction step, and on numerous practical optimizations. For compacted de Bruijn graph construction, GGCAT achieves speed-ups of 3–21× compared to the state-of-the-art tool Cuttlefish 2 (Khan and Patro, Genome Biology, 2022). When constructing the colored variant, GGCAT achieves speed-ups of 5–39× compared to the state-of-the-art tool BiFrost (Holley and Melsted, Genome Biology, 2020). Additionally, GGCAT is up to 480× faster than BiFrost for batch sequence queries on colored graphs.
A common neural code for representing imagined and inferred tastes

Inferences about the taste of foods are a key aspect of our everyday experience of food choice. Despite this, gustatory mental imagery is a relatively under-studied aspect of our mental lives. In the present study, we examined subjects during high-field fMRI as they actively imagined basic tastes and subsequently viewed pictures of foods dominant in those specific taste qualities. Imagined tastes elicited activity in the bilateral dorsal mid-insula, one of the primary cortical regions responsive to the experience of taste. In addition, within this region we reliably decoded imagined tastes according to their dominant quality - sweet, sour, or salty – thus indicating that, like actual taste, imagined taste activates distinct quality-specific neural patterns. Using a cross-task decoding analysis, we found that the neural patterns for imagined tastes and food pictures in the mid-insula were reliably similar and quality-specific, suggesting a common code for representing taste quality regardless of whether explicitly imagined or automatically inferred when viewing food. These findings have important implications for our understanding of the mechanisms of mental imagery and the multimodal nature of presumably primary sensory brain regions like the dorsal mid-insula.
Loss of MLL3/4 decouples enhancer H3K4 monomethylation, H3K27 acetylation, and gene activation during ESC differentiation

Enhancers are essential in defining cell fates through the control of cell type specific gene expression. Enhancer activation is a multi-step process involving chromatin remodelers and histone modifiers including the monomethylation of H3K4 (H3K4me1) by MLL3 (KMT2C) and MLL4 (KMT2D). MLL3/4 are thought to be critical for enhancer activation and cognate gene expression including through the recruitment of acetyltransferases for H3K27. Here we test this model by evaluating the impact of MLL3/4 loss on chromatin and transcription during early embryonic stem cell differentiation. We find that MLL3/4 activity is required at most if not all sites that gain or lose H3K4me1 but is largely dispensable at sites that remain stably methylated during this transition. This requirement extends to H3K27 acetylation (H3K27ac) at most transitional sites. However, many sites gain H3K27ac independent of MLL3/4 or H3K4me1 including enhancers regulating key factors in early differentiation. Furthermore, despite the failure to gain active histone marks at thousands of enhancers, transcriptional activation of nearby genes is largely unaffected, thus uncoupling the regulation of these chromatin events from transcriptional changes during this transition. These data challenge current models of enhancer activation and imply distinct mechanisms between stable and dynamically changing enhancers. Collectively, our study highlights gaps in knowledge about the steps and epistatic relationships of enzymes necessary for enhancer activation and cognate gene transcription.
Adversarial Attacks on Protein Language Models

Deep Learning models for protein structure prediction, such as AlphaFold2, leverage Transformer architectures and their attention mechanism to capture structural and functional properties of amino acid sequences. Despite the high accuracy of predictions, biologically insignificant perturbations of the input sequences, or even single point mutations, can lead to substantially different 3d structures. On the other hand, protein language models are often insensitive to biologically relevant mutations that induce misfolding or dysfunction (e.g. missense mutations). Precisely, predictions of the 3d coordinates do not reveal the structure-disruptive effect of these mutations. Therefore, there is an evident inconsistency between the biological importance of mutations and the resulting change in structural prediction. Inspired by this problem, we introduce the concept of adversarial perturbation of protein sequences in continuous embedding spaces of protein language models. Our method relies on attention scores to detect the most vulnerable amino acid positions in the input sequences. Adversarial mutations are biologically diverse from their references and are able to significantly alter the resulting 3d structures.
Multi-omics of Circular RNAs and Their Responses to Hormones in Moso Bamboo (Phyllostachys edulis)

