Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Adaptive Skeleton Approaches to Detect Self-Organized Coalitions From Brain Functional Networks Through Probabilistic Mixture Models

Detecting self-organized coalitions from functional networks is one of the most important ways to uncover functional mechanisms in the brain. Determining these raises well-known technical challenges in terms of scale imbalance, outliers and hard-examples. In this article, we propose a novel self-adaptive skeleton approach to detect coalitions through an approximation method based on probabilistic mixture models. The nodes in the networks are characterized in terms of robust k-order complete subgraphs (k-clique) as essential substructures. The k-clique enumeration algorithm quickly enumerates all k-cliques in a parallel manner for a given network. Then, the cliques, from max-clique down to min-clique, of each order k, are hierarchically embedded into a probabilistic mixture model. They are self-adapted to the corresponding structure density of coalitions in the brain functional networks through different order k. All the cliques are merged and evolved into robust skeletons to sustain each unbalanced coalition by eliminating outliers and separating overlaps. We call this the k-CLIque Merging Evolution (CLIME) algorithm. The experimental results illustrate that the proposed approaches are robust to density variation and coalition mixture and can enable the effective detection of coalitions from real brain functional networks. There exist potential cognitive functional relations between the regions of interest in the coalitions revealed by our methods, which suggests the approach can be usefully applied in neuroscientific studies.
Deliberative Breakdowns in the Social Representation Process

This article examines breakdowns that occur when readers at partisan news websites attempt to understand a challenging news event. We conduct the work with the 2017 Alabama senate race as the empirical context marked by the nomination of Republican Roy Moore (a challenging news event for the left-leaning readers), and the story of his alleged sexual misconduct (a challenging news event for the right-leaning readers). To examine how readers attempt to understand these events, we scrape reader comments from two partisan news websites. Our analysis relies on and further elaborates the social representation theory and argumentation theory to identify obstacles that prevent successful progression of social representation processes: rhetorical, epistemological, and emotional breakdowns. The findings from our data reveal indicators of rhetorical breakdowns (greater occurrence of fallacious and non-argumentative reader comments) and epistemological breakdowns (greater use of doxastic comments) both tied to how challenging the news event is as well as indicators of emotional breakdowns (greater occurrence of attack posture) tied to the emotionally charged nature of the news event. We interpret the findings as a balancing act between protecting pre-existing representations and acknowledging the challenging news event. The indicators of potential breakdowns we find enhance our understanding of partisan political discourse viewed through the lens of social representation processes. The article discusses these contributions, including elaborations to social representation theory, and discusses implications of the work.
Tiered Sampling

We introduce Tiered Sampling, a novel technique for estimating the count of sparse motifs in massive graphs whose edges are observed in a stream. Our technique requires only a single pass on the data and uses a memory of fixed size M, which can be magnitudes smaller than the number of edges. Our methods address the challenging task of counting sparse motifs—sub-graph patterns—that have a low probability of appearing in a sample of M edges in the graph, which is the maximum amount of data available to the algorithms in each step. To obtain an unbiased and low variance estimate of the count, we partition the available memory into tiers (layers) of reservoir samples. While the base layer is a standard reservoir sample of edges, other layers are reservoir samples of sub-structures of the desired motif. By storing more frequent sub-structures of the motif, we increase the probability of detecting an occurrence of the sparse motif we are counting, thus decreasing the variance and error of the estimate. While we focus on the designing and analysis of algorithms for counting 4-cliques, we present a method which allows generalizing Tiered Sampling to obtain high-quality estimates for the number of occurrence of any sub-graph of interest, while reducing the analysis effort due to specific properties of the pattern of interest. We present a complete analytical analysis and extensive experimental evaluation of our proposed method using both synthetic and real-world data. Our results demonstrate the advantage of our method in obtaining high-quality approximations for the number of 4 and 5-cliques for large graphs using a very limited amount of memory, significantly outperforming the single edge sample approach for counting sparse motifs in large scale graphs.
Machine learning modeling of the effects of media formulated with various yeast extracts on heterologous protein production in Escherichia coli

In microbial manufacturing, yeast extract is an important component of the growth media. The production of heterologous proteins often varies because of the yeast extract composition. To identify why this reduces protein production, the effects of yeast extract composition on the growth and green fluorescent protein (GFP) production of engineered Escherichia coli were investigated using a deep neural network (DNN)‐mediated metabolomics approach. We observed 205 peaks from the various yeast extracts using gas chromatography‐mass spectrometry. Principal component analyses of the peaks identified at least three different clusters. Using 20 different compositions of yeast extract in M9 media, the yields of cells and GFP in the yeast extract‐containing media were higher than those in the control without yeast extract by approximately 3.0‐ to 5.0‐fold and 1.5‐ to 2.0‐fold, respectively. We compared machine learning models and found that DNN best fit the data. To estimate the importance of each variable, we performed DNN with a mean increase error calculation based on a permutation algorithm. This method identified the significant components of yeast extract. DNN learning with varying numbers of input variables provided the number of significant components. The influence of specific components on cell growth and GFP production was confirmed with a validation cultivation.
Interleukin‐18, IL‐18 binding protein and IL‐18 receptor expression in asthma: a hypothesis showing IL‐18 promotes epithelial cell differentiation

