Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Caliber Compliant Vascular Grafts Based on Elastin-Like Recombinamers for in situ Tissue Engineering

Vascular disease is a leading cause of death worldwide, but surgical options are restricted by the limited availability of autologous vessels, and the suboptimal performance of prosthetic vascular grafts. This is especially evident for coronary artery by-pass grafts, whose small caliber is associated with a high occlusion propensity. Despite the potential of tissue-engineered grafts, compliance mismatch, dilatation, thrombus formation, and the lack of functional elastin are still major limitations leading to graft failure. This calls for advanced materials and fabrication schemes to achieve improved control on the grafts' properties and performance. Here, bioinspired materials and technical textile components are combined to create biohybrid cell-free implants for endogenous tissue regeneration. Clickable elastin-like recombinamers are processed to form an open macroporous 3D architecture to favor cell ingrowth, while being endowed with the non-thrombogenicity and the elastic behavior of the native elastin. The textile components (i.e., warp-knitted and electrospun meshes) are designed to confer suture retention, long-term structural stability, burst strength, and compliance. Notably, by controlling the electrospun layer's thickness, the compliance can be modulated over a wide range of values encompassing those of native vessels. The grafts support cell ingrowth, extracellular matrix deposition and endothelium development in vitro. Overall, the fabrication strategy results in promising off-the-shelf hemocompatible vascular implants for in situ tissue engineering by addressing the known limitations of bioartificial vessel substitutes.
High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of “linking genotype and phenotype,” considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders.
Familial Mediterranean Fever in Chinese Children: A Case Series

Background: Familial Mediterranean fever (FMF) is an inherited auto-inflammatory disorder and is extremely rare in Chinese. This study aimed to investigate the demographic, clinical, and genetic features of FMF in a series of Chinese pediatric patients. Methods: This was a retrospective case series of children with recurrent febrile or inflammatory episodes and referred to the Peking Union Medical College Hospital between 06/2013 and 06/2018. All suspected patients were genetically diagnosed and met the Tel-Hashomer criteria for FMF. Demographic, clinical, genetic, and treatment characteristics were collected. Descriptive statistics were used. Results: Eleven patients were included (seven boys and four girls). The median age at the time of disease onset was 7.1 (range, 3–12) years, while the median age at diagnosis was 10.9 (range, 6–15) years. The median delay in diagnosis was 2.1 years (range, 6 months to 6.7 years). Fever (100%, 11/11) was the most common symptom, followed by joint pain (63.6%, 7/11), rash (54.5%, 6/11), abdominal pain (36.4%, 4/11), and oral ulcers (18.2%, 2/11), without evidence of amyloidosis. C-reactive protein (81.8%, 9/11) and erythrocyte sedimentation (90.9%, 10/11) were increased during attacks. All patients harbored one to five different MEFV mutations, with E148Q and L110P being the most frequent. A novel non-synonymous mutation F636Y in exon 10 was discovered. Favorable responses to colchicine was observed in all six treated patients. Conclusion: The most common variants in our study were E148Q and L110P. F636Y may found for the first time. Colchicine led to favorable responses in all treated patients.
AR-ia: Volumetric Opera for Mobile Augmented Reality

Motivated by recent availability of augmented and virtual reality platforms, we tackle the challenging problem of immersive storytelling experiences on mobile devices. In particular, we show an end-to-end system to generate 3D assets that enable real-time rendering of an opera on high end mobile phones. We call our system AR-ia and in this paper we walk through the main components and technical challenges of such a system, showing how to deliver an immersive mixed reality experience in every user’s living room.
Dependence measure for length-biased survival data using copulas

Abstract The linear correlation coefficient of Bravais-Pearson is considered a powerful indicator when the dependency relationship is linear and the error variate is normally distributed. Unfortunately in finance and in survival analysis the dependency relationship may not be linear. In such case, the use of rank-based measures of dependence, like Kendall’s tau or Spearman rho are recommended. In this direction, under length-biased sampling, measures of the degree of dependence between the survival time and the covariates appear to have not received much intention in the literature. Our goal in this paper, is to provide an alternative indicator of dependence measure, based on the concept of information gain, using the parametric copulas. In particular, the extension of the Kent’s [18] dependence measure to length-biased survival data is proposed. The performance of the proposed method is demonstrated through simulations studies.
Visual analytics of urban transportation from a bike-sharing and taxi perspective

