bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time-series Classification for Patients under Active Surveillance and Screening Using Echo State Network
Authors: Li Zonglun , Zaikin Alexey , Blyuss Oleg 
Published: April 2023

AbstractOver the past few decades, more and more patients come on follow-up studies such as active surveillance and screening, which results in a vast amount of time-series data in the health department. Each Patient typically has a small but different number of visits to the doctor and the time interval between the visits is heterogeneous. Nowadays, many machine learning tasks in relation to time series data are carried out using deep recurrent neural networks (RNN). However, deep neural networks consume enormous computational power as all weights in the network need to be trained through back-propagation. Conversely, echo state network (ESN), another form of RNN, demonstrates low training cost and the potential of it is still largely untapped. Therefore, in this article we will develop a new methodology that can classify aforementioned time-series data using the echo state network. We will also discuss how to address the heterogeneity in the time interval arising from the data of this type and how our model can also potentially fit other time-series data.
EnGens: a computational framework for generation and analysis of representative protein conformational ensembles
Authors: Conev Anja , Rigo Mauricio Menagatti , Devaurs Didier , Fonseca André Faustino , Kalavadwala Hussain , de Freitas Martiela Vaz , Clementi Cecilia , Zanatta Geancarlo , Antunes Dinler Amaral , Kavraki Lydia 
Published: April 2023

AbstractProteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conformational ensembles. In this work we: (1) provide an overview of existing methods and tools for protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples found in the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.
Uncovering the hidden structure of dynamic T cell composition in peripheral blood during cancer immunotherapy: a topic modeling approach
Authors: Peng Xiyu , Lee Jasme , Adamow Matthew , Maher Colleen , Postow Michael A. , Callahan Margaret K. , Panageas Katherine S. , Shen Ronglai 
Published: April 2023

AbstractImmune checkpoint inhibitors (ICIs), now mainstays in the treatment of cancer treatment, show great potential but only benefit a subset of patients. A more complete understanding of the immunological mechanisms and pharmacodynamics of ICI in cancer patients will help identify the patients most likely to benefit and will generate knowledge for the development of next-generation ICI regimens. We set out to interrogate the early temporal evolution of T cell populations from longitudinal single-cell flow cytometry data. We developed an innovative statistical and computational approach using a Latent Dirichlet Allocation (LDA) model that extends the concept of topic modeling used in text mining. This powerful unsupervised learning tool allows us to discover compositional topics within immune cell populations that have distinct functional and differentiation states and are biologically and clinically relevant. To illustrate the model’s utility, we analyzed ∼17 million T cells obtained from 138 pre- and on-treatment peripheral blood samples from a cohort of melanoma patients treated with ICIs. We identified three latent dynamic topics: a T-cell exhaustion topic that recapitulates a LAG3+ predominant patient subgroup with poor clinical outcome; a naive topic that shows association with immune-related toxicity; and an immune activation topic that emerges upon ICI treatment. We identified that a patient subgroup with a high baseline of the naïve topic has a higher toxicity grade. While the current application is demonstrated using flow cytometry data, our approach has broader utility and creates a new direction for translating single-cell data into biological and clinical insights.
InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes
Authors: Salimi Adel , Rahmani Saeid , Sharifi-Zarchi Ali 
Published: April 2023

