Miracle-dz/UniFAR-FTUs huggingface dataset:
The natural axis of transmitter receptor distribution in the human cerebral cortex

Significance Communication between cells in the brain relies on different types of transmitter receptors. Can we uncover organizational principles that harness the diversity of such signatures across the brain? We focus on the human cerebral cortex and demonstrate that the distribution of receptors forms a natural axis that stretches from association to sensory areas. Moreover, traversing this axis entails changes in the diversity, excitability, and mirrored density that reflect a basic division in receptor types, that is, ionotropic and metabotropic receptors. The unraveled principles offer explanatory depth for diverse phenomena and entail concrete, testable predictions. Transmitter receptors constitute a key component of the molecular machinery for intercellular communication in the brain. Recent efforts have mapped the density of diverse transmitter receptors across the human cerebral cortex with an unprecedented level of detail. Here, we distill these observations into key organizational principles. We demonstrate that receptor densities form a natural axis in the human cerebral cortex, reflecting decreases in differentiation at the level of laminar organization and a sensory-to-association axis at the functional level. Along this natural axis, key organizational principles are discerned: progressive molecular diversity (increase of the diversity of receptor density); excitation/inhibition (increase of the ratio of excitatory-to-inhibitory receptor density); and mirrored, orderly changes of the density of ionotropic and metabotropic receptors. The uncovered natural axis formed by the distribution of receptors aligns with the axis that is formed by other dimensions of cortical organization, such as the myelo- and cytoarchitectonic levels. Therefore, the uncovered natural axis constitutes a unifying organizational feature linking multiple dimensions of the cerebral cortex, thus bringing order to the heterogeneity of cortical organization.
Emergence of synchronized multicellular mechanosensing from spatiotemporal integration of heterogeneous single-cell information transfer

We quantitatively characterize how noisy and heterogeneous behaviors of individual cells are integrated across a population toward multicellular synchronization by studying the calcium dynamics in mechanically stimulated monolayers of endothelial cells. We used information-theory to quantify the asymmetric information-transfer between pairs of cells and define quantitative measures of how single cells receive or transmit information in the multicellular network. We find that cells take different roles in intercellular information-transfer and that this heterogeneity is associated with synchronization. Cells tended to maintain their roles between consecutive cycles of mechanical stimuli and reinforced them over time, suggesting the existence of a cellular “memory” in intercellular information transfer. Interestingly, we identified a subpopulation of cells characterized by higher probability of both receiving and transmitting information. These “communication hub” roles were stable - once a cell switched to a “communication hub” role it was less probable to switch to other roles. This stableness property of the cells led to gradual enrichment of communication hubs that was associated with the establishment of synchronization. Our analysis demonstrated that multicellular synchronization was established by effective information spread from the (local) single cell to the (global) group scale in the multicellular network. Altogether, we suggest that multicellular synchronization is driven by single cell communication properties, including heterogeneity, functional memory and information flow.
Runx1 shapes the chromatin landscape via a cascade of direct and indirect targets

Runt-related transcription factor 1 (Runx1) can act as both an activator and a repressor. Here we show that CRISPR-mediated deletion of Runx1 in an embryonic kidney-derived cell (mK4) results in large-scale genome-wide changes to chromatin accessibility and gene expression. Open chromatin regions near down-regulated loci are enriched for Runx sites, remain bound by Runx2, but lose chromatin accessibility and expression in Runx1 knockout cells. Unexpectedly, regions near upregulated genes are depleted of Runx sites and are instead enriched for Zeb transcription factor binding sites. Re-expressing Zeb2 in Runx1 knockout cells restores suppression. These data confirm that Runx1 activity is uniquely needed to maintain open chromatin at many loci, and demonstrate that genome-scale derepression is an indirect consequence of losing Runx1-dependent Zeb expression.
Effect of biochar on the growth of Poncirus trifoliata (L.) Raf. seedlings in Gannan acidic red soil

