bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ambient RNA analysis reveals misinterpreted and masked cell types in brain single-nuclei datasets
Authors: Caglayan Emre , Liu Yuxiang , Konopka Genevieve 
Published: March 2022

AbstractAmbient RNA contamination in single-cell RNA sequencing (RNA-seq) is a significant problem, but its consequences are poorly understood. Here, we show that ambient RNAs in brain single-nuclei RNA-seq can have a nuclear or extra-nuclear origin with distinct gene set signatures. Both ambient RNA signatures are predominantly neuronal and we find that some previously annotated neuronal cell types are distinguished by ambient RNA. Strikingly, we also detect pervasive neuronal ambient RNA contamination in all glial cell types unless glia and neurons are physically separated prior to sequencing. We demonstrate that this contamination can be removedin silico. We also show that previous annotations of immature oligodendrocytes are likely glial cells contaminated with ambient RNAs. After ambient RNA removal, we detect extremely rare, committed oligodendrocyte progenitor cells, which were infrequently annotated in previous adult human brain datasets. Together, these results provide an in-depth analysis of ambient RNA contamination in brain single-cell datasets.
A Strategy to Quantify Myofibroblast Activation on a Continuous Spectrum
Authors: Hillsley Alexander , Santoso Matthew , Engels Sean M. , Contreras Lydia M. , Rosales Adrianne M. 
Published: March 2022

AbstractMyofibroblasts are a highly secretory and contractile phenotype most commonly identified by the de novo expression and assembly of alpha-smooth muscle actin stress fibers. Traditionally, this activation process has been thought of as a binary process, with cells being labeled as “activated” or “quiescent (non-activated)”. More recently, this view has been expanded to consider activation on a continuous spectrum. However, there is no established method to quantify a cell’s position on this spectrum, and as a result, the binary labeling system is still widely used. While transcriptomic analyses provide a continuous measure of myofibroblast markers, a faster and more facile screening method is needed. To this end, we utilized optical microscopy and machine learning methods to quantify myofibroblast activation on a spectrum. We first measured size and shape features of over 1,000 individual cardiac fibroblasts and found that these features provide enough information to predict activation state, on the binary scale, with 94% accuracy as compared to manual classification. We next performed dimensionality reduction techniques on these features to create a continuous scale of activation. Importantly, this new classification system captures a range of fibroblast activation states, but still possesses inherent bias due to choice of morphological features. Thus, we next used self-supervised machine learning to create a second continuous labeling system free from biases associated with the manually measured features. Lastly, we compared our findings for mechanically activated cardiac fibroblasts to a distribution of cell phenotypes generated from transcriptomic data using single-cell RNA sequencing. Altogether, these results demonstrate a continuous spectrum of activation from fibroblast to myofibroblast and provide a strategy to quantify a cell’s position on that spectrum.
A deep unsupervised language model for protein design
Authors: Ferruz Noelia , Schmidt Steffen , Höcker Birte 
Published: March 2022

AbstractProtein design aims to build new proteins from scratch thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in the field of natural language processing (NLP) has enabled the implementation of ever-growing language models capable of understanding and generating text with human-like capabilities. Given the many similarities between human languages and protein sequences, the use of NLP models offers itself for predictive tasks in protein research. Motivated by the evident success of generative Transformer-based language models such as the GPT-x series, we developed ProtGPT2, a language model trained on protein space that generatesde novoprotein sequences that follow the principles of natural ones. In particular, the generated proteins display amino acid propensities which resemble natural proteins. Disorder and secondary structure prediction indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yielded well-folded non-idealized structures with embodiments as well as large loops and revealed new topologies not captured in current structure databases. ProtGPT2 has learned to speak the protein language. It has the potential to generatede novoproteins in a high throughput fashion in a matter of seconds. The model is easy-to-use and freely available.
Fabrication of Conductive Hollow Microfibers for Encapsulation of Astrocyte Cells
Authors: Alimoradi Nima , Nasirian Vahid , Aykar Saurabh S. , McNamara Marilyn C. , Niaraki-Asli Amir Ehsan , Montazami Reza , Makowski Andrew , Hashemi Nicole N. 
Published: March 2022

