bluuebunny/crossref_metadata_2025_split huggingface dataset:
Decoding of human identity by computer vision and neuronal vision
Authors: Zhang Yipeng , Aghajan Zahra M. , Ison Matias , Lu Qiujing , Tang Hanlin , Kalender Guldamla , Monsoor Tonmoy , Zheng Jie , Kreiman Gabriel , Roychowdhury Vwani , Fried Itzhak 
Published: October 2021

SummaryExtracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes1,2. This is the same challenge faced by the nervous system and partially addressed by the concept cells—neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL)3–6. Yet, access to neurons representing a particular concept is limited due to these neurons’ sparse coding. It is conceivable, however, that the information required for such decoding is present in relatively small neuronal populations. To evaluate how well neuronal populations encode identity information in natural settings, we recorded neuronal activity from multiple brain regions of nine neurosurgical epilepsy patients implanted with depth electrodes, while the subjects watched an episode of the TV series “24”. We implemented DL models that used the time-varying population neural data as inputs and decoded the visual presence of the main characters in each frame. Before training and testing the DL models, we devised a minimally supervised CV algorithm (with comparable performance against manually-labelled data7) to detect and label all the important characters in each frame. This methodology allowed us to compare “computer vision” with “neuronal vision”—footprints associated with each character present in the activity of a subset of neurons—and identify the brain regions that contributed to this decoding process. We then tested the DL models during a recognition memory task following movie viewing where subjects were asked to recognize clip segments from the presented episode. DL model activations were not only modulated by the presence of the corresponding characters but also by participants’ subjective memory of whether they had seen the clip segment, and by the associative strengths of the characters in the narrative plot. The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks. Further, the results suggest that the information required to robustly decode concepts is present in the population activity of only tens of neurons even in brain regions beyond MTL.
Climate shapes flowering periods across plant communities
Authors: Stephens Ruby E. , Sauquet Hervé , Guerin Greg R. , Jiang Mingkai , Falster Daniel , Gallagher Rachael V. 
Published: October 2021

AbstractAimClimate shapes the composition and function of plant communities globally, but it remains unclear how this influence extends to floral traits. Flowering phenology, or the time period in which a species flowers, has well-studied relationships with climatic signals at the species level but has rarely been explored at a cross-community and continental scale. Here, we characterise the distribution of flowering periods (months of flowering) across continental plant communities encompassing six biomes, and determine the influence of climate on community flowering period lengths.LocationAustraliaTaxonFlowering plantsMethodsWe combined plant composition and abundance data from 629 standardised floristic surveys (AusPlots) with data on flowering period from the AusTraits database and additional primary literature for 2,983 species. We assessed abundance-weighted community mean flowering periods across biomes and tested their relationship with climatic annual means and the predictability of climate conditions using regression models.ResultsCombined, temperature and precipitation (annual mean and predictability) explain 29% of variation in continental community flowering period. Plant communities with higher mean temperatures and lower mean precipitation have longer mean flowering periods. Moreover, plant communities in climates with predictable temperatures and, to a lesser extent, predictable precipitation have shorter mean flowering periods. Flowering period varies by biome, being longest in deserts and shortest in alpine and montane communities. For instance, desert communities experience low and unpredictable precipitation and high, unpredictable temperatures and have longer mean flowering periods, with desert species typically flowering at any time of year in response to rain.Main conclusionsOur findings demonstrate the role of current climate conditions in shaping flowering periods across biomes, with implications under climate change. Shifts in flowering periods across climatic gradients reflect changes in plant strategies, affecting patterns of plant growth and reproduction as well as the availability of floral resources across the landscape.
Elevated transcriptional pausing of RNA polymerase II underlies acquired resistance to radiotherapy
Authors: Liu Honglu , Yu Chunhong , Zhang Na , Meng Yang , Huang Canhua , Hu Chunhong , Chen Fang , Xiao Zhiqiang , Zhang Zhuohua , Shao Hao , Yuan Kai 
Published: October 2021

