Miracle-dz/UniFAR-FTUs huggingface dataset:
The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.
Isolation of Murine Brain and Lung Microvascular Endothelial Cells

[Abstract] This protocol describes how to isolate murine endothelial cells from newborn mice brain and 3-month-old mice lung by modifying the original protocols (Sobczak et al. , 2010; Ruck et al. , 2014). We have used the protocol to analyze mRNA expression level in brain endothelial cells (Sawaguchi et al. , 2017). Isolated lung endothelial cells were expanded in vitro for various downstream experiments such as gene expression analysis and cell-based signaling assay.
Gelfoam Embolization Technique to Prevent Bone Cement Leakage during Percutaneous Vertebroplasty: Comparative Study of Gelfoam only vs. Gelfoam with Venography

Objective Percutaneous vertebroplasty (VP) has been used for the safe treatment of osteoporotic compression fracture. However, cement leakage is the most common complication. To reduce the leakage of bone cement, we did the gelfoam embolization during VP. The purpose of this study is to compare the safety and feasibility of different two gelfoam embolization technique during VP. Methods Total 127 patients (146 level) who had the thoracolumbar osteoporotic compression fracture were enrolled. Group A was treated by gelfoam-only technique and, Group B was treated by gelfoam with venography technique. We compared the incidence of bone cement leakage between two groups using post-operative computed tomography scan and X-ray. Results Seventy-four patients (81 levels) were treated with gelfoam-only technique (A), and 53 patients (65 levels) were treated with gelfoam with venography technique (B). There were 22 leakages on group A, and 19 leakages on group B. There was no statistical significant difference between two groups (Chi-square test, p-value =0.958). Incidence of leakage to spinal canal was 11 levels in Group A, 3 levels in group B, and there was statistical significant difference (Fisher's exact test, p-value=0.027). Conclusion Complication induced by the bone cement leakage are the most careful point during VP. Gelfoam embolization with venography is very easy and safe method. Gelfoam with venography technique could make lower the incidence of cement leakage to spinal canal.
Verifpal: Cryptographic Protocol Analysis for the Real World

Verifpal is a new automated modeling framework and verifier for cryptographic protocols, optimized with heuristics for common-case protocol specifications, that aims to work better for real-world practitioners, students and engineers without sacrificing comprehensive formal verification features. In order to achieve this, Verifpal introduces a new, intuitive language for modeling protocols that is easier to write and understand than the languages employed by existing tools. Its formal verification paradigm is also designed explicitly to provide protocol modeling that avoids user error. Verifpal is able to model protocols under an active attacker with unbounded sessions and fresh values, and supports queries for advanced security properties such as forward secrecy or key compromise impersonation. Furthermore, Verifpal's semantics have been formalized within the Coq theorem prover, and Verifpal models can be automatically translated into Coq as well as into ProVerif models for further verification. Verifpal has already been used to verify security properties for Signal, Scuttlebutt, TLS 1.3 as well as the first formal model for the DP-3T pandemic-tracing protocol, which we present in this work. Through Verifpal, we show that advanced verification with formalized semantics and sound logic can exist without any expense towards the convenience of real-world practitioners.
High performance ultrasound simulation using Monte-Carlo simulation: a GPU ray-tracing implementation

Medical ultrasound has gained relevance in medical diagnosis, procedure guidance and emergentology. It is a low cost, safe and non-invasive practice, while obtaining real-time results. Limitations on working hours and ultrasound equipment have made simulation the main alternative for training, since it eliminates the need for a real ultrasound machine and allows the modeling of a large number of case studies. Patient 11 from the 3D-IRCADb-01 CT-scans data-set was used to generate corresponding OBJ mesh files. A JSON file carries relevant information of the scene and tissues involved in it. Monte-Carlo simulation over GPU is used to improve ray tracing performance on a deterministic surface model. An improvement in ray tracing performance and throughput is achieved by the presented model. In execution cases presented, a minimum global acceleration of 30% is reached, while reducing ray tracing times by at least 80%. Monte-Carlo simulation allows a more realistic image generation than the previous deterministic model. Low memory usage gives room to implement the remaining stages of the process on GPU. A correct parametrization of tissues is required in order to achieve improved image quality.
Patch-based surface morphometry feature selection with federated group lasso regression

Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer’s disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.
SIERA: The Seismic Information Extended Reality Analytics Tool

Three-dimensional seismic data is notoriously challenging to visualize effectively and analyze efficiently due to its huge volumetric format and highly complex nature. This process becomes even more complicated when attempting to analyze machine learning results produced from seismic datasets and see how such results relate back to the original data. SIERA presents a solution to this problem through visualizing seismic data in an extended reality environment as highly customizable 3D data visualizations, and via physical movement, allow one to easily navigate these large, 3D information spaces effectively. This allows for a more immersive and intuitive way to interact with seismic data and machine learning results, providing an improved experience over conventional analytics tools.
Whole-Genome Assemblies for Three Yersinia pestis Strains Isolated in Erenhot, China

To explore the genetic diversity of Yersinia pestis in Erenhot, China, and their relationship with Mongolia strains, we collected and sequenced three Y. pestis strains from Erenhot, China, in 2018. Here, we report the draft genome sequences of three Y. pestis bv. Medievalis strains belonging to the 2.MED phylogroup that were circulating in Meriones unguiculatus populations. ABSTRACT To explore the genetic diversity of Yersinia pestis strains in Erenhot, China, and their relationship with Mongolian strains, we collected and sequenced three Y. pestis strains from Erenhot, China, in 2018. Here, we report the draft genome sequences of three Y. pestis bv. Medievalis strains belonging to the 2.MED phylogroup that were circulating in Meriones unguiculatus populations.
Analysis and classification of lung and muscular tissues in ultrasound images using 2D wavelet transform and machine learning

Ultrasound has been considered a safe and accurate alternative to radiography and computerized tomography to diagnose lung diseases such as pneumonia. However, speckle noise, artifacts or certain conditions can difficult image interpretation. For example, in some cases, the pleura line cannot be observed. This work proposes an approach for discriminating between lung and muscular tissues in ultrasound images. We evaluated the symlet and daubechies wavelets for feature extraction, principal component analysis and recursive backward elimination for feature selection, and supervised learning methods for classification. Statistical moments and the energy of the second horizontal coefficient and peak-to-peak root mean squared ratio were the features more outstanding over the rest. The best model was obtained with recursive backward elimination for feature selection and knearest neighbor for classification. Tissue classification was possible with a mean accuracy of 97.5% and area under the curve of 99%. These results offer great insights on the recognition of lung and muscular tissues, which could improve the effectiveness of automatic segmentation and analysis algorithms.
Deep learning for coronary artery segmentation in x-ray angiograms using a patch-based training

This paper presents a new method for coronary artery segmentation in X-ray angiograms based on deep learning and a patch-based training. The blood vessel segmentation is performed using the U-Net convolutional neural network, which has been trained using patches extracted from the original angiograms instead of using complete images. The publicly available database of coronary angiograms DCA1 containing 130 angiograms with their respective ground-truth has been used to generate the training patterns and subsequently to evaluate and compare the segmentation performance of the proposed method. The hyper-parameter configuration used for training the U-Net parameters has been selected from 90 possible combinations according to five binary classification metrics. Each combination involving the selection of a patch size, weight assigned to the blood vessel class, and learning rate used by the optimization method, has been used in order to train the U-Net parameters with patterns extracted from a set of 100 images. The segmentation performance of the proposed method is compared with five specialized blood vessel segmentation methods from the state of the art using a test set of 30 images, achieving the highest accuracy (0.977) and Dice similarity coefficient (0.779). Moreover, the experimental results have also shown that the proposed method is suitable to be integrated into a computer-aided system to support decision making in medical practice.
Quantitative Hormone Signaling Output Analyses of Arabidopsis thaliana Interactions With Virulent and Avirulent Hyaloperonospora arabidopsidis Isolates at Single-Cell Resolution

