bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plants stand still but hide: imperfect and heterogeneous detection is the rule when counting plants
Authors: Perret Jan , Besnard Aurélien , Charpentier Anne , Papuga Guillaume 
Published: September 2022

AbstractThe estimation of population size and its variation across space and time largely relies on counts of individuals, generally carried out within spatial units such as quadrats or sites. Missing individuals during counting (i.e. imperfect detection) results in biased estimates of population size and trends. Imperfect detection has been shown to be the rule in animal studies, and most studies now correct for this bias by estimating detection probability. Yet this correction remains exceptional in plant studies, suggesting that most plant ecologists implicitly assume that all individuals are always detected.To assess if this assumption is valid, we conducted a field experiment to estimate individual detection probability in plant counts conducted in 1×1 m quadrats. We selected 30 herbaceous plant species along a gradient of conspicuousness at 24 sites along a gradient of habitat closure, and asked groups of observers to count individuals in 10 quadrats using three counting methods requiring progressively increasing times to complete (quick count, unlimited count and cell count). In total, 158 participants took part in the experiment, allowing an analysis of the results of 5,024 counts.Over all field sessions, no observer succeeded in detecting all the individuals in the 10 quadrats. The mean detection rate was 0.44 (ranging from 0.11 to 0.82) for the quick count, 0.59 for the unlimited count (range 0.18–0.87) and 0.74 for the cell count (range 0.46-0.94).Detection probability increased with the conspicuousness of the target species and decreased with the density of individuals and habitat closure. The observer’s experience in botany had little effect on detection probability, whereas detection was strongly affected by the time observers spent counting. Yet although the more time-consuming methods increased detection probability, none achieved perfect detection, nor did they reduce the effect on detection probability of the variables we measured.Synthesis. Our results show that detection is imperfect and highly heterogeneous when counting plants. To avoid biased estimates when assessing the size, temporal or spatial trends of plant populations, plant ecologists should use methods that estimate the detection probability of individuals rather than relying on raw counts.
Metamodelling of a two-population spiking neural network
Authors: Skaar Jan-Eirik W. , Haug Nicolai , Stasik Alexander J. , Plesser Hans Ekkehard , Einevoll Gaute T. , Tøndel Kristin 
Published: September 2022

AbstractIn computational neuroscience, hypotheses are often formulated as bottom-up mechanistic models of the systems in question, consisting of differential equations that can be numerically integrated forward in time. Candidate models can then be validated by comparison against experimental data. The model outputs of neural network models depend on both neuron parameters, connectivity parameters and other model inputs. Successful model fitting requires sufficient exploration of the model parameter space, which can be computationally demanding. Additionally, identifying degeneracy in the parameters, i.e. different combinations of parameter values that produce similar outputs, is of interest, as they define the subset of models consistent with the data. In this computational study, we apply metamodels to a two-population recurrent spiking network of point-neurons, the so-called Brunel network. Metamodels are data-driven approximations to more complex models with more desirable computational properties, which can be run considerably faster than the original model. Specifically, we apply two different metamodelling techniques, Masked Autoregressive Flows and Deep Gaussian Process Regression, to estimate the power spectra of two different signals; the population spiking activities and the local field potential. We find that the metamodels are able to accurately model the power spectra in the asynchronous irregular regime. Using the metamodels, we invert the network, and estimate the probability distributions over parameters that can give rise to observed simulation outputs. We find that these distributions correctly identify parameter combinations that give similar model outputs, and that the parameters are constrained to different degrees by observing the two different signals.Author summaryIn computational neuroscience, mechanistic models are used to simulate networks of neurons. These models exhibit complex dynamics, and the parameters of the neurons and connections between neurons shape the model’s behaviour. Due to the model complexity, running the simulations and fitting the model to experimental data can be computationally demanding. In this study, we apply metamodelling techniques, data-driven approximations that are much faster to run, to two different signals generated by a two-population recurrent network model, the population spiking activities and the local field potential (LFP). Further, we invert the metamodels, and demonstrate that it can reliably find the different combinations of parameters that can give rise to an observed simulation output. We compare the metamodels on both the forward and inverse problem, and investigate to what degree the parameters are constrained by the two different signals.
Unravelling the impact of aging on the human endothelial lncRNA transcriptome
Authors: Drekolia Maria-Kyriaki , Talyan Sweta , Emidio Rebeca Cordellini , Boon Reinier Abraham , Guenther Stefan , Looso Mario , Dumbović Gabrijela , Bibli Sofia-Iris 
Published: September 2022