The urban transportation system is the footstone of a city’s infrastructure, and the booming bike-sharing system has become a vital part of urban transportation. Understanding the bike-sharing system and traditional taxi system as well as their similarities and differences are essential for bike-sharing rebalancing, taxi dispatching, and urban planning. However, due to the sparseness of record data and the difference in service regions, the relationship between them is indeed obscure, and previous solutions mostly focus only on a single system. In this paper, we propose a visual analytics system to investigate the similarities and differences between bike-sharing and taxi systems. The service region for each bike station is created to fuse bike-sharing data and taxi data. We harness two three-order tensors to represent them in a unified framework to generate potential patterns by tensor decomposition. The visual analytics system integrates two spatiotemporal data sources by analyzing the patterns that are typical of each data source and the patterns that are common to both data sources to assist users in better discovering the relationships between the taxi system and the bike-sharing system. We demonstrate the effectiveness of our system through real-world case studies. The urban transportation system is the footstone of a city’s infrastructure, and the booming bike-sharing system has become a vital part of urban transportation. Understanding the bike-sharing system and traditional taxi system as well as their similarities and differences are essential for bike-sharing rebalancing, taxi dispatching, and urban planning. However, due to the sparseness of record data and the difference in service regions, the relationship between them is indeed obscure, and previous solutions mostly focus only on a single system. In this paper, we propose a visual analytics system to investigate the similarities and differences between bike-sharing and taxi systems. The service region for each bike station is created to fuse bike-sharing data and taxi data. We harness two three-order tensors to represent them in a unified framework to generate potential patterns by tensor decomposition. The visual analytics system integrates two spatiotemporal data sources by analyzing the patterns that are typical of each data source and the patterns that are common to both data sources to assist users in better discovering the relationships between the taxi system and the bike-sharing system. We demonstrate the effectiveness of our system through real-world case studies.
Understanding Financial Transaction Documents using Natural Language Processing

In this paper, we share our experiences creating NLP based AI platform for finance - Appzen (http://www.appzen.com). AppZen's auditing technology is being utilized by over 500 enterprise customers including multiple Fortune 500 companies for auditing employee expenses. AppZen's technology can process, analyze and identify relationships between various kinds of transaction documents such as - receipts, invoices, contracts and purchase orders. Each type of transaction document requires custom processing and analysis due to the diversity in language and structure of the document. Contracts typically require deep understanding of the content such as identifying sentence structures, identifying entities and relationships between them compared to receipts and invoices, which are somewhat semi-structured and require a different kind of processing. We elaborate on the challenges we have experienced and use of NLP in conjunction with a lightweight semantic layer to alleviate these challenges.
Scalable Cross-lingual Document Similarity through Language-specific Concept Hierarchies

With the ongoing growth in number of digital articles in a wider set of languages and the expanding use of different languages, we need annotation methods that enable browsing multi-lingual corpora. Multilingual probabilistic topic models have recently emerged as a group of semi-supervised machine learning models that can be used to perform thematic explorations on collections of texts in multiple languages. However, these approaches require theme-aligned training data to create a language-independent space. This constraint limits the amount of scenarios that this technique can offer solutions to train and makes it difficult to scale up to situations where a huge collection of multi-lingual documents are required during the training phase. This paper presents an unsupervised document similarity algorithm that does not require parallel or comparable corpora, or any other type of translation resource. The algorithm annotates topics automatically created from documents in a single language with cross-lingual labels and describes documents by hierarchies of multi-lingual concepts from independently-trained models. Experiments performed on the English, Spanish and French editions of JCR-Acquis corpora reveal promising results on classifying and sorting documents by similar content.
Contextual Entity Disambiguation in Domains with Weak Identity Criteria: Disambiguating Golden Age Amsterdamers

Entity disambiguation is a widely investigated topic, and many matching algorithms have been proposed. However, this task has not yet been satisfactorily addressed when the domain of interest provides poor or incomplete data with little discriminating power. In these cases, the use of content fields such as name and date is not enough and the simple use of relations with other entities is not of much help when these related entities also need disambiguation before they can be used. Therefore, we propose an approach for the disambiguation of clustered resources using context (related entities that are also clustered) as evidence for reconciling matched entities. We test the proposed method on datasets of historical records from Amsterdam in the 17th century for which context is available, and we compare the results of the proposed approach to a gold standard generated by three experts, which we make available online. The results show that the proposed approach manages to meaningfully use context for isolating identity sub-clusters with higher quality by eliminating potentially false positive matches.
Several Link Keys are Better than One, or Extracting Disjunctions of Link Key Candidates