The phytohormones salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) are central regulators of biotic and abiotic stress responses in Arabidopsis thaliana. Here, we generated modular fluorescent protein-based reporter lines termed COLORFUL-PR1pro, -VSP2pro, and -PDF1.2apro. These feature hormone-controlled nucleus-targeted transcriptional output sensors and the simultaneous constitutive expression of spectrally separated nuclear reference and plasma membrane-localized reporters. This set-up allowed the study of cell-type specific hormone activities, cellular viability and microbial invasion. Moreover, we developed a software-supported high-throughput confocal microscopy imaging protocol for output quantification to resolve the spatio-temporal dynamics of respective hormonal signaling activities at single-cell resolution. Proof-of-principle analyses in A. thaliana leaves revealed distinguished hormone sensitivities in mesophyll, epidermal pavement and stomatal guard cells, suggesting cell type-specific regulatory protein activities. In plant-microbe interaction studies, we found that virulent and avirulent Hyaloperonospora arabidopsidis (Hpa) isolates exhibit different invasion dynamics and induce spatio-temporally distinct hormonal activity signatures. On the cellular level, these hormone-controlled reporter signatures demarcate the nascent sites of Hpa entry and progression, and highlight initiation, transduction and local containment of immune signals.
A Multi-Sectoral Approach Improves Early Child Development in a Disadvantaged Community in Peru: Role of Community Gardens, Nutrition Workshops and Enhanced Caregiver-Child Interaction: Project “Wawa Illari”

Background: Multi-dimensional monitoring evaluation and learning strategies are needed to address the complex set of factors that affect early child development in marginalized populations, but few studies have explored their effectiveness. Objective: To compare improvement of health and development of children 0–3 years between intervention communities (IC) and control communities (CC) from peripheral settlements of Lima. Sequential interventions included: (1) home and community gardens, (2) conscious nutrition, and (3) parenting workshops following the International Child Development Program (ICDP). Methods: Interventions were delivered by community health promoters (CHPs) using a “step-by-step” learning system. Both IC and CC were monitored before the interventions began, at 8 and 12 months (n = 113 IC and 127 CC children). Data were collected on household characteristics, diet, food security, health indicators (history of diarrhea and respiratory infections, hemoglobin, intestinal parasites, anthropometry), caregiver-child interactions and stress, and achievement of Pan-American Health Organization age-specific developmental milestones. Stepwise multiple logistic regressions were used to determine if the interventions affected food insecurity, as well as motor, social/cognitive and language delays. Results: At baseline, 2.6% were categorized as “suspected developmental delay” and 14.2% were on “alert for development delay. ” Food insecurity, diarrhea and respiratory infections were lowered following the interventions. Through the “step-by-step” approach, caregivers in IC gained skills in gardening, conscious nutrition and parenting that reduced the risk of food insecurity [Adjusted Risk Ratio = 0.20 (95% CI: 0.08–0.51)] and language delay [0.39 (0.19–0.82)] but not motor or social/cognitive delay. Use of a multiple micronutrient supplement decreased the risk of motor delay [0.12 (0.03–0.56)], but more pets were associated with higher risk of motor [3.24 (1.47–7.14)] and social/cognitive delay [2.72 (1.33–5.55)], and of food insecurity [1.73 (1.13–2.66)]. Conclusion: The combined interventions delivered by CHPs helped to mitigate the impact of adversity on food insecurity and language delay. Additional improvements may have been detected if the interventions had continued for a longer time. Our results indicate that control of infections and pets may be needed to achieve measurable results for motor and social/cognitive development. Continuous monitoring facilitated adjusting implementation strategies and achieving positive developmental outcomes.
Performance Improvement for Detecting Brain Function Using fNIRS: A Multi-Distance Probe Configuration With PPL Method

