bluuebunny/crossref_metadata_2025_split huggingface dataset:
StackTADB: a stacking-based ensemble learning model for predicting the boundaries of topologically associating domains (TADs) accurately in fruit flies
Authors: Wu Hao , Zhang Pengyu , Ai Zhaoheng , Wei Leyi , Zhang Hongming , Yang Fan , Cui Lizhen 
Published: February 2022

AbstractChromosome is composed of many distinct chromatin domains, referred to variably as topological domains or topologically associating domains (TADs). The domains are stable across different cell types and highly conserved across species, thus these chromatin domains have been considered as the basic units of chromosome folding and regarded as an important secondary structure in chromosome organization. However, the identification of TAD boundaries is still a great challenge due to the high cost and low resolution of Hi-C data or experiments. In this study, we propose a novel ensemble learning framework, termed as StackTADB, for predicting the boundaries of TADs. StackTADB integrates four base classifiers including Random Forest, Logistic Regression, K-NearestNeighbor and Support Vector Machine. From the analysis of a series of examinations on the data set in the previous study, it is concluded that StackTADB has optimal performance in six metrics, AUC, Accuracy, MCC, Precision, Recall and F1 score, and it is superior to the existing methods. In addition, the comparison of the performance of multiple features shows that Kmers-based features play an essential role in predicting TADs boundaries of fruit flies, and we also apply the SHapley Additive exPlanations (SHAP) framework to interpret the predictions of StackTADB to identify the reason why Kmers-based features are vital. The experimental results show that the subsequences matching the BEAF-32 motif play a crucial role in predicting the boundaries of TADs. The source code is freely available at https://github.com/HaoWuLab-Bioinformatics/StackTADB and the webserver of StackTADB is freely available at http://hwtad.sdu.edu.cn:8002/StackTADB.
KaruBioNet: a network and discussion group for a better collaboration and structuring of bioinformatics in Guadeloupe (French West Indies)
Authors: Couvin David , Dereeper Alexis , Meyer Damien F , Noroy Christophe , Gaete Stanie , Bhakkan Bernard , Poullet Nausicaa , Gaspard Sarra , Bezault Etienne , Marcelino Isabel , Pruneau Ludovic , Segretier Wilfried , Stattner Erick , Cazenave Damien , Garnier Maëlle , Pot Matthieu , Tressières Benoît , Deloumeaux Jacqueline , Breurec Sébastien , Ferdinand Séverine , Gonzalez-Rizzo Silvina , Reynaud Yann 
Published: February 2022

AbstractSummarySequencing and other biological data are now more frequently available and at a lower price. Mutual tools and strategies are needed to analyze the huge amount of heterogeneous data generated by several research teams and devices. Bioinformatics represents a growing field in the scientific community globally. This multidisciplinary field provides a great amount of tools and methods that can be used to conduct scientific studies in a more strategic way. Coordinated actions and collaborations are needed to find more innovative and accurate methods for a better understanding of real-life data. A wide variety of organizations are contributing to KaruBioNet in Guadeloupe (French West Indies), a Caribbean archipelago. The purpose of this group is to foster collaboration and mutual aid among people from different disciplines using a ‘one health’ approach, for a better comprehension and surveillance of humans, plants or animals’ health and diseases. The KaruBioNet network particularly aims to help researchers in their studies related to ‘omics’ data, but also more general aspects concerning biological data analysis. This transdisciplinary network is a platform for discussion, sharing, training and support between scientists interested in bioinformatics and related fields. Starting from a little archipelago in the Caribbean, we envision to facilitate exchange between other Caribbean partners in the future, knowing that the Caribbean is a region with non-negligible biodiversity which should be preserved and protected. Joining forces with other Caribbean countries or territories would strengthen scientific collaborative impact in the region. Information related to this network can be found at: http://www.pasteur-guadeloupe.fr/karubionet.html. Furthermore, a dedicated ‘Galaxy KaruBioNet’ platform is available at: http://calamar.univ-ag.fr/c3i/galaxy_karubionet.html.Availability and implementation Information about KaruBioNet is availabe at: http://www.pasteur-guadeloupe.fr/karubionet.htmlContactdcouvin@pasteur-guadeloupe.frSupplementary informationSupplementary data are available at Bioinformatics Advances online.
GMNN2CD: identification of circRNA–disease associations based on variational inference and graph Markov neural networks
Authors: Niu Mengting , Zou Quan , Wang Chunyu 
Published: February 2022