AbstractAs the mainstay modality for many malignancies, particularly inoperable solid tumors such as nasopharyngeal carcinoma (NPC), ionizing radiation (IR) induces a variety of lesions in genomic DNA, evoking a multipronged DNA damage response to interrupt many cellular processes including transcription. The turbulence in transcription, depending on the nature of DNA lesions, encompasses local blockage of RNA polymerase II (RNAPII) near the damage sites, as well as a less understood genome-wide alteration. How the transcriptional change influences the effectiveness of radiotherapy remains unclear. Using a panel of NPC and lung cancer cell lines, we observe increased phosphorylation at serine 5 (pS5) of the RNAPII after IR, indicating an accumulation of paused RNAPII. Remarkably, a similar increase of pS5 is seen in IR-resistant cells. ChIP-seq analysis of RNAPII distribution confirms this increased pausing both in IR-treated and IR-resistant NPC cells, notably on genes involved in radiation response and cell cycle. Accordingly, many of these genes show downregulated transcripts abundance in IR-resistant cells, and individual knockdown some of them such as TP53 and NEK7 endows NPC cells with varying degrees of IR-resistance. Decreasing pS5 of RNAPII and hence tuning down transcriptional pausing by inhibiting CDK7 reverses IR-resistance both in cell culture and xenograft models. Our results therefore uncover an unexpected link between elevated transcriptional pausing and IR-resistance. Given the recurrent NPC tissues display a steady increase in pS5 compared to the paired primary tissues, we suggest that CDK7 inhibitors can be used in combination with radiotherapy to increase sensitivity and thwart resistance.
Additive, synergic and antagonistic interactions between maternal immune activation and peripubertal stress in cocaine addiction-like behaviour, morphofunctional brain parameters and striatal transcriptome
Authors: Capellán Roberto , Orihuel Javier , Marcos Alberto , Ucha Marcos , Moreno-Fernández Mario , Casquero-Veiga Marta , Soto-Montenegro María Luisa , Desco Manuel , Oteo-Vives Marta , Ibáñez-Moragues Marta , Magro-Calvo Natalia , Morcillo Miguel Ángel , Ambrosio Emilio , Higuera-Matas Alejandro 
Published: October 2021

AbstractSubstance use disorders are more prevalent in schizophrenia, worsening its course and prognosis. Here, we used a double-hit rat model, combining maternal immune activation (MIA) and peripubertal stress (PUS), to study cocaine addiction and the underlying neurobehavioural alterations. We injected lipopolysaccharide or saline on gestational days 15 and 16 to pregnant rats. Their male offspring were then subjected to 5 episodes of unpredictable stress every other day during adolescence (from postnatal day 28 to 38). When rats reached adulthood, we studied cocaine addiction-like behaviour, impulsivity, conditioning processes and several aspects of brain structure and function by MRI, PET and RNAseq. MIA facilitated the acquisition of cocaine self-administration while PUS reduced cocaine intake, an effect that was reversed by MIA. MIA increased motivation for cocaine and reversed the effects of PUS during extended access. Incubation of seeking was unaffected. Neither hit alone nor their combination impacted Pavlovian or instrumental conditioning or impulsiveness. At the brain level, PUS reduced hippocampal volume and hyperactivated the dorsal subiculum. MIA+PUS altered the structure and function of the dorsal striatum increasing its volume and interfering with glutamatergic dynamics. MIA did not affect the gene expression of the nucleus accumbens but, when combined with PUS, modulated specific genes that could account for the restored cocaine intake. PUS had a profound effect on the dorsal striatal transcriptome however, this was obliterated when PUS occurred in animals with MIA. These results describe a complex interplay between MIA and stress on neurodevelopment and in the susceptibility to develop cocaine addiction.
scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model
Authors: Tran Andy , Yang Pengyi , Yang Jean Y.H. , Ormerod John T. 
Published: October 2021

AbstractCell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.
Structural Diversity of Photoswitchable Sphingolipids for Optodynamic Control of Lipid Raft Microdomains
Authors: Hartrampf Nina , Leitao Samuel M. , Winter Nils , Toombs-Ruane Henry , Frank James A. , Schwille Petra , Trauner Dirk , Franquelim Henri G. 
Published: October 2021