In asthma, genome‐wide association studies have shown that interleukin‐18 (IL‐18) receptor 1 gene (IL‐18R1) and sputum IL‐18 are increased during exacerbations. However, the role of the IL‐18 axis in bronchial epithelial function is unclear. To investigate IL‐18, IL‐18 binding protein (BP) and IL‐18R expression in bronchial biopsies and sputum samples from patients with asthma, and to determine its functional role using in vitro bronchial epithelial cells.
The WZA: A window-based method for characterizing genotype-environment association

Genotype environment association (GEA) studies have the potential to elucidate the genetic basis of local adaptation in natural populations. Specifically, GEA approaches look for a correlation between allele frequencies and putatively selective features of the environment. Genetic markers with extreme evidence of correlation with the environment are presumed to be tagging the location of alleles that contribute to local adaptation. In this study, we propose a new method for GEA studies called the weighted-Z analysis (WZA) that combines information from closely linked sites into analysis windows in a way that was inspired by methods for calculating FST. We analyze simulations modelling local adaptation to heterogeneous environments either using a GEA method that controls for population structure or an uncorrected approach. In the majority of cases we tested, the WZA either outperformed single-SNP based approaches or performed similarly. The WZA outperformed individual SNP approaches when the measured environment is not perfectly correlated with the true selection pressure or when a small number of individuals or demes was sampled. We apply the WZA to previously published data from lodgepole pine and identified candidate loci that were not found in the original study.
Coordinated action of multiple transporters in the acquisition of essential cationic amino acids by the intracellular parasite Toxoplasma gondii

Intracellular parasites of the phylum Apicomplexa are dependent on the scavenging of essential amino acids from their hosts. We previously identified a large family of apicomplexan-specific plasma membrane-localized amino acid transporters, the ApiATs, and showed that the Toxoplasma gondii transporter TgApiAT1 functions in the selective uptake of arginine. TgApiAT1 is essential for parasite virulence, but dispensable for parasite growth in medium containing high concentrations of arginine, indicating the presence of at least one other arginine transporter. Here we identify TgApiAT6-1 as the second arginine transporter. Using a combination of parasite assays and heterologous characterisation of TgApiAT6-1 in Xenopus laevis oocytes, we demonstrate that TgApiAT6-1 is a general cationic amino acid transporter that mediates both the high-affinity uptake of lysine and the low-affinity uptake of arginine. TgApiAT6-1 is the primary lysine transporter in the disease-causing tachyzoite stage of T. gondii and is essential for parasite proliferation. We demonstrate that the uptake of cationic amino acids by TgApiAT6-1 is ‘trans-stimulated’ by cationic and neutral amino acids and is likely promoted by an inwardly negative membrane potential. These findings demonstrate that T. gondii has evolved overlapping transport mechanisms for the uptake of essential cationic amino acids, and we draw together our findings into a comprehensive model that highlights the finely-tuned, regulated processes that mediate cationic amino acid scavenging by these intracellular parasites. Author summary The causative agent of toxoplasmosis, Toxoplasma gondii, is a versatile intracellular parasite that can proliferate within nucleated cells of virtually all warm-blooded organisms. In order to survive, T. gondii must scavenge the cationic amino acids lysine and arginine from their hosts. In a previous study, we demonstrated that a plasma membrane-localized protein called TgApiAT1 facilitates the uptake of arginine into the parasite. We found that parasites lacking TgApiAT1 could proliferate when cultured in medium containing high concentrations of arginine, suggesting the existence of an additional uptake pathway for arginine. In the present study, we demonstrate that this second uptake pathway is mediated by TgApiAT6-1, a protein belonging to the same solute transporter family as TgApiAT1. We show that TgApiAT6-1 is the major lysine transporter of the parasite, and that it is critical for parasite proliferation. Furthermore, we demonstrate that TgApiAT6-1 can transport arginine into parasites in conditions where arginine concentrations are high and lysine concentrations are comparatively lower. These data support a model for the finely-tuned acquisition of essential cationic amino acids that involves multiple transporters, and which likely contributes to these parasites being able to survive and proliferate within a wide variety of host cell types.
Contractile ring constriction and septation in fission yeast are integrated mutually stabilizing processes