ABSTRACT Soil acidification has become a serious problem for citrus cultivation in China. As a soil amendment, biochar is expected to increase soil pH as well as soil fertility. In this study, we assessed the effect of biochar on Trifoliate orange, the most frequently used citrus rootstock, in a pot experiment using acidic red soil from the Gannan citrus production area. Plant height and shoot diameter of Poncirus trifoliata (L.) Raf. seedlings increased significantly after biochar was added to soils. This positive effect was further evidenced by the increased plant biomass and leaf net photosynthetic rate. The root system architecture (RSA) was evaluated based on root length, root surface area, root volume and root tip. Biochar amendment significantly increased the total absorptive surface area of the root system. Due to the significant role of arbuscular mycorrhizal fungi (AMF) in citrus root nutrient uptake, the AMF colonization and community in Poncirus roots were investigated. The AMF colonization rate was not significantly affected by biochar, whereas AMF diversity increased upon biochar treatment. In addition, the biochar treatment resulted in increases in soil pH, organic matter and mineral nutrients. Together, our results suggest that the positive effects of biochar on the growth performance of Poncirus seedlings can be attributed to the substantial augmentation of soil fertility, increased soil pH, optimized RSA and improved AMF species composition.
Temporal stability and genetic diversity of 48-year old T-series phages

T-series phages have been model organisms for molecular biology since the 1940s. Given that these phages have been stocked, distributed, and propagated for decades across the globe, there exists the potential for genetic drift to accumulate between stocks over time. Here we compared the temporal stability and genetic relatedness of laboratory-maintained phage stocks with a T-series collection from 1972. Only the T-even phages produced viable virions. We obtained complete genomes of these T-even phages, along with two contemporary T4 stocks. Performing comparative genomics, we found 12 and 16 nucleotide variations, respectively, in the genomes of T2 and T6; whereas there were ~172 nucleotide variations between T4-sublines when compared with NCBI RefSeq genome. To account for the possibility of artefacts in the NCBI RefSeq, we used the 1972 T4 stock as a reference and compared genetic and phenotypic variations between T4-sublines. Genomic analysis predicted nucleotide variations in genes associated with DNA metabolism and structural proteins. We did not however, observe any differences in growth characteristics or host range between the T4-sublines. Our study highlights the potential for genetic drift between individually maintained T-series phage stocks, yet after 48-years, this has not resulted in phenotypic alternations in these important model organisms. Importance T-series bacteriophages have been used throughout the world for various molecular biology researches, which were critical for establishing the fundamentals of molecular biology – from the structure of DNA to advanced gene-editing tools. These model bacteriophages help keep research data consistent and comparable between laboratories. However, we observed genetic variability when compared contemporary sublines of T4-phages to a 48-year old stock of T4. This may have effects in the comparability of results obtained using T4 phage. Here, we highlighted the genomic differences between T4 sublines and examined phenotypic differences in phage replication parameters. We observed limited genomic changes but no phenotypic variations between T4 sublines. Our research highlights the possibility of genetic drift in model bacteriophages.
DAF-18/PTEN inhibits germline zygotic gene activation during primordial germ cell quiescence

Quiescence, an actively-maintained reversible state of cell cycle arrest, is not well understood. PTEN is one of the most frequently lost tumor suppressors in human cancers and regulates quiescence of stem cells and cancer cells. In C. elegans mutant for daf-18, the sole C. elegans PTEN ortholog, primordial germ cells (PGCs) divide inappropriately in starvation conditions, in a TOR-dependent manner. Here, we further investigated the role of daf-18 in maintaining PGC quiescence. We found that maternal or zygotic daf-18 is sufficient to maintain cell cycle quiescence, that daf-18 acts in the germ line and soma, and that daf-18 affects timing of PGC divisions in fed animals. Importantly, our results also implicate daf-18 in zygotic germline gene activation, though not in germline fate specification. However, TOR is less important to zygotic germline gene expression, suggesting that in the absence of food daf-18/PTEN prevents inappropriate germline zygotic gene activation and cell division by distinct mechanisms.
Mechanisms and heterogeneity of mineral use by natural colonies of the cyanobacterium Trichodesmium

The keystone marine nitrogen fixer Trichodesmium thrives in high dust environments, and while experimental observations suggest that Trichodesmium colonies can access the essential nutrient iron from dust particles, it is not known the extent to which this occurs in the field. Here we demonstrate that Trichodesmium colonies actively process mineral particles in nature with direct molecular impacts. Microscopy and synchrotron-based imaging demonstrated heterogeneous associations with particles consistent with iron oxide and iron silicate minerals. Metaproteomic analysis of individual colonies revealed enrichment of biogeochemically-relevant proteins including photosynthesis proteins and metalloproteins containing iron, nickel, copper and zinc when particles were present. The iron-storage protein ferritin was particularly enriched implying accumulation of particle-derived iron, and multiple iron acquisition pathways including Fe(II), Fe(III), and Fe-siderophore transporters were engaged, including evidence of superoxide-driven particle dissolution. While the particles clearly provided iron, there was also evidence that the concentrated metals had toxic effects. The molecular mechanisms allowing Trichodesmium to interact with particulate minerals are fundamental to its success and global impact on nitrogen biogeochemistry, and may contribute to the leaching of particulate trace metals with implications for global iron and carbon cycling.
Applying SCA for high-accuracy cortical auditory ERPs in children