AbstractThe manufacturing of 3D cell scaffoldings provides advantages for modeling diseases and injuries by physiologically relevant platforms. A triple-flow microfluidic device was developed to rapidly fabricate alginate/graphene hollow microfibers based on the gelation of alginate induced with CaCl2. This five-channel pattern actualized continuous mild fabrication of hollow fibers under an optimized flowing rate ratio of 300: 200: 100 μL.min−1. The polymer solution was 2.5% alginate in 0.1% graphene, and a 30% polyethylene glycol solution was used as the sheath and core solutions. The morphology and physical properties of microstructures were investigated by scanning electron microscopy, electrochemical, and surface area analyzers. Subsequently, these conductive microfibers’ biocompatibility was studied by encapsulating mouse astrocyte cells within these scaffolds. The cells could successfully survive both the manufacturing process and prolonged encapsulation for up to 8 days. These unique 3D hollow scaffolds could significantly enhance the available surface area for nutrient transport to the cells. In addition, these conductive hollow scaffolds illustrated unique advantages such as 0.728 cm3.gr−1 porosity and twice more electrical conductivity in comparison to alginate scaffolds. The results confirm the potential of these scaffolds as a microenvironment that supports cell growth.
Effects of disease emergence on invasive grass impacts
Authors: Kendig Amy E. , Adhikari Ashish , Lane Brett R. , Wojan Christopher M. , Kortessis Nicholas , Simon Margaret W. , Barfield Michael , Harmon Philip F. , Holt Robert D. , Clay Keith , Goss Erica M. , Flory S. Luke 
Published: March 2022

AbstractInvasive species impact ecosystems through their large abundances and strong per capita effects. Enemies can regulate abundances and per capita effects, but are notably absent for many new invaders. However, invaders acquire enemies over time and as they spread; processes hypothesized to mitigate negative invader impacts by reducing abundance or per capita effects. Alternatively, properties of invaders or acquired enemies, such as an enemy’s ability to attack multiple species, may hinder enemy mitigation of invader impacts. We used field experiments to evaluate disease mitigation of invader impacts using the invasive grassMicrostegium vimineum, which hosts an emerging fungal disease, and a native grass competitor,Elymus virginicus. We manipulated competition through density gradients of each plant species, and we reduced ambient foliar diseases with fungicide and autoclaving. We then modeled long-term population dynamics with field-estimated parameters. In the field, disease did not reduce invader abundance or per capita effects. The invader amplified disease on itself and the competitor, and disease reduced invader and competitor fitness components (e.g., germination). The dynamical model predicted that disease impacts on the competitor are greater than on the invader, such that disease will reduce invader abundance by 18%, and competitor abundance by 88%, over time. Our study suggests that enemies acquired by invaders will not necessarily mitigate invader impacts if the invader amplifies the enemy and the enemy attacks and suppresses competitor species.
The Musashi proteins are post-transcriptional activators of protein expression and alternative exon splicing in vertebrate photoreceptors
Authors: Matalkah Fatimah , Jeong Bohye , Sheridan Macie , Horstick Eric , Ramamurthy Visvanathan , Stoilov Peter 
Published: March 2022