In common with other cellular machineries, the actomyosin contractile ring that divides cells during cytokinesis does not operate in isolation. Contractile rings in animal cells interact with contiguous actomyosin cortex, while ring constriction in many cell-walled organisms couples tightly to cell wall growth. In fission yeast, a septum grows in the wake of the constricting ring, ensuring cytokinesis leaves two daughter cells fully enclosed by cell wall. Here we mathematical modeled the integrated constriction-septation system in fission yeast, with a kinetic growth model evolving the 3D septum shape coupled to a molecularly explicit simulation of the contractile ring highly constrained by experimental data. Simulations revealed influences in both directions, stabilizing the ring-septum system as a whole. By providing a smooth circular anchoring surface for the ring, the inner septum leading edge stabilized ring organization and tension production; by mechanically regulating septum circularity and in-plane growth, ring tension stabilized septum growth and shape. Genetic or pharmacological perturbation of either subsystem destabilized this delicate balance, precipitating uncontrolled positive feedback with disastrous morphological and functional consequences. Thus, high curvature septum irregularities triggered bridging instabilities, in which contractile ring segments became unanchored. Bridging abolished the local tension-mediated septum shape regulation, exacerbating the irregularity in a mutually destabilizing runaway process. Our model explains a number of previously mysterious experimental observations, including unanchoring of ring segments observed in cells with mutations in the septum-growing β-glucan synthases, and irregular septa in cells with mutations in the contractile ring myosin-II Myo2. Thus, the contractile ring and cell wall growth cellular machineries operate as a single integrated system, whose stability relies on mutual regulation by the two subsystems.
In Vivo Imaging and Quantification of Carbon Tracer Dynamics in Nodulated Root Systems of Pea Plants

Legumes associate with root colonizing rhizobia that provide fixed nitrogen to its plant host in exchange for recently fixed carbon. There is a lack in understanding how individual plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. One reason is that most approaches are based on destructive sampling, making quantification of localized carbon allocation dynamics in the root system difficult. We established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11C tracer towards individual nodules in a three-dimensional (3D) root system of pea (Pisum sativum). Nitrate was used for triggering the shutdown of biological nitrogen fixation (BNF) expected to rapidly affect carbon allocation dynamics in the root-nodule system. This nitrate treatment lead to a reduction of 11C tracer allocation to nodules by 40% – 47% in 5 treated plants while the variation in control plants was less than 11%. The established experimental pipeline enabled for the first time that several plants could consistently be labelled and measured using 11C tracer in a PET approach to quantify C-allocation to individual nodules following a BNF shutdown. This demonstrates the strength of using 11C tracers in a PET approach for non-invasive quantification of dynamic carbon allocation in several growing plants over several days. A major advantage of the approach is the possibility to investigate carbon dynamics in small regions of interest in a 3D system such as nodules in comparison to whole plant development. One sentence summary Positron Emission Tomography for quantification of carbon allocation dynamics in individual nodules within a 3D root system revealed strong effect of nitrate on carbon allocation.
Oiling the gears of memory: quercetin exposure during memory formation, consolidation, and recall enhances memory in Lymnaea stagnalis

Memory formation (short-term, intermediate-term, and long-term) is an integral process of cognition which allows individuals to retain important information and is influenced by various intrinsic and extrinsic factors. A major extrinsic factor influencing cognition across taxa is diet, which may contain rich sources of molecular agents with antioxidant, anti-inflammatory, and memory enhancing properties that potentially enhance cognitive ability. A common and abundant flavonoid present in numerous food substances is quercetin (Q) which is also known to upregulate cyclic AMP response element binding protein (CREB) in several animals including our model system Lymnaea stagnalis. Since CREB is known to be involved in long term memory (LTM) formation, we investigated the role of Q-exposure on memory formation, consolidation, and recall during operant conditioning of aerial respiratory behaviour in Lymnaea. Snails were exposed to Q 3h before or after training to ascertain its effects on LTM. Additionally, we investigated the effect of the combined presentation of a single reinforcing stimulus (at 24h post-training or 24h before training) and Q-exposure on both LTM formation and reconsolidation. Our data indicate that Q-exposure acts on the different phases of memory formation, consolidation, and recall leading to enhanced LTM formation. Summary Statement Quercetin enhances long-term memory formation acting on the different phases of memory formation, consolidation, and recall.
Macrophage inflammatory and regenerative response periodicity is programmed by cell cycle and chromatin state