AbstractThe incidence and prevalence of cardiovascular disease is highest among the elderly. There is a need to further understand the mechanisms behind endothelial cell aging in order to achieve vascular rejuvenation and minimize the onset of age-related vascular diseases. Long non-coding RNAs (lncRNAs) have been proposed to regulate numerous processes in the human genome, yet their function in vascular aging and their therapeutic potential remain largely unknown. This is primarily because the majority of studies investigating the impact of aging on lncRNA expression heavily rely on in vitro studies based on replicative senescence. Here, using a unique collection of young and aged endothelial cells isolated from native human arteries, we sought to characterize the age-related alterations in lncRNA expression profiles. We were able to detect a total of 4463 lncRNAs expressed in the human endothelium from which ∼17% (798) were altered in advanced age. One of the most affected lncRNAs in aging was the primate-specific, Prostate Cancer Associated Transcript (PCAT) 14. In our follow up analysis, using single molecule RNA FISH, we showed that PCAT14 is relatively abundant, localized almost exclusively in the nucleus of young endothelial cells, and silenced in the aged endothelium. Functionally, our studies proposed that PCAT14 affects multiple endothelial cell functions including endothelial cell migration, sprouting and inflammatory responses. Taken together, our data highlight that endothelial cell aging correlates with altered expression of lncRNAs, which could impair the endothelial regenerative capacity and enhance inflammatory phenotypes.
The amyloid precursor protein regulates synaptic transmission at medial perforant path synapses
Authors: Lenz Maximilian , Eichler Amelie , Kruse Pia , Galanis Christos , Kleidonas Dimitrios , Jedlicka Peter , Müller Ulrike , Deller Thomas , Vlachos Andreas 
Published: September 2022

SUMMARYThe perforant path provides the main cortical excitatory input to the hippocampus. Due to its important role in information processing and coding, entorhinal projections to the dentate gyrus have been studied in considerable detail. Nevertheless, a characterization of synaptic transmission between individual connected pairs of entorhinal stellate cells and dentate granule cells is still pending. Here, we have used organotypic entorhino-hippocampal tissue cultures, in which the entorhino-dentate (EC-GC) projection is present and EC-GC pairs can be studied using whole-cell patch clamp recordings. Using cultures of wildtype mice, the properties of EC-GC synapses formed by afferents from the lateral and medial entorhinal cortex were compared and differences in short-term plasticity were revealed. Since the perforant path is severely affected in Alzheimer’s disease, we used cultures of APP-deficient mice to address the role of the amyloid-precursor protein (APP) at this synapse. APP-deficiency caused alterations in excitatory neurotransmission at medial perforant path synapses that were accompanied by transcriptomic and ultrastructural changes. Moreover, the deletion of pre- but not postsynaptic APP through the local injection of Cre-expressing AAVs in conditional APPflox/flox tissue cultures increased the efficacy of neurotransmission at perforant path synapses. Together, these data suggest a physiological role for presynaptic APP at medial perforant path synapses, which may be adversely affected under conditions of altered APP processing.
Substrate availability and not thermal-acclimation controls microbial temperature sensitivity response to long term warming
Authors: Domeignoz-Horta Luiz A. , Pold Grace , Erb Hailey , Sebag David , Verrecchia Eric , Northen Trent , Louie Katherine , Eloe-Fadrosh Emiley , Pennacchio Christa , Knorr Melissa A. , Frey Serita D. , Melillo Jerry M. , DeAngelis Kristen M. 
Published: September 2022

AbstractMicrobes are responsible for cycling carbon (C) through soils, and the predictions of how soil C stocks change with warming are highly sensitive to the assumptions made about the mechanisms controlling the microbial physiology response to climate warming. Two mechanisms, microbial thermal-acclimation and changes in the quantity and quality of substrates available for microbial metabolism have been suggested to explain the long-term warming impact on microbial physiology. Yet studies disentangling these two mechanisms are lacking. To resolve the drivers of changes in microbial physiology in response to long-term warming, we sampled soils from 13- and 28-year old soil warming experiments in different seasons. We performed short-term laboratory incubations across a range of temperatures to measure the relationship between temperature sensitivity of physiology (growth, respiration, carbon use efficiency and extracellular enzyme activity) and the chemical composition of soil organic matter. We observed apparent thermal acclimation in microbial processes important for C cycling, but only when warming had exacerbated the seasonally-induced, already small soil organic matter pools. Irrespective of warming, greater quantity and quality of soil carbon enhanced the extracellular enzymatic pool and its temperature sensitivity. We suggest that fresh litter input into the system seasonally cancels apparent thermal acclimation of C-cycling processes. Our findings reveal that long-term warming has indirectly affected microbial physiology via reduced C availability in this system, implying that earth system models including these negative feedbacks may be best suited to describe long-term warming impact in soils.
Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling
Authors: Kaste Joshua A.M. , Shachar-Hill Yair 
Published: September 2022