To improve the spatial resolution of imaging and get more effective brain function information, a multi-distance probe configuration with three distances (28.2, 40, and 44.7 mm) and 52 channels is designed. At the same time, a data conversion method of modified Beer–Lambert law (MBLL) with partial pathlength (PPL) is proposed. In the experiment, three kinds of tasks, grip of left hand, grip of right hand, and rest, are performed with eight healthy subjects. First, with a typical single-distance probe configuration (30 mm, 24 channels), the feasibility of the proposed MBLL with PPL is preliminarily validated. Further, the characteristic of the proposed method is evaluated with the multi-distance probe configuration. Compared with MBLL with differential pathlength factor (DPF), the proposed MBLL with PPL is able to acquire more obvious concentration change and can achieve higher classification accuracy of the three tasks. Then, with the proposed method, the performance of the multi-distance probe configuration is discussed. Results show that, compared with a single distance, the combination of the three distances has better spatial resolution and could explore more accurate brain activation information. Besides, the classification accuracy of the three tasks obtained with the combination of three distances is higher than that of any combination of two distances. Also, with the combination of the three distances, the two-class classification between different tasks is carried out. Both theory and experimental results demonstrate that, using multi-distance probe configuration and the MBLL with PPL method, the performance of brain function detected by NIRS can be improved.
Human Expressions of Object Preference Affect Dogs’ Perceptual Focus, but Not Their Action Choices

Inspired by work on infants, we investigated whether dogs’ behaviors are guided by human displays of preference, contrasting with the animals’ own choices. In a rewarded fetching task, dogs override their own interest toward “disgusting” objects and retrieve what the owner prefers. However, in previous research, both objects were inherently neutral to the dogs and they might have chosen the owner’s object because a “happy owner” predicts a positive outcome. If dogs are indeed able to override their own interests, we expected them to fetch the owner’s object even if (1) they would prefer another one and (2) do not receive a reward for it. Two objects were compared, a toy (hoop) and a bracelet. After establishing that the toy was preferred by all dogs in an initial test of preference, we applied a two-choice procedure to test if either fetching or looking at the objects from a distance would be affected by the owner’s choice. In Study 1, the owner demonstrated happiness toward the bracelet and disgust toward the toy with both facial and body gestures accompanied by verbalizations. Then the owner asked the dog to fetch, without providing additional guiding cues. All dogs fetched the toy, indicating that their own choice was not overcome by the positive emotional state signaled by the owner. To avoid direct contact with the objects, in Study 2 we placed the objects on an unreachable spot after the emotion demonstration and measured the duration of looking at the objects. In the “bracelet” (non-matching) group the owners demonstrated happiness toward the bracelet and disgust toward the toy, similar to Study 1. In the “toy” (matching) group the owners showed happiness toward the toy and disgust toward the bracelet. When the objects were placed on the unreachable spot, dogs looked at both objects for the same amount of time in the non-matching group, but longer at the toy in the matching group. Although the studies did not demonstrate that dogs override their own preferences for an object, the results suggested that the owners’ expressed preference was perceived by the dogs and guided their perceptual focus.
A Custom Ultra-Low-Cost 3D Bioprinter Supports Cell Growth and Differentiation

Advances in 3D bioprinting have allowed the use of stem cells along with biomaterials and growth factors toward novel tissue engineering approaches. However, the cost of these systems along with their consumables is currently extremely high, limiting their applicability. To address this, we converted a 3D printer into an open source 3D bioprinter and produced a customized bioink based on accessible alginate/gelatin precursors, leading to a cost-effective solution. The bioprinter’s resolution, including line width, spreading ratio and extrusion uniformity measurements, along with the rheological properties of the bioinks were analyzed, revealing high bioprinting accuracy within the printability window. Following the bioprinting process, cell survival and proliferation were validated on HeLa Kyoto and HEK293T cell lines. In addition, we isolated and 3D bioprinted postnatal neural stem cell progenitors derived from the mouse subventricular zone as well as mesenchymal stem cells derived from mouse bone marrow. Our results suggest that our low-cost 3D bioprinter can support cell proliferation and differentiation of two different types of primary stem cell populations, indicating that it can be used as a reliable tool for developing efficient research models for stem cell research and tissue engineering.