ABSTRACTThe Musashi proteins, MSI1 and MSI2, are conserved RNA binding proteins with a role in the maintenance and renewal of stem cells. Contrasting with this role, retina and terminally differentiated photoreceptor cells express high levels of MSI1 and MSI2, indicating that the two proteins have a role unrelated to maintaining undifferentiated cell state. Here we show that the Musashi proteins are essential in mature photoreceptors. Combined knockout of Msi1 and Msi2 lead to loss of the retina response to light and progressive photoreceptor cell death. The two proteins are fully redundant as individual deletion of Msi1 or Msi2 did not produce a phenotype. To define the molecular functions underlying the requirement for Musashi in photoreceptors, we delineated their RNA targets and analyzed the effect of the combined Msi1/Msi2 knockout on transcript levels, pre-mRNA splicing, and protein expressions. We show distinct nuclear and cytoplasmic functions for the Musashi proteins in photoreceptor cells. In the nucleus, Musashi binding to the downstream proximal intron promotes splicing of alternative exons. Surprisingly, four conserved photoreceptor-specific alternative exons in genes critical for vision proved to be dispensable, leaving open questions about the selective pressures that lead to the conservation of these exons and the contribution of alternative splicing to the phenotype of the Musashi knockout. In the photoreceptor cell cytoplasm MSI1 and MSI2 act as activators of protein expression. The combined knockout of Msi1 and Msi2 reduced the levels of multiple proteins including proteins required for vision and photoreceptor survival.
Choroid plexus tissue perfusion and secretory function in rats measured by non-invasive MRI reveal significant effects of anesthesia
Authors: Lee Hedok , Ozturk Burhan , Stringer Michael S. , MacIntosh Bradley J. , Rothman Douglas , Benveniste Helene 
Published: March 2022

AbstractThe choroid plexus (ChP) of the cerebral ventricles is a source of cerebrospinal fluid (CSF) production and also plays a key role in immune surveillance at the level of blood-to-CSF-barrier (BCSFB). In this study, we quantify ChP blood perfusion and BCSFB mediated water exchange from arterial blood into ventricular CSF using non-invasive continuous arterial spin labelling magnetic resonance imaging (CASL-MRI). Systemic administration of anti-diuretic hormone (vasopressin) was used to validate BCSFB mediated water exchange as a metric of choroidal CSF secretory function. To further investigate the coupling between ChP perfusion and CSF secretory function, we characterized the effects of two anesthetic regimens known to have large-scale differential effects on cerebral blood flow. For quantification of ChP blood perfusion a multi-compartment perfusion model was employed, and we discovered that partial volume correction improved measurement accuracy. Vasopressin significantly reduced both ChP blood perfusion and CSF secretory function. ChP perfusion was significantly higher with pure isoflurane anesthesia (2-2.5%) when compared to a balanced anesthesia with dexmedetomidine and low-dose isoflurane (1.0 %), however there was no significant difference in BCSFB water secretory function. In summary, here we introduce a non-invasive, robust, and spatially resolved in vivo imaging platform to quantify ChP perfusion as well as BCSFB water secretory function which can be applied to study coupling of these two key parameters in future clinical translational studies.
Does a postpartum “Green Star” family planning decision aid for adolescent mothers reduce decisional conflict? : A quasi-experimental study
Authors: Mushy Stella E. , Shishido Eri , Horiuchi Shigeko 
Published: March 2022

AbstractAimTo our knowledge, there are still no studies in Tanzania regarding decision aids on long-acting reversible contraception. We evaluated the effects of our postpartum “Green Star” family planning decision aid on decisional conflict, knowledge, satisfaction, and uptake of long-acting reversible contraception among pregnant adolescents in Tanzania.MethodsWe used a facility-based quasi-experimental design with control. The participants were purposively recruited and randomly assigned (intervention, n = 33; control, n = 33). The intervention received the routine family planning counseling and decision aid. The control received only the routine family planning counseling. The primary outcome was change in decisional conflict measured using the validated Decisional Conflict Scale (DCS). The secondary outcomes were knowledge, satisfaction, and contraception uptake. We hypothesized that pregnant adolescents who use the decision aid will have a lower DCS score.ResultsWe recruited 66 pregnant adolescents; 62 completed the study. Participants in the intervention had a lower mean difference score in the DCS than participants in the control (intervention: -24.7 [SD 7.99] vs. control: -11.6 [SD 10.9], t = -5.53, p < 0.001). The mean difference score in knowledge was significantly higher in the intervention than in the control (intervention: 4.53 [SD 2.54] vs. control: 2.0 [SD 1.45], t = 4.88, p < 0.001). The mean score of satisfaction was significantly higher in the intervention than in the control (intervention: 100 [SD 0.0] vs. control: 55.8 [SD 30.7], t = 8.112, p < 0.001). Choice of contraception was significantly higher in the intervention [29 (45.3%)] than in the control [13 (20.3%)] (x2 = 17.73, p < 0.001).ConclusionThe postpartum “Green Star” family planning decision aid was useful as it lowered decisional conflict, improved knowledge and satisfaction with decision making, and enhanced contraception uptake. The decision aid demonstrated positive applicability and affordability for pregnant adolescents in Tanzania.
Deep learning in human neurons predicts mechanistic subtypes of Parkinson’s
Authors: D’Sa Karishma , Evans James R. , Virdi Gurvir S. , Vecchi Giulia , Adam Alex , Bertolli Ottavia , Fleming James , Chang Hojong , Athauda Dilan , Choi Minee L. , Gandhi Sonia 
Published: March 2022

