bluuebunny/crossref_metadata_2025_split huggingface dataset:
RGC-TBRS Personal Air Pollutant Exposure versus Health Condition and Perception Pilot Study for Young Asthmatics in Hong Kong (2019/2020)
Authors: Chow Kenyon , Han Yang , Li Victor OK , Lam Jacqueline CK , Lee So-lun , Wong Wilfred , Lau Yu-lung 
Published: June 2021

AbstractThis is a report of the RGC-TBRS funded observational pilot study which examines the effects of personal exposures to three types of air pollutants, namely, PM1.0, PM2.5, and PM10, on personal health condition and perception of young asthmatics (aged 12 – 15) in Hong Kong. This is the first study to investigate the relationship between PM1.0 and FEV1 and FVC of young asthmatics in Hong Kong, based on personal exposures obtained from portable sensors. Our preliminary results show that a higher level of PM1.0, PM2.5 and PM10 would deteriorate the health conditions of young asthmatics in HK. All correlations between particulates and lung functions are significant and negative, including PM1.0 exposure vs. FEV1 (R2=12%; p=0.023), PM1.0 exposure vs. FVC (R2=15%; p=0.010), PM2.5 exposure vs. FEV1 (R2=13%; p=0.019), PM2.5 exposure vs. FVC (R2=16%; p=0.008), PM10 exposure vs. FEV1 (R2=14%; p=0.012), and PM10 exposure vs. FVC (R2=18%; p=0.005). Moreover, after accounting for covariates, including age, gender, body mass index (BMI), temperature, and relative humidity, we found a significant relationship between PM1.0 exposure vs. FVC (Coefficient=-0.1224; p=0.032), PM2.5 exposure vs. FVC (Coefficient=-0.1177; p=0.021), PM10 exposure vs. FEV1 (Coefficient=-0.0703; p=0.019), and PM10 exposure vs. FVC (Coefficient=-0.1204; p=0.006). Further, using the pilot study data, we have performed a power analysis to estimate the sample size for our follow-up main study. Based on the primary null hypothesis that personal PM exposure would not change the FEV1 and FVC of young asthmatics in HK, the lowest sample size that gives 80% power at a 5% significance level is 107. Hence, the sample size (or the total number of participated asthma subjects) expected for the follow-up longitudinal clinical study should be 125 (after adjusting for the non-compliance and withdrawal of subjects). Our pilot study has demonstrated the feasibility of research into the effects of personal air pollutant exposure on health condition and health perception. Our follow-up study will address the challenges identified in the pilot study, based on the proposed follow-up actions for subject engagement, data collection, and data analysis.
In vivo imaging and quantification of carbon tracer dynamics in nodulated root systems of pea plants
Authors: Metzner Ralf , Chlubek Antonia , Bühler Jonas , Pflugfelder Daniel , Schurr Ulrich , Huber Gregor , Koller Robert , Jahnke Siegfried 
Published: June 2021

AbstractLegumes associate with root colonizing rhizobia that provide fixed nitrogen to its plant host in exchange for recently fixed carbon. There is a lack in understanding how individual plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. One reason is that most approaches are based on destructive sampling, making quantification of localized carbon allocation dynamics in the root system difficult. We established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11C tracer towards individual nodules in a three-dimensional (3D) root system of pea (Pisum sativum). Nitrate was used for triggering the shutdown of biological nitrogen fixation (BNF) expected to rapidly affect carbon allocation dynamics in the root-nodule system. This nitrate treatment lead to a reduction of 11C tracer allocation to nodules by 40% – 47% in 5 treated plants while the variation in control plants was less than 11%. The established experimental pipeline enabled for the first time that several plants could consistently be labelled and measured using 11C tracer in a PET approach to quantify C-allocation to individual nodules following a BNF shutdown. This demonstrates the strength of using 11C tracers in a PET approach for non-invasive quantification of dynamic carbon allocation in several growing plants over several days. A major advantage of the approach is the possibility to investigate carbon dynamics in small regions of interest in a 3D system such as nodules in comparison to whole plant development.One sentence summaryPositron Emission Tomography for quantification of carbon allocation dynamics in individual nodules within a 3D root system revealed strong effect of nitrate on carbon allocation.
Rapid and reproducible MALDI-TOF-based method for detection Vancomycin-resistant Enterococcus faecium using classifying algorithms
Authors: Candela Ana , Arroyo Manuel J. , Sánchez-Molleda Ángela , Méndez Gema , Rodriguez-Temporal David , Quiroga Lidia , Ruiz Adrián , Cercenado Emilia , Marín Mercedes , Muñoz Patricia , Mancera Luis , Rodríguez-Sánchez Belén 
Published: June 2021

