bluuebunny/crossref_metadata_2025_split huggingface dataset:
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection
Authors: Abbas Asmaa , Abdelsamea Mohammed M. , Gaber Mohamed Medhat 
Published: June 2020

AbstractDue to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for Transfer learning (4S-DT) model.4S-DTencourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabelled chest X-ray images.4S-DThelps in improving the robustness of knowledge transformation via a downstream learning strategy with a class-decomposition layer to simplify the local structure of the data.4S-DTcan deal with any irregularities in the image dataset by investigating its class boundaries using a downstream class-decomposition mechanism. We used 50,000 unlabelled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar.4S-DThas achieved a high accuracy of 99.8% (95% CI: 99.44 %, 99.98%) in the detection of COVID-19 cases on a large dataset and an accuracy of 97.54% (95% CI: 96.22%, 98.91%) on an extended test set enriched by augmented images of a small dataset, out of which all real COVID-19 cases were detected, which was the highest accuracy obtained when compared to other methods.
TzanckNet: A convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases
Authors: Noyan Mehmet Alican , Durdu Murat , Eskiocak Ali Haydar 
Published: June 2020

AbstractTzanck smear test is a low-cost, rapid and reliable tool which can be used for the diagnosis of many erosive-vesiculobullous, tumoral and granulomatous diseases. Currently its use is limited mainly due to lack of experience in interpretation of the smears. We developed a deep learning model, TzanckNet, that can identify cells in Tzanck smear test findings. TzanckNet was trained on a retrospective development dataset of 2260 Tzanck smear images collected between December 2006 – December 2019. The finalized model was evaluated using a prospective validation dataset of 359 Tzanck smear images collected from 15 patients during January 2020. It is designed to recognize six cell types (acantholytic cells, eosinophils, hypha, multinucleated giant cells, normal keratinocytes and tadpole cells). For 359 images and 6 cell types, TzanckNet made 2154 predictions. The accuracy was 94.3 % (95% CI 93.4 to 95.3), the sensitivity was 83.7 % (95% CI 80.3 to 87.0) and the specificity was 97.3 % (95% CI 96.5 to 98.1). The area under the receiver operating characteristic curve was 0.974. Our results show that TzanckNet has the potential to lower the experience barrier needed to use this test, broadening its user base, and hence improving patient well-being.
Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography
Authors: Singla Jitin , White Kate L. , Stevens Raymond C. , Alber Frank 
Published: June 2020

AbstractCryo-electron tomography provides the opportunity for unsupervised discovery of endogenous complexes in situ. This process usually requires particle picking, clustering and alignment of subtomograms to produce an average structure of the complex. When applied to heterogeneous samples, template-free clustering and alignment of subtomograms can potentially lead to the discovery of structures for unknown endogenous complexes. However, such methods require useful scoring functions to measure the quality of aligned subtomogram clusters, which can be compromised by contaminations from misclassified complexes and alignment errors. To our knowledge, a comprehensive survey to assess the effectiveness of scoring functions for ranking the quality of subtomogram clusters does not exist yet. Here, we provide such a study and assess a total of 15 scoring functions for evaluating the quality of the subtomogram clusters, which differ in the amount of structural misalignments and contaminations due to misclassified complexes. We assessed both experimental and simulated subtomograms as ground truth data sets. Our analysis shows that the robustness of scoring functions varies largely. Most scores are sensitive to the signal-to-noise ratio of subtomograms and often require Gaussian filtering as preprocessing for improved performance. Two scoring functions, Spectral SNR-based Fourier Shell Correlation and Pearson Correlation in the Fourier domain with missing wedge correction, show a robust ranking of subtomogram clusters even without any preprocessing and irrespective of SNR levels of subtomograms. Of these two scoring functions, Spectral SNR-based Fourier Shell Correlation was fastest to compute and is a better choice for handling large numbers of subtomograms. Our results provide a guidance for choosing a scoring function for template-free approaches to detect complexes from heterogeneous samples.
Automatic Generation of Personalised Skeletal Models of the Lower Limb from Three-Dimensional Bone Geometries
Authors: Modenese Luca , Renault Jean-Baptiste 
Published: June 2020