ABSTRACTParkinson’s disease (PD) is a common, devastating, and incurable neurodegenerative disorder. Several molecular mechanisms have been proposed to drive PD, with genetic and pathological evidence pointing towards aberrant protein homeostasis and mitochondrial dysfunction. PD is clinically highly heterogeneous, it is likely that different mechanisms underlie the pathology in different individuals, each requiring a specific targeted treatment. Recent advances in stem cell technology and fluorescent live-cell imaging have enabled the generation of patient-derived neurons with different mechanistic subtypes of PD. Here, we performed multi-dimensional fluorescent labelling of organelles in iPSC-derived neurons, in healthy control cells, and in four different disease subclasses. We generated a machine learning-based model that can simultaneously predict the presence of disease, and its primary mechanistic subtype. We independently trained a series of classifiers using both quantitative single-cell fluorescence variables and images to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whilst image based deep neural networks predict control, and four distinct disease subtypes with an accuracy of 95%. The classifiers achieve their accuracy across all subtypes primarily utilizing the organellar features of the mitochondria, with additional contribution of the lysosomes, confirming their biological importance in PD. Taken together, we show that machine learning approaches applied to patient-derived cells are able to predict disease subtypes, demonstrating that this approach may be used to guide personalized treatment approaches in the future.
Genome-wide analysis of the non-coding RNA synthetic genetic network reveals extensive plasticity and unique phenotypes in yeast
Authors: Fraczek Marcin G , Timouma Soukaina , Balarezo-Cisneros Laura Natalia , Parker Steven , Duangrattanalert Kobchai , Feng Matthew Wenjie , Griffiths-Jones Sam , O’Keefe Raymond T , Delneri Daniela 
Published: March 2022

ABSTRACTRecent studies identified non-coding RNAs (ncRNAs) with unknown function that are responsible for major fitness changes in yeast. To understand ncRNA interplay and aid their functional assignment, the synthetic genetic array (SGA) methodology was employed to create >15,000 double mutants and to score their epistasis in different environments. Unlike the protein network, ncRNAs mostly displayed positive epistasis in rich medium. Interestingly, the negative interactions significantly increased under stressors, showing environmental-dependent functions for ncRNAs. No correlation was found between the network of ncRNAs and that of their neighbouring genes, suggesting functional independence. The U3 paralogs, SNR17A and SNR17B, share the majority of genetic interactions in rich medium as expected. For example, SUT480 interacted with both paralogs and its function was linked to 18S rRNA processing. However, under stressors, a large number of unique epistatic interactions were observed, supporting the notion that SNR17A and SNR17B have diverged and sub-functionalised after genome duplication.
RF4Del: A Random Forest approach for accurate deletion detection
Authors: Xavier Roberto , Fiston-Lavier Anna-Sophie , Alves Ronnie C.O. , Cherif Emira 
Published: March 2022