Link keys express conditions under which instances of two classes of different RDF data sets may be considered as equal. As such, they can be used for data interlinking. There exist algorithms to extract link key candidates from RDF data sets and different measures have been defined to evaluate the quality of link key candidates individually. For certain data sets, however, it may be necessary to use more than one link key on a pair of classes to retrieve a more complete set of links. To this end, in this paper, we define disjunction of link keys, propose strategies to extract disjunctions of link key candidates from RDF data, and apply existing quality measures to evaluate them. We also report on experiments with these strategies.
FaceDrive: Facial Expression Driven Operation to Control Virtual Supernumerary Robotic Arms

Supernumerary Robotic Arms (SRAs) can make physical activities easier, but require cooperation with the operator. To improve cooperation, we predict the operator’s intentions by using his/her Facial Expressions (FEs). We propose to map FEs to SRAs commands (e.g. grab, release). To measure FEs, we used a optical sensor-based approach (here inside a HMD), the sensors data are fed to a SVM classifying them in FEs. The SRAs can then carry out commands by predicting the operator’s FEs (and arguably, the operator’s intention). We made a Virtual reality Environment (VE) with SRAs and synchronizable avatar to investigate the most suitable mapping between FEs and SRAs. In SIGGRAPH Asia 2019, the user can manipulate virtual SRAs using his/her FEs.
Exploration of the Transcriptional Landscape of ALPPS Reveals the Pathways of Accelerated Liver Regeneration

Background and Aims: ALPPS (associating liver partition and portal vein ligation for staged hepatectomy), a novel 2-staged hepatectomy, dramatically accelerates liver regeneration and thus enables extensive liver tumor resection. The signaling networks underlying the ALPPS-induced accelerated regeneration process are largely unknown. Methods: We performed transcriptome profiling (TP) of liver tissue obtained from a mouse model of ALPPS, standard hepatectomy (68% model), and additional control surgeries (sham, PVL and Tx). We also performed TP using human liver biopsies (n = 5) taken from the occluded lobe and the future liver remnant (FLR) during the first step of ALPPS surgery (4–5 h apart). We used Oncofinder computational tools, which covers 378 ISPs, for unsupervised, unbiased quantification of ISP activity. Results: Gene expression cluster analysis revealed an ALPPS specific signature: the IGF1R Signaling Pathway (Cell survival), the ILK Pathway (Induced cell proliferation), and the IL-10 Pathway (Stability determination) were significantly enriched, whereas the activity of the Interferon Pathway (Transcription) was reduced (p < 0.05). Further, the PAK- and ILK-associated ISPs were activated at an earlier time point, reflecting significant acceleration of liver regeneration (p < 0.001). These pathways, which were also recovered in human liver biopsies, control cell growth and proliferation, inflammatory response, and hypoxia-related processes. Conclusions: ALPPS is not a straightforward addition of portal vein ligation (PVL) plus transection—it is more. The early stages of normal and accelerated liver regeneration are clearly discernible by a significantly increased and earlier activation of a small number of signaling pathways. Compounds mimicking these responses may help to improve the ALPPS method and further reduce the hospitalization time of the patient.
Transcranial Direct Current Stimulation to Improve the Dysfunction of Descending Pain Modulatory System Related to Opioids in Chronic Non-cancer Pain: An Integrative Review of Neurobiology and Meta-Analysis