AbstractThe generation of personalised and patient-specific musculoskeletal models is currently a cumbersome and time-consuming task that normally requires several processing hours and trained operators. We believe that this aspect discourages the use of computational models even when appropriate data are available and personalised biomechanical analysis would be beneficial. In this paper we present a computational tool that enables the fully automatic generation of skeletal models of the lower limb from three-dimensional bone geometries, normally obtained by segmentation of medical images. This tool was evaluated against four manually created lower limb models finding remarkable agreement in the computed joint parameters, well within human operator repeatability. The coordinate systems origins were identified with maximum differences between 0.5 mm (hip joint) and 5.9 mm (subtalar joint), while the joint axes presented discrepancies between 1° (knee joint) to 11° (subtalar joint). To prove the robustness of the methodology, the models were built from four datasets including both genders, anatomies ranging from juvenile to elderly and bone geometries reconstructed from high-quality computed tomography as well as lower-quality magnetic resonance imaging scans. The entire workflow, implemented in MATLAB scripting language, executed in seconds and required no operator intervention, creating lower extremity models ready to use for kinematic and kinetic analysis or as baselines for more advanced musculoskeletal modelling approaches, of which we provide some practical examples. We auspicate that this technical advancement, together with upcoming progress in medical image segmentation techniques, will promote the use of personalised models in larger-scale studies than those hitherto undertaken.
ReCodLiver0.9: Overcoming challenges in genome-scale metabolic reconstruction of a non-model species
Authors: Hanna Eileen Marie , Zhang Xiaokang , Eide Marta , Fallahi Shirin , Furmanek Tomasz , Yadetie Fekadu , Zielinski Daniel Craig , Goksøyr Anders , Jonassen Inge 
Published: June 2020

AbstractThe availability of genome sequences, annotations and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.Author summaryGenome-scale metabolic models (GEMs) are constructed based upon reconstructed networks that are carried out by an organism. The underlying biochemical knowledge in such networks can be transformed into mathematical models that could serve as a platform to answer biological questions. The availability of high-throughput biological data, including genomics, proteomics, and metabolomics data, supports the generation of such models for a large number of organisms. Nevertheless, challenges arise for non-model species which are typically less annotated. In this paper, we discuss these challenges and possible solutions in the context of generation of a draft liver reconstruction of Atlantic cod (Gadus morhua). We also show how experimental data, here gene expression data, can be mapped to the resulting model to understand the metabolic response of cod liver to environmental toxicants.
Anisotropic random walks reveal chemotaxis signaling output in run-reversing bacteria
Authors: Antani Jyot D. , Sumali Anita X. , Lele Tanmay P. , Lele Pushkar P. 
Published: June 2020

AbstractThe bias for a particular direction of rotation of the flagellar motor is a sensitive readout of chemotaxis signaling, which mediates bacterial migration towards favorable chemical environments. The rotational bias has not been characterized inHelicobacter pylori, which limits our understanding of the signaling dynamics. Here, we determined thatH. pyloriswim faster (slower) whenever their flagella rotate counterclockwise (clockwise) by analyzing their hydrodynamic interactions with bounding surfaces. The anisotropy in swimming speeds helped quantify the fraction of the time that the cells swam slower to report the first measurements of the bias. A stochastic model of run-reversals indicated that the anisotropy promotes faster spread compared to isotropic swimmers. The approach further revealed that the diffuse spread ofH. pyloriis likely limited at the physiological temperature due to increased reversal frequencies. Thus, anisotropic run-reversals make it feasible to study signal-response relations in the chemotaxis network in non-model bacterial species.Impact StatementAnisotropy in run and reversal swimming speeds promotes the spread ofH. pyloriand reveals temperature-dependent behavior of the flagellar switch.
13C Electron Nuclear Double Resonance Spectroscopy Shows Acetyl-CoA Synthase Binds Two Substrate CO in Multiple Binding Modes and Reveals the Importance of a CO-Binding ‘Alcove’
Authors: James Christopher D. , Wiley Seth , Ragsdale Stephen W. , Hoffman Brian M. 
Published: June 2020