ABSTRACTQuantification of gene expression is a crucial task in biomedical studies. Although high-throughput methods enable rapid and simultaneous expression quantification of coding or non-coding regions, qPCR is still vastly used due to its high availability, sensitivity, specificity, reproducibility, low cost, and ease of use. A limitation of qPCR has been the need of internal controls or reference genes (RGs) with stable expression in different conditions, to normalize the expression level of the other target genes. So far, several stability criteria and numerous methods for selecting a group of RGs have been proposed, however, important challenges must be addressed. Here we introduce a mathematical basis for precise modeling of qPCR expression normalization and justify widely used stability measures of RGs. We then propose a family of scale-invariant functions, as an alternative to the geometric mean, to optimize aggregated expression of RGs. We provide closed-form optimizations for several scale-invariant aggregation functions. Among them, we show the superiority of weighted geometric mean, whose parameters optimize standard deviation as the stability measure of aggregated RGs expression. We provide experimental support for this finding using real data of solid tumors and liquid biopsies of different sample sizes.The proposed methods can be easily integrated in the existing qPCR expression normalization pipelines of genes and non-coding RNAs. We also provide an implementation of the proposed methods as an R package, with GPU acceleration.Availability and implementationhttps://github.com/asalimih/InterOptContactasalimih@gmail.com
Rising minimum temperatures contribute to 50 years of shifting Arctic and boreal butterfly communities in North America
Authors: Shirey Vaughn , Neupane Naresh , Guralnick Robert , Ries Leslie 
Published: April 2023

AbstractGlobal climate change has been identified as a major driver of observed insect declines, yet in many regions there are critical knowledge gaps for how communities are responding to climate. Poleward regions are of particular interest because warming is most rapid while biodiversity data are most sparse. Building on recent advances in occupancy modeling of presence-only data, we reconstructed 50 years (1970-2019) of butterfly population trends in response to rising minimum temperatures in one of the most under sampled regions of the continent. Among 90 modeled species, we found that cold-adapted species are far more often in decline compared to their warm-adapted, more southerly distributed counterparts. Further, in a post-hoc analysis using species’ traits, we find that species’ range-wide average annual temperature and wingspan are a consistent predictor of occupancy changes. Species with warmer ranges and larger wingspans were most likely to be increasing in occupancy. Our results provide the first look at macroscale butterfly biodiversity shifts in a critically under sampled region of North America. Further, these results highlight the potential of leveraging the wealth of presence only data, the most abundant source of historical insect biodiversity. New approaches to the modeling of presence only data will match recent increases in community science participation with sparse historical records to reconstruct trends even in poorly sampled regions.
Amphiphilic particle-stabilized nanoliter droplet reactors with a multi-modal portable reader for distributive biomarker quantification
Authors: Shah Vishwesh , Yang Xilin , Arnheim Alyssa , Udani Shreya , Tseng Derek , Luo Yi , Ouyang Mengxing , Destgeer Ghulam , Garner Omai , Koydemir Hatice , Ozcan Aydogan , Carlo Dino Di 
Published: April 2023

AbstractCompartmentalization, leveraging microfluidics, enables highly sensitive assays; but the requirement for significant infrastructure for their design, build, and operation limits access. Newer multi-material particle-based technologies thermodynamically stabilize monodisperse droplets as individual reaction compartments with simple liquid handling steps, precluding the need for expensive microfluidic equipment. Here, we further improve the accessibility of this lab on a particle technology to resource-limited settings by combining this assay system with a portable multi-modal reader, thus enabling nanoliter droplet assays in an accessible platform. We show the utility of this platform in measuring N-terminal propeptide B-type natriuretic peptide (NT-proBNP), a heart failure biomarker, in complex medium and patient samples. We report a limit of detection of ∼0.05 ng/ml and a linear response between 0.2 – 2 ng/ml in spiked plasma samples. We also show that, owing to the plurality of measurements per sample, “swarm” sensing acquires better statistical quantitation with a portable reader. Monte Carlo simulations show the increasing capability of this platform to differentiate between negative and positive samples, i.e. below or above the clinical cut-off for acute heart failure (∼0.1ng/ml), as a function of the number of particles measured. Our platform measurements correlate with gold standard ELISA measurement in cardiac patient samples, and achieve lower variation in measurement across samples compared to the standard well plate-based ELISA. Thus, we show the capabilities of a cost-effective droplet-reader system in accurately measuring biomarkers in nanoliter droplets for diseases that disproportionally affect underserved communities in resource-limited settings.
Study replication: Shape discrimination in a conditioning procedure on the jumping spiderPhidippus regius
Authors: Mannino Eleonora , Regolin Lucia , Moretto Enzo , De Agrò Massimo 
Published: April 2023