Cell cycle (CC) is a fundamental biological process with robust, cyclical gene expression programs to facilitate cell division. In the immune system, a productive immune response requires the expansion of pathogen-responsive cell types, but whether CC also confers unique gene expression programs that inform the subsequent immunological response remains unclear. Here we demonstrate that single macrophages adopt different plasticity states in CC, which is a major source of heterogeneity in response to polarizing cytokines. Specifically, macrophage plasticity to interferon gamma (IFNG) is substantially reduced, while interleukin 4 (IL-4) can induce S-G2/M-biased gene expression. Additionally, IL-4 polarization shifts the CC-phase distribution of the population towards G2/M phase, providing a mechanism for reduced IFNG-induced repolarization. Finally, we show that macrophages express tissue remodeling genes in the S-G2/M-phases of CC, that can be also detected in vivo during muscle regeneration. Therefore, macrophage inflammatory and regenerative responses are gated by CC in a cyclical phase-dependent manner. Highlights Single-cell chromatin maps reveal heterogeneous macrophage polarization states Cell cycle coincides with heterogeneity and alters macrophage plasticity to polarizing cytokines Macrophage polarization is a cell cycle phase-dependent immunological process S-G2/M-biased gene expression is linked to tissue remodeling and detected in proliferating macrophages during muscle regeneration
Land-surface evapotranspiration derived from a first-principles primary production model

Evapotranspiration (ET) links the water and carbon cycles in the atmosphere, hydrosphere, and biosphere. In this study, we develop an ET modelling framework based on the idea that the transpiration and carbon uptake are closely coupled, as predicted by the ‘least-cost hypothesis’ that canopy conductance acclimates to environmental variations. According to eco-evolutionary optimality theory, which has been previously applied in monitoring and modelling land-surface processes, the total costs (per unit carbon fixed) for maintaining transpiration and carboxylation capacities should be minimized. We calculate gross primary production (GPP) assuming that the light- and Rubisco-limited rates of photosynthesis, described by the classical biochemical model of photosynthesis, are coordinated on an approximately weekly time scale. Transpiration (T) is then calculated via acclimated canopy conductance, with no need for plant type- or biome-specific parameters. ET is finally calculated from T using an empirical function of light, temperature, soil water content and foliage cover to predict the T/ET ratio at each site. The GPP estimates were well supported by (weekly) GPP data at 20 widely distributed eddy-covariance flux sites (228 site-years), with correlation coefficients (r) = 0.81 and root-mean-square error (RMSE) = 18.7 gC week−1 and Nash-Sutcliffe efficiency (NSE) = 0.61. Predicted ET was also well supported, with r =0.85, RMSE = 5.5 mm week–1 and NSE = 0.66. Estimated T/ET ratios (0.43–0.74) showed significant positive relationships to radiation, precipitation and green vegetation cover and negative relationships to temperature and modelled T (r = 0.84). Aspects of this framework could be improved, notably the estimation of T/ET. Nonetheless, we see the application of eco-evolutionary principles as a promising direction for water resources research, eliminating the uncertainty introduced by the need to specify multiple parameters, and leveraging the power of remotely sensed vegetation cover data as a key indicator of ecosystem function.
Protein Kinase Cγ Mutations Drive Spinocerebellar Ataxia Type 14 by Impairing Autoinhibition

Spinocerebellar ataxia type 14 (SCA14) is a neurodegenerative disease caused by germline mutations in the diacylglycerol (DG)/Ca2+-regulated protein kinase C gamma (PKCγ), leading to Purkinje cell degeneration and progressive cerebellar dysfunction. The majority of the approximately 50 mutations identified in PKCγ cluster to the DG-sensing C1 domains. Here, we use a FRET-based activity reporter to show that ataxia-associated PKCγ mutations enhance basal activity by compromising autoinhibition. Although impaired autoinhibition generally leads to PKC degradation, the C1 domain mutations protect PKCγ from phorbol ester-induced downregulation. Furthermore, it is the degree of disrupted autoinhibition, not changes in the amplitude of agonist- stimulated activity, that correlate with disease severity. Specifically, a SCA14 mutation in which phenylalanine 48 in the C1A domain is deleted had high basal activity both in cells and in vitro, yet was unresponsive to agonist stimulation. Validating that the pathology arises from disrupted autoinhibition, we show that the degree of impaired autoinhibition correlates inversely with age of disease onset in patients: mutations that cause high basal activity are associated with early onset, whereas those that only modestly increase basal activity, including a previously undescribed mutation D115Y, are associated with later onset. Molecular modeling indicates that almost all SCA14 mutations that are not in the C1 domains are at interfaces with the C1B domain, and bioinformatics analysis reveals that mutations in the C1B domain are under-represented in cancer. Thus, clustering of SCA14 mutations to the C1B domain provides a unique mechanism to enhance PKCγ basal activity while protecting the enzyme from downregulation. One Sentence Summary SCA14 driver mutations in PKCγ impair autoinhibition, with defect correlating inversely with age of disease onset.