ABSTRACTVancomycin-resistant Enterococcus faecium has become a health threat over the last 20 years due to its ability to rapidly spread and cause outbreaks in hospital settings. Although MALDI-TOF MS has already demonstrated its usefulness for accurate identification of E. faecium, its implementation for antimicrobial resistance detection is still under evaluation. The reproducibility of MALDI-TOF MS for peak analysis and its performance for correct discrimination of vancomycin susceptible isolates (VSE) from those hosting the VanA and VanB resistance mechanisms was evaluated in this study. For the first goal, intra-spot, inter-spot -technical- and inter-day -biological- reproducibility was assayed. The capability of MALDI-TOF to discriminate VSE isolates from VanA VRE and VanB VRE strains was carried out on protein spectra from 178 E. faecium unique clinical isolates −92 VSE, 31 VanA VRE, 55 VanB VRE-, processed with Clover MS Data Analysis software. Unsupervised (Principal Component Analysis –PCA-) and supervised algorithms (Support Vector Machine -SVM-, Random Forest -RF- and Partial Least Squares-Discriminant Analysis -PLS-DA-) were applied. The reproducibility assay showed lower variability for normalized data (p<0.0001) and for the peaks within the 3000-9000 m/z range. Besides, 80.9%, 79.21% and 77.53% VSE vs VRE (VanA + VanB) discrimination was achieved by applying SVM, RF and PLS-DA, respectively. Correct differentiation of VanA from VanB VRE isolates was obtained by SVM in 86.65% cases. The implementation MALDI-TOF MS and peak analysis could represent a rapid and effective tool for VRE screening. However, further improvements are needed to increase the accuracy of this approach.
Elucidating the genome of Emydura subglobosa through manual annotation and computational biology tools
Authors: Yu Megan R. 
Published: June 2021

AbstractRapid advancements in automated genomic technologies have uncovered many unique findings about the turtle genome and its associated features including olfactory gene expansions and duplications of toll-like receptors. However, automated technologies often result in a high frequency of errors through the process of assembly and annotation and highlight the need for manual annotation. In this study, we have manually annotated four genes of the red-bellied short-neck turtle (Emydura subglobosa), an understudied outgroup of turtles representing a diverse lineage. We improved upon initial ab initio gene predictions through homology-based evidence and generated refined consensus models. Through functional, localization, and structural analyses of the predicted proteins, we have discovered conserved genes encoding proteins that play a role in C21-steroid hormone biosynthetic processes, Vitamin A uptake, collagen/elastin integrity, tumor suppression, and fatty acid catabolism. Overall, these findings further our knowledge about the genetic features underlying turtle physiology, morphology, and longevity, which could have important implications for the treatment of human diseases and evolutionary studies.
Fetal-like reversion in the regenerating intestine is regulated by mesenchymal Asporin
Authors: Iqbal Sharif , Andersson Simon , Nestaite Ernesta , Pentinmikko Nalle , Kumar Ashish , Borshagovski Daniel , Webb Anna , Saarinen Tuure , Juuti Anne , Ori Alessandro , Varjosalo Markku , Pietiläinen Kirsi H. , Jensen Kim B. , Oudhoff Menno , Katajisto Pekka 
Published: June 2021

ABSTRACTEpithelial tissues undergo fetal-like cellular reprogramming to regenerate after damage1,2. Although the mesenchyme and the extracellular matrix (ECM) play critical roles in tissue homeostasis and regeneration2–5, their role in repurposing developmental programs in epithelium is unknown. To model epithelial regeneration, we culture intestinal epithelium on decellularized small intestinal scaffold (iECM), and identify Asporin (Aspn), an ECM bound proteoglycan, as a critical mediator of cellular reprogramming. Aspn is produced by the mesenchyme, and we show that its effect on epithelial Tgfβ-signalling via CD44 is critical for fetal-like conversion. Furthermore, we demonstrate that Aspn is transiently increased upon chemotherapy-induced damage and pivotal for a timely induction of the fetal-like state and tissue regeneration. In summary, we establish a platform for modelling epithelial injury responses ex vivo, and show that the mesenchymal Aspn-producing niche controls tissue repair by regulating epithelial fetal-like reprogramming.
Environment Classification for Robotic Leg Prostheses and Exoskeletons using Deep Convolutional Neural Networks
Authors: Laschowski Brokoslaw , McNally William , Wong Alexander , McPhee John 
Published: June 2021

AbstractRobotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, the current locomotion mode recognition systems being developed for intelligent high-level control and decision-making use mechanical, inertial, and/or neuromuscular data, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, we designed and evaluated an advanced environment classification system that uses computer vision and deep learning to forward predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust locomotion mode transitions. In this study, we first reviewed the development of the ExoNet database – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a hierarchical labelling architecture. We then trained and tested over a dozen state-of-the-art deep convolutional neural networks (CNNs) on the ExoNet database for large-scale image classification of the walking environments, including: EfficientNetB0, InceptionV3, MobileNet, MobileNetV2, VGG16, VGG19, Xception, ResNet50, ResNet101, ResNet152, DenseNet121, DenseNet169, and DenseNet201. Lastly, we quantitatively compared the benchmarked CNN architectures and their environment classification predictions using an operational metric called NetScore, which balances the image classification accuracy with the computational and memory storage requirements (i.e., important for onboard real-time inference). Although we designed this environment classification system to support the development of next-generation environment-adaptive locomotor control systems for robotic prostheses and exoskeletons, applications could extend to humanoids, autonomous legged robots, powered wheelchairs, and assistive devices for persons with visual impairments.
Identification of public submitted tick images: a neural network approach
Authors: Justen Lennart , Carlsmith Duncan , Paskewitz Susan M. , Bartholomay Lyric C. , Bron Gebbiena M. 
Published: June 2021

AbstractTicks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated and by providing researchers and public health agencies with better data on tick activity and geographic range. We report the development and validation of a convolutional neural network, a type of deep learning algorithm, trained on a dataset of more than 12,000 user-generated tick images. The model, which we call “TickIDNet,” is trained to identify the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. At baseline, TickIDNet scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network’s ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet’s performance can be increased by using confidence thresholds to introduce an “unsure” class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.
A single-cell atlas of de novo β-cell regeneration reveals the contribution of hybrid β/δ-cells to diabetes recovery in zebrafish
Authors: Singh Sumeet Pal , Chawla Prateek , Hnatiuk Alisa , Kamel Margrit , Silva Luis Delgadillo , Spanjard Bastiaan , Eski Sema Elif , Janjuha Sharan , Olivares Pedro , Kayisoglu Oezge , Rost Fabian , Bläsche Juliane , Kränkel Annekathrin , Petzold Andreas , Kurth Thomas , Reinhardt Susanne , Junker Jan Philipp , Ninov Nikolay 
Published: June 2021

AbstractRegeneration-competent species possess the ability to reverse the progression of severe diseases by restoring the function of the damaged tissue. However, the cellular dynamics underlying this capability remain unexplored. Here, we use single-cell transcriptomics to map de novo β-cell regeneration during induction and recovery from diabetes in zebrafish. We show that the zebrafish has evolved two distinct types of somatostatin-producing δ-cells, which we term δ1- and δ2-cells. Moreover, we characterize a small population of glucose-responsive islet cells, which share the hormones and fate-determinants of both β- and δ1-cells. The transcriptomic analysis of β-cell regeneration reveals that β/δ hybrid cells constitute a prominent source of insulin-expression during diabetes recovery. Using in vivo calcium imaging and cell tracking, we further show that the hybrid cells form de novo and acquire glucose-responsiveness in the course of regeneration. The overexpression of dkk3, a gene enriched in hybrid cells, increases their formation in the absence of β-cell injury. Finally, interspecies comparison shows that plastic δ1-cells are partially related to PP-cells in the human pancreas. Our work provides an atlas of β-cell regeneration and indicates that the rapid formation of glucose-responsive hybrid cells contributes to the resolution of diabetes in zebrafish
A δ-cell subpopulation with a pro-β-cell identity confers efficient age-independent recovery in a zebrafish model of diabetes
Authors: Carril Pardo Claudio A. , Massoz Laura , Dupont Marie A. , Bergemann David , Bourdouxhe Jordane , Lavergne Arnaud , Tarifeño-Saldivia Estefania , Helker Christian S. M. , Stainier Didier Y. R. , Peers Bernard , Voz Marianne L. , Manfroid Isabelle 
Published: June 2021