AbstractMotivationThe accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as Flux Balance Analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy.ResultsRelative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into Flux Balance Analysis predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13C Metabolic Flux Analysis (MFA) compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps, as measured by weighted averaged percent error values, dropped from between 169-180% and 94-103% in high light and low light conditions, respectively, to between 10-12% and 9-11%, depending on the gene expression dataset used. The incorporation of gene expression data into the modeling process also substantially altered the predicted carbon and energy economy of the plant.AvailabilityCode is available fromhttps://github.com/Gibberella/ArabidopsisGeneExpressionWeightsContactyairhill@msu.edu
Bridging The Evolving Semantics: A Data Driven Approach to Knowledge Discovery In Biomedicine
Authors: Jha Kishlay 
Published: September 2022

AbstractRecent progress in biological, medical and health-care technologies, and innovations in wearable sensors provide us with unprecedented opportunities to accumulate massive data to understand disease prognosis and develop personalized treatments and interventions. These massive data supplemented with rapid growth in computing infrastructure has enabled bio-medical researchers to perform more comprehensive experiments and detailed case-studies. At the same time, performing these experiments are not only monetarily expensive but also time consuming. Thus, there is a growing need to provide tools to the researchers that will allow them to pose queries that will assist them in focusing on interesting “hypotheses”. However, such a tool would require capabilities to derive inferences based on existing known relationship between medical concepts. In this paper, we tackle this problem as one of non-factoid question answering wherein we try to answer the user-post questions by leveraging both authoritative as well as social media posts. While the former provides us with well knowledge on well researched topics, the latter provides us with real-time feedback on variety of topics like adverse drug effect (ADE), symptoms-drug relationship, etc. The challenge with leveraging the authoritative sources to infer answers for non-factoid question lies in: (a) The effective navigation of the answer search-space for timely response to the queries, (b) Ranking the candidate answers derived in step-(a) to enable non-trivial and novel discoveries, and (c) Being robust to perform confirmatory as well as discovery type of tasks.
Streamlined single-molecule RNA-FISH of core clock mRNAs in clock neurons in whole mount Drosophila brains
Authors: Yuan Ye , Padilla Marc-Antonio , Clark Dunham , Yadlapalli Swathi 
Published: September 2022

AbstractCircadian clocks are ∼24-hour timekeepers that control rhythms in almost all aspects of our behavior and physiology. While it is well known that subcellular localization of core clock proteins plays a critical role in circadian regulation, very little is known about the spatiotemporal organization of core clock mRNAs and its role in generating ∼24-hr circadian rhythms. Here we describe a streamlined single molecule Fluorescence In Situ Hybridization (smFISH) protocol and a fully automated analysis pipeline to precisely quantify the number and subcellular location of mRNAs of Clock, a core circadian transcription factor, in individual clock neurons in whole mount Drosophila adult brains. Specifically, we used ∼48 fluorescent oligonucleotide probes that can bind to an individual Clock mRNA molecule, which can then be detected as a diffraction-limited spot. Further, we developed a machine learning-based approach for 3-D cell segmentation, based on a pretrained encoder-decoder convolutional neural network, to automatically identify the cytoplasm and nuclei of clock neurons. We combined our segmentation model with a spot counting algorithm to detect Clock mRNA spots in individual clock neurons. Our results demonstrate that the number of Clock mRNA molecules cycle in large ventral lateral clock neurons (lLNvs) with peak levels at ZT4 (4 hours after lights are turned on) with ~80 molecules/neuron and trough levels at ZT16 with ∼30 molecules/neuron. Our streamlined smFISH protocol and deep learning-based analysis pipeline can be employed to quantify the number and subcellular location of any mRNA in individual clock neurons in Drosophila brains. Further, this method can open mechanistic and functional studies into how spatiotemporal localization of clock mRNAs affect circadian rhythms.
Decomposing spatial heterogeneity of cell trajectories with Paella
Authors: Hou Wenpin , Ji Zhicheng 
Published: September 2022

ABSTRACTSpatial transcriptomics provides a unique opportunity to study continuous biological processes in a spatial context. We developed Paella, a computational method to decompose a cell trajectory into multiple spatial sub-trajectories and identify genes with differential temporal patterns across spatial sub-trajectories. Applied to spatial transcriptomics datasets of cancer, Paella identified spatially varying genes associated with tumor progression, providing insights into the spatial heterogeneity of cancer development.
The mitochondrial Cu+transporter PiC2 (SLC25A3) is a target of MTF1 and contributes to the development of skeletal musclein vitro
Authors: McCann Cat , Quinteros Michael , Adelugba Ifeoluwa , Morgada Marcos N. , Castelblanco Aida R. , Davis Emily J. , Lanzirotti Antonio , Hainer Sarah J. , Vila Alejandro J. , Navea Juan G. , Padilla-Benavides Teresita 
Published: September 2022