Circular RNAs are endogenous non-coding RNAs with covalently closed structures, which have important functions in plants. However, their biogenesis, degradation, and function upon treatment with gibberellins (GA) and auxins (NAA) remain unknown. Here, we systematically identified and characterized expression patterns, evolutionary conservation, genomic features, and internal structures of circRNAs using RNase R-treated libraries from moso bamboo (Phyllostachys edulis) seedlings. Moreover, we investigated the biogenesis of circRNAs dependent on both cis- and trans-regulation. We determined details regarding the function of circRNAs, including their roles in regulating microRNA-related genes and modulating the alternative splicing of their linear counterparts. Importantly, we developed a customized degradome sequencing approach to detect microRNA-mediated cleavage of circRNAs. Finally, we present a comprehensive view of the participation of circRNAs in the regulation of hormone metabolism upon treatment of bamboo seedlings with gibberellins (GA) and auxins (NAA). Collectively, our study uncovers important features of circRNAs including overall characteristics, biogenesis, function, and microRNA-mediated degradation of circRNAs in moso bamboo.
Reward modulates visual responses in the superficial superior colliculus of mice

The processing of sensory input is constantly adapting to behavioral demands and internal states. The drive to obtain reward, e.g., searching for water when thirsty, is a strong behavioral demand and associating the reward with its source, a certain environment or action, is paramount for survival. Here, we show that water reward increases subsequent visual activity in the superficial layers of the superior colliculus (SC), which receive direct input from the retina and belong to the earliest stages of visual processing. We trained mice of either sex to perform a visual decision task and recorded the activity of neurons in the SC using two-photon calcium imaging and high-density electrophysiological recordings. Responses to visual stimuli in around 20% of visually responsive neurons in the superficial SC were affected by reward delivered in the previous trial. Reward mostly increased visual responses independent from modulations due to pupil size changes. The modulation of visual responses by reward could not be explained by movements like licking. It was specific to responses to the following visual stimulus, independent of slow fluctuations in neural activity and independent of how often the stimulus was previously rewarded. Electrophysiological recordings confirmed these results and revealed that reward affected the early phase of the visual response around 80 ms after stimulus onset. Modulation of visual responses by reward, but not pupil size, significantly improved the performance of a population decoder to detect visual stimuli, indicating the relevance of reward modulation for the visual performance of the animal. SIGNIFICANCE STATEMENT To learn which actions lead to food, water, or safety, it is necessary to integrate the receiving of reward with sensory stimuli related to the reward. Cortical stages of sensory processing have been shown to represent stimulus-reward associations. Here, we show, however, that reward influences neurons at a much earlier stage of sensory processing, the superior colliculus (SC), receiving direct input from the retina. Visual responses were increased shortly after the animal received the water reward, which led to an improved stimulus signal in the population of these visual neurons. Reward modulation of early visual responses may thus improve perception of visual environments predictive of reward.
Tuft cells mediate commensal remodeling of the small intestinal antimicrobial landscape

Succinate produced by the commensal protist Tritrichomonas musculis (T. mu) stimulates chemosensory tuft cells, resulting in intestinal type 2 immunity. Tuft cells express the succinate receptor SUCNR1, yet this receptor does not mediate anti-helminth immunity nor alter protist colonization. Here, we report that microbial-derived succinate increases Paneth cell numbers and profoundly alters the antimicrobial peptide (AMP) landscape in the small intestine. Succinate was sufficient to drive this epithelial remodeling, but not in mice lacking tuft cell chemosensory components required to detect this metabolite. Tuft cells respond to succinate by stimulating type 2 immunity, leading to interleukin-13-mediated epithelial and AMP expression changes. Moreover, type 2 immunity decreases the total number of mucosa-associated bacteria and alters the small intestinal microbiota composition. These findings demonstrate that a single metabolite produced by commensals, like T. mu, can markedly shift the intestinal AMP profile and suggest that tuft cells utilize SUCNR1 to modulate bacterial homeostasis.
Helicobacter pylori FlgV forms a flagellar motor ring structure required for optimal motility