Abstract

Motivation
With the analysis of the characteristic and function of circular RNAs (circRNAs), people have realized that they play a critical role in the diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for searching the etiopathogenesis and treatment of diseases. Nevertheless, it is inefficient to learn new associations only through biotechnology.

Results
Consequently, we present a computational method, GMNN2CD, which employs a graph Markov neural network (GMNN) algorithm to predict unknown circRNA–disease associations. First, used verified associations, we calculate semantic similarity and Gaussian interactive profile kernel similarity (GIPs) of the disease and the GIPs of circRNA and then merge them to form a unified descriptor. After that, GMNN2CD uses a fusion feature variational map autoencoder to learn deep features and uses a label propagation map autoencoder to propagate tags based on known associations. Based on variational inference, GMNN alternate training enhances the ability of GMNN2CD to obtain high-efficiency high-dimensional features from low-dimensional representations. Finally, 5-fold cross-validation of five benchmark datasets shows that GMNN2CD is superior to the state-of-the-art methods. Furthermore, case studies have shown that GMNN2CD can detect potential associations.

Availability and implementation
The source code and data are available at https://github.com/nmt315320/GMNN2CD.git.
MetaMutationalSigs: comparison of mutational signature refitting results made easy
Authors: Pandey Palash , Arora Sanjeevani , Rosen Gail L 
Published: February 2022

Abstract

Motivation
The analysis of mutational signatures is becoming increasingly common in cancer genetics, with emerging implications in cancer evolution, classification, treatment decision and prognosis. Recently, several packages have been developed for mutational signature analysis, with each using different methodology and yielding significantly different results. Because of the non-trivial differences in tools’ refitting results, researchers may desire to survey and compare the available tools, in order to objectively evaluate the results for their specific research question, such as which mutational signatures are prevalent in different cancer types.

Results
Due to the need for effective comparison of refitting mutational signatures, we introduce a user-friendly software that can aggregate and visually present results from different refitting packages.

Availability and implementation
MetaMutationalSigs is implemented using R and python and is available for installation using Docker and available at: https://github.com/EESI/MetaMutationalSigs.
Structural connectivity associated with the sense of body ownership: a diffusion tensor imaging and disconnection study in patients with bodily awareness disorder
Authors: Errante Antonino , Rossi Sebastiano Alice , Ziccarelli Settimio , Bruno Valentina , Rozzi Stefano , Pia Lorenzo , Fogassi Leonardo , Garbarini Francesca 
Published: February 2022

Abstract
The brain mechanisms underlying the emergence of a normal sense of body ownership can be investigated starting from pathological conditions in which body awareness is selectively impaired. Here, we focused on pathological embodiment, a body ownership disturbance observed in brain-damaged patients who misidentify other people’s limbs as their own. We investigated whether such body ownership disturbance can be classified as a disconnection syndrome, using three different approaches based on diffusion tensor imaging: (i) reconstruction of disconnectome maps in a large sample (N = 70) of stroke patients with and without pathological embodiment; (ii) probabilistic tractography, performed on the age-matched healthy controls (N = 16), to trace cortical connections potentially interrupted in patients with pathological embodiment and spared in patients without this pathological condition; (iii) probabilistic ‘in vivo’ tractography on two patients without and one patient with pathological embodiment. The converging results revealed the arcuate fasciculus and the third branch of the superior longitudinal fasciculus as mainly involved fibre tracts in patients showing pathological embodiment, suggesting that this condition could be related to the disconnection between frontal, parietal and temporal areas. This evidence raises the possibility of a ventral self-body recognition route including regions where visual (computed in occipito-temporal areas) and sensorimotor (stored in premotor and parietal areas) body representations are integrated, giving rise to a normal sense of body ownership.
A prospective validation of a deep learning-based automated workflow for the interpretation of the echocardiogram
Authors: Tromp J , Bauer DN , Claggett BL , Frost M , Iversen MB , Prasad N , Petrie M , Larson MG , Ezekowitz JA , Solomon SD 
Published: February 2022