Overlapping neurophysiological signals are the main obstacle preventing from using cortical event-related potentials (ERPs) in clinical settings. Children ERPs are particularly affected by this problem, as their cerebral cortex is still maturing. To overcome this problem, we applied a new version of Spike-density Component Analysis (SCA), an analysis method recently introduced, to isolate with high accuracy the neural components of auditory ERP responses (AEPs) in 8-year-old children. Electroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard ERP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC). SCA-HSAC most successfully allowed the extraction of AEPs for each child, revealing that the most consistent components were P1 and N2. An immature N1 component was also detected. Superior accuracy in isolating neural components at the individual level even in children was demonstrated for SCA-HSAC over other SCA approaches. Reliable methods of extraction of neurophysiological signals at the individual level are crucial for the application of cortical AEPs for routine diagnostic exams in clinical settings both in children and adults. Highlights Spike-density component analysis (SCA) was validated on children ERPs SCA extracted overlapping neural components from auditory ERPs (AEPs) Child AEPs were modelled at the individual level
A simple (2+ε)-approximation algorithm for Split Vertex Deletion

A split graph is a graph whose vertex set can be partitioned into a clique and a stable set. Given a graph $G$ and weight function $w: V(G) \to \mathbb{Q}_{\geq 0}$, the Split Vertex Deletion (SVD) problem asks to find a minimum weight set of vertices $X$ such that $G-X$ is a split graph. It is easy to show that a graph is a split graph if and only it it does not contain a $4$-cycle, $5$-cycle, or a two edge matching as an induced subgraph. Therefore, SVD admits an easy $5$-approximation algorithm. On the other hand, for every $\delta >0$, SVD does not admit a $(2-\delta)$-approximation algorithm, unless P=NP or the Unique Games Conjecture fails. For every $\epsilon >0$, Lokshtanov, Misra, Panolan, Philip, and Saurabh recently gave a randomized $(2+\epsilon)$-approximation algorithm for SVD. In this work we give an extremely simple deterministic $(2+\epsilon)$-approximation algorithm for SVD.
Autonomous Programming for General Purposes: Theory

The universal Turing Machine (TM) is a model for Von Neumann computers — general-purpose computers. A human brain, linked with its biological body, can inside-skull-autonomously learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a robot can accomplish the same. This theoretical work shows how the Developmental Network (DN), linked with its robot body, can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM — Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This paper extends the Church–Turing thesis to a stronger version — a GENISAMA TM is capable of Autonomous Programming for General Purposes (APFGP) — and proves both the Church–Turing thesis and its stronger version.
DeepEthogram: a machine learning pipeline for supervised behavior classification from raw pixels

Researchers commonly acquire videos of animal behavior and quantify the prevalence of behaviors of interest to study nervous system function, the effects of gene mutations, and the efficacy of pharmacological therapies. This analysis is typically performed manually and is therefore immensely time consuming, often limited to a small number of behaviors, and variable across researchers. Here, we created DeepEthogram: software that takes raw pixel values of videos as input and uses machine learning to output an ethogram, the set of user-defined behaviors of interest present in each frame of a video. We used convolutional neural network models that compute motion in a video, extract features from motion and single frames, and classify these features into behaviors. These models classified behaviors with greater than 90% accuracy on single frames in videos of flies and mice, matching expert-level human performance. The models accurately predicted even extremely rare behaviors, required little training data, and generalized to new videos and subjects. DeepEthogram runs rapidly on common scientific computer hardware and has a graphical user interface that does not require programming by the end-user. We anticipate DeepEthogram will enable the rapid, automated, and reproducible assignment of behavior labels to every frame of a video, thus accelerating all those studies that quantify behaviors of interest. Code is available at: https://github.com/jbohnslav/deepethogram
Seasonal Changes in Stable Carbon Isotope Ratios within Annual Rings of Pinus radiata Reflect Environmental Regulation of Growth Processes