AbstractEPR and Electron Nuclear Double Resonance spectroscopies here characterize CO binding to the active-site A cluster of wild-type (WT) Acetyl-CoA Synthase (ACS) and two variants, F229W and F229A. The A-cluster binds CO to a proximal Ni (Nip) that bridges a [4Fe-4S] cluster and distal Nid. An alcove seen in the ACS crystal-structure near the A-cluster, defined by hydrophobic residues including F229, forms a cage surrounding a Xe mimic of CO and is suggested to ‘cradle’ this CO. Previously, we only knew WT ACS bound a single CO in the Ared-CO intermediate, here seen as forming Nip(I)-CO with CO on-axis of the dz2odd-electron orbital (g⊥>g‖∼2). The two-dimensional field-frequency pattern of 2K-35 GHz13C-ENDOR spectra collected across the Ared-CO EPR envelope now reveals a second CO bound in the dz2orbital’s equatorial plane. This WT A-cluster conformer dominates the nearly-conservative F229W variant, but13C-ENDOR reveals a minority “A” conformation with (g‖>g⊥∼2) characteristic of a ‘cloverleaf’ (eg. dx2-y2) odd-electron orbital, and with Nipbinding two, apparently ‘in-plane’ CO. Disruption of the alcove through introduction of the smaller alanine residue in the F229A variant diminishes conversion to Ni(I) ∼tenfold and introduces extensive cluster flexibility.13C-ENDOR shows the F229A cluster is mostly (60%) in the “A” conformation, but with ∼20% each of the WT conformer and an “O” state in which dz2Nip(I) (g⊥>g‖∼2) surprisingly lacks CO. This paper thus demonstrates the importance of an intact alcove in forming and stabilizing the Ni(I)-CO intermediate in the Wood-Ljungdahl pathway of anaerobic CO and CO2fixation.
Spatially explicit modelling of kelp-grazer interactions: a seascape approach for effective habitat enhancement using artificial reefs
Authors: Filippo Ferrario , Suskiewicz Thew , Johnson Ladd Erik 
Published: June 2020

AbstractArtificial structures are sprawling along the coast affecting the aspect and the functioning of shallow coastal seascapes. For years, the ecology of artificial structures has been investigated mainly in contrast to natural coastal habitats. However, it is increasingly emerging that structuring processes, such as trophic interactions, can depend on properties of the surrounding landscape. Heterogeneity of coastal seafloor and habitats could thus play a major role in determining the variability of ecological outcomes on artificial structures.Artificial reefs are being used in coastal areas in attempts to restore and enhance marine habitats and communities, including large brown seaweed (“kelp”), to offset habitat loss and mitigate coastal development impacts. The outcome of enhancement projects using artificial reefs have not always been either consistent or positive. Overlooking the effect of strong ecological interactions adds a high level of uncertainty and can undermine the success of these efforts.In Eastern Canada, top-down control exerted by green sea urchins (Strongylocentrotus droebachiensis) can seriously compromise the success of artificial reefs for kelp enhancement. Importantly, urchin interactions with macroalgae are likely to be influenced by the bottom composition. A seascape approach could thus integrate behavior and habitat heterogeneity.We investigated whether the local seascape could create zones of differential grazing risk for kelp outplanting kelp (Alaria esculenta) on artificial blocks on an heterogenous bottom. Adopting a spatially explicit framework, we determined how seascape affected the urchin use of the habitat and used this information to map the grazing risk throughout the area.Kelp survival was a function of frequency of urchin presence throughout the study site. While urchins avoided sandy patches, bottom composition and algal cover modulated the within-patch urchin use of the habitat. This translated in the heterogeneity of grazing risk intensity.Synthesis and applications. The presence of discrete seascape features locally increased the grazing risk for kelp by differentially affecting the urchin’s usage of the habitat, even within the same bottom patch. Incorporating this information when planning artificial reefs could minimize the detrimental grazing risk thus increasing the rate of success and ensuring lasting results.
Modeling the effect of COVID-19 disease on the cardiac function: a computational study
Authors: Regazzoni Francesco , Vergara Christian , Dede’ Luca , Zunino Paolo , Guglielmo Marco , Scrofani Roberto , Fusini Laura , Cogliati Chiara , Pontone Gianluca , Quarteroni Alfio 
Published: June 2020

ABSTRACTBackgroundThe effect of COVID-19 on the cardiac function and on the vascular system increases the morbidity and mortality of infected subjects with cardiovascular diseases.ObjectivesTo provide preliminary results on cardiac global outcomes (such as cardiac output, ventricular pressures) obtained by means of computational models in plausible scenarios characterized by COVID-19.MethodsWe considered a lumped parameters computational model of the cardiovascular system, which models, from the mechanical point of view, the systemic and pulmonary circulations, the four cardiac valves and the four heart chambers, through mathematical equations of the underlying physical processes. To study the effect of COVID-19, we varied the heart rate, the contractility and the pulmonary resistances in suitable ranges.ResultsOur computations on individuals with both otherwise normal and impaired cardiac functions revealed that COVID-19 worsen cardiac function, as shown by a decrease of some cardiac biomarkers values such as cardiac output and ejection fraction. In the case of existing impaired cardiac function, the presence of COVID-19 lead to values outside the normal ranges.ConclusionsComputational models revealed to be an effective tool to study the effect of COVID-19 on the cardiovascular system. Such effect could be significant for patients with impaired cardiac function. This is especially useful to perform a sensitivity analysis of the hemodynamics for different conditions.CONDENSED ABSTRACTEmerging studies address how COVID-19 infection might impact the cardiovascular system. This relates particularly to the development of myocardial injury, acute coronary syndrome, myocarditis, arrhythmia, and heart failure. Prospective treatment approach is advised for these patients. By the assessment of conventional important biomarkers obtained with new sources as a 0-dimentional computational model, we propose a new study protocol as an effective method to evaluate short-term prognosis. The clinical protocol proposed will help to rapidly identify which patients require intensive monitoring, diagnostic strategy and most adequate therapy.
The crystal structure of the Ca2+-ATPase 1 fromListeria monocytogenesreveals a pump primed for dephosphorylation
Authors: Hansen Sara Basse , Dyla Mateusz , Neumann Caroline , Hoegh Quistgaard Esben Meldgaard , Andersen Jacob Lauwring , Kjaergaard Magnus , Nissen Poul 
Published: June 2020