Abstract

Funding Acknowledgements
Type of funding sources: Private company. Main funding source(s): Us2.ai
Background. Deep learning can automate the interpretation of medical imaging tests. This study aimed to prospectively assess the interchangeability of deep learning algorithms with expert human measurements for interpreting echocardiographic studies, the primary method for assessing cardiac structure and function.
Methods. We compared a deep learning interpretation of 23 echocardiographic parameters—including cardiac volumes, ejection fraction, and Doppler measurements—with three repeated measurements by core lab human experts in a prospective study for submission to the United States Food and Drug Administration (FDA). The primary outcome metric was the individual equivalence coefficient (IEC), which compares the disagreement between deep learning and human readers relative to the disagreement among human readers. The pre-determined non-inferiority criterion was 0.25 for the upper bound of the 95% confidence interval (CI). Secondary outcomes included measures of agreement, including the mean absolute deviation.
Results. We included 602 studies from 600 participants (421 with heart failure, 179 controls, 69% women) with a mean age of 57 ± 16 years. The point estimates of IEC were all &lt;0, indicating that the disagreement between the deep learning and human measures were lower than the disagreement among three core lab readers, and the upper bound of the 95% CI of IECs fell below the prespecified success criterion of 0.25. Secondary endpoints showed good agreement of automated with human expert measurements (Figure), with comparable or lower mean absolute deviations between automated and human experts relative to the mean absolute deviation among human experts.
Conclusion. This prospective validation study demonstrated excellent agreement between deep learning and expert human interpretation for a wide range of echocardiographic measurements. These results highlight the potential of deep learning algorithms to improve efficiency and reduce costs of echocardiography. Abstract Figure.
Three-dimensional transesophageal echocardiographic assessment of valvar and ventricular aspects in simple and complex mitral valve prolapse
Authors: Capotosto L , Tanzilli G , Iannucci G , Viceconte N , Placanica G , Mancone M , Mangieri E , Miraldi F , Vitarelli A 
Published: February 2022

Abstract

Funding Acknowledgements
Type of funding sources: None.
Purpose. In mitral valve prolapse (MVP), preoperative assessment of repairability depends primarily on the complexity of the prolapse and is one of the most challenging roles of the echocardiologist. The aim of this study was to investigate whether three-dimensional transesophageal echocardiography helps in the evaluation of both valvar/annular and ventricular aspects in simple (single segment) and complex (multisegmental) MVP.
Methods. We studied 24 patients with mitral regurgitation due to MVP using three-dimensional transesophageal echocardiography. Mitral valve (MV), left atrium (LA) and left ventricle (LV) were analyzed. Leaflets surface area, mitral annulus antero-posterior and inter-commissural diameters, area and circumference were determined. Mitral annular disjunction (MAD) defined as a separation between LA-MV junction and LV attachment was evaluated. Papillary muscle to annulus plane distances and inter-papillary distance were obtained. LV global longitudinal and circumferential strain was assessed by speckle tracking echocardiography.
Results. Thirteen patients had complex MVP and 11 had simple MVP. Prolapse volume was 6.37 ± 4.68 mL in complex MVP and 1.59 ± 1.21 mL in simple MVP (p = 0.004). Prolapse height was 10.1 ± 2.9 mm in complex MVP and 5.8 ± 3.2 mm in simple MVP (p = 0.003). Volume/height ratio was 0.58 ± 0.29 in complex MVP and 0.21 ± 0.14 in simple MVP (p = 0.001). LV longitudinal strain had a negative correlation with change in prolapse height (r = 0.58, p &lt; 0.001), whereas LV circumferential strain correlated with change in prolapse volume (r = 0.53, p &lt; 0.005). Annular area was 1037 ± 191mm2 in simple MVP and 1842 ± 336mm2 in complex MVP (p &lt; 0.01). MAD was evident in 8 complex MVP patients (61%) and 2 simple MVP patients (18%, p &lt; 0.05). The 3D extension of MAD was significantly related to total (r = 0.54; p = 0.002) and anterior leaflet surface area (r = 0.52; p = 0.004), length from papillary muscles to coaptation (r = 0.61; p = 0.002) and regurgitant orifice (r = 0.66; p &lt; 0.001).
Conclusions. In MVP patients 3DTEE helps to refine the characterization of simple and complex prolapse by a combined assessment of leaflets/annular details and ventricular attachment.
Right ventricular strain in in COVID-19 patients: a single-center experience
Authors: Loquias M , Valencia O , Calimbas J , Pelat J 
Published: February 2022