AbstractJumping spiders possess a unique visual system, split into 8 different eyes and divided into two fully independent visual pathways. This peculiar organization begs the question of how visual information is processed, and whether the classically recognized gestalt rules of perception hold true. In a previous experiment, we tested the ability of jumping spiders to associate a geometrical shape with a reward (sucrose solution), and then to generalize the learned association to a partially occluded version of it. The occluded shape was presented together with a broken version of the same shape. The former should be perceived as a whole shape only in the case the animals, like humans, are able of amodally complete an object partly hidden by an occluder; otherwise, the two shapes would be perceived as identical. There, the spiders learned the association but failed to generalize. Here, we present a replication of the experiment, with an increased number of subjects, a DeepLabCut-based scoring procedure, and an improved statistical analysis. The results of the experiment follow closely the direction of the effects observed in the previous work but fail to raise to significance. We discuss the importance of study replication, and we especially highlight the use of automated scoring procedures to maximize objectivity in behavioural studies.
A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets
Authors: Castrignanò Alberto , Bardini Roberta , Savino Alessandro , Di Carlo Stefano 
Published: April 2023

AbstractTissue Engineering (TE) and Regenerative Medicine (RM) aim to replicate and replace tissues for curing disease. However, full tissue integration and homeostasis are still far from reach. Biofabrication is an emerging field that identifies the processes required for generating biologically functional products with the desired structural organization and functionality and can potentially revolutionize the regenerative medicine domain, which aims to use patients’ cells to restore the structure and function of damaged tissues and organs. However, biofabrication still has limitations in the quality of processes and products. Biofabrication processes are often improved empirically, but this is slow, costly, and provides partial results. Computational approaches can tap into biofabrication underused potential, supporting analysis, modeling, design, and optimization of biofabrication processes, speeding up their improvement towards a higher quality of products and subsequent higher clinical relevance. This work proposes a reinforcement learning-based computational design space exploration methodology to generate optimal in-silico protocols for the simulated fabrication of epithelial sheets. The optimization strategy relies on a Deep Reinforcement Learning (DRL) algorithm, the Advantage-Actor Critic, which relies on a neural network model for learning. In contrast, simulations rely on the PalaCell2D simulation framework. Validation demonstrates the proposed approach on two protocol generation targets: maximizing the final number of obtained cells and optimizing the spatial organization of the cell aggregate.
Time-course of antipredator behavioral changes induced by the helminthPomphorhynchus laevisin its intermediate hostGammarus pulex: the switch in manipulation according to parasite developmental stage differs between behaviors
Authors: Rigaud Thierry , Balourdet Aude , Bauer Alexandre 
Published: April 2023

AbstractMany trophically transmitted parasites with complex life cycles manipulate their intermediate host’s antipredatory defenses in ways facilitating their transmission to final host by predation. Some parasites also protect the intermediate host from predation when noninfective during its ontogeny. The acanthocephalanPomphorynchus laevis, a fish intestinal helminth, infecting freshwater gammarid amphipods as intermediate hosts, is using such a strategy of protection-then-exposure to predation by the definitive host. However, the whole time-course of this sequence of behavioral switch is not yet known, and only one antipredator behavior has been studied to date. Here we show that the protective part of this manipulation begins quite late during the parasite ontogeny, suggesting that the advantages overpass the costs induced by this protective manipulation only at this stage. We confirmed that the refuge use behavior is showing a switch in the few days following the stage mature for the definitive host (switching from overuse to underuse). However, such a switch was not observed for the gammarids activity rate, a behavior also known to make the host less conspicuous to predators when weak. While we predicted a low activity during early development stages, then a switch to high activity, we observed general decrease in activity during the parasite ontogeny. The possible causes for this discrepancy between behaviors are discussed. All these behavioral changes were observed mostly when animals were tested in water scented by potential predators (here brown trout), suggesting condition-dependent manipulation.
Electrophysiological signatures of anxiety in Parkinson’s disease
Authors: Yassine Sahar , Almarouk Sourour , Gschwandtner Ute , Auffret Manon , Hassan Mahmoud , Verin Marc , Fuhr Peter 
Published: April 2023