AbstractSphingolipids are a structurally diverse class of lipids predominantly found in the plasma membrane of eukaryotic cells. These lipids can laterally segregate with other saturated lipids and cholesterol into lipid rafts; liquid-ordered (Lo) microdomains that act as organizing centers within biomembranes. Owing the vital role of sphingolipids for lipid segregation, controlling their lateral localization is of utmost significance. Hence, we made use of the light-induced trans-cis isomerization of azobenzene-modified acyl chains, to develop a set of photoswitchable sphingolipids, with different headgroups (hydroxyl, galactosyl, phosphocholine) and backbones (sphingosine, phytosphingosine, tetrahydropyran (THP)-blocked sphingosine), able to shuttle between liquid-ordered (Lo) and liquid-disordered (Ld) regions of model membranes upon irradiation with UV-A (λ = 365 nm) and blue (λ = 470 nm) light, respectively. Using combined high-speed atomic force microscopy, fluorescence microscopy, and force spectroscopy, we investigated how these active sphingolipids laterally remodel supported bilayers upon photo-isomerization, notably in terms of domain area changes, height mismatch, line tension, and membrane piercing. Hereby, we show that all sphingosine-(Azo-β-Gal-Cer, Azo-SM, Azo-Cer) and phytosphingosine-based (Azo-α-Gal-PhCer, Azo-PhCer) photolipids behave similarly, promoting a reduction in Lo domain area when in the UV-adapted cis-isoform. In contrast, azo-sphingolipids having THP groups that block H-bonding at the sphingosine backbone (Azo-THP-SM, Azo-THP-Cer) induce an increase in the Lo domain area when in cis, accompanied by a major rise in height mismatch and line tension. These changes were fully reversible upon blue light-triggered isomerization of the various lipids back to trans, pinpointing the role of interfacial interactions for the formation of stable Lo lipid raft domains.
PeakBot: Machine learning based chromatographic peak picking
Authors: Bueschl Christoph , Doppler Maria , Varga Elisabeth , Seidl Bernhard , Flasch Mira , Warth Benedikt , Zanghellini Juergen 
Published: October 2021

AbstractMotivationChromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task.ResultsA machine-learning framework entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time vs. m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot’s architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box.In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion.PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs.AvailabilityThe package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot.Contactchristoph.bueschl@univie.ac.atSupplementary informationSupplementary data are available at Bioinformatics online.
Evolution of reduced dormancy during range expansions
Authors: Travis Justin M. J. , Mynard Poppy , Bocedi Greta 
Published: October 2021

AbstractThere is increasing evidence that life-history traits can evolve rapidly during range expansion and that this evolution can impact the ecological dynamics of population spread. While dispersal evolution during range expansion has received substantial attention, dormancy (dispersal in time) has not. Here, we use an individual-based model to investigate the evolution of seed dormancy during range expansion. When a population is at spatial equilibrium our model produces results that are consistent with previous theoretical studies: seed dormancy evolves due to kin competition and the degree of dormancy increases as temporal environmental variation increases. During range expansions we consistently observe evolution towards reduced rates of dormancy at the front. Behind the front there is selection for higher rates of dormancy. Notably, the decreased dormancy towards the expanding margin reduces the regional resilience of recently expanded populations to a series of harsh years. We discuss how dormancy evolution during range expansion, and its consequences for spatial population dynamics, may impact other evolutionary responses to environmental change. We end with suggestions for future theoretical and empirical work.
Dormant replication origin firing links replication stress to whole chromosomal instability in human cancer
Authors: Schmidt Ann-Kathrin , Böhly Nicolas , Zhang Xiaoxiao , Slusarenko Benjamin O. , Hennecke Magdalena , Kschischo Maik , Bastians Holger 
Published: October 2021

AbstractChromosomal instability (CIN) is a hallmark of cancer and comprises structural CIN (S-CIN) and whole chromosome instability (W-CIN). Replication stress (RS), a condition of slowed or stalled DNA replication during S phase, has been linked to S-CIN, whereas defects in mitosis leading to chromosome missegregation and aneuploidy can account for W-CIN. It is well established that RS can activate additional replication origin firing that is considered as a rescue mechanism to suppress chromosomal instability in the presence of RS. In contrast, we show here that an increase in replication origin firing during S phase can contribute to W-CIN in human cancer cells. Increased origin firing can be specifically triggered by overexpression of origin firing genes including GINS1 and CDC45, whose elevated expression significantly correlates with W-CIN in human cancer specimens. Moreover, endogenous mild RS present in cancer cells characterized by W-CIN or modulation of the origin firing regulating ATR-CDK1-RIF1 axis induces dormant origin firing, which is sufficient to trigger chromosome missegregation and W-CIN. Importantly, chromosome missegregation upon increased dormant origin firing is mediated by increased microtubule growth rates leading to the generation of lagging chromosomes in mitosis, a condition prevalent in chromosomally unstable cancer cells. Thus, our study identified increased or dormant replication origin firing as a hitherto unrecognized, but cancer-relevant trigger for chromosomal instability.
Unravelling the complex genetic basis of growth in New Zealand silver trevally (Pseudocaranx georgianus)
Authors: Valenza-Troubat Noemie , Montanari Sara , Ritchie Peter , Wellenreuther Maren 
Published: October 2021