Abstract

Funding Acknowledgements
Type of funding sources: None.

BACKGROUND
Assessment of right ventricular systolic function using strain imaging analysis from two-dimensional echocardiography has been identified to have powerful predictive value. Utilization of right ventricular strain may provide additional information in the management of COVID-19 patients.

OBJECTIVE
To determine and analyze the right ventricular systolic function using longitudinal strain imaging among COVID-19 patients.

METHOD
This is a prospective cohort study of the right ventricular function using speckle tracking echocardiography among COVID-19 patients. The study included two dimensional (2D) echocardiographic studies among 137 adult patients with laboratory-confirmed COVID-19 from September to November 2020. Analysis of Variance (ANOVA) was used to compare more than two groups with numerical data. Pearson Correlation was utilized to determine correlation between numerical variables.

RESULTS
The results showed a total of 35 patients (25.54%) to have abnormal right ventricular free wall strain. The results showed that there was a significant direct correlation between right ventricular free wall strain and the echocardiographic parameters of tricuspid annular plane systolic excursion (TAPSE) (r = 0.277; p = 0.001), S’ (r = 0.166; p = 0.050), right ventricular fractional area change (r = 0.298; p &lt; 0.0001) and left ventricular ejection fraction (LVEF) (r = 0.176; p = 0.040). There was a significant inverse correlation noted between right ventricular free wall strain and the echocardiographic parameters of the tricuspid regurgitation (r=-0.284; p = 0.001), pulmonary arterial systolic pressure (r=-0.209; p = 0.014) and left atrial size (r=-0.209; p = 0.014). There was a significant difference in the right ventricular free wall strain according to the severity of COVID-19 infection (p = 0.032). Moreover, a significant difference was also noted between right ventricular free wall strain and mortality (p = 0.0001). The mean right ventricular free wall strain of patients who died was significantly lower than those who were discharged with a mean of 18.92% and 23.59% respectively.

CONCLUSION
Right ventricular free wall strain using speckle tracking echocardiography, can be used for risk stratification for patients with COVID-19. It also showed that it is has significant correlation with the severity of the disease and mortality. These findings together with other conventional echocardiographic parameters, may provide clinicians additional information in the management of these patients.
Usefulness of myocardial work to predict long-term hard cardiovascular outcomes after ST-segment elevation acute myocardial infarction
Authors: Timoteo A , Moura Branco L , Galrinho A , Branco Mano T , Rio P , Viegas J , Cruz Ferreira R 
Published: February 2022

Abstract

Funding Acknowledgements
Type of funding sources: None.

Background
Left ventricular (LV) global longitudinal strain has demonstrated incremental prognostic value over LV ejection fraction (LVEF) in patients with ST-segment-elevation acute myocardial infarction. However, LV global longitudinal strain (GLS) does not take into consideration the effect of afterload. Myocardial work (MW) by speckle-tracking echocardiography integrates blood pressure measurements (afterload) with LV GLS and it has been recently demonstrated that Global Work Efficiency (GWE) predicts long-term all-cause mortality. It remains to be demonstrated if MW indices are associated with hard cardiovascular endpoints. The present study aimed to investigate the prognostic value of LV MW obtained from pressure-strain loops with echocardiography in patients with ST-segment-elevation myocardial infarction.