ABSTRACTThe loading of copper (Cu) into cytochrome c oxidase (COX) in mitochondria is essential for energy production in cells. Extensive studies have been performed with mitochondrial cuproenzymes, such as Sco1, Sco2 and Cox17, which contributes to the metallation of the oxidase. However, limited information is available on the upstream mechanism of Cu transport and delivery to mitochondria, especially through Cu-impermeable membranes, in mammalian cells. The mitochondrial phosphate transporter SLC25A3, also known as PiC2, is also able to bind Cu+and acts as an active copper transporter in eukaryotic cells through these membranes, and ultimately aid in the metallation of COX. We used a well-established differentiation model of primary myoblasts derived from mouse satellite cells, where Cu availability is necessary for growth and maturation, and showed PiC2 is a target of MTF1, its expression is induced during myogenesis and favored by Cu supplementation. PiC2 deletion using CRISPR/Cas9 showed that the transporter is required for proliferation and differentiation of primary myoblasts, as both processes are delayed uponPiC2knock-out. The effects ofPiC2deletion were ameliorated by the addition of Cu to the growth medium, implying the deleterious effects ofPiC2knockout in myoblasts may be in part due to a failure to deliver sufficient Cu to the mitochondria, which can be compensated by other mitochondrial cuproproteins. Co-localization and co-immunoprecipitation of PiC2 and COX also strongly suggest that PiC2 may act to directly load Cu into COX, which was verified byin vitroCu+-transfer experiments. The data indicate an important role for PiC2 in both the delivery of Cu to the mitochondria, COX and, subsequently, the differentiation of primary myoblasts.
Activity in the human superior colliculus associated with reaching for tactile targets
Authors: Prabhu Nikhil G , Himmelbach Marc 
Published: September 2022

AbstractThe superior colliculus (SC) plays a major role in orienting movements of eyes and the head and in the allocation of attention. Functions of the SC have been mostly investigated in animal models, including non-human primates. Differences in the SC’s anatomy and function between different species question extrapolations of these studies to humans without further validation. Few electrophysiological and neuroimaging studies in animal models and humans have reported an unexpected role of the SC in visually guided reaching movements. Using BOLD fMRI imaging, we sought to decipher if the SC is also active during reaching movements guided by tactile stimulation. Participants executed reaching movements to visual and tactile target positions. When contrasted against visual and tactile stimulation without reaching, we found increased SC activity with reaching not only for visual but also for tactile targets. We conclude that the SC’s involvement in reaching does not rely on visual inputs. It is also independent from a specific sensory modality. Our results indicate a general involvement of the human SC in upper limb reaching movements.
Bayesian inference of admixture graphs on Native American and Arctic populations
Authors: Nielsen Svend V , Vaughn Andrew H. , Leppälä Kalle , Landis Michael J. , Mailund Thomas , Nielsen Rasmus 
Published: September 2022

AbstractAdmixture graphs are mathematical structures that describe the ancestry of populations in terms of divergence and merging (admixing) of ancestral populations as a graph. An admixture graph consists of a graph topology, branch lengths, and admixture proportions. The branch lengths and admixture proportions can be estimated using numerous numerical optimization methods, but inferring the topology involves a combinatorial search for which no polynomial algorithm is known. In this paper, we present a reversible jump MCMC algorithm for sampling high-probability admixture graphs and show that this approach works well both as a heuristic search for a single best-fitting graph and for summarizing shared features extracted from posterior samples of graphs. We apply the method to 11 Native American and Siberian populations and exploit the shared structure of high-probability graphs to address the relationship between Saqqaq, Inuit, Koryaks, and Athabascans. Our analyses show that the Saqqaq is not a good proxy for the previously identified gene flow from Arctic people into the Na-Dene speaking Athabascans.Author SummaryOne way of summarizing historical relationships between genetic samples is by constructing an admixture graph. An admixture graph describes the demographic history of a set of populations as a directed acyclic graph representing population splits and mergers. The inference of admixture graphs is currently done via greedy search algorithms that may fail to find the global optimum. We here improve on these approaches by developing a novel MCMC sampling method, AdmixtureBayes, that can sample from the posterior distribution of admixture graphs. This enables an efficient search of the entire state space as well as the ability to report a level of confidence in the sampled graphs. We apply AdmixtureBayes to a set of Native American and Arctic genomes to reconstruct the demographic history of these populations and report posterior probabilities of specific admixture events. While some previous studies have identified the ancient Saqqaq culture as a source of introgression into Athabascans, we instead find that it is the Siberian Koryak population, not the Saqqaq, that serves as the best proxy for gene flow into Athabascans.