Background: Opioid long-term therapy can produce tolerance, opioid-induced hyperalgesia (OIH), and it induces dysfunction in pain descending pain inhibitory system (DPIS). Objectives: This integrative review with meta-analysis aimed: (i) To discuss the potential mechanisms involved in analgesic tolerance and opioid-induced hyperalgesia (OIH). (ii) To examine how the opioid can affect the function of DPIS. (ii) To show evidence about the tDCS as an approach to treat acute and chronic pain. (iii) To discuss the effect of tDCS on DPIS and how it can counter-regulate the OIH. (iv) To draw perspectives for the future about the tDCS effects as an approach to improve the dysfunction in the DPIS in chronic non-cancer pain. Methods: Relevant published randomized clinical trials (RCT) comparing active (irrespective of the stimulation protocol) to sham tDCS for treating chronic non-cancer pain were identified, and risk of bias was assessed. We searched trials in PubMed, EMBASE and Cochrane trials databases. tDCS protocols accepted were application in areas of the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), or occipital area. Results: Fifty-nine studies were fully reviewed, and 24 with moderate to the high-quality methodology were included. tDCS improved chronic pain with a moderate effect size [pooled standardized mean difference; −0.66; 95% confidence interval (CI) −0.91 to −0.41]. On average, active protocols led to 27.26% less pain at the end of treatment compared to sham [95% CI; 15.89–32.90%]. Protocol varied in terms of anodal or cathodal stimulation, areas of stimulation (M1 and DLPFC the most common), number of sessions (from 5 to 20) and current intensity (from 1 to 2 mA). The time of application was 20 min in 92% of protocols. Conclusion: In comparison with sham stimulation, tDCS demonstrated a superior effect in reducing chronic pain conditions. They give perspectives that the top-down neuromodulator effects of tDCS are a promising approach to improve management in refractory chronic not-cancer related pain and to enhance dysfunctional neuronal circuitries involved in the DPIS and other pain dimensions and improve pain control with a therapeutic opioid-free. However, further studies are needed to determine individualized protocols according to a biopsychosocial perspective.
Binary Space Partitioning Visibility Tree for Polygonal Light Rendering

In this paper, we present a method to render shadows for physically-based materials under polygonal light sources. Direct illumination calculation from a polygonal light source involves the triple product integral of the lighting, the bidirectional reflectance distribution function (BRDF), and the visibility function over the polygonal domain, which is computation intensive. To achieve real-time performance, work on polygonal light shading exploits analytic solutions of boundary integrals along the edges of the polygonal light at the cost of lacking shadowing effects. We introduce a hierarchical representation for the pre-computed visibility function to retain the merits of closed-form solutions for boundary integrals. Our method subdivides the polygonal light into a set of polygons visible from a point to be shaded. Experimental results show that our method can render complex shadows with a GGX microfacet BRDF from polygonal light sources at interactive frame rates.
An Approach Toward a Prediction of the Presence of Asbestos in Buildings Based on Incomplete Temporal Descriptions of Marketed Products

Since 1997, the production, import and sale of asbestos\footnoteNaturally occurring mineral fibres which were used due to their insulating properties. have been banned in France. However, there are still millions of tons scattered in factories, buildings, or hospitals. In this paper we propose a method for predicting the presence of asbestos products in buildings based on temporal data that describes the probability of the presence of asbestos in marketed products.
Multiplicity and concentration results for magnetic relativistic Schrödinger equations

Abstract In this paper, we consider the following magnetic pseudo-relativistic Schrödinger equation εi∇−A(x)2+m2u+V(x)u=f(|u|)uinRN, $$\begin{array}{} \displaystyle \sqrt{\left(\frac{\varepsilon}{i}\nabla-A(x)\right)^2+m^2}u+V(x)u= f(|u|)u \quad {\rm in}\,\,\mathbb{R}^N, \end{array}$$ where ε > 0 is a parameter, m > 0, N ≥ 1, V : ℝN → ℝ is a continuous scalar potential satisfies V(x) ≥ − V0 > − m for any x ∈ ℝN and f : ℝN → ℝ is a continuous function. Under a local condition imposed on the potential V, we discuss the number of nontrivial solutions with the topology of the set where the potential attains its minimum. We proof our results via variational methods, penalization techniques and Ljusternik-Schnirelmann theory.
Natural hazards and their impact on rural settlements in NE Romania – A cartographical approach

Abstract Natural hazards are the most significant threats in rural areas of Romania, while landslides, floods and bank river erosion are the geomorphological processes that impose the greatest risk in the Moldavian Plateau. We have identified 189 of disappeared, displaced and partially affected villages (in the area between Siret and Prut Rivers), using old cartographic materials as primary tool, and overlapping them with the present situation: ortophoto imagery and LiDAR derived high-resolution Digital Elevation Models (DEMs). The main natural hazards that affected the settlements were landslides (63%), floods (26%) and river bank erosion processes (11%) and in 19% of the cases the settlement completely disappeared. We also present seven study cases, three for landslides, three for floods and one for river bank erosion in order to detail how these natural hazards impacted the physical environment of the settlements. In the Moldavian Plateau the mentioned natural hazards played an important role in the dynamics of the settlement network, with variations induced mainly by the socio-political characteristics and not necessarily by the frequency variation of the natural hazard events. Such studies are needed in order to depict the vulnerability and risk scenarios, especially for the back-analysis, but also considering that future climate changes might impose changes in hazard, vulnerability and risk associated to settlements.