AbstractRestoring damaged β-cells in diabetic patients by harnessing the plasticity of other pancreatic cells raises the questions of the efficiency of the process and of the functionality of the new Insulin-expressing cells. To overcome the weak regenerative capacity of mammals, we used regeneration-prone zebrafish to study β-cells arising following destruction. We show that most new insulin cells differ from the original β-cells as they are Somatostatin+ Insulin+, but are nevertheless functional and normalize glycemia. These bihormonal cells are transcriptionally close to a subset of δ-cells in normal islets characterized by the expression of somatostatin 1.1 (sst1.1), the β-cell genes pdx1, slc2a2 and gck, and the machinery for glucose-induced Insulin secretion. β-cell destruction triggers massive sst1.1 δ-cell conversion to bihormonal cells. Our work shows that their pro- β-cell identity predisposes this zebrafish δ-cell subpopulation to efficient age-independent neogenesis of Insulin-producing cells and provides clues to restoring functional β-cells in mammalian diabetes models.
Metric Multidimensional Scaling for Large Single-Cell Data Sets using Neural Networks
Authors: Canzar Stefan , Do Van Hoan , Jelić Slobodan , Laue Sören , Matijević Domagoj , Prusina Tomislav 
Published: June 2021

AbstractMetric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a neural network based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.
Effects of trypsin on aggregation, disaggregation and aggregate morphology of red blood cells in autologous plasma and serum
Authors: Sheremet’ev Yury A. 
Published: June 2021

AbstractWe study the influence of trypsin on aggregation, disaggregation, and aggregate morphology of RBCs in autologous plasma and serum. The effect of trypsin on the surface charge of red blood cells and the aggregation of glutaraldehyde fixed cells after treatment with the enzyme was also studied. RBC aggregation was studied by means of an aggregometer and microscopic observations. The results obtained in this study indicate that trypsin treatment increases RBCs aggregation in autologous plasma and serum. The disaggregation of erythrocytes after trypsin treatment considerably decreased in autologous plasma and serum. Increase in the strength of red blood cell aggregates was observed in autologous plasma and serum. The microscopic images of RBCs aggregates indicate the formation of globular (pathologic) structures of aggregates in autologous plasma and serum. Trypsin decrease the surface charge of RBCs. In autologous plasma and serum, the cup shapes of RBCs appear. The control RBCs fixed with glutaraldehyde were not aggregated after their placement in autologous plasma. At the same time, red blood cells pretreated with trypsin and fixed with glutaraldehyde interact with each other in autologous plasma. The physiological significance of glycoproteins of erythrocyte surface for RBCs aggregation was discussed.
The Clade Displacement Index: how to detect horizontal gene transfers in unrooted gene trees
Authors: Aleksander Ciach Michał 
Published: June 2021

AbstractWhile most genes of any organism are inherited vertically - i.e. from its parent organisms - sometimes they can be exchanged between unrelated species in a process known as the horizontal gene transfer (HGT). Studies of HGT contribute to our knowledge about the mechanisms of evolution, including the emergence of new pathogens, and a great deal of effort has been put into different methods of finding transferred genes. The golden standard of HGT detection is the analysis of the incongruence between the gene and the species trees. Those methods typically require rooted trees, in which the direction of evolution is known. Gene trees are typically unrooted, and rooting them is yet another step in HGT analysis, prone to errors which may lead to wrong conclusions. A natural question arises: can HGTs be detected in gene trees without rooting them at all?It turns out that, for a particular, yet broad, class of transfers, the answer to this question is: yes. It also turns out that the same methodology can be applied to complement the bootstrap support in assessing the stability of gene tree topology. In this article, we present the Clade Displacement Index, a measure of shift of a given clade’s location between two trees. We derive algorithms to compute it and give several examples of its applications to HGT detection and gene tree stability analysis. We finish by pointing out directions for further studies and an example that shows that not all HGTs are detectable without knowing the location of the root of the gene tree.A Jupyter Notebook with the implementation and applications of CDI described in this paper is available at https://github.com/mciach/CDI