AbstractAnxiety is a common non-motor symptom in Parkinson’s disease (PD) occurring in up to 31% of the patients and affecting their quality of life. Despite the high prevalence, anxiety symptoms in PD are often underdiagnosed and, therefore, undertreated. To date, functional and structural neuroimaging studies have contributed to our understanding of the motor and cognitive symptomatology of PD. Yet, the underlying pathophysiology of anxiety symptoms in PD remains largely unknown and studies on their neural correlates are missing. Here, we used resting state electroencephalography (RS-EEG) of 68 non-demented PD patients with or without clinically-defined anxiety and 25 healthy controls (HC) to assess spectral and functional connectivity fingerprints characterizing the PD-related anxiety. When comparing the brain activity of the PD anxious group (PD-A, N=18) to both PD non-anxious (PD-NA, N=50) and HC groups (N=25) at baseline, our results showed increased fronto-parietal delta power and decreased frontal beta power depicting the PD-A group. Results also revealed hyper-connectivity networks predominating in delta, theta and gamma bands against prominent hypo-connectivity networks in alpha and beta bands as network signatures of anxiety in PD where the frontal, temporal, limbic and insular lobes exhibited the majority of significant connections. Moreover, the revealed EEG-based electrophysiological signatures were strongly associated with the clinical scores of anxiety over the course of the disease. We believe that the identification of the electrophysiological correlates of anxiety in PD using EEG is conducive toward more accurate prognosis and diagnosis and can ultimately support the development of new therapeutics strategies.
Group 2 innate lymphoid cells constrain type 3/17 lymphocytes in shared stromal niches to restrict liver fibrosis
Authors: Sbierski-Kind Julia , Cautivo Kelly M , Wagner Johanna C , Dahlgren Madelene W , Nilsson Julia , Krasilnikov Maria , Mroz Nicholas M. , Lizama Carlos O. , Gan Anna Lu , Matatia Peri R , Taruselli Marcela T , Chang Anthony A , Caryotakis Sofia , O’Leary Claire E , Kotas Maya , Mattis Aras N , Peng Tien , Locksley Richard M , Molofsky Ari B 
Published: April 2023

SUMMARYGroup 2 innate lymphoid cells (ILC2s) cooperate with adaptive Th2 cells as key organizers of tissue type 2 immune responses, while a spectrum of innate and adaptive lymphocytes coordinate early type 3/17 immunity. Both type 2 and type 3/17 lymphocyte associated cytokines are linked to tissue fibrosis, but how their dynamic and spatial topographies may direct beneficial or pathologic organ remodelling is unclear. Here we used volumetric imaging in models of liver fibrosis, finding accumulation of periportal and fibrotic tract IL-5+lymphocytes, predominantly ILC2s, in close proximity to expanded type 3/17 lymphocytes and IL-33highniche fibroblasts. Ablation of IL-5+lymphocytes worsened carbon tetrachloride-and bile duct ligation-induced liver fibrosis with increased niche IL-17A+type 3/17 lymphocytes, predominantly γδ T cells. In contrast, concurrent ablation of IL-5+and IL-17A+lymphocytes reduced this progressive liver fibrosis, suggesting a cross-regulation of type 2 and type 3 lymphocytes at specialized fibroblast niches that tunes hepatic fibrosis.
Active Transport by Cytoplasmic Dynein Maintains the Localization of MAP2 in Developing Neurons
Authors: Yonemura Yoji , Sakai Yuri , Nakata Rinaho , Hagita-Tatsumoto Ayaka , Miyasaka Tomohiro , Misonou Hiroaki 
Published: April 2023