AbstractEfficiently detecting genomic structural variants (SVs) is a key step to grasp the “missing heritability” underlying complex traits involved in major evolutionary processes such as speciation, phenotypic plasticity, and adaptive responses. Yet, the SV-based genotype/trait association studies are still largely overlooked mainly due to the lack of reliable detection methods. Here, we present a random forest (RF) method for accurate deletion identification: RF4Del. By relying on the analysis of the mapping profiles, data already available in most sequencing projects, RF4Del can easily and quickly call deletions.Several classic and ensemble learning strategies were carefully evaluated using proper benchmark data. RF4Del was trained and tested on simulated data from the model species Drosophila melanogaster to detect deletions. The model consists of 13 features extracted from a mapping file. We show that RF4Del outperforms established SV callers (DELLY, Pindel) with higher overall performance (F1-score > 0.75; 6x-12x sequence coverage) and is less affected by low sequencing coverage and deletion size variations. RF4Del could learn from a compilation of sequence patterns linked to a given SV. Such models can then be combined to form a learning system able to detect all types of SVs in a given genome, beyond the one used in our study. https://github.com/alvesrcoo/eletric-scheep
High nutrient loads hinder successful restoration of natural habitats in freshwater wetlands
Authors: Moeslund Jesper Erenskjold , Andersen Dagmar Kappel , Brunbjerg Ane Kirstine , Fløjgaard Camilla , Nygaard Bettina , Ejrnæs Rasmus 
Published: March 2022

AbstractRestoration of natural processes in ecosystems is key to halt the biodiversity crisis. Here, we evaluate 20 different stream-valley wetland restoration projects – mainly rewetting – in a large region in Denmark in terms of successful restoration of natural wetland habitats. We used quadratic discriminant analysis and generalized linear models to compare the projects’ 80 vegetation plots with >50.000 natural wetland-habitat reference plots and modelled the influence of time, grazing, rewetting and nutrient availability on the study plots’ probabilities of belonging to such natural habitats and their richness of high-quality habitat indicator species. In our study, the probability of a restored wetland being a natural wetland habitat – almost always an alkaline fen – was generally below 10 %. Also, we only found half as many indicator species in restored wetlands than in reference wetlands and we demonstrated that the number of characteristic alkaline fen species did not deviate from what could be expected under the prevailing nutrient conditions. We found a negative effect of nutrient availability on the number of high-quality habitat indicator species and the lowest probability of plots being natural wetlands in the most nutrient rich plots. The effect of grazing was only positive in the first years after restoration and only in the most nutrient rich plots, while the effect of rewetting sites to their historical hydrological conditions was generally negative. Our findings reveal that unnaturally high nutrient availability is probably the core limiting factor for successful restoration of natural wetlands and their associated plant diversity.Implications for practice- To successfully restore natural and characteristic freshwater wetland habitats focus on recreating natural processes and conditions is needed- Restoring natural hydrology and grazing is not enough, the soil and water must be naturally nutrient poor for successful restoration of these habitats- Restoration of stream-valley wetlands such as alkaline springs and fens is more likely to be successful in spring-dominated landscapes where clean groundwater diffusely exfiltrates the soil
A genomic analysis of parasite-mediated population differentiation in a metapopulation
Authors: Halter Meret , Du Pasquier Louis , Ebert Dieter , Fields Peter D. 
Published: March 2022

AbstractUnderstanding the genetics of host adaptation is a powerful approach to study host – parasite interactions. Hosts are often assumed to have a simple genetic architecture underlying resistance. However, in natural populations the genetics are rarely known and the link between host adaptation and evolutionary models cannot be easily established. To shed light on the genetic basis of host evolution in the presence and absence of parasites we studied a highly dynamic rockpool metapopulation of the planktonic crustacean Daphnia magna and its microsporidian parasite Hamiltosporidium tvaerminnensis. We examined genome-wide allele frequencies estimated from pooled Illumina sequencing (Pool-seq) of 12 subpopulations of a metapopulation. Subpopulations that had evolved for several years with the parasite were contrasted to uninfected subpopulations, with the aim to find genomic sites of diversifying selection. Consistent with earlier attempts to find resistance genes in this system, we observe many minor-effect outliers, suggesting that the response of the host to this parasite is based on a quantitative-trait architecture. We found 34 outliers across 11 genomic regions that indicate increased differentiation between population groups as well as signs of positive selection. Some of these regions contain immune-related genes, of which four are likely involved in immune downregulation. Our findings show that in the presence of the microsporidium parasite, hosts evolve in a complex polygenic way, driving population differentiation in the metapopulation under study. Such evolutionary differentiation is a powerful mechanism to maintain genetic diversity in spatially structured populations.