Seasonal cycles in stable carbon isotope composition (δ13C) were measured within annual rings of Pinus radiata D. Don from trees in two plantation sites which differ markedly in annual water balance. The amplitudes of seasonal δ13C variation at the wet and dry sites were 1–2 and 4 respectively. Mean δ13C values from the wet site were 3 more 13C depleted than those from the dry site implying lower water-use efficiency (carbon assimilation per unit transpiration). A process-based, leaf-level model of stomatal conductance and CO2 assimilation was combined with a water balance model to estimate the average daily intercellular CO2 concentration (ci). Over two growing seasons at each site there was generally good agreement between mean canopy-level ci derived from the tree-ring δ13C data and modelled leaf-level ci levels. Further, the ratio of annual CO2 assimilation to transpiration estimated by the model for each site correlated with the differences in water-use efficiency between the sites. Carbon isotope discrimination of leaves in the canopy was thus reflected directly in the stem wood, and its dynamic variation within a site, as well as between sites, was driven principally by the interaction of seasonally changing micrometeorological variables and soil water availability.
Gut microbiome dysbiosis is associated with elevated toxic bile acids in Parkinson’s disease

The gut microbiome can impact brain health and is altered in Parkinson’s disease (PD) patients. Here, we investigate changes in the functional microbiome in the appendix of PD patients relative to controls by metatranscriptomic analysis. We find microbial dysbiosis affecting lipid metabolism, particularly an upregulation of bacteria responsible for secondary bile acid synthesis. Proteomic and transcript analysis corroborates a disruption in cholesterol homeostasis and lipid catabolism. Bile acid analysis reveals an increase in microbially-derived, toxic secondary bile acids. Synucleinopathy in mice induces similar microbiome alterations to those of PD patients. The mouse model of synucleinopathy has elevated DCA and LCA. An analysis of blood markers shows evidence of biliary abnormalities early in PD, including elevated alkaline phosphatase and bilirubin. Increased bilirubin levels are also evident before PD diagnosis. In sum, microbially-derived toxic bile acids are heightened in PD and biliary changes may even precede the onset of overt motor symptoms.
Quantifying the Brain Predictivity of Artificial Neural Networks With Nonlinear Response Mapping

Quantifying the similarity between artificial neural networks (ANNs) and their biological counterparts is an important step towards building more brain-like artificial intelligence systems. Recent efforts in this direction use neural predictivity, or the ability to predict the responses of a biological brain given the information in an ANN (such as its internal activations), when both are presented with the same stimulus. We propose a new approach to quantifying neural predictivity by explicitly mapping the activations of an ANN to brain responses with a nonlinear function, and measuring the error between the predicted and actual brain responses. Further, we propose to use a neural network to approximate this mapping function by training it on a set of neural recordings. The proposed method was implemented within the Tensorflow framework and evaluated on a suite of 8 state-of-the-art image recognition ANNs. Our experiments suggest that the use of a non-linear mapping function leads to higher neural predictivity. Our findings also reaffirm the observation that the latest advances in classification performance of image recognition ANNs are not matched by improvements in their neural predictivity. Finally, we examine the impact of pruning, a widely used ANN optimization, on neural predictivity, and demonstrate that network sparsity leads to higher neural predictivity.
Predicting the Key Regulators of Cell Identity in Human Adult Pancreas

Cellular identity during development is under the control of transcription factors that form gene regulatory networks. However, the transcription factors and gene regulatory networks underlying cellular identity in the human adult pancreas remain largely unexplored. Here, we integrate multiple single-cell RNA-sequencing datasets of the human adult pancreas, totaling 7393 cells, and comprehensively reconstruct gene regulatory networks. We show that a network of 142 transcription factors forms distinct regulatory modules that characterize pancreatic cell types. We present evidence that our approach identifies regulators of cell identity in the human adult pancreas. We predict that HEYL, BHLHE41 and JUND are active in acinar, beta and alpha cells, respectively, and show that these proteins are present in the human adult pancreas as well as in human induced pluripotent stem cell (hiPSC)-derived islet cells. Using single-cell transcriptomics, we found that JUND represses beta cell genes in hiPSC-alpha cells. Both BHLHE41 and JUND depletion seemed to increase the number of sc-enterochromaffin cells in hiPSC-derived islets. The comprehensive gene regulatory network atlas can be explored interactively online. We anticipate our analysis to be the starting point for a more sophisticated dissection of how transcription factors regulate cell identity in the human adult pancreas. Furthermore, given that transcription factors are major regulators of embryo development and are often perturbed in diseases, a comprehensive understanding of how transcription factors work will be relevant in development and disease. HIGHLIGHTS Reconstruction of gene regulatory networks for human adult pancreatic cell types An interactive resource to explore and visualize gene expression and regulatory states Prediction of putative transcription factors that drive pancreatic cell identity BHLHE41 depletion in primary islets induces apoptosis