The bacterium Helicobacter pylori has a large flagellar motor that generates significantly higher torque than the archetypical Escherichia coli motor. To understand how H. pylori navigates the viscous environment of the stomach, it is essential to establish how specific motor components contribute to efficient motility. We show here that the protein FlgV, required for motility in Campylobacter jejuni, forms a novel ring associated with the MS and C rings in H. pylori. Deletion of flgV from H. pylori B128 or a highly motile variant of H. pylori G27 (G27M) resulted in reduced motility in soft agar medium. Based on comparative analyses of in-situ flagellar motor structures of H. pylori wild-type and ΔflgV mutants, the reduced motility of the ΔflgV mutants and the location of the FlgV ring suggest it stabilizes interactions between the MS and C rings and/or plays a role in switching the direction of flagellar rotation. Overall, these results identify a novel motor accessory likely adapted to promote flagellar function for bacterial colonization of high-load environments such as the gastric mucosa.
Inferring time-aware models of cancer progression using Timed Hazard Networks

Analysis of the sequential accumulation of genetic events, such as mutations and copy number alterations, is key to understanding disease dynamics and may provide insights into the design of targeted therapies. Oncogenetic graphical models are computational methods that use genetic event profiles from cross-sectional genomic data to infer the statistical dependencies between events and thereby deduce their temporal order of occurrence. Existing research focuses mainly on the development of graph structure learning algorithms. However, no algorithm explicitly links the oncogenetic graph with the temporal differences of samples in an analytic way. In this paper, we propose a novel statistical framework Timed Hazard Networks (TimedHN), that treat progression times as hidden variables and jointly infers oncogenetic graph and pseudo-time order of samples. We modeled the accumulation process as a continuous-time Markov chain and developed an efficient gradient computation algorithm for the optimization. Experiment results using synthetic data showed that our method outperforms the state-of-the-art in graph reconstruction. We highlighted the differences between TimedHN and competing methods on a luminal breast cancer dataset and illustrated the potential utility of the proposed method. Implementation and data are available at https://github.com/puar-playground/TimedHN
Title: Temperature seasonality and nutrient enrichment drive intra-annual community turnover in global grasslands

In many grasslands, species with specific traits occupy unique temporal positions within 35 communities. Such intra-annual segregation is predicted to be greatest in systems with high 36 variability (i.e., seasonality) in precipitation and temperature because fluctuating environmental 37 conditions select for species that capitalize on resources at different times. However, because 38 most studies on intra-annual community dynamics have been conducted at individual sites, 39 relationships between seasonality and intra-annual turnover at global scales have not yet been 40 identified. Furthermore, the same characteristics that promote species-specific responses to 41 seasonal fluctuations in environmental conditions may also drive species-specific responses to 42 global change drivers such as eutrophication. Research provides evidence that eutrophication 43 alters inter-annual plant community dynamics yet understanding of how it alters intra-annual 44 dynamics remains limited. 45 We used early-season and late-season compositional data collected from 10 grassland 46 sites around the world to ask how seasonality in precipitation and temperature as well as nutrient 47 enrichment shape intra-annual turnover in plant communities. In addition, we assessed whether 48 changes in the abundances of specific functional groups including annual forbs, perennial forbs, 49 C3 and C4 graminoids, and legumes underpin compositional differences between early- and late- 50 season communities and treatments. We found that temperature seasonality and intra-annual 51 turnover were positively related but observed no relationship between precipitation seasonality 52 and intra-annual turnover. These results conflict with theoretical predictions from earlier studies 53 which suggest precipitation seasonality drives intra-annual turnover in plant communities. In 54 addition, nutrient enrichment increased intra-annual turnover; this pattern was underpinned by 55 increased replacement of species between early- and late-season communities in fertilized conditions. This finding mirrors patterns observed at inter-annual scales and suggests fertilization can alter compositional dynamics via similar mechanisms at varied temporal scales. Finally, 58 fertilization reduced the abundance of C4 graminoids and legumes and eliminated intra-annual 59 differences in these groups. In contrast, fertilization resulted in intra-annual differences in C3 60 graminoids which were not observed in control conditions, and increased abundance of C3 61 graminoids and annual forbs overall. Our study provides novel insight into how seasonality and 62 nutrient enrichment shape intra-annual grassland dynamics and highlights how discerning these 63 patterns is essential to our understanding of biodiversity in these systems. 64