AbstractGrowth directly influences production rate and therefore is one of the most important and well-studied trait in animal breeding. However, understanding the genetic basis of growth has been hindered by its typically complex polygenic architecture. Here, we performed quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) for 10 growth traits that were observed over two years in 1,100 F1captive-bred trevally (Pseudocaranx georgianus). We constructed the first high-density linkage map for trevally, which included 19,861 single nucleotide polymorphism (SNP) markers, and discovered eight QTLs for height, length and weight on linkage groups 3, 14 and 18. Using GWAS, we further identified 113 SNP-trait associations, uncovering 10 genetic hot spots involved in growth. Two of the markers found in the GWAS co-located with the QTLs previously mentioned, demonstrating that combining QTL mapping and GWAS represents a powerful approach for the identification and validation of loci controlling complex traits. This is the first study of its kind for trevally. Our findings provide important insights into the genetic architecture of growth in this species and supply a basis for fine mapping QTLs, marker-assisted selection, and further detailed functional analysis of the genes underlying growth in trevally.
FX-Cell: Quantitative cell release from fixed plant tissues for single-cell genomics
Authors: Marchant D. Blaine , Nelms Brad , Walbot Virginia 
Published: October 2021

ABSTRACTSingle-cell RNA-sequencing (scRNA-seq) can provide invaluable insight into cell development, cell type identification, and plant evolution. However, the resilience of the cell wall makes it difficult to dissociate plant tissues and release individual cells for single-cell analysis. Here, we show that plant organs can be rapidly and quantitatively dissociated into cells if fixed prior to enzymatic digestion. Fixation enables digestion at high temperatures at which enzymatic activity is optimal and stabilizes the plant cell cytoplasm, rendering cells resistant to mechanical shear force while maintaining high quality RNA. This protocol, FX-Cell, releases four to ten-fold more recoverable cells than optimized protoplasting methods applied to maize anthers or root tips with no cell type biases and can be readily applied to a variety of plant taxa and tissues with no optimization. FX-Cell and scRNA-seq analysis were applied to maize anthers for which 95% of the cells were dispersed and provided suitable scRNA-seq data for the identification of anther cell types with marker genes and well-understood biological functions, including rare meiocytes (∼1% anther cells). In addition, the scRNA-seq data provided putative marker genes and gene ontology information for the identification of unknown cell types. FX-Cell also preserves the morphology of the isolated cells, permitting cell type identification without staining. Ultimately, FX-Cell can be applied to a range of plant species and tissues with minimal to no optimization paving the way for plant scRNA-seq analyses in non-model taxa and tissues.
Multi-layer Regulation of Rubisco in Response to Altered Carbon Status in Synechococcus elongatus PCC 7942
Authors: Singh Amit K. , Santos-Merino María , Sakkos Jonathan K. , Walker Berkley J. , Ducat Daniel C 
Published: October 2021

AbstractPhotosynthetic organisms possess a variety of mechanisms to achieve balance between absorbed light (source) and the capacity to metabolically utilize or dissipate this energy (sink). While regulatory processes that detect changes in metabolic status/balance are relatively well-studied in plants, analogous pathways remain poorly characterized in photosynthetic microbes. Herein, we explore systemic changes that result from alterations in carbon availability in the model cyanobacterium Synechococcus elongatus PCC 7942 by taking advantage of an engineered strain where influx/efflux of a central carbon metabolite, sucrose, can be regulated experimentally. We observe that induction of a high-flux sucrose export pathway leads to depletion of internal carbon storage pools (glycogen), and concurrent increases in photosynthetic parameters. Further, a proteome-wide analysis and fluorescence reporter-based analysis revealed that upregulated factors following the activation of the metabolic sink are strongly concentrated on ribulose-1,5-bisphosphate carboxylase-oxygenase (Rubisco) and axillary modules involved in Rubisco maturation. Carboxysome number and Rubisco activity also increase following engagement of sucrose secretion. Conversely, reversing the flux of sucrose by feeding exogenous sucrose heterologously results in increased glycogen pools, decreased Rubisco abundance, decreased photosystem II quantum efficiency, and carboxysome reorganization. Our data suggest that Rubisco activity and organization are key outputs connected to regulatory pathways involved in metabolic balancing in cyanobacteria.One sentence summaryRubisco activity and organization are key outputs involve in source-sink balancing in cyanobacteria.