ABSTRACTMAP2 has been widely used as a marker of neuronal dendrites because of its extensive restriction in the somatodendritic region of neurons. Despite that, how the precise localization of such a soluble protein is established and maintained against thermal forces and diffusion has been elusive and long remained a mystery in neuroscience. In this study, we aimed to uncover the mechanism behind how MAP2 is retained in the somatodendritic region. Using GFP-tagged MAP2 expressed in cultured hippocampal neurons, we discovered a crucial protein region responsible for the localization of MAP2, the serine/proline-rich (S/P) region. Our pulse-chase live-cell imaging revealed the slow but steady migration of MAP2 toward distal dendrites, which was not observed in a MAP2 mutant lacking the S/P region, indicating that S/P-dependent transport is vital for the proper localization of MAP2. Furthermore, our experiments using an inhibitor of cytoplasmic Dynein, ciliobrevin D, as well as Dynein knockdown, showed that cytoplasmic Dynein is involved in the transport of MAP2 in dendrites. We also found that Dynein complex binds to MAP2 through the S/P region in heterologous cells. Using mathematical modeling based on experimental data, we confirmed that an intermittent active transport mechanism is essential. Thus, we propose that the cytoplasmic Dynein recruits and transports free MAP2 toward distal dendrites, thereby maintaining the precise dendritic localization of MAP2 in neurons. Our findings shed light on the previously unknown mechanism behind MAP2 localization and provide a new direction for soluble protein trafficking research in the field of cell biology of neurons.
Spinal Cord Injury regulates circular RNA expression in axons
Authors: Siddiq Mustafa M. , Toro Carlos A. , Johnson Nicholas P. , Hansen Jens , Xiong Yuguang , Mellado Wilfredo , Tolentino Rosa E. , Johnson Kaitlin , Jayaraman Gomathi , Suhail Zaara , Harlow Lauren , Beaumont Kristin G. , Sebra Robert , Willis Dianna E. , Cardozo Christopher P. , Iyengar Ravi 
Published: April 2023

AbstractIntroductionNeurons transport mRNA and translational machinery to axons for local translation. After spinal cord injury (SCI),de novotranslation is assumed to enable neurorepair. Knowledge of the identity of axonal mRNAs that participate in neurorepair after SCI is limited. We sought to identify and understand how axonal RNAs play a role in axonal regeneration.MethodsWe obtained preparations enriched in axonal mRNAs from control and SCI rats by digesting spinal cord tissue with cold-active protease (CAP). The digested samples were then centrifuged to obtain a supernatant that were then sequenced. We used bioinformatics analyses to identify DEGS and map them to various biological processes. We validated the DEGs by RT-qPCR and RNA-scope.ResultsThe supernatant fraction was highly enriched for axonal mRNA. Using Gene Ontology, the second most significant pathway for all differentially expressed genes (DEGs) was axonogenesis. Among the DEGs was Rims2, which is predominately a circular RNA (circRNA) in the CNS. We show that Rims2 RNA within spinal cord axons is circular. We found an additional 200 putative circRNAs in the axonal-enriched fraction. Knockdown in primary rat cortical neurons of the RNA editing enzyme ADAR1, which inhibits formation of circRNAs, significantly increased axonal outgrowth. Focusing on Rims2 we used Circular RNA Interactome to predict that several of the miRNAs that bind to circRims2 also bind to the 3’UTR of GAP-43, PTEN or CREB1, all known regulators of axonal outgrowth. Axonally-translated GAP-43 supports axonal elongation and we detect GAP-43 mRNA in the rat axons by RNAscope.DiscussionBy using our method for enrichment of axonal RNA, we detect SCI induced DEGs, including circRNA such as Rims2. Ablation of ADAR1, the enzyme that regulates circRNA formation, promotes axonal outgrowth of cortical neurons. We developed a pathway model using Circular RNA Interactome that indicates that Rims2 through miRNAs can regulate the axonal translation GAP-43 a known regulator of axonal regeneration indicating that axonal mRNA contribute to regeneration.