AbstractMany bacteria export intracellular calcium using active transporters homologous to the sarco/endoplasmic reticulum Ca2+-ATPase (SERCA). Here we present three crystal structures of Ca2+-ATPase 1 from Listeria monocytogenes (LMCA1). Structures with BeF3-mimicking a phosphoenzyme state reveal a closed state, which is intermediate between the outward-open E2P and the proton-occluded E2-P* conformations known for SERCA. It suggests that LMCA1 in the E2P state is pre-organized for dephosphorylation upon Ca2+release, consistent with the rapid dephosphorylation observed in single-molecule studies. An arginine side-chain occupies the position equivalent to calcium binding site I in SERCA, leaving a single Ca2+-binding site in LMCA1, corresponding to SERCA site II. Observing no putative transport pathways dedicated to protons, we infer a direct proton counter transport through the Ca2+exchange pathways. The LMCA1 structures provide insight into the evolutionary divergence and conserved features of this important class of ion transporters.
Diversity of mosquitoes (Diptera: Culicidae) collected in different types of larvitraps in an Amazon rural settlement
Authors: Feijó Almeida Jessica , Matos Belchior Heliana Christy , Ríos-Velásquez Claudia María , Costa Pessoa Felipe Arley 
Published: June 2020

AbstractAnthropogenic environments provide favorable conditions for some species, which are especially suitable for mosquitoes that present eclecticism at the moment of choice for the site of oviposition. The present study, the diversity of mosquitoes in plastic container, bamboo internode, and tire placed in forest, the forest edge, and peridomicile environments in a rural settlement area was assessed. Eighteen sampling points were chosen, delimited by a buffer 200 m, placed in environments: forest, forest edge, and peridomicile. In each environment, larvitraps were installed, separated by a minimum distance of 7 m and 1 m from the ground. A total of 10,131 immature mosquitoes of 20 species were collected. The most abundant species wasCulex urichii(29.5%), followed byTrichoprosopom digitatum(27.1%), andCx. (Melanoconion) sp. (10,4%). There was a difference in the composition of immature mosquito populations between larvitraps (p<0.0005), and the plastic container hosted a greater diversity of species, whereas tires presented a greater abundance of individuals. The forest, forest edge, and peridomicile environments were also significantly different with regard to diversity of immature mosquito populations (p<0.0010). The forest edge was the environment with the greatest diversity of species, followed by the peridomicile and forest environments. In the forest and peridomicile, plastic container larvitraps had the greatest diversity, whereas the forest edge tire presented the largest number of individuals. Further, tire larvitraps collected the largest number of individuals in all environments. We identified 10 species associated with the bamboo internode and tire. We also observed the preference of species for artificial larvitraps, such as the plastic container and tire, even in wild environments. These artificial objects may represent a risk factor for the population living in this region, as all vector species found in the study were present in plastic containers and tires.
Correlation Coefficient Based Automatic Optic Disc Detection from Retinal Images
Authors: Anand S. , Prabhadevi R. , Rini D. 
Published: June 2020

ABSTRACTIn this paper an algorithm to detect the optic disc (OD) automatically is described. The proposed method is based on the circular brightness of the OD and its correlation coefficient. At first the peak intensity points are taken, a mask is generated for the given image which gives the circular bright regions of the image. To locate the OD accurately, a pattern is generated which is similar to the OD. By correlating the retinal image with the pattern generated, the maximum correlation of the pattern with the OD is obtained. On locating the coordinates of maximum correlation, the exact location of the OD is detected. The proposed algorithm has been tested with DRIVE database images and an average OD detection accuracy of 95% was obtained for healthy and pathological retinas respectively.