Methods
A total of 132 consecutive patients admitted with an ST-segment-elevation acute myocardial infarction (mean age 62 ± 12 years; 76% men) that survived to discharge were retrospectively analysed. LVEF, GLS and all LVMW indices were measured by transthoracic echocardiography before discharge (4.9 ± 2.4 days after admission). All patients had at least a two-year follow-up (mean follow-up of 820 ± 155 days). Outcomes: all-cause mortality, major acute cardiovascular events (a composite of cardiovascular mortality, myocardial infarction, stroke, unplanned cardiovascular admission) and heart failure hospitalization.

Results
In the two-year follow-up, 6 patients died, there were 18 patients with MACE, and 3 patients were hospitalized with heart failure. We confirmed that for all-cause mortality, GWE and GWI showed better discrimination compared to GLS (Table), with a cut-off of 83% and 1066 mmHg% (log-rank &lt; 0,001) respectively. However, ROC curve comparisons were not significantly different. For MACE, the performance of all methods is suboptimal, with an AUC &lt; 0.65 for all variables, except for GLS. For heart failure admission, performance is slightly better, but GLS is still the best parameter to predict this event.

Conclusions
LVGWE and GWI are better predictors of all-cause mortality compared to GLS, but MW indices failed to demonstrate a prognostic impact in long-term cardiovascular events. Prospective studies are warranted to confirm this finding. Abstract Figure.
Early systolic lengthening in patients with ST-elevation myocardial infarction: a novel tool for risk stratification
Authors: Ferreira C , Freitas A , Martinho S , Goncalves V , Almeida J , Campos G , Rosa J , Guimaraes J , Baptista R , Castro G , Goncalves L 
Published: February 2022

Abstract

Funding Acknowledgements
Type of funding sources: None.

Background
After an ST-elevation myocardial infarction (STEMI), the risk of subsequent cardiovascular events is high. Risk stratification at index hospitalization remains a core challenge, especially in patients with subtle changes in LV function. Early systolic lengthening (ESL) may occur in ischemic myocardial segments with reduced contractile force and it has been correlated with infarct size and prognosis in STEMI patients with mildly impaired LV function. In this study, we aimed to evaluate the correlation of ESL with common echocardiographic parameters and its prognostic value in STEMI patients with preserved LV function.

Methods
We retrospectively included all the patients admitted to an intensive care unit with STEMI and a left ventricle ejection fraction ≥55% from January to June of 2016. Patients with inadequate image quality for speckle tracking echocardiographic examination were excluded (n = 14). We evaluated the ESL index, defined as follows: [−100×(peak positive systolic strain/peak negative strain in cardiac cycle)], and ESL duration.

Results
A total of 37 patients were included in the study. Mean age was 63 ± 12 years with a male preponderance (81%). All patients were submitted to complete revascularization. Median values of the ESL index and ESL duration were 7% (IQR, 4%–10%) and 37 msec (IQR, 21–55 msec), respectively. No significant differences were found between ESL index and ESL duration groups, except for a higher prevalence of heart failure at hospitalization in both highest groups, and women were more prevalent in the ESL index higher group (Tables 1 and 2). ESL index was correlated with post-systolic index (PSI) (r2 = 0.34, p = 0.04) and showed a weak correlation with E/A ratio (r2=-0.37, p = 0.02). ESL duration was correlated with ESL index (r2 = 0.76, p &lt; 0.001) and PSI (r2 = 0.43, p = 0.008). During a median follow-up of 3.2 years (interquartile range, 2.9–3.4 years), 7 (18.9%) patients experienced major adverse cardiovascular events (MACE), a composite of heart failure admission, myocardial infarction, and all-cause mortality Both ESL index (HR 2.5; 95%CI 1.2–5.3; P = 0.02) and ESL duration (HR 1.7; 95%CI 1.1–2.7; P = 0.02) were independent predictors of MACE. Both associations remained significant after adjusting for clinical confounders.

Conclusions
In our cohort of STEMI patients with preserved LV function, assessment of ESL yielded important and significant prognostic information on MACE. ESL may be a useful tool to enhance routine risk stratification in this population. Abstract TABLE 1